模版匹配——在大量的图片中找到与模版相似的图像

本文主要是介绍模版匹配——在大量的图片中找到与模版相似的图像，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

传统的特征匹配算法：

通过opencv自带的matchtemplate方法识别发现对形变、旋转的效果不是很好，后来尝试利用orb特征、sift特征匹配，由于车辆很多特征很相似，也不能很好的区分，如利用sift特征匹配效果如下：

代码：

import shutil
import cv2
import numpy as np
import osdef calculate_match_score(img1, img2):"""计算两张图像的匹配分数"""# 创建SIFT对象sift = cv2.SIFT_create()# 检测SIFT关键点和描述符keypoints1, descriptors1 = sift.detectAndCompute(img1, None)keypoints2, descriptors2 = sift.detectAndCompute(img2, None)if descriptors1 is None or descriptors2 is None:return 0  # 如果无法计算描述符，则匹配分数为0# 创建BFMatcher对象bf = cv2.BFMatcher(cv2.NORM_L2, crossCheck=True)matches = bf.match(descriptors1, descriptors2)# 计算匹配度（匹配点数量与总点数的比值）num_matches = len(matches)total_points = len(keypoints1) + len(keypoints2)if total_points > 0:match_score = num_matches / total_pointselse:match_score = 0return match_score * 1000def template_match_folder(template_img, folder):"""在文件夹中查找与模板图像匹配的图像"""all_img_list = {}folder_name = os.path.basename(template_img).split("_")[0]save_folder = os.path.join("G:", "ss", folder_name)os.makedirs(save_folder, exist_ok=True)for des_img_name in os.listdir(folder):des_img_path = os.path.join(folder, des_img_name)# 读取目标图像des_img = cv2.imread(des_img_path)if des_img is None:print(f"无法读取图像 {des_img_path}")continueheight, width = des_img.shape[:2]des_img_area = height * widthif des_img_area < 50 * 65:continue# 计算匹配分数match_score = calculate_match_score(template_img, des_img)if match_score > 200:all_img_list[des_img_name] = match_scoresave_img_path = os.path.join(save_folder, des_img_name)shutil.copy(des_img_path, save_img_path)return all_img_listdef template_folder_match_des_folder(template_folder, folder):"""遍历模板文件夹，匹配每个模板图像与目标文件夹中的图像"""for template_name in os.listdir(template_folder):template_path = os.path.join(template_folder, template_name)template_img = cv2.imread(template_path)if template_img is None:print(f"无法读取模板图像 {template_path}")continueall_img_list = template_match_folder(template_img, folder)with open("1.txt", "a", encoding="utf-8") as f:f.write(str(all_img_list))f.write("\n")# 主程序入口
template_folder = r"G:\dataset\M3FD\M3FD_Detection\templates"
folder = r"G:\dataset\M3FD\M3FD_Detection\cut_imgs"template_folder_match_des_folder(template_folder, folder)

