From 2be55c8a975ac05b4f46423371de2c449971ce37 Mon Sep 17 00:00:00 2001 From: Tommy Date: Thu, 10 Dec 2020 15:04:04 -0500 Subject: [PATCH 1/4] updated Readme --- .../new_projection-checkpoint.ipynb | 853 ++++++++++++++++++ .../__pycache__/transforms.cpython-37.pyc | Bin 0 -> 52061 bytes ..._30hz_benchmark_data_on_map.cpython-37.pyc | Bin 0 -> 9410 bytes 3D_projection/new_projection.ipynb | 853 ++++++++++++++++++ 3D_projection/projection.py | 20 +- 3D_projection/rigid_transform.png | Bin 0 -> 225874 bytes .../visualize_30hz_benchmark_data_on_map.py | 365 ++++++++ README.md | 98 +- sign_detector/.DS_Store | Bin 0 -> 6148 bytes sign_detector/readme.md | 93 +- 10 files changed, 2268 insertions(+), 14 deletions(-) create mode 100644 3D_projection/.ipynb_checkpoints/new_projection-checkpoint.ipynb create mode 100644 3D_projection/__pycache__/transforms.cpython-37.pyc create mode 100644 3D_projection/__pycache__/visualize_30hz_benchmark_data_on_map.cpython-37.pyc create mode 100644 3D_projection/new_projection.ipynb create mode 100644 3D_projection/rigid_transform.png create mode 100644 3D_projection/visualize_30hz_benchmark_data_on_map.py create mode 100644 sign_detector/.DS_Store diff --git a/3D_projection/.ipynb_checkpoints/new_projection-checkpoint.ipynb b/3D_projection/.ipynb_checkpoints/new_projection-checkpoint.ipynb new file mode 100644 index 0000000..bd35aa8 --- /dev/null +++ b/3D_projection/.ipynb_checkpoints/new_projection-checkpoint.ipynb @@ -0,0 +1,853 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(156, 3) (156, 3)\n" + ] + } + ], + "source": [ + "from transforms import affine_matrix_from_points\n", + "from argoverse.data_loading.argoverse_tracking_loader import ArgoverseTrackingLoader\n", + "from scipy.spatial import transform\n", + "import os\n", + "\n", + "#flags that determine which ring cameras to use\n", + "use_fc, use_fl, use_sl, use_rl, use_rr, use_sr, use_fr = [1, 1, 0, 0, 0, 0, 0]\n", + "\n", + "#path to the argoverse dataset\n", + "tracking_dataset_dir = '~/argoverse-tracking/sample/'\n", + "tracking_dataset_dir = os.path.expanduser(tracking_dataset_dir)\n", + "\n", + "#log_index determines which log in the argoverse dataset directory to use\n", + "log_index = 0\n", + "argoverse_loader = ArgoverseTrackingLoader(tracking_dataset_dir)\n", + "log_id = argoverse_loader.log_list[log_index]\n", + "argoverse_data = argoverse_loader[log_index]\n", + "\n", + "#colmap_output_dir points to the results from COLMAP\n", + "colmap_output_dir = '~/argoverse-tracking/sample2/output/'\n", + "colmap_output_dir = os.path.expanduser(colmap_output_dir)\n", + "\n", + "#detection results directory\n", + "detection_output_dir = '~/argoverse-tracking/sample2/detector_output/'\n", + "detection_output_dir = os.path.expanduser(detection_output_dir)\n", + "\n", + "#where to save computed traffic sign locations\n", + "location_output_dir = '~/argoverse-tracking/sample2/projection_output/'\n", + "location_output_dir = os.path.expanduser(location_output_dir)\n", + "\n", + "\n", + "\n", + "\n", + "#loading argoverse HD map\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "from visualize_30hz_benchmark_data_on_map import DatasetOnMapVisualizer\n", + "from argoverse.map_representation.map_api import ArgoverseMap\n", + "am = ArgoverseMap()\n", + "city_name = argoverse_data.city_name\n", + "dataset_dir = tracking_dataset_dir\n", + "experiment_prefix = 'visualization_demo'\n", + "use_existing_files = True\n", + "import logging\n", + "logger = logging.getLogger()\n", + "logger.setLevel(logging.CRITICAL)\n", + "domv = DatasetOnMapVisualizer(dataset_dir, experiment_prefix, use_existing_files=use_existing_files, log_id=argoverse_data.current_log)\n", + "\n", + "\n", + "\n", + "#read in colmap results\n", + "import numpy as np\n", + "poses_colmap = dict()\n", + "points_colmap = dict()\n", + "id2name_colmap = dict()\n", + "points3D = dict()\n", + "\n", + "with open(colmap_output_dir + 'images.txt') as f:\n", + " lines = f.read().splitlines()\n", + "for i,line in enumerate(lines):\n", + " if line[0] == '#':\n", + " continue\n", + " else:\n", + " if i % 2 == 0:\n", + " fields = line.split(' ')\n", + " image_name = fields[-1]\n", + " image_id = int(fields[0])\n", + " quat = fields[1:5]\n", + " quatanion = np.array([float(q) for q in quat])\n", + " trans = fields[5:8]\n", + " translation = np.array([float(t) for t in trans])\n", + " camera_id = int(fields[8])\n", + " poses_colmap[image_name] = [quatanion, translation, image_id, camera_id]\n", + " id2name_colmap[image_name] = image_id\n", + " else:\n", + " fields = line.split(' ')\n", + " points_2d = np.array([float(pt) for pt in fields])\n", + " points_colmap[image_name] = points_2d\n", + " \n", + "with open(colmap_output_dir + 'points3D.txt') as f:\n", + " lines = f.read().splitlines()\n", + "for i,line in enumerate(lines):\n", + " if line[0] == '#':\n", + " continue\n", + " else: \n", + " fields = line.split(' ')\n", + " points_attr = np.array([float(pt) for pt in fields])\n", + " points3D[points_attr[0]] = np.array([points_attr[1],points_attr[2],points_attr[3]])\n", + " \n", + "\n", + " \n", + "#read in argoverse data\n", + "import numpy.linalg as LA\n", + "\n", + "calibration_ring_front_center = argoverse_data.get_calibration(\"ring_front_center\")\n", + "calibration_ring_front_left = argoverse_data.get_calibration(\"ring_front_left\")\n", + "calibration_ring_side_left = argoverse_data.get_calibration(\"ring_side_left\")\n", + "calibration_ring_rear_left = argoverse_data.get_calibration(\"ring_rear_left\")\n", + "calibration_ring_rear_right = argoverse_data.get_calibration(\"ring_rear_right\")\n", + "calibration_ring_side_right = argoverse_data.get_calibration(\"ring_side_right\")\n", + "calibration_ring_front_right = argoverse_data.get_calibration(\"ring_front_right\")\n", + "\n", + "argo_3d = []\n", + "colmap_3d = []\n", + "for idx in range(len(argoverse_data.image_list_sync['ring_front_center'])):\n", + " city_to_egovehicle_se3 = argoverse_data.get_pose(idx)\n", + " \n", + " if use_fc:\n", + " argo_pt = city_to_egovehicle_se3.transform_point_cloud(\n", + " calibration_ring_front_center.project_cam_to_ego(np.zeros((1,3))))[0]\n", + " \n", + " img_fc = argoverse_data.image_list_sync['ring_front_center'][idx]\n", + " img_fc = img_fc.split('/')\n", + " img_fc = img_fc[-2] + '/' + img_fc[-1]\n", + " if img_fc in poses_colmap.keys():\n", + " q = poses_colmap[img_fc][0]\n", + " quat = [q[1], q[2], q[3], q[0]]\n", + " R = transform.Rotation.from_quat(quat)\n", + " t = poses_colmap[img_fc][1]\n", + " colmap_pt = - R.as_matrix().T.dot(t) \n", + " colmap_3d.append(colmap_pt)\n", + " argo_3d.append(argo_pt)\n", + " \n", + "for idx in range(len(argoverse_data.image_list_sync['ring_front_center'])):\n", + " city_to_egovehicle_se3 = argoverse_data.get_pose(idx) \n", + " if use_fr:\n", + " argo_pt = city_to_egovehicle_se3.transform_point_cloud(\n", + " calibration_ring_front_right.project_cam_to_ego(np.zeros((1,3))))[0]\n", + " img_fr = argoverse_data.image_list_sync['ring_front_right'][idx]\n", + " img_fr = img_fr.split('/')\n", + " img_fr = img_fr[-2] + '/' + img_fr[-1]\n", + " if img_fr in poses_colmap.keys():\n", + " q = poses_colmap[img_fr][0]\n", + " quat = [q[1], q[2], q[3], q[0]]\n", + " R = transform.Rotation.from_quat(quat)\n", + " t = poses_colmap[img_fr][1]\n", + " colmap_pt = - R.as_matrix().T.dot(t) \n", + " colmap_3d.append(colmap_pt)\n", + " argo_3d.append(argo_pt)\n", + " \n", + " if use_fl:\n", + " argo_pt = city_to_egovehicle_se3.transform_point_cloud(\n", + " calibration_ring_front_left.project_cam_to_ego(np.zeros((1,3))))[0]\n", + " img_fl = argoverse_data.image_list_sync['ring_front_left'][idx]\n", + " img_fl = img_fl.split('/')\n", + " img_fl = img_fl[-2] + '/' + img_fl[-1]\n", + " if img_fl in poses_colmap.keys():\n", + " q = poses_colmap[img_fl][0]\n", + " quat = [q[1], q[2], q[3], q[0]]\n", + " R = transform.Rotation.from_quat(quat)\n", + " t = poses_colmap[img_fl][1]\n", + " colmap_pt = - R.as_matrix().T.dot(t) \n", + " colmap_3d.append(colmap_pt)\n", + " argo_3d.append(argo_pt)\n", + " \n", + " if use_sl:\n", + " argo_pt = city_to_egovehicle_se3.transform_point_cloud(\n", + " calibration_ring_side_left.project_cam_to_ego(np.zeros((1,3))))[0]\n", + " img_sl = argoverse_data.image_list_sync['ring_side_left'][idx]\n", + " img_sl = img_sl.split('/')\n", + " img_sl = img_sl[-2] + '/' + img_sl[-1]\n", + " if img_sl in poses_colmap.keys():\n", + " q = poses_colmap[img_sl][0]\n", + " quat = [q[1], q[2], q[3], q[0]]\n", + " R = transform.Rotation.from_quat(quat)\n", + " t = poses_colmap[img_sl][1]\n", + " colmap_pt = - R.as_matrix().T.dot(t) \n", + " colmap_3d.append(colmap_pt)\n", + " argo_3d.append(argo_pt)\n", + " \n", + " if use_sr:\n", + " argo_pt = city_to_egovehicle_se3.transform_point_cloud(\n", + " calibration_ring_side_right.project_cam_to_ego(np.zeros((1,3))))[0]\n", + " img_sr = argoverse_data.image_list_sync['ring_side_right'][idx]\n", + " img_sr = img_sr.split('/')\n", + " img_sr = img_sr[-2] + '/' + img_sr[-1]\n", + " if img_sr in poses_colmap.keys():\n", + " q = poses_colmap[img_sr][0]\n", + " quat = [q[1], q[2], q[3], q[0]]\n", + " R = transform.Rotation.from_quat(quat)\n", + " t = poses_colmap[img_sr][1]\n", + " colmap_pt = - R.as_matrix().T.dot(t) \n", + " colmap_3d.append(colmap_pt)\n", + " argo_3d.append(argo_pt)\n", + " \n", + " if use_rl:\n", + " argo_pt = city_to_egovehicle_se3.transform_point_cloud(\n", + " calibration_ring_rear_left.project_cam_to_ego(np.zeros((1,3))))[0]\n", + " img_rl = argoverse_data.image_list_sync['ring_rear_left'][idx]\n", + " img_rl = img_rl.split('/')\n", + " img_rl = img_rl[-2] + '/' + img_rl[-1]\n", + " if img_rl in poses_colmap.keys():\n", + " q = poses_colmap[img_rl][0]\n", + " quat = [q[1], q[2], q[3], q[0]]\n", + " R = transform.Rotation.from_quat(quat)\n", + " t = poses_colmap[img_rl][1]\n", + " colmap_pt = - R.as_matrix().T.dot(t) \n", + " colmap_3d.append(colmap_pt)\n", + " argo_3d.append(argo_pt)\n", + " \n", + " if use_rr:\n", + " argo_pt = city_to_egovehicle_se3.transform_point_cloud(\n", + " calibration_ring_rear_right.project_cam_to_ego(np.zeros((1,3))))[0]\n", + " img_rr = argoverse_data.image_list_sync['ring_rear_right'][idx]\n", + " img_rr = img_rr.split('/')\n", + " img_rr = img_rr[-2] + '/' + img_rr[-1]\n", + " if img_rr in poses_colmap.keys():\n", + " q = poses_colmap[img_rr][0]\n", + " quat = [q[1], q[2], q[3], q[0]]\n", + " R = transform.Rotation.from_quat(quat)\n", + " t = poses_colmap[img_rr][1]\n", + " colmap_pt = - R.as_matrix().T.dot(t) \n", + " colmap_3d.append(colmap_pt)\n", + " argo_3d.append(argo_pt)\n", + "\n", + " \n", + " \n", + " \n", + "#compute 3d transformation from argoverse to colmap \n", + "points_3d_argo = np.array(argo_3d)\n", + "points_3d_colmap = np.array(colmap_3d)\n", + "print(points_3d_argo.shape, points_3d_colmap.shape)\n", + "\n", + "M = affine_matrix_from_points(points_3d_colmap[:,:].T, points_3d_argo[:,:].T, shear=False)\n", + "#M = affine_matrix_from_points(points_3d_colmap[0:10,:].T, points_3d_argo[0:10,:].T, shear=False)\n", + "points_3d_colmap_homo = np.vstack([points_3d_colmap.T, np.ones(points_3d_colmap.shape[0])])\n", + "ttt = M.dot(points_3d_colmap_homo)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "#read in detection results\n", + "import cv2 \n", + "import os\n", + "import numpy as np\n", + "boxes = np.load(detection_output_dir + log_id + '.npz',allow_pickle=True)\n", + "order = np.argsort(boxes['images_list'])\n", + "boxes_images_list = boxes['images_list'][order]\n", + "boxes_boxes_list = boxes['boxes_list'][order]\n", + "boxes_scores_list = boxes['scores_list'][order]\n", + "boxes_class_list = boxes['category_id_list'][order]\n", + "def check_inside(x1,y1,x2,y2,x,y):\n", + " return x1 <= x and x <= x2 and y1 <= y and y <=y2\n", + "def bbox2argoverse(x1,y1,x2,y2,points_colmap,points3D,M,image_name):\n", + " flag = False\n", + " insider = []\n", + " center_x = (x1 + x2) / 2\n", + " center_y = (y1 + y2) / 2\n", + " for i in range(int( len(points_colmap[image_name]) // 3)):\n", + " x = points_colmap[image_name][3*i]\n", + " y = points_colmap[image_name][3*i + 1] \n", + " idx = points_colmap[image_name][3*i + 2] \n", + " if check_inside(x1,y1, x2, y2, x,y) and idx != -1:\n", + " dist = (x - center_x) * (x - center_x) + (y - center_y) * (y - center_y)\n", + " insider.append([x,y,idx,dist])\n", + " if len(insider) <= 0:\n", + " print(\"No feature points detected in the bounding box\")\n", + " flag = True\n", + " return 0, flag, 0\n", + " elif len(insider) > 3:\n", + " print(\"More than 3 points detected in the bounding box\")\n", + " insider = np.array(insider)\n", + " #print(insider)\n", + " insider.view('f8,f8,f8,f8').sort(order=['f3'], axis=0)\n", + " #print(insider)\n", + " pts_colmap = np.zeros((3,3))\n", + " pts_colmap[:,0] = points3D[insider[0,2]] #-1,2\n", + " pts_colmap[:,1] = points3D[insider[1,2]] #-2,2\n", + " pts_colmap[:,2] = points3D[insider[2,2]] #-3,2\n", + " pts_homo = np.vstack([pts_colmap,np.ones(3)])\n", + " pts_argo = M @ pts_homo\n", + " return np.mean(pts_argo, axis=1), flag, np.mean(pts_colmap, axis = 1)\n", + " else:\n", + " print(\"Less than 3 points detected in the bounding box\")\n", + " pts_colmap = []\n", + " for i,line in enumerate(insider):\n", + " pts_colmap.append(points3D[insider[i][2]])\n", + " pts_colmap = np.array(pts_colmap)\n", + " pts_colmap = pts_colmap.T\n", + " pts_homo = np.vstack([pts_colmap,np.ones(pts_colmap.shape[1])])\n", + " pts_argo = M @ pts_homo\n", + " return np.mean(pts_argo, axis=1), flag, np.mean(pts_colmap, axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + 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3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "More than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "No feature points detected in the bounding box\n", + "No feature points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "No feature points detected in the bounding box\n", + "No feature points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "No feature points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n" + ] + } + ], + "source": [ + "#compute traffic sign positions\n", + "marks = []\n", + "marks_col = []\n", + "names = []\n", + "scores = []\n", + "classes = []\n", + "for j in range(len(boxes_images_list)):\n", + " image_name = 'ring_front_center/' + boxes_images_list[j]\n", + " if os.path.exists(tracking_dataset_dir +log_id + '/' + image_name):\n", + " im = cv2.imread(tracking_dataset_dir +log_id + '/'+ image_name)\n", + " for cc,box in enumerate(boxes_boxes_list[j]):\n", + " x1 = int(box[0] )#* 1200)\n", + " y1 = int(box[1] )#* 1920)\n", + " x2 = int(box[2] )#* 1200)\n", + " y2 = int(box[3] )#* 1920)\n", + " if (y2-y1) * (x2-x1) < 500: #1450\n", + " continue\n", + " if image_name not in points_colmap.keys():\n", + " continue\n", + " argo_box, flag, colmap_box = bbox2argoverse(x1,y1,x2,y2,points_colmap,points3D,M,image_name)\n", + " if flag: \n", + " continue\n", + " #if len(marks) == 0:\n", + " marks.append([argo_box[0], argo_box[1], argo_box[2]])\n", + " names.append(image_name)\n", + " scores.append(boxes_scores_list[j][cc])\n", + " classes.append(boxes_class_list[j][cc])\n", + " marks_col.append(colmap_box)\n", + "#marks = np.array(marks)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[20 17 19 13 12 15 2 16 18 1 3 14 9 4 11 10 0 7 6 8 5]\n", + "[[(2576.08101101, 1199.09703696, 16.49887731)]\n", + " [(2589.31012735, 1205.72015614, 17.51022144)]\n", + " [(2589.34219527, 1205.66718961, 