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pip install pyautogui

"""
图像对比分析工具
功能：实现像素级差异、结构相似性(SSIM)、直方图相似度和特征匹配率四种对比方法
作者：智能助手
版本：1.2
日期：2025-02-27"""
import os
import cv2
import pyautogui
import numpy as np
from skimage.metrics import structural_similarity as ssim
from matplotlib import pyplot as plt
from datetime import datetimeclass ImageComparator:def __init__(self, img1_path, img2_path):"""初始化图像比较器参数：img1_path: 第一张图片路径img2_path: 第二张图片路径"""# 读取并预处理图像self.img1 = self._preprocess_image(cv2.imread(img1_path))self.img2 = self._preprocess_image(cv2.imread(img2_path))self._validate_images()  # 验证图像有效性def _validate_images(self):"""验证图像有效性并统一尺寸"""if self.img1 is None or self.img2 is None:raise ValueError("无法加载图像文件")if self.img1.shape != self.img2.shape:self._resize_images()  # 自动调整图像尺寸def _resize_images(self, method=cv2.INTER_AREA):"""统一图像尺寸参数：method: 插值方法，默认使用区域插值(速度快)"""h, w = self.img1.shape[:2]self.img2 = cv2.resize(self.img2, (w, h), interpolation=method)def _preprocess_image(self, img):"""图像预处理流程：1. 高斯模糊降噪2. CLAHE对比度受限直方图均衡化"""# 高斯模糊（核大小5x5，标准差自动计算）img = cv2.GaussianBlur(img, (5, 5), 0)# 转换到LAB颜色空间处理明度通道lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)l, a, b = cv2.split(lab)# 创建CLAHE对象（对比度限制2.0，网格大小8x8）clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))l_clahe = clahe.apply(l)  # 应用对比度均衡# 合并通道并转回BGR空间lab = cv2.merge((l_clahe, a, b))return cv2.cvtColor(lab, cv2.COLOR_LAB2BGR)def pixel_diff(self, threshold=5, visualize=False):"""像素级差异分析参数：threshold: 差异百分比阈值（默认5%）visualize: 是否可视化差异返回：差异百分比和是否超过阈值"""# 计算绝对差异矩阵diff = cv2.absdiff(self.img1, self.img2)# 计算总差异量（三通道总和）diff_sum = np.sum(diff)# 计算差异百分比（相对于最大可能差异）diff_percent = diff_sum / (self.img1.size * 255) * 100if visualize:self._plot_images(diff, "Pixel Difference")return {"difference_percent": round(diff_percent, 2),"is_different": diff_percent > threshold}def ssim_compare(self, visualize=False):"""结构相似性指数（SSIM）计算参数：visualize: 是否可视化差异图返回：SSIM分数和相似性判断"""# 转换为灰度图像gray1 = cv2.cvtColor(self.img1, cv2.COLOR_BGR2GRAY)gray2 = cv2.cvtColor(self.img2, cv2.COLOR_BGR2GRAY)# 计算SSIM（使用11x11高斯窗口）score, diff = ssim(gray1, gray2, full=True)# 将差异矩阵转换为0-255范围diff = (diff * 255).astype("uint8")if visualize:self._plot_images(diff, f"SSIM {score:.2f}")return {"ssim_score": round(score, 3),"is_similar": score > 0.95  # 经验阈值}def histogram_compare(self, method=cv2.HISTCMP_CORREL):"""直方图相似度计算参数：method: 比较方法（默认相关性）可选方法：HISTCMP_CORREL: 相关性（0-1）HISTCMP_CHISQR: 卡方检验（越小越相似）HISTCMP_BHATTACHARYYA: 巴氏距离（0-1，0最相似）"""# 计算三维直方图（每个通道256级）hist1 = cv2.calcHist([self.img1], [0, 1, 2], None, [256, 256, 256], [0, 256, 0, 256, 0, 256])hist2 = cv2.calcHist([self.img2], [0, 1, 2], None, [256, 256, 256], [0, 256, 0, 256, 0, 256])# 归一化直方图cv2.normalize(hist1, hist1, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX)cv2.normalize(hist2, hist2, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX)# 计算直方图相似度similarity = cv2.compareHist(hist1, hist2, method)return round(similarity, 3)def feature_match(self):"""特征匹配率计算使用ORB特征检测器+暴力匹配返回：优质匹配率（匹配距离<50的比例）"""# 初始化ORB检测器orb = cv2.ORB_create()# 检测关键点和计算描述子kp1, des1 = orb.detectAndCompute(self.img1, None)kp2, des2 = orb.detectAndCompute(self.img2, None)# 当没有检测到特征点时返回0if des1 is None or des2 is None:return 0.0# 创建暴力匹配器（汉明距离）bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)matches = bf.match(des1, des2)# 按距离排序matches = sorted(matches, key=lambda x: x.distance)# 筛选优质匹配（距离<50）good_matches = [m for m in matches if m.distance < 50]# 计算优质匹配率match_rate = len(good_matches) / len(matches) if matches else 0return round(match_rate, 3)def _plot_images(self, diff, title):"""可视化对比结果参数：diff: 差异矩阵title: 图表标题"""# 获取屏幕分辨率用于调整显示大小screen_width, screen_height = pyautogui.size()plt.figure(figsize=(screen_width / 100, screen_height / 100))# 子图布局说明：# 221 -> 2行2列的第1个位置# 223 -> 2行2列的第3个位置# 122 -> 1行2列的第2个位置# 原始图像对比plt.subplot(221), plt.imshow(cv2.cvtColor(self.img1, cv2.COLOR_BGR2RGB))plt.title('Image 1'), plt.axis('off')plt.subplot(223), plt.imshow(cv2.cvtColor(self.img2, cv2.COLOR_BGR2RGB))plt.title('Image 2'), plt.axis('off')# 差异图显示plt.subplot(122), plt.imshow(diff, cmap='gray')plt.title(title), plt.axis('off')# 保存并关闭图表（避免内存泄漏）plt.tight_layout()plt.savefig(f"res_{title.split(' ')[0]}.png")plt.close()def generate_report(self, results, filename="report.html"):"""生成HTML格式的测试报告参数：results: 包含各指标的字典filename: 输出文件名"""html_template = f"""<html><head><title>图像对比报告 {datetime.now().strftime("%Y-%m-%d %H:%M")}</title><style>/* 表格样式 */table {{ border-collapse: collapse; width: 80%;margin: 20px auto;box-shadow: 0 1px 3px rgba(0,0,0,0.2);}}th, td {{border: 1px solid #ddd;padding: 12px;text-align: left;}}th {{background-color: #4CAF50;color: white;}}tr:nth-child(even) {{ background-color: #f8f9fa; }}/* 图片容器样式 */.image-container {{text-align: center;margin: 20px;}}img {{ max-width: 45%;margin: 10px;box-shadow: 0 2px 4px rgba(0,0,0,0.1);}}</style></head><body><h2 style="text-align: center;">图像对比分析报告</h2><!-- 结果汇总表格 --><table><tr><th>指标</th><th>结果</th><th>判断标准</th></tr><tr><td>像素差异率</td><td>{results['pixel_diff']}%</td><td>阈值 &lt;5%</td></tr><tr><td>SSIM评分</td><td>{results['ssim']}</td><td>阈值 &gt;0.95</td></tr><tr><td>直方图相似度</td><td>{results['histogram']}</td><td>相关性 &gt;0.9</td></tr><tr><td>特征匹配率</td><td>{results['feature_match']}</td><td>优质率 &gt;30%</td></tr></table><!-- 差异图展示 --><div class="image-container"><h3>差异可视化</h3><img src="res_Pixel.png" alt="像素差异图"><img src="res_SSIM.png" alt="SSIM差异图"></div></body></html>"""# 写入HTML文件with open(filename, 'w', encoding='utf-8') as f:f.write(html_template)print(f"报告已生成: {filename}")if __name__ == "__main__":# 文件路径img1_path = "qian-c.png"img2_path = "qian-w.png"# 检查文件是否存在if not os.path.exists(img1_path):print(f"文件不存在: {img1_path}")if not os.path.exists(img2_path):print(f"文件不存在: {img2_path}")# 使用示例comparator = ImageComparator(img1_path, img2_path)# 执行各对比方法pixel_results = comparator.pixel_diff(visualize=True)ssim_results = comparator.ssim_compare(visualize=True)histogram_results = comparator.histogram_compare()feature_results = comparator.feature_match()# 汇总结果results = {"pixel_diff": pixel_results["difference_percent"],"ssim": ssim_results["ssim_score"],"histogram": histogram_results,"feature_match": feature_results}# 生成报告comparator.generate_report(results)

对比结果