智能秤分析
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tools/picdir_to_picdir_similar.py
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tools/picdir_to_picdir_similar.py
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from similar_analysis import SimilarAnalysis
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import os
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import pickle
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from tools.image_joint import merge_imgs
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import yaml
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from PIL import Image
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import torch
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from sklearn.metrics.pairwise import cosine_similarity
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import numpy as np
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import matplotlib.pyplot as plt
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'''
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轨迹图与标准库之间的相似度分析
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1.用于生成轨迹图与标准库中所有图片的相似度
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2.用于分析轨迹图与标准库比对选取策略的判断
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'''
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class picDirSimilarAnalysis(SimilarAnalysis):
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def __init__(self):
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super(picDirSimilarAnalysis, self).__init__()
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with open('../configs/pic_pic_similar.yml', 'r') as f:
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self.conf = yaml.load(f, Loader=yaml.FullLoader)
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if not os.path.exists(self.conf['data']['total_pkl']):
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# self.create_total_feature()
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self.create_total_pkl()
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if os.path.exists(self.conf['data']['total_pkl']):
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self.all_dicts = self.load_dict_from_pkl()
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def is_image_file(self, filename):
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"""
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检查文件是否为图像文件
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"""
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image_extensions = ('.jpg', '.jpeg', '.png', '.bmp', '.gif', '.tiff')
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return filename.lower().endswith(image_extensions)
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def create_total_pkl(self): # 将目录下所有的图片特征存入pkl文件
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all_images_feature_dict = {}
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for roots, dirs, files in os.walk(self.conf['data']['data_dir']):
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for file_name in files:
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if self.is_image_file(file_name):
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try:
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print(f"处理图像 {os.sep.join([roots, file_name])}")
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feature = self.extract_features(os.sep.join([roots, file_name]))
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except Exception as e:
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print(f"处理图像 {os.sep.join([roots, file_name])} 时出错: {e}")
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feature = None
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all_images_feature_dict[os.sep.join([roots, file_name])] = feature
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if not os.path.exists(self.conf['data']['total_pkl']):
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with open(self.conf['data']['total_pkl'], 'wb') as f:
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pickle.dump(all_images_feature_dict, f)
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def load_dict_from_pkl(self):
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with open(self.conf['data']['total_pkl'], 'rb') as f:
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data = pickle.load(f)
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print(f"字典已从 {self.conf['data']['total_pkl']} 加载")
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return data
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def get_image_files(self, folder_path):
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"""
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获取文件夹中的所有图像文件
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"""
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image_files = []
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for root, _, files in os.walk(folder_path):
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for file in files:
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if self.is_image_file(file):
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image_files.append(os.path.join(root, file))
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return image_files
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def extract_features(self, image_path):
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feature_dict = self.get_feature(image_path)
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return feature_dict[image_path]
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def create_one_similarity_matrix(self, folder1_path, folder2_path):
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images1 = self.get_image_files(folder1_path)
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images2 = self.get_image_files(folder2_path)
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print(f"文件夹1 ({folder1_path}) 包含 {len(images1)} 张图像")
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print(f"文件夹2 ({folder2_path}) 包含 {len(images2)} 张图像")
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if len(images1) == 0 or len(images2) == 0:
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raise ValueError("至少有一个文件夹中没有图像文件")
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# 提取文件夹1中的所有图像特征
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features1 = []
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print("正在提取文件夹1中的图像特征...")
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for i, img_path in enumerate(images1):
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try:
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# feature = self.extract_features(img_path)
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feature = self.all_dicts[img_path]
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features1.append(feature.cpu().numpy())
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# if (i + 1) % 10 == 0:
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# print(f"已处理 {i + 1}/{len(images1)} 张图像")
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except Exception as e:
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print(f"处理图像 {img_path} 时出错: {e}")
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features1.append(None)
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# 提取文件夹2中的所有图像特征
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features2 = []
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print("正在提取文件夹2中的图像特征...")
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for i, img_path in enumerate(images2):
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try:
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# feature = self.extract_features(img_path)
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feature = self.all_dicts[img_path]
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features2.append(feature.cpu().numpy())
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# if (i + 1) % 10 == 0:
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# print(f"已处理 {i + 1}/{len(images2)} 张图像")
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except Exception as e:
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print(f"处理图像 {img_path} 时出错: {e}")
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features2.append(None)
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# 移除处理失败的图像
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valid_features1 = []
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valid_images1 = []
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for i, feature in enumerate(features1):
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if feature is not None:
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valid_features1.append(feature)
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valid_images1.append(images1[i])
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valid_features2 = []
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valid_images2 = []
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for i, feature in enumerate(features2):
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if feature is not None:
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valid_features2.append(feature)
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valid_images2.append(images2[i])
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# print(f"文件夹1中成功处理 {len(valid_features1)} 张图像")
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# print(f"文件夹2中成功处理 {len(valid_features2)} 张图像")
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if len(valid_features1) == 0 or len(valid_features2) == 0:
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raise ValueError("没有成功处理任何图像")
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# 计算相似度矩阵
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print("正在计算相似度矩阵...")
