drive-guardian/run.py at main · bennettai/drive-guardian · GitHub

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import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
import cv2
import os
import time
import pickle
from sklearn.svm import LinearSVC
from sklearn.preprocessing import StandardScaler
from skimage.feature import hog
from sklearn.model_selection import train_test_split
from scipy.ndimage.measurements import label
from moviepy.editor import VideoFileClip
from IPython.display import HTML
from skimage.feature import hog

import numpy as np
import cv2
import pickle
import glob
from tracker import tracker
from moviepy.editor import VideoFileClip
from IPython.display import HTML

dist_pickle = pickle.load(open("calibration_pickle.p", "rb"))
mtx = dist_pickle["mtx"]
dist = dist_pickle["dist"]

def abs_sobel_thresh(img, orient='x', sobel_kernel=3, thresh=(0,255)):
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    if orient == 'x':
        abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0))
    if orient == 'y':
        abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1))

    scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
    binary_output = np.zeros_like(scaled_sobel)

    binary_output[(scaled_sobel >= thresh[0]) & (scaled_sobel <= thresh[1])] = 1
    return binary_output

def mag_thresh(img, sobel_kernel=3, mag_thresh=(0,255)):
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
    sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)

    gradmag = np.sqrt(sobelx**2+sobely**2)
    scale_factor = np.max(gradmag)/255
    gradmag = (gradmag/scale_factor).astype(np.uint8)

    binary_output = np.zeros_like(gradmag)
    binary_output[(gradmag >= mag_thresh[0]) & (gradmag <= mag_thresh[1])] = 1
    return binary_output

def dir_threshold(image, sobel_kernel=3, thresh=(0, np.pi/2)):
    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
    sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)

    with np.errstate(divide='ignore', invalid='ignore'):
        absgraddir = np.absolute(np.arctan(sobely/sobelx))
        binary_output = np.zeros_like(absgraddir)
        binary_output[(absgraddir >= thresh[0]) & (absgraddir <= thresh[1])] = 1
    return binary_output

def color_threshold(image, sthresh=(0,255), vthresh=(0,255), lthresh=(0,255)):
    hls = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
    s_channel = hls[:,:,2]
    s_binary = np.zeros_like(s_channel)
    s_binary[(s_channel >= sthresh[0]) & (s_channel <= sthresh[1])] = 1

    hsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
    v_channel = hsv[:,:,2]
    v_binary = np.zeros_like(v_channel)
    v_binary[(v_channel >= vthresh[0]) & (v_channel <= vthresh[1])] = 1

    output = np.zeros_like(s_channel)
    output[(s_binary == 1) & (v_binary == 1)] = 1
    return output

def window_mask(width, height, img_ref, center, level):
    output = np.zeros_like(img_ref)
    output[int(img_ref.shape[0] - (level + 1) * height):int(img_ref.shape[0] - level * height),
    max(0, int(center - width / 2)):min(int(center + width / 2), img_ref.shape[1])] = 1
    return output

def process_image(image):

    img = cv2.undistort(image, mtx, dist, None, mtx)

    preprocessImage = np.zeros_like(img[:,:,0])
    gradx = abs_sobel_thresh(img, orient='x', thresh=(12,255))
    grady = abs_sobel_thresh(img, orient='x', thresh=(25,255))
    c_binary = color_threshold(img, sthresh=(100,255),vthresh=(50,255))
    preprocessImage[((gradx==1)&(grady==1)|(c_binary==1))] = 255

    img_size = (img.shape[1], img.shape[0])
    bot_width = .75 #changed from .76
    mid_width = .1 #changed this value - seemed to work a lot better than 0.08
    height_pct = .62
    bottom_trim = .935
    src = np.float32([[img.shape[1]*(.5-mid_width/2),img.shape[0]*height_pct],[img.shape[1]*(.5+mid_width/2),img.shape[0]*height_pct],
                      [img.shape[1]*(.5+bot_width/2),img.shape[0]*bottom_trim],[img.shape[1]*(.5-bot_width/2),img.shape[0]*bottom_trim]])
    offset = img_size[0]*.25
    dst = np.float32([[offset, 0], [img_size[0]-offset, 0],[img_size[0]-offset, img_size[1]],[offset, img_size[1]]])

