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b/scripts/image_test/results/similarity_pivot_20250506_153638.csv new file mode 100644 index 0000000..0ee1454 --- /dev/null +++ b/scripts/image_test/results/similarity_pivot_20250506_153638.csv @@ -0,0 +1,4 @@ +image1,image2,ahash,color_hist,dhash,md5,phash,sift,ssim +additional_1.jpg,additional_2.jpg,0.984375,0.9870948370376991,0.921875,0.0,0.875,0.579540165689286,0.19861068520387853 +additional_1.jpg,additional_3.jpg,1.0,0.921953366880821,0.96875,0.0,0.9375,0.5848237706872704,0.18287979964654466 +additional_3.jpg,additional_2.jpg,0.984375,0.9566729875886791,0.921875,0.0,0.9375,0.5776319463836918,0.2204994764088436 diff --git a/scripts/image_test/results/similarity_results_20250506_153638.csv b/scripts/image_test/results/similarity_results_20250506_153638.csv new file mode 100644 index 0000000..cf460a4 --- /dev/null +++ b/scripts/image_test/results/similarity_results_20250506_153638.csv @@ -0,0 +1,22 @@ +image1,image2,algorithm,similarity,compute_time +additional_1.jpg,additional_3.jpg,md5,0.0,0.0008852481842041016 +additional_1.jpg,additional_3.jpg,phash,0.9375,0.03219485282897949 +additional_1.jpg,additional_3.jpg,ahash,1.0,0.014646530151367188 +additional_1.jpg,additional_3.jpg,dhash,0.96875,0.015238761901855469 +additional_1.jpg,additional_3.jpg,color_hist,0.921953366880821,0.05095839500427246 +additional_1.jpg,additional_3.jpg,sift,0.5848237706872704,0.9035642147064209 +additional_1.jpg,additional_3.jpg,ssim,0.18287979964654466,0.09257769584655762 +additional_1.jpg,additional_2.jpg,md5,0.0,0.0008139610290527344 +additional_1.jpg,additional_2.jpg,phash,0.875,0.017491579055786133 +additional_1.jpg,additional_2.jpg,ahash,0.984375,0.015668869018554688 +additional_1.jpg,additional_2.jpg,dhash,0.921875,0.015781641006469727 +additional_1.jpg,additional_2.jpg,color_hist,0.9870948370376991,0.013017416000366211 +additional_1.jpg,additional_2.jpg,sift,0.579540165689286,0.8017916679382324 +additional_1.jpg,additional_2.jpg,ssim,0.19861068520387853,0.08038783073425293 +additional_3.jpg,additional_2.jpg,md5,0.0,0.0008535385131835938 +additional_3.jpg,additional_2.jpg,phash,0.9375,0.017593860626220703 +additional_3.jpg,additional_2.jpg,ahash,0.984375,0.01582646369934082 +additional_3.jpg,additional_2.jpg,dhash,0.921875,0.015839099884033203 +additional_3.jpg,additional_2.jpg,color_hist,0.9566729875886791,0.013316154479980469 +additional_3.jpg,additional_2.jpg,sift,0.5776319463836918,0.8478097915649414 +additional_3.jpg,additional_2.jpg,ssim,0.2204994764088436,0.08063888549804688 diff --git a/scripts/image_test/run_dupe_checker.sh b/scripts/image_test/run_dupe_checker.sh new file mode 100755 index 0000000..099268d --- /dev/null +++ b/scripts/image_test/run_dupe_checker.sh @@ -0,0 +1,97 @@ +#!/bin/bash +# 用于运行简化版图像相似度检测工具的脚本 + +# 确保脚本在出错时退出 +set -e + +# 颜色定义 +GREEN='\033[0;32m' +YELLOW='\033[1;33m' +RED='\033[0;31m' +NC='\033[0m' # No Color + +# 打印带颜色的消息 +print_green() { + echo -e "${GREEN}$1${NC}" +} + +print_yellow() { + echo -e "${YELLOW}$1${NC}" +} + +print_red() { + echo -e "${RED}$1${NC}" +} + +# 安装必要的Python依赖 +install_dependencies() { + print_yellow "安装必要的Python依赖..." + pip install numpy opencv-python pillow matplotlib scikit-image scipy pandas imagehash + print_green "依赖安装完成！" +} + +# 检查测试图像目录 +check_images_dir() { + TEST_IMAGES_DIR="./scripts/image_test/test_images" + mkdir -p "$TEST_IMAGES_DIR" + + # 检查测试图像目录中是否有图像文件 + image_count=$(find "$TEST_IMAGES_DIR" -type f \( -name "*.jpg" -o -name "*.jpeg" -o -name "*.png" -o -name "*.bmp" \) | wc -l) + + if [ "$image_count" -eq 0 ]; then + print_red "错误: 测试图像目录 '$TEST_IMAGES_DIR' 中没有图像文件!" + print_yellow "请在此目录中放置要检测相似度的图像文件后再运行此脚本。" + print_yellow "您至少需要放置2个或更多图像文件进行比较。" + exit 1 + else + print_green "找到 $image_count 个图像文件，可以进行检测。" + fi +} + +# 确保输出目录存在 +prepare_output_dir() { + print_yellow "准备输出目录..." + OUTPUT_DIR="./scripts/image_test/results" + mkdir -p "$OUTPUT_DIR" + print_green "输出目录准备完成！" +} + +# 运行检测脚本 +run_check() { + print_yellow "运行图像相似度检测..." + TEST_IMAGES_DIR="./scripts/image_test/test_images" + OUTPUT_DIR="./scripts/image_test/results" + + python ./scripts/image_test/simple_dupe_checker.py --input_dir "$TEST_IMAGES_DIR" --output_dir "$OUTPUT_DIR" + + print_green "检测完成！" + print_yellow "结果保存在: $OUTPUT_DIR" +} + +# 主函数 +main() { + print_green "===== 简化版图像相似度检测工具 =====" + + # 检查Python是否可用 + if ! command -v python &> /dev/null; then + print_red "错误: 未找到Python。请确保已安装Python 3.6+。" + exit 1 + fi + + # 安装依赖 + install_dependencies + + # 检查测试图像目录 + check_images_dir + + # 准备输出目录 + prepare_output_dir + + # 运行检测 + run_check + + print_green "===== 检测完毕 =====" +} + +# 执行主函数 +main \ No newline at end of file diff --git a/scripts/image_test/simple_dupe_checker.py b/scripts/image_test/simple_dupe_checker.py new file mode 100644 index 0000000..352b6f9 --- /dev/null +++ b/scripts/image_test/simple_dupe_checker.py @@ -0,0 +1,416 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +""" +简化版图像相似度检测器 +直接计算指定目录中图片之间的相似度，输出表格结果 +""" + +import os +import sys +import hashlib +import argparse +import time +import numpy as np +import cv2 +from PIL import Image +import matplotlib.pyplot as plt +import pandas as pd +from skimage.metrics import structural_similarity as ssim +import imagehash +from datetime import datetime + +class SimpleImageDupeChecker: + """简化版图像相似度检测器""" + + def __init__(self): + """初始化检测器""" + # 算法列表 + self.algorithms = { + 'md5': self.compare_md5, + 'phash': self.compare_phash, + 'ahash': self.compare_ahash, + 'dhash': self.compare_dhash, + 'color_hist': self.compare_color_histogram, + 'sift': self.compare_sift, + 'ssim': self.compare_ssim + } + + # 初始化SIFT检测器 + self.sift = cv2.SIFT_create() + # FLANN匹配器参数 + FLANN_INDEX_KDTREE = 1 + index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5) + search_params = dict(checks=50) + self.flann = cv2.FlannBasedMatcher(index_params, search_params) + + def get_md5(self, image_path): + """计算图像的MD5哈希值""" + with open(image_path, 'rb') as f: + md5hash = hashlib.md5(f.read()).hexdigest() + return md5hash + + def compare_md5(self, image1_path, image2_path): + """比较两张图像的MD5哈希值相似度""" + hash1 = self.get_md5(image1_path) + hash2 = self.get_md5(image2_path) + + # 如果哈希完全相同返回1.0，否则返回0.0 + return 1.0 if hash1 == hash2 else 0.0 + + def get_phash(self, image_path, hash_size=8): + """计算图像的感知哈希(pHash)""" + img = Image.open(image_path).convert('L').resize((hash_size * 4, hash_size * 4), Image.LANCZOS) + return imagehash.phash(img, hash_size=hash_size) + + def compare_phash(self, image1_path, image2_path): + """比较两张图像的感知哈希(pHash)相似度""" + hash1 = self.get_phash(image1_path) + hash2 = self.get_phash(image2_path) + + # 计算哈希相似度（64位哈希的汉明距离） + hash_diff = hash1 - hash2 + max_diff = 64.0 # 8x8哈希的最大汉明距离 + similarity = 1.0 - (hash_diff / max_diff) + return max(0.0, similarity) # 确保相似度不低于0 + + def get_ahash(self, image_path, hash_size=8): + """计算图像的平均哈希(aHash)""" + img = Image.open(image_path).convert('L').resize((hash_size, hash_size), Image.LANCZOS) + return imagehash.average_hash(img, hash_size=hash_size) + + def compare_ahash(self, image1_path, image2_path): + """比较两张图像的平均哈希(aHash)相似度""" + hash1 = self.get_ahash(image1_path) + hash2 = self.get_ahash(image2_path) + + # 计算哈希相似度 + hash_diff = hash1 - hash2 + max_diff = 64.0 # 8x8哈希的最大汉明距离 + similarity = 1.0 - (hash_diff / max_diff) + return max(0.0, similarity) + + def get_dhash(self, image_path, hash_size=8): + """计算图像的差值哈希(dHash)""" + img = Image.open(image_path).convert('L').resize((hash_size + 1, hash_size), Image.LANCZOS) + return imagehash.dhash(img, hash_size=hash_size) + + def compare_dhash(self, image1_path, image2_path): + """比较两张图像的差值哈希(dHash)相似度""" + hash1 = self.get_dhash(image1_path) + hash2 = self.get_dhash(image2_path) + + # 计算哈希相似度 + hash_diff = hash1 - hash2 + max_diff = 64.0 # 8x8哈希的最大汉明距离 + similarity = 1.0 - (hash_diff / max_diff) + return max(0.0, similarity) + + def get_color_histogram(self, image_path): + """计算图像的颜色直方图""" + img = cv2.imread(image_path) + img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) + + # 计算HSV颜色空间的直方图 + hist = cv2.calcHist([img], [0, 1, 2], None, [8, 8, 8], [0, 180, 0, 256, 0, 256]) + cv2.normalize(hist, hist, 0, 1.0, cv2.NORM_MINMAX) + return hist.flatten() + + def compare_color_histogram(self, image1_path, image2_path): + """比较两张图像的颜色直方图相似度""" + hist1 = self.get_color_histogram(image1_path) + hist2 = self.get_color_histogram(image2_path) + + # 计算直方图相似度 + similarity = cv2.compareHist(hist1, hist2, cv2.HISTCMP_CORREL) + return max(0.0, similarity) # 确保相似度不低于0 + + def get_sift_features(self, image_path): + """获取图像的SIFT特征点和描述符""" + img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE) + keypoints, descriptors = self.sift.detectAndCompute(img, None) + return keypoints, descriptors + + def compare_sift(self, image1_path, image2_path): + """比较两张图像的SIFT特征点相似度""" + kp1, des1 = self.get_sift_features(image1_path) + kp2, des2 = self.get_sift_features(image2_path) + + # 如果没有足够的特征点，返回低相似度 + if des1 is None or des2 is None or len(des1) < 2 or len(des2) < 2: + return 0.0 + + # 使用FLANN匹配器查找最佳匹配 + matches = self.flann.knnMatch(des1, des2, k=2) + + # 应用比率测试，筛选好的匹配 + good_matches = [] + for m, n in matches: + if m.distance < 0.7 * n.distance: + good_matches.append(m) + + # 计算相似度 + similarity = len(good_matches) / max(len(kp1), len(kp2)) if max(len(kp1), len(kp2)) > 0 else 0 + return min(1.0, similarity) # 确保相似度不超过1 + + def compare_ssim(self, image1_path, image2_path): + """比较两张图像的结构相似性(SSIM)""" + img1 = cv2.imread(image1_path, cv2.IMREAD_GRAYSCALE) + img2 = cv2.imread(image2_path, cv2.IMREAD_GRAYSCALE) + + # 确保两张图像尺寸相同 + h, w = min(img1.shape[0], img2.shape[0]), min(img1.shape[1], img2.shape[1]) + img1 = cv2.resize(img1, (w, h)) + img2 = cv2.resize(img2, (w, h)) + + # 计算SSIM + similarity = ssim(img1, img2) + return max(0.0, similarity) # 确保相似度不低于0 + + def check_images(self, input_dir, output_dir=None): + """ + 检查目录中所有图像之间的相似度 + + Args: + input_dir: 输入图像目录 + output_dir: 输出结果目录，如果为None则使用input_dir + + Returns: + 包含相似度信息的DataFrame + """ + # 如果未指定输出目录，使用输入目录 + if output_dir is None: + output_dir = input_dir + os.makedirs(output_dir, exist_ok=True) + + # 获取所有图像文件 + print("正在读取图像文件...") + image_files = [] + for file in os.listdir(input_dir): + file_path = os.path.join(input_dir, file) + if os.path.isfile(file_path) and file.