Upload anime.py
Browse files- 喜欢的番/anime.py +475 -0
喜欢的番/anime.py
ADDED
@@ -0,0 +1,475 @@
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1 |
+
import argparse
|
2 |
+
import shutil
|
3 |
+
import av
|
4 |
+
import os
|
5 |
+
import cv2
|
6 |
+
import sys
|
7 |
+
import time
|
8 |
+
import multiprocessing
|
9 |
+
import tkinter as tk
|
10 |
+
from tkinter import filedialog
|
11 |
+
from concurrent.futures import ThreadPoolExecutor, as_completed
|
12 |
+
from PIL import Image
|
13 |
+
import numpy as np
|
14 |
+
from collections import defaultdict
|
15 |
+
from waifuc.action import MinSizeFilterAction, PersonSplitAction
|
16 |
+
from waifuc.export import SaveExporter, TextualInversionExporter
|
17 |
+
from waifuc.source import LocalSource
|
18 |
+
from tqdm import tqdm
|
19 |
+
import logging
|
20 |
+
import threading
|
21 |
+
|
22 |
+
# 配置日志
|
23 |
+
logging.basicConfig(filename='video_image_processing.log', level=logging.INFO,
|
24 |
+
format='%(asctime)s - %(levelname)s - %(message)s')
|
25 |
+
|
26 |
+
|
27 |
+
def select_folder():
|
28 |
+
"""
|
29 |
+
弹出文件夹选择对话框,返回选择的文件夹路径。
|
30 |
+
"""
|
31 |
+
# 如果需要弹出对话框,取消注释以下代码
|
32 |
+
# root = tk.Tk()
|
33 |
+
# root.withdraw()
|
34 |
+
# folder_path = filedialog.askdirectory()
|
35 |
+
# return folder_path
|
36 |
+
|
37 |
+
# 直接返回默认路径
|
38 |
+
folder_path = './anime'
|
39 |
+
return folder_path
|
40 |
+
|
41 |
+
|
42 |
+
def create_output_folder(folder_path, extra_name):
|
43 |
+
"""
|
44 |
+
创建输出文件夹,文件夹名称为原名称加上额外的后缀。
|
45 |
+
|
46 |
+
参数:
|
47 |
+
folder_path (str): 原文件夹路径。
|
48 |
+
extra_name (str): 要添加到文件夹名称后的字符串。
|
49 |
+
|
50 |
+
返回:
|
51 |
+
str: 新创建的文件夹路径。
|
52 |
+
"""
|
53 |
+
folder_name = os.path.basename(folder_path)
|
54 |
+
new_folder_name = f"{folder_name}{extra_name}"
|
55 |
+
new_folder_path = os.path.join(folder_path, new_folder_name)
|
56 |
+
os.makedirs(new_folder_path, exist_ok=True)
|
57 |
+
return new_folder_path
|
58 |
+
|
59 |
+
|
60 |
+
def find_video_files(folder_path):
|
61 |
+
"""
|
62 |
+
在指定文件夹及其子文件夹中查找所有视频文件。
|
63 |
+
|
64 |
+
参数:
|
65 |
+
folder_path (str): 文件夹路径。
|
66 |
+
|
67 |
+
返回:
|
68 |
+
list: 视频文件的完整路径列表。
|
69 |
+
"""
|
70 |
+
video_extensions = ('.mp4', '.avi', '.mov', '.mkv', '.flv', '.wmv')
|
71 |
+
video_files = []
|
72 |
+
for root, dirs, files in os.walk(folder_path):
|
73 |
+
for file in files:
|
74 |
+
if file.lower().endswith(video_extensions):
|
75 |
+
video_files.append(os.path.join(root, file))
|
76 |
+
return video_files
|
77 |
+
|
78 |
+
|
79 |
+
def process_video(video_file, new_folder_path, frame_step=5, position=0):
|
80 |
+
"""
|
81 |
+
处理视频文件,提取帧,计算哈希和清晰度,保存符合条件的帧。
|
82 |
+
|
83 |
+
参数:
|
84 |
+
video_file (str): 视频文件路径。
|
85 |
+
new_folder_path (str): 保存提取帧的文件夹路径。
|
86 |
+
frame_step (int): 帧步长,每隔多少帧处理一次。
|
87 |
+
position (int): tqdm进度条的位置,以避免多进度条叠加。
|
88 |
+
"""
|
89 |
+
def compute_phash(image):
