import os import time import sys import gc from typing import List import cv2 import torch import numpy as np from tqdm import tqdm from torchvision import transforms sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) from backend.config import config from backend.inpaint.sttn.auto_sttn import InpaintGenerator from backend.inpaint.utils.sttn_utils import Stack, ToTorchFormatTensor from backend.tools.inpaint_tools import get_inpaint_area_by_mask, is_frame_number_in_ab_sections from backend.tools.video_io import FramePrefetcher from backend.tools.hardware_accelerator import HardwareAccelerator # 定义图像预处理方式 _to_tensors = transforms.Compose([ Stack(), # 将图像堆叠为序列 ToTorchFormatTensor() # 将堆叠的图像转化为PyTorch张量 ]) class STTNInpaint: def __init__(self, device, model_path): self.device = device # 1. 创建InpaintGenerator模型实例并装载到选择的设备上 self.model = InpaintGenerator().to(self.device) # 2. 载入预训练模型的权重，转载模型的状态字典 self.model.load_state_dict(torch.load(model_path, map_location='cpu')['netG']) # 3. # 将模型设置为评估模式 self.model.eval() # 模型输入用的宽和高 self.model_input_width, self.model_input_height = 640, 120 # 2. 设置相连帧数 self.neighbor_stride = config.sttnNeighborStride.value self.ref_length = config.sttnReferenceLength.value def __call__(self, input_frames: List[np.ndarray], input_mask: np.ndarray): """ :param input_frames: 原视频帧 :param mask: 字幕区域mask """ _, mask = cv2.threshold(input_mask, 127, 1, cv2.THRESH_BINARY) mask = mask[:, :, None] H_ori, W_ori = mask.shape[:2] H_ori = int(H_ori + 0.5) W_ori = int(W_ori + 0.5) # 确定去字幕的垂直高度部分 split_h = int(W_ori * 3 / 16) inpaint_area = get_inpaint_area_by_mask(W_ori, H_ori, split_h, mask) # 初始化帧存储变量 # 高分辨率帧存储列表（浅拷贝 + 逐帧 copy，避免 deepcopy 开销） frames_hr = [f.copy() for f in input_frames] frames_scaled = {} # 存放缩放后帧的字典 comps = {} # 存放补全后帧的字典 # 存储最终的视频帧 inpainted_frames = [] for k in range(len(inpaint_area)): frames_scaled[k] = [] # 为每个去除部分初始化一个列表 # 读取并缩放帧 for j in range(len(frames_hr)): image = frames_hr[j] # 对每个去除部分进行切割和缩放 for k in range(len(inpaint_area)): image_crop = image[inpaint_area[k][0]:inpaint_area[k][1], :, :] # 切割 image_resize = cv2.resize(image_crop, (self.model_input_width, self.model_input_height)) # 缩放 frames_scaled[k].append(image_resize) # 将缩放后的帧添加到对应列表 # 处理每一个去除部分 for k in range(len(inpaint_area)): # 调用inpaint函数进行处理 comps[k] = self.inpaint(frames_scaled[k]) # 如果存在去除部分 if inpaint_area: for j in range(len(frames_hr)): frame = frames_hr[j] # 取出原始帧 # 对于模式中的每一个段落 for k in range(len(inpaint_area)): comp = cv2.resize(comps[k][j], (W_ori, split_h)) # 将补全帧缩放回原大小 comp = cv2.cvtColor(comp.astype(np.uint8), cv2.COLOR_BGR2RGB) # 转换颜色空间 # 获取遮罩区域并进行图像合成 mask_area = mask[inpaint_area[k][0]:inpaint_area[k][1], :] # 取出遮罩区域 # 实现遮罩区域内的图像融合 frame[inpaint_area[k][0]:inpaint_area[k][1], :, :] = mask_area * comp + (1 - mask_area) * frame[inpaint_area[k][0]:inpaint_area[k][1], :, :] # 将最终帧添加到列表 inpainted_frames.append(frame) # print(f'processing frame, {len(frames_hr) - j} left') else: inpainted_frames = frames_hr return inpainted_frames @staticmethod def read_mask(path): img = cv2.imread(path, 0) # 转为binary mask ret, img = cv2.threshold(img, 127, 1, cv2.THRESH_BINARY) img = img[:, :, None] return img def get_ref_index(self, neighbor_ids, length): """ 采样整个视频的参考帧 """ # 初始化参考帧的索引列表 ref_index = [] # 在视频长度范围内根据ref_length逐步迭代 for i in range(0, length, self.ref_length): # 如果当前帧不在近邻帧中 if i not in neighbor_ids: # 将它添加到参考帧列表 ref_index.append(i) # 返回参考帧索引列表 return ref_index def inpaint(self, frames: List[np.ndarray]): """ 使用STTN完成空洞填充（空洞即被遮罩的区域） """ frame_length = len(frames) # 