chore: 更新最新代码
This commit is contained in:
琉璃月光
90
image/del_photo/change.py
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90
image/del_photo/change.py
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import os
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from PIL import Image
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import numpy as np
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def is_large_gray(image, gray_lower_threshold=70, gray_upper_threshold=230, gray_ratio_threshold=0.7):
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"""
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判断图片是否大面积为灰色（基于像素的颜色值）
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参数：
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- image: 图片对象（PIL Image）
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- gray_lower_threshold: 灰色下限（低于此值不算“灰色”）
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- gray_upper_threshold: 灰色上限（高于此值不算“灰色”）
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- gray_ratio_threshold: 灰色像素占比阈值（>70% 算大面积灰色）
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返回：True 表示是大面积灰色，应删除
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"""
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# 将图片转换为 numpy 数组
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img_array = np.array(image)
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# 获取图片的尺寸
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height, width, _ = img_array.shape
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total_pixels = height * width
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# 判断是否为灰色像素（R、G、B 值都在 gray_lower_threshold 和 gray_upper_threshold 之间）
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gray_pixels = np.sum(
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(img_array[:, :, 0] >= gray_lower_threshold) &
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(img_array[:, :, 0] <= gray_upper_threshold) &
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(img_array[:, :, 1] >= gray_lower_threshold) &
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(img_array[:, :, 1] <= gray_upper_threshold) &
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(img_array[:, :, 2] >= gray_lower_threshold) &
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(img_array[:, :, 2] <= gray_upper_threshold)
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)
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gray_ratio = gray_pixels / total_pixels
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return gray_ratio > gray_ratio_threshold
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def process_images_in_folder(input_folder, output_folder):
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"""
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遍历文件夹，旋转图片并根据条件保存到输出文件夹
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"""
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# 创建输出文件夹（如果不存在）
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if not os.path.exists(output_folder):
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os.makedirs(output_folder)
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# 支持的图片格式
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supported_formats = ('.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.tif', '.webp')
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for filename in os.listdir(input_folder):
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file_path = os.path.join(input_folder, filename)
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if not os.path.isfile(file_path):
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continue
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if not filename.lower().endswith(supported_formats):
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continue
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print(f"处理: {filename}")
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try:
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with Image.open(file_path) as img:
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# 判断是否为大面积灰色
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if is_large_gray(img):
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print(f" 🔴 不保存大面积灰色图片: {filename}")
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continue # 不保存该图片
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# 否则：打开并旋转 180 度
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rotated_img = img.rotate(180, expand=False)
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# 构建新的保存路径
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save_path = os.path.join(output_folder, filename)
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# 保持原格式保存（覆盖原图）
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rotated_img.save(save_path, format=img.format)
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print(f" ✅ 已旋转并保存至: {save_path}")
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except Exception as e:
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print(f" ❌ 处理失败 {filename}: {e}")
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# ================ 使用示例 ================
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if __name__ == "__main__":
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folder = "/media/hx/disk/folder_5"
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output_folder = "/media/hx/disk/folder_5"
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if not os.path.exists(folder):
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print("❌ 输入文件夹不存在！")
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else:
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process_images_in_folder(folder, output_folder)
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print("✅ 所有图片处理完成！")
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118
image/del_photo/del_image_gray.py
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image/del_photo/del_image_gray.py
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import os
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from pathlib import Path
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from PIL import Image
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import numpy as np
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def is_grayscale_image(image_path, saturation_threshold=0.05, gray_intensity_threshold=200):
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"""
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判断图像是否为“灰色图片”（低饱和度或接近灰度）
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:param image_path: 图像路径
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:param saturation_threshold: 饱和度阈值（0~1），越低越可能是灰色
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:param gray_intensity_threshold: 亮度阈值，过滤纯白/纯黑
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:return: True 表示是灰色图，应删除
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"""
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try:
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img = Image.open(image_path)
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# 转为 RGB（处理灰度图自动转为 3 通道）
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if img.mode != 'RGB':
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img = img.convert('RGB')
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# 转为 numpy 数组
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rgb = np.array(img).astype(np.float32) # (H, W, 3)
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H, W, _ = rgb.shape
