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ytracking/utils/plots.py create mode 100644 ytracking/utils/segment/__init__.py create mode 100644 ytracking/utils/segment/augmentations.py create mode 100644 ytracking/utils/segment/dataloaders.py create mode 100644 ytracking/utils/segment/general.py create mode 100644 ytracking/utils/segment/loss.py create mode 100644 ytracking/utils/segment/metrics.py create mode 100644 ytracking/utils/segment/plots.py create mode 100644 ytracking/utils/torch_utils.py create mode 100644 ytracking/utils/triton.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..af5a89f --- /dev/null +++ b/.gitignore @@ -0,0 +1,12 @@ +OneByOneSimilarity.py +Return_purchase_test_analysis.py +compairsonOneByOne/ +comparative/ +comparisonData/ +comparisonResult/ +data_test/ +imageQualityData/ +search_library/ +Single_purchase_data/ +*.pth +*.pt \ No newline at end of file diff --git a/.idea/.gitignore b/.idea/.gitignore new file mode 100644 index 0000000..35410ca --- /dev/null +++ b/.idea/.gitignore @@ -0,0 +1,8 @@ +# 默认忽略的文件 +/shelf/ +/workspace.xml +# 基于编辑器的 HTTP 客户端请求 +/httpRequests/ +# Datasource local storage ignored files +/dataSources/ +/dataSources.local.xml diff --git a/.idea/deployment.xml b/.idea/deployment.xml new file mode 100644 index 0000000..bdccf07 --- /dev/null +++ b/.idea/deployment.xml @@ -0,0 +1,15 @@ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..105ce2d --- /dev/null +++ b/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/.idea/kmeans.iml b/.idea/kmeans.iml new file mode 100644 index 0000000..5a5b8f5 --- /dev/null +++ b/.idea/kmeans.iml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100644 index 0000000..9b80e10 --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,7 @@ + + + + + + \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml new file mode 100644 index 0000000..b6f9888 --- /dev/null +++ b/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml new file mode 100644 index 0000000..94a25f7 --- /dev/null +++ b/.idea/vcs.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/ComparisonAnalysis.py b/ComparisonAnalysis.py new file mode 100644 index 0000000..954ca5e --- /dev/null +++ b/ComparisonAnalysis.py @@ -0,0 +1,169 @@ +import os.path +import shutil + +import numpy as np +import matplotlib.pyplot as plt + + +def showHist(err, correct, date): + err = np.array(err) + correct = np.array(correct) + + fig, axs = plt.subplots(2, 1) + axs[0].hist(err, bins=50, edgecolor='black') + axs[0].set_xlim([0, 1]) + axs[0].set_title('err') + axs[1].hist(correct, bins=50, edgecolor='black') + axs[1].set_xlim([0, 1]) + axs[1].set_title('correct') + plt.title(date) + plt.savefig('comparisonResult/Hist_' + date + '.png') + plt.show() + + +def showgrid(recall, prec, date): + x = np.linspace(start=-0, stop=1, num=11, endpoint=True).tolist() + plt.figure(figsize=(10, 6)) + plt.plot(x, recall, color='red', label='recall') + plt.plot(x, prec, color='blue', label='PrecisePos') + plt.legend() + plt.xlabel('threshold') + plt.title(date) + # plt.ylabel('Similarity') + plt.grid(True, linestyle='--', alpha=0.5) + # plt.savefig('accuracy_recall_grid.png') + plt.savefig('comparisonResult/grid_' + date + '.png') + plt.show() + plt.close() + + +def read_txt_file(filePth): + with open(filePth, 'r', encoding='utf-8') as f: + lines = f.readlines() + dict = {} + all_list = [] + barcode_list = [] + similarity_list = [] + split_flag = False + clean_lines = [line.strip().replace("'", '').replace('"', '') for line in lines] + for line in clean_lines: + stripped_line = line.strip() + if not stripped_line: + split_flag = False + all_list.append(dict) + dict = {} + barcode_list, similarity_list = [], [] + continue + + label = line.split(':')[0] + value = line.split(':')[1] + if label == 'SeqDir': + dict['SeqDir'] = value + if label == 'Deleted': + dict['Deleted'] = value + if label == 'List': + split_flag = True + continue + if split_flag: + dict['barcode'] = barcode_list + dict['similarity'] = similarity_list + barcode_list.append(label) + similarity_list.append(value) + all_list.append(dict) + all_list = [d for d in all_list if d] + return all_list + + +def move_file(seqdirs, filePth): + comparisonData = os.path.basename(filePth).split('.')[0] + comparisonData = os.sep.join(['D:\Project\ieemoo\image_quality_assessment\comparisonData', comparisonData]) + print('comparisonData', comparisonData) + for seqdir in seqdirs: + print(seqdir[0], seqdir[1]) + err_pair = os.sep.join([comparisonData, 'err_pair', seqdir[1]]) + if not os.path.exists(err_pair): + os.makedirs(err_pair) + for comparison in os.listdir(comparisonData): + print(os.sep.join([comparisonData, comparison])) + print(os.sep.join([err_pair, comparison])) + try: + if seqdir[0] in comparison: + shutil.copytree(os.sep.join([comparisonData, comparison]), + os.sep.join([err_pair, comparison])) + if seqdir[1] in comparison: + shutil.copytree(os.sep.join([comparisonData, comparison]), + os.sep.join([err_pair, comparison])) + except Exception as e: + continue + + +def compute_recall_precision(err_similarity, correct_similarity, date=None): + # ths = np.linspace(0, 1, 11) + ths = np.linspace(start=-0, stop=1, num=11, endpoint=True).tolist() + recall, prec = [], [] + for th in ths: + TP = len([num for num in correct_similarity if num >= th]) + FP = len([num for num in err_similarity if num >= th]) + if (TP + FP) == 0: + prec.append(1) + recall.append(0) + else: + prec.append(TP / (TP + FP)) + recall.append(TP / (len(err_similarity) + len(correct_similarity))) + # print(recall) + # print(prec) + showgrid(recall, prec, date) + return recall, prec + + +def deal_one_file(filePth): + date = filePth.split('\\')[-1].split('.')[0] + all_list = read_txt_file(filePth) + num = 0 + err_barcode_list, correct_barcode_list, err_similarity, correct_similarity, seqdirs = [], [], [], [], [] + for s_list in all_list: + seqdir = s_list['SeqDir'].strip() + delete = s_list['Deleted'].strip() + barcodes = [s.strip() for s in s_list['barcode']] + similarity = [float(s.strip()) for s in s_list['similarity']] + if delete in barcodes[:1]: + num += 1 + correct_barcode_list.append(delete) + correct_similarity.append(similarity[0]) + else: + seqdirs.append((seqdir, delete)) + err_barcode_list.append(delete) + err_similarity.append(similarity[0]) + + compute_recall_precision(err_similarity, correct_similarity, date) + showHist(err_similarity, correct_similarity, date) + move_file(seqdirs, filePth) # 统计错误对 + return err_similarity, correct_similarity + + +def main(filesPth): + err_similarities, correct_similarities = [], [] + dir_lists = os.listdir(filesPth) + # dir_lists = ['deletedBarcode_10_0716_pm_3.txt'] + for