import os import cv2 import tqdm import imageio import numpy as np from PIL import Image import torch import torch.nn.functional as F from hugging_face.tools.download_util import load_file_from_url from utils import gen_dilate, gen_erosion, read_frame_from_videos from matanyone.inference.inference_core import InferenceCore from matanyone.utils.get_default_model import get_matanyone_model import warnings warnings.filterwarnings("ignore") @torch.inference_mode() @torch.cuda.amp.autocast() def process_video(input_path, mask_path, output_path, ckpt_path=None, n_warmup=10, r_erode=10, r_dilate=10, suffix="", save_image=False, max_size=-1): """ Process a video to remove the background using a single frame mask. Args: input_path: Path to the input video mask_path: Path to the first-frame segmentation mask (white foreground, black background) output_path: Directory to save the output ckpt_path: Path to the model checkpoint (will download if not provided) n_warmup: Number of warmup iterations for the first frame r_erode: Erosion kernel size for the input mask r_dilate: Dilation kernel size for the input mask suffix: Suffix to add to the output filename save_image: Whether to save individual frames max_size: Maximum size for the shorter dimension (downsamples if > 0) Returns: Paths to the output videos (foreground and alpha) """ # download model if not provided if ckpt_path is None: pretrain_model_url = "https://github.com/pq-yang/MatAnyone/releases/download/v1.0.0/matanyone.pth" ckpt_path = load_file_from_url(pretrain_model_url, 'pretrained_models') # load MatAnyone model print(f"Loading model from {ckpt_path}...") matanyone = get_matanyone_model(ckpt_path) # init inference processor processor = InferenceCore(matanyone, cfg=matanyone.cfg) # inference parameters r_erode = int(r_erode) r_dilate = int(r_dilate) n_warmup = int(n_warmup) max_size = int(max_size) # load input frames print(f"Loading video from {input_path}...") vframes, fps, length, video_name = read_frame_from_videos(input_path) repeated_frames = vframes[0].unsqueeze(0).repeat(n_warmup, 1, 1, 1) # repeat the first frame for warmup vframes = torch.cat([repeated_frames, vframes], dim=0).float() length += n_warmup # update length # resize if needed if max_size > 0: h, w = vframes.shape[-2:] min_side = min(h, w) if min_side > max_size: new_h = int(h / min_side * max_size) new_w = int(w / min_side * max_size) vframes = F.interpolate(vframes, size=(new_h, new_w), mode="area") # set output paths os.makedirs(output_path, exist_ok=True) if suffix != "": video_name = f'{video_name}_{suffix}' if save_image: os.makedirs(f'{output_path}/{video_name}', exist_ok=True) os.makedirs(f'{output_path}/{video_name}/pha', exist_ok=True) os.makedirs(f'{output_path}/{video_name}/fgr', exist_ok=True) # load the first-frame mask print(f"Loading mask from {mask_path}...") mask = Image.open(mask_path).convert('L') mask = np.array(mask) bgr = (np.array([120, 255, 155], dtype=np.float32)/255).reshape((1, 1, 3)) # green screen to paste fgr objects = [1] # [optional] erode & dilate if r_dilate > 0: mask = gen_dilate(mask, r_dilate, r_dilate) if r_erode > 0: mask = gen_erosion(mask, r_erode, r_erode) mask = torch.from_numpy(mask).cuda() if max_size > 0: # resize needed mask = F.interpolate(mask.unsqueeze(0).unsqueeze(0), size=(new_h, new_w), mode="nearest") mask = mask[0,0] # inference start print("Processing video frames...") phas = [] fgrs = [] for ti in tqdm.tqdm(range(length)): # load the image as RGB; normalization is done within the model image = vframes[ti] image_np = np.array(image.permute(1,2,0)) # for output visualize image = (image / 255.).cuda().float() # for network input if ti == 0: output_prob = processor.step(image, mask, objects=objects) # encode given mask output_prob = processor.step(image, first_frame_pred=True) # first frame for prediction else: if ti <= n_warmup: output_prob = processor.step(image, first_frame_pred=True) # reinit as the first frame for prediction else: output_prob = processor.step(image) # convert output probabilities to alpha matte mask = processor.output_prob_to_mask(output_prob) # visualize prediction pha = mask.unsqueeze(2).cpu().numpy() com_np = image_np / 255. * pha + bgr * (1 - pha) # DONOT save the warmup frame if ti > (n_warmup-1): com_np = (com_np*255).astype(np.uint8) pha = (pha*255).astype(np.uint8) fgrs.append(com_np) phas.append(pha) if save_image: cv2.imwrite(f'{output_path}/{video_name}/pha/{str(ti-n_warmup).zfill(5)}.png', pha) cv2.imwrite(f'{output_path}/{video_name}/fgr/{str(ti-n_warmup).zfill(5)}.png', com_np[...,[2,1,0]]) phas = np.array(phas) fgrs = np.array(fgrs) fgr_path = f'{output_path}/{video_name}_fgr.mp4' pha_path = f'{output_path}/{video_name}_pha.mp4' print(f"Saving foreground video to {fgr_path}") imageio.mimwrite(fgr_path, fgrs, fps=fps, quality=7) print(f"Saving alpha video to {pha_path}") imageio.mimwrite(pha_path, phas, fps=fps, quality=7) return fgr_path, pha_path if __name__ == '__main__': import argparse parser = argparse.ArgumentParser(description="MatAnyone - Video background removal with a single frame mask") parser.add_argument('-i', '--input_path', type=str, required=True, help='Path of the input video or frame folder.') parser.add_argument('-m', '--mask_path', type=str, required=True, help='Path of the first-frame segmentation mask.') parser.add_argument('-o', '--output_path', type=str, default="results/", help='Output folder. Default: results') parser.add_argument('-c', '--ckpt_path', type=str, default=None, help='Path of the MatAnyone model. Will download if not provided.') parser.add_argument('-w', '--warmup', type=int, default=10, help='Number of warmup iterations for the first frame alpha prediction.') parser.add_argument('-e', '--erode_kernel', type=int, default=10, help='Erosion kernel on the input mask.') parser.add_argument('-d', '--dilate_kernel', type=int, default=10, help='Dilation kernel on the input mask.') parser.add_argument('--suffix', type=str, default="", help='Suffix to specify different target when saving, e.g., target1.') parser.add_argument('--save_image', action='store_true', default=False, help='Save output frames. Default: False') parser.add_argument('--max_size', type=int, default=-1, help='When positive, the video will be downsampled if min(w, h) exceeds. Default: -1 (means no limit)') args = parser.parse_args() process_video( input_path=args.input_path, mask_path=args.mask_path, output_path=args.output_path, ckpt_path=args.ckpt_path, n_warmup=args.warmup, r_erode=args.erode_kernel, r_dilate=args.dilate_kernel, suffix=args.suffix, save_image=args.save_image, max_size=args.max_size )