mirror of
https://github.com/YaoFANGUK/video-subtitle-remover.git
synced 2026-03-02 08:24:42 +08:00
312 lines
15 KiB
Python
312 lines
15 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 import config
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from backend.inpaint.sttn.auto_sttn import InpaintGenerator
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from backend.inpaint.utils.sttn_utils import Stack, ToTorchFormatTensor
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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 STTNInpaint:
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def __init__(self):
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self.device = config.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(config.STTN_MODEL_PATH, map_location=self.device)['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 = 640, 120
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# 2. 设置相连帧数
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self.neighbor_stride = config.STTN_NEIGHBOR_STRIDE
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self.ref_length = config.STTN_REFERENCE_LENGTH
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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 = cv2.threshold(input_mask, 127, 1, cv2.THRESH_BINARY)
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mask = 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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split_h = int(W_ori * 3 / 16)
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inpaint_area = self.get_inpaint_area_by_mask(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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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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# 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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image_resize = cv2.resize(image_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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# 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])
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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], :, :] = mask_area * comp + (1 - mask_area) * frame[inpaint_area[k][0]:inpaint_area[k][1], :, :]
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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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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]):
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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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# 把特征张量转移到指定的设备（CPU或GPU）
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feats = feats.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.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(feats[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(np.uint8)
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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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@staticmethod
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def get_inpaint_area_by_mask(H, h, mask):
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"""
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获取字幕去除区域，根据mask来确定需要填补的区域和高度
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"""
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# 存储绘画区域的列表
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inpaint_area = []
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# 从视频底部的字幕位置开始，假设字幕通常位于底部
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to_H = from_H = H
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# 从底部向上遍历遮罩
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while from_H != 0:
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if to_H - h < 0:
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# 如果下一段会超出顶端，则从顶端开始
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from_H = 0
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to_H = h
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else:
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# 确定段的上边界
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from_H = to_H - h
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# 检查当前段落是否包含遮罩像素
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if not np.all(mask[from_H:to_H, :] == 0) and np.sum(mask[from_H:to_H, :]) > 10:
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# 如果不是第一个段落，向下移动以确保没遗漏遮罩区域
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if to_H != H:
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move = 0
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while to_H + move < H and not np.all(mask[to_H + move, :] == 0):
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move += 1
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# 确保没有越过底部
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if to_H + move < H and move < h:
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to_H += move
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from_H += move
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# 将该段落添加到列表中
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if (from_H, to_H) not in inpaint_area:
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inpaint_area.append((from_H, to_H))
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else:
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break
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# 移动到下一个段落
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to_H -= h
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return inpaint_area # 返回绘画区域列表
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class STTNVideoInpaint:
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def read_frame_info_from_video(self):
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# 使用opencv读取视频
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reader = cv2.VideoCapture(self.video_path)
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# 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
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frame_info = {
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'W_ori': int(reader.get(cv2.CAP_PROP_FRAME_WIDTH) + 0.5), # 视频的原始宽度
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'H_ori': int(reader.get(cv2.CAP_PROP_FRAME_HEIGHT) + 0.5), # 视频的原始高度
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'fps': reader.get(cv2.CAP_PROP_FPS), # 视频的帧率
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'len': int(reader.get(cv2.CAP_PROP_FRAME_COUNT) + 0.5) # 视频的总帧数
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}
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# 返回视频读取对象、帧信息和视频写入对象
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return reader, frame_info
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def __init__(self, video_path, mask_path=None, clip_gap=None):
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# STTNInpaint视频修复实例初始化
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self.sttn_inpaint = STTNInpaint()
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# 视频和掩码路径
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self.video_path = video_path
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self.mask_path = mask_path
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# 设置输出视频文件的路径
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self.video_out_path = os.path.join(
