智能秤分析

tools/dataset.py

@@ -24,7 +24,7 @@ def get_transform(cfg):
         T.Normalize(mean=[cfg['transform']['img_mean']], std=[cfg['transform']['img_std']]),
     ])
     test_transform = T.Compose([
-        # T.Lambda(pad_to_square),  # 补边
+        T.Lambda(pad_to_square),  # 补边
         T.ToTensor(),
         T.Resize((cfg['transform']['img_size'], cfg['transform']['img_size']), antialias=True),
         T.ConvertImageDtype(torch.float32),

tools/event_similar_analysis.py

@@ -0,0 +1,144 @@
+from similar_analysis import SimilarAnalysis
+import os
+import pickle
+from tools.image_joint import merge_imgs
+
+
+class EventSimilarAnalysis(SimilarAnalysis):
+    def __init__(self):
+        super(EventSimilarAnalysis, self).__init__()
+        self.fn_one2one_event, self.fp_one2one_event = self.One2one_similar_analysis()
+        self.fn_one2sn_event, self.fp_one2sn_event = self.One2Sn_similar_analysis()
+        if os.path.exists(self.conf['event']['pickle_path']):
+            print('pickle file exists')
+        else:
+            self.target_image = self.get_path()
+
+    def get_path(self):
+        events = [self.fn_one2one_event, self.fp_one2one_event,
+                  self.fn_one2sn_event, self.fp_one2sn_event]
+        event_image_path = []
+        barcode_image_path = []
+        for event in events:
+            for event_name, bcd in event:
+                event_sub_image = os.sep.join([self.conf['event']['event_save_dir'],
+                                               event_name,
+                                               'subimgs'])
+                barcode_images = os.sep.join([self.conf['event']['stdlib_image_path'],
+                                              bcd])
+                for image_name in os.listdir(event_sub_image):
+                    event_image_path.append(os.sep.join([event_sub_image, image_name]))
+                for barcode in os.listdir(barcode_images):
+                    barcode_image_path.append(os.sep.join([barcode_images, barcode]))
+        return list(set(event_image_path + barcode_image_path))
+
+
+    def write_dict_to_pickle(self, data):
+        """将字典写入pickle文件."""
+        with open(self.conf['event']['pickle_path'], 'wb') as file:
+            pickle.dump(data, file)
+
+    def get_dict_to_pickle(self):
+        with open(self.conf['event']['pickle_path'], 'rb') as f:
+            data = pickle.load(f)
+        return data
+
+    def create_total_feature(self):
+        feature_dicts = self.get_feature_map(self.target_image)
+        self.write_dict_to_pickle(feature_dicts)
+        print(feature_dicts)
+
+    def One2one_similar_analysis(self):
+        fn_event, fp_event = [], []
+        with open(self.conf['event']['oneToOneTxt'], 'r') as f:
+            lines = f.readlines()
+        for line in lines:
+            print(line.strip().split(' '))
+            event_infor = line.strip().split(' ')
+            label = event_infor[0]
+            event_name = event_infor[1]
+            bcd = event_infor[2]
+            simi1 = event_infor[3]
+            simi2 = event_infor[4]
+            if label == 'same' and float(simi2) < self.conf['event']['oneToOne_max_th']:
+                print(event_name, bcd, simi1)
+                fn_event.append((event_name, bcd))
+            elif label == 'diff' and float(simi2) > self.conf['event']['oneToSn_min_th']:
+                fp_event.append((event_name, bcd))
+        return fn_event, fp_event
+
+    def One2Sn_similar_analysis(self):
+        fn_event, fp_event = [], []
+        with open(self.conf['event']['oneToOneTxt'], 'r') as f:
+            lines = f.readlines()
+        for line in lines:
+            print(line.strip().split(' '))
+            event_infor = line.strip().split(' ')
+            label = event_infor[0]
+            event_name = event_infor[1]
+            bcd = event_infor[2]
+            simi = event_infor[3]
+            if label == 'fn':
+                print(event_name, bcd, simi)
+                fn_event.append((event_name, bcd))
+            elif label == 'fp':
+                fp_event.append((event_name, bcd))
+        return fn_event, fp_event
+
+    def save_joint_image(self, img_pth1, img_pth2, feature_dicts, record):
+        feature_dict1 = feature_dicts[img_pth1]
+        feature_dict2 = feature_dicts[img_pth2]
+        similarity = self.get_similarity(feature_dict1.cpu().numpy(),
+                                         feature_dict2.cpu().numpy())
+        dir_name = img_pth1.split('/')[-3]
+        save_path = os.sep.join([self.conf['data']['image_joint_pth'], dir_name, record])
+        if "fp" in record:
+            if similarity > 0.8:
+                merge_imgs(img_pth1,
+                           img_pth2,
+                           self.conf,
+                           similarity,
+                           label=None,
+                           cam=self.cam,
+                           save_path=save_path)
+        else:
+            if similarity < 0.8:
+                merge_imgs(img_pth1,
+                           img_pth2,
+                           self.conf,
+                           similarity,
+                           label=None,
+                           cam=self.cam,
+                           save_path=save_path)
+        print(similarity)
+
+    def get_contrast(self, feature_dicts):
+        events_compare = [self.fp_one2one_event, self.fn_one2one_event, self.fp_one2sn_event, self.fn_one2sn_event]
+        event_record = ['fp_one2one', 'fn_one2one', 'fp_one2sn', 'fn_one2sn']
+        for event_compare, record in zip(events_compare, event_record):
+            for img, img_std in event_compare:
+                imgs_pth1 = os.sep.join([self.conf['event']['event_save_dir'],
+                                         img,
+                                         'subimgs'])
+                imgs_pth2 = os.sep.join([self.conf['event']['stdlib_image_path'],
+                                         img_std])
+                for img1 in os.listdir(imgs_pth1):
+                    for img2 in os.listdir(imgs_pth2):
+                        img_pth1 = os.sep.join([imgs_pth1, img1])
+                        img_pth2 = os.sep.join([imgs_pth2, img2])
+                        try:
+                            self.save_joint_image(img_pth1, img_pth2, feature_dicts, record)
+                        except Exception as e:
+                            continue
+                            print(e)
+
+
+if __name__ == '__main__':
+    event_similar_analysis = EventSimilarAnalysis()
+    if os.path.exists(event_similar_analysis.conf['event']['pickle_path']):
+        print('pickle file exists')
+    else:
+        event_similar_analysis.create_total_feature()  # 生成pickle文件, 生成时间较长，生成一个文件即可
+    feature_dicts = event_similar_analysis.get_dict_to_pickle()
+    # all_compare_img = event_similar_analysis.get_image_map()
+    event_similar_analysis.get_contrast(feature_dicts)  # 获取比对结果

