# -*- coding: utf-8 -*- import os import cv2 import argparse import imageio import numpy as np import scipy.ndimage from PIL import Image from tqdm import tqdm import torch import torchvision from model.modules.flow_comp_raft import RAFT_bi from model.recurrent_flow_completion import RecurrentFlowCompleteNet from model.propainter import InpaintGenerator from utils.download_util import load_file_from_url from core.utils import to_tensors from model.misc import get_device import warnings warnings.filterwarnings("ignore") pretrain_model_url = 'https://github.com/sczhou/ProPainter/releases/download/v0.1.0/' def imwrite(img, file_path, params=None, auto_mkdir=True): if auto_mkdir: dir_name = os.path.abspath(os.path.dirname(file_path)) os.makedirs(dir_name, exist_ok=True) return cv2.imwrite(file_path, img, params) # resize frames def resize_frames(frames, size=None): if size is not None: out_size = size process_size = (out_size[0]-out_size[0]%8, out_size[1]-out_size[1]%8) frames = [f.resize(process_size) for f in frames] else: out_size = frames[0].size process_size = (out_size[0]-out_size[0]%8, out_size[1]-out_size[1]%8) if not out_size == process_size: frames = [f.resize(process_size) for f in frames] return frames, process_size, out_size # read frames from video def read_frame_from_videos(frame_root): if frame_root.endswith(('mp4', 'mov', 'avi', 'MP4', 'MOV', 'AVI')): # input video path video_name = os.path.basename(frame_root)[:-4] vframes, aframes, info = torchvision.io.read_video(filename=frame_root, pts_unit='sec') # RGB frames = list(vframes.numpy()) frames = [Image.fromarray(f) for f in frames] 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)) frame = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) frames.append(frame) fps = None size = frames[0].size return frames, fps, size, video_name def binary_mask(mask, th=0.1): mask[mask>th] = 1 mask[mask<=th] = 0 return mask # read frame-wise masks def read_mask(mpath, length, size, flow_mask_dilates=8, mask_dilates=5): masks_img = [] masks_dilated = [] flow_masks = [] if mpath.endswith(('jpg', 'jpeg', 'png', 'JPG', 'JPEG', 'PNG')): # input single img path masks_img = [Image.open(mpath)] else: mnames = sorted(os.listdir(mpath)) for mp in mnames: masks_img.append(Image.open(os.path.join(mpath, mp))) for mask_img in masks_img: if size is not None: mask_img = mask_img.resize(size, Image.NEAREST) mask_img = np.array(mask_img.convert('L')) # Dilate 8 pixel so that all known pixel is trustworthy if flow_mask_dilates > 0: flow_mask_img = scipy.ndimage.binary_dilation(mask_img, iterations=flow_mask_dilates).astype(np.uint8) else: flow_mask_img = binary_mask(mask_img).astype(np.uint8) # Close the small holes inside the foreground objects # flow_mask_img = cv2.morphologyEx(flow_mask_img, cv2.MORPH_CLOSE, np.ones((21, 21),np.uint8)).astype(bool) # flow_mask_img = scipy.ndimage.binary_fill_holes(flow_mask_img).astype(np.uint8) flow_masks.append(Image.fromarray(flow_mask_img * 255)) if mask_dilates > 0: mask_img = scipy.ndimage.binary_dilation(mask_img, iterations=mask_dilates).astype(np.uint8) else: mask_img = binary_mask(mask_img).astype(np.uint8) masks_dilated.append(Image.fromarray(mask_img * 255)) if len(masks_img) == 1: flow_masks = flow_masks * length masks_dilated = masks_dilated * length return flow_masks, masks_dilated def