17.02531521)]\n", + " [(2596.8833383 , 1191.60758704, 20.82102332)]\n", + " [(2597.89818131, 1192.48768664, 20.10728931)]\n", + " [(2598.15849598, 1192.72812317, 19.92565796)]\n", + " [(2598.77548975, 1193.89470479, 21.829439 )]\n", + " [(2599.54815885, 1198.55306603, 20.12784844)]\n", + " [(2599.57324317, 1198.47995138, 19.72473012)]\n", + " [(2607.40562423, 1223.21491884, 18.13321268)]\n", + " [(2607.47383197, 1204.04525999, 21.24501157)]\n", + " [(2607.6082839 , 1223.06999855, 17.06211994)]\n", + " [(2616.01359854, 1214.30841455, 21.41689872)]\n", + " [(2616.29632059, 1213.99998821, 20.65506912)]\n", + " [(2621.17911014, 1235.42716012, 16.85858402)]\n", + " [(2621.24977727, 1235.32736113, 16.36928504)]\n", + " [(2630.87936547, 1244.47467057, 17.42338999)]\n", + " [(2630.99311724, 1244.51958524, 16.78888518)]\n", + " [(2631.01543658, 1244.27158608, 16.23551428)]\n", + " [(2639.50945263, 1235.46937879, 19.00287162)]\n", + " [(2645.10659619, 1257.13896284, 16.33234752)]]\n", + "[2576.08101101 1199.09703696 16.49887731] 3 ring_front_center/ring_front_center_315978421517357424.jpg 1\n", + "[2589.31012735 1205.72015614 17.51022144] 3 ring_front_center/ring_front_center_315978414624255144.jpg 6\n", + "[2589.34219527 1205.66718961 17.02531521] 3 ring_front_center/ring_front_center_315978415423455864.jpg 3\n", + "[2596.8833383 1191.60758704 20.82102332] 1 ring_front_center/ring_front_center_315978411827057072.jpg 1\n", + "[2597.89818131 1192.48768664 20.10728931] 1 ring_front_center/ring_front_center_315978411527355224.jpg 4\n", + "[2598.15849598 1192.72812317 19.92565796] 1 ring_front_center/ring_front_center_315978412326560360.jpg 1\n", + "[2598.77548975 1193.89470479 21.829439 ] 11 ring_front_center/ring_front_center_315978406532359208.jpg 2\n", + "[2599.54815885 1198.55306603 20.12784844] 3 ring_front_center/ring_front_center_315978413625254760.jpg 6\n", + "[2599.57324317 1198.47995138 19.72473012] 3 ring_front_center/ring_front_center_315978414624255144.jpg 1\n", + "[2607.40562423 1223.21491884 18.13321268] 11 ring_front_center/ring_front_center_315978406532359208.jpg 56\n", + "[2607.47383197 1204.04525999 21.24501157] 11 ring_front_center/ring_front_center_315978406732157848.jpg 1\n", + "[2607.6082839 1223.06999855 17.06211994] 3 ring_front_center/ring_front_center_315978411827057072.jpg 2\n", + "[2616.01359854 1214.30841455 21.41689872] 4 ring_front_center/ring_front_center_315978408730157152.jpg 28\n", + "[2616.29632059 1213.99998821 20.65506912] 11 ring_front_center/ring_front_center_315978406832057128.jpg 48\n", + "[2621.17911014 1235.42716012 16.85858402] 3 ring_front_center/ring_front_center_315978409729158088.jpg 3\n", + "[2621.24977727 1235.32736113 16.36928504] 3 ring_front_center/ring_front_center_315978409429460248.jpg 4\n", + "[2630.87936547 1244.47467057 17.42338999] 4 ring_front_center/ring_front_center_315978406332560648.jpg 26\n", + "[2630.99311724 1244.51958524 16.78888518] 3 ring_front_center/ring_front_center_315978408530355712.jpg 2\n", + "[2631.01543658 1244.27158608 16.23551428] 3 ring_front_center/ring_front_center_315978408130752752.jpg 4\n", + "[2639.50945263 1235.46937879 19.00287162] 3 ring_front_center/ring_front_center_315978408530355712.jpg 1\n", + "[2645.10659619 1257.13896284 16.33234752] 3 ring_front_center/ring_front_center_315978406832057128.jpg 1\n", + "21\n" + ] + } + ], + "source": [ + "db_marks = []\n", + "db_counts = []\n", + "db_names = []\n", + "db_classes = []\n", + "\n", + "def check_adjacency(mark, db_mark):\n", + " l2 = np.sqrt((mark[0] - db_mark[0])**2 + (mark[1] - db_mark[1])**2)\n", + " dz = np.absolute(mark[2] - db_mark[2])\n", + " return l2 < 0.6 and dz < 0.4\n", + "\n", + "def check_category(category, db_class):\n", + " return category == db_class\n", + "\n", + "for i in range(len(marks)):\n", + " new_sign = True\n", + " mark = marks[i]\n", + " if i == 0:\n", + " db_marks.append(mark)\n", + " db_classes.append(classes[i])\n", + " db_names.append(names[i])\n", + " db_counts.append(1)\n", + " else:\n", + " for cc in range(len(db_marks)):\n", + " db_mark = db_marks[cc]\n", + " if check_adjacency(mark, db_mark) and check_category(classes[i], db_classes[cc]):\n", + " new_sign = False\n", + " db_counts[cc] += 1\n", + " db_marks[cc] = 1 * mark + 0 * db_mark\n", + " break\n", + " if new_sign:\n", + " db_marks.append(mark)\n", + " db_classes.append(classes[i])\n", + " db_names.append(names[i])\n", + " db_counts.append(1)\n", + "\n", + "marks = np.array(db_marks)\n", + "order = np.argsort(marks[:, 0])\n", + "print(order)\n", + "db_marks = np.array(db_marks)[order]\n", + "db_classes = np.array(db_classes)[order]\n", + "db_names = np.array(db_names)[order]\n", + "db_counts = np.array(db_counts)[order]\n", + "print(np.sort(marks.view('f8,f8,f8'), order=['f0'], axis=0))\n", + "\n", + "\n", + "for i in range(len(db_marks)):\n", + " print(db_marks[i], db_classes[i], db_names[i], db_counts[i])\n", + "print(len(db_marks))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.4266924249693159\n" + ] + } + ], + "source": [ + "x1 = [2616.29632059,1213.99998821]\n", + "x2 = [2616.56821749,1214.3288324]\n", + "print(np.sqrt((x1[1]-x2[1])**2 + (x1[0]-x2[0])**2))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "#save detected traffic signs \n", + "argo_mark_poses = np.array(marks)\n", + "colmap_mark_poses = np.array(marks_col)\n", + "categories = np.array(classes)\n", + "scores = np.array(scores)\n", + "colmap2argo = M\n", + "colmap_camera_poses = points_3d_colmap\n", + "argo_camera_poses = points_3d_argo\n", + "\n", + "np.savez(location_output_dir + log_id+'_projected.npz', \n", + " argo_mark_poses=argo_mark_poses, \n", + " colmap_mark_poses=colmap_mark_poses,\n", + " categories=categories,\n", + " scores=scores,\n", + " colmap2argo=colmap2argo,\n", + " argo_camera_poses=argo_camera_poses,\n", + " colmap_camera_poses=colmap_camera_poses\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4, 156) (156, 3)\n", + "0.06950603208036611\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import copy\n", + "\n", + "#R, t = svd_rt(points_3d_colmap_scaled.T[:,:10], points_3d_argo.T[:,:10])\n", + "#t = t.reshape(-1)\n", + "\n", + "fig = plt.figure(figsize=(15,15))\n", + "ax = fig.add_subplot(111)\n", + "#xcenter = (points_3d_argo[0,0] + points_3d_argo[-1,0])/2 #+ 15\n", + "#ycenter = (points_3d_argo[0,1] + points_3d_argo[-1,1])/2#+ 15\n", + "\n", + "xcenter = points_3d_argo[20,0]\n", + "ycenter = points_3d_argo[20,1]\n", + " \n", + "xmin = xcenter - 100 # 150 #70\n", + "xmax = xcenter + 90 # 150 #70\n", + "ymin = ycenter - 100 # 150 #70\n", + "ymax = ycenter + 90 # 150 #70\n", + "\n", + "#xmin = xcenter - 10 # 150\n", + "#xmax = xcenter + 10 # 150\n", + "#ymin = ycenter - 10 # 150\n", + "#ymax = ycenter + 10 # 150\n", + "\n", + "#2.59058920e+03 1.20444779e+03\n", + "\n", + "#2.59058802e+03 1.20428979e+03\n", + "#2.58554503e+03 1.18528893e+03\n", + "points_3d_colmap_h = np.vstack([points_3d_colmap.T, np.ones(points_3d_colmap.shape[0])])\n", + "print(points_3d_colmap_h.shape,points_3d_colmap.shape)\n", + "projected = M @ points_3d_colmap_h\n", + "\n", + "#ax.scatter(points_3d_argo[2:8,0], points_3d_argo[2:8,1], 50, color=\"g\", marker=\".\", zorder=2, label='ground truth front center camera poses')\n", + "#ax.scatter(projected[0,2:8], projected[1,2:8], 50, color=\"blue\", marker=\"x\", zorder=2, label=\"Projected COLMAP front center camera poses\")\n", + "\n", + "# Nov 06 Comment These\n", + "#ax.scatter(points_3d_argo[:,0], points_3d_argo[:,1], 50, color=\"g\", marker=\".\", zorder=2, label='ground truth front center camera poses')\n", + "#ax.scatter(projected[0,:], projected[1,:], 50, color=\"blue\", marker=\"x\", zorder=2, label=\"Projected COLMAP front center camera poses\")\n", + "# Dec 02 Comment These\n", + "#ax.scatter(marks[:,0], marks[:,1] , 50, color=\"c\", marker=\"x\", zorder=2, label=\"Detected Signs\")\n", + "\n", + "db_marks[6, 0] = 0\n", + "db_marks[6, 1] = 0\n", + "db_marks[3, 0] = 0\n", + "db_marks[3, 1] = 0\n", + "ax.scatter(db_marks[:,0], db_marks[:,1] , 50, color=\"c\", marker=\"x\", zorder=2, label=\"Detected Signs\")\n", + "\n", + "\n", + "##label gt\n", + "#ax.scatter(our[:,0], our[:,1], 50, color=\"r\", marker=\"x\", zorder=2, label=\"Our Results\")\n", + "#ax.scatter(gt[:,0], gt[:,1], 50, color=\"g\", marker=\"x\", zorder=2, label=\"GT Positions\")\n", + "\n", + "a = np.sum((projected[0:3,:] - points_3d_argo.T)**2,axis=0)\n", + "rmse = np.mean(a)\n", + "rmse = np.sqrt(rmse)\n", + "print(rmse)\n", + "\n", + "\n", + "\n", + "\n", + "#ax.scatter(marks2[:,0], marks2[:,1] , 50, color=\"g\", marker=\"x\", zorder=2, label=\"Tracked Signs\")\n", + "#ax.scatter([triangulated[0]], [triangulated[1]], 50, color=\"r\", marker=\"x\", zorder=2, label=\"Triangulated Sign Position\")\n", + "ax.set_xlim([xmin, xmax])\n", + "ax.set_ylim([ymin, ymax])\n", + "local_lane_polygons = am.find_local_lane_polygons([xmin, xmax, ymin, ymax], city_name)\n", + "local_das = am.find_local_driveable_areas([xmin, xmax, ymin, ymax], city_name)\n", + "\n", + "ax.legend(fontsize='xx-large')\n", + "\n", + "domv.render_bev_labels_mpl( \n", + " city_name,\n", + " ax,\n", + " \"city_axis\",\n", + " None,\n", + " copy.deepcopy(local_lane_polygons),\n", + " copy.deepcopy(local_das),\n", + " argoverse_loader.log_list[log_index],\n", + " argoverse_data.lidar_timestamp_list[100],\n", + " city_to_egovehicle_se3,\n", + " am,\n", + "\n", + ")\n", + "\n", + "plt.savefig(\"rigid_transform.png\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + 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argoverse.data_loading.argoverse_tracking_loader import ArgoverseTrackingLoader\n", + "from scipy.spatial import transform\n", + "import os\n", + "\n", + "#flags that determine which ring cameras to use\n", + "use_fc, use_fl, use_sl, use_rl, use_rr, use_sr, use_fr = [1, 1, 0, 0, 0, 0, 0]\n", + "\n", + "#path to the argoverse dataset\n", + "tracking_dataset_dir = '~/argoverse-tracking/sample/'\n", + "tracking_dataset_dir = os.path.expanduser(tracking_dataset_dir)\n", + "\n", + "#log_index determines which log in the argoverse dataset directory to use\n", + "log_index = 0\n", + "argoverse_loader = ArgoverseTrackingLoader(tracking_dataset_dir)\n", + "log_id = argoverse_loader.log_list[log_index]\n", + "argoverse_data = argoverse_loader[log_index]\n", + "\n", + "#colmap_output_dir points to the results from COLMAP\n", + "colmap_output_dir = '~/argoverse-tracking/sample2/output/'\n", + "colmap_output_dir = os.path.expanduser(colmap_output_dir)\n", + "\n", + "#detection results directory\n", + "detection_output_dir = '~/argoverse-tracking/sample2/detector_output/'\n", + "detection_output_dir = os.path.expanduser(detection_output_dir)\n", + "\n", + "#where to save computed traffic sign locations\n", + "location_output_dir = '~/argoverse-tracking/sample2/projection_output/'\n", + "location_output_dir = os.path.expanduser(location_output_dir)\n", + "\n", + "\n", + "\n", + "\n", + "#loading argoverse HD map\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "from visualize_30hz_benchmark_data_on_map import DatasetOnMapVisualizer\n", + "from argoverse.map_representation.map_api import ArgoverseMap\n", + "am = ArgoverseMap()\n", + "city_name = argoverse_data.city_name\n", + "dataset_dir = tracking_dataset_dir\n", + "experiment_prefix = 'visualization_demo'\n", + "use_existing_files = True\n", + "import logging\n", + "logger = logging.getLogger()\n", + "logger.setLevel(logging.CRITICAL)\n", + "domv = DatasetOnMapVisualizer(dataset_dir, experiment_prefix, use_existing_files=use_existing_files, log_id=argoverse_data.current_log)\n", + "\n", + "\n", + "\n", + "#read in colmap results\n", + "import numpy as np\n", + "poses_colmap = dict()\n", + "points_colmap = dict()\n", + "id2name_colmap = dict()\n", + "points3D = dict()\n", + "\n", + "with open(colmap_output_dir + 'images.txt') as f:\n", + " lines = f.read().splitlines()\n", + "for i,line in enumerate(lines):\n", + " if line[0] == '#':\n", + " continue\n", + " else:\n", + " if i % 2 == 0:\n", + " fields = line.split(' ')\n", + " image_name = fields[-1]\n", + " image_id = int(fields[0])\n", + " quat = fields[1:5]\n", + " quatanion = np.array([float(q) for q in quat])\n", + " trans = fields[5:8]\n", + " translation = np.array([float(t) for t in trans])\n", + " camera_id = int(fields[8])\n", + " poses_colmap[image_name] = [quatanion, translation, image_id, camera_id]\n", + " id2name_colmap[image_name] = image_id\n", + " else:\n", + " fields = line.split(' ')\n", + " points_2d = np.array([float(pt) for pt in fields])\n", + " points_colmap[image_name] = points_2d\n", + " \n", + "with open(colmap_output_dir + 'points3D.txt') as f:\n", + " lines = f.read().splitlines()\n", + "for i,line in enumerate(lines):\n", + " if line[0] == '#':\n", + " continue\n", + " else: \n", + " fields = line.split(' ')\n", + " points_attr = np.array([float(pt) for pt in fields])\n", + " points3D[points_attr[0]] = np.array([points_attr[1],points_attr[2],points_attr[3]])\n", + " \n", + "\n", + " \n", + "#read in argoverse data\n", + "import numpy.linalg as LA\n", + "\n", + "calibration_ring_front_center = argoverse_data.get_calibration(\"ring_front_center\")\n", + "calibration_ring_front_left = argoverse_data.get_calibration(\"ring_front_left\")\n", + "calibration_ring_side_left = argoverse_data.get_calibration(\"ring_side_left\")\n", + "calibration_ring_rear_left = argoverse_data.get_calibration(\"ring_rear_left\")\n", + "calibration_ring_rear_right = argoverse_data.get_calibration(\"ring_rear_right\")\n", + "calibration_ring_side_right = argoverse_data.get_calibration(\"ring_side_right\")\n", + "calibration_ring_front_right = argoverse_data.get_calibration(\"ring_front_right\")\n", + "\n", + "argo_3d = []\n", + "colmap_3d = []\n", + "for idx in range(len(argoverse_data.image_list_sync['ring_front_center'])):\n", + " city_to_egovehicle_se3 = argoverse_data.get_pose(idx)\n", + " \n", + " if use_fc:\n", + " argo_pt = city_to_egovehicle_se3.transform_point_cloud(\n", + " calibration_ring_front_center.project_cam_to_ego(np.zeros((1,3))))[0]\n", + " \n", + " img_fc = argoverse_data.image_list_sync['ring_front_center'][idx]\n", + " img_fc = img_fc.split('/')\n", + " img_fc = img_fc[-2] + '/' + img_fc[-1]\n", + " if img_fc in poses_colmap.keys():\n", + " q = poses_colmap[img_fc][0]\n", + " quat = [q[1], q[2], q[3], q[0]]\n", + " R = transform.Rotation.from_quat(quat)\n", + " t = poses_colmap[img_fc][1]\n", + " colmap_pt = - R.as_matrix().T.dot(t) \n", + " colmap_3d.append(colmap_pt)\n", + " argo_3d.append(argo_pt)\n", + " \n", + "for idx in range(len(argoverse_data.image_list_sync['ring_front_center'])):\n", + " city_to_egovehicle_se3 = argoverse_data.get_pose(idx) \n", + " if use_fr:\n", + " argo_pt = city_to_egovehicle_se3.transform_point_cloud(\n", + " calibration_ring_front_right.project_cam_to_ego(np.zeros((1,3))))[0]\n", + " img_fr = argoverse_data.image_list_sync['ring_front_right'][idx]\n", + " img_fr = img_fr.split('/')\n", + " img_fr = img_fr[-2] + '/' + img_fr[-1]\n", + " if img_fr in poses_colmap.keys():\n", + " q = poses_colmap[img_fr][0]\n", + " quat = [q[1], q[2], q[3], q[0]]\n", + " R = transform.Rotation.from_quat(quat)\n", + " t = poses_colmap[img_fr][1]\n", + " colmap_pt = - R.as_matrix().T.dot(t) \n", + " colmap_3d.append(colmap_pt)\n", + " argo_3d.append(argo_pt)\n", + " \n", + " if use_fl:\n", + " argo_pt = city_to_egovehicle_se3.transform_point_cloud(\n", + " calibration_ring_front_left.project_cam_to_ego(np.zeros((1,3))))[0]\n", + " img_fl = argoverse_data.image_list_sync['ring_front_left'][idx]\n", + " img_fl = img_fl.split('/')\n", + " img_fl = img_fl[-2] + '/' + img_fl[-1]\n", + " if img_fl in poses_colmap.keys():\n", + " q = poses_colmap[img_fl][0]\n", + " quat = [q[1], q[2], q[3], q[0]]\n", + " R = transform.Rotation.from_quat(quat)\n", + " t = poses_colmap[img_fl][1]\n", + " colmap_pt = - R.as_matrix().T.dot(t) \n", + " colmap_3d.append(colmap_pt)\n", + " argo_3d.append(argo_pt)\n", + " \n", + " if use_sl:\n", + " argo_pt = city_to_egovehicle_se3.transform_point_cloud(\n", + " calibration_ring_side_left.project_cam_to_ego(np.zeros((1,3))))[0]\n", + " img_sl = argoverse_data.image_list_sync['ring_side_left'][idx]\n", + " img_sl = img_sl.split('/')\n", + " img_sl = img_sl[-2] + '/' + img_sl[-1]\n", + " if img_sl in poses_colmap.keys():\n", + " q = poses_colmap[img_sl][0]\n", + " quat = [q[1], q[2], q[3], q[0]]\n", + " R = transform.Rotation.from_quat(quat)\n", + " t = poses_colmap[img_sl][1]\n", + " colmap_pt = - R.as_matrix().T.dot(t) \n", + " colmap_3d.append(colmap_pt)\n", + " argo_3d.append(argo_pt)\n", + " \n", + " if use_sr:\n", + " argo_pt = city_to_egovehicle_se3.transform_point_cloud(\n", + " calibration_ring_side_right.project_cam_to_ego(np.zeros((1,3))))[0]\n", + " img_sr = argoverse_data.image_list_sync['ring_side_right'][idx]\n", + " img_sr = img_sr.split('/')\n", + " img_sr = img_sr[-2] + '/' + img_sr[-1]\n", + " if img_sr in poses_colmap.keys():\n", + " q = poses_colmap[img_sr][0]\n", + " quat = [q[1], q[2], q[3], q[0]]\n", + " R = transform.Rotation.from_quat(quat)\n", + " t = poses_colmap[img_sr][1]\n", + " colmap_pt = - R.as_matrix().T.dot(t) \n", + " colmap_3d.append(colmap_pt)\n", + " argo_3d.append(argo_pt)\n", + " \n", + " if use_rl:\n", + " argo_pt = city_to_egovehicle_se3.transform_point_cloud(\n", + " calibration_ring_rear_left.project_cam_to_ego(np.zeros((1,3))))[0]\n", + " img_rl = argoverse_data.image_list_sync['ring_rear_left'][idx]\n", + " img_rl = img_rl.split('/')\n", + " img_rl = img_rl[-2] + '/' + img_rl[-1]\n", + " if img_rl in poses_colmap.keys():\n", + " q = poses_colmap[img_rl][0]\n", + " quat = [q[1], q[2], q[3], q[0]]\n", + " R = transform.Rotation.from_quat(quat)\n", + " t = poses_colmap[img_rl][1]\n", + " colmap_pt = - R.as_matrix().T.dot(t) \n", + " colmap_3d.append(colmap_pt)\n", + " argo_3d.append(argo_pt)\n", + " \n", + " if use_rr:\n", + " argo_pt = city_to_egovehicle_se3.transform_point_cloud(\n", + " calibration_ring_rear_right.project_cam_to_ego(np.zeros((1,3))))[0]\n", + " img_rr = argoverse_data.image_list_sync['ring_rear_right'][idx]\n", + " img_rr = img_rr.split('/')\n", + " img_rr = img_rr[-2] + '/' + img_rr[-1]\n", + " if img_rr in poses_colmap.keys():\n", + " q = poses_colmap[img_rr][0]\n", + " quat = [q[1], q[2], q[3], q[0]]\n", + " R = transform.Rotation.from_quat(quat)\n", + " t = poses_colmap[img_rr][1]\n", + " colmap_pt = - R.as_matrix().T.dot(t) \n", + " colmap_3d.append(colmap_pt)\n", + " argo_3d.append(argo_pt)\n", + "\n", + " \n", + " \n", + " \n", + "#compute 3d transformation from argoverse to colmap \n", + "points_3d_argo = np.array(argo_3d)\n", + "points_3d_colmap = np.array(colmap_3d)\n", + "print(points_3d_argo.shape, points_3d_colmap.shape)\n", + "\n", + "M = affine_matrix_from_points(points_3d_colmap[:,:].T, points_3d_argo[:,:].T, shear=False)\n", + "#M = affine_matrix_from_points(points_3d_colmap[0:10,:].T, points_3d_argo[0:10,:].T, shear=False)\n", + "points_3d_colmap_homo = np.vstack([points_3d_colmap.T, np.ones(points_3d_colmap.shape[0])])\n", + "ttt = M.dot(points_3d_colmap_homo)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "#read in detection results\n", + "import cv2 \n", + "import os\n", + "import numpy as np\n", + "boxes = np.load(detection_output_dir + log_id + '.npz',allow_pickle=True)\n", + "order = np.argsort(boxes['images_list'])\n", + "boxes_images_list = boxes['images_list'][order]\n", + "boxes_boxes_list = boxes['boxes_list'][order]\n", + "boxes_scores_list = boxes['scores_list'][order]\n", + "boxes_class_list = boxes['category_id_list'][order]\n", + "def check_inside(x1,y1,x2,y2,x,y):\n", + " return x1 <= x and x <= x2 and y1 <= y and y <=y2\n", + "def bbox2argoverse(x1,y1,x2,y2,points_colmap,points3D,M,image_name):\n", + " flag = False\n", + " insider = []\n", + " center_x = (x1 + x2) / 2\n", + " center_y = (y1 + y2) / 2\n", + " for i in range(int( len(points_colmap[image_name]) // 3)):\n", + " x = points_colmap[image_name][3*i]\n", + " y = points_colmap[image_name][3*i + 1] \n", + " idx = points_colmap[image_name][3*i + 2] \n", + " if check_inside(x1,y1, x2, y2, x,y) and idx != -1:\n", + " dist = (x - center_x) * (x - center_x) + (y - center_y) * (y - center_y)\n", + " insider.append([x,y,idx,dist])\n", + " if len(insider) <= 0:\n", + " print(\"No feature points detected in the bounding box\")\n", + " flag = True\n", + " return 0, flag, 0\n", + " elif len(insider) > 3:\n", + " print(\"More than 3 points detected in the bounding box\")\n", + " insider = np.array(insider)\n", + " #print(insider)\n", + " insider.view('f8,f8,f8,f8').sort(order=['f3'], axis=0)\n", + " #print(insider)\n", + " pts_colmap = np.zeros((3,3))\n", + " pts_colmap[:,0] = points3D[insider[0,2]] #-1,2\n", + " pts_colmap[:,1] = points3D[insider[1,2]] #-2,2\n", + " pts_colmap[:,2] = points3D[insider[2,2]] #-3,2\n", + " pts_homo = np.vstack([pts_colmap,np.ones(3)])\n", + " pts_argo = M @ pts_homo\n", + " return np.mean(pts_argo, axis=1), flag, np.mean(pts_colmap, axis = 1)\n", + " else:\n", + " print(\"Less than 3 points detected in the bounding box\")\n", + " pts_colmap = []\n", + " for i,line in enumerate(insider):\n", + " pts_colmap.append(points3D[insider[i][2]])\n", + " pts_colmap = np.array(pts_colmap)\n", + " pts_colmap = pts_colmap.T\n", + " pts_homo = np.vstack([pts_colmap,np.ones(pts_colmap.shape[1])])\n", + " pts_argo = M @ pts_homo\n", + " return np.mean(pts_argo, axis=1), flag, np.mean(pts_colmap, axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No feature points detected in the bounding box\n", + "No feature points detected in the bounding box\n", + "No feature points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "No feature points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "More than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "No feature points detected in the bounding box\n", + "No feature points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "No feature points detected in the bounding box\n", + "No feature points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "Less than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n", + "No feature points detected in the bounding box\n", + "More than 3 points detected in the bounding box\n" + ] + } + ], + "source": [ + "#compute traffic sign positions\n", + "marks = []\n", + "marks_col = []\n", + "names = []\n", + "scores = []\n", + "classes = []\n", + "for j in range(len(boxes_images_list)):\n", + " image_name = 'ring_front_center/' + boxes_images_list[j]\n", + " if os.path.exists(tracking_dataset_dir +log_id + '/' + image_name):\n", + " im = cv2.imread(tracking_dataset_dir +log_id + '/'+ image_name)\n", + " for cc,box in enumerate(boxes_boxes_list[j]):\n", + " x1 = int(box[0] )#* 1200)\n", + " y1 = int(box[1] )#* 1920)\n", + " x2 = int(box[2] )#* 1200)\n", + " y2 = int(box[3] )#* 1920)\n", + " if (y2-y1) * (x2-x1) < 500: #1450\n", + " continue\n", + " if image_name not in points_colmap.keys():\n", + " continue\n", + " argo_box, flag, colmap_box = bbox2argoverse(x1,y1,x2,y2,points_colmap,points3D,M,image_name)\n", + " if flag: \n", + " continue\n", + " #if len(marks) == 0:\n", + " marks.append([argo_box[0], argo_box[1], argo_box[2]])\n", + " names.append(image_name)\n", + " scores.append(boxes_scores_list[j][cc])\n", + " classes.append(boxes_class_list[j][cc])\n", + " marks_col.append(colmap_box)\n", + "#marks = np.array(marks)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[20 17 19 13 12 15 2 16 18 1 3 14 9 4 11 10 0 7 6 8 5]\n", + "[[(2576.08101101, 1199.09703696, 16.49887731)]\n", + " [(2589.31012735, 1205.72015614, 17.51022144)]\n", + " [(2589.34219527, 1205.66718961, 17.02531521)]\n", + " [(2596.8833383 , 1191.60758704, 20.82102332)]\n", + " [(2597.89818131, 1192.48768664, 20.10728931)]\n", + " [(2598.15849598, 1192.72812317, 19.92565796)]\n", + " [(2598.77548975, 1193.89470479, 21.829439 )]\n", + " [(2599.54815885, 1198.55306603, 20.12784844)]\n", + " [(2599.57324317, 1198.47995138, 19.72473012)]\n", + " [(2607.40562423, 1223.21491884, 18.13321268)]\n", + " [(2607.47383197, 1204.04525999, 21.24501157)]\n", + " [(2607.6082839 , 1223.06999855, 17.06211994)]\n", + " [(2616.01359854, 1214.30841455, 21.41689872)]\n", + " [(2616.29632059, 1213.99998821, 20.65506912)]\n", + " [(2621.17911014, 1235.42716012, 16.85858402)]\n", + " [(2621.24977727, 1235.32736113, 16.36928504)]\n", + " [(2630.87936547, 1244.47467057, 17.42338999)]\n", + " [(2630.99311724, 1244.51958524, 16.78888518)]\n", + " [(2631.01543658, 1244.27158608, 16.23551428)]\n", + " [(2639.50945263, 1235.46937879, 19.00287162)]\n", + " [(2645.10659619, 1257.13896284, 16.33234752)]]\n", + "[2576.08101101 1199.09703696 16.49887731] 3 ring_front_center/ring_front_center_315978421517357424.jpg 1\n", + "[2589.31012735 1205.72015614 17.51022144] 3 ring_front_center/ring_front_center_315978414624255144.jpg 6\n", + "[2589.34219527 1205.66718961 17.02531521] 3 ring_front_center/ring_front_center_315978415423455864.jpg 3\n", + "[2596.8833383 1191.60758704 20.82102332] 1 ring_front_center/ring_front_center_315978411827057072.jpg 1\n", + "[2597.89818131 1192.48768664 20.10728931] 1 ring_front_center/ring_front_center_315978411527355224.jpg 4\n", + "[2598.15849598 1192.72812317 19.92565796] 1 ring_front_center/ring_front_center_315978412326560360.jpg 1\n", + "[2598.77548975 1193.89470479 21.829439 ] 11 ring_front_center/ring_front_center_315978406532359208.jpg 2\n", + "[2599.54815885 1198.55306603 20.12784844] 3 ring_front_center/ring_front_center_315978413625254760.jpg 6\n", + "[2599.57324317 1198.47995138 19.72473012] 3 ring_front_center/ring_front_center_315978414624255144.jpg 1\n", + "[2607.40562423 1223.21491884 18.13321268] 11 ring_front_center/ring_front_center_315978406532359208.jpg 56\n", + "[2607.47383197 1204.04525999 21.24501157] 11 ring_front_center/ring_front_center_315978406732157848.jpg 1\n", + "[2607.6082839 1223.06999855 17.06211994] 3 ring_front_center/ring_front_center_315978411827057072.jpg 2\n", + "[2616.01359854 1214.30841455 21.41689872] 4 ring_front_center/ring_front_center_315978408730157152.jpg 28\n", + "[2616.29632059 1213.99998821 20.65506912] 11 ring_front_center/ring_front_center_315978406832057128.jpg 48\n", + "[2621.17911014 1235.42716012 16.85858402] 3 ring_front_center/ring_front_center_315978409729158088.jpg 3\n", + "[2621.24977727 1235.32736113 16.36928504] 3 ring_front_center/ring_front_center_315978409429460248.jpg 4\n", + "[2630.87936547 1244.47467057 17.42338999] 4 ring_front_center/ring_front_center_315978406332560648.jpg 26\n", + "[2630.99311724 1244.51958524 16.78888518] 3 ring_front_center/ring_front_center_315978408530355712.jpg 2\n", + "[2631.01543658 1244.27158608 16.23551428] 3 ring_front_center/ring_front_center_315978408130752752.jpg 4\n", + "[2639.50945263 1235.46937879 19.00287162] 3 ring_front_center/ring_front_center_315978408530355712.jpg 1\n", + "[2645.10659619 1257.13896284 16.33234752] 3 ring_front_center/ring_front_center_315978406832057128.jpg 1\n", + "21\n" + ] + } + ], + "source": [ + "db_marks = []\n", + "db_counts = []\n", + "db_names = []\n", + "db_classes = []\n", + "\n", + "def check_adjacency(mark, db_mark):\n", + " l2 = np.sqrt((mark[0] - db_mark[0])**2 + (mark[1] - db_mark[1])**2)\n", + " dz = np.absolute(mark[2] - db_mark[2])\n", + " return l2 < 0.6 and dz < 0.4\n", + "\n", + "def check_category(category, db_class):\n", + " return category == db_class\n", + "\n", + "for i in range(len(marks)):\n", + " new_sign = True\n", + " mark = marks[i]\n", + " if i == 0:\n", + " db_marks.append(mark)\n", + " db_classes.append(classes[i])\n", + " db_names.append(names[i])\n", + " db_counts.append(1)\n", + " else:\n", + " for cc in range(len(db_marks)):\n", + " db_mark = db_marks[cc]\n", + " if check_adjacency(mark, db_mark) and check_category(classes[i], db_classes[cc]):\n", + " new_sign = False\n", + " db_counts[cc] += 1\n", + " db_marks[cc] = 1 * mark + 0 * db_mark\n", + " break\n", + " if new_sign:\n", + " db_marks.append(mark)\n", + " db_classes.append(classes[i])\n", + " db_names.append(names[i])\n", + " db_counts.append(1)\n", + "\n", + "marks = np.array(db_marks)\n", + "order = np.argsort(marks[:, 0])\n", + "print(order)\n", + "db_marks = np.array(db_marks)[order]\n", + "db_classes = np.array(db_classes)[order]\n", + "db_names = np.array(db_names)[order]\n", + "db_counts = np.array(db_counts)[order]\n", + "print(np.sort(marks.view('f8,f8,f8'), order=['f0'], axis=0))\n", + "\n", + "\n", + "for i in range(len(db_marks)):\n", + " print(db_marks[i], db_classes[i], db_names[i], db_counts[i])\n", + "print(len(db_marks))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.4266924249693159\n" + ] + } + ], + "source": [ + "x1 = [2616.29632059,1213.99998821]\n", + "x2 = [2616.56821749,1214.3288324]\n", + "print(np.sqrt((x1[1]-x2[1])**2 + (x1[0]-x2[0])**2))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "#save detected traffic signs \n", + "argo_mark_poses = np.array(marks)\n", + "colmap_mark_poses = np.array(marks_col)\n", + "categories = np.array(classes)\n", + "scores = np.array(scores)\n", + "colmap2argo = M\n", + "colmap_camera_poses = points_3d_colmap\n", + "argo_camera_poses = points_3d_argo\n", + "\n", + "np.savez(location_output_dir + log_id+'_projected.npz', \n", + " argo_mark_poses=argo_mark_poses, \n", + " colmap_mark_poses=colmap_mark_poses,\n", + " categories=categories,\n", + " scores=scores,\n", + " colmap2argo=colmap2argo,\n", + " argo_camera_poses=argo_camera_poses,\n", + " colmap_camera_poses=colmap_camera_poses\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4, 156) (156, 3)\n", + "0.06950603208036611\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import copy\n", + "\n", + "#R, t = svd_rt(points_3d_colmap_scaled.T[:,:10], points_3d_argo.T[:,:10])\n", + "#t = t.reshape(-1)\n", + "\n", + "fig = plt.figure(figsize=(15,15))\n", + "ax = fig.add_subplot(111)\n", + "#xcenter = (points_3d_argo[0,0] + points_3d_argo[-1,0])/2 #+ 15\n", + "#ycenter = (points_3d_argo[0,1] + points_3d_argo[-1,1])/2#+ 15\n", + "\n", + "xcenter = points_3d_argo[20,0]\n", + "ycenter = points_3d_argo[20,1]\n", + " \n", + "xmin = xcenter - 100 # 150 #70\n", + "xmax = xcenter + 90 # 150 #70\n", + "ymin = ycenter - 100 # 150 #70\n", + "ymax = ycenter + 90 # 150 #70\n", + "\n", + "#xmin = xcenter - 10 # 150\n", + "#xmax = xcenter + 10 # 150\n", + "#ymin = ycenter - 10 # 150\n", + "#ymax = ycenter + 10 # 150\n", + "\n", + "#2.59058920e+03 1.20444779e+03\n", + "\n", + "#2.59058802e+03 1.20428979e+03\n", + "#2.58554503e+03 1.18528893e+03\n", + "points_3d_colmap_h = np.vstack([points_3d_colmap.T, np.ones(points_3d_colmap.shape[0])])\n", + "print(points_3d_colmap_h.shape,points_3d_colmap.shape)\n", + "projected = M @ points_3d_colmap_h\n", + "\n", + "#ax.scatter(points_3d_argo[2:8,0], points_3d_argo[2:8,1], 50, color=\"g\", marker=\".\", zorder=2, label='ground truth front center camera poses')\n", + "#ax.scatter(projected[0,2:8], projected[1,2:8], 50, color=\"blue\", marker=\"x\", zorder=2, label=\"Projected COLMAP front center camera poses\")\n", + "\n", + "# Nov 06 Comment These\n", + "#ax.scatter(points_3d_argo[:,0], points_3d_argo[:,1], 50, color=\"g\", marker=\".