tools/getpairs.py

@@ -9,8 +9,23 @@ import logging
 class PairGenerator:
     """Generate positive and negative image pairs for contrastive learning."""
 
-    def __init__(self):
+    def __init__(self, original_path):
         self._setup_logging()
+        self.original_path = original_path
+        self._delete_space()
+
+    def _delete_space(self):  # 删除图片文件名中的空格
+        print(self.original_path)
+        for root, dirs, files in os.walk(self.original_path):
+            for file_name in files:
+                if file_name.endswith('.jpg' or '.png'):
+                    n_file_name = file_name.replace(' ', '')
+                    os.rename(os.path.join(root, file_name), os.path.join(root, n_file_name))
+                if 'rotate' in file_name:
+                    os.remove(os.path.join(root, file_name))
+            for dir_name in dirs:
+                n_dir_name = dir_name.replace(' ', '')
+                os.rename(os.path.join(root, dir_name), os.path.join(root, n_dir_name))
 
     def _setup_logging(self):
         """Configure logging settings."""
@@ -188,11 +203,11 @@ class PairGenerator:
 
 
 if __name__ == "__main__":
-    original_path = '/home/lc/data_center/scatter/v4/val'
+    original_path = '/home/lc/data_center/contrast_data/v1/extra'
     parent_dir = str(Path(original_path).parent)
-    generator = PairGenerator()
+    generator = PairGenerator(original_path)
 