extrapolation(video_ori, scale): """Prepares the data for video outpainting. """ nFrame = len(video_ori) imgW, imgH = video_ori[0].size # Defines new FOV. imgH_extr = int(scale[0] * imgH) imgW_extr = int(scale[1] * imgW) imgH_extr = imgH_extr - imgH_extr % 8 imgW_extr = imgW_extr - imgW_extr % 8 H_start = int((imgH_extr - imgH) / 2) W_start = int((imgW_extr - imgW) / 2) # Extrapolates the FOV for video. frames = [] for v in video_ori: frame = np.zeros(((imgH_extr, imgW_extr, 3)), dtype=np.uint8) frame[H_start: H_start + imgH, W_start: W_start + imgW, :] = v frames.append(Image.fromarray(frame)) # Generates the mask for missing region. masks_dilated = [] flow_masks = [] dilate_h = 4 if H_start > 10 else 0 dilate_w = 4 if W_start > 10 else 0 mask = np.ones(((imgH_extr, imgW_extr)), dtype=np.uint8) mask[H_start+dilate_h: H_start+imgH-dilate_h, W_start+dilate_w: W_start+imgW-dilate_w] = 0 flow_masks.append(Image.fromarray(mask * 255)) mask[H_start: H_start+imgH, W_start: W_start+imgW] = 0 masks_dilated.append(Image.fromarray(mask * 255)) flow_masks = flow_masks * nFrame masks_dilated = masks_dilated * nFrame return frames, flow_masks, masks_dilated, (imgW_extr, imgH_extr) def get_ref_index(mid_neighbor_id, neighbor_ids, length, ref_stride=10, ref_num=-1): ref_index = [] if ref_num == -1: for i in range(0, length, ref_stride): if i not in neighbor_ids: ref_index.append(i) else: start_idx = max(0, mid_neighbor_id - ref_stride * (ref_num // 2)) end_idx = min(length, mid_neighbor_id + ref_stride * (ref_num // 2)) for i in range(start_idx, end_idx, ref_stride): if i not in neighbor_ids: if len(ref_index) > ref_num: break ref_index.append(i) return ref_index if __name__ == '__main__': # device = torch.device("cuda" if torch.cuda.is_available() else "cpu") device = get_device() parser = argparse.ArgumentParser() parser.add_argument( '-i', '--video', type=str, default='inputs/object_removal/bmx-trees', help='Path of the input video or image folder.') parser.add_argument( '-m', '--mask', type=str, default='inputs/object_removal/bmx-trees_mask', help='Path of the mask(s) or mask folder.') parser.add_argument( '-o', '--output', type=str, default='results', help='Output folder. Default: results') parser.add_argument( "--resize_ratio", type=float, default=1.0, help='Resize scale for processing video.') parser.add_argument( '--height', type=int, default=-1, help='Height of the processing video.') parser.add_argument( '--width', type=int, default=-1, help='Width of the processing video.') parser.add_argument( '--mask_dilation', type=int, default=4, help='Mask dilation for video and flow masking.') parser.add_argument( "--ref_stride", type=int, default=10, help='Stride of global reference frames.') parser.add_argument( "--neighbor_length", type=int, default=10, help='Length of local neighboring frames.') parser.add_argument( "--subvideo_length", type=int, default=80, help='Length of sub-video for long video inference.') parser.add_argument( "--raft_iter", type=int, default=20, help='Iterations for RAFT inference.') parser.add_argument( '--mode', default='video_inpainting', choices=['video_inpainting', 'video_outpainting'], help="Modes: video_inpainting / video_outpainting") parser.add_argument( '--scale_h', type=float, default=1.0, help='Outpainting scale of height for video_outpainting