High Quality

hugging_face/app.py

@@ -140,16 +140,6 @@ def get_frames_from_video(video_input, video_state):
         print("read_frame_source:{} error. {}\n".format(video_path, str(e)))
     image_size = (frames[0].shape[0],frames[0].shape[1]) 
 
-    # resize if resolution too big
-    if image_size[0]>=1280 and image_size[0]>=1280:
-        scale = 1080 / min(image_size)
-        new_w = int(image_size[1] * scale)
-        new_h = int(image_size[0] * scale)
-        # update frames
-        frames = [cv2.resize(f, (new_w, new_h), interpolation=cv2.INTER_AREA) for f in frames]
-        # update image_size
-        image_size = (frames[0].shape[0],frames[0].shape[1]) 
-
     # initialize video_state
     video_state = {
         "user_name": user_name,
@@ -363,7 +353,7 @@ def generate_video_from_frames(frames, output_path, fps=30, gray2rgb=False, audi
     video_temp_path = output_path.replace(".mp4", "_temp.mp4")
     
     # resize back to ensure input resolution
-    imageio.mimwrite(video_temp_path, frames, fps=fps, quality=7, 
+    imageio.mimwrite(video_temp_path, frames, fps=fps, quality=10, 
                      codec='libx264', ffmpeg_params=["-vf", f"scale={w}:{h}"])
     
     # add audio to video if audio path exists
@@ -450,7 +440,6 @@ description = r"""
 🔥 MatAnyone is a practical human video matting framework supporting target assignment 🎯.<br>
 🎪 Try to drop your video/image, assign the target masks with a few clicks, and get the the matting results 🤡!<br>
 
-*Note: Due to the online GPU memory constraints, any input with too big resolution will be resized to 1080p.<br>*
 🚀 <b> If you encounter any issue (e.g., frozen video output) or wish to run on higher resolution inputs, please consider <u>duplicating this space</u> or 
 <u>launching the <a href='https://github.com/pq-yang/MatAnyone?tab=readme-ov-file#-interactive-demo' target='_blank'>demo</a> locally</u> following the GitHub instructions.</b>
 """