update libs

libs/common/img_proc.py

@@ -199,7 +199,7 @@ def generate_xy_map(bbox, resolution, global_size):
     resolution (height, width): target resolution
     global_size (height, width): the size of original image
     """
-    map_height, map_width = resolution
+    map_width, map_height = resolution
     g_height, g_width = global_size
     x_start, x_end = 2*bbox[0]/g_width - 1, 2*bbox[2]/g_width - 1
     y_start, y_end = 2*bbox[1]/g_height - 1, 2*bbox[3]/g_height - 1

libs/model/egonet.py

@@ -76,11 +76,12 @@ def crop_single_instance(self,
         Crop a single instance given an image and bounding box.
         """
         bbox = to_npy(bbox)
-        target_ar = resolution[0] / resolution[1]
+        width, height = resolution
+        target_ar = height / width
         ret = modify_bbox(bbox, target_ar)
         c, s, r = ret['c'], ret['s'], 0.
         # xy_dict: parameters for adding xy coordinate maps
-        trans = get_affine_transform(c, s, r, resolution)
+        trans = get_affine_transform(c, s, r, (height, width))
         instance = cv2.warpAffine(img,
                                   trans,
                                   (int(resolution[0]), int(resolution[1])),
@@ -114,7 +115,7 @@ def crop_instances(self,
         all_instances = []
         # each record stores attributes of one instance
         all_records = []
-        target_ar = resolution[0] / resolution[1]
+        target_ar = resolution[1] / resolution[0]
         for idx, path in enumerate(annot_dict['path']):
             data_numpy = self.load_cv2(path)
             boxes = annot_dict['boxes'][idx]
@@ -432,8 +433,9 @@ def get_keypoints(self,
             instances = instances.cuda()
         output = self.HC(instances)
         # local part coordinates
+        width, height = self.resolution
         local_coord = output[1].data.cpu().numpy()
-        local_coord *= self.resolution[0]
+        local_coord *= np.array(self.resolution).reshape(1, 1, 2)
         # transform local part coordinates to screen coordinates
         centers = [records[i]['center'] for i in range(len(records))]
         scales = [records[i]['scale'] for i in range(len(records))]
@@ -442,7 +444,7 @@ def get_keypoints(self,
             trans_inv = get_affine_transform(centers[instance_idx],
                                              scales[instance_idx], 
                                              rots[instance_idx], 
-                                             self.resolution, 
+                                             (height, width), 
                                              inv=1
                                              )
             screen_coord = affine_transform_modified(local_coord[instance_idx], 

tools/inference.py

@@ -77,7 +77,14 @@ def filter_conf(record, thres=0.0):
         }
     return True, filterd_record
 
-def gather_dict(request, references, filter_c=True, larger=True, thres=0.):
+def gather_dict(request, 
+                references, 
+                filter_c=True, 
+                larger=True, 
+                thres=0.,
+                target_ar=1.,
+                enlarge=1.2
+                ):
     """
     Gather a annotation dictionary from the prepared detections as requsted.
     """
@@ -104,8 +111,8 @@ def gather_dict(request, references, filter_c=True, larger=True, thres=0.):
             # enlarge the input bounding box if needed            
             for instance_id in range(len(bbox)):
                 bbox[instance_id] = np.array(modify_bbox(bbox[instance_id], 
-                                                         target_ar=1, 
-                                                         enlarge=1.2
+                                                         target_ar=target_ar, 
+                                                         enlarge=enlarge
                                                          )['bbox']
                                              )
         ret['boxes'].append(bbox)
@@ -158,8 +165,14 @@ def inference(testset, model, results, cfgs):
             merge(all_records, record)
         if cfgs['use_pred_box']:
             # use detected bounding box from any 2D/3D detector
-            thres = cfgs['conf_thres'] if 'conf_thres' in cfgs else 0.
-            annot_dict = gather_dict(meta, results['pred'], thres=thres)
+            thres = cfgs.get('conf_thres', 0.)
+            width, height = cfgs['heatmapModel']['input_size']
+            enlarge = cfgs['dataset'].get('enlarge_factor', 1.2)
+            annot_dict = gather_dict(meta, results['pred'], 
+                                     thres=thres,
+                                     target_ar=height/width,
+                                     enlarge=enlarge
+                                     )
             if len(annot_dict['path']) != 0:
                 record2 = model(annot_dict)
                 # update drawings

tools/train_lifting.py

@@ -49,7 +49,7 @@ def main():
             os.mkdir(save_path)
         # save the model and the normalization statistics
         torch.save(cascade[0].cpu().state_dict(), 
-                   os.path.join(save_path, 'L.th')
+                   os.path.join(save_path, 'L.pth')
                    )
         np.save(os.path.join(save_path, 'LS.npy'), train_dataset.statistics)
         logger.info('=> saving final model state to {}'.format(save_path))