     # Example usage:
     pairs = generator.get_pairs(original_path,
-                                output_txt=os.sep.join([parent_dir, 'cross_same.txt']))  # Individual pairs
+                                output_txt=os.sep.join([parent_dir, 'extra_cross_same.txt']))  # Individual pairs
     # groups = generator.get_group_pairs('val')  # Group pairs

tools/image_joint.py

@@ -18,31 +18,49 @@ def merge_imgs(img1_path, img2_path, conf, similar=None, label=None, cam=None, s
         img2 = Image.open(img2_path)
         img1 = img1.resize((224, 224))
         img2 = img2.resize((224, 224))
+        new_img = Image.new('RGB', (img1.width + img2.width + 10, img1.height))
         # save_path = conf['data']['image_joint_pth']
     else:
         assert cam is not None, 'cam is None'
         img1 = cam.get_hot_map(img1_path)
         img2 = cam.get_hot_map(img2_path)
+        img1_ori = Image.open(img1_path)
+        img2_ori = Image.open(img2_path)
+        img1_ori = img1_ori.resize((224, 224))
+        img2_ori = img2_ori.resize((224, 224))
+        new_img = Image.new('RGB',
+                            (img1.width + img2.width + 10,
+                             img1.height + img2.width + 10))
         # save_path = conf['heatmap']['image_joint_pth']
     # print('img1_path', img1)
     # print('img2_path', img2)
-    if not os.path.exists(os.sep.join([save_path, str(label)])):
+    if not os.path.exists(os.sep.join([save_path, str(label)])) and (label is not None):
         os.makedirs(os.sep.join([save_path, str(label)]))
-    if save_path is None:
         save_path = os.sep.join([save_path, str(label)])
-    img_name = os.path.basename(img1_path).split('.')[0] + '_' + os.path.basename(img2_path).split('.')[0] + '.png'
+    if save_path is None:
+        # save_path = os.sep.join([save_path, str(label)])
+        pass
+    # img_name = os.path.basename(img1_path).split('.')[0] + '_' + os.path.basename(img2_path).split('.')[0] + '.png'
+    img_name = os.path.basename(img1_path).split('.')[0][:30] + '_' + os.path.basename(img2_path).split('.')[0][
+                                                                      :30] + '.png'
     assert img1.height == img2.height
 
-    new_img = Image.new('RGB', (img1.width + img2.width + 10, img1.height))
+
 
     # print('new_img', new_img)
-    new_img.paste(img1, (0, 0))
-    new_img.paste(img2, (img1.width + 10, 0))
+    if not conf['heatmap']['show_heatmap']:
+        new_img.paste(img1, (0, 0))
+        new_img.paste(img2, (img1.width + 10, 0))
+    else:
+        new_img.paste(img1_ori, (10, 10))
+        new_img.paste(img2_ori, (img2_ori.width + 20, 10))
+        new_img.paste(img1, (10, img1.height+20))
+        new_img.paste(img2, (img2.width+20, img2.height+20))
 
     if similar is not None:
-        if label == '1' and similar > 0.5:
+        if label == '1' and (similar > 0.5 or similar < 0.25):
             save = False
-        elif label == '0' and similar < 0.5:
+        elif label == '0' and similar > 0.25:
             save = False
         similar = str(similar) + '_' + str(label)
         draw = ImageDraw.Draw(new_img)

tools/model_rknn_transform.py

@@ -122,7 +122,7 @@ if __name__ == '__main__':
 