    M = cv2.getPerspectiveTransform(src, dst)
    Minv = cv2.getPerspectiveTransform(dst, src)
    warped = cv2.warpPerspective(preprocessImage, M, img_size,flags=cv2.INTER_LINEAR)

    window_width = 25
    window_height = 80
    curve_centers = tracker(Mywindow_width=window_width,Mywindow_height=window_height,Mymargin=25,My_ym=10/720,My_xm=4/384,Mysmooth_factor=15)
    window_centroids = curve_centers.find_window_centroids(warped)

    l_points = np.zeros_like(warped)
    r_points = np.zeros_like(warped)

    rightx = []
    leftx = []

    for level in range(0,len(window_centroids)):
        l_mask = window_mask(window_width,window_height,warped,window_centroids[level][0],level)
        r_mask = window_mask(window_width,window_height,warped,window_centroids[level][1],level)

        leftx.append(window_centroids[level][0])
        rightx.append(window_centroids[level][1])

        l_points[(l_points==255)|((l_mask==1))] = 255
        r_points[(r_points==255)|((r_mask==1))] = 255

    template = np.array(r_points+l_points,np.uint8)
    zero_channel = np.zeros_like(template)
    template = np.array(cv2.merge((zero_channel,template,zero_channel)),np.uint8)
    warpage = np.array(cv2.merge((warped,warped,warped)),np.uint8)
    result = cv2.addWeighted(warpage,1,template,0.5,0.0)

    yvals = range(0,warped.shape[0])
    res_yvals = np.arange(warped.shape[0]-(window_height/2),0,-window_height)

    left_fit = np.polyfit(res_yvals,leftx,2)
    left_fitx = left_fit[0]*yvals*yvals + left_fit[1]*yvals + left_fit[2]
    left_fitx = np.array(left_fitx,np.int32)

    right_fit = np.polyfit(res_yvals,rightx,2)
    right_fitx = right_fit[0]*yvals*yvals + right_fit[1]*yvals + right_fit[2]
    right_fitx = np.array(right_fitx,np.int32)

    left_lane = np.array(list(zip(np.concatenate((left_fitx-window_width/2,left_fitx[::-1]+window_width/2),axis=0),np.concatenate((yvals,yvals[::-1]),axis=0))),np.int32)

    right_lane = np.array(list(zip(np.concatenate((right_fitx-window_width/2,right_fitx[::-1]+window_width/2),axis=0),np.concatenate((yvals,yvals[::-1]),axis=0))),np.int32)

    inner_lane = np.array(list(zip(np.concatenate((left_fitx+window_width/2,right_fitx[::-1]+window_width/2),axis=0),np.concatenate((yvals,yvals[::-1]),axis=0))),np.int32)

    road = np.zeros_like(img)
    road_bkg = np.zeros_like(img)
    cv2.fillPoly(road,[left_lane],color=[255,0,0])
    cv2.fillPoly(road,[inner_lane],color=[0,255,0])
    cv2.fillPoly(road,[right_lane],color=[0,0,255])
    cv2.fillPoly(road_bkg,[left_lane],color=[255,255,255])
    cv2.fillPoly(road_bkg,[right_lane],color=[255,255,255])

    road_warped = cv2.warpPerspective(road,Minv,img_size,flags=cv2.INTER_LINEAR)
    road_warped_bkg = cv2.warpPerspective(road_bkg,Minv,img_size,flags=cv2.INTER_LINEAR)

    base = cv2.addWeighted(img, 1.0, road_warped_bkg, -1.0, 0.0)
    result = cv2.addWeighted(base,1.0,road_warped,0.7,0.0)

    #measure pixels in y and x directions
    ym_per_pix = curve_centers.ym_per_pix
    xm_per_pix = curve_centers.xm_per_pix

    curve_fit_cr = np.polyfit(np.array(res_yvals,np.float32)*ym_per_pix,np.array(leftx,np.float32)*xm_per_pix,2)
    curverad = ((1+(2*curve_fit_cr[0]*yvals[-1]*ym_per_pix+curve_fit_cr[1])**2)**1.5)/np.absolute(2*curve_fit_cr[0]) #remember that it's the equation from the lesson (derivatives) - radius of curvature

    camera_center = (left_fitx[-1] + right_fitx[-1])/2
    center_diff = (camera_center-warped.shape[1]/2)*xm_per_pix
    side_pos = 'left'
    if center_diff <= 0:
        side_pos = 'right'

    cv2.putText(result,'Radius of curvature = '+str(round(curverad,3))+'(m)',(50,50),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,255),2)
    cv2.putText(result,'Vehicle is '+str(abs(round(center_diff,3)))+'m '+side_pos+' of center',(50,100), cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,255),2)

    return result

output_vid = 'lane_tracker.mp4'
input_vid = 'project_video.mp4'

clip1 = VideoFileClip(input_vid)
video_clip = clip1.fl_image(process_image)
video_clip.write_videofile(output_vid, audio=False)