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https://github.com/YaoFANGUK/video-subtitle-remover.git
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f78e985e1c
添加任务列表组件并优化视频加载逻辑 支持可视化显示字幕区域 整理所有模型, 分别为STTN智能擦除, STTN字幕检测, LAMA, ProPainter, OpenCV 提高处理性能 新增CPU运行模式并优化多语言支持 修复Propainter模式部分视频报错 本次提交新增了CPU运行模式，适用于无GPU加速的场景。同时，优化了多语言支持，新增了日语、韩语、越南语等语言配置文件，并更新了README文档以反映新的运行模式和多语言支持。此外，修复了部分代码逻辑，提升了系统的稳定性和兼容性。
180 lines
8.5 KiB
Python
180 lines
8.5 KiB
Python
import copy
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import time
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import cv2
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import numpy as np
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import torch
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from torchvision import transforms
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from typing import List
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import sys
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import os
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
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from backend.config import config
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from backend.inpaint.sttn.network_sttn import InpaintGenerator
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from backend.inpaint.utils.sttn_utils import Stack, ToTorchFormatTensor
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from backend.tools.inpaint_tools import get_inpaint_area_by_mask
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# 定义图像预处理方式
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_to_tensors = transforms.Compose([
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Stack(), # 将图像堆叠为序列
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ToTorchFormatTensor() # 将堆叠的图像转化为PyTorch张量
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])
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class STTNDetInpaint:
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def __init__(self, device, model_path):
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self.device = device
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# 1. 创建InpaintGenerator模型实例并装载到选择的设备上
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self.model = InpaintGenerator().to(self.device)
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# 2. 载入预训练模型的权重，转载模型的状态字典
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self.model.load_state_dict(torch.load(model_path, map_location='cpu')['netG'])
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# 3. # 将模型设置为评估模式
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self.model.eval()
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# 模型输入用的宽和高
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self.model_input_width, self.model_input_height = 432, 240
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# 2. 设置相连帧数
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self.neighbor_stride = config.sttnNeighborStride.value
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self.ref_length = config.sttnReferenceLength.value
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def __call__(self, input_frames: List[np.ndarray], input_mask: np.ndarray):
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"""
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:param input_frames: 原视频帧
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:param mask: 字幕区域mask
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"""
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mask = input_mask[:, :, None]
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H_ori, W_ori = mask.shape[:2]
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H_ori = int(H_ori + 0.5)
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W_ori = int(W_ori + 0.5)
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# 确定去字幕的垂直高度部分
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if H_ori > W_ori:
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split_h = int(H_ori * 5 / 9)
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else:
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split_h = int(W_ori * 5 / 18)
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inpaint_area = get_inpaint_area_by_mask(W_ori, H_ori, split_h, mask)
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# 初始化帧存储变量
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# 高分辨率帧存储列表
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frames_hr = copy.deepcopy(input_frames)
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frames_scaled = {} # 存放缩放后帧的字典
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masks_scaled = {} # 存放缩放后遮罩的字典
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comps = {} # 存放补全后帧的字典
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# 存储最终的视频帧
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inpainted_frames = []
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for k in range(len(inpaint_area)):
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frames_scaled[k] = [] # 为每个去除部分初始化一个列表
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masks_scaled[k] = [] # 为每个去除部分初始化一个列表
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# 读取并缩放帧
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for j in range(len(frames_hr)):
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image = frames_hr[j]
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# 对每个去除部分进行切割和缩放
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for k in range(len(inpaint_area)):
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image_crop = image[inpaint_area[k][0]:inpaint_area[k][1], :, :] # 切割
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mask_crop = mask[inpaint_area[k][0]:inpaint_area[k][1], :, :] # 切割
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image_resize = cv2.resize(image_crop, (self.model_input_width, self.model_input_height)) # 缩放
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mask_resize = cv2.resize(mask_crop, (self.model_input_width, self.model_input_height)) # 缩放
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frames_scaled[k].append(image_resize) # 将缩放后的帧添加到对应列表
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masks_scaled[k].append(mask_resize) # 将缩放后的遮罩添加到对应列表
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# 处理每一个去除部分
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for k in range(len(inpaint_area)):
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# 调用inpaint函数进行处理
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comps[k] = self.inpaint(frames_scaled[k], masks_scaled[k])
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# 如果存在去除部分
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if inpaint_area:
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for j in range(len(frames_hr)):
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frame = frames_hr[j] # 取出原始帧
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# 对于模式中的每一个段落
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for k in range(len(inpaint_area)):
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comp = cv2.resize(comps[k][j], (W_ori, split_h)) # 将补全帧缩放回原大小
