Update web-demos/hugging_face/app.py

web-demos/hugging_face/app.py

@@ -321,7 +321,7 @@ def inpaint_video(video_state, resize_ratio_number, dilate_radius_number, raft_i
                                                    neighbor_length=neighbor_length_number, 
                                                    ref_stride=ref_stride_number)   # numpy array, T, H, W, 3
 
-    video_output = generate_video_from_frames(inpainted_frames, output_path="./result/inpaint/{}".format(video_state["video_name"]), fps=float(fps)) # import video_input to name the output video
+    video_output = generate_video_from_frames(inpainted_frames, output_path="./result/inpaint/{}".format(video_state["video_name"]), fps=fps) # import video_input to name the output video
 
     return video_output, operation_log, operation_log
 
@@ -336,15 +336,28 @@ def generate_video_from_frames(frames, output_path, fps=30):
         fps (int, optional): The frame rate of the output video. Defaults to 30.
     """
     # Приведение fps к обычному float (из np.float64, если нужно)
-    fps = float(fps)
+    import numpy as np
+    import torch
+    import torchvision
+    import os
+    from fractions import Fraction
 
-    frames = torch.from_numpy(np.asarray(frames))  # shape: (T, H, W, C)
-    
-    # Убедимся, что директория существует
+    # Convert fps to a clean format
+    if isinstance(fps, np.generic):
+        fps = fps.item()
+    fps = Fraction(float(fps)).limit_denominator()
+
+    # Ensure all frames are the same shape
+    assert all(f.shape == frames[0].shape for f in frames), "All frames must have the same shape"
+
+    # Convert to tensor (T, H, W, C)
+    frames = torch.from_numpy(np.asarray(frames))
+
+    # Ensure output directory exists
     if not os.path.exists(os.path.dirname(output_path)):
         os.makedirs(os.path.dirname(output_path))
 
-    # Пишем видео
+    # Write the video
     torchvision.io.write_video(output_path, frames, fps=fps, video_codec="libx264")
     return output_path