├── .gitignore ├── LICENSE ├── README.md ├── image ├── no_watermark.jpg └── origin.jpg ├── requirements.txt └── watermark_remover.py /.gitignore: -------------------------------------------------------------------------------- 1 | /venv 2 | /output 3 | /video 4 | log 5 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # WatermarkRemover 2 | 3 | 一个基于LAMA模型的视频水印移除工具，能够批量清除视频中的固定水印。 4 | 5 | ## 效果展示 6 | 7 | 原始帧 8 | 9 | 10 | 去除水印 11 | 12 | 13 | ## 系统要求 14 | 15 | - Python 3.10 16 | 17 | ## 安装步骤 18 | 19 | - 克隆仓库 20 | 21 | ```bash 22 | git clone https://github.com/lxulxu/WatermarkRemover.git 23 | cd WatermarkRemover 24 | ``` 25 | 26 | - 创建并激活虚拟环境（可选，推荐） 27 | 28 | ```bash 29 | python -m venv venv 30 | # Windows 31 | venv\Scripts\activate 32 | ``` 33 | 34 | - 安装基础依赖 35 | 36 | ```bash 37 | pip install -r requirements.txt 38 | ``` 39 | 40 | - 安装PyTorch（二选一） 41 | 42 | 1. CPU版本 43 | 44 | ```bash 45 | pip install torch 46 | ``` 47 | 48 | 2. GPU版本（需要NVIDIA显卡） 49 | 50 | - 安装CUDA Toolkit 51 | 52 | 访问 [NVIDIA CUDA下载页面](https://developer.nvidia.com/cuda-downloads)，选择对应的操作系统和版本。 53 | 54 | - 安装cuDNN 55 | 56 | 访问 [NVIDIA cuDNN下载页面](https://developer.nvidia.com/cudnn-downloads)，选择与CUDA版本匹配的cuDNN。 57 | 58 | - 安装GPU版本的PyTorch 59 | 60 | 访问 [PyTorch官方网站](https://pytorch.org/get-started/locally/)，选择与CUDA版本匹配的命令安装，例如： 61 | 62 | 63 | ```bash 64 | pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126 65 | ``` 66 | 67 | 68 | 69 | ​ 程序会自动检测是否有可用的GPU输出相关信息并自动选择处理方式。 70 | 71 | ## 使用方法 72 | 73 | ### 基本用法 74 | 75 | 处理单个视频目录中的所有视频： 76 | 77 | ```bash 78 | python watermark_remover.py --input /path/to/videos --output /path/to/output 79 | ``` 80 | 81 | ### 带预览的处理 82 | 83 | ```bash 84 | python watermark_remover.py --input /path/to/videos --output /path/to/output --preview 85 | ``` 86 | 87 | ### 命令行参数 88 | 89 | | 参数 | 简写 | 说明 | 默认值 | 90 | | ----------- | ---- | ---------------------- | -------------- | 91 | | `--input` | `-i` | 包含视频文件的输入目录 | `.` (当前目录) | 92 | | `--output` | `-o` | 处理后视频的输出目录 | `output` | 93 | | `--preview` | `-p` | 启用处理效果预览 | 禁用 | 94 | 95 | ## 工作流程 96 | 97 | 1. **水印区域选择**：程序会显示视频一帧，手动框选水印区域后按**SPACE**或**ENTER**键继续。 98 | 2. **效果预览**（可选）：显示处理效果预览，按**SPACE**或**ENTER**键确认或按**ESC**键取消退出程序。 99 | 3. **视频处理**：初次运行程序使用LAMA模型需较长时间下载模型。 100 | 4. **输出结果** 101 | 102 | ## 局限性 103 | 104 | - 只能处理固定位置的水印（不支持移动水印） 105 | - 同一批处理的视频尺寸必须一致 106 | - 同一批处理的视频水印必须一致 107 | 108 | ## 常见问题 109 | 110 | **Q: 安装CUDA后出现错误？** 111 | A: 确保安装的CUDA、cuDNN和PyTorch版本相互兼容。参考PyTorch官方网站的兼容性表格。 112 | 113 | 114 | 115 | ## Star History 116 | 117 | [![Star History Chart](https://api.star-history.com/svg?repos=lxulxu/WatermarkRemover&type=Date)](https://star-history.com/#lxulxu/WatermarkRemover&Date) 118 | -------------------------------------------------------------------------------- /image/no_watermark.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lxulxu/WatermarkRemover/bcdb617b4fdab17aa29fcedc7dff61506f6c2990/image/no_watermark.jpg -------------------------------------------------------------------------------- /image/origin.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lxulxu/WatermarkRemover/bcdb617b4fdab17aa29fcedc7dff61506f6c2990/image/origin.jpg -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | lama_cleaner==1.2.5 2 | moviepy==2.1.2 3 | numpy==2.2.3 4 | opencv_python==4.11.0.86 5 | tqdm==4.67.1 6 | -------------------------------------------------------------------------------- /watermark_remover.