Python updates

inference.py

@@ -160,11 +160,22 @@ def show_alpha_floorplan(dt_xyz, side_l=512, border_color=None):
     return dt_floorplan
 
 
+class NpEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, np.integer):
+            return int(obj)
+        if isinstance(obj, np.floating):
+            return float(obj)
+        if isinstance(obj, np.ndarray):
+            return obj.tolist()
+        return json.JSONEncoder.default(self, obj)
+
+
 def save_pred_json(xyz, ration, save_path):
     # xyz[..., -1] = -xyz[..., -1]
     json_data = xyz2json(xyz, ration)
     with open(save_path, 'w') as f:
-        f.write(json.dumps(json_data, indent=4) + '\n')
+        f.write(json.dumps(json_data, indent=4, cls=NpEncoder) + '\n')
     return json_data
 
 

postprocessing/dula/layout.py

@@ -155,15 +155,11 @@ def fit_layout(floor_xz, need_cube=False, show=False, block_eps=5):
         plt.show()
 
     pred = np.uint8(ans)
-    pred_polys = cv2.findContours(pred, 1, 3)
-    if isinstance(pred_polys, tuple):
-        if len(pred_polys) == 3:
-            pred_polys = pred_polys[1]
-        else:
-            pred_polys = pred_polys[0]
 
-    pred_polys.sort(key=lambda x: cv2.contourArea(x), reverse=True)
-    pred_poly = pred_polys[0]
+
+    pred_polys, hierarchy = cv2.findContours(pred, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    pred_polys = sorted(pred_polys, key=cv2.contourArea)
+    pred_poly = pred_polys[-1]
     # findContours may produce errors, which are enforced here 
     for i in range(len(pred_poly)):
         p1 = pred_poly[i][0]

requirements.txt

@@ -1,15 +1,15 @@
-numpy==1.21.0
-torch==1.7.1
-torchvision==0.8.2
+numpy==2.2.6
+torch==2.7.1
+torchvision==0.22.1
 yacs==0.1.8
-einops==0.3.0
-opencv-python==4.5.3.56
-pylsd-nova==1.2.0
-tqdm==4.64.0
-scipy==1.8.1
-termcolor==1.1.0
-shapely==1.8.2
-imageio==2.19.2
-open3d==0.15.2
-gdown==4.4.0
-gradio==3.0.5
+einops==0.8.0
+opencv-python==4.12.0.88
+pylsd-nova==1.2.1
+tqdm==4.67.1
+scipy==1.14.1
+termcolor==3.0.1
+shapely==2.1.0
+imageio==2.37.0
+open3d==0.19.0
+gdown==5.2.0
+gradio==5.27.0

utils/conversion.py

@@ -52,7 +52,7 @@ def get_lon(w, is_np, b=None):
 
 
 def pixel2uv(pixel, w=1024, h=512, axis=None):
-    pixel = pixel.astype(np.float) if isinstance(pixel, np.ndarray) else pixel.float()
+    pixel = pixel.astype(np.float64) if isinstance(pixel, np.ndarray) else pixel.float()
     # +0.5 will make left/right and up/down coordinates symmetric
     if axis is None:
         u = (pixel[..., 0:1] + 0.5) / w
@@ -203,19 +203,19 @@ def uv2pixel(uv, w=1024, h=512, axis=None, need_round=True):
     elif axis == 0:
         pu = uv * w - 0.5
         if need_round:
-            pu = pu.round().astype(np.int) if isinstance(uv, np.ndarray) else pu.round().int()
+            pu = pu.round().astype(np.int64) if isinstance(uv, np.ndarray) else pu.round().int()
         return pu
     elif axis == 1:
         pv = uv * h - 0.5
         if need_round:
-            pv = pv.round().astype(np.int) if isinstance(uv, np.ndarray) else pv.round().int()
+            pv = pv.round().astype(np.int64) if isinstance(uv, np.ndarray) else pv.round().int()
         return pv
     else:
         assert False, "axis error"
 
     lst = [pu, pv]
     if need_round:
-        pixel = np.concatenate(lst, axis=-1).round().astype(np.int) if isinstance(uv, np.ndarray) else torch.cat(lst,
+        pixel = np.concatenate(lst, axis=-1).round().astype(np.int64) if isinstance(uv, np.ndarray) else torch.cat(lst,
                                                                                                                  dim=-1).round().int()
     else:
         pixel = np.concatenate(lst, axis=-1) if isinstance(uv, np.ndarray) else torch.cat(lst, dim=-1)

visualization/floorplan.py

@@ -29,7 +29,7 @@ def draw_floorplan(xz, fill_color=None, border_color=None, side_l=512, show_radi
     if fill_color is None:
         fill_color = [1]
 
-    board = np.zeros([side_l, side_l, len(fill_color)], dtype=np.float)
+    board = np.zeros([side_l, side_l, len(fill_color)], dtype=np.float64)
 
     if show_radius is None:
         show_radius = np.linalg.norm(xz, axis=-1).max()
@@ -43,7 +43,7 @@ def draw_floorplan(xz, fill_color=None, border_color=None, side_l=512, show_radi
     # |------------u
     xz[:, 1] = -xz[:, 1]
     xz += side_l // 2  # moving to center
-    xz = xz.astype(np.int)
+    xz = xz.astype(np.int64)
     cv2.fillPoly(board, [xz], fill_color)
     if border_color:
         cv2.drawContours(board, [xz], 0, border_color, 2)
@@ -101,7 +101,7 @@ def draw_iou_floorplan(dt_xz, gt_xz, show_radius=None, show=False, side_l=512,
     gt_floorplan = Image.fromarray((gt_floorplan * 255).astype(np.uint8), mode='RGBA')
     iou_floorplan = Image.alpha_composite(gt_floorplan, dt_floorplan)
 
-    back = np.zeros([side_l, side_l, len(fill_color)], dtype=np.float)
+    back = np.zeros([side_l, side_l, len(fill_color)], dtype=np.float64)
     back[..., :] = [0.8, 0.8, 0.8, 1]
     back = Image.fromarray((back * 255).astype(np.uint8), mode='RGBA')
 

visualization/grad.py

@@ -22,7 +22,7 @@ def convert_img(value, h, need_nor=True, cmap=None):
     elif cmap == cv2.COLORMAP_PLASMA:
         grad_img = cv2.applyColorMap((grad_img * 255).astype(np.uint8), colormap=cmap)
         grad_img = grad_img[..., ::-1]
-        grad_img = grad_img.astype(np.float) / 255.0
+        grad_img = grad_img.astype(np.float64) / 255.0
     elif cmap == 'HSV':
         grad_img = np.round(grad_img * 1000) / 1000.0
         grad_img = grad_img[..., np.newaxis].repeat(3, axis=-1)
@@ -31,7 +31,7 @@ def convert_img(value, h, need_nor=True, cmap=None):
         grad_img[..., 2] = 255
         grad_img = grad_img.astype(np.uint8)
         grad_img = cv2.cvtColor(grad_img, cv2.COLOR_HSV2RGB)
-        grad_img = grad_img.astype(np.float) / 255.0
+        grad_img = grad_img.astype(np.float64) / 255.0
     return grad_img