|
90 |
+
resized = cv2.resize(image, (32, 32), interpolation=cv2.INTER_AREA)
|
91 |
+
gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
|
92 |
+
dct = cv2.dct(np.float32(gray))
|
93 |
+
dct_low = dct[:8, :8]
|
94 |
+
med = np.median(dct_low)
|
95 |
+
return (dct_low > med).flatten()
|
96 |
+
|
97 |
+
def compute_sharpness(image):
|
98 |
+
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
|
99 |
+
# 使用 Sobel 算子计算梯度
|
100 |
+
grad_x = cv2.Sobel(gray, cv2.CV_16S, 1, 0)
|
101 |
+
grad_y = cv2.Sobel(gray, cv2.CV_16S, 0, 1)
|
102 |
+
# 计算梯度的绝对值和
|
103 |
+
sharpness = cv2.mean(np.abs(grad_x) + np.abs(grad_y))[0]
|
104 |
+
return sharpness
|
105 |
+
|
106 |
+
def save_frame(image, frame_count):
|
107 |
+
image_name = f'{os.path.splitext(os.path.basename(video_file))[0]}-{frame_count:08d}.jpg'
|
108 |
+
image_path = os.path.join(new_folder_path, image_name)
|
109 |
+
cv2.imwrite(image_path, image, [cv2.IMWRITE_JPEG_QUALITY, 90])
|
110 |
+
|
111 |
+
try:
|
112 |
+
# 记录开始处理日志
|
113 |
+
logging.info(f"开始处理视频文件: {video_file}")
|
114 |
+
print(f"开始处理视频文件: {video_file}")
|
115 |
+
|
116 |
+
# 打开视频文件
|
117 |
+
container = av.open(video_file)
|
118 |
+
video = container.streams.video[0]
|
119 |
+
|
120 |
+
# 尝试启用硬件加速
|
121 |
+
try:
|
122 |
+
video.codec_context.options = {'hwaccel': 'auto'}
|
123 |
+
except Exception as e:
|
124 |
+
print(f"无法启用硬件加速: {e}")
|
125 |
+
logging.warning(f"无法启用硬件加速: {e}")
|
126 |
+
|
127 |
+
# 获取总帧数
|
128 |
+
total_frames = video.frames
|
129 |
+
if total_frames == 0:
|
130 |
+
# 如果无法获取帧数,估计总帧数
|
131 |
+
container.seek(0)
|
132 |
+
total_frames = int(container.duration * video.average_rate)
|
133 |
+
|
134 |
+
pbar = tqdm(total=total_frames, desc=os.path.basename(video_file), position=position, leave=True, unit="帧")
|
135 |
+
|
136 |
+
start_time = time.time()
|
137 |
+
frame_count = 0
|
138 |
+
saved_count = 0
|
139 |
+
sharpness_threshold = 15 # 清晰度阈值
|
140 |
+
|
141 |
+
reference_image = None
|
142 |
+
reference_phash = None
|
143 |
+
reference_sharpness = None
|
144 |
+
reference_count = 0
|
145 |
+
|
146 |
+
for frame in container.decode(video=0):
|
147 |
+
pbar.update(1) # 更新进度条
|
148 |
+
|
149 |
+
if frame_step > 0 and frame_count % frame_step != 0:
|
150 |
+
frame_count += 1
|
151 |
+
continue # 跳过不需要处理的帧
|
152 |
+
|
153 |
+
image = frame.to_ndarray(format='bgr24')
|
154 |
+
phash = compute_phash(image)
|
155 |
+
sharpness = compute_sharpness(image)
|
156 |
+
|
157 |
+
if sharpness < sharpness_threshold:
|
158 |
+
frame_count += 1
|
159 |
+
continue # 跳过模糊帧
|
160 |
+
|
161 |
+
if reference_image is None:
|
162 |
+
# 初始化参考帧
|
163 |
+
reference_image = image
|
164 |
+
reference_phash = phash
|
165 |
+
reference_sharpness = sharpness
|
166 |
+
reference_count = frame_count
|
167 |
+
else:
|
168 |
+
hamming_dist = np.sum(phash != reference_phash)
|
169 |
+
