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imgcompare.py
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313
imgcompare.py
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import os.path
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import shutil
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import numpy as np
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from ytracking.track_ import *
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from contrast.test_logic import group_image, inference
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from tools.Interface import AiInterface, AiClass
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from tools.config import cfg, gvalue
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from tools.initModel import models
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from scipy.spatial.distance import cdist
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from dealdata import get_keams
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import matplotlib.pyplot as plt
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from contrast.model.resnet_pre import resnet18
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from prettytable import PrettyTable
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from sklearn.cluster import KMeans
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models.initModel()
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ai_obj = AiClass()
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def showComprehensiveHistogram(data, title):
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bins = np.arange(0, 1.01, 0.1)
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plt.hist(data, bins, edgecolor='black')
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plt.title(title)
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plt.xlabel('Similarity')
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plt.ylabel('Frequency')
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# plt.show()
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plt.savefig(title + '.png')
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def showHistogram():
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fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(10, 8))
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bins = np.arange(0, 1.01, 0.1)
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axs[0, 0].hist(gvalue.back_return_similarity, bins=bins, edgecolor='black')
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axs[0, 0].set_title('back_return_similarity')
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axs[0, 0].set_xlabel('Similarity')
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axs[0, 0].set_ylabel('Frequency')
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axs[0, 0].legend(labels=['back_return_similarity'])
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axs[0, 1].hist(gvalue.back_add_similarity, bins=bins, edgecolor='black')
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axs[0, 1].set_title('back_add_similarity')
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axs[0, 1].set_xlabel('Similarity')
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axs[0, 1].set_ylabel('Frequency')
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axs[0, 1].legend(labels=['back_add_similarity'])
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axs[1, 0].hist(gvalue.front_return_similarity, bins=bins, edgecolor='black')
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axs[1, 0].set_title('front_return_similarity')
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axs[1, 0].set_xlabel('Similarity')
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axs[1, 0].set_ylabel('Frequency')
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axs[1, 0].legend(labels=['front_return_similarity'])
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axs[1, 1].hist(gvalue.front_add_similarity, bins=bins, edgecolor='black')
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axs[1, 1].set_title('front_add_similarity')
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axs[1, 1].set_xlabel('Similarity')
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axs[1, 1].set_ylabel('Frequency')
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axs[1, 1].legend(labels=['front_add_similarity'])
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# 显示图形
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plt.tight_layout()
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plt.savefig('multiple_histograms.png')
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plt.close(fig)
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# plt.show()
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def showgrid():
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y_back_return = get_count_number(gvalue.back_return_similarity)
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y_back_add = get_count_number(gvalue.back_add_similarity)
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y_front_return = get_count_number(gvalue.front_return_similarity)
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y_front_add = get_count_number(gvalue.front_add_similarity)
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y_comprehensive = get_count_number(gvalue.comprehensive_similarity)
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x = np.linspace(start=0.1, stop=1.0, num=10, endpoint=True).tolist()
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plt.figure(figsize=(10, 6))
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plt.plot(x, y_back_return, color='red', label='back_return')
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plt.plot(x, y_back_add, color='blue', label='back_add')
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plt.plot(x, y_front_return, color='green', label='front_return')
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plt.plot(x, y_front_add, color='purple', label='front_add')
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plt.plot(x, y_comprehensive, color='orange', label='comprehensive')
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plt.legend()
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plt.xlabel('Similarity')
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plt.ylabel('Frequency')
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plt.grid(True, linestyle='--', alpha=0.5)
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plt.savefig('multiple_grid.png')
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plt.close()
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def showtable(): # 在指定相似度下离群点统计
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temp_lists = [get_count_number(gvalue.back_return_similarity),
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get_count_number(gvalue.back_add_similarity),
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get_count_number(gvalue.front_return_similarity),
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get_count_number(gvalue.front_add_similarity),
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get_count_number(gvalue.comprehensive_similarity)]
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rows = []
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table = PrettyTable()
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tablename = ['back_return', 'back_add', 'front_return', 'front_add', 'comprehensive']
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table.field_names = ['name', '0.1', '0.2', '0.3', '0.4', '0.5', '0.6', '0.7', '0.8', '0.9', '1.0']
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for List, name in zip(temp_lists, tablename):
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o_data = [round(data / List[-1], 3) for data in List]
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o_data.insert(0, name)
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rows.append(o_data)