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comp = cv2.cvtColor(np.array(comp).astype(np.uint8), cv2.COLOR_BGR2RGB) # 转换颜色空间
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# 获取遮罩区域并进行图像合成
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mask_area = mask[inpaint_area[k][0]:inpaint_area[k][1], :] # 取出遮罩区域
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# 实现遮罩区域内的图像融合
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frame[inpaint_area[k][0]:inpaint_area[k][1], :, :] = comp
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# 将最终帧添加到列表
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inpainted_frames.append(frame)
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# print(f'processing frame, {len(frames_hr) - j} left')
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return inpainted_frames
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@staticmethod
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def read_mask(path):
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img = cv2.imread(path, 0)
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# 转为binary mask
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ret, img = cv2.threshold(img, 127, 1, cv2.THRESH_BINARY)
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img = img[:, :, None]
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return img
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def get_ref_index(self, neighbor_ids, length):
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"""
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采样整个视频的参考帧
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"""
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# 初始化参考帧的索引列表
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ref_index = []
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# 在视频长度范围内根据ref_length逐步迭代
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for i in range(0, length, self.ref_length):
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# 如果当前帧不在近邻帧中
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if i not in neighbor_ids:
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# 将它添加到参考帧列表
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ref_index.append(i)
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# 返回参考帧索引列表
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return ref_index
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def inpaint(self, frames: List[np.ndarray], masks: List[np.ndarray]):
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"""
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使用STTN完成空洞填充（空洞即被遮罩的区域）
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"""
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frame_length = len(frames)
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# 对帧进行预处理转换为张量，并进行归一化
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feats = _to_tensors(frames).unsqueeze(0) * 2 - 1
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binary_masks = [np.expand_dims((np.array(m) > 0.5).astype(np.uint8), 2) for m in masks]
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# 将掩码转换为张量
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masks = (_to_tensors(masks).unsqueeze(0) > 0.5).float()
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# 把特征张量转移到指定的设备（CPU或GPU）
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feats, masks = feats.to(self.device), masks.to(self.device)
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# 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
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comp_frames = [None] * frame_length
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# 关闭梯度计算，用于推理阶段节省内存并加速
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with torch.no_grad():
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# 将处理好的帧通过编码器，产生特征表示
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feats = self.model.encoder((feats*(1-masks).float()).view(frame_length, 3, self.model_input_height, self.model_input_width))
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# 获取特征维度信息
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_, c, feat_h, feat_w = feats.size()
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# 调整特征形状以匹配模型的期望输入
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feats = feats.view(1, frame_length, c, feat_h, feat_w)
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# 获取重绘区域
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# 在设定的邻居帧步幅内循环处理视频
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for f in range(0, frame_length, self.neighbor_stride):
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# 计算邻近帧的ID
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neighbor_ids = [i for i in range(max(0, f - self.neighbor_stride), min(frame_length, f + self.neighbor_stride + 1))]
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# 获取参考帧的索引
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ref_ids = self.get_ref_index(neighbor_ids, frame_length)
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# 同样关闭梯度计算
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with torch.no_grad():
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# 通过模型推断特征并传递给解码器以生成完成的帧
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pred_feat = self.model.infer(
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feats[0, neighbor_ids + ref_ids, :, :, :], masks[0, neighbor_ids + ref_ids, :, :, :])
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# 将预测的特征通过解码器生成图片，并应用激活函数tanh，然后分离出张量
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pred_img = torch.tanh(self.model.decoder(pred_feat[:len(neighbor_ids), :, :, :])).detach()
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# 将结果张量重新缩放到0到255的范围内（图像像素值）
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pred_img = (pred_img + 1) / 2
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# 将张量移动回CPU并转为NumPy数组
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pred_img = pred_img.cpu().permute(0, 2, 3, 1).numpy() * 255
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# 遍历邻近帧
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for i in range(len(neighbor_ids)):
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idx = neighbor_ids[i]
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# 将预测的图片转换为无符号8位整数格式
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img = np.array(pred_img[i]).astype(
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np.uint8)*binary_masks[idx] + frames[idx] * (1-binary_masks[idx])
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if comp_frames[idx] is None:
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# 如果该位置为空，则赋值为新计算出的图片
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comp_frames[idx] = img
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else:
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# 如果此位置之前已有图片，则将新旧图片混合以提高质量
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comp_frames[idx] = comp_frames[idx].astype(np.float32) * 0.5 + img.astype(np.float32) * 0.5
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# 返回处理完成的帧序列
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return comp_frames
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