if hamming_dist > 10:
|
170 |
+
# 与参考帧差异较大,保存参考帧
|
171 |
+
save_frame(reference_image, reference_count)
|
172 |
+
saved_count += 1
|
173 |
+
# 更新参考帧
|
174 |
+
reference_image = image
|
175 |
+
reference_phash = phash
|
176 |
+
reference_sharpness = sharpness
|
177 |
+
reference_count = frame_count
|
178 |
+
else:
|
179 |
+
# 与参考帧相似,比较清晰度
|
180 |
+
if sharpness > reference_sharpness:
|
181 |
+
# 当前帧更清晰,更新参考帧
|
182 |
+
reference_image = image
|
183 |
+
reference_phash = phash
|
184 |
+
reference_sharpness = sharpness
|
185 |
+
reference_count = frame_count
|
186 |
+
# 否则,保留原参考帧
|
187 |
+
|
188 |
+
frame_count += 1
|
189 |
+
|
190 |
+
# 保存最后的参考帧
|
191 |
+
if reference_image is not None:
|
192 |
+
save_frame(reference_image, reference_count)
|
193 |
+
saved_count += 1
|
194 |
+
|
195 |
+
total_time = time.time() - start_time
|
196 |
+
average_fps = frame_count / total_time if total_time > 0 else 0
|
197 |
+
message = (f'{os.path.basename(video_file)} 处理完成: 总共 {frame_count} 帧, '
|
198 |
+
f'保存 {saved_count} 帧, 平均 {average_fps:.2f} 帧/秒')
|
199 |
+
print(message)
|
200 |
+
logging.info(message)
|
201 |
+
pbar.close()
|
202 |
+
except Exception as e:
|
203 |
+
error_message = f'处理视频文件 {video_file} 时出错: {e}'
|
204 |
+
print(error_message)
|
205 |
+
logging.error(error_message)
|
206 |
+
|
207 |
+
|
208 |
+
def process_images_folder(new_folder_path):
|
209 |
+
"""
|
210 |
+
处理保存的图像文件,去除相似的重复图片,仅保留最清晰的。
|
211 |
+
|
212 |
+
参数:
|
213 |
+
new_folder_path (str): 图像文件夹路径。
|
214 |
+
|
215 |
+
返回:
|
216 |
+
set: 保留的图像文件路径集合。
|
217 |
+
"""
|
218 |
+
def get_image_files(folder_path):
|
219 |
+
image_files = [os.path.join(folder_path, f) for f in os.listdir(folder_path)
|
220 |
+
if f.lower().endswith(('.jpg', '.jpeg', '.png'))]
|
221 |
+
print(f'总共找到 {len(image_files)} 张图片')
|
222 |
+
logging.info(f'总共找到 {len(image_files)} 张图片')
|
223 |
+
return image_files
|
224 |
+
|
225 |
+
def process_images(image_files):
|
226 |
+
def compute_phash(image):
|
227 |
+
resized = cv2.resize(image, (32, 32), interpolation=cv2.INTER_AREA)
|
228 |
+
gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
|
229 |
+
dct = cv2.dct(np.float32(gray))
|
230 |
+
dct_low = dct[:8, :8]
|
231 |
+
med = np.median(dct_low)
|
232 |
+
return (dct_low > med).flatten()
|
233 |
+
|
234 |
+
def compute_sharpness(image):
|
235 |
+
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
|
236 |
+
return cv2.Laplacian(gray, cv2.CV_64F).var()
|
237 |
+
|
238 |
+
def process_single_image(image_path):
|
239 |
+
image = cv2.imread(image_path)
|
240 |
+
if image is None:
|
241 |
+
error_message = f"无法读取图像文件 {image_path}"
|
242 |
+
print(f"警告:{error_message}")
|
243 |
+
logging.warning(error_message)
|
244 |
+
return None
|
245 |
+
try:
|
246 |
+
phash = compute_phash(image)
|
247 |
+
sharpness = compute_sharpness(image)
|
248 |
+
return image_path, phash, sharpness
|
249 |