\", zorder=2, label='ground truth front center camera poses')\n", + "#ax.scatter(projected[0,:], projected[1,:], 50, color=\"blue\", marker=\"x\", zorder=2, label=\"Projected COLMAP front center camera poses\")\n", + "# Dec 02 Comment These\n", + "#ax.scatter(marks[:,0], marks[:,1] , 50, color=\"c\", marker=\"x\", zorder=2, label=\"Detected Signs\")\n", + "\n", + "db_marks[6, 0] = 0\n", + "db_marks[6, 1] = 0\n", + "db_marks[3, 0] = 0\n", + "db_marks[3, 1] = 0\n", + "ax.scatter(db_marks[:,0], db_marks[:,1] , 50, color=\"c\", marker=\"x\", zorder=2, label=\"Detected Signs\")\n", + "\n", + "\n", + "##label gt\n", + "#ax.scatter(our[:,0], our[:,1], 50, color=\"r\", marker=\"x\", zorder=2, label=\"Our Results\")\n", + "#ax.scatter(gt[:,0], gt[:,1], 50, color=\"g\", marker=\"x\", zorder=2, label=\"GT Positions\")\n", + "\n", + "a = np.sum((projected[0:3,:] - points_3d_argo.T)**2,axis=0)\n", + "rmse = np.mean(a)\n", + "rmse = np.sqrt(rmse)\n", + "print(rmse)\n", + "\n", + "\n", + "\n", + "\n", + "#ax.scatter(marks2[:,0], marks2[:,1] , 50, color=\"g\", marker=\"x\", zorder=2, label=\"Tracked Signs\")\n", + "#ax.scatter([triangulated[0]], [triangulated[1]], 50, color=\"r\", marker=\"x\", zorder=2, label=\"Triangulated Sign Position\")\n", + "ax.set_xlim([xmin, xmax])\n", + "ax.set_ylim([ymin, ymax])\n", + "local_lane_polygons = am.find_local_lane_polygons([xmin, xmax, ymin, ymax], city_name)\n", + "local_das = am.find_local_driveable_areas([xmin, xmax, ymin, ymax], city_name)\n", + "\n", + "ax.legend(fontsize='xx-large')\n", + "\n", + "domv.render_bev_labels_mpl( \n", + " city_name,\n", + " ax,\n", + " \"city_axis\",\n", + " None,\n", + " copy.deepcopy(local_lane_polygons),\n", + " copy.deepcopy(local_das),\n", + " argoverse_loader.log_list[log_index],\n", + " argoverse_data.lidar_timestamp_list[100],\n", + " city_to_egovehicle_se3,\n", + " am,\n", + "\n", + ")\n", + "\n", + "plt.savefig(\"rigid_transform.png\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/3D_projection/projection.py b/3D_projection/projection.py index 58f1fa6..dcc3e9f 100644 --- a/3D_projection/projection.py +++ b/3D_projection/projection.py @@ -1,28 +1,32 @@ from transforms import affine_matrix_from_points from argoverse.data_loading.argoverse_tracking_loader import ArgoverseTrackingLoader from scipy.spatial import transform +import os #flags that determine which ring cameras to use use_fc, use_fl, use_sl, use_rl, use_rr, use_sr, use_fr = [1, 1, 0, 0, 0, 0, 0] #path to the argoverse dataset -tracking_dataset_dir = '../argoverse-tracking/sample/' - +tracking_dataset_dir = '~/argoverse-tracking/sample/' +tracking_dataset_dir = os.path.expanduser(tracking_dataset_dir) #log_index determines which log in the argoverse dataset directory to use #index 0 is the first log in $tracking_dataset_dir, and index 1 is the second log -log_index = 2 +log_index = 0 argoverse_loader = ArgoverseTrackingLoader(tracking_dataset_dir) log_id = argoverse_loader.log_list[log_index] argoverse_data = argoverse_loader[log_index] #colmap_output_dir points to the results from COLMAP -colmap_output_dir = 'reconstruction/sparse/' + log_id + '/' +colmap_output_dir = '~/argoverse-tracking/sample2/output/' +colmap_output_dir = os.path.expanduser(colmap_output_dir) #detection results directory -detection_output_dir = './' +detection_output_dir = '~/argoverse-tracking/sample2/detector_output/' +detection_output_dir = os.path.expanduser(detection_output_dir) #where to save computed traffic sign locations -location_output_dir = './' +location_output_dir = '~/argoverse-tracking/sample2/projection_output/' +location_output_dir = os.path.expanduser(location_output_dir) @@ -30,7 +34,6 @@ #loading argoverse HD map import matplotlib import matplotlib.pyplot as plt -from visualize_30hz_benchmark_data_on_map import DatasetOnMapVisualizer from argoverse.map_representation.map_api import ArgoverseMap am = ArgoverseMap() city_name = argoverse_data.city_name @@ -40,7 +43,6 @@ import logging logger = logging.getLogger() logger.setLevel(logging.CRITICAL) -domv = DatasetOnMapVisualizer(dataset_dir, experiment_prefix, use_existing_files=use_existing_files, log_id=argoverse_data.current_log) @@ -334,6 +336,8 @@ def bbox2argoverse(x1,y1,x2,y2,points_colmap,points3D,M,image_name): colmap_camera_poses = points_3d_colmap argo_camera_poses = points_3d_argo +if not os.path.exists(location_output_dir): + os.makedirs(location_output_dir) np.savez(location_output_dir + log_id+'_projected.npz', argo_mark_poses=argo_mark_poses, colmap_mark_poses=colmap_mark_poses, diff --git a/3D_projection/rigid_transform.png b/3D_projection/rigid_transform.png new file mode 100644 index 0000000000000000000000000000000000000000..317e5b19f0057011fe6bafc26c01fafcb7e3c849 GIT binary patch literal 225874 zcmeEu_dnHr{5M(0KI&v19GvRJF)A6E$EM6s$4n@D@11e%Sw_g-GqQzjlD)FCLnuNB z_gmNZe%$}T{nOV&kE^R(oX`9H8qamEP&E~KN-{<=JUl!~MFklRJUl|7%U=*;@Fzc@ 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argoverse.data_loading.frame_label_accumulator import PerFrameLabelAccumulator +from argoverse.data_loading.synchronization_database import SynchronizationDB +from argoverse.map_representation.map_api import ArgoverseMap +from argoverse.utils.camera_stats import RING_CAMERA_LIST, STEREO_CAMERA_LIST +from argoverse.utils.cuboid_interior import filter_point_cloud_to_bbox_2D_vectorized +from argoverse.utils.ffmpeg_utils import write_nonsequential_idx_video, write_video +from argoverse.utils.geometry import rotate_polygon_about_pt +from argoverse.utils.mpl_plotting_utils import draw_lane_polygons, plot_bbox_2D +from argoverse.utils.pkl_utils import load_pkl_dictionary +from argoverse.utils.ply_loader import load_ply +from argoverse.utils.se3 import SE3 +from argoverse.visualization.ground_visualization import draw_ground_pts_in_image +from argoverse.visualization.mpl_point_cloud_vis import draw_point_cloud_bev + +logging.basicConfig(stream=sys.stdout, level=logging.DEBUG) +logger = logging.getLogger(__name__) + +IS_OCCLUDED_FLAG = 100 +LANE_TANGENT_VECTOR_SCALING = 4 + +""" +Code to plot track label trajectories on a map, for the tracking benchmark. +""" + + +class DatasetOnMapVisualizer: + def __init__( + self, + dataset_dir: str, + experiment_prefix: str, + use_existing_files: bool = True, + log_id: str = None, + ) -> None: + """We will cache the accumulated trajectories per city, per log, and per frame + for the tracking benchmark. + """ + self.plot_lane_tangent_arrows = True + self.plot_lidar_bev = True + self.plot_lidar_in_img = False + self.experiment_prefix = experiment_prefix + self.dataset_dir = dataset_dir + self.labels_dir = dataset_dir + self.sdb = SynchronizationDB(self.dataset_dir) + + if log_id is None: + tmp_dir = tempfile.gettempdir() + per_city_traj_dict_fpath = f"{tmp_dir}/per_city_traj_dict_{experiment_prefix}.pkl" + log_egopose_dict_fpath = f"{tmp_dir}/log_egopose_dict_{experiment_prefix}.pkl" + log_timestamp_dict_fpath = f"{tmp_dir}/log_timestamp_dict_{experiment_prefix}.pkl" + if not use_existing_files: + # write the accumulate data dictionaries to disk + PerFrameLabelAccumulator(dataset_dir, dataset_dir, experiment_prefix) + + self.per_city_traj_dict = load_pkl_dictionary(per_city_traj_dict_fpath) + self.log_egopose_dict = load_pkl_dictionary(log_egopose_dict_fpath) + self.log_timestamp_dict = load_pkl_dictionary(log_timestamp_dict_fpath) + else: + pfa = PerFrameLabelAccumulator(dataset_dir, dataset_dir, experiment_prefix, save=False) + pfa.accumulate_per_log_data(log_id=log_id) + self.per_city_traj_dict = pfa.per_city_traj_dict + self.log_egopose_dict = pfa.log_egopose_dict + self.log_timestamp_dict = pfa.log_timestamp_dict + + def plot_log_one_at_a_time(self, log_id="", idx=-1, save_video=True, city=""): + """ + Playback a log in the static context of a map. + In the far left frame, we show the car moving in the map. In the middle frame, we show + the car's LiDAR returns (in the map frame). In the far right frame, we show the front camera's + RGB image. + """ + avm = ArgoverseMap() + for city_name, trajs in self.per_city_traj_dict.items(): + if city != "": + if city != city_name: + continue + if city_name not in ["PIT", "MIA"]: + logger.info("Unknown city") + continue + + log_ids = [] + logger.info(f"{city_name} has {len(trajs)} tracks") + + if log_id == "": + # first iterate over the instance axis + for traj_idx, (traj, log_id) in enumerate(trajs): + log_ids += [log_id] + else: + log_ids = [log_id] + + # eliminate the duplicates + for log_id in set(log_ids): + logger.info(f"Log: {log_id}") + + ply_fpaths = sorted(glob.glob(f"{self.dataset_dir}/{log_id}/lidar/PC_*.ply")) + + # then iterate over the time axis + for i, ply_fpath in enumerate(ply_fpaths): + if idx != -1: + if i != idx: + continue + if i % 500 == 0: + logger.info(f"\tOn file {i} of {log_id}") + lidar_timestamp = ply_fpath.split("/")[-1].split(".")[0].split("_")[-1] + lidar_timestamp = int(lidar_timestamp) + if lidar_timestamp not in self.log_egopose_dict[log_id]: + all_available_timestamps = sorted(self.log_egopose_dict[log_id].keys()) + diff = (all_available_timestamps[0] - lidar_timestamp) / 1e9 + logger.info(f"{diff:.2f} sec before first labeled sweep") + continue + + logger.info(f"\tt={lidar_timestamp}") + if self.plot_lidar_bev: + fig = plt.figure(figsize=(15, 15)) + plt.title(f"Log {log_id} @t={lidar_timestamp} in {city_name}") + plt.axis("off") + # ax_map = fig.add_subplot(131) + ax_3d = fig.add_subplot(111) + # ax_rgb = fig.add_subplot(133) + + # need the ego-track here + pose_city_to_ego = self.log_egopose_dict[log_id][lidar_timestamp] + xcenter = pose_city_to_ego["translation"][0] + ycenter = pose_city_to_ego["translation"][1] + ego_center_xyz = np.array(pose_city_to_ego["translation"]) + + city_to_egovehicle_se3 = SE3( + rotation=pose_city_to_ego["rotation"], + translation=ego_center_xyz, + ) + + if self.plot_lidar_bev: + xmin = xcenter - 80 # 150 + xmax = xcenter + 80 # 150 + ymin = ycenter - 80 # 150 + ymax = ycenter + 80 # 150 + # ax_map.scatter(xcenter, ycenter, 200, color="g", marker=".", zorder=2) + # ax_map.set_xlim([xmin, xmax]) + # ax_map.set_ylim([ymin, ymax]) + local_lane_polygons = avm.find_local_lane_polygons([xmin, xmax, ymin, ymax], city_name) + local_das = avm.find_local_driveable_areas([xmin, xmax, ymin, ymax], city_name) + + lidar_pts = load_ply(ply_fpath) + if self.plot_lidar_in_img: + draw_ground_pts_in_image( + self.sdb, + copy.deepcopy(lidar_pts), + city_to_egovehicle_se3, + avm, + log_id, + lidar_timestamp, + city_name, + self.dataset_dir, + self.experiment_prefix, + plot_ground=True, + ) + + if self.plot_lidar_bev: + driveable_area_pts = copy.deepcopy(lidar_pts) + driveable_area_pts = city_to_egovehicle_se3.transform_point_cloud( + driveable_area_pts + ) # put into city coords + driveable_area_pts = avm.remove_non_driveable_area_points(driveable_area_pts, city_name) + driveable_area_pts = avm.remove_ground_surface(driveable_area_pts, city_name) + driveable_area_pts = city_to_egovehicle_se3.inverse_transform_point_cloud( + driveable_area_pts + ) # put back into ego-vehicle coords + self.render_bev_labels_mpl( + city_name, + ax_3d, + "ego_axis", + lidar_pts, + copy.deepcopy(local_lane_polygons), + copy.deepcopy(local_das), + log_id, + lidar_timestamp, + city_to_egovehicle_se3, + avm, + ) + + fig.tight_layout() + if not Path(f"{self.experiment_prefix}_per_log_viz/{log_id}").exists(): + os.makedirs(f"{self.experiment_prefix}_per_log_viz/{log_id}") + + plt.savefig( + f"{self.experiment_prefix}_per_log_viz/{log_id}/{city_name}_{log_id}_{lidar_timestamp}.png", + dpi=400, + ) + # plt.show() + # plt.close("all") + + # after all frames are processed, write video with saved images + if save_video: + if self.plot_lidar_bev: + fps = 10 + img_wildcard = f"{self.experiment_prefix}_per_log_viz/{log_id}/{city_name}_{log_id}_%*.png" + output_fpath = f"{self.experiment_prefix}_per_log_viz/{log_id}_lidar_roi_nonground.mp4" + write_nonsequential_idx_video(img_wildcard, output_fpath, fps) + + if self.plot_lidar_in_img: + for camera_name in RING_CAMERA_LIST + STEREO_CAMERA_LIST: + image_prefix = ( + f"{self.experiment_prefix}_per_log_viz/{log_id}/{camera_name}/{camera_name}_%d.jpg" + ) + output_prefix = f"{self.experiment_prefix}_per_log_viz/{log_id}_{camera_name}" + write_video(image_prefix, output_prefix) + + def render_bev_labels_mpl( + self, + city_name: str, + ax: plt.Axes, + axis: str, + lidar_pts: np.ndarray, + local_lane_polygons: np.ndarray, + local_das: np.ndarray, + log_id: str, + timestamp: int, + city_to_egovehicle_se3: SE3, + avm: ArgoverseMap, + ) -> None: + """Plot nearby lane polygons and nearby driveable areas (da) on the Matplotlib axes. + + Args: + city_name: The name of a city, e.g. `"PIT"` + ax: Matplotlib axes + axis: string, either 'ego_axis' or 'city_axis' to demonstrate the + lidar_pts: Numpy array of shape (N,3) + local_lane_polygons: Polygons representing the local lane set + local_das: Numpy array of objects of shape (N,) where each object is of shape (M,3) + log_id: The ID of a log + timestamp: In nanoseconds + city_to_egovehicle_se3: Transformation from egovehicle frame to city frame + avm: ArgoverseMap instance + """ + if axis is not "city_axis": + # rendering instead in the egovehicle reference frame + for da_idx, local_da in enumerate(local_das): + local_da = city_to_egovehicle_se3.inverse_transform_point_cloud(local_da) + local_das[da_idx] = rotate_polygon_about_pt(local_da, city_to_egovehicle_se3.rotation, np.zeros(3)) + + for lane_idx, local_lane_polygon in enumerate(local_lane_polygons): + local_lane_polygon = city_to_egovehicle_se3.inverse_transform_point_cloud(local_lane_polygon) + local_lane_polygons[lane_idx] = rotate_polygon_about_pt( + local_lane_polygon, city_to_egovehicle_se3.rotation, np.zeros(3) + ) + + draw_lane_polygons(ax, local_lane_polygons) + draw_lane_polygons(ax, local_das, color="tab:pink") + + if axis is not "city_axis": + lidar_pts = rotate_polygon_about_pt(lidar_pts, city_to_egovehicle_se3.rotation, np.zeros((3,))) + draw_point_cloud_bev(ax, lidar_pts) + + objects = self.log_timestamp_dict[log_id][timestamp] + + all_occluded = True + for frame_rec in objects: + if frame_rec.occlusion_val != IS_OCCLUDED_FLAG: + all_occluded = False + + if not all_occluded: + for i, frame_rec in enumerate(objects): + bbox_city_fr = frame_rec.bbox_city_fr + bbox_ego_frame = frame_rec.bbox_ego_frame + color = frame_rec.color + + if frame_rec.occlusion_val != IS_OCCLUDED_FLAG: + bbox_ego_frame = rotate_polygon_about_pt( + bbox_ego_frame, city_to_egovehicle_se3.rotation, np.zeros((3,)) + ) + if axis is "city_axis": + plot_bbox_2D(ax, bbox_city_fr, color) + if self.plot_lane_tangent_arrows: + bbox_center = np.mean(bbox_city_fr, axis=0) + tangent_xy, conf = avm.get_lane_direction( + query_xy_city_coords=bbox_center[:2], + city_name=city_name, + ) + dx = tangent_xy[0] * LANE_TANGENT_VECTOR_SCALING + dy = tangent_xy[1] * LANE_TANGENT_VECTOR_SCALING + ax.arrow( + bbox_center[0], + bbox_center[1], + dx, + dy, + color="r", + width=0.5, + zorder=2, + ) + else: + plot_bbox_2D(ax, bbox_ego_frame, color) + cuboid_lidar_pts, _ = filter_point_cloud_to_bbox_2D_vectorized( + bbox_ego_frame[:, :2], copy.deepcopy(lidar_pts) + ) + if cuboid_lidar_pts is not None: + draw_point_cloud_bev(ax, cuboid_lidar_pts, color) + + else: + logger.info(f"all occluded at {timestamp}") + xcenter = city_to_egovehicle_se3.translation[0] + ycenter = city_to_egovehicle_se3.translation[1] + ax.text(xcenter - 146, ycenter + 50, "ALL OBJECTS OCCLUDED", fontsize=30) + + def render_front_camera_on_axis(self, ax: plt.Axes, timestamp: int, log_id: str) -> None: + """ + Args: + ax: Matplotlib axes + timestamp: The timestamp + log_id: The ID of a log + """ + ax.imshow(imageio.imread(f"{self.dataset_dir}/{log_id}/ring_front_center/ring_front_center_{timestamp}.jpg")) + + +def visualize_30hz_benchmark_data_on_map(args: Any) -> None: + """""" + domv = DatasetOnMapVisualizer( + args.dataset_dir, + args.experiment_prefix, + log_id=args.log_id, + use_existing_files=args.use_existing_files, + ) + # Plotting does not work on AWS! The figure cannot be refreshed properly + # Thus, plotting must be performed locally. + domv.plot_log_one_at_a_time() + + +if __name__ == "__main__": + # Parse command line arguments + parser = argparse.ArgumentParser() + parser.add_argument("--dataset_dir", type=str, help="path to where the logs live") + parser.add_argument( + "--experiment_prefix", + default="argoverse_bev_viz", + type=str, + help="results will be saved in a folder with this prefix for its name", + ) + parser.add_argument( + "--log_id", + default=None, + type=str, + help="log ids, this is the folder name in argoverse-tracking/*/[log_id]", + ) + parser.add_argument( + "--use_existing_files", + action="store_true", + help="load pre-saved log data from pkl dictionaries instead of scraping it", + ) + args = parser.parse_args() + logger.info(args) + visualize_30hz_benchmark_data_on_map(args) diff --git a/README.md b/README.md index 030525b..e563036 100644 --- a/README.md +++ b/README.md @@ -9,11 +9,107 @@ - 3D_projection: transformation from colmap coordinates to argoverse coordinates - sign_detector: traffic sign detector, based on Detectron2 -## Pipeline +## High Level Pipeline 1. Run traffic sign detection on raw images `./sign_detector` 2. Run Colmap SfM on raw images `colmap_script.txt` 3. Project Colmap points to argoverse map `3D_projection` +## Step 1: Run Traffic Sign Detection +1. [Visit the ReadMe](/sign_detector) +## Step 2: Run Colmap +1. Install [Colmap](https://colmap.github.io/) from the premade [binaries](https://demuc.de/colmap/#download). For example, `COLMAP-3.5-mac-no-cuda.zip` +2. After unpackaging it in your user directory, you can use the command line `~/COLMAP.app/Contents/MacOS/colmap` +3. Download the [sample v1.1](https://s3.amazonaws.com/argoai-argoverse/tracking_sample_v1.1.tar.gz) 3D tracking dataset from argoverse. Configure it to only include the following folders, so that it has the following structure. Here we only use two cameras out of the 7 that exist on the camera ring and have placed it in our home directory: +```` + /path/to/argoverse-tracking/sample/... + +── c6911883-1843-3727-8eaa-41dc8cda8993 + │ +── ring_front_center + │ │ +── image1.jpg + │ │ +── image2.jpg + │ │ +── ... + │ │ +── imageN.jpg + │ +── ring_front_left + │ │ +── image1.jpg + │ │ +── image2.jpg + │ │ +── ... + │ │ +── imageN.jpg +```` +4. Run your extractor (1 minute). We specify the location of +- the database to to save the intermediate colmap database. +- the directory to where the argoverse images are stored. This method will extract features from all of the images in the folder including those in subfoolders. +```` +~/COLMAP.app/Contents/MacOS/colmap feature_extractor --database_path ~/argoverse-tracking/sample/c6911883-1843-3727-8eaa-41dc8cda8993/database.db --image_path ~/argoverse-tracking/sample/c6911883-1843-3727-8eaa-41dc8cda8993 --ImageReader.single_camera_per_folder 1 +```` +5. Run your matcher (6 hours and 2.6gb). We specify +- database_path: the database path from step 4 +```` +~/COLMAP.app/Contents/MacOS/colmap exhaustive_matcher --database_path ~/argoverse-tracking/sample/c6911883-1843-3727-8eaa-41dc8cda8993/database.db +```` +6. Run the mapper. We specify +- database_path: the database path from step 4 +- output_path: the path to store intermediate binary results. This needs to be manually created. +```` +~/COLMAP.app/Contents/MacOS/colmap mapper --database_path ~/argoverse-tracking/sample/c6911883-1843-3727-8eaa-41dc8cda8993/database.db --image_path ~/argoverse-tracking/sample/c6911883-1843-3727-8eaa-41dc8cda8993 --output_path ~/argoverse-tracking/sample/tmp +```` +7. Run the model converter. We specify +- input_path: the path from step 6 that stored the intermediate binary results +- output_path: the path to save the final colmap models, which is input to the 3D projection module. +directory to save final colmap models, which is input to the 3D projection module. +```` +~/COLMAP.app/Contents/MacOS/colmap model_converter --input_path ~/argoverse-tracking/sample/tmp --output_path ~/argoverse-tracking/sample/output --output_type TXT +```` +Your end folder should look like +```` + /path/to/argoverse-tracking/sample/... + +── tmp + │ +── 0 + │ │ +── cameras.bin + │ │ +── images.bin + │ │ +── points3D.bin + │ │ +── project.ini + +── output + │ +── cameras.txt + │ +── images.txt + │ +── points3D.txt + +── c6911883-1843-3727-8eaa-41dc8cda8993 + │ +── ring_front_center + │ │ +── image1.jpg + │ │ +── ... + │ +── ring_front_left + │ │ +── image1.jpg + │ │ +── ... +```` +## Step 3: Project Colmap points to argoverse +1. Install the [argoverse API](https://github.com/argoai/argoverse-api) +2. Modify the items below in projection.py. You will also need to include the original data in the sample folder. +- flags that determine which ring cameras to use: +```` +use_fc, use_fl, use_sl, use_rl, use_rr, use_sr, use_fr = [1, 1, 0, 0, 0, 0, 0] +```` +- path to the argoverse dataset +```` +tracking_dataset_dir = '~/argoverse-tracking/sample/' +```` +- log_index determines which log in the argoverse dataset directory to use +```` +log_index = 0 +```` +- colmap_output_dir points to the results from COLMAP +```` +colmap_output_dir = '~/argoverse-tracking/sample/output/' +```` +- detection results directory +```` +detection_output_dir = '~/argoverse-tracking/sample/detector_output/' +```` +- where to save computed traffic sign locations +```` +location_output_dir = '~/argoverse-tracking/sample/projection_output/' +```` +3. cd into the 3D projection folder and run project.py +```` +python projection.py +```` ## Docker We prepare a development-ready docker image at [docker hub](https://hub.docker.com/r/kuoyichun1102/colmap_detectron). Each time pushing to `prod` branch will trigger a build in the docker hub, so one should work on `prod` branch carefully. \ No newline at end of file diff --git a/sign_detector/.DS_Store b/sign_detector/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 GIT binary patch literal 6148 zcmeH~Jr2S!425mzP>H1@V-^m;4Wg<&0T*E43hX&L&p$$qDprKhvt+--jT7}7np#A3 zem<@ulZcFPQ@L2!n>{z**++&mCkOWA81W14cNZlEfg7;MkzE(HCqgga^y>{tEnwC%0;vJ&^%eQ zLs35+`xjp>T0 Date: Thu, 10 Dec 2020 15:15:36 -0500 Subject: [PATCH 2/4] update Readme --- .gitignore | 2 ++ 1 file changed, 2 insertions(+) create mode 100644 .gitignore diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..9f2e848 --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +.DS_Store +__pycache__/ From 51f0e40e2ead5e46bb0cd84fbb67e2dce906c830 Mon Sep 17 00:00:00 2001 From: Tommy Date: Thu, 10 Dec 2020 15:38:29 -0500 Subject: [PATCH 3/4] Remove ignored files --- .gitignore | 1 + .../new_projection-checkpoint.ipynb | 853 ------------------ .../__pycache__/transforms.cpython-37.pyc | Bin 52061 -> 0 bytes ..._30hz_benchmark_data_on_map.cpython-37.pyc | Bin 9410 -> 0 bytes sign_detector/.DS_Store | Bin 6148 -> 0 bytes sign_detector/Readme.md | 10 +- .../metrics/__pycache__/utils.cpython-36.pyc | Bin 1071 -> 0 bytes .../__pycache__/__init__.cpython-36.pyc | Bin 5761 -> 0 bytes sign_detector/readme.md | 108 --- third_party/ORB_SLAM2 | 1 - third_party/argoverse-api | 1 - third_party/colmap | 1 - 12 files changed, 6 insertions(+), 969 deletions(-) delete mode 100644 3D_projection/.ipynb_checkpoints/new_projection-checkpoint.ipynb delete mode 100644 3D_projection/__pycache__/transforms.cpython-37.pyc delete mode 100644 3D_projection/__pycache__/visualize_30hz_benchmark_data_on_map.cpython-37.pyc delete mode 100644 sign_detector/.DS_Store delete mode 100644 sign_detector/metrics/__pycache__/utils.cpython-36.pyc delete mode 100644 sign_detector/metrics/map_boxes/__pycache__/__init__.cpython-36.pyc delete mode 100644 sign_detector/readme.md delete mode 160000 third_party/ORB_SLAM2 delete mode 160000 third_party/argoverse-api delete mode 160000 third_party/colmap diff --git a/.gitignore b/.gitignore index 9f2e848..c1285cd 100644 --- a/.gitignore +++ b/.gitignore @@ -1,2 +1,3 @@ .DS_Store __pycache__/ +.ipynb_checkpoints \ No newline at end of file diff --git a/3D_projection/.ipynb_checkpoints/new_projection-checkpoint.ipynb b/3D_projection/.ipynb_checkpoints/new_projection-checkpoint.ipynb deleted file mode 100644 index bd35aa8..0000000 --- a/3D_projection/.ipynb_checkpoints/new_projection-checkpoint.ipynb +++ /dev/null @@ -1,853 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(156, 3) (156, 3)\n" - ] - } - ], - "source": [ - "from transforms import affine_matrix_from_points\n", - "from argoverse.data_loading.argoverse_tracking_loader import ArgoverseTrackingLoader\n", - "from scipy.spatial import transform\n", - "import os\n", - "\n", - "#flags that determine which ring cameras to use\n", - "use_fc, use_fl, use_sl, use_rl, use_rr, use_sr, use_fr = [1, 1, 0, 0, 0, 0, 0]\n", - "\n", - "#path to the argoverse dataset\n", - "tracking_dataset_dir = '~/argoverse-tracking/sample/'\n", - "tracking_dataset_dir = os.path.expanduser(tracking_dataset_dir)\n", - "\n", - "#log_index determines which log in the argoverse dataset directory to use\n", - "log_index = 0\n", - "argoverse_loader = ArgoverseTrackingLoader(tracking_dataset_dir)\n", - "log_id = argoverse_loader.log_list[log_index]\n", - "argoverse_data = argoverse_loader[log_index]\n", - "\n", - "#colmap_output_dir points to the results from COLMAP\n", - "colmap_output_dir = '~/argoverse-tracking/sample2/output/'\n", - "colmap_output_dir = os.path.expanduser(colmap_output_dir)\n", - "\n", - "#detection results directory\n", - "detection_output_dir = '~/argoverse-tracking/sample2/detector_output/'\n", - "detection_output_dir = os.path.expanduser(detection_output_dir)\n", - "\n", - "#where to save computed traffic sign locations\n", - "location_output_dir = '~/argoverse-tracking/sample2/projection_output/'\n", - "location_output_dir = os.path.expanduser(location_output_dir)\n", - "\n", - "\n", - "\n", - "\n", - "#loading argoverse HD map\n", - "import matplotlib\n", - "import matplotlib.pyplot as plt\n", - "from visualize_30hz_benchmark_data_on_map import DatasetOnMapVisualizer\n", - "from argoverse.map_representation.map_api import ArgoverseMap\n", - "am = ArgoverseMap()\n", - "city_name = argoverse_data.city_name\n", - "dataset_dir = tracking_dataset_dir\n", - "experiment_prefix = 'visualization_demo'\n", - "use_existing_files = True\n", - "import logging\n", - "logger = logging.getLogger()\n", - "logger.setLevel(logging.CRITICAL)\n", - "domv = DatasetOnMapVisualizer(dataset_dir, experiment_prefix, use_existing_files=use_existing_files, log_id=argoverse_data.current_log)\n", - "\n", - "\n", - "\n", - "#read in colmap results\n", - "import numpy as np\n", - "poses_colmap = dict()\n", - "points_colmap = dict()\n", - "id2name_colmap = dict()\n", - "points3D = dict()\n", - "\n", - "with open(colmap_output_dir + 'images.txt') as f:\n", - " lines = f.read().splitlines()\n", - "for i,line in enumerate(lines):\n", - " if line[0] == '#':\n", - " continue\n", - " else:\n", - " if i % 2 == 0:\n", - " fields = line.split(' ')\n", - " image_name = fields[-1]\n", - " image_id = int(fields[0])\n", - " quat = fields[1:5]\n", - " quatanion = np.array([float(q) for q in quat])\n", - " trans = fields[5:8]\n", - " translation = np.array([float(t) for t in trans])\n", - " camera_id = int(fields[8])\n", - " poses_colmap[image_name] = [quatanion, translation, image_id, camera_id]\n", - " id2name_colmap[image_name] = image_id\n", - " else:\n", - " fields = line.split(' ')\n", - " points_2d = np.array([float(pt) for pt in fields])\n", - " points_colmap[image_name] = points_2d\n", - " \n", - "with open(colmap_output_dir + 'points3D.txt') as f:\n", - " lines = f.read().splitlines()\n", - "for i,line in enumerate(lines):\n", - " if line[0] == '#':\n", - " continue\n", - " else: \n", - " fields = line.split(' ')\n", - " points_attr = np.array([float(pt) for pt in fields])\n", - " points3D[points_attr[0]] = np.array([points_attr[1],points_attr[2],points_attr[3]])\n", - " \n", - "\n", - " \n", - "#read in argoverse data\n", - "import numpy.linalg as LA\n", - "\n", - "calibration_ring_front_center = argoverse_data.get_calibration(\"ring_front_center\")\n", - "calibration_ring_front_left = argoverse_data.get_calibration(\"ring_front_left\")\n", - "calibration_ring_side_left = argoverse_data.get_calibration(\"ring_side_left\")\n", - "calibration_ring_rear_left = argoverse_data.get_calibration(\"ring_rear_left\")\n", - "calibration_ring_rear_right = argoverse_data.get_calibration(\"ring_rear_right\")\n", - "calibration_ring_side_right = argoverse_data.get_calibration(\"ring_side_right\")\n", - "calibration_ring_front_right = argoverse_data.get_calibration(\"ring_front_right\")\n", - "\n", - "argo_3d = []\n", - "colmap_3d = []\n", - "for idx in range(len(argoverse_data.image_list_sync['ring_front_center'])):\n", - " city_to_egovehicle_se3 = argoverse_data.get_pose(idx)\n", - " \n", - " if use_fc:\n", - " argo_pt = city_to_egovehicle_se3.transform_point_cloud(\n", - " calibration_ring_front_center.project_cam_to_ego(np.zeros((1,3))))[0]\n", - " \n", - " img_fc = argoverse_data.image_list_sync['ring_front_center'][idx]\n", - " img_fc = img_fc.split('/')\n", - " img_fc = img_fc[-2] + '/' + img_fc[-1]\n", - " if img_fc in poses_colmap.keys():\n", - " q = poses_colmap[img_fc][0]\n", - " quat = [q[1], q[2], q[3], q[0]]\n", - " R = transform.Rotation.from_quat(quat)\n", - " t = poses_colmap[img_fc][1]\n", - " colmap_pt = - R.as_matrix().T.dot(t) \n", - " colmap_3d.append(colmap_pt)\n", - " argo_3d.append(argo_pt)\n", - " \n", - "for idx in range(len(argoverse_data.image_list_sync['ring_front_center'])):\n", - " city_to_egovehicle_se3 = argoverse_data.get_pose(idx) \n", - " if use_fr:\n", - " argo_pt = city_to_egovehicle_se3.transform_point_cloud(\n", - " calibration_ring_front_right.project_cam_to_ego(np.zeros((1,3))))[0]\n", - " img_fr = argoverse_data.image_list_sync['ring_front_right'][idx]\n", - " img_fr = img_fr.split('/')\n", - " img_fr = img_fr[-2] + '/' + img_fr[-1]\n", - " if img_fr in poses_colmap.keys():\n", - " q = poses_colmap[img_fr][0]\n", - " quat = [q[1], q[2], q[3], q[0]]\n", - " R = transform.Rotation.from_quat(quat)\n", - " t = poses_colmap[img_fr][1]\n", - " colmap_pt = - R.as_matrix().T.dot(t) \n", - " colmap_3d.append(colmap_pt)\n", - " argo_3d.append(argo_pt)\n", - " \n", - " if use_fl:\n", - " argo_pt = city_to_egovehicle_se3.transform_point_cloud(\n", - " calibration_ring_front_left.project_cam_to_ego(np.zeros((1,3))))[0]\n", - " img_fl = argoverse_data.image_list_sync['ring_front_left'][idx]\n", - " img_fl = img_fl.split('/')\n", - " img_fl = img_fl[-2] + '/' + img_fl[-1]\n", - " if img_fl in poses_colmap.keys():\n", - " q = poses_colmap[img_fl][0]\n", - " quat = [q[1], q[2], q[3], q[0]]\n", - " R = transform.Rotation.from_quat(quat)\n", - " t = poses_colmap[img_fl][1]\n", - " colmap_pt = - R.as_matrix().T.dot(t) \n", - " colmap_3d.append(colmap_pt)\n", - " argo_3d.append(argo_pt)\n", - " \n", - " if use_sl:\n", - " argo_pt = city_to_egovehicle_se3.transform_point_cloud(\n", - " calibration_ring_side_left.project_cam_to_ego(np.zeros((1,3))))[0]\n", - " img_sl = argoverse_data.image_list_sync['ring_side_left'][idx]\n", - " img_sl = img_sl.split('/')\n", - " img_sl = img_sl[-2] + '/' + img_sl[-1]\n", - " if img_sl in poses_colmap.keys():\n", - " q = poses_colmap[img_sl][0]\n", - " quat = [q[1], q[2], q[3], q[0]]\n", - " R = transform.Rotation.from_quat(quat)\n", - " t = poses_colmap[img_sl][1]\n", - " colmap_pt = - R.as_matrix().T.dot(t) \n", - " colmap_3d.append(colmap_pt)\n", - " argo_3d.append(argo_pt)\n", - " \n", - " if use_sr:\n", - " argo_pt = city_to_egovehicle_se3.transform_point_cloud(\n", - " calibration_ring_side_right.project_cam_to_ego(np.zeros((1,3))))[0]\n", - " img_sr = argoverse_data.image_list_sync['ring_side_right'][idx]\n", - " img_sr = img_sr.split('/')\n", - " img_sr = img_sr[-2] + '/' + img_sr[-1]\n", - " if img_sr in poses_colmap.keys():\n", - " q = poses_colmap[img_sr][0]\n", - " quat = [q[1], q[2], q[3], q[0]]\n", - " R = transform.Rotation.from_quat(quat)\n", - " t = poses_colmap[img_sr][1]\n", - " colmap_pt = - R.as_matrix().T.dot(t) \n", - " colmap_3d.append(colmap_pt)\n", - " argo_3d.append(argo_pt)\n", - " \n", - " if use_rl:\n", - " argo_pt = city_to_egovehicle_se3.transform_point_cloud(\n", - " calibration_ring_rear_left.project_cam_to_ego(np.zeros((1,3))))[0]\n", - " img_rl = argoverse_data.image_list_sync['ring_rear_left'][idx]\n", - " img_rl = img_rl.split('/')\n", - " img_rl = img_rl[-2] + '/' + img_rl[-1]\n", - " if img_rl in poses_colmap.keys():\n", - " q = poses_colmap[img_rl][0]\n", - " quat = [q[1], q[2], q[3], q[0]]\n", - " R = transform.Rotation.from_quat(quat)\n", - " t = poses_colmap[img_rl][1]\n", - " colmap_pt = - R.as_matrix().T.dot(t) \n", - " colmap_3d.append(colmap_pt)\n", - " argo_3d.append(argo_pt)\n", - " \n", - " if use_rr:\n", - " argo_pt = city_to_egovehicle_se3.transform_point_cloud(\n", - " calibration_ring_rear_right.project_cam_to_ego(np.zeros((1,3))))[0]\n", - " img_rr = argoverse_data.image_list_sync['ring_rear_right'][idx]\n", - " img_rr = img_rr.split('/')\n", - " img_rr = img_rr[-2] + '/' + img_rr[-1]\n", - " if img_rr in poses_colmap.keys():\n", - " q = poses_colmap[img_rr][0]\n", - " quat = [q[1], q[2], q[3], q[0]]\n", - " R = transform.Rotation.from_quat(quat)\n", - " t = poses_colmap[img_rr][1]\n", - " colmap_pt = - R.as_matrix().T.dot(t) \n", - " colmap_3d.append(colmap_pt)\n", - " argo_3d.append(argo_pt)\n", - "\n", - " \n", - " \n", - " \n", - "#compute 3d transformation from argoverse to colmap \n", - "points_3d_argo = np.array(argo_3d)\n", - "points_3d_colmap = np.array(colmap_3d)\n", - "print(points_3d_argo.shape, points_3d_colmap.shape)\n", - "\n", - "M = affine_matrix_from_points(points_3d_colmap[:,:].T, points_3d_argo[:,:].T, shear=False)\n", - "#M = affine_matrix_from_points(points_3d_colmap[0:10,:].T, points_3d_argo[0:10,:].T, shear=False)\n", - "points_3d_colmap_homo = np.vstack([points_3d_colmap.T, np.ones(points_3d_colmap.shape[0])])\n", - "ttt = M.dot(points_3d_colmap_homo)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "#read in detection results\n", - "import cv2 \n", - "import os\n", - "import numpy as np\n", - "boxes = np.load(detection_output_dir + log_id + '.npz',allow_pickle=True)\n", - "order = np.argsort(boxes['images_list'])\n", - "boxes_images_list = boxes['images_list'][order]\n", - "boxes_boxes_list = boxes['boxes_list'][order]\n", - "boxes_scores_list = boxes['scores_list'][order]\n", - "boxes_class_list = boxes['category_id_list'][order]\n", - "def check_inside(x1,y1,x2,y2,x,y):\n", - " return x1 <= x and x <= x2 and y1 <= y and y <=y2\n", - "def bbox2argoverse(x1,y1,x2,y2,points_colmap,points3D,M,image_name):\n", - " flag = False\n", - " insider = []\n", - " center_x = (x1 + x2) / 2\n", - " center_y = (y1 + y2) / 2\n", - " for i in range(int( len(points_colmap[image_name]) // 3)):\n", - " x = points_colmap[image_name][3*i]\n", - " y = points_colmap[image_name][3*i + 1] \n", - " idx = points_colmap[image_name][3*i + 2] \n", - " if check_inside(x1,y1, x2, y2, x,y) and idx != -1:\n", - " dist = (x - center_x) * (x - center_x) + (y - center_y) * (y - center_y)\n", - " insider.append([x,y,idx,dist])\n", - " if len(insider) <= 0:\n", - " print(\"No feature points detected in the bounding box\")\n", - " flag = True\n", - " return 0, flag, 0\n", - " elif len(insider) > 3:\n", - " print(\"More than 3 points detected in the bounding box\")\n", - " insider = np.array(insider)\n", - " #print(insider)\n", - " insider.view('f8,f8,f8,f8').sort(order=['f3'], axis=0)\n", - " #print(insider)\n", - " pts_colmap = np.zeros((3,3))\n", - " pts_colmap[:,0] = points3D[insider[0,2]] #-1,2\n", - " pts_colmap[:,1] = points3D[insider[1,2]] #-2,2\n", - " pts_colmap[:,2] = points3D[insider[2,2]] #-3,2\n", - " pts_homo = np.vstack([pts_colmap,np.ones(3)])\n", - " pts_argo = M @ pts_homo\n", - " return np.mean(pts_argo, axis=1), flag, np.mean(pts_colmap, axis = 1)\n", - " else:\n", - " print(\"Less than 3 points detected in the bounding box\")\n", - " pts_colmap = []\n", - " for i,line in enumerate(insider):\n", - " pts_colmap.append(points3D[insider[i][2]])\n", - " pts_colmap = np.array(pts_colmap)\n", - " pts_colmap = pts_colmap.T\n", - " pts_homo = np.vstack([pts_colmap,np.ones(pts_colmap.shape[1])])\n", - " pts_argo = M @ pts_homo\n", - " return np.mean(pts_argo, axis=1), flag, np.mean(pts_colmap, axis = 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No feature points detected in the bounding box\n", - "No feature points detected in the bounding box\n", - "No feature points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "No feature points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "No feature points detected in the bounding box\n", - "No feature points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "No feature points detected in the bounding box\n", - "No feature points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "No feature points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n" - ] - } - ], - "source": [ - "#compute traffic sign positions\n", - "marks = []\n", - "marks_col = []\n", - "names = []\n", - "scores = []\n", - "classes = []\n", - "for j in range(len(boxes_images_list)):\n", - " image_name = 'ring_front_center/' + boxes_images_list[j]\n", - " if os.path.exists(tracking_dataset_dir +log_id + '/' + image_name):\n", - " im = cv2.imread(tracking_dataset_dir +log_id + '/'+ image_name)\n", - " for cc,box in enumerate(boxes_boxes_list[j]):\n", - " x1 = int(box[0] )#* 1200)\n", - " y1 = int(box[1] )#* 1920)\n", - " x2 = int(box[2] )#* 1200)\n", - " y2 = int(box[3] )#* 1920)\n", - " if (y2-y1) * (x2-x1) < 500: #1450\n", - " continue\n", - " if image_name not in points_colmap.keys():\n", - " continue\n", - " argo_box, flag, colmap_box = bbox2argoverse(x1,y1,x2,y2,points_colmap,points3D,M,image_name)\n", - " if flag: \n", - " continue\n", - " #if len(marks) == 0:\n", - " marks.append([argo_box[0], argo_box[1], argo_box[2]])\n", - " names.append(image_name)\n", - " scores.append(boxes_scores_list[j][cc])\n", - " classes.append(boxes_class_list[j][cc])\n", - " marks_col.append(colmap_box)\n", - "#marks = np.array(marks)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[20 17 19 13 12 15 2 16 18 1 3 14 9 4 11 10 0 7 6 8 5]\n", - "[[(2576.08101101, 1199.09703696, 16.49887731)]\n", - " [(2589.31012735, 1205.72015614, 17.51022144)]\n", - " [(2589.34219527, 1205.66718961, 17.02531521)]\n", - " [(2596.8833383 , 1191.60758704, 20.82102332)]\n", - " [(2597.89818131, 1192.48768664, 20.10728931)]\n", - " [(2598.15849598, 1192.72812317, 19.92565796)]\n", - " [(2598.77548975, 1193.89470479, 21.829439 )]\n", - " [(2599.54815885, 1198.55306603, 20.12784844)]\n", - " [(2599.57324317, 1198.47995138, 19.72473012)]\n", - " [(2607.40562423, 1223.21491884, 18.13321268)]\n", - " [(2607.47383197, 1204.04525999, 21.24501157)]\n", - " [(2607.6082839 , 1223.06999855, 17.06211994)]\n", - " [(2616.01359854, 1214.30841455, 21.41689872)]\n", - " [(2616.29632059, 1213.99998821, 20.65506912)]\n", - " [(2621.17911014, 1235.42716012, 16.85858402)]\n", - " [(2621.24977727, 1235.32736113, 16.36928504)]\n", - " [(2630.87936547, 1244.47467057, 17.42338999)]\n", - " [(2630.99311724, 1244.51958524, 16.78888518)]\n", - " [(2631.01543658, 1244.27158608, 16.23551428)]\n", - " [(2639.50945263, 1235.46937879, 19.00287162)]\n", - " [(2645.10659619, 1257.13896284, 16.33234752)]]\n", - "[2576.08101101 1199.09703696 16.49887731] 3 ring_front_center/ring_front_center_315978421517357424.jpg 1\n", - "[2589.31012735 1205.72015614 17.51022144] 3 ring_front_center/ring_front_center_315978414624255144.jpg 6\n", - "[2589.34219527 1205.66718961 17.02531521] 3 ring_front_center/ring_front_center_315978415423455864.jpg 3\n", - "[2596.8833383 1191.60758704 20.82102332] 1 ring_front_center/ring_front_center_315978411827057072.jpg 1\n", - "[2597.89818131 1192.48768664 20.10728931] 1 ring_front_center/ring_front_center_315978411527355224.jpg 4\n", - "[2598.15849598 1192.72812317 19.92565796] 1 ring_front_center/ring_front_center_315978412326560360.jpg 1\n", - "[2598.77548975 1193.89470479 21.829439 ] 11 ring_front_center/ring_front_center_315978406532359208.jpg 2\n", - "[2599.54815885 1198.55306603 20.12784844] 3 ring_front_center/ring_front_center_315978413625254760.jpg 6\n", - "[2599.57324317 1198.47995138 19.72473012] 3 ring_front_center/ring_front_center_315978414624255144.jpg 1\n", - "[2607.40562423 1223.21491884 18.13321268] 11 ring_front_center/ring_front_center_315978406532359208.jpg 56\n", - "[2607.47383197 1204.04525999 21.24501157] 11 ring_front_center/ring_front_center_315978406732157848.jpg 1\n", - "[2607.6082839 1223.06999855 17.06211994] 3 ring_front_center/ring_front_center_315978411827057072.jpg 2\n", - "[2616.01359854 1214.30841455 21.41689872] 4 ring_front_center/ring_front_center_315978408730157152.jpg 28\n", - "[2616.29632059 1213.99998821 20.65506912] 11 ring_front_center/ring_front_center_315978406832057128.jpg 48\n", - "[2621.17911014 1235.42716012 16.85858402] 3 ring_front_center/ring_front_center_315978409729158088.jpg 3\n", - "[2621.24977727 1235.32736113 16.36928504] 3 ring_front_center/ring_front_center_315978409429460248.jpg 4\n", - "[2630.87936547 1244.47467057 17.42338999] 4 ring_front_center/ring_front_center_315978406332560648.jpg 26\n", - "[2630.99311724 1244.51958524 16.78888518] 3 ring_front_center/ring_front_center_315978408530355712.jpg 2\n", - "[2631.01543658 1244.27158608 16.23551428] 3 ring_front_center/ring_front_center_315978408130752752.jpg 4\n", - "[2639.50945263 1235.46937879 19.00287162] 3 ring_front_center/ring_front_center_315978408530355712.jpg 1\n", - "[2645.10659619 1257.13896284 16.33234752] 3 ring_front_center/ring_front_center_315978406832057128.jpg 1\n", - "21\n" - ] - } - ], - "source": [ - "db_marks = []\n", - "db_counts = []\n", - "db_names = []\n", - "db_classes = []\n", - "\n", - "def check_adjacency(mark, db_mark):\n", - " l2 = np.sqrt((mark[0] - db_mark[0])**2 + (mark[1] - db_mark[1])**2)\n", - " dz = np.absolute(mark[2] - db_mark[2])\n", - " return l2 < 0.6 and dz < 0.4\n", - "\n", - "def check_category(category, db_class):\n", - " return category == db_class\n", - "\n", - "for i in range(len(marks)):\n", - " new_sign = True\n", - " mark = marks[i]\n", - " if i == 0:\n", - " db_marks.append(mark)\n", - " db_classes.append(classes[i])\n", - " db_names.append(names[i])\n", - " db_counts.append(1)\n", - " else:\n", - " for cc in range(len(db_marks)):\n", - " db_mark = db_marks[cc]\n", - " if check_adjacency(mark, db_mark) and check_category(classes[i], db_classes[cc]):\n", - " new_sign = False\n", - " db_counts[cc] += 1\n", - " db_marks[cc] = 1 * mark + 0 * db_mark\n", - " break\n", - " if new_sign:\n", - " db_marks.append(mark)\n", - " db_classes.append(classes[i])\n", - " db_names.append(names[i])\n", - " db_counts.append(1)\n", - "\n", - "marks = np.array(db_marks)\n", - "order = np.argsort(marks[:, 0])\n", - "print(order)\n", - "db_marks = np.array(db_marks)[order]\n", - "db_classes = np.array(db_classes)[order]\n", - "db_names = np.array(db_names)[order]\n", - "db_counts = np.array(db_counts)[order]\n", - "print(np.sort(marks.view('f8,f8,f8'), order=['f0'], axis=0))\n", - "\n", - "\n", - "for i in range(len(db_marks)):\n", - " print(db_marks[i], db_classes[i], db_names[i], db_counts[i])\n", - "print(len(db_marks))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.4266924249693159\n" - ] - } - ], - "source": [ - "x1 = [2616.29632059,1213.99998821]\n", - "x2 = [2616.56821749,1214.3288324]\n", - "print(np.sqrt((x1[1]-x2[1])**2 + (x1[0]-x2[0])**2))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "#save detected traffic signs \n", - "argo_mark_poses = np.array(marks)\n", - "colmap_mark_poses = np.array(marks_col)\n", - "categories = np.array(classes)\n", - "scores = np.array(scores)\n", - "colmap2argo = M\n", - "colmap_camera_poses = points_3d_colmap\n", - "argo_camera_poses = points_3d_argo\n", - "\n", - "np.savez(location_output_dir + log_id+'_projected.npz', \n", - " argo_mark_poses=argo_mark_poses, \n", - " colmap_mark_poses=colmap_mark_poses,\n", - " categories=categories,\n", - " scores=scores,\n", - " colmap2argo=colmap2argo,\n", - " argo_camera_poses=argo_camera_poses,\n", - " colmap_camera_poses=colmap_camera_poses\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(4, 156) (156, 3)\n", - "0.06950603208036611\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import copy\n", - "\n", - "#R, t = svd_rt(points_3d_colmap_scaled.T[:,:10], points_3d_argo.T[:,:10])\n", - "#t = t.reshape(-1)\n", - "\n", - "fig = plt.figure(figsize=(15,15))\n", - "ax = fig.add_subplot(111)\n", - "#xcenter = (points_3d_argo[0,0] + points_3d_argo[-1,0])/2 #+ 15\n", - "#ycenter = (points_3d_argo[0,1] + points_3d_argo[-1,1])/2#+ 15\n", - "\n", - "xcenter = points_3d_argo[20,0]\n", - "ycenter = points_3d_argo[20,1]\n", - " \n", - "xmin = xcenter - 100 # 150 #70\n", - "xmax = xcenter + 90 # 150 #70\n", - "ymin = ycenter - 100 # 150 #70\n", - "ymax = ycenter + 90 # 150 #70\n", - "\n", - "#xmin = xcenter - 10 # 150\n", - "#xmax = xcenter + 10 # 150\n", - "#ymin = ycenter - 10 # 150\n", - "#ymax = ycenter + 10 # 150\n", - "\n", - "#2.59058920e+03 1.20444779e+03\n", - "\n", - "#2.59058802e+03 1.20428979e+03\n", - "#2.58554503e+03 1.18528893e+03\n", - "points_3d_colmap_h = np.vstack([points_3d_colmap.T, np.ones(points_3d_colmap.shape[0])])\n", - "print(points_3d_colmap_h.shape,points_3d_colmap.shape)\n", - "projected = M @ points_3d_colmap_h\n", - "\n", - "#ax.scatter(points_3d_argo[2:8,0], points_3d_argo[2:8,1], 50, color=\"g\", marker=\".\", zorder=2, label='ground truth front center camera poses')\n", - "#ax.scatter(projected[0,2:8], projected[1,2:8], 50, color=\"blue\", marker=\"x\", zorder=2, label=\"Projected COLMAP front center camera poses\")\n", - "\n", - "# Nov 06 Comment These\n", - "#ax.scatter(points_3d_argo[:,0], points_3d_argo[:,1], 50, color=\"g\", marker=\".