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.tiff', '.gif')): + image_files.append(file_path) + + # 检查图像数量 + if len(image_files) == 0: + print(f"错误: 在目录 '{input_dir}' 中没有找到图像文件") + return None + + print(f"找到 {len(image_files)} 个图像文件，开始计算相似度...") + + # 结果列表 + results = [] + + # 计算所有图像对的相似度 + total_pairs = len(image_files) * (len(image_files) - 1) // 2 + pair_count = 0 + + for i, img1 in enumerate(image_files): + img1_name = os.path.basename(img1) + + for j, img2 in enumerate(image_files): + if j <= i: # 跳过重复比较 + continue + + img2_name = os.path.basename(img2) + pair_count += 1 + print(f"比较图像对 {pair_count}/{total_pairs}: {img1_name} vs {img2_name}") + + # 对每种算法计算相似度 + for alg_name, compare_func in self.algorithms.items(): + try: + start_time = time.time() + similarity = compare_func(img1, img2) + compute_time = time.time() - start_time + + results.append({ + 'image1': img1_name, + 'image2': img2_name, + 'algorithm': alg_name, + 'similarity': similarity, + 'compute_time': compute_time + }) + + except Exception as e: + print(f" 算法 {alg_name} 比较出错: {e}") + results.append({ + 'image1': img1_name, + 'image2': img2_name, + 'algorithm': alg_name, + 'similarity': None, + 'compute_time': None, + 'error': str(e) + }) + + # 创建DataFrame + df = pd.DataFrame(results) + + # 保存结果 + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + csv_path = os.path.join(output_dir, f"similarity_results_{timestamp}.csv") + df.to_csv(csv_path, index=False) + print(f"结果已保存至: {csv_path}") + + # 创建透视表 + pivot_df = df.pivot_table( + index=['image1', 'image2'], + columns='algorithm', + values='similarity', + aggfunc='first' + ).reset_index() + + # 保存透视表 + pivot_path = os.path.join(output_dir, f"similarity_pivot_{timestamp}.csv") + pivot_df.to_csv(pivot_path, index=False) + print(f"透视表已保存至: {pivot_path}") + + # 显示结果摘要 + self.print_summary(df) + + # 生成可视化 + self.visualize_results(df, output_dir) + + return df + + def print_summary(self, df): + """打印结果摘要""" + print("\n===== 图像相似度检测结果摘要 =====") + + # 按算法分组计算平均相似度 + alg_summary = df.groupby('algorithm')['similarity'].agg(['mean', 'min', 'max', 'std']).reset_index() + alg_summary = alg_summary.sort_values('mean', ascending=False) + + print("\n各算法平均相似度:") + for _, row in alg_summary.iterrows(): + print(f" {row['algorithm']:<12}: 平均值 = {row['mean']:.4f} (最小值: {row['min']:.4f}, 最大值: {row['max']:.4f}, 标准差: {row['std']:.4f})") + + # 显示最相似的图像对 + print("\n相似度最高的图像对:") + for alg in df['algorithm'].unique(): + alg_df = df[df['algorithm'] == alg] + if not alg_df.empty: + max_idx = alg_df['similarity'].idxmax() + max_row = alg_df.loc[max_idx] + print(f" {alg:<12}: {max_row['image1']} 和 {max_row['image2']} (相似度: {max_row['similarity']:.4f})") + + # 显示最不相似的图像对 + print("\n相似度最低的图像对:") + for alg in df['algorithm'].unique(): + alg_df = df[df['algorithm'] == alg] + if not alg_df.empty: + min_idx = alg_df['similarity'].idxmin() + min_row = alg_df.loc[min_idx] + print(f" {alg:<12}: {min_row['image1']} 和 {min_row['image2']} (相似度: {min_row['similarity']:.4f})") + + def visualize_results(self, df, output_dir): + """可视化结果""" + print("\n生成结果可视化...") + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + + # 1. 