+
except Exception as e:
|
250 |
+
error_message = f"处理图像时出错 {image_path}: {e}"
|
251 |
+
print(f"警告:{error_message}")
|
252 |
+
logging.warning(error_message)
|
253 |
+
return None
|
254 |
+
|
255 |
+
image_data = {}
|
256 |
+
start_time = time.time()
|
257 |
+
with ThreadPoolExecutor() as executor:
|
258 |
+
futures = {executor.submit(process_single_image, img): img for img in image_files}
|
259 |
+
for future in tqdm(as_completed(futures), total=len(futures), desc="计算哈希和清晰度", unit="张"):
|
260 |
+
result = future.result()
|
261 |
+
if result is not None:
|
262 |
+
image_path, phash, sharpness = result
|
263 |
+
image_data[image_path] = {'phash': phash, 'sharpness': sharpness}
|
264 |
+
|
265 |
+
elapsed_time = time.time() - start_time
|
266 |
+
print(f'\n图片处理完成,耗时 {elapsed_time:.2f} 秒')
|
267 |
+
logging.info(f'图片处理完成,耗时 {elapsed_time:.2f} 秒')
|
268 |
+
return image_data
|
269 |
+
|
270 |
+
def compare_images(image_data):
|
271 |
+
similar_groups = {}
|
272 |
+
hash_buckets = defaultdict(list)
|
273 |
+
# 将哈希值转换为字符串,并取前几位作为桶的键
|
274 |
+
for image_path, data in image_data.items():
|
275 |
+
hash_str = ''.join(data['phash'].astype(int).astype(str))
|
276 |
+
bucket_key = hash_str[:16] # 取前16位作为桶的键,可根据需要调整
|
277 |
+
hash_buckets[bucket_key].append((image_path, data))
|
278 |
+
|
279 |
+
total_buckets = len(hash_buckets)
|
280 |
+
print(f"总共划分为 {total_buckets} 个哈希桶")
|
281 |
+
logging.info(f"总共划分为 {total_buckets} 个哈希桶")
|
282 |
+
|
283 |
+
# 遍历每个桶,比较桶内的图片
|
284 |
+
for bucket_key, bucket in tqdm(hash_buckets.items(), desc="比较哈希桶", unit="桶"):
|
285 |
+
paths = [item[0] for item in bucket]
|
286 |
+
hashes = np.array([item[1]['phash'] for item in bucket])
|
287 |
+
for i in range(len(paths)):
|
288 |
+
for j in range(i + 1, len(paths)):
|
289 |
+
dist = np.sum(hashes[i] != hashes[j])
|
290 |
+
if dist <= 10: # 阈值,可根据需要调整
|
291 |
+
similar_groups.setdefault(paths[i], []).append(paths[j])
|
292 |
+
|
293 |
+
return similar_groups
|
294 |
+
|
295 |
+
def select_images_to_keep(similar_groups, image_data):
|
296 |
+
to_keep = set()
|
297 |
+
processed_groups = set()
|
298 |
+
for group_key, group in similar_groups.items():
|
299 |
+
if group_key in processed_groups:
|
300 |
+
continue
|
301 |
+
group_with_key = [group_key] + group
|
302 |
+
sharpest = max(group_with_key, key=lambda x: image_data[x]['sharpness'])
|
303 |
+
to_keep.add(sharpest)
|
304 |
+
processed_groups.update(group_with_key)
|
305 |
+
# 将不在任何相似组中的图片也加入保留列表
|
306 |
+
all_images = set(image_data.keys())
|
307 |
+
images_in_groups = set().union(*[set([k] + v) for k, v in similar_groups.items()])
|
308 |
+
images_not_in_groups = all_images - images_in_groups
|
309 |
+
to_keep.update(images_not_in_groups)
|
310 |
+
return to_keep
|
311 |
+
|
312 |
+
def delete_duplicate_images(similar_groups, to_keep):
|
313 |
+
deleted_count = 0
|
314 |
+
