Fix typo: num_peroson -> num_person in gen_kpts.py and call sites

Agent-Logs-Url: https://github.com/AdaptiveMotorControlLab/FMPose3D/sessions/2136af56-29a8-4635-9ed4-faf9b8146672

Co-authored-by: xiu-cs <81274389+xiu-cs@users.noreply.github.com>

Author: Copilot

demo/vis_in_the_wild.py

@@ -113,7 +113,7 @@ def show3Dpose(vals, ax):
 def get_pose2D(path, output_dir, type):
 
     print('\nGenerating 2D pose...')
-    keypoints, scores = hrnet_pose(path, det_dim=416, num_peroson=1, gen_output=True, type=type)
+    keypoints, scores = hrnet_pose(path, det_dim=416, num_person=1, gen_output=True, type=type)
     keypoints, scores, valid_frames = h36m_coco_format(keypoints, scores)
     re_kpts = revise_kpts(keypoints, scores, valid_frames)
     print('Generating 2D pose successful!')

fmpose3d/lib/hrnet/gen_kpts.py

@@ -99,7 +99,7 @@ def model_load(config):
 
     return model
 
-def gen_from_image(args, frame, people_sort, human_model, pose_model, det_dim=416, num_peroson=1, gen_output=False):
+def gen_from_image(args, frame, people_sort, human_model, pose_model, det_dim=416, num_person=1, gen_output=False):
 
     bboxs, scores = yolo_det(frame, human_model, reso=det_dim, confidence=args.thred_score)
     if bboxs is None or not bboxs.any():
@@ -117,7 +117,7 @@ def gen_from_image(args, frame, people_sort, human_model, pose_model, det_dim=41
     if people_track.shape[0] == 1:
         people_track_ = people_track[-1, :-1].reshape(1, 4)
     elif people_track.shape[0] >= 2:
-        people_track_ = people_track[-num_peroson:, :-1].reshape(num_peroson, 4)
+        people_track_ = people_track[-num_person:, :-1].reshape(num_person, 4)
         people_track_ = people_track_[::-1]
     else:
         return [], []
@@ -129,7 +129,7 @@ def gen_from_image(args, frame, people_sort, human_model, pose_model, det_dim=41
 
     with torch.no_grad():
         # bbox is coordinate location
-        inputs, origin_img, center, scale = PreProcess(frame, track_bboxs, cfg, num_peroson)
+        inputs, origin_img, center, scale = PreProcess(frame, track_bboxs, cfg, num_person)
 
         inputs = inputs[:, [2, 1, 0]]
 
@@ -140,8 +140,8 @@ def gen_from_image(args, frame, people_sort, human_model, pose_model, det_dim=41
         # compute coordinate
         preds, maxvals = get_final_preds(cfg, output.clone().cpu().numpy(), np.asarray(center), np.asarray(scale))
 
-    kpts = np.zeros((num_peroson, 17, 2), dtype=np.float32)
-    scores = np.zeros((num_peroson, 17), dtype=np.float32)
+    kpts = np.zeros((num_person, 17, 2), dtype=np.float32)
+    scores = np.zeros((num_person, 17), dtype=np.float32)
     for i, kpt in enumerate(preds):
         kpts[i] = kpt
 
@@ -151,7 +151,7 @@ def gen_from_image(args, frame, people_sort, human_model, pose_model, det_dim=41
     return kpts, scores
 
 
-def gen_video_kpts(path, det_dim=416, num_peroson=1, gen_output=False, type='image'):
+def gen_video_kpts(path, det_dim=416, num_person=1, gen_output=False, type='image'):
     # Ensure checkpoints are downloaded only when HRNet is actually requested
     ensure_checkpoints()
 
@@ -168,7 +168,7 @@ def gen_video_kpts(path, det_dim=416, num_peroson=1, gen_output=False, type='ima
     scores_result = []
     if type == "image":
         frame = cv2.imread(path)
-        kpts, scores = gen_from_image(args1, frame, people_sort, human_model, pose_model, det_dim=det_dim, num_peroson=num_peroson, gen_output=gen_output)
+        kpts, scores = gen_from_image(args1, frame, people_sort, human_model, pose_model, det_dim=det_dim, num_person=num_person, gen_output=gen_output)
         kpts_result.append(kpts)
         scores_result.append(scores)
 
@@ -180,7 +180,7 @@ def gen_video_kpts(path, det_dim=416, num_peroson=1, gen_output=False, type='ima
             if not ret:
                 continue
 
-            kpts, scores = gen_from_image(args1, frame, people_sort, human_model, pose_model, det_dim=det_dim, num_peroson=num_peroson, gen_output=gen_output)
+            kpts, scores = gen_from_image(args1, frame, people_sort, human_model, pose_model, det_dim=det_dim, num_person=num_person, gen_output=gen_output)
             kpts_result.append(kpts)
             scores_result.append(scores)
 

tests/test_demo_human.py

@@ -38,7 +38,7 @@ def test_2d_pose_estimation(test_image_path, test_output_dir):
     from fmpose3d.lib.preprocess import h36m_coco_format, revise_kpts
 
     # Run 2D pose estimation
-    keypoints, scores = hrnet_pose(test_image_path, det_dim=416, num_peroson=1, gen_output=True, type='image')
+    keypoints, scores = hrnet_pose(test_image_path, det_dim=416, num_person=1, gen_output=True, type='image')
 
     # Check output shapes
     assert keypoints is not None, "Keypoints should not be None"