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import numpy as np 3 | import glob 4 | from moviepy import VideoFileClip 5 | import os 6 | import sys 7 | import argparse 8 | from tqdm import tqdm 9 | from lama_cleaner.model_manager import ModelManager 10 | from lama_cleaner.schema import Config, HDStrategy 11 | import time 12 | from datetime import timedelta 13 | import numpy as np 14 | import torch 15 | 16 | class WatermarkDetector: 17 | def __init__(self, num_sample_frames=10, min_frame_count=7, dilation_kernel_size=5): 18 | self.num_sample_frames = num_sample_frames 19 | self.min_frame_count = min_frame_count 20 | self.dilation_kernel_size = dilation_kernel_size 21 | self.roi = None 22 | 23 | def get_first_valid_frame(self, video_clip, threshold=10): 24 | total_frames = int(video_clip.fps * video_clip.duration) 25 | frame_indices = [int(i * total_frames / self.num_sample_frames) for i in range(self.num_sample_frames)] 26 | 27 | for idx in frame_indices: 28 | frame = video_clip.get_frame(idx / video_clip.fps) 29 | if frame.mean() > threshold: 30 | return frame 31 | 32 | return video_clip.get_frame(0) 33 | 34 | def select_roi(self, video_clip): 35 | frame = self.get_first_valid_frame(video_clip) 36 | frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR) 37 | 38 | display_height = 720 39 | scale_factor = display_height / frame.shape[0] 40 | display_width = int(frame.shape[1] * scale_factor) 41 | display_frame = cv2.resize(frame, (display_width, display_height)) 42 | 43 | instructions = "Select ROI and press SPACE or ENTER" 44 | font = cv2.FONT_HERSHEY_SIMPLEX 45 | cv2.putText(display_frame, instructions, (10, 30), font, 1, (255, 255, 255), 2, cv2.LINE_AA) 46 | 47 | r = cv2.selectROI(display_frame) 48 | cv2.destroyAllWindows() 49 | 50 | self.roi = ( 51 | int(r[0] / scale_factor), 52 | int(r[1] / scale_factor), 53 | int(r[2] / scale_factor), 54 | int(r[3] / scale_factor) 55 | ) 56 | 57 | return self.roi 58 | 59 | def detect_watermark_in_frame(self, frame): 60 | if self.roi is None: 61 | raise ValueError("ROI hasn't been selected yet. Call select_roi first.") 62 | 63 | roi_frame = frame[self.roi[1]:self.roi[1] + self.roi[3], self.roi[0]:self.roi[0] + self.roi[2]] 64 | gray_frame = cv2.cvtColor(roi_frame, cv2.COLOR_BGR2GRAY) 65 | _, binary_frame = cv2.threshold(gray_frame, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) 66 | 67 | mask = np.zeros_like(frame[:, :, 0], dtype=np.uint8) 68 | mask[self.roi[1]:self.roi[1] + self.roi[3], self.roi[0]:self.roi[0] + self.roi[2]] = binary_frame 69 | 70 | return mask 71 | 72 | def generate_mask(self, video_clip): 73 | if self.roi is None: 74 | self.select_roi(video_clip) 75 | 76 | total_frames = int(video_clip.duration * video_clip.fps) 77 | frame_indices = [int(i * total_frames / self.num_sample_frames) for i in range(self.num_sample_frames)] 78 | frames = [video_clip.get_frame(idx / video_clip.fps) for idx in frame_indices] 79 | 80 | masks = [self.detect_watermark_in_frame(frame) for frame in frames] 81 | 82 | final_mask = sum((mask == 255).astype(np.uint8) for mask in masks) 83 | final_mask = np.where(final_mask >= self.min_frame_count, 255, 0).astype(np.uint8) 84 | 85 | kernel = np.ones((self.dilation_kernel_size, self.dilation_kernel_size), np.uint8) 86 | dilated_mask = cv2.dilate(final_mask, kernel, iterations=2) 87 | 88 | return dilated_mask 89 | 90 | def get_roi_coordinates(self, watermark_mask, margin=50): 91 | y_indices, x_indices = np.where(watermark_mask > 0) 92 | if len(y_indices) == 0 or len(x_indices) == 0: 93 | raise ValueError("No watermark region found in mask") 94 | 95 | y_min = max(0, np.min(y_indices) - margin) 96 | y_max = min(watermark_mask.shape[0], np.max(y_indices) + margin) 97 | x_min = max(0, np.min(x_indices) - margin) 98 | x_max = min(watermark_mask.shape[1], np.max(x_indices) + margin) 99 | 100 | return (y_min, y_max, x_min, x_max) 101 | 102 | def extract_roi_mask(self, watermark_mask, roi_coords): 103 | y_min, y_max, x_min, x_max = roi_coords 104 | return watermark_mask[y_min:y_max, x_min:x_max] 105 | 106 | def preview_effect(self, video_clip, watermark_mask, model, config, max_height=720): 107 | frame = self.get_first_valid_frame(video_clip) 108 | 109 | result = lama_inpaint(frame, watermark_mask, model, config) 110 | 111 | h, w = result.shape[:2] 112 | 113 | scale_factor = max_height / h 114 | display_width = int(w * scale_factor) 115 | 116 | display_result = cv2.resize(result, (display_width, max_height)) 117 | 118 | instructions = "Processed Result | Press ENTER to continue, ESC to exit" 119 | font = cv2.FONT_HERSHEY_SIMPLEX 120 | cv2.putText(display_result, instructions, (10, 30), font, 0.7, (255, 255, 255), 2, cv2.LINE_AA) 121 | 122 | cv2.imshow("Watermark Removal Result", display_result) 123 | key = cv2.waitKey(0) 124 | cv2.destroyAllWindows() 125 | 126 | return key != 27 127 | 128 | 129 | def get_video_info(video_clip): 130 | info = { 131 | "resolution": f"{int(video_clip.w)}x{int(video_clip.h)}", 132 | "duration": f"{video_clip.duration:.2f}秒", 133 | "total_frames": int(video_clip.duration * video_clip.fps), 134 | "fps": f"{video_clip.fps:.2f}", 135 | "format": "mp4" 136 | } 137 | return info 138 | 139 | def check_gpu(): 140 | if torch.cuda.is_available(): 141 | device = "cuda" 142 | gpu_name = torch.cuda.get_device_name(0) 143 | return True, device, gpu_name 144 | else: 145 | return False, "cpu", None 146 | 147 | def initialize_lama(device="cpu"): 148 | model = ModelManager(name="lama", device=device) 149 | 150 | config = Config( 151 | ldm_steps=25, 152 | hd_strategy=HDStrategy.ORIGINAL, 153 | hd_strategy_crop_margin=32, 154 | hd_strategy_crop_trigger_size=2048, 155 | hd_strategy_resize_limit=2048, 156 | ) 157 | 158 | return model, config 159 | 160 | def lama_inpaint(frame, mask, model, config): 161 | mask_binary = np.where(mask > 0, 255, 0).astype(np.uint8) 162 | result_rgb = model(frame, mask_binary, config) 163 | 164 | if result_rgb.dtype == np.float64: 165 | if np.max(result_rgb) <= 1.0: 166 | result_rgb = (result_rgb * 255).astype(np.uint8) 167 | else: 168 | result_rgb = result_rgb.astype(np.uint8) 169 | 170 | return result_rgb 171 | 172 | def ensure_directory_exists(directory): 173 | if not os.path.exists(directory): 174 | try: 175 | os.makedirs(directory) 176 | return True 177 | except OSError as error: 178 | print(f"Error creating directory {directory}: {error}") 179 | return False 180 | 181 | temp_file = os.path.join(directory, f"temp_{time.time()}.tmp") 182 | try: 183 | with open(temp_file, 'w') as f: 184 | f.write("test") 185 | os.remove(temp_file) 186 | return True 187 | except Exception as e: 188 | print(f"No write permission in directory {directory}: {e}") 189 | return False 190 | 191 | def is_valid_video_file(file): 192 | try: 193 | with VideoFileClip(file) as video_clip: 194 | return True 195 | except Exception as e: 196 | print(f"Invalid video file: {file}, Error: {e}") 197 | return False 198 | 199 | class WatermarkProcessor: 200 | def __init__(self, model, config, roi_coords, roi_mask): 201 | self.model = model 202 | self.config = config 203 | self.roi_coords = roi_coords 204 | self.roi_mask = roi_mask 205 | 206 | def extract_roi(self, frame_bgr): 207 | y_min, y_max, x_min, x_max = self.roi_coords 208 | return frame_bgr[y_min:y_max, x_min:x_max] 209 | 210 | def process_frame(self, frame_bgr, frame_index): 211 | y_min, y_max, x_min, x_max = self.roi_coords 212 | roi = self.extract_roi(frame_bgr) 213 | 214 | processed_roi = lama_inpaint(roi, self.roi_mask, self.model, self.config) 215 | processed_roi = cv2.cvtColor(processed_roi, cv2.COLOR_BGR2RGB) 216 | 217 | blend_mask = cv2.GaussianBlur(self.roi_mask.astype(np.float32), (21, 21), 0) / 255.0 218 | 219 | result = frame_bgr.copy() 220 | result[y_min:y_max, x_min:x_max] = ( 221 | blend_mask[:, :, np.newaxis] * processed_roi + 222 | (1 - blend_mask[:, :, np.newaxis]) * roi 223 | ) 224 | 225 | return result 226 | 227 | 228 | def process_video(video_clip, output_path, watermark_mask, model, config): 229 | video_info = get_video_info(video_clip) 230 | start_time = time.time() 231 | 232 | y_indices, x_indices = np.where(watermark_mask > 0) 233 | if len(y_indices) == 0 or len(x_indices) == 0: 234 | print("No watermark region found in mask") 235 | return video_info 236 | 237 | margin = 10 238 | y_min, y_max = max(0, np.min(y_indices) - margin), min(video_clip.h, np.max(y_indices) + margin) 239 | x_min, x_max = max(0, np.min(x_indices) - margin), min(video_clip.w, np.max(x_indices) + margin) 240 | 241 | roi_coords = (y_min, y_max, x_min, x_max) 242 | roi_mask = watermark_mask[y_min:y_max, x_min:x_max] 243 | 244 | processor = WatermarkProcessor(model, config, roi_coords, roi_mask) 245 | 246 | frame_count = 0 247 | total_frames = video_info["total_frames"] 248 | progress_bar = tqdm(total=total_frames, desc="Processing Frames", unit="frames") 249 | 250 | def process_frame_wrapper(frame): 251 | nonlocal frame_count 252 | frame_bgr = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR) 253 | result = processor.process_frame(frame_bgr, frame_count) 254 | 255 | frame_count += 1 256 | progress_bar.update(1) 257 | return cv2.cvtColor(result, cv2.COLOR_BGR2RGB) 258 | 259 | processed_video = video_clip.image_transform(lambda frame: process_frame_wrapper(frame)) 260 | processed_video.write_videofile(f"{output_path}.mp4", codec="libx264", logger=None) 261 | 262 | progress_bar.close() 263 | 264 | end_time = time.time() 265 | processing_time = end_time - start_time 266 | processing_info = { 267 | "video_info": video_info, 268 | "processing_time": str(timedelta(seconds=int(processing_time))) 269 | } 270 | 271 | return processing_info 272 | 273 | def parse_args(): 274 | parser = argparse.ArgumentParser(description="Video Watermark Remover") 275 | parser.add_argument("--input", "-i", type=str, default=".", help="Input directory containing videos") 276 | parser.add_argument("--output", "-o", type=str, default="output", help="Output directory") 277 | parser.add_argument("--preview", "-p", action="store_true", help="Preview effect before processing") 278 | return parser.parse_args() 279 | 280 | if __name__ == "__main__": 281 | args = parse_args() 282 | 283 | input_dir = args.input 284 | output_dir = args.output 285 | preview_enabled = args.preview 286 | 287 | has_gpu, device, gpu_name = check_gpu() 288 | 289 | if has_gpu: 290 | print(f"GPU detected: {gpu_name}") 291 | print("Using GPU for processing") 292 | use_device = "cuda" 293 | else: 294 | print("No GPU detected, using CPU for processing") 295 | use_device = "cpu" 296 | 297 | if not ensure_directory_exists(output_dir): 298 | sys.exit(1) 299 | 300 | if os.path.isdir(input_dir): 301 | videos = [f for f in glob.glob(os.path.join(input_dir, "*")) if os.path.isfile(f) and is_valid_video_file(f)] 302 | else: 303 | print(f"Input path {input_dir} is not a directory") 304 | sys.exit(1) 305 | 306 | if not videos: 307 | print(f"No valid video files found in {input_dir}") 308 | sys.exit(1) 309 | 310 | watermark_detector = WatermarkDetector() 311 | watermark_mask = None 312 | 313 | lama_model, lama_config = initialize_lama(device=use_device) 314 | 315 | for video in videos: 316 | print(f"Processing {video}") 317 | video_clip = VideoFileClip(video) 318 | 319 | if watermark_mask is None: 320 | watermark_mask = watermark_detector.generate_mask(video_clip) 321 | 322 | if preview_enabled: 323 | if not watermark_detector.preview_effect(video_clip, watermark_mask, lama_model, lama_config): 324 | print("Processing cancelled by user") 325 | break 326 | 327 | video_name = os.path.basename(video) 328 | output_video_path = os.path.join(output_dir, os.path.splitext(video_name)[0]) 329 | 330 | processing_info = process_video(video_clip, output_video_path, watermark_mask, lama_model, lama_config) 331 | 332 | print(f"Successfully processed {video_name}") 333 | print(f" Resolution: {processing_info['video_info']['resolution']}") 334 | print(f" Duration: {processing_info['video_info']['duration']}") 335 | print(f" FPS: {processing_info['video_info']['fps']}") 336 | print(f" Total frames: {processing_info['video_info']['total_frames']}") 337 | print(f" Processing time: {processing_info['processing_time']}") --------------------------------------------------------------------------------