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os.path.dirname(os.path.abspath(self.video_path)),
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f"{os.path.basename(self.video_path).rsplit('.', 1)[0]}_no_sub.mp4"
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)
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# 配置可在一次处理中加载的最大帧数
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if clip_gap is None:
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self.clip_gap = config.STTN_MAX_LOAD_NUM
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else:
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self.clip_gap = clip_gap
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def __call__(self, input_mask=None, input_sub_remover=None, tbar=None):
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# 读取视频帧信息
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reader, frame_info = self.read_frame_info_from_video()
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if input_sub_remover is not None:
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writer = input_sub_remover.video_writer
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else:
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# 创建视频写入对象，用于输出修复后的视频
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writer = cv2.VideoWriter(self.video_out_path, cv2.VideoWriter_fourcc(*"mp4v"), frame_info['fps'], (frame_info['W_ori'], frame_info['H_ori']))
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# 计算需要迭代修复视频的次数
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rec_time = frame_info['len'] // self.clip_gap if frame_info['len'] % self.clip_gap == 0 else frame_info['len'] // self.clip_gap + 1
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# 计算分割高度，用于确定修复区域的大小
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split_h = int(frame_info['W_ori'] * 3 / 16)
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if input_mask is None:
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# 读取掩码
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mask = self.sttn_inpaint.read_mask(self.mask_path)
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else:
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_, mask = cv2.threshold(input_mask, 127, 1, cv2.THRESH_BINARY)
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mask = mask[:, :, None]
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# 得到修复区域位置
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inpaint_area = self.sttn_inpaint.get_inpaint_area_by_mask(frame_info['H_ori'], split_h, mask)
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# 遍历每一次的迭代次数
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for i in range(rec_time):
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start_f = i * self.clip_gap # 起始帧位置
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end_f = min((i + 1) * self.clip_gap, frame_info['len']) # 结束帧位置
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print('Processing:', start_f + 1, '-', end_f, ' / Total:', frame_info['len'])
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frames_hr = [] # 高分辨率帧列表
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frames = {} # 帧字典，用于存储裁剪后的图像
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comps = {} # 组合字典，用于存储修复后的图像
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# 初始化帧字典
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for k in range(len(inpaint_area)):
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frames[k] = []
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# 读取和修复高分辨率帧
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for j in range(start_f, end_f):
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success, image = reader.read()
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frames_hr.append(image)
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for k in range(len(inpaint_area)):
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# 裁剪、缩放并添加到帧字典
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image_crop = image[inpaint_area[k][0]:inpaint_area[k][1], :, :]
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image_resize = cv2.resize(image_crop, (self.sttn_inpaint.model_input_width, self.sttn_inpaint.model_input_height))
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frames[k].append(image_resize)
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# 对每个修复区域运行修复
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for k in range(len(inpaint_area)):
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comps[k] = self.sttn_inpaint.inpaint(frames[k])
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# 如果有要修复的区域
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if inpaint_area is not []:
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for j in range(end_f - start_f):
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if input_sub_remover is not None and input_sub_remover.gui_mode:
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original_frame = copy.deepcopy(frames_hr[j])
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else:
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original_frame = None
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frame = frames_hr[j]
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for k in range(len(inpaint_area)):
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# 将修复的图像重新扩展到原始分辨率，并融合到原始帧
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comp = cv2.resize(comps[k][j], (frame_info['W_ori'], split_h))
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comp = cv2.cvtColor(np.array(comp).astype(np.uint8), cv2.COLOR_BGR2RGB)
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mask_area = mask[inpaint_area[k][0]:inpaint_area[k][1], :]
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frame[inpaint_area[k][0]:inpaint_area[k][1], :, :] = mask_area * comp + (1 - mask_area) * frame[inpaint_area[k][0]:inpaint_area[k][1], :, :]
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writer.write(frame)
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if input_sub_remover is not None:
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if tbar is not None:
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input_sub_remover.update_progress(tbar, increment=1)
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if original_frame is not None and input_sub_remover.gui_mode:
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input_sub_remover.preview_frame = cv2.hconcat([original_frame, frame])
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# 释放视频写入对象
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writer.release()
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if __name__ == '__main__':
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mask_path = '../../test/test.png'
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video_path = '../../test/test.mp4'
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# 记录开始时间
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start = time.time()
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sttn_video_inpaint = STTNVideoInpaint(video_path, mask_path, clip_gap=config.STTN_MAX_LOAD_NUM)
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sttn_video_inpaint()
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print(f'video generated at {sttn_video_inpaint.video_out_path}')
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print(f'time cost: {time.time() - start}')
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