     # Build model
     print('--> Building model')
-    ret = rknn.build(do_quantization=True,
+    ret = rknn.build(do_quantization=False,  # True
                      dataset='./dataset.txt',
                      rknn_batch_size=conf['models']['rknn_batch_size'])
     # ret = rknn.build(do_quantization=False, dataset='./dataset.txt')

tools/picdir_to_picdir_similar.py

@@ -0,0 +1,242 @@
+from similar_analysis import SimilarAnalysis
+import os
+import pickle
+from tools.image_joint import merge_imgs
+import yaml
+from PIL import Image
+import torch
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+import matplotlib.pyplot as plt
+
+'''
+轨迹图与标准库之间的相似度分析
+1.用于生成轨迹图与标准库中所有图片的相似度
+2.用于分析轨迹图与标准库比对选取策略的判断
+'''
+
+
+class picDirSimilarAnalysis(SimilarAnalysis):
+    def __init__(self):
+        super(picDirSimilarAnalysis, self).__init__()
+        with open('../configs/pic_pic_similar.yml', 'r') as f:
+            self.conf = yaml.load(f, Loader=yaml.FullLoader)
+        if not os.path.exists(self.conf['data']['total_pkl']):
+            # self.create_total_feature()
+            self.create_total_pkl()
+        if os.path.exists(self.conf['data']['total_pkl']):
+            self.all_dicts = self.load_dict_from_pkl()
+
+    def is_image_file(self, filename):
+        """
+        检查文件是否为图像文件
+        """
+        image_extensions = ('.jpg', '.jpeg', '.png', '.bmp', '.gif', '.tiff')
+        return filename.lower().endswith(image_extensions)
+
+    def create_total_pkl(self):  # 将目录下所有的图片特征存入pkl文件
+        all_images_feature_dict = {}
+        for roots, dirs, files in os.walk(self.conf['data']['data_dir']):
+            for file_name in files:
+                if self.is_image_file(file_name):
+                    try:
+                        print(f"处理图像 {os.sep.join([roots, file_name])}")
+                        feature = self.extract_features(os.sep.join([roots, file_name]))
+                    except Exception as e:
+                        print(f"处理图像 {os.sep.join([roots, file_name])} 时出错: {e}")
+                        feature = None
+                    all_images_feature_dict[os.sep.join([roots, file_name])] = feature
+        if not os.path.exists(self.conf['data']['total_pkl']):
+            with open(self.conf['data']['total_pkl'], 'wb') as f:
+                pickle.dump(all_images_feature_dict, f)
+
+    def load_dict_from_pkl(self):
+        with open(self.conf['data']['total_pkl'], 'rb') as f:
+            data = pickle.load(f)
+        print(f"字典已从 {self.conf['data']['total_pkl']} 加载")
+        return data
+
+    def get_image_files(self, folder_path):
+        """
+        获取文件夹中的所有图像文件
+        """
+        image_files = []
+        for root, _, files in os.walk(folder_path):
+            for file in files:
+                if self.is_image_file(file):
+                    image_files.append(os.path.join(root, file))
+        return image_files
+
+    def extract_features(self, image_path):
+        feature_dict = self.get_feature(image_path)
+        return feature_dict[image_path]
+
+    def create_one_similarity_matrix(self, folder1_path, folder2_path):
+        images1 = self.get_image_files(folder1_path)
+        images2 = self.get_image_files(folder2_path)
+
+        print(f"文件夹1 ({folder1_path}) 包含 {len(images1)} 张图像")
+        print(f"文件夹2 ({folder2_path}) 包含 {len(images2)} 张图像")
+
+        if len(images1) == 0 or len(images2) == 0:
+            raise ValueError("至少有一个文件夹中没有图像文件")
+
+        # 提取文件夹1中的所有图像特征
+        features1 = []
+        print("正在提取文件夹1中的图像特征...")
+        for i, img_path in enumerate(images1):
+            try:
+                # feature = self.extract_features(img_path)
+                feature = self.all_dicts[img_path]
+                features1.append(feature.cpu().numpy())
+                # if (i + 1) % 10 == 0:
+                #     print(f"已处理 {i + 1}/{len(images1)} 张图像")
+            except Exception as e:
+                print(f"处理图像 {img_path} 时出错: {e}")
+                features1.append(None)
+
+        # 提取文件夹2中的所有图像特征
+        features2 = []
+        print("正在提取文件夹2中的图像特征...")
+        for i, img_path in enumerate(images2):
+            try:
+                # feature = self.extract_features(img_path)
+                feature = self.all_dicts[img_path]
+                features2.append(feature.cpu().numpy())
+                # if (i + 1) % 10 == 0:
+                #     print(f"已处理 {i + 1}/{len(images2)} 张图像")
+            except Exception as e:
+                print(f"处理图像 {img_path} 时出错: {e}")
+                features2.append(None)
+
+        # 移除处理失败的图像
+        valid_features1 = []
+        valid_images1 = []
+        for i, feature in enumerate(features1):
