[Fix] Bug fixes in data converters and transforms

mmhuman3d/data/data_converters/agora.py

@@ -105,7 +105,7 @@ def convert_by_mode(self, dataset_path: str, out_path: str,
             for idx in tqdm(range(len(df))):
                 imgname = df.iloc[idx]['imgPath']
                 if self.res == (1280, 720):
-                    imgname.replace('.png', '_1280x720.png')
+                    imgname = imgname.replace('.png', '_1280x720.png')
                 img_path = os.path.join('images', mode, imgname)
                 valid_pers_idx = np.where(df.iloc[idx].at['isValid'])[0]
                 for pidx in valid_pers_idx:
@@ -118,7 +118,7 @@ def convert_by_mode(self, dataset_path: str, out_path: str,
 
                     # obtain keypoints
                     keypoints2d = df.iloc[idx]['gt_joints_2d'][pidx]
-                    if self.res == '1280x720':
+                    if self.res == (1280, 720):
                         keypoints2d *= (720 / 2160)
                     keypoints3d = df.iloc[idx]['gt_joints_3d'][pidx]
 

mmhuman3d/data/data_converters/posetrack.py

@@ -49,23 +49,24 @@ def convert_by_mode(self, dataset_path: str, out_path: str,
         for ann_file in tqdm(ann_files):
             json_data = mmcv.load(ann_file)
 
-            counter = 0
-            for im, ann in zip(json_data['images'], json_data['annotations']):
-                # sample every 10 image and check image is labelled
-                if counter % 10 != 0 and not im['is_labeled']:
-                    continue
+            imgs = {}
+            for img in json_data['images']:
+                imgs[img['id']] = img
+
+            for ann in json_data['annotations']:
+                image_id = ann['image_id']
+                image_path = str(imgs[image_id]['file_name'])
+
                 keypoints2d = np.array(ann['keypoints']).reshape(17, 3)
                 keypoints2d[keypoints2d[:, 2] > 0, 2] = 1
                 # check if all major body joints are annotated
                 if sum(keypoints2d[5:, 2] > 0) < 12:
                     continue
 
-                image_path = im['file_name']
                 image_abs_path = os.path.join(dataset_path, image_path)
                 if not os.path.exists(image_abs_path):
                     print('{} does not exist!'.format(image_abs_path))
                     continue
-                counter += 1
                 bbox_xywh = np.array(ann['bbox'])
 
                 # store data

mmhuman3d/data/data_converters/surreal.py

@@ -184,10 +184,10 @@ def convert_by_mode(self, dataset_path: str, out_path: str,
                         success, image = vidcap.read()
                         if not success:
                             break
-                        frame += 1
                         # image name
                         imgname = os.path.join(img_dir,
                                                'frame_%06d.jpg' % frame)
+                        frame += 1
                         # save image
                         cv2.imwrite(imgname, image)