mode.') parser.add_argument( '--scale_w', type=float, default=1.2, help='Outpainting scale of width for video_outpainting mode.') parser.add_argument( '--save_fps', type=int, default=24, help='Frame per second. Default: 24') parser.add_argument( '--save_frames', action='store_true', help='Save output frames. Default: False') parser.add_argument( '--fp16', action='store_true', help='Use fp16 (half precision) during inference. Default: fp32 (single precision).') args = parser.parse_args() # Use fp16 precision during inference to reduce running memory cost use_half = True if args.fp16 else False if device == torch.device('cpu'): use_half = False frames, fps, size, video_name = read_frame_from_videos(args.video) if not args.width == -1 and not args.height == -1: size = (args.width, args.height) if not args.resize_ratio == 1.0: size = (int(args.resize_ratio * size[0]), int(args.resize_ratio * size[1])) frames, size, out_size = resize_frames(frames, size) fps = args.save_fps if fps is None else fps save_root = os.path.join(args.output, video_name) if not os.path.exists(save_root): os.makedirs(save_root, exist_ok=True) if args.mode == 'video_inpainting': frames_len = len(frames) flow_masks, masks_dilated = read_mask(args.mask, frames_len, size, flow_mask_dilates=args.mask_dilation, mask_dilates=args.mask_dilation) w, h = size elif args.mode == 'video_outpainting': assert args.scale_h is not None and args.scale_w is not None, 'Please provide a outpainting scale (s_h, s_w).' frames, flow_masks, masks_dilated, size = extrapolation(frames, (args.scale_h, args.scale_w)) w, h = size else: raise NotImplementedError # for saving the masked frames or video masked_frame_for_save = [] for i in range(len(frames)): mask_ = np.expand_dims(np.array(masks_dilated[i]),2).repeat(3, axis=2)/255. img = np.array(frames[i]) green = np.zeros([h, w, 3]) green[:,:,1] = 255 alpha = 0.6 # alpha = 1.0 fuse_img = (1-alpha)*img + alpha*green fuse_img = mask_ * fuse_img + (1-mask_)*img masked_frame_for_save.append(fuse_img.astype(np.uint8)) frames_inp = [np.array(f).astype(np.uint8) for f in frames] frames = to_tensors()(frames).unsqueeze(0) * 2 - 1 flow_masks = to_tensors()(flow_masks).unsqueeze(0) masks_dilated = to_tensors()(masks_dilated).unsqueeze(0) frames, flow_masks, masks_dilated = frames.to(device), flow_masks.to(device), masks_dilated.to(device) ############################################## # set up RAFT and flow competition model ############################################## ckpt_path = load_file_from_url(url=os.path.join(pretrain_model_url, 'raft-things.pth'), model_dir='weights', progress=True, file_name=None) fix_raft = RAFT_bi(ckpt_path, device) ckpt_path = load_file_from_url(url=os.path.join(pretrain_model_url, 'recurrent_flow_completion.pth'), model_dir='weights', progress=True, file_name=None) fix_flow_complete = RecurrentFlowCompleteNet(ckpt_path) for p in fix_flow_complete.parameters(): p.requires_grad = False fix_flow_complete.to(device) fix_flow_complete.eval() ############################################## # set up ProPainter model ############################################## ckpt_path = load_file_from_url(url=os.path.join(pretrain_model_url, 'ProPainter.pth'), model_dir='weights', progress=True, file_name=None) model = InpaintGenerator(model_path=ckpt_path).to(device) model.eval() ############################################## # ProPainter