import os import cv2 import tqdm import random import imageio import numpy as np from PIL import Image import torch import torchvision import torch.nn.functional as F from matanyone.model.matanyone import MatAnyone from matanyone.inference.inference_core import InferenceCore import warnings warnings.filterwarnings("ignore") IMAGE_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.JPG', '.JPEG', '.PNG') VIDEO_EXTENSIONS = ('.mp4', '.mov', '.avi', '.MP4', '.MOV', '.AVI') def read_frame_from_videos(frame_root): if frame_root.endswith(VIDEO_EXTENSIONS): # Video file path video_name = os.path.basename(frame_root)[:-4] frames, _, info = torchvision.io.read_video(filename=frame_root, pts_unit='sec', output_format='TCHW') # RGB fps = info['video_fps'] else: video_name = os.path.basename(frame_root) frames = [] fr_lst = sorted(os.listdir(frame_root)) for fr in fr_lst: frame = cv2.imread(os.path.join(frame_root, fr))[...,[2,1,0]] # RGB, HWC frames.append(frame) fps = 24 # default frames = torch.Tensor(np.array(frames)).permute(0, 3, 1, 2).contiguous() # TCHW length = frames.shape[0] return frames, fps, length, video_name def gen_dilate(alpha, min_kernel_size, max_kernel_size): kernel_size = random.randint(min_kernel_size, max_kernel_size) kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (kernel_size,kernel_size)) fg_and_unknown = np.array(np.not_equal(alpha, 0).astype(np.float32)) dilate = cv2.dilate(fg_and_unknown, kernel, iterations=1)*255 return dilate.astype(np.float32) def gen_erosion(alpha, min_kernel_size, max_kernel_size): kernel_size = random.randint(min_kernel_size, max_kernel_size) kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (kernel_size,kernel_size)) fg = np.array(np.equal(alpha, 255).astype(np.float32)) erode = cv2.erode(fg, kernel, iterations=1)*255 return erode.astype(np.float32) @torch.inference_mode() @torch.cuda.amp.autocast() 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): matanyone = MatAnyone.from_pretrained("PeiqingYang/MatAnyone").cuda().eval() 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 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 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 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) 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)