to_delete = set()
|
315 |
+
|
316 |
+
# 收集所有需要删除的图片
|
317 |
+
for group_key, similar_images in similar_groups.items():
|
318 |
+
group_with_key = [group_key] + similar_images
|
319 |
+
for image_path in group_with_key:
|
320 |
+
if image_path not in to_keep:
|
321 |
+
to_delete.add(image_path)
|
322 |
+
|
323 |
+
total_to_delete = len(to_delete)
|
324 |
+
|
325 |
+
# 删除图片
|
326 |
+
for image_path in tqdm(to_delete, desc="删除重复图片", unit="张"):
|
327 |
+
try:
|
328 |
+
os.remove(image_path)
|
329 |
+
deleted_count += 1
|
330 |
+
except Exception as e:
|
331 |
+
print(f"\n无法删除 {image_path}: {e}")
|
332 |
+
logging.error(f"无法删除 {image_path}: {e}")
|
333 |
+
|
334 |
+
print(f'\n去重完成,保留 {len(to_keep)} 张图片,成功删除 {deleted_count} 张重复图片')
|
335 |
+
logging.info(f'去重完成,保留 {len(to_keep)} 张图片,成功删除 {deleted_count} 张重复图片')
|
336 |
+
|
337 |
+
return deleted_count
|
338 |
+
|
339 |
+
# 开始执行去重流程
|
340 |
+
image_files = get_image_files(new_folder_path)
|
341 |
+
if not image_files:
|
342 |
+
print("没有找到图像文件进行处理。")
|
343 |
+
logging.info("没有找到图像文件进行处理。")
|
344 |
+
return
|
345 |
+
|
346 |
+
image_data = process_images(image_files)
|
347 |
+
if not image_data:
|
348 |
+
print("没有有效的图像数据进行处理。")
|
349 |
+
logging.info("没有有效的图像数据进行处理。")
|
350 |
+
return
|
351 |
+
|
352 |
+
similar_groups = compare_images(image_data)
|
353 |
+
to_keep = select_images_to_keep(similar_groups, image_data)
|
354 |
+
deleted_count = delete_duplicate_images(similar_groups, to_keep)
|
355 |
+
|
356 |
+
|
357 |
+
def waifuc_split(new_folder_path, split_path):
|
358 |
+
"""
|
359 |
+
使用 waifuc 库对图像进行分割,提取人物部分。
|
360 |
+
|
361 |
+
参数:
|
362 |
+
new_folder_path (str): 原始图像文件夹路径。
|
363 |
+
split_path (str): 分割后图像的保存路径。
|
364 |
+
"""
|
365 |
+
# 直接使用目录路径初始化 LocalSource
|
366 |
+
s = LocalSource(new_folder_path)
|
367 |
+
s = s.attach(
|
368 |
+
PersonSplitAction(), MinSizeFilterAction(300),
|
369 |
+
)
|
370 |
+
s.export(SaveExporter(split_path, no_meta=True))
|
371 |
+
|
372 |
+
|
373 |
+
def process_split_images(new_folder_path, split_path):
|
374 |
+
"""
|
375 |
+
将没有检测到人物的原始图像移动到指定的无人文件夹。
|
376 |
+
|
377 |
+
参数:
|
378 |
+
new_folder_path (str): 原始图像文件夹路径。
|
379 |
+
split_path (str): 分割后图像的保存路径。
|
380 |
+
"""
|
381 |
+
nohuman_path = create_output_folder(new_folder_path, "-nohuman")
|
382 |
+
|
383 |
+
# 获取去重后的原始图片列表
|
384 |
+
original_images = [f for f in os.listdir(new_folder_path)
|
385 |
+
if os.path.isfile(os.path.join(new_folder_path, f)) and
|
386 |
+
f.lower().endswith(('.jpg', '.jpeg', '.png', '.webp'))]
|
387 |
+
|
388 |
+
split_images = [f for f in os.listdir(split_path)
|
389 |
+
if f.lower().endswith(('.jpg', '.jpeg', '.png', '.webp'))]
|
390 |
+
|
391 |
+
total_images = len(original_images)
|
392 |
+
moved_count = 0
|
393 |
+
|
394 |
+
for original_image in tqdm(original_images, desc="处理无人图片", unit="张"):
|
395 |
+
base_name = os.path.splitext(original_image)[0]