对帧进行预处理转换为张量，并进行归一化 feats = _to_tensors(frames).unsqueeze(0) * 2 - 1 # 把特征张量转移到指定的设备（CPU或GPU） feats = feats.to(self.device) # 初始化一个与视频长度相同的列表，用于存储处理完成的帧 comp_frames = [None] * frame_length # 统一关闭梯度计算，用于推理阶段节省内存并加速 with torch.no_grad(): # 将处理好的帧通过编码器，产生特征表示 feats = self.model.encoder(feats.view(frame_length, 3, self.model_input_height, self.model_input_width)) # 获取特征维度信息 _, c, feat_h, feat_w = feats.size() # 调整特征形状以匹配模型的期望输入 feats = feats.view(1, frame_length, c, feat_h, feat_w) # 在设定的邻居帧步幅内循环处理视频 for f in range(0, frame_length, self.neighbor_stride): # 计算邻近帧的ID neighbor_ids = [i for i in range(max(0, f - self.neighbor_stride), min(frame_length, f + self.neighbor_stride + 1))] # 获取参考帧的索引 ref_ids = self.get_ref_index(neighbor_ids, frame_length) # 通过模型推断特征并传递给解码器以生成完成的帧 pred_feat = self.model.infer(feats[0, neighbor_ids + ref_ids, :, :, :]) # 将预测的特征通过解码器生成图片，并应用激活函数tanh pred_img = torch.tanh(self.model.decoder(pred_feat[:len(neighbor_ids), :, :, :])) # 将结果张量重新缩放到0到255的范围内 pred_img = (pred_img + 1) / 2 # 将张量移动回CPU并转为NumPy数组 pred_img = pred_img.cpu().permute(0, 2, 3, 1).numpy() * 255 # 遍历邻近帧 for i in range(len(neighbor_ids)): idx = neighbor_ids[i] img = pred_img[i].astype(np.uint8) 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 # 返回处理完成的帧序列 return comp_frames class STTNAutoInpaint: def read_frame_info_from_video(self): # 使用opencv读取视频 reader = cv2.VideoCapture(self.video_path) # 获取视频的宽度, 高度, 帧率和帧数信息并存储在frame_info字典中 frame_info = { 'W_ori': int(reader.get(cv2.CAP_PROP_FRAME_WIDTH) + 0.5), # 视频的原始宽度 'H_ori': int(reader.get(cv2.CAP_PROP_FRAME_HEIGHT) + 0.5), # 视频的原始高度 'fps': reader.get(cv2.CAP_PROP_FPS), # 视频的帧率 'len': int(reader.get(cv2.CAP_PROP_FRAME_COUNT) + 0.5) # 视频的总帧数 } # 返回视频读取对象、帧信息和视频写入对象 return reader, frame_info def __init__(self, device, model_path, video_path, mask_path=None, clip_gap=None): # STTNInpaint视频修复实例初始化 self.sttn_inpaint = STTNInpaint(device, model_path) # 视频和掩码路径 self.video_path = video_path self.mask_path = mask_path # 设置输出视频文件的路径 self.video_out_path = os.path.join( os.path.dirname(os.path.abspath(self.video_path)), f"{os.path.basename(self.video_path).rsplit('.', 1)[0]}_no_sub.mp4" ) # 配置可在一次处理中加载的最大帧数 if clip_gap is None: self.clip_gap = config.getSttnMaxLoadNum() else: self.clip_gap = clip_gap def __call__(self, input_mask=None, input_sub_remover=None, tbar=None): reader = None writer = None try: # 读取视频帧信息 reader, frame_info = self.read_frame_info_from_video() # 使用帧预读取，I/O 与推理重叠 prefetcher = FramePrefetcher(reader) if input_sub_remover is not None: ab_sections = input_sub_remover.ab_sections writer = input_sub_remover.video_writer else: ab_sections = None # 创建视频写入对象，用于输出修复后的视频 writer = cv2.VideoWriter(self.video_out_path, cv2.VideoWriter_fourcc(*"mp4v"), frame_info['fps'], (frame_info['W_ori'], frame_info['H_ori'])) # 计算分割高度，用于确定修复区域的大小 split_h = int(frame_info['W_ori'] * 3 / 16) if input_mask is None: # 读取掩码 mask = self.sttn_inpaint.read_mask(self.mask_path) else: _, mask = cv2.threshold(input_mask, 127, 1, cv2.THRESH_BINARY) mask = mask[:, :, None] # 得到修复区域位置 inpaint_area = get_inpaint_area_by_mask(frame_info['W_ori'], frame_info['H_ori'], split_h, mask) # 根据可用显存动态调整 clip_gap，避免 OOM effective_clip_gap = self.clip_gap vram_mb = HardwareAccelerator.instance().get_available_vram_mb() if vram_mb > 0: # 估算每帧约需 (W * H * 3 * 4) bytes，clip_gap帧约需 clip_gap * W * H * 12 bytes（含中间张量） bytes_per_frame = frame_info['W_ori'] * frame_info['H_ori'] * 12 max_frames_by_vram = int(vram_mb * 1024 * 1024 / bytes_per_frame) max_frames_by_vram = max(max_frames_by_vram, 10) # 至少10帧 effective_clip_gap = min(self.clip_gap, max_frames_by_vram) if effective_clip_gap < self.clip_gap: tqdm.write(f'GPU VRAM: {vram_mb:.0f}MB, adjusting clip_gap: {self.clip_gap} -> {effective_clip_gap}') # 计算需要迭代修复视频的次数 rec_time = frame_info['len'] // effective_clip_gap if frame_info['len'] % effective_clip_gap == 0 else frame_info['len'] // effective_clip_gap + 1 # 遍历每一次的迭代次数 for i in range(rec_time): start_f = i * effective_clip_gap # 起始帧位置 end_f = min((i + 1) * effective_clip_gap, frame_info['len']) # 结束帧位置 tqdm.write(f'Processing: {start_f + 1} - {end_f} / Total: {frame_info['len']}') frames_hr = [] # 高分辨率帧列表 frames = {} # 帧字典，用于存储裁剪后的图像 comps = {} # 组合字典，用于存储修复后的图像 # 初始化帧字典 for k in range(len(inpaint_area)): frames[k] = [] # 读取和修复高分辨率帧 valid_frames_count = 0 for j in range(start_f, end_f): success, image = prefetcher.read() if not success: print(f"Warning: Failed to read frame {j}.") break frames_hr.append(image) valid_frames_count += 1 if is_frame_number_in_ab_sections(j, ab_sections): for k in range(len(inpaint_area)): # 裁剪、缩放并添加到帧字典 image_crop = image[inpaint_area[k][0]:inpaint_area[k][1], :, :] image_resize = cv2.resize(image_crop, (self.sttn_inpaint.model_input_width, self.sttn_inpaint.model_input_height)) frames[k].append(image_resize) # 如果没有读取到有效帧，则跳过当前迭代 if valid_frames_count == 0: print(f"Warning: No valid frames found in range {start_f+1}-{end_f}. Skipping this segment.") continue # 对每个修复区域运行修复 for k in range(len(inpaint_area)): if len(frames[k]) > 0: # 确保有帧可以处理 comps[k] = self.sttn_inpaint.inpaint(frames[k]) else: comps[k] = [] # 如果有要修复的区域 if inpaint_area and valid_frames_count > 0: # 创建一个映射，记录哪些帧被处理了以及它们在frames[k]中的索引 processed_frames_map = {} processed_idx = 0 # 构建映射关系 for j in range(start_f, end_f): if j - start_f < valid_frames_count and is_frame_number_in_ab_sections(j, ab_sections): processed_frames_map[j - start_f] = processed_idx processed_idx += 1 # 应用修复结果 for j in range(valid_frames_count): if input_sub_remover is not None and input_sub_remover.gui_mode: original_frame = frames_hr[j].copy() else: original_frame = None frame = frames_hr[j] # 只有被处理过的帧才应用修复结果 if j in processed_frames_map: comp_idx = processed_frames_map[j] for k in range(len(inpaint_area)): if comp_idx < len(comps[k]): # 确保索引有效 # 将修复的图像重新扩展到原始分辨率，并融合到原始帧 comp = cv2.resize(comps[k][comp_idx], (frame_info['W_ori'], split_h)) comp = cv2.cvtColor(comp.astype(np.uint8), cv2.COLOR_BGR2RGB) mask_area = mask[inpaint_area[k][0]:inpaint_area[k][1], :] frame[inpaint_area[k][0]:inpaint_area[k][1], :, :] = mask_area * comp + (1 - mask_area) * frame[inpaint_area[k][0]:inpaint_area[k][1], :, :] writer.write(frame) if input_sub_remover is not None: if tbar is not None: input_sub_remover.update_progress(tbar, increment=1) if original_frame is not None and input_sub_remover.gui_mode: input_sub_remover.update_preview_with_comp(original_frame, frame) # 每个chunk处理完后清理GPU缓存 del frames_hr, frames, comps gc.collect() if torch.cuda.is_available(): torch.cuda.empty_cache() except Exception as e: print(f"Error during video processing: {str(e)}") # 不抛出异常，允许程序继续执行 finally: if reader: prefetcher.release() if writer: writer.release() if __name__ == '__main__': mask_path = '../../test/test.png' video_path = '../../test/test.mp4' # 记录开始时间 start = time.time() sttn_video_inpaint = STTNAutoInpaint(video_path, mask_path, clip_gap=config.getSttnMaxLoadNum()) sttn_video_inpaint() print(f'video generated at {sttn_video_inpaint.video_out_path}') print(f'time cost: {time.time() - start}')