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if H * W == 0:
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return True # 空图
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# 转为 HSV（手动计算避免 PIL 的 hsv 转换问题）
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r, g, b = rgb[..., 0], rgb[..., 1], rgb[..., 2]
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max_c = np.maximum(np.maximum(r, g), b)
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min_c = np.minimum(np.minimum(r, g), b)
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delta = max_c - min_c
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# 饱和度 S = delta / max_c
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with np.errstate(divide='ignore', invalid='ignore'):
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s = np.where(max_c == 0, 0, delta / max_c)
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# 只取非纯黑区域的饱和度（避免纯黑区域干扰）
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valid_s = s[(max_c > 10) & (max_c < gray_intensity_threshold)] # 忽略极暗和极亮
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if len(valid_s) == 0:
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return True # 全黑或全白
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# 计算平均饱和度
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avg_saturation = valid_s.mean()
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# 如果平均饱和度很低，认为是灰色图
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return avg_saturation < saturation_threshold
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except Exception as e:
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print(f"⚠️ 无法读取图像 {image_path}: {e}")
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return True # 出错的图也删除（可选）
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def delete_gray_images(folder_path, extensions=None, dry_run=False):
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"""
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删除文件夹中的灰色图片
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:param folder_path: 图片文件夹路径
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:param extensions: 支持的图片格式
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:param dry_run: 如果为 True，只打印不删除
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"""
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if extensions is None:
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extensions = ['.jpg', '.jpeg', '.png', '.bmp', '.tiff']
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folder = Path(folder_path)
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if not folder.exists():
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print(f"❌ 文件夹不存在: {folder_path}")
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return
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image_files = []
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for ext in extensions:
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image_files.extend(folder.glob(f'*{ext}'))
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image_files.extend(folder.glob(f'*{ext.upper()}'))
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if not image_files:
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print(f"🔍 文件夹中没有找到图片: {folder_path}")
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return
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print(f"🔍 扫描到 {len(image_files)} 张图片...")
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deleted_count = 0
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for img_path in image_files:
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if is_grayscale_image(img_path):
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print(f"🗑️ 灰色图: {img_path.name}")
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if not dry_run:
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try:
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img_path.unlink() # 删除文件
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print(f"✅ 已删除: {img_path.name}")
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deleted_count += 1
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except Exception as e:
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print(f"❌ 删除失败 {img_path.name}: {e}")
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else:
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print(f"✅ 彩色图: {img_path.name} (保留)")
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print("\n" + "=" * 50)
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if dry_run:
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print(f"🧪 模拟完成，共发现 {deleted_count} 张灰色图将被删除")
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else:
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print(f"✅ 删除完成！共删除 {deleted_count} 张灰色图片")
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print(f"📁 保留图片数: {len(image_files) - deleted_count}")
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print("=" * 50)
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# ================== 用户配置 ==================
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FOLDER_PATH = "/media/hx/disk/folder_5" # 修改为你的图片文件夹
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DRY_RUN = False # 先设为 True 测试，确认无误后再改为 False
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# ================== 执行 ==================
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if __name__ == "__main__":
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print(f"🚀 开始检测并删除灰色图片...")
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delete_gray_images(
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folder_path=FOLDER_PATH,
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dry_run=DRY_RUN
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)
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103
image/del_photo/del_image_ssim.py
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103
image/del_photo/del_image_ssim.py
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import os
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import cv2
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from skimage.metrics import structural_similarity as ssim
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def calculate_ssim(image_path1, image_path2):
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"""
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计算两张图片的 SSIM 相似度
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"""
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# 读取图像
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img1 = cv2.imread(image_path1)
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img2 = cv2.imread(image_path2)
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if img1 is None:
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print(f"❌ 无法读取图片1: {image_path1}")
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return None
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if img2 is None:
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print(f"❌ 无法读取图片2: {image_path2}")
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return None
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# 转为灰度图
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gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
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gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
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# 确保尺寸一致
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if gray1.shape != gray2.shape:
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print("⚠️ 图像尺寸不一致，正在调整...")
