import spaces import gradio as gr import cv2 import numpy as np import time import random from PIL import Image import torch torch.jit.script = lambda f: f from transparent_background import Remover @spaces.GPU() def doo(video, mode, progress=gr.Progress()): if mode == 'Fast': remover = Remover(mode='fast') else: remover = Remover() cap = cv2.VideoCapture(video) total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) # Get total frames writer = None tmpname = random.randint(111111111, 999999999) processed_frames = 0 start_time = time.time() while cap.isOpened(): ret, frame = cap.read() if ret is False: break if time.time() - start_time >= 20 * 60 - 5: print("GPU Timeout is coming") cap.release() writer.release() return str(tmpname) + '.mp4' frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) img = Image.fromarray(frame).convert('RGB') if writer is None: writer = cv2.VideoWriter(str(tmpname) + '.mp4', cv2.VideoWriter_fourcc(*'mp4v'), cap.get(cv2.CAP_PROP_FPS), img.size) processed_frames += 1 print(f"Processing frame {processed_frames}") progress(processed_frames / total_frames, desc=f"Processing frame {processed_frames}/{total_frames}") out = remover.process(img, type='green') writer.write(cv2.cvtColor(np.array(out), cv2.COLOR_BGR2RGB)) cap.release() writer.release() return str(tmpname) + '.mp4' title = "🎞️ Video Background Removal Tool 🎥" description = """*Please note that if your video file is long (has a high number of frames), there is a chance that processing break due to GPU timeout. In this case, consider trying Fast mode.""" examples = [['./input.mp4']] iface = gr.Interface( fn=doo, inputs=["video", gr.components.Radio(['Normal', 'Fast'], label='Select mode', value='Normal', info='Normal is more accurate, but takes longer. | Fast has lower accuracy so the process will be faster.')], outputs="video", examples=examples, title=title, description=description ) iface.launch()