+            if feature is not None:
+                valid_features1.append(feature)
+                valid_images1.append(images1[i])
+
+        valid_features2 = []
+        valid_images2 = []
+        for i, feature in enumerate(features2):
+            if feature is not None:
+                valid_features2.append(feature)
+                valid_images2.append(images2[i])
+
+        # print(f"文件夹1中成功处理 {len(valid_features1)} 张图像")
+        # print(f"文件夹2中成功处理 {len(valid_features2)} 张图像")
+
+        if len(valid_features1) == 0 or len(valid_features2) == 0:
+            raise ValueError("没有成功处理任何图像")
+
+        # 计算相似度矩阵
+        print("正在计算相似度矩阵...")
+        similarity_matrix = cosine_similarity(valid_features1, valid_features2)
+
+        return similarity_matrix, valid_images1, valid_images2
+
+    def get_group_similarity_matrix(self, folder_path):
+        tracking_folder = os.sep.join([folder_path, 'tracking'])
+        standard_folder = os.sep.join([folder_path, 'standard_slim'])
+        for dir_name in os.listdir(tracking_folder):
+            tracking_dir = os.sep.join([tracking_folder, dir_name])
+            standard_dir = os.sep.join([standard_folder, dir_name])
+            similarity_matrix, valid_images1, valid_images2 = self.create_one_similarity_matrix(tracking_dir,
+                                                                                                standard_dir)
+            mean_similarity = np.mean(similarity_matrix)
+            std_similarity = np.std(similarity_matrix)
+            max_similarity = np.max(similarity_matrix)
+            min_similarity = np.min(similarity_matrix)
+            print(f"文件夹 {dir_name} 的相似度矩阵已计算完成 "
+                  f"均值：{mean_similarity}  标准差：{std_similarity}  最大值：{max_similarity} 最小值：{min_similarity}")
+            result = f"{os.path.basename(standard_folder)} {dir_name} {mean_similarity:.3f} {std_similarity:.3f} {max_similarity:.3f} {min_similarity:.3f}"
+            with open(self.conf['data']['result_txt'], 'a') as f:
+                f.write(result + '\n')
+
+
+def read_result_txt():
+    parts = []
+    value_num = 2
+    with open('../configs/pic_pic_similar.yml', 'r') as f:
+        conf = yaml.load(f, Loader=yaml.FullLoader)
+        f.close()
+    with open(conf['data']['result_txt'], 'r') as f:
+        lines = f.readlines()
+    for line in lines:
+        line = line.strip()
+        if line:
+            parts.append(line.split(' '))
+    parts = np.array(parts)
+    print(parts)
+    labels = ['Mean', 'Std', 'Max', 'Min']
+    while value_num < 6:
+        dicts = {}
+        for barcode, value in zip(parts[:, 1], parts[:, value_num]):
+            if barcode in dicts:
+                dicts[barcode].append(float(value))
+            else:
+                dicts[barcode] = [float(value)]
+        get_histogram(dicts, labels[value_num - 2])
+        value_num += 1
+    f.close()
+
+
+def get_histogram(data, label=None):
+    # 准备数据
+    categories = list(data.keys())
+    values1 = [data[cat][0] for cat in categories]  # 第一个值
+    values2 = [data[cat][1] for cat in categories]  # 第二个值
+
+    # 设置柱状图的位置
+    x = np.arange(len(categories))  # 标签位置
+    width = 0.35  # 柱状图的宽度
+
+    # 创建图形和轴
+    fig, ax = plt.subplots(figsize=(10, 6))
+
+    # 绘制柱状图
+    bars1 = ax.bar(x - width / 2, values1, width, label='standard', color='red', alpha=0.7)
+    bars2 = ax.bar(x + width / 2, values2, width, label='standard_slim', color='green', alpha=0.7)
+
+    # 在每个柱状图上显示数值
+    for bar in bars1:
+        height = bar.get_height()
+        ax.annotate(f'{height:.3f}',
+                    xy=(bar.get_x() + bar.get_width() / 2, height),
+                    xytext=(0, 3),  # 3点垂直偏移
+                    textcoords="offset points",
+                    ha='center', va='bottom',
+                    fontsize=12)
+
+    for bar in bars2:
+        height = bar.get_height()
+        ax.annotate(f'{height:.3f}',
+                    xy=(bar.get_x() + bar.get_width() / 2, height),
+                    xytext=(0, 3),  # 3点垂直偏移
+                    textcoords="offset points",
+                    ha='center', va='bottom',
+                    fontsize=12)
+
+    # 添加标签和标题
+    if label is None:
+        label = ''
+    ax.set_xlabel('barcode')
+    ax.set_ylabel('Values')
+    ax.set_title(label)
+    ax.set_xticks(x)
+    ax.set_xticklabels(categories)
+    ax.legend()
+
+    # 添加网格
+    ax.grid(True, alpha=0.3)
+
+    # 调整布局并显示
+    plt.tight_layout()
+    plt.show()
+
+
+if __name__ == '__main__':
+    # picTopic_matrix = picDirSimilarAnalysis()
+    # picTopic_matrix.get_group_similarity_matrix('/home/lc/data_center/image_analysis/pic_pic_similar_maxtrix')
+    read_result_txt()