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# print(rows)
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table.add_rows(rows)
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print(table)
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def compute_similarity_matrix(featurelists):
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"""计算图片之间的余弦相似度矩阵"""
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# 计算所有向量对之间的余弦相似度
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cosine_similarities = 1 - cdist(featurelists, featurelists, metric='cosine')
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cosine_similarities = np.around(cosine_similarities, decimals=3)
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return cosine_similarities
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def remove_empty_folders(root_dir):
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for foldername, subfolders, files in os.walk(root_dir):
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if not subfolders and not files: # 如果当前文件夹无子文件夹且无文件
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print(f"Removing empty folder: {foldername}")
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try:
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shutil.rmtree(foldername) # 删除空文件夹
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except Exception as e:
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print(f"Error removing folder {foldername}: {e}")
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def cosine_similarity(vec_mean, vecs, k=False, y_pred=None): # 余弦角相似度
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all_similarity = []
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if not k:
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vec_mean = np.array(vec_mean)
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for ovec in vecs:
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ovec = np.array(ovec)
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cos_sim = ovec.dot(vec_mean) / (np.linalg.norm(vec_mean) * np.linalg.norm(ovec))
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all_similarity.append(cos_sim)
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else:
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for nu, ks in enumerate(y_pred):
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ovec = np.array(vecs[nu])
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vecmean = np.array(vec_mean[ks])
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cos_sim = ovec.dot(vecmean) / (np.linalg.norm(vecmean) * np.linalg.norm(ovec))
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all_similarity.append(cos_sim)
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# print(all_similarity)
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return all_similarity
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def get_count_number(numbers):
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count_less = []
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thresholds = np.linspace(start=0.1, stop=1.0, num=10, endpoint=True).tolist()
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for threshold in thresholds:
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count_less.append(sum(map(lambda x: x < threshold, numbers)))
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print(count_less)
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return count_less
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def shuntVideo_imgs(obj: AiInterface, rootpth, vpth, ): # 制作单trackid下的相似度矩阵
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videospth = os.sep.join([rootpth, vpth])
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for videoname in os.listdir(videospth):
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if videoname.endswith('mp4'):
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cameraId = '0' if videoname.split('_')[2] == 'back' else '1'
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videopth = os.sep.join([videospth, videoname])
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save_imgs_dir = os.sep.join([rootpth, 'images', videoname.split('.')[0]])
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if not os.path.exists(save_imgs_dir):
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os.makedirs(save_imgs_dir)
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track_boxes, features_dict, frame_id_img = run(models, source=videopth)
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allimages, trackIdList = obj.getTrackingBox(track_boxes,
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features_dict,
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cameraId,
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frame_id_img,
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save_imgs_dir)
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featList = get_feature_list(allimages)
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cosine_similarities = compute_similarity_matrix(featList)
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print(len(cosine_similarities))
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print(cosine_similarities)
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def get_feature_list(allimages, actionModel=True):
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featList = []
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groups = group_image(allimages, batch=64)
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if not actionModel:
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groups = [groups]
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for group in groups:
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for img in group:
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feat_tensor = inference(img, models.similarityModel, actionModel)
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for fe in feat_tensor:
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if fe.device == 'cpu':
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fe_np = fe.squeeze().detach().numpy()
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else:
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fe_np = fe.squeeze().detach().cpu().numpy()
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featList.append(fe_np)
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return featList
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def k_similarity(imgs_pth, k, actionModel=False): # k个聚类中心向量与每个图片的相似度
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remove_empty_folders(imgs_pth)
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for imgdirs in os.listdir(imgs_pth):
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imgpth = []
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for img in os.listdir(os.sep.join([imgs_pth, imgdirs])):
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imgpth.append(os.sep.join([imgs_pth, imgdirs, img]))
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featList = get_feature_list(imgpth, actionModel)
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# assert all(len(lst) == len(featList[0]) for lst in featList)
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if len(featList) < k:
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continue
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featList = np.array(featList)
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Kmeans = KMeans(n_clusters=k)
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y_pred = Kmeans.fit_predict(featList)
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ores = cosine_similarity(Kmeans.cluster_centers_, featList, k=True, y_pred=y_pred)
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if 'back_return' in imgdirs:
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gvalue.back_return_similarity += ores
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elif 'back_add' in imgdirs:
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gvalue.back_add_similarity += ores
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elif 'front_return' in imgdirs:
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gvalue.front_return_similarity += ores
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elif 'front_add' in imgdirs:
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gvalue.front_add_similarity += ores
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gvalue.comprehensive_similarity += ores
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showtable() # 离群点表格
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def average_similarity(imgs_pth, actionModel=False): # 平均向量与每个图片的相似度
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remove_empty_folders(imgs_pth)
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for imgdirs in os.listdir(imgs_pth):
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imgpth = []
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if len(os.listdir(os.sep.join([imgs_pth, imgdirs]))) < 10:
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continue
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for img in os.listdir(os.sep.join([imgs_pth, imgdirs])):
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imgpth.append(os.sep.join([imgs_pth, imgdirs, img]))
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featList = get_feature_list(imgpth, actionModel)
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assert all(len(lst) == len(featList[0]) for lst in featList)
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vec_mean = [sum(column) / len(featList) for column in zip(*featList)]
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ores = cosine_similarity(vec_mean, featList)
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if 'back_return' in imgdirs:
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gvalue.back_return_similarity += ores
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elif 'back_add' in imgdirs:
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gvalue.back_add_similarity += ores
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elif 'front_return' in imgdirs:
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gvalue.front_return_similarity += ores
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elif 'front_add' in imgdirs:
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gvalue.front_add_similarity += ores
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gvalue.comprehensive_similarity += ores
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showHistogram() # 绘制直方图
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showgrid() # 绘制折线图
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showtable() # 离群点表格
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showComprehensiveHistogram(gvalue.comprehensive_similarity, 'comprehensive_similarity')
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def barcode_similarity(rootpths):
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for dir in os.listdir(rootpths):
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if dir == 'barcode_similarity':
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continue
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new_dir = os.sep.join([rootpths, 'barcode_similarity', dir])
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if not os.path.exists(new_dir):
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os.makedirs(new_dir)
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else:
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continue
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rootpth = os.sep.join([rootpths, dir]) # 6934660520292
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imgs_pth = [os.sep.join([rootpth, name]) for name in os.listdir(rootpth)]
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featList = get_feature_list(imgs_pth, False)
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cosine_similarities = compute_similarity_matrix(featList)
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num = 0
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for i in range(cosine_similarities.shape[0]):
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cols = np.where(cosine_similarities[i, :] > 0.5)[0]
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if len(cols) > num:
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num = len(cols)
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max_cols = cols
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imgPth = [os.sep.join([rootpth, imgName]) for imgName in [os.listdir(rootpth)[i] for i in max_cols]]
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for img in imgPth:
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try:
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shutil.copy(img, new_dir)
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except Exception as e:
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print(e)
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continue
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# shutil.copy(img, new_dir)
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# print(imgPth)
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# print(featList)
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# print(imgs_pth)
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def compare_two_img(img1, img2):
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img1_feature = get_feature_list(img1, False)[0]
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img2_feature = get_feature_list(img2, False)[0]
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cos_sim = img1_feature.dot(img2_feature) / (np.linalg.norm(img1_feature) * np.linalg.norm(img2_feature))
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print(cos_sim)
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if __name__ == '__main__':
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rootpth = 'Single_purchase_data'
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'''
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制作单trackid下的相似度矩阵
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'''
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# vpth = 'test_video'
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# shuntVideo_imgs(ai_obj, rootpth, vpth)
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'''
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平均向量与每个图片的相似度
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'''
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imgs_pth = os.sep.join([rootpth, 'images'])
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average_similarity(imgs_pth)
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'''
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k值聚类中心向量与每个图片的相似度
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'''
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# imgs_pth = os.sep.join([rootpth, 'images'])
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# k_similarity(imgs_pth, k=3)
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'''
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计算筛选单个barcode相似度集中最多的图片
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'''
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# rootpths = 'data_test'
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# barcode_similarity(rootpths)
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'''
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对比两张图的相似度
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'''
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# img1 = ['C:/Users/HP/Desktop/maskBackImg.jpg']
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# img2 = ['C:/Users/HP/Desktop/frontImgMask.jpg']
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# compare_two_img(img1, img2)
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