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 import torch from PIL import Image, ImageOps import numpy as np import imageio from moviepy.editor import * os.mkdir("data") os.rename("best.ckpt", "models/best.ckpt") os.mkdir("dataout") def get_frames(video_in): frames = [] #resize the video clip = VideoFileClip(video_in) #check fps if clip.fps > 30: print("vide 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") 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") fps = cap.get(cv2.CAP_PROP_FPS) print("video fps: " + str(fps)) i=0 while(cap.isOpened()): ret, frame = cap.read() if ret == False: break cv2.imwrite('kang'+str(i)+'.jpg',frame) frames.append('kang'+str(i)+'.jpg') i+=1 cap.release() cv2.destroyAllWindows() print("broke the video into frames") return frames, fps def create_video(frames, fps, type): print("building video result") clip = ImageSequenceClip(frames, fps=fps) clip.write_videofile(type + "_result.mp4", fps=fps) return type + "_result.mp4" def magic_lama(img): i = img img = Image.open(img) 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) 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" 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) 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;") final_vid = create_video(result_frames, fps, "cleaned") files = [final_vid] return final_vid inputs = [gr.Video(label="Input", source="upload", type="filepath")] outputs = [gr.Video(label="output")] title = "LaMa Video Watermark Remover" description = "LaMa: Resolution-robust Large Mask Inpainting with Fourier Convolutions.
This demo in meant to be used as a watermark remover on Modelscope generated videos.
Simply upload your modelscope video and hit Submit" article = "

Resolution-robust Large Mask Inpainting with Fourier Convolutions | Github Repo

" example = ["./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).launch()