tools/similar_analysis.py

@@ -10,7 +10,7 @@ import yaml
 import os
 
 
-class analysis:
+class SimilarAnalysis:
     def __init__(self):
         with open('../configs/similar_analysis.yml', 'r') as f:
             self.conf = yaml.load(f, Loader=yaml.FullLoader)
@@ -53,6 +53,7 @@ class analysis:
     def get_feature_map(self, all_imgs):
         feature_dicts = {}
         for img_pth in all_imgs:
+            print(f"Processing {img_pth}")
             feature_dict = self.get_feature(img_pth)
             feature_dicts = dict(ChainMap(feature_dict, feature_dicts))
         return feature_dicts
@@ -85,7 +86,7 @@ class analysis:
             feature_dict2 = feature_dicts[img_pth2]
             similarity = self.get_similarity(feature_dict1.cpu().numpy(),
                                              feature_dict2.cpu().numpy())
-            dir_name = img_pth1.split(os.sep)[-3]
+            dir_name = img_pth1.split('/')[-3]
             save_path = os.sep.join([self.conf['data']['image_joint_pth'], dir_name])
             if similarity > 0.7:
                 merge_imgs(img_pth1,
@@ -99,7 +100,7 @@ class analysis:
 
 
 if __name__ == '__main__':
-    ana = analysis()
+    ana = SimilarAnalysis()
     all_imgs = ana.create_total_feature()
     feature_dicts = ana.get_feature_map(all_imgs)
     all_compare_img = ana.get_image_map()