update some deprecated from gradio v3

app.py

@@ -1,10 +1,4 @@
 import os
-os.system("wget https://huggingface.co/akhaliq/lama/resolve/main/best.ckpt")
-os.system("pip install imageio")
-os.system("pip install albumentations==0.5.2")
-os.system("pip install opencv-python")
-os.system("pip install ffmpeg-python")
-os.system("pip install moviepy")
 import cv2
 import paddlehub as hub
 import gradio as gr
@@ -13,103 +7,108 @@ from PIL import Image, ImageOps
 import numpy as np
 import imageio
 from moviepy.editor import *
-os.mkdir("data")
+
+# Download and set up necessary files and folders
+os.system("wget https://huggingface.co/akhaliq/lama/resolve/main/best.ckpt")
+os.system("pip install imageio albumentations==0.5.2 opencv-python ffmpeg-python moviepy")
+os.makedirs("models", exist_ok=True)
 os.rename("best.ckpt", "models/best.ckpt")
-os.mkdir("dataout")
+os.makedirs("data", exist_ok=True)
+os.makedirs("dataout", exist_ok=True)
 
 def get_frames(video_in):
     frames = []
-    #resize the video
     clip = VideoFileClip(video_in)
-    
-    #check fps
+
+    # Resize and adjust FPS
     if clip.fps > 30:
-        print("vide rate is over 30, resetting to 30")
+        print("Video rate is over 30, resetting to 30")
         clip_resized = clip.resize(height=256)
         clip_resized.write_videofile("video_resized.mp4", fps=30)
     else:
-        print("video rate is OK")
+        print("Video rate is OK")
         clip_resized = clip.resize(height=256)
         clip_resized.write_videofile("video_resized.mp4", fps=clip.fps)
-    
-    print("video resized to 512 height")
-    
-    # Opens the Video file with CV2
-    cap= cv2.VideoCapture("video_resized.mp4")
-    
+
+    # Extract frames
+    cap = cv2.VideoCapture("video_resized.mp4")
     fps = cap.get(cv2.CAP_PROP_FPS)
-    print("video fps: " + str(fps))
-    i=0
-    while(cap.isOpened()):
+    print("Video fps:", fps)
+    i = 0
+    while cap.isOpened():
         ret, frame = cap.read()
-        if ret == False:
+        if not ret:
             break
-        cv2.imwrite('kang'+str(i)+'.jpg',frame)
-        frames.append('kang'+str(i)+'.jpg')
-        i+=1
+        frame_path = f'kang_{i}.jpg'
+        cv2.imwrite(frame_path, frame)
+        frames.append(frame_path)
+        i += 1
     
     cap.release()
     cv2.destroyAllWindows()
-    print("broke the video into frames")
-    
+    print("Video broken into frames")
     return frames, fps
 
-def create_video(frames, fps, type):
-    print("building video result")
+def create_video(frames, fps, type_name):
+    print("Building video result")
     clip = ImageSequenceClip(frames, fps=fps)
-    clip.write_videofile(type + "_result.mp4", fps=fps)
-    
-    return type + "_result.mp4"
+    output_file = f"{type_name}_result.mp4"
+    clip.write_videofile(output_file, fps=fps)
+    return output_file
 
-
-def magic_lama(img):
-    
-    i = img
-    img = Image.open(img)
+def magic_lama(img_path):
+    img = Image.open(img_path)
     mask = Image.open("./masks/modelscope-mask.png")
     inverted_mask = ImageOps.invert(mask)
-    
-    
-    imageio.imwrite(f"./data/data.png", img)
-    imageio.imwrite(f"./data/data_mask.png", inverted_mask)
+
+    imageio.imwrite("./data/data.png", img)
+    imageio.imwrite("./data/data_mask.png", inverted_mask)
     os.system('python predict.py model.path=/home/user/app/ indir=/home/user/app/data/ outdir=/home/user/app/dataout/ device=cpu')
-    return f"./dataout/data_mask.png"
+    return "./dataout/data_mask.png"
 
 def infer(video_in):
-    # 1. break video into frames and get FPS
-    break_vid = get_frames(video_in)
-    frames_list= break_vid[0]
-    fps = break_vid[1]
-    #n_frame = int(trim_value*fps)
+    frames_list, fps = get_frames(video_in)
     n_frame = len(frames_list)
     
