用python写一下批量图像增强数据集扩比如充度增强、对比度增强、旋转图像、翻转图像、仿射变化扩充图像、错切变化扩充图像、HSV数据增强、平移扩充

在Python中，我们可以使用一些库如`PIL`（Pillow）和`OpenCV`来进行批量图像增强。这里是一个简单的例子，展示如何进行常见的数据增强操作：

from PIL import Image, ImageEnhance, ImageOps
import cv2
import numpy as np

def augment_images(image_list, transform_list):
    augmented_images = []
    for image in image_list:
        # 图像增强
        enhancer_brightness = ImageEnhance.Brightness(image)
        brightness_enhanced = enhancer_brightness.enhance(1.5)  # 对比度增强

        enhancer_contrast = ImageEnhance.Contrast(brightness_enhanced)
        contrast_enhanced = enhancer_contrast.enhance(1.2)

        # 旋转和翻转
        rotated_image = contrast_enhanced.rotate(45)  # 随机角度旋转
        flipped_image = rotated_image.transpose(Image.FLIP_LEFT_RIGHT)  # 水平翻转

        # 仿射变换和错切变化
        width, height = flipped_image.size
        center = (width // 2, height // 2)
        rotation_matrix = cv2.getRotationMatrix2D(center, 45, 1.0)
        transformed_image = cv2.warpAffine(np.array(flipped_image), rotation_matrix, (width, height))
        
        # HSV数据增强
        hsv_image = cv2.cvtColor(transformed_image, cv2.COLOR_BGR2HSV)
        h, s, v = cv2.split(hsv_image)
        lower_saturation = np.clip(s - 50, 0, 255).astype('uint8')
        upper_saturation = np.clip(s + 50, 0, 255).astype('uint8')
        hsv_image = cv2.merge((h, upper_saturation, v))

        # 平移变换
        dx, dy = np.random.randint(-5, 6, size=2)  # 随机平移范围
        translation_matrix = np.float32([[1, 0, dx], [0, 1, dy]])
        translated_image = cv2.warpAffine(hsv_image, translation_matrix, (width, height))

        augmented_images.append(translated_image)
    
    return augmented_images

# 示例：处理一张图片并生成增强后的图片列表
original_image = Image.open("example.jpg")
augmented_dataset = augment_images([original_image], [True] * len(original_image))

# 打印增强后的图片数量
print(f"增强后的图片数量：{len(augmented_dataset)}")

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