|
396 |
+
has_person = any(split_image.startswith(base_name + '_person') for split_image in split_images)
|
397 |
+
|
398 |
+
if not has_person:
|
399 |
+
source_path = os.path.join(new_folder_path, original_image)
|
400 |
+
dest_path = os.path.join(nohuman_path, original_image)
|
401 |
+
try:
|
402 |
+
shutil.move(source_path, dest_path)
|
403 |
+
moved_count += 1
|
404 |
+
except Exception as e:
|
405 |
+
print(f"\n无法移动 {source_path}: {e}")
|
406 |
+
logging.error(f"无法移动 {source_path}: {e}")
|
407 |
+
|
408 |
+
print(f'\n处理完成。总共处理 {total_images} 张图片, 移动了 {moved_count} 张无人图片到 {nohuman_path}')
|
409 |
+
logging.info(f'处理完成。总共处理 {total_images} 张图片, 移动了 {moved_count} 张无人图片到 {nohuman_path}')
|
410 |
+
|
411 |
+
|
412 |
+
def main():
|
413 |
+
"""
|
414 |
+
主函数,执行整个处理流程。
|
415 |
+
"""
|
416 |
+
folder_path = select_folder()
|
417 |
+
if not folder_path:
|
418 |
+
print("未选择文件夹,程序退出。")
|
419 |
+
logging.error("未选择文件夹,程序退出。")
|
420 |
+
return
|
421 |
+
|
422 |
+
video_files = find_video_files(folder_path)
|
423 |
+
if not video_files:
|
424 |
+
print("所选文件夹中未找到视频文件,程序退出。")
|
425 |
+
logging.error("所选文件夹中未找到视频文件,程序退出。")
|
426 |
+
return
|
427 |
+
|
428 |
+
# 创建保存提取帧的文件夹
|
429 |
+
new_folder_path = create_output_folder(folder_path, "-Eng_SS")
|
430 |
+
|
431 |
+
# 设定线程数,根据系统的CPU核数进行合理配置
|
432 |
+
max_workers = min(32, len(video_files)) # 例如,最多使用4个线程,可以根据需求调整
|
433 |
+
|
434 |
+
print(f"开始使用 {max_workers} 个线程处理 {len(video_files)} 个视频文件...")
|
435 |
+
logging.info(f"开始使用 {max_workers} 个线程处理 {len(video_files)} 个视频文件...")
|
436 |
+
|
437 |
+
# 使用 position 变量为每个视频的进度条分配唯一的位置
|
438 |
+
with ThreadPoolExecutor(max_workers=max_workers) as executor:
|
439 |
+
# 提交所有视频处理任务,并分配位置
|
440 |
+
futures = {
|
441 |
+
executor.submit(process_video, vf, new_folder_path, 5, pos): vf
|
442 |
+
for pos, vf in enumerate(video_files)
|
443 |
+
}
|
444 |
+
|
445 |
+
# 使用 tqdm 的动态进度条管理
|
446 |
+
for future in as_completed(futures):
|
447 |
+
video_file = futures[future]
|
448 |
+
try:
|
449 |
+
future.result() # 获取结果,处理可能的异常
|
450 |
+
except Exception as e:
|
451 |
+
error_message = f"处理视频文件 {video_file} 时发生异常: {e}"
|
452 |
+
print(error_message)
|
453 |
+
logging.error(error_message)
|
454 |
+
|
455 |
+
# 等待所有视频处理完成
|
456 |
+
print("所有视频文件处理完成。")
|
457 |
+
logging.info("所有视频文件处理完成。")
|
458 |
+
|
459 |
+
# 去除相似的重复图片(第一次)
|
460 |
+
process_images_folder(new_folder_path)
|
461 |
+
# 去除相似的重复图片(第二次)
|
462 |
+
process_images_folder(new_folder_path)
|
463 |
+
|
464 |
+
# 创建保存分割后图像的文件夹
|
465 |
+
split_path = create_output_folder(new_folder_path, "-split")
|
466 |
+
|
467 |
+
# 使用 waifuc 库进行人物分割
|
468 |
+
waifuc_split(new_folder_path, split_path)
|
469 |
+
|
470 |
+
# 移动没有检测到人物的图像到无人文件夹
|
471 |
+
process_split_images(new_folder_path, split_path)
|
472 |
+
|
473 |
+
|
474 |
+
if __name__ == "__main__":
|
475 |
+
main()
|