效果：

模版图像：

算法匹配结果：

模版图像：

算法匹配结果：

深度学习匹配算法：

通过resne提取图像特征，计算余弦相似度。再映射至hsv和lab颜色空间计算颜色的相似度，共同去评估模版与目标的相似度。

代码：

import torch
import torchvision.transforms as transforms
from torchvision import models
from PIL import Image
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
import cv2
import shutil
import os
import concurrent.futures
from tqdm import tqdm# 检查CUDA是否可用并选择设备
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(device)
# 加载预训练的 ResNet 模型并将其移动到GPU
model = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
model = model.to(device)  # 将模型移动到GPU
model.eval()  # 设置模型为评估模式# 定义图像预处理步骤
preprocess = transforms.Compose([transforms.Resize(256),transforms.CenterCrop(224),transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])def preprocess_image(image):"""将图像预处理为模型输入格式"""if isinstance(image, str):image = Image.open(image).convert('RGB')if isinstance(image, np.ndarray):image = Image.fromarray(image)if isinstance(image, Image.Image):image = preprocess(image)image = image.unsqueeze(0).to(device)  # 增加一个批次维度并将图像移动到GPUreturn imageelse:raise TypeError("Unsupported image type: {}".format(type(image)))def get_features(image):"""提取图像特征"""image = preprocess_image(image)# 使用模型提取特征with torch.no_grad():features = model(image)return features.cpu().numpy().flatten()  # 将特征从GPU移动到CPU并展平def get_color_features(image):"""提取图像颜色直方图特征"""if isinstance(image, str):image = Image.open(image).convert('RGB')if isinstance(image, np.ndarray):image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)elif isinstance(image, Image.Image):image = np.array(image.convert('RGB'))else:raise TypeError("Unsupported image type: {}".format(type(image)))# 转换到 HSV 和 Lab 颜色空间hsv_image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)lab_image = cv2.cvtColor(image, cv2.COLOR_RGB2Lab)# 计算 HSV 颜色直方图hist_h = cv2.calcHist([hsv_image], [0], None, [256], [0, 256]).flatten()hist_s = cv2.calcHist([hsv_image], [1], None, [256], [0, 256]).flatten()hist_v = cv2.calcHist([hsv_image], [2], None, [256], [0, 256]).flatten()# 计算 Lab 颜色直方图hist_l = cv2.calcHist([lab_image], [0], None, [256], [0, 256]).flatten()hist_a = cv2.calcHist([lab_image], [1], None, [256], [-128, 128]).flatten()hist_b = cv2.calcHist([lab_image], [2], None, [256], [-128, 128]).flatten()# 计算颜色矩（均值和标准差）mean_hsv = np.mean(hsv_image, axis=(0, 1))std_hsv = np.std(hsv_image, axis=(0, 1))mean_lab = np.mean(lab_image, axis=(0, 1))std_lab = np.std(lab_image, axis=(0, 1))# 归一化直方图hist_h /= hist_h.sum() if hist_h.sum() > 0 else 1hist_s /= hist_s.sum() if hist_s.sum() > 0 else 1hist_v /= hist_v.sum() if hist_v.sum() > 0 else 1hist_l /= hist_l.sum() if hist_l.sum() > 0 else 1hist_a /= hist_a.sum() if hist_a.sum() > 0 else 1hist_b /= hist_b.sum() if hist_b.sum() > 0 else 1# 合并特征并进行标准化color_features = np.concatenate([hist_h, hist_s, hist_v, hist_l, hist_a, hist_b, mean_hsv, std_hsv, mean_lab, std_lab])color_features = (color_features - np.mean(color_features)) / (np.std(color_features) + 1e-6)  # 标准化return color_featuresdef compare_images(image1, image2):"""比较两张图像的相似性"""# 提取图像特征features1 = get_features(image1)features2 = get_features(image2)# 提取颜色特征color_features1 = get_color_features(image1)color_features2 = get_color_features(image2)similarity_reset = cosine_similarity([features1], [features2])[0][0]similarity_color = cosine_similarity([color_features1], [color_features2])[0][0]return similarity_reset, similarity_colordef calculate_match_score(img1, img2):"""计算SIFT匹配度"""# 创建SIFT对象sift = cv2.SIFT_create()# 检测SIFT关键点和描述符keypoints1, descriptors1 = sift.detectAndCompute(img1, None)keypoints2, descriptors2 = sift.detectAndCompute(img2, None)# 创建BFMatcher对象bf = cv2.BFMatcher(cv2.NORM_L2, crossCheck=True)matches = bf.match(descriptors1, descriptors2)# 计算匹配度（匹配点数量与总点数的比值）num_matches = len(matches)total_points = len(keypoints1) + len(keypoints2)if total_points > 100:match_score = num_matches / total_pointselse:match_score = 0return match_score * 1000def process_image_pair(template_img_path, des_img_path, save_folder):"""处理图像对并保存符合条件的图像"""template_img = cv2.imread(template_img_path)des_img = cv2.imread(des_img_path)height, width = des_img.shape[:2]des_img_area = height * widthif des_img_area < 50 * 65:return Nonesimilarity_reset_score, similarity_color_score = compare_images(template_img, des_img)if similarity_reset_score > 0.8 and similarity_color_score > 0.998:des_img_name = os.path.basename(des_img_path)save_img_path = os.path.join(save_folder, des_img_name)shutil.copy(des_img_path, save_img_path)return {des_img_name: similarity_reset_score}return Nonedef template_match_folder(template_path, folder, max_workers=8):"""处理文件夹中的所有图像"""all_img_list = {}template_img = cv2.imread(template_path)save_folder = os.path.join("G:\\fff", os.path.basename(template_path).split("_")[0])os.makedirs(save_folder, exist_ok=True)with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:futures = []for des_img_name in os.listdir(folder):des_img_path = os.path.join(folder, des_img_name)futures.append(executor.submit(process_image_pair, template_path, des_img_path, save_folder))for future in concurrent.futures.as_completed(futures):result = future.result()if result:all_img_list.update(result)return all_img_listdef template_folder_match_des_folder(template_folder, folder, max_workers=8):"""处理模板文件夹和目标文件夹"""for template_name in tqdm(os.listdir(template_folder)):template_path = os.path.join(template_folder, template_name)all_img_list = template_match_folder(template_path, folder, max_workers)with open("3.txt", "a", encoding="utf-8") as f:f.write(str(all_img_list))f.write("\n")# 示例路径（根据实际情况修改）
template_folder = r"G:\dataset\M3FD\M3FD_Detection\templates"
folder = r"G:\dataset\M3FD\M3FD_Detection\cut_imgs"# 调整 max_workers 的值以控制并行处理的数量
template_folder_match_des_folder(template_folder, folder, max_workers=4)