name in dir_lists: + filePth = os.sep.join([filesPth, name]) + err_similarity, correct_similarity = deal_one_file(filePth) + err_similarities += err_similarity + correct_similarities += correct_similarity + compute_recall_precision(err_similarities, correct_similarities, date='zong') + showHist(err_similarities, correct_similarities, date='zong') + print('END') + + +if __name__ == '__main__': + # filePth = 'D:\Project\ieemoo\image_quality_assessment\comparisonResult\deletedBarcode_10_0722_pm_01.txt' + # read_txt_file(filePth) + + filesPth = 'comparisonResult' + main(filesPth) + + # for name in os.listdir('comparisonData\deletedBarcode_15_0628_pm\err_pair'): + # path = os.sep.join(['comparisonData\deletedBarcode_15_0628_pm\err_pair', name]) + # print(len(os.listdir(path))) + # if len(os.listdir(path)) < 2: + # print(path) diff --git a/__pycache__/ComparisonAnalysis.cpython-38.pyc b/__pycache__/ComparisonAnalysis.cpython-38.pyc new 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b/contrast/.idea/contrast_nettest.iml @@ -0,0 +1,12 @@ + + + + + + + + + + \ No newline at end of file diff --git a/contrast/.idea/inspectionProfiles/Project_Default.xml b/contrast/.idea/inspectionProfiles/Project_Default.xml new file mode 100644 index 0000000..0d5fe0d --- /dev/null +++ b/contrast/.idea/inspectionProfiles/Project_Default.xml @@ -0,0 +1,19 @@ + + + + \ No newline at end of file diff --git a/contrast/.idea/inspectionProfiles/profiles_settings.xml b/contrast/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..105ce2d --- /dev/null +++ b/contrast/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/contrast/.idea/misc.xml b/contrast/.idea/misc.xml new file mode 100644 index 0000000..dc9ea49 --- /dev/null +++ b/contrast/.idea/misc.xml @@ -0,0 +1,4 @@ + + + + \ No newline at end of file diff --git a/contrast/.idea/modules.xml b/contrast/.idea/modules.xml new file mode 100644 index 0000000..cda59cb --- /dev/null +++ b/contrast/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/contrast/.idea/vcs.xml b/contrast/.idea/vcs.xml new file mode 100644 index 0000000..6c0b863 --- /dev/null +++ b/contrast/.idea/vcs.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/contrast/README.md b/contrast/README.md new file mode 100644 index 0000000..4dcbad2 --- /dev/null +++ b/contrast/README.md @@ -0,0 +1,60 @@ +# Build Your Own Face Recognition Model + +训练你自己的人脸识别模型！ + +人脸识别从原始的 Softmax Embbedding，经过2015年 Facenet 领衔的 triple loss metric learning，然后是 additional margin metric learning。这次的系列博客实现的是2018年提出的 ArcFace 。 + + +### 依赖 +```py +Python >= 3.6 +pytorch >= 1.0 +torchvision +imutils +pillow == 6.2.0 +tqdm +``` + +### 数据准备 + ++ 下载WebFace（百度一下）以及干净的图片列表（[BaiduYun](http://pan.baidu.com/s/1hrKpbm8)）用于训练 ++ 下载LFW（[BaiduYun](https://pan.baidu.com/s/12IKEpvM8-tYgSaUiz_adGA) 提取码 u7z4）以及[测试列表](https://github.com/ronghuaiyang/arcface-pytorch/blob/master/lfw_test_pair.txt)用于测试 ++ 删除WebFace中的脏数据，使用`utils.py` + +### 配置参数 + +见`config.py` + +### 训练 + +天然支持单机多GPU训练 + +```py +export CUDA_VISIBLE_DEVICES=0,1 +python train.py +``` + +### 测试 + +```py +python test.py +``` + +### 博客 + +虽然有关人脸识别的介绍已经很多了，但受到许多 [Build-Your-Own-x](https://github.com/danistefanovic/build-your-own-x) 文章的启发，就想写一个 Build Your Own Face Model 的博客，愿于他人有益。 + ++ 001 [数据准备](./blog/data.md) ++ 002 [模型架构](./blog/model.md) ++ 003 [损失函数](./blog/loss.md) ++ 004 [度量函数](./blog/metric.md) ++ 005 [训练](./blog/train.md) ++ 006 [测试](./blog/test.md) + +### 致谢 + +虽然并未注明，但本项目中有一些代码直接复制或者修改自以下仓库，许可证与之相同： + ++ [insightFace](https://github.com/deepinsight/insightface/tree/master/recognition) ++ [insightFace_Pytorch](https://github.com/TreB1eN/InsightFace_Pytorch) ++ [arcface-pytorch](https://github.com/ronghuaiyang/arcface-pytorch) diff --git a/contrast/__pycache__/img_data.cpython-310.pyc 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torch.cuda.is_available() else 'cpu') + +config = Config() diff --git a/contrast/dataset.py b/contrast/dataset.py new file mode 100644 index 0000000..538c4bf --- /dev/null +++ b/contrast/dataset.py @@ -0,0 +1,21 @@ +from torch.utils.data import DataLoader +from torchvision.datasets import ImageFolder + +from config import config as conf + + +def load_data(conf, training=True): + if training: + dataroot = conf.train_root + transform = conf.train_transform + batch_size = conf.train_batch_size + else: + dataroot = conf.test_root + transform = conf.test_transform + batch_size = conf.test_batch_size + + data = ImageFolder(dataroot, transform=transform) + class_num = len(data.classes) + loader = DataLoader(data, batch_size=batch_size, shuffle=True, + pin_memory=conf.pin_memory, num_workers=conf.num_workers, drop_last=True) + return loader, class_num \ No newline at end of file diff --git a/contrast/img_data.py b/contrast/img_data.py new file mode 100644 index 0000000..28645f8 --- /dev/null +++ b/contrast/img_data.py @@ -0,0 +1,129 @@ + + +### Topn Top10 +# queueImgs = { +# # '4260290263776_01-11':'test_img/img_queue/4260290263776_11_01.jpg', +# # '4260290263776_02-10':'test_img/img_queue/4260290263776_10_02.jpg', +# # '3500610085338_01-93':'test_img/img_queue/3500610085338_93_01.jpg', +# '3500610085338_01-90':'test_img/img_queue/3500610085338_90_01.jpg', +# # '01-93':'test_img/img_queue/93_01.jpg', +# # '01-90':'test_img/img_queue/90_02.jpg', +# # '01-94':'test_img/img_queue/01-94.jpg', +# } +# queueImgs_add = { +# '3500610085338_01': ['contrast/test_img/img_queue/3500610085338_93_01.jpg', 'contrast/test_img/img_queue/3500610085338_92_01.jpg'], +# '4260290263776_01': ['contrast/test_img/img_queue/4260290263776_11_01.jpg'], +# 'barcode_flag': True, +# 'add_flag': True, +# } +## 输入格式 macID_barcode_trackID macID_trackID +queueImgs_add = { +# 'aaaa_084501446314_2': [ +# 'contrast/test_img/img_queue/output_image31.png', +# 'contrast/test_img/img_queue/output_image32.png', +# 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'test_imgs_0314\\70f754e2b269_6924743915817_1\\20240312-132731-6e617463-6e88-487c-88a7-23c108f23a17_null_70f754e2b269_0_132731777-f99296fe-220c-4f47-9f34-3e7cdbd8f6b0_3262.jpg', 'test_imgs_0314\\70f754e2b269_6924743915817_1\\20240312-132731-6e617463-6e88-487c-88a7-23c108f23a17_null_70f754e2b269_0_132731777-f99296fe-220c-4f47-9f34-3e7cdbd8f6b0_3274.jpg', 'test_imgs_0314\\70f754e2b269_6924743915817_1\\20240312-132731-6e617463-6e88-487c-88a7-23c108f23a17_null_70f754e2b269_0_132731777-f99296fe-220c-4f47-9f34-3e7cdbd8f6b0_3290.jpg', 'test_imgs_0314\\70f754e2b269_6924743915817_1\\20240312-133114-f7a5c66a-8245-438a-af59-9afb60dfc51a_null_70f754e2b269_0_133115092-90241b70-a85c-47d3-ad2e-ebe9f4b3f968_384.jpg'], +'70f754e2b269_6920459905012_1': ['contrast\\test_imgs_0322\\70f754e2b269_6920459905012_1\\01_20240312-113153-04f718d6-701f-4c31-8fd3-5a68a605ea04_6901285991219_70f754078bb4_0_113158231-45d01e14-b13d-4ef0-804e-844b189afda0_1115.jpg', 