\", zorder=2, label='ground truth front center camera poses')\n", - "#ax.scatter(projected[0,:], projected[1,:], 50, color=\"blue\", marker=\"x\", zorder=2, label=\"Projected COLMAP front center camera poses\")\n", - "# Dec 02 Comment These\n", - "#ax.scatter(marks[:,0], marks[:,1] , 50, color=\"c\", marker=\"x\", zorder=2, label=\"Detected Signs\")\n", - "\n", - "db_marks[6, 0] = 0\n", - "db_marks[6, 1] = 0\n", - "db_marks[3, 0] = 0\n", - "db_marks[3, 1] = 0\n", - "ax.scatter(db_marks[:,0], db_marks[:,1] , 50, color=\"c\", marker=\"x\", zorder=2, label=\"Detected Signs\")\n", - "\n", - "\n", - "##label gt\n", - "#ax.scatter(our[:,0], our[:,1], 50, color=\"r\", marker=\"x\", zorder=2, label=\"Our Results\")\n", - "#ax.scatter(gt[:,0], gt[:,1], 50, color=\"g\", marker=\"x\", zorder=2, label=\"GT Positions\")\n", - "\n", - "a = np.sum((projected[0:3,:] - points_3d_argo.T)**2,axis=0)\n", - "rmse = np.mean(a)\n", - "rmse = np.sqrt(rmse)\n", - "print(rmse)\n", - "\n", - "\n", - "\n", - "\n", - "#ax.scatter(marks2[:,0], marks2[:,1] , 50, color=\"g\", marker=\"x\", zorder=2, label=\"Tracked Signs\")\n", - "#ax.scatter([triangulated[0]], [triangulated[1]], 50, color=\"r\", marker=\"x\", zorder=2, label=\"Triangulated Sign Position\")\n", - "ax.set_xlim([xmin, xmax])\n", - "ax.set_ylim([ymin, ymax])\n", - "local_lane_polygons = am.find_local_lane_polygons([xmin, xmax, ymin, ymax], city_name)\n", - "local_das = am.find_local_driveable_areas([xmin, xmax, ymin, ymax], city_name)\n", - "\n", - "ax.legend(fontsize='xx-large')\n", - "\n", - "domv.render_bev_labels_mpl( \n", - " city_name,\n", - " ax,\n", - " \"city_axis\",\n", - " None,\n", - " copy.deepcopy(local_lane_polygons),\n", - " copy.deepcopy(local_das),\n", - " argoverse_loader.log_list[log_index],\n", - " argoverse_data.lidar_timestamp_list[100],\n", - " city_to_egovehicle_se3,\n", - " am,\n", - "\n", - ")\n", - "\n", - "plt.savefig(\"rigid_transform.png\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - 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zw}UKr0JlT-G1i}>9Bm4sMdGP6Pz7Q!Q$F0H5z63InZpHVMCZHX#$@9ikS;xmZA8Xfp*+;N+P(r8Lpgtmq+P)VoZTgY1Xf^2yp@3@xTVEw$e9f+z1-$j&TGH>5V!Z diff --git a/sign_detector/readme.md b/sign_detector/readme.md deleted file mode 100644 index a00f7b7..0000000 --- a/sign_detector/readme.md +++ /dev/null @@ -1,108 +0,0 @@ -# Sign Detector - -#### Build the detectorn2 dictionary file based on the Mapillary Traffic Sign Dataset - -1. Request the dataset: https://www.mapillary.com/dataset/trafficsign - -2. Split the images and annotations based on the split files (provided in split_files) - - ``` - python split_imgs_anns.py --root_dir [root_dir_of_dataset] --path_to_img [path_to_store_imgs] --path_to_ann [path_to_store_annotations] --path_to_split_file [path_to_the_split_file] - ``` - -3. Generate the annotation file in detectron2 format (Pre-defined 6 classes) - - ``` - python trafficsigns_to_detectron_6_class.py --path_to_img [path_to_splited_imgs] --path_to_ann [--path_to_splited_annotations] --path_to_pkl [path_to_the_annotation_file] - ``` - - ![Pre-defined_6_classes](Pre-defined_6_classes.png) - -4. Test the annotation file - - ``` - python test_dict_6_class.py --path_to_pkl [path_to_the_annotation_file] - ``` - ![annotation_example](annotation_example.jpg) - - - - -#### Test on the pre-build model - -1. Set up the enviornment: - - Use environment.yml to build the needed environement - - ``` - conda env create -f environment.yml - ``` - -2. Download model weight: - - Download link: https://drive.google.com/drive/folders/1tF7upcJhw_5rtj11odlgaO4ZlW2AjMPn?usp=sharing - -3. Test on images: - - Put the images in the test_imgs directory - - Execute the script: - - ``` - python test.py - ``` - - Example output result: - - - -![test_out_example](test_out_example.jpg) - -#### Generate .npz from a sequence - -1. Download a test sequence from argoverse dataset: https://www.argoverse.org/data.html#tracking-link - -2. Execute the script to get .npz using the following command: - - ``` - python gen_npz.py --path_to_pkl [path_to_the_annotation_file] --path_to_model [path_to_the_trained_weights] ----path_to_seq [path_to_the_sequence] --output_dir [output_directory] - ``` - - Output .npz will be generated under $(output_dir)/record along with the generated output images in .mp4 format - -#### Train the model - -1. Set up the enviornment: - - Use environment.yml to build the needed environment - - ``` - conda env create -f environment.yml - ``` - -2. Train the model - ``` - python train.py --path_to_pkl [path_to_the_annotation_file] --output_dir [output directory to store all the outputs] - ``` -3. Test on images: - - Put the images in the test_imgs directory - - Execute the script: - - ``` - python test.py - ``` - -#### Run metrics on the model - -1. Run metrics: - - ``` - python metrics/validate_6.py --path_to_model [path to your model] - ``` - -2. You can get the mAP for each class and the corresponding PR curve - - - diff --git a/third_party/ORB_SLAM2 b/third_party/ORB_SLAM2 deleted file mode 160000 index f9e240f..0000000 --- a/third_party/ORB_SLAM2 +++ /dev/null @@ -1 +0,0 @@ -Subproject commit f9e240f9accb5e965d7df3b21b92304c67573c0d diff --git a/third_party/argoverse-api b/third_party/argoverse-api deleted file mode 160000 index a057f70..0000000 --- a/third_party/argoverse-api +++ /dev/null @@ -1 +0,0 @@ -Subproject commit a057f70d2c426209b55f3196a4a4ef15a5edad0d diff --git a/third_party/colmap b/third_party/colmap deleted file mode 160000 index b7ce38f..0000000 --- a/third_party/colmap +++ /dev/null @@ -1 +0,0 @@ -Subproject commit b7ce38ff276228b8aff7186f6abd99b463ede10d From 48176b85b2de6fafcc8be1624f2b9782663f2bc0 Mon Sep 17 00:00:00 2001 From: Tommy Date: Thu, 10 Dec 2020 17:07:43 -0500 Subject: [PATCH 4/4] minor update --- .gitignore | 4 +- 3D_projection/new_projection.ipynb | 853 ----------------------------- 3D_projection/projection.py | 6 +- 3D_projection/rigid_transform.png | Bin 225874 -> 0 bytes sign_detector/Readme.md | 2 +- 5 files changed, 6 insertions(+), 859 deletions(-) delete mode 100644 3D_projection/new_projection.ipynb delete mode 100644 3D_projection/rigid_transform.png diff --git a/.gitignore b/.gitignore index c1285cd..3d5449c 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,3 @@ .DS_Store -__pycache__/ -.ipynb_checkpoints \ No newline at end of file +.ipynb_checkpoints +__pycache__/ \ No newline at end of file diff --git a/3D_projection/new_projection.ipynb b/3D_projection/new_projection.ipynb deleted file mode 100644 index bd35aa8..0000000 --- a/3D_projection/new_projection.ipynb +++ /dev/null @@ -1,853 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(156, 3) (156, 3)\n" - ] - } - ], - "source": [ - "from transforms import affine_matrix_from_points\n", - "from argoverse.data_loading.argoverse_tracking_loader import ArgoverseTrackingLoader\n", - "from scipy.spatial import transform\n", - "import os\n", - "\n", - "#flags that determine which ring cameras to use\n", - "use_fc, use_fl, use_sl, use_rl, use_rr, use_sr, use_fr = [1, 1, 0, 0, 0, 0, 0]\n", - "\n", - "#path to the argoverse dataset\n", - "tracking_dataset_dir = '~/argoverse-tracking/sample/'\n", - "tracking_dataset_dir = os.path.expanduser(tracking_dataset_dir)\n", - "\n", - "#log_index determines which log in the argoverse dataset directory to use\n", - "log_index = 0\n", - "argoverse_loader = ArgoverseTrackingLoader(tracking_dataset_dir)\n", - "log_id = argoverse_loader.log_list[log_index]\n", - "argoverse_data = argoverse_loader[log_index]\n", - "\n", - "#colmap_output_dir points to the results from COLMAP\n", - "colmap_output_dir = '~/argoverse-tracking/sample2/output/'\n", - "colmap_output_dir = os.path.expanduser(colmap_output_dir)\n", - "\n", - "#detection results directory\n", - "detection_output_dir = '~/argoverse-tracking/sample2/detector_output/'\n", - "detection_output_dir = os.path.expanduser(detection_output_dir)\n", - "\n", - "#where to save computed traffic sign locations\n", - "location_output_dir = '~/argoverse-tracking/sample2/projection_output/'\n", - "location_output_dir = os.path.expanduser(location_output_dir)\n", - "\n", - "\n", - "\n", - "\n", - "#loading argoverse HD map\n", - "import matplotlib\n", - "import matplotlib.pyplot as plt\n", - "from visualize_30hz_benchmark_data_on_map import DatasetOnMapVisualizer\n", - "from argoverse.map_representation.map_api import ArgoverseMap\n", - "am = ArgoverseMap()\n", - "city_name = argoverse_data.city_name\n", - "dataset_dir = tracking_dataset_dir\n", - "experiment_prefix = 'visualization_demo'\n", - "use_existing_files = True\n", - "import logging\n", - "logger = logging.getLogger()\n", - "logger.setLevel(logging.CRITICAL)\n", - "domv = DatasetOnMapVisualizer(dataset_dir, experiment_prefix, use_existing_files=use_existing_files, log_id=argoverse_data.current_log)\n", - "\n", - "\n", - "\n", - "#read in colmap results\n", - "import numpy as np\n", - "poses_colmap = dict()\n", - "points_colmap = dict()\n", - "id2name_colmap = dict()\n", - "points3D = dict()\n", - "\n", - "with open(colmap_output_dir + 'images.txt') as f:\n", - " lines = f.read().splitlines()\n", - "for i,line in enumerate(lines):\n", - " if line[0] == '#':\n", - " continue\n", - " else:\n", - " if i % 2 == 0:\n", - " fields = line.split(' ')\n", - " image_name = fields[-1]\n", - " image_id = int(fields[0])\n", - " quat = fields[1:5]\n", - " quatanion = np.array([float(q) for q in quat])\n", - " trans = fields[5:8]\n", - " translation = np.array([float(t) for t in trans])\n", - " camera_id = int(fields[8])\n", - " poses_colmap[image_name] = [quatanion, translation, image_id, camera_id]\n", - " id2name_colmap[image_name] = image_id\n", - " else:\n", - " fields = line.split(' ')\n", - " points_2d = np.array([float(pt) for pt in fields])\n", - " points_colmap[image_name] = points_2d\n", - " \n", - "with open(colmap_output_dir + 'points3D.txt') as f:\n", - " lines = f.read().splitlines()\n", - "for i,line in enumerate(lines):\n", - " if line[0] == '#':\n", - " continue\n", - " else: \n", - " fields = line.split(' ')\n", - " points_attr = np.array([float(pt) for pt in fields])\n", - " points3D[points_attr[0]] = np.array([points_attr[1],points_attr[2],points_attr[3]])\n", - " \n", - "\n", - " \n", - "#read in argoverse data\n", - "import numpy.linalg as LA\n", - "\n", - "calibration_ring_front_center = argoverse_data.get_calibration(\"ring_front_center\")\n", - "calibration_ring_front_left = argoverse_data.get_calibration(\"ring_front_left\")\n", - "calibration_ring_side_left = argoverse_data.get_calibration(\"ring_side_left\")\n", - "calibration_ring_rear_left = argoverse_data.get_calibration(\"ring_rear_left\")\n", - "calibration_ring_rear_right = argoverse_data.get_calibration(\"ring_rear_right\")\n", - "calibration_ring_side_right = argoverse_data.get_calibration(\"ring_side_right\")\n", - "calibration_ring_front_right = argoverse_data.get_calibration(\"ring_front_right\")\n", - "\n", - "argo_3d = []\n", - "colmap_3d = []\n", - "for idx in range(len(argoverse_data.image_list_sync['ring_front_center'])):\n", - " city_to_egovehicle_se3 = argoverse_data.get_pose(idx)\n", - " \n", - " if use_fc:\n", - " argo_pt = city_to_egovehicle_se3.transform_point_cloud(\n", - " calibration_ring_front_center.project_cam_to_ego(np.zeros((1,3))))[0]\n", - " \n", - " img_fc = argoverse_data.image_list_sync['ring_front_center'][idx]\n", - " img_fc = img_fc.split('/')\n", - " img_fc = img_fc[-2] + '/' + img_fc[-1]\n", - " if img_fc in poses_colmap.keys():\n", - " q = poses_colmap[img_fc][0]\n", - " quat = [q[1], q[2], q[3], q[0]]\n", - " R = transform.Rotation.from_quat(quat)\n", - " t = poses_colmap[img_fc][1]\n", - " colmap_pt = - R.as_matrix().T.dot(t) \n", - " colmap_3d.append(colmap_pt)\n", - " argo_3d.append(argo_pt)\n", - " \n", - "for idx in range(len(argoverse_data.image_list_sync['ring_front_center'])):\n", - " city_to_egovehicle_se3 = argoverse_data.get_pose(idx) \n", - " if use_fr:\n", - " argo_pt = city_to_egovehicle_se3.transform_point_cloud(\n", - " calibration_ring_front_right.project_cam_to_ego(np.zeros((1,3))))[0]\n", - " img_fr = argoverse_data.image_list_sync['ring_front_right'][idx]\n", - " img_fr = img_fr.split('/')\n", - " img_fr = img_fr[-2] + '/' + img_fr[-1]\n", - " if img_fr in poses_colmap.keys():\n", - " q = poses_colmap[img_fr][0]\n", - " quat = [q[1], q[2], q[3], q[0]]\n", - " R = transform.Rotation.from_quat(quat)\n", - " t = poses_colmap[img_fr][1]\n", - " colmap_pt = - R.as_matrix().T.dot(t) \n", - " colmap_3d.append(colmap_pt)\n", - " argo_3d.append(argo_pt)\n", - " \n", - " if use_fl:\n", - " argo_pt = city_to_egovehicle_se3.transform_point_cloud(\n", - " calibration_ring_front_left.project_cam_to_ego(np.zeros((1,3))))[0]\n", - " img_fl = argoverse_data.image_list_sync['ring_front_left'][idx]\n", - " img_fl = img_fl.split('/')\n", - " img_fl = img_fl[-2] + '/' + img_fl[-1]\n", - " if img_fl in poses_colmap.keys():\n", - " q = poses_colmap[img_fl][0]\n", - " quat = [q[1], q[2], q[3], q[0]]\n", - " R = transform.Rotation.from_quat(quat)\n", - " t = poses_colmap[img_fl][1]\n", - " colmap_pt = - R.as_matrix().T.dot(t) \n", - " colmap_3d.append(colmap_pt)\n", - " argo_3d.append(argo_pt)\n", - " \n", - " if use_sl:\n", - " argo_pt = city_to_egovehicle_se3.transform_point_cloud(\n", - " calibration_ring_side_left.project_cam_to_ego(np.zeros((1,3))))[0]\n", - " img_sl = argoverse_data.image_list_sync['ring_side_left'][idx]\n", - " img_sl = img_sl.split('/')\n", - " img_sl = img_sl[-2] + '/' + img_sl[-1]\n", - " if img_sl in poses_colmap.keys():\n", - " q = poses_colmap[img_sl][0]\n", - " quat = [q[1], q[2], q[3], q[0]]\n", - " R = transform.Rotation.from_quat(quat)\n", - " t = poses_colmap[img_sl][1]\n", - " colmap_pt = - R.as_matrix().T.dot(t) \n", - " colmap_3d.append(colmap_pt)\n", - " argo_3d.append(argo_pt)\n", - " \n", - " if use_sr:\n", - " argo_pt = city_to_egovehicle_se3.transform_point_cloud(\n", - " calibration_ring_side_right.project_cam_to_ego(np.zeros((1,3))))[0]\n", - " img_sr = argoverse_data.image_list_sync['ring_side_right'][idx]\n", - " img_sr = img_sr.split('/')\n", - " img_sr = img_sr[-2] + '/' + img_sr[-1]\n", - " if img_sr in poses_colmap.keys():\n", - " q = poses_colmap[img_sr][0]\n", - " quat = [q[1], q[2], q[3], q[0]]\n", - " R = transform.Rotation.from_quat(quat)\n", - " t = poses_colmap[img_sr][1]\n", - " colmap_pt = - R.as_matrix().T.dot(t) \n", - " colmap_3d.append(colmap_pt)\n", - " argo_3d.append(argo_pt)\n", - " \n", - " if use_rl:\n", - " argo_pt = city_to_egovehicle_se3.transform_point_cloud(\n", - " calibration_ring_rear_left.project_cam_to_ego(np.zeros((1,3))))[0]\n", - " img_rl = argoverse_data.image_list_sync['ring_rear_left'][idx]\n", - " img_rl = img_rl.split('/')\n", - " img_rl = img_rl[-2] + '/' + img_rl[-1]\n", - " if img_rl in poses_colmap.keys():\n", - " q = poses_colmap[img_rl][0]\n", - " quat = [q[1], q[2], q[3], q[0]]\n", - " R = transform.Rotation.from_quat(quat)\n", - " t = poses_colmap[img_rl][1]\n", - " colmap_pt = - R.as_matrix().T.dot(t) \n", - " colmap_3d.append(colmap_pt)\n", - " argo_3d.append(argo_pt)\n", - " \n", - " if use_rr:\n", - " argo_pt = city_to_egovehicle_se3.transform_point_cloud(\n", - " calibration_ring_rear_right.project_cam_to_ego(np.zeros((1,3))))[0]\n", - " img_rr = argoverse_data.image_list_sync['ring_rear_right'][idx]\n", - " img_rr = img_rr.split('/')\n", - " img_rr = img_rr[-2] + '/' + img_rr[-1]\n", - " if img_rr in poses_colmap.keys():\n", - " q = poses_colmap[img_rr][0]\n", - " quat = [q[1], q[2], q[3], q[0]]\n", - " R = transform.Rotation.from_quat(quat)\n", - " t = poses_colmap[img_rr][1]\n", - " colmap_pt = - R.as_matrix().T.dot(t) \n", - " colmap_3d.append(colmap_pt)\n", - " argo_3d.append(argo_pt)\n", - "\n", - " \n", - " \n", - " \n", - "#compute 3d transformation from argoverse to colmap \n", - "points_3d_argo = np.array(argo_3d)\n", - "points_3d_colmap = np.array(colmap_3d)\n", - "print(points_3d_argo.shape, points_3d_colmap.shape)\n", - "\n", - "M = affine_matrix_from_points(points_3d_colmap[:,:].T, points_3d_argo[:,:].T, shear=False)\n", - "#M = affine_matrix_from_points(points_3d_colmap[0:10,:].T, points_3d_argo[0:10,:].T, shear=False)\n", - "points_3d_colmap_homo = np.vstack([points_3d_colmap.T, np.ones(points_3d_colmap.shape[0])])\n", - "ttt = M.dot(points_3d_colmap_homo)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "#read in detection results\n", - "import cv2 \n", - "import os\n", - "import numpy as np\n", - "boxes = np.load(detection_output_dir + log_id + '.npz',allow_pickle=True)\n", - "order = np.argsort(boxes['images_list'])\n", - "boxes_images_list = boxes['images_list'][order]\n", - "boxes_boxes_list = boxes['boxes_list'][order]\n", - "boxes_scores_list = boxes['scores_list'][order]\n", - "boxes_class_list = boxes['category_id_list'][order]\n", - "def check_inside(x1,y1,x2,y2,x,y):\n", - " return x1 <= x and x <= x2 and y1 <= y and y <=y2\n", - "def bbox2argoverse(x1,y1,x2,y2,points_colmap,points3D,M,image_name):\n", - " flag = False\n", - " insider = []\n", - " center_x = (x1 + x2) / 2\n", - " center_y = (y1 + y2) / 2\n", - " for i in range(int( len(points_colmap[image_name]) // 3)):\n", - " x = points_colmap[image_name][3*i]\n", - " y = points_colmap[image_name][3*i + 1] \n", - " idx = points_colmap[image_name][3*i + 2] \n", - " if check_inside(x1,y1, x2, y2, x,y) and idx != -1:\n", - " dist = (x - center_x) * (x - center_x) + (y - center_y) * (y - center_y)\n", - " insider.append([x,y,idx,dist])\n", - " if len(insider) <= 0:\n", - " print(\"No feature points detected in the bounding box\")\n", - " flag = True\n", - " return 0, flag, 0\n", - " elif len(insider) > 3:\n", - " print(\"More than 3 points detected in the bounding box\")\n", - " insider = np.array(insider)\n", - " #print(insider)\n", - " insider.view('f8,f8,f8,f8').sort(order=['f3'], axis=0)\n", - " #print(insider)\n", - " pts_colmap = np.zeros((3,3))\n", - " pts_colmap[:,0] = points3D[insider[0,2]] #-1,2\n", - " pts_colmap[:,1] = points3D[insider[1,2]] #-2,2\n", - " pts_colmap[:,2] = points3D[insider[2,2]] #-3,2\n", - " pts_homo = np.vstack([pts_colmap,np.ones(3)])\n", - " pts_argo = M @ pts_homo\n", - " return np.mean(pts_argo, axis=1), flag, np.mean(pts_colmap, axis = 1)\n", - " else:\n", - " print(\"Less than 3 points detected in the bounding box\")\n", - " pts_colmap = []\n", - " for i,line in enumerate(insider):\n", - " pts_colmap.append(points3D[insider[i][2]])\n", - " pts_colmap = np.array(pts_colmap)\n", - " pts_colmap = pts_colmap.T\n", - " pts_homo = np.vstack([pts_colmap,np.ones(pts_colmap.shape[1])])\n", - " pts_argo = M @ pts_homo\n", - " return np.mean(pts_argo, axis=1), flag, np.mean(pts_colmap, axis = 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No feature points detected in the bounding box\n", - "No feature points detected in the bounding box\n", - "No feature points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "No feature points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "No feature points detected in the bounding box\n", - "No feature points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "No feature points detected in the bounding box\n", - "No feature points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "Less than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n", - "No feature points detected in the bounding box\n", - "More than 3 points detected in the bounding box\n" - ] - } - ], - "source": [ - "#compute traffic sign positions\n", - "marks = []\n", - "marks_col = []\n", - "names = []\n", - "scores = []\n", - "classes = []\n", - "for j in range(len(boxes_images_list)):\n", - " image_name = 'ring_front_center/' + boxes_images_list[j]\n", - " if os.path.exists(tracking_dataset_dir +log_id + '/' + image_name):\n", - " im = cv2.imread(tracking_dataset_dir +log_id + '/'+ image_name)\n", - " for cc,box in enumerate(boxes_boxes_list[j]):\n", - " x1 = int(box[0] )#* 1200)\n", - " y1 = int(box[1] )#* 1920)\n", - " x2 = int(box[2] )#* 1200)\n", - " y2 = int(box[3] )#* 1920)\n", - " if (y2-y1) * (x2-x1) < 500: #1450\n", - " continue\n", - " if image_name not in points_colmap.keys():\n", - " continue\n", - " argo_box, flag, colmap_box = bbox2argoverse(x1,y1,x2,y2,points_colmap,points3D,M,image_name)\n", - " if flag: \n", - " continue\n", - " #if len(marks) == 0:\n", - " marks.append([argo_box[0], argo_box[1], argo_box[2]])\n", - " names.append(image_name)\n", - " scores.append(boxes_scores_list[j][cc])\n", - " classes.append(boxes_class_list[j][cc])\n", - " marks_col.append(colmap_box)\n", - "#marks = np.array(marks)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[20 17 19 13 12 15 2 16 18 1 3 14 9 4 11 10 0 7 6 8 5]\n", - "[[(2576.08101101, 1199.09703696, 16.49887731)]\n", - " [(2589.31012735, 1205.72015614, 17.51022144)]\n", - " [(2589.34219527, 1205.66718961, 17.02531521)]\n", - " [(2596.8833383 , 1191.60758704, 20.82102332)]\n", - " [(2597.89818131, 1192.48768664, 20.10728931)]\n", - " [(2598.15849598, 1192.72812317, 19.92565796)]\n", - " [(2598.77548975, 1193.89470479, 21.829439 )]\n", - " [(2599.54815885, 1198.55306603, 20.12784844)]\n", - " [(2599.57324317, 1198.47995138, 19.72473012)]\n", - " [(2607.40562423, 1223.21491884, 18.13321268)]\n", - " [(2607.47383197, 1204.04525999, 21.24501157)]\n", - " [(2607.6082839 , 1223.06999855, 17.06211994)]\n", - " [(2616.01359854, 1214.30841455, 21.41689872)]\n", - " [(2616.29632059, 1213.99998821, 20.65506912)]\n", - " [(2621.17911014, 1235.42716012, 16.85858402)]\n", - " [(2621.24977727, 1235.32736113, 16.36928504)]\n", - " [(2630.87936547, 1244.47467057, 17.42338999)]\n", - " [(2630.99311724, 1244.51958524, 16.78888518)]\n", - " [(2631.01543658, 1244.27158608, 16.23551428)]\n", - " [(2639.50945263, 1235.46937879, 19.00287162)]\n", - " [(2645.10659619, 1257.13896284, 16.33234752)]]\n", - "[2576.08101101 1199.09703696 16.49887731] 3 ring_front_center/ring_front_center_315978421517357424.jpg 1\n", - "[2589.31012735 1205.72015614 17.51022144] 3 ring_front_center/ring_front_center_315978414624255144.jpg 6\n", - "[2589.34219527 1205.66718961 17.02531521] 3 ring_front_center/ring_front_center_315978415423455864.jpg 3\n", - "[2596.8833383 1191.60758704 20.82102332] 1 ring_front_center/ring_front_center_315978411827057072.jpg 1\n", - "[2597.89818131 1192.48768664 20.10728931] 1 ring_front_center/ring_front_center_315978411527355224.jpg 4\n", - "[2598.15849598 1192.72812317 19.92565796] 1 ring_front_center/ring_front_center_315978412326560360.jpg 1\n", - "[2598.77548975 1193.89470479 21.829439 ] 11 ring_front_center/ring_front_center_315978406532359208.jpg 2\n", - "[2599.54815885 1198.55306603 20.12784844] 3 ring_front_center/ring_front_center_315978413625254760.jpg 6\n", - "[2599.57324317 1198.47995138 19.72473012] 3 ring_front_center/ring_front_center_315978414624255144.jpg 1\n", - "[2607.40562423 1223.21491884 18.13321268] 11 ring_front_center/ring_front_center_315978406532359208.jpg 56\n", - "[2607.47383197 1204.04525999 21.24501157] 11 ring_front_center/ring_front_center_315978406732157848.jpg 1\n", - "[2607.6082839 1223.06999855 17.06211994] 3 ring_front_center/ring_front_center_315978411827057072.jpg 2\n", - "[2616.01359854 1214.30841455 21.41689872] 4 ring_front_center/ring_front_center_315978408730157152.jpg 28\n", - "[2616.29632059 1213.99998821 20.65506912] 11 ring_front_center/ring_front_center_315978406832057128.jpg 48\n", - "[2621.17911014 1235.42716012 16.85858402] 3 ring_front_center/ring_front_center_315978409729158088.jpg 3\n", - "[2621.24977727 1235.32736113 16.36928504] 3 ring_front_center/ring_front_center_315978409429460248.jpg 4\n", - "[2630.87936547 1244.47467057 17.42338999] 4 ring_front_center/ring_front_center_315978406332560648.jpg 26\n", - "[2630.99311724 1244.51958524 16.78888518] 3 ring_front_center/ring_front_center_315978408530355712.jpg 2\n", - "[2631.01543658 1244.27158608 16.23551428] 3 ring_front_center/ring_front_center_315978408130752752.jpg 4\n", - "[2639.50945263 1235.46937879 19.00287162] 3 ring_front_center/ring_front_center_315978408530355712.jpg 1\n", - "[2645.10659619 1257.13896284 16.33234752] 3 ring_front_center/ring_front_center_315978406832057128.jpg 1\n", - "21\n" - ] - } - ], - "source": [ - "db_marks = []\n", - "db_counts = []\n", - "db_names = []\n", - "db_classes = []\n", - "\n", - "def check_adjacency(mark, db_mark):\n", - " l2 = np.sqrt((mark[0] - db_mark[0])**2 + (mark[1] - db_mark[1])**2)\n", - " dz = np.absolute(mark[2] - db_mark[2])\n", - " return l2 < 0.6 and dz < 0.4\n", - "\n", - "def check_category(category, db_class):\n", - " return category == db_class\n", - "\n", - "for i in range(len(marks)):\n", - " new_sign = True\n", - " mark = marks[i]\n", - " if i == 0:\n", - " db_marks.append(mark)\n", - " db_classes.append(classes[i])\n", - " db_names.append(names[i])\n", - " db_counts.append(1)\n", - " else:\n", - " for cc in range(len(db_marks)):\n", - " db_mark = db_marks[cc]\n", - " if check_adjacency(mark, db_mark) and check_category(classes[i], db_classes[cc]):\n", - " new_sign = False\n", - " db_counts[cc] += 1\n", - " db_marks[cc] = 1 * mark + 0 * db_mark\n", - " break\n", - " if new_sign:\n", - " db_marks.append(mark)\n", - " db_classes.append(classes[i])\n", - " db_names.append(names[i])\n", - " db_counts.append(1)\n", - "\n", - "marks = np.array(db_marks)\n", - "order = np.argsort(marks[:, 0])\n", - "print(order)\n", - "db_marks = np.array(db_marks)[order]\n", - "db_classes = np.array(db_classes)[order]\n", - "db_names = np.array(db_names)[order]\n", - "db_counts = np.array(db_counts)[order]\n", - "print(np.sort(marks.view('f8,f8,f8'), order=['f0'], axis=0))\n", - "\n", - "\n", - "for i in range(len(db_marks)):\n", - " print(db_marks[i], db_classes[i], db_names[i], db_counts[i])\n", - "print(len(db_marks))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.4266924249693159\n" - ] - } - ], - "source": [ - "x1 = [2616.29632059,1213.99998821]\n", - "x2 = [2616.56821749,1214.3288324]\n", - "print(np.sqrt((x1[1]-x2[1])**2 + (x1[0]-x2[0])**2))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "#save detected traffic signs \n", - "argo_mark_poses = np.array(marks)\n", - "colmap_mark_poses = np.array(marks_col)\n", - "categories = np.array(classes)\n", - "scores = np.array(scores)\n", - "colmap2argo = M\n", - "colmap_camera_poses = points_3d_colmap\n", - "argo_camera_poses = points_3d_argo\n", - "\n", - "np.savez(location_output_dir + log_id+'_projected.npz', \n", - " argo_mark_poses=argo_mark_poses, \n", - " colmap_mark_poses=colmap_mark_poses,\n", - " categories=categories,\n", - " scores=scores,\n", - " colmap2argo=colmap2argo,\n", - " argo_camera_poses=argo_camera_poses,\n", - " colmap_camera_poses=colmap_camera_poses\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(4, 156) (156, 3)\n", - "0.06950603208036611\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import copy\n", - "\n", - "#R, t = svd_rt(points_3d_colmap_scaled.T[:,:10], points_3d_argo.T[:,:10])\n", - "#t = t.reshape(-1)\n", - "\n", - "fig = plt.figure(figsize=(15,15))\n", - "ax = fig.add_subplot(111)\n", - "#xcenter = (points_3d_argo[0,0] + points_3d_argo[-1,0])/2 #+ 15\n", - "#ycenter = (points_3d_argo[0,1] + points_3d_argo[-1,1])/2#+ 15\n", - "\n", - "xcenter = points_3d_argo[20,0]\n", - "ycenter = points_3d_argo[20,1]\n", - " \n", - "xmin = xcenter - 100 # 150 #70\n", - "xmax = xcenter + 90 # 150 #70\n", - "ymin = ycenter - 100 # 150 #70\n", - "ymax = ycenter + 90 # 150 #70\n", - "\n", - "#xmin = xcenter - 10 # 150\n", - "#xmax = xcenter + 10 # 150\n", - "#ymin = ycenter - 10 # 150\n", - "#ymax = ycenter + 10 # 150\n", - "\n", - "#2.59058920e+03 1.20444779e+03\n", - "\n", - "#2.59058802e+03 1.20428979e+03\n", - "#2.58554503e+03 1.18528893e+03\n", - "points_3d_colmap_h = np.vstack([points_3d_colmap.T, np.ones(points_3d_colmap.shape[0])])\n", - "print(points_3d_colmap_h.shape,points_3d_colmap.shape)\n", - "projected = M @ points_3d_colmap_h\n", - "\n", - "#ax.scatter(points_3d_argo[2:8,0], points_3d_argo[2:8,1], 50, color=\"g\", marker=\".\", zorder=2, label='ground truth front center camera poses')\n", - "#ax.scatter(projected[0,2:8], projected[1,2:8], 50, color=\"blue\", marker=\"x\", zorder=2, label=\"Projected COLMAP front center camera poses\")\n", - "\n", - "# Nov 06 Comment These\n", - "#ax.scatter(points_3d_argo[:,0], points_3d_argo[:,1], 50, color=\"g\", marker=\".\", zorder=2, label='ground truth front center camera poses')\n", - "#ax.scatter(projected[0,:], projected[1,:], 50, color=\"blue\", marker=\"x\", zorder=2, label=\"Projected COLMAP front center camera poses\")\n", - "# Dec 02 Comment These\n", - "#ax.scatter(marks[:,0], marks[:,1] , 50, color=\"c\", marker=\"x\", zorder=2, label=\"Detected Signs\")\n", - "\n", - "db_marks[6, 0] = 0\n", - "db_marks[6, 1] = 0\n", - "db_marks[3, 0] = 0\n", - "db_marks[3, 1] = 0\n", - "ax.scatter(db_marks[:,0], db_marks[:,1] , 50, color=\"c\", marker=\"x\", zorder=2, label=\"Detected Signs\")\n", - "\n", - "\n", - "##label gt\n", - "#ax.scatter(our[:,0], our[:,1], 50, color=\"r\", marker=\"x\", zorder=2, label=\"Our Results\")\n", - "#ax.scatter(gt[:,0], gt[:,1], 50, color=\"g\", marker=\"x\", zorder=2, label=\"GT Positions\")\n", - "\n", - "a = np.sum((projected[0:3,:] - points_3d_argo.T)**2,axis=0)\n", - "rmse = np.mean(a)\n", - "rmse = np.sqrt(rmse)\n", - "print(rmse)\n", - "\n", - "\n", - "\n", - "\n", - "#ax.scatter(marks2[:,0], marks2[:,1] , 50, color=\"g\", marker=\"x\", zorder=2, label=\"Tracked Signs\")\n", - "#ax.scatter([triangulated[0]], [triangulated[1]], 50, color=\"r\", marker=\"x\", zorder=2, label=\"Triangulated Sign Position\")\n", - "ax.set_xlim([xmin, xmax])\n", - "ax.set_ylim([ymin, ymax])\n", - "local_lane_polygons = am.find_local_lane_polygons([xmin, xmax, ymin, ymax], city_name)\n", - "local_das = am.find_local_driveable_areas([xmin, xmax, ymin, ymax], city_name)\n", - "\n", - "ax.legend(fontsize='xx-large')\n", - "\n", - "domv.render_bev_labels_mpl( \n", - " city_name,\n", - " ax,\n", - " \"city_axis\",\n", - " None,\n", - " copy.deepcopy(local_lane_polygons),\n", - " copy.deepcopy(local_das),\n", - " argoverse_loader.log_list[log_index],\n", - " argoverse_data.lidar_timestamp_list[100],\n", - " city_to_egovehicle_se3,\n", - " am,\n", - "\n", - ")\n", - "\n", - "plt.savefig(\"rigid_transform.png\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/3D_projection/projection.py b/3D_projection/projection.py index dcc3e9f..9af9384 100644 --- a/3D_projection/projection.py +++ b/3D_projection/projection.py @@ -17,15 +17,15 @@ argoverse_data = argoverse_loader[log_index] #colmap_output_dir points to the results from COLMAP -colmap_output_dir = '~/argoverse-tracking/sample2/output/' +colmap_output_dir = '~/argoverse-tracking/sample/output/' colmap_output_dir = os.path.expanduser(colmap_output_dir) #detection results directory -detection_output_dir = '~/argoverse-tracking/sample2/detector_output/' +detection_output_dir = '~/argoverse-tracking/sample/detector_output/' detection_output_dir = os.path.expanduser(detection_output_dir) #where to save computed traffic sign locations -location_output_dir = '~/argoverse-tracking/sample2/projection_output/' +location_output_dir = '~/argoverse-tracking/sample/projection_output/' location_output_dir = os.path.expanduser(location_output_dir) diff --git 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Request the dataset: https://www.mapillary.com/dataset/trafficsign