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h, w = min(gray1.shape[0], gray2.shape[0]), min(gray1.shape[1], gray2.shape[1])
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gray1 = cv2.resize(gray1, (w, h))
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gray2 = cv2.resize(gray2, (w, h))
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# 计算 SSIM
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try:
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similarity = ssim(gray1, gray2)
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return similarity
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except Exception as e:
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print(f"❌ SSIM 计算失败: {e}")
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return None
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def delete_similar_consecutive_images(folder_path, threshold=0.95, extensions=None):
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"""
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删除相似度高于阈值的连续图片
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:param folder_path: 图片所在的文件夹路径
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:param threshold: SSIM 阈值，默认为 0.95
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:param extensions: 支持的图片格式列表，默认为 ['.jpg', '.jpeg', '.png']
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"""
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if extensions is None:
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extensions = ['.jpg', '.jpeg', '.png']
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folder = os.path.abspath(folder_path)
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if not os.path.exists(folder):
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print(f"❌ 文件夹不存在: {folder_path}")
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return
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# 获取所有图片文件路径
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image_files = []
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for ext in extensions:
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image_files.extend([os.path.join(folder, f) for f in os.listdir(folder) if f.lower().endswith(ext)])
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if not image_files:
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print(f"🔍 文件夹中没有找到图片: {folder_path}")
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return
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# 按文件名排序以确保顺序正确
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image_files.sort()
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print(f"🔍 扫描到 {len(image_files)} 张图片...")
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deleted_count = 0
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# 遍历每一对连续的图片
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for i in range(len(image_files) - 1):
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img_path1 = image_files[i]
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img_path2 = image_files[i + 1]
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similarity = calculate_ssim(img_path1, img_path2)
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if similarity is not None and similarity > threshold:
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print(f"🗑️ 删除相似图片: {img_path2} (SSIM: {similarity:.4f})")
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try:
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os.remove(img_path2)
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deleted_count += 1
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except Exception as e:
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print(f"❌ 删除失败 {img_path2}: {e}")
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else:
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print(f"✅ 保留图片: {img_path2}")
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print("\n" + "=" * 50)
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print(f"✅ 删除完成！共删除 {deleted_count} 张相似图片")
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print(f"📁 保留图片数: {len(image_files) - deleted_count}")
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print("=" * 50)
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# ================== 用户配置 ==================
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FOLDER_PATH = "/media/hx/disk/folder_5" # 修改为你的图片文件夹路径
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THRESHOLD = 0.90 # SSIM 阈值
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# ================== 执行 ==================
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if __name__ == "__main__":
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print(f"🚀 开始检测并删除相似图片...")
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delete_similar_consecutive_images(
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folder_path=FOLDER_PATH,
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threshold=THRESHOLD
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)
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63
image/del_photo/ssim.py
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63
image/del_photo/ssim.py
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import cv2
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from skimage.metrics import structural_similarity as ssim
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import os
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def calculate_ssim(image_path1, image_path2):
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"""
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计算两张图片的 SSIM 相似度
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"""
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# 读取图像
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img1 = cv2.imread(image_path1)
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img2 = cv2.imread(image_path2)
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if img1 is None:
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print(f"❌ 无法读取图片1: {image_path1}")
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return None
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if img2 is None:
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print(f"❌ 无法读取图片2: {image_path2}")
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return None
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# 转为灰度图
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gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
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gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
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# 确保尺寸一致
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if gray1.shape != gray2.shape:
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print("⚠️ 图像尺寸不一致，正在调整...")
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h, w = min(gray1.shape[0], gray2.shape[0]), min(gray1.shape[1], gray2.shape[1])
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gray1 = cv2.resize(gray1, (w, h))
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gray2 = cv2.resize(gray2, (w, h))
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# 计算 SSIM
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try:
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similarity = ssim(gray1, gray2)
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return similarity
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except Exception as e:
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print(f"❌ SSIM 计算失败: {e}")
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return None
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# ==================== 使用示例 ====================
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if __name__ == "__main__":
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# 替换成你本地的两张图片路径
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path1 = "/home/hx/桌面/image/image/frame_20250805_120334_585.jpg"
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path2 = "/home/hx/桌面/image/image/frame_20250805_120334_570.jpg"
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# 检查文件是否存在
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if not os.path.exists(path1):
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print(f"文件不存在: {path1}")
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print("请修改 path1 为实际存在的图片路径")
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elif not os.path.exists(path2):
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print(f"文件不存在: {path2}")
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print("请修改 path2 为实际存在的图片路径")
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else:
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print("正在计算 SSIM...")
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sim = calculate_ssim(path1, path2)
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if sim is not None:
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print(f"✅ SSIM 相似度: {sim:.4f}")
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if sim > 0.9:
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print("🔴 太相似（>0.9），应跳过重复帧")
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elif sim > 0.7:
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print("🟡 较相似，内容变化不大")
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else:
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print("🟢 差异明显，建议保存")
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