绘制条形图 - 各算法的平均相似度 + plt.figure(figsize=(10, 6)) + avg_similarity = df.groupby('algorithm')['similarity'].mean().reset_index() + avg_similarity = avg_similarity.sort_values('similarity', ascending=False) + + plt.bar(avg_similarity['algorithm'], avg_similarity['similarity'], color='skyblue') + plt.xlabel('算法') + plt.ylabel('平均相似度') + plt.title('各算法的平均相似度') + plt.xticks(rotation=45) + plt.grid(True, linestyle='--', alpha=0.7) + plt.tight_layout() + + bar_chart_path = os.path.join(output_dir, f"algorithm_avg_similarity_{timestamp}.png") + plt.savefig(bar_chart_path) + + # 2. 绘制热力图 - 各图像对的相似度矩阵 + # 为每个算法生成一个热力图 + image_names = sorted(list(set(df['image1'].unique()) | set(df['image2'].unique()))) + + for alg in df['algorithm'].unique(): + plt.figure(figsize=(10, 8)) + + # 创建热力图数据 + heatmap_data = np.zeros((len(image_names), len(image_names))) + + # 填充热力图数据 + for idx, row in df[df['algorithm'] == alg].iterrows(): + i = image_names.index(row['image1']) + j = image_names.index(row['image2']) + heatmap_data[i, j] = row['similarity'] + heatmap_data[j, i] = row['similarity'] # 对称矩阵 + + # 热力图对角线设为1.0（自己与自己相似度为1） + for i in range(len(image_names)): + heatmap_data[i, i] = 1.0 + + # 创建热力图 + plt.imshow(heatmap_data, cmap='viridis') + plt.colorbar(label='相似度') + plt.title(f'{alg} 算法的图像相似度热力图') + + # 设置坐标轴刻度 + plt.xticks(np.arange(len(image_names)), image_names, rotation=90) + plt.yticks(np.arange(len(image_names)), image_names) + + # 添加文本标签 + for i in range(len(image_names)): + for j in range(len(image_names)): + text = plt.text(j, i, f"{heatmap_data[i, j]:.2f}", + ha="center", va="center", + color="w" if heatmap_data[i, j] < 0.7 else "black") + + plt.tight_layout() + heatmap_path = os.path.join(output_dir, f"similarity_heatmap_{alg}_{timestamp}.png") + plt.savefig(heatmap_path) + + # 3. 箱线图 - 各算法的相似度分布 + plt.figure(figsize=(10, 6)) + + # 准备数据 + data = [] + labels = [] + + for alg in sorted(df['algorithm'].unique()): + alg_data = df[df['algorithm'] == alg]['similarity'] + if not alg_data.empty: + data.append(alg_data) + labels.append(alg) + + # 创建箱线图 + plt.boxplot(data, labels=labels) + plt.ylabel('相似度') + plt.title('各算法的相似度分布') + plt.grid(True, linestyle='--', alpha=0.7) + plt.tight_layout() + + boxplot_path = os.path.join(output_dir, f"algorithm_boxplot_{timestamp}.png") + plt.savefig(boxplot_path) + + # 关闭所有图形 + plt.close('all') + + print(f"可视化图表已保存到:\n 条形图: {bar_chart_path}\n 箱线图: {boxplot_path}") + print(f" 各算法热力图已保存到输出目录") + +def main(): + """主函数""" + parser = argparse.ArgumentParser(description='简化版图像相似度检测工具') + parser.add_argument('--input_dir', '-i', type=str, default='./scripts/image_test/test_images', + help='输入图像目录路径') + parser.add_argument('--output_dir', '-o', type=str, default=None, + help='输出结果目录路径，默认与输入目录相同') + + args = parser.parse_args() + + print("===== 简化版图像相似度检测工具 =====") + print(f"输入目录: {args.input_dir}") + print(f"输出目录: {args.output_dir or args.input_dir}") + + checker = SimpleImageDupeChecker() + checker.check_images(args.input_dir, args.output_dir) + + print("\n===== 检测完成 =====") + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/scripts/image_test/test_images/additional_1.jpg b/scripts/image_test/test_images/additional_1.jpg new file mode 100644 index 0000000..d9be66e Binary files /dev/null and b/scripts/image_test/test_images/additional_1.jpg differ diff --git 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