inference ############################################## video_length = frames.size(1) print(f'\nProcessing: {video_name} [{video_length} frames]...') with torch.no_grad(): # ---- compute flow ---- if frames.size(-1) <= 640: short_clip_len = 12 elif frames.size(-1) <= 720: short_clip_len = 8 elif frames.size(-1) <= 1280: short_clip_len = 4 else: short_clip_len = 2 # use fp32 for RAFT if frames.size(1) > short_clip_len: gt_flows_f_list, gt_flows_b_list = [], [] for f in range(0, video_length, short_clip_len): end_f = min(video_length, f + short_clip_len) if f == 0: flows_f, flows_b = fix_raft(frames[:,f:end_f], iters=args.raft_iter) else: flows_f, flows_b = fix_raft(frames[:,f-1:end_f], iters=args.raft_iter) gt_flows_f_list.append(flows_f) gt_flows_b_list.append(flows_b) torch.cuda.empty_cache() gt_flows_f = torch.cat(gt_flows_f_list, dim=1) gt_flows_b = torch.cat(gt_flows_b_list, dim=1) gt_flows_bi = (gt_flows_f, gt_flows_b) else: gt_flows_bi = fix_raft(frames, iters=args.raft_iter) torch.cuda.empty_cache() if use_half: frames, flow_masks, masks_dilated = frames.half(), flow_masks.half(), masks_dilated.half() gt_flows_bi = (gt_flows_bi[0].half(), gt_flows_bi[1].half()) fix_flow_complete = fix_flow_complete.half() model = model.half() # ---- complete flow ---- flow_length = gt_flows_bi[0].size(1) if flow_length > args.subvideo_length: pred_flows_f, pred_flows_b = [], [] pad_len = 5 for f in range(0, flow_length, args.subvideo_length): s_f = max(0, f - pad_len) e_f = min(flow_length, f + args.subvideo_length + pad_len) pad_len_s = max(0, f) - s_f pad_len_e = e_f - min(flow_length, f + args.subvideo_length) pred_flows_bi_sub, _ = fix_flow_complete.forward_bidirect_flow( (gt_flows_bi[0][:, s_f:e_f], gt_flows_bi[1][:, s_f:e_f]), flow_masks[:, s_f:e_f+1]) pred_flows_bi_sub = fix_flow_complete.combine_flow( (gt_flows_bi[0][:, s_f:e_f], gt_flows_bi[1][:, s_f:e_f]), pred_flows_bi_sub, flow_masks[:, s_f:e_f+1]) pred_flows_f.append(pred_flows_bi_sub[0][:, pad_len_s:e_f-s_f-pad_len_e]) pred_flows_b.append(pred_flows_bi_sub[1][:, pad_len_s:e_f-s_f-pad_len_e]) torch.cuda.empty_cache() pred_flows_f = torch.cat(pred_flows_f, dim=1) pred_flows_b = torch.cat(pred_flows_b, dim=1) pred_flows_bi = (pred_flows_f, pred_flows_b) else: pred_flows_bi, _ = fix_flow_complete.forward_bidirect_flow(gt_flows_bi, flow_masks) pred_flows_bi = fix_flow_complete.combine_flow(gt_flows_bi, pred_flows_bi, flow_masks) torch.cuda.empty_cache() # ---- image propagation ---- masked_frames = frames * (1 - masks_dilated) subvideo_length_img_prop = min(100, args.subvideo_length) # ensure a minimum of 100 frames for image propagation if video_length > subvideo_length_img_prop: updated_frames, updated_masks = [], [] pad_len = 10 for f in range(0, video_length, subvideo_length_img_prop): s_f = max(0, f - pad_len) e_f = min(video_length, f + subvideo_length_img_prop + pad_len) pad_len_s = max(0, f) - s_f pad_len_e = e_f - min(video_length, f + subvideo_length_img_prop) b, t, _, _, _ = masks_dilated[:, s_f:e_f].size() pred_flows_bi_sub = (pred_flows_bi[0][:, s_f:e_f-1], pred_flows_bi[1][:, s_f:e_f-1]) prop_imgs_sub, updated_local_masks_sub = model.img_propagation(masked_frames[:, s_f:e_f], pred_flows_bi_sub, masks_dilated[:, s_f:e_f], 'nearest') updated_frames_sub = frames[:, s_f:e_f] * (1 - masks_dilated[:, s_f:e_f]) + \ prop_imgs_sub.view(b, t, 3, h, w) * masks_dilated[:, s_f:e_f] updated_masks_sub = updated_local_masks_sub.view(b, t, 1, h, w) updated_frames.append(updated_frames_sub[:, pad_len_s:e_f-s_f-pad_len_e]) updated_masks.append(updated_masks_sub[:, pad_len_s:e_f-s_f-pad_len_e]) torch.cuda.empty_cache() updated_frames = torch.cat(updated_frames, dim=1) updated_masks = torch.cat(updated_masks, dim=1) else: b, t, _, _, _ = masks_dilated.size() prop_imgs, updated_local_masks = model.img_propagation(masked_frames, pred_flows_bi, masks_dilated, 'nearest') updated_frames = frames * (1 - masks_dilated) + prop_imgs.view(b, t, 3, h, w) * masks_dilated updated_masks = updated_local_masks.view(b, t, 1, h, w) torch.cuda.empty_cache() ori_frames = frames_inp comp_frames = [None] * video_length neighbor_stride = args.neighbor_length // 2 if video_length > args.subvideo_length: ref_num = args.subvideo_length // args.ref_stride else: ref_num = -1 # ---- feature propagation + transformer ---- for f in tqdm(range(0, video_length, neighbor_stride)): neighbor_ids = [ i for i in range(max(0, f - neighbor_stride), min(video_length, f + neighbor_stride + 1)) ] ref_ids = get_ref_index(f, neighbor_ids, video_length, args.ref_stride, ref_num) selected_imgs = updated_frames[:, neighbor_ids + ref_ids, :, :, :] selected_masks = masks_dilated[:, neighbor_ids + ref_ids, :, :, :] selected_update_masks = updated_masks[:, neighbor_ids + ref_ids, :, :, :] selected_pred_flows_bi = (pred_flows_bi[0][:, neighbor_ids[:-1], :, :, :], pred_flows_bi[1][:, neighbor_ids[:-1], :, :, :]) with torch.no_grad(): # 1.0 indicates mask l_t = len(neighbor_ids) # pred_img = selected_imgs # results of image propagation pred_img = model(selected_imgs, selected_pred_flows_bi, selected_masks, selected_update_masks, l_t) pred_img = pred_img.view(-1, 3, h, w) pred_img = (pred_img + 1) / 2 pred_img = pred_img.cpu().permute(0, 2, 3, 1).numpy() * 255 binary_masks = masks_dilated[0, neighbor_ids, :, :, :].cpu().permute( 0, 2, 3, 1).numpy().astype(np.uint8) for i in range(len(neighbor_ids)): idx = neighbor_ids[i] img = np.array(pred_img[i]).astype(np.uint8) * binary_masks[i] \ + ori_frames[idx] * (1 - binary_masks[i]) if comp_frames[idx] is None: comp_frames[idx] = img else: comp_frames[idx] = comp_frames[idx].astype(np.float32) * 0.5 + img.astype(np.float32) * 0.5 comp_frames[idx] = comp_frames[idx].astype(np.uint8) torch.cuda.empty_cache() # save each frame if args.save_frames: for idx in range(video_length): f = comp_frames[idx] f = cv2.resize(f, out_size, interpolation = cv2.INTER_CUBIC) f = cv2.cvtColor(f, cv2.COLOR_BGR2RGB) img_save_root = os.path.join(save_root, 'frames', str(idx).zfill(4)+'.png') imwrite(f, img_save_root) # if args.mode == 'video_outpainting': # comp_frames = [i[10:-10,10:-10] for i in comp_frames] # masked_frame_for_save = [i[10:-10,10:-10] for i in masked_frame_for_save] # save videos frame masked_frame_for_save = [cv2.resize(f, out_size) for f in masked_frame_for_save] comp_frames = [cv2.resize(f, out_size) for f in comp_frames] imageio.mimwrite(os.path.join(save_root, 'masked_in.mp4'), masked_frame_for_save, fps=fps, quality=7) imageio.mimwrite(os.path.join(save_root, 'inpaint_out.mp4'), comp_frames, fps=fps, quality=7) print(f'\nAll results are saved in {save_root}') torch.cuda.empty_cache()