### 使用Python和OpenCV进行图像差异检测 #### 准备工作 为了使用Python和OpenCV来比较两幅图像之间的差异，首先需要安装必要的库。可以通过pip命令轻松完成此操作。 ```bash pip install opencv-python numpy matplotlib ``` #### 加载并预处理图像 加载待比较的两个图像文件，并将其转换为灰度图以便后续处理： ```python import cv2 import numpy as np from matplotlib import pyplot as plt def load_images(image_path1, image_path2): """Load two images and convert them to grayscale.""" img1 = cv2.imread(image_path1, cv2.IMREAD_GRAYSCALE) img2 = cv2.imread(image_path2, cv2.IMREAD_GRAYSCALE) # Resize both images so they have the same dimensions. height, width = min(img1.shape[0], img2.shape[0]), min(img1.shape[1], img2.shape[1]) img1_resized = cv2.resize(img1[:height,:width], dsize=(64, 64), interpolation=cv2.INTER_CUBIC)[^4] img2_resized = cv2.resize(img2[:height,:width], dsize=(64, 64), interpolation=cv2.INTER_CUBIC) return img1_resized, img2_resized ``` #### 计算绝对误差图 通过计算两张图片对应位置像素值之差可以得到它们之间存在的不同之处。这里采用的是简单的减法运算加上取绝对值得到的结果作为最终输出。 ```python def compute_abs_diff_image(img1, img2): """Compute absolute difference between two images.""" abs_diff_img = cv2.absdiff(img1, img2) return abs_diff_img ``` #### 显示结果 最后一步就是展示原始输入以及经过处理后的差异图像给用户查看了。这有助于直观理解哪些部分发生了改变。 ```python def show_results(original_img1, original_img2, diff_result): fig, axes = plt.subplots(1, 3, figsize=(15, 5)) ax = axes.ravel() ax[0].imshow(original_img1, cmap='gray') ax[0].set_title('Original Image 1') ax[1].imshow(original_img2, cmap='gray') ax[1].set_title('Original Image 2') ax[2].imshow(diff_result, cmap='hot', vmin=0, vmax=255) ax[2].set_title('Difference Result') for a in ax: a.axis('off') plt.tight_layout() plt.show() ``` #### 完整流程示例 下面给出了一段完整的代码片段用于执行上述各个函数调用来完成整个过程。 ```python if __name__ == '__main__': path_to_first_image = 'path/to/image1.png' path_to_second_image = 'path/to/image2.png' first_gray, second_gray = load_images(path_to_first_image, path_to_second_image) diff_map = compute_abs_diff_image(first_gray, second_gray) show_results(first_gray, second_gray, diff_map) ```