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similarity_matrix = cosine_similarity(valid_features1, valid_features2)
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return similarity_matrix, valid_images1, valid_images2
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def get_group_similarity_matrix(self, folder_path):
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tracking_folder = os.sep.join([folder_path, 'tracking'])
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standard_folder = os.sep.join([folder_path, 'standard_slim'])
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for dir_name in os.listdir(tracking_folder):
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tracking_dir = os.sep.join([tracking_folder, dir_name])
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standard_dir = os.sep.join([standard_folder, dir_name])
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similarity_matrix, valid_images1, valid_images2 = self.create_one_similarity_matrix(tracking_dir,
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standard_dir)
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mean_similarity = np.mean(similarity_matrix)
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std_similarity = np.std(similarity_matrix)
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max_similarity = np.max(similarity_matrix)
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min_similarity = np.min(similarity_matrix)
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print(f"文件夹 {dir_name} 的相似度矩阵已计算完成 "
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f"均值：{mean_similarity} 标准差：{std_similarity} 最大值：{max_similarity} 最小值：{min_similarity}")
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result = f"{os.path.basename(standard_folder)} {dir_name} {mean_similarity:.3f} {std_similarity:.3f} {max_similarity:.3f} {min_similarity:.3f}"
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with open(self.conf['data']['result_txt'], 'a') as f:
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f.write(result + '\n')
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def read_result_txt():
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parts = []
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value_num = 2
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with open('../configs/pic_pic_similar.yml', 'r') as f:
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conf = yaml.load(f, Loader=yaml.FullLoader)
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f.close()
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with open(conf['data']['result_txt'], 'r') as f:
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lines = f.readlines()
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for line in lines:
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line = line.strip()
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if line:
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parts.append(line.split(' '))
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parts = np.array(parts)
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print(parts)
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labels = ['Mean', 'Std', 'Max', 'Min']
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while value_num < 6:
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dicts = {}
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for barcode, value in zip(parts[:, 1], parts[:, value_num]):
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if barcode in dicts:
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dicts[barcode].append(float(value))
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else:
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dicts[barcode] = [float(value)]
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get_histogram(dicts, labels[value_num - 2])
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value_num += 1
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f.close()
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def get_histogram(data, label=None):
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# 准备数据
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categories = list(data.keys())
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values1 = [data[cat][0] for cat in categories] # 第一个值
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values2 = [data[cat][1] for cat in categories] # 第二个值
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# 设置柱状图的位置
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x = np.arange(len(categories)) # 标签位置
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width = 0.35 # 柱状图的宽度
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# 创建图形和轴
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fig, ax = plt.subplots(figsize=(10, 6))
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# 绘制柱状图
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bars1 = ax.bar(x - width / 2, values1, width, label='standard', color='red', alpha=0.7)
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bars2 = ax.bar(x + width / 2, values2, width, label='standard_slim', color='green', alpha=0.7)
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# 在每个柱状图上显示数值
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for bar in bars1:
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height = bar.get_height()
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ax.annotate(f'{height:.3f}',
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xy=(bar.get_x() + bar.get_width() / 2, height),
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xytext=(0, 3), # 3点垂直偏移
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textcoords="offset points",
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ha='center', va='bottom',
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fontsize=12)
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for bar in bars2:
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height = bar.get_height()
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ax.annotate(f'{height:.3f}',
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xy=(bar.get_x() + bar.get_width() / 2, height),
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xytext=(0, 3), # 3点垂直偏移
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textcoords="offset points",
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ha='center', va='bottom',
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fontsize=12)
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# 添加标签和标题
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if label is None:
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label = ''
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ax.set_xlabel('barcode')
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ax.set_ylabel('Values')
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ax.set_title(label)
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ax.set_xticks(x)
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ax.set_xticklabels(categories)
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ax.legend()
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# 添加网格
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ax.grid(True, alpha=0.3)
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# 调整布局并显示
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plt.tight_layout()
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plt.show()
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if __name__ == '__main__':
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# picTopic_matrix = picDirSimilarAnalysis()
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# picTopic_matrix.get_group_similarity_matrix('/home/lc/data_center/image_analysis/pic_pic_similar_maxtrix')
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read_result_txt()
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