import os import cv2 import tqdm import numpy as np from PIL import Image import torch import torch.nn.functional as F from matanyone2.inference.inference_core import InferenceCore from matanyone2.utils.get_default_model import get_matanyone2_model from matanyone2.utils.device import get_default_device, safe_autocast_decorator from matanyone2.utils.inference_utils import gen_dilate, gen_erosion, read_frame_from_videos import warnings warnings.filterwarnings("ignore") device = get_default_device() @torch.inference_mode() @safe_autocast_decorator() def main(input_path, mask_path, output_path, ckpt_path, n_warmup=10, r_erode=10, r_dilate=10, suffix="", save_image=False, max_size=-1): video_name = os.path.basename(os.path.dirname(input_path)) # load MatAnyone model matanyone2 = get_matanyone2_model(ckpt_path, device) # init inference processor processor = InferenceCore(matanyone2, cfg=matanyone2.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 vframes, fps, length, _ = 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 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).float().to(device) 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 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.).float().to(device) # 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 = np.round(np.clip(com_np * 255.0, 0, 255)).astype(np.uint8) pha = np.round(np.clip(pha * 255.0, 0, 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(4)}.png', pha) cv2.imwrite(f'{output_path}/{video_name}/fgr/{str(ti-n_warmup).zfill(4)}.png', com_np[...,[2,1,0]]) # [optional] save videos for better visualization # import imageio # phas = np.array(phas) # fgrs = np.array(fgrs) # imageio.mimwrite(f'{output_path}/{video_name}_fgr.mp4', fgrs, fps=fps, quality=7) # imageio.mimwrite(f'{output_path}/{video_name}_pha.mp4', phas, fps=fps, quality=7) if __name__ == '__main__': import argparse parser = argparse.ArgumentParser() parser.add_argument('-i', '--input_path', type=str, default="inputs/video/test-sample1.mp4", help='Path of the input video or frame folder.') parser.add_argument('-m', '--mask_path', type=str, default="inputs/mask/test-sample1.png", 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="pretrained_models/matanyone.pth", help='Path of the MatAnyone model.') parser.add_argument('-w', '--warmup', type=str, default="10", help='Number of warmup iterations for the first frame alpha prediction.') parser.add_argument('-e', '--erode_kernel', type=str, default="10", help='Erosion kernel on the input mask.') parser.add_argument('-d', '--dilate_kernel', type=str, 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=str, default="-1", help='When positive, the video will be downsampled if min(w, h) exceeds. Default: -1 (means no limit)') args = parser.parse_args() main(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)