-    if n_frame >= len(frames_list):
-        print("video is shorter than the cut value")
-        n_frame = len(frames_list)
-    
-    # 2. prepare frames result arrays
     result_frames = []
-    print("set stop frames to: " + str(n_frame))
-    
-    for i in frames_list[0:int(n_frame)]:
-        lama_frame = magic_lama(i)
-        lama_frame = Image.open(lama_frame)
-        imageio.imwrite(f"cleaned_frame_{i}", lama_frame)
-        result_frames.append(f"cleaned_frame_{i}")
-        print("frame " + i + "/" + str(n_frame) + ": done;")
+    for i, frame_path in enumerate(frames_list[:n_frame]):
+        lama_frame = magic_lama(frame_path)
+        cleaned_frame_path = f"cleaned_frame_{i}.jpg"
+        Image.open(lama_frame).save(cleaned_frame_path)
+        result_frames.append(cleaned_frame_path)
+        print(f"Processed frame {i + 1}/{n_frame}")
 
-    
-    final_vid = create_video(result_frames, fps, "cleaned")
+    final_video = create_video(result_frames, fps, "cleaned")
+    return final_video
 
-    files = [final_vid]
+# Gradio Interface
+inputs = gr.Video(label="Input", source="upload", type="filepath")
+outputs = gr.Video(label="Output")
+title = "LaMa Video Watermark Remover"
+description = (
+    "<p style='text-align: center'>LaMa: Resolution-robust Large Mask Inpainting with Fourier Convolutions.<br />"
+    "This demo is meant to be used as a watermark remover on Modelscope generated videos.<br />"
+    "Simply upload your Modelscope video and hit Submit.</p>"
+)
+article = (
+    "<p style='text-align: center'><a href='https://arxiv.org/abs/2109.07161' target='_blank'>Resolution-robust Large Mask Inpainting with Fourier Convolutions</a> | "
+    "<a href='https://github.com/saic-mdal/lama' target='_blank'>Github Repo</a></p>"
+)
+examples = [
+    "./examples/modelscope-astronaut-horse.mp4",
+    "./examples/modelscope-panda.mp4",
+    "./examples/modelscope-spiderman-surfing.mp4"
+]
 
-    return final_vid
+demo = gr.Interface(
+    fn=infer,
+    inputs=inputs,
+    outputs=outputs,
+    title=title,
+    description=description,
+    article=article,
+    examples=examples
+)
 
-inputs = [gr.Video(label="Input", source="upload", type="filepath")]
-outputs = [gr.Video(label="output")]
-title = "LaMa Video Watermark Remover"
-description = "<p style='text-align: center'>LaMa: Resolution-robust Large Mask Inpainting with Fourier Convolutions. <br />This demo in meant to be used as a watermark remover on Modelscope generated videos. <br />Simply upload your modelscope video and hit Submit</p>"
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2109.07161' target='_blank'>Resolution-robust Large Mask Inpainting with Fourier Convolutions</a> | <a href='https://github.com/saic-mdal/lama' target='_blank'>Github Repo</a></p>"
-examples = ["./examples/modelscope-astronaut-horse.mp4", "./examples/modelscope-panda.mp4", "./examples/modelscope-spiderman-surfing.mp4"]
-gr.Interface(infer, inputs, outputs, title=title,
-             description=description, article=article, examples=examples).launch()
+# Launch with prevent_thread_lock in case it's needed for async compatibility in v4
+demo.launch(prevent_thread_lock=True)