Merge remote-tracking branch 'origin/master'

Author: zh-plus

common/visualization.py

@@ -170,7 +170,7 @@ def update_video(i):
 
                 for n, ax in enumerate(ax_3d):
                     pos = poses[n][i]
-                    lines_3d[n][j - 1][0].set_xdata([pos[j, 0], pos[j_parent, 0]])
+                    lines_3d[n][j - 1][0].set_xdata(np.array([pos[j, 0], pos[j_parent, 0]])) # Hotfix matplotlib's bug. https://github.com/matplotlib/matplotlib/pull/20555
                     lines_3d[n][j - 1][0].set_ydata([pos[j, 1], pos[j_parent, 1]])
                     lines_3d[n][j - 1][0].set_3d_properties([pos[j, 2], pos[j_parent, 2]], zdir='z')
 

joints_detectors/Alphapose/fn.py

@@ -6,7 +6,7 @@
 import cv2
 import numpy as np
 import torch
-from torch._six import string_classes, int_classes
+from torch._six import string_classes
 
 RED = (0, 0, 255)
 GREEN = (0, 255, 0)
@@ -57,7 +57,7 @@ def collate_fn(batch):
         if elem.shape == ():  # scalars
             py_type = float if elem.dtype.name.startswith('float') else int
             return numpy_type_map[elem.dtype.name](list(map(py_type, batch)))
-    elif isinstance(batch[0], int_classes):
+    elif isinstance(batch[0], int):
         return torch.LongTensor(batch)
     elif isinstance(batch[0], float):
         return torch.DoubleTensor(batch)
@@ -194,7 +194,7 @@ def vis_frame(frame, im_res, format='coco'):
             bg = img.copy()
             cv2.circle(bg, (int(cor_x / 2), int(cor_y / 2)), 2, p_color[n], -1)
             # Now create a mask of logo and create its inverse mask also
-            transparency = max(0, min(1, kp_scores[n]))
+            transparency = max(0, min(1, kp_scores[n].item()))
             img = cv2.addWeighted(bg, transparency, img, 1 - transparency, 0)
 
         # Draw proposal score on the head
@@ -219,7 +219,7 @@ def vis_frame(frame, im_res, format='coco'):
                 polygon = cv2.ellipse2Poly((int(mX), int(mY)), (int(length / 2), int(stickwidth)), int(angle), 0, 360, 1)
                 cv2.fillConvexPoly(bg, polygon, line_color[i])
                 # cv2.line(bg, start_xy, end_xy, line_color[i], (2 * (kp_scores[start_p] + kp_scores[end_p])) + 1)
-                transparency = max(0, min(1, 0.5 * (kp_scores[start_p] + kp_scores[end_p])))
+                transparency = max(0, min(1, 0.5 * (kp_scores[start_p] + kp_scores[end_p]).item()))
                 img = cv2.addWeighted(bg, transparency, img, 1 - transparency, 0)
     img = cv2.resize(img, (width, height), interpolation=cv2.INTER_CUBIC)
     return img

pose_trackers/PoseFlow/requirements.txt

@@ -5,5 +5,5 @@ opencv_contrib_python==3.4.2.16
 matplotlib==2.2.2
 tqdm==4.23.4
 Image==1.5.25
-Pillow==6.2.0
+Pillow==8.2.0
 munkres==1.0.12