# -*- coding: utf-8 -*- import os import cv2 import numpy as np import scipy.ndimage from PIL import Image import torch import torchvision from backend import config from backend.inpaint.video.model.modules.flow_comp_raft import RAFT_bi from backend.inpaint.video.model.recurrent_flow_completion import RecurrentFlowCompleteNet from backend.inpaint.video.model.propainter import InpaintGenerator from backend.inpaint.video.core.utils import to_tensors from backend.inpaint.video.model.misc import get_device import warnings warnings.filterwarnings("ignore") 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 = [] # 如果传入的直接为numpy array if isinstance(mpath, np.ndarray): masks_img = [Image.fromarray(mpath)] # input single img path else: if isinstance(mpath, str): if mpath.endswith(('jpg', 'jpeg', 'png', 'JPG', 'JPEG', 'PNG')): 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: 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 class VideoInpaint: def __init__(self, sub_video_length=config.PROPAINTER_MAX_LOAD_NUM, use_fp16=True): self.device = get_device() self.use_fp16 = use_fp16 self.use_half = True if self.use_fp16 else False if self.device == torch.device('cpu'): self.use_half = False # Length of sub-video for long video inference. self.sub_video_length = sub_video_length # Length of local neighboring frames.' self.neighbor_length = 10 # Mask dilation for video and flow masking self.mask_dilation = 4 # Stride of global reference frames self.ref_stride = 10 # Iterations for RAFT inference self.raft_iter = 20 # Stride of global reference frames self.ref_stride = 10 # 设置raft模型 self.fix_raft = self.init_raft_model() # 设置fix_flow模型 self.fix_flow_complete = self.init_fix_flow_model() # 设置inpaint模型 self.model = self.init_inpaint_model() def init_raft_model(self): # set up RAFT and flow competition model return RAFT_bi(os.path.join(config.VIDEO_INPAINT_MODEL_PATH, 'raft-things.pth'), self.device) def init_fix_flow_model(self): fix_flow_complete_model = RecurrentFlowCompleteNet( os.path.join(config.VIDEO_INPAINT_MODEL_PATH, 'recurrent_flow_completion.pth')) for p in fix_flow_complete_model.parameters(): p.requires_grad = False fix_flow_complete_model.to(self.device) fix_flow_complete_model.eval() return fix_flow_complete_model def init_inpaint_model(self): # set up ProPainter model return InpaintGenerator(model_path=os.path.join(config.VIDEO_INPAINT_MODEL_PATH, 'ProPainter.pth')).to( self.device).eval() def inpaint(self, frames, mask): if isinstance(frames[0], np.ndarray): frames = [Image.fromarray(cv2.cvtColor(f, cv2.COLOR_BGR2RGB)) for f in frames] size = frames[0].size frames_len = len(frames) flow_masks, masks_dilated = read_mask(mask, frames_len, size, flow_mask_dilates=self.mask_dilation, mask_dilates=self.mask_dilation) w, h = size # 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(self.device), flow_masks.to(self.device), masks_dilated.to( self.device) video_length = frames.size(1) 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 = self.fix_raft(frames[:, f:end_f], iters=self.raft_iter) else: flows_f, flows_b = self.fix_raft(frames[:, f - 1:end_f], iters=self.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 = self.fix_raft(frames, iters=self.raft_iter) torch.cuda.empty_cache() if self.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 = self.fix_flow_complete.half() self.model = self.model.half() # ---- complete flow ---- flow_length = gt_flows_bi[0].size(1) if flow_length > self.sub_video_length: pred_flows_f, pred_flows_b = [], [] pad_len = 5 for f in range(0, flow_length, self.sub_video_length): s_f = max(0, f - pad_len) e_f = min(flow_length, f + self.sub_video_length + pad_len) pad_len_s = max(0, f) - s_f pad_len_e = e_f - min(flow_length, f + self.sub_video_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) # ensure a minimum of 100 frames for image propagation subvideo_length_img_prop = min(100, self.sub_video_length) 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 = self.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 = self.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 = self.neighbor_length // 2 if video_length > self.sub_video_length: ref_num = self.sub_video_length // self.ref_stride else: ref_num = -1 # ---- feature propagation + transformer ---- for f in 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, self.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 = self.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 videos frame comp_frames = [cv2.cvtColor(i, cv2.COLOR_RGB2BGR) for i in comp_frames] return comp_frames def read_frames(v_path): video_cap = cv2.VideoCapture(v_path) video_frames = [] while True: ret, frame = video_cap.read() if not ret: break video_frames.append(frame) video_frames = [Image.fromarray(f) for f in video_frames] return video_frames if __name__ == '__main__': # VideoInpaint video_inpaint = VideoInpaint(sub_video_length=80) frames = read_frames('/home/yao/Documents/Project/video-subtitle-remover/local_test/test1.mp4') mask = cv2.imread('/home/yao/Documents/Project/video-subtitle-remover/local_test/test1_mask.png') inpainted_frames = video_inpaint.inpaint(frames, mask) save_root = '/home/yao/Documents/Project/video-subtitle-remover/local_test/' video_out_path = os.path.join(save_root, 'inpaint_out.mp4') print("size: ", inpainted_frames[0].shape) video_writer = cv2.VideoWriter(video_out_path, cv2.VideoWriter_fourcc(*'mp4v'), 24, (640, 360)) for comp_frame in inpainted_frames: video_writer.write(comp_frame) video_writer.release() print(f'\nAll results are saved in {save_root}')