'contrast\\test_imgs_0322\\70f754e2b269_6920459905012_1\\01_20240312-134500-7b626d7b-a2c3-4208-ad63-f12c7324f1ed_6901285991219_70f754e2ab13_0_134512348-b8803fad-59e6-4407-8cb2-1c61e628f641_1118.jpg'], +'70f754e2b269_6920459905012_2': ['contrast\\test_imgs_0322\\70f754e2b269_6920459905012_1\\90_01.jpg'], + +# 'barcode_list': ['aaaa_084501446314','aaaa_4260290263776'], + 'barcode_flag': False, + 'add_flag': False, +} +queueImgs_back = { + 'aaaa_084501446314_2': [ + 'contrast/test_img/img_queue/output_image31.png', + 'contrast/test_img/img_queue/output_image32.png', + 'contrast/test_img/img_queue/output_image33.png', + ], + 'aaaa_084501446314e_1': [ + 'contrast/test_img/img_queue/output_image31.png', + 'contrast/test_img/img_queue/output_image32.png', ], + + 'barcode_list': ['aaaa_084501446314','aaaa_4260290263776'], + 'barcode_flag': False, + 'add_flag': False, +} + +temp_imgs = { + '3500610085338_01': ['test_img/img_queue/3500610085338_93_01.jpg', 'test_img/img_queue/3500610085338_92_01.jpg'], + '4260290263776_01': ['test_img/img_queue/4260290263776_11_01.jpg'], + '4066600601951_01':['test_img/img_queue/4066600601951_144_01.jpg'], +} + +library_imgs = { + '3500610085338':['test_img/img_queue/3500610085338_93_01.jpg', 'test_img/img_queue/3500610085338_92_01.jpg'], + '4066600601951':['test_img/img_queue/4066600601951_144_01.jpg'], + '4260290263776':['test_img/img_queue/4260290263776_11_01.jpg'], +} + +temp_imgs = { + '3500610085338_01':['test_img/img_queue/3500610085338_93_01.jpg','test_img/img_queue/3500610085338_92_01.jpg'], + '4066600601951_01':['test_img/img_queue/4066600601951_144_01.jpg'], + '4260290263776_01':['test_img/img_queue/4260290263776_11_01.jpg'], + } +# library_imgs = { +# '3500610085338_01-93':'test_img/img_queue/3500610085338_93_01.jpg', +# '3500610085338_01-92':'test_img/img_queue/3500610085338_92_01.jpg', +# '3500610085338_01-81':'test_img/img_queue/3500610085338_81_01.jpg', +# '3500610085338_01-24':'test_img/img_queue/3500610085338_24_01.jpg', +# '3500610085338_01-90':'test_img/img_queue/3500610085338_90_01.jpg', +# '4260290263776_01-11':'test_img/img_queue/4260290263776_11_01.jpg', +# } + + +# library_imgs = {'4260290263776_01-11':'test_img/img_queue/4260290263776_11_01.jpg', +# '4260290263776_02-10':'test_img/img_queue/4260290263776_10_02.jpg', +# '3500610085338_01-93':'test_img/img_queue/3500610085338_93_01.jpg', +# # '3500610085338_01-90':'test_img/img_queue/3500610085338_90_01.jpg', +# } +### 测试同一个Id不同的图片匹配结果输出Top10不同，最终取相似度最高的10个barcode 输出 +## Top10 +# queueImgs = { +# '01-93':'test_img/img_queue/93_01.jpg', +# '01-94':'test_img/img_queue/01-94.jpg', +# } +# +# library_imgs ={ +# '3500610085338_01-93':'test_img/img_queue/3500610085338_93_01.jpg', +# '3500610085338_01-90':'test_img/img_queue/3500610085338_90_01.jpg', +# '4260290263776_01-11':'test_img/img_queue/4260290263776_11_01.jpg', +# '4260290263776_02-10':'test_img/img_queue/4260290263776_10_02.jpg', +# } + +### Top1 + +# queueImgs = { +# '3500610085338_01-93':'test_img/img_queue/3500610085338_93_01.jpg', +# '3500610085338_01-90':'test_img/img_queue/3500610085338_90_01.jpg', +# } +# library_imgs = { +# # '850009021359_01-24':'test_img/library_imgs/850009021359_24_01.jpg', +# '3500610085338_01-93':'test_img/img_queue/3500610085338_93_01.jpg', +# '3500610085338_01-92':'test_img/img_queue/3500610085338_92_01.jpg', +# '3500610085338_01-81':'test_img/img_queue/3500610085338_81_01.jpg', +# '3500610085338_01-24':'test_img/img_queue/3500610085338_24_01.jpg', +# # '3500610085338_01-90':'test_img/img_queue/3500610085338_90_01.jpg', +# # '4066600601951_01-144':'test_img/library_imgs/4066600601951_144_01.jpg', +# # '4260290263776_01-11':'test_img/img_queue/4260290263776_11_01.jpg', +# } + + + + + +if __name__ == "__main__": + # 定义一个字典 + my_dict = {'a': 3, 'b': 1, 'c': 2} + my_dict['b'] = 4 + # 将字典转换为列表并按值进行排序 + sorted_list = sorted(my_dict.items(), key=lambda x: x[1]) + + print(my_dict) \ No newline at end of file diff --git a/contrast/logic.py b/contrast/logic.py new file mode 100644 index 0000000..75f116a --- /dev/null +++ b/contrast/logic.py @@ -0,0 +1,66 @@ +import sys +import torch + +from tools.config import cfg as conf +sys.path.append('contrast') +# from config import config as conf +# from model import resnet18, MobileNetV3_Large +from test_logic import similarity_interface +from img_data import queueImgs_add + +# import pymilvus + + +class datacollection: + barcode_flag = None + add_flag = None + queImgsDict = None + mainMilvus = None + tempLibList = None + model = None + barcode_list = None + actionModel = True # 是否是运行模式， False是测试模式 True是运行模式 + + +class similarityResult: + top10 = None + top1 = None + tempLibList = None + topn = None + + +class similarity: + def __init__(self): + pass + + def getSimilarity(self, model, dataCollection, similarityRes): + dataCollection.mainMilvus = model.milvusModel + dataCollection.model = model.similarityModel + # try: + if dataCollection.add_flag: + if dataCollection.barcode_flag: # 加购 有barcode -> 输出top10和top1 + similarityRes.top10, similarityRes.top1, similarityRes.tempLibList = similarity_interface( + dataCollection) + print(f"top10: {similarityRes.top10}\ntop1: {similarityRes.top1}") + else: # 加购 无barcode -> 输出top10 + similarityRes.top10, similarityRes.tempLibList = similarity_interface(dataCollection) + else: # 退购 -> 输出top10和topn + if dataCollection.barcode_flag: + similarityRes.top10, similarityRes.top1, similarityRes.topn = similarity_interface(dataCollection) + else: + similarityRes.top10, similarityRes.topn = similarity_interface(dataCollection) + return similarityRes + # except pymilvus.exceptions.SchemaNotReadyException as SchemaNotReadyException: ###当前特征库不存在 + # print('pymilvus.exceptions.SchemaNotReadyException', SchemaNotReadyException) + +def main(): + data_collection = datacollection() + similarityRes = similarityResult() + data_collection.barcode_flag = queueImgs_add['barcode_flag'] + data_collection.add_flag = queueImgs_add['add_flag'] + data_collection.queImgsDict = queueImgs_add + similarity().getSimilarity(data_collection, similarityRes) + + +if __name__ == '__main__': + main() diff --git a/contrast/main_barcodes.json b/contrast/main_barcodes.json new file mode 100644 index 0000000..7497a7a --- /dev/null +++ b/contrast/main_barcodes.json @@ -0,0 +1,1894 @@ +{ + "main_barcode": [ + "6924743915848", + "084501222314", + "084501446314", + "3701375402228", + "6921168500956", + "6944649700065", + "3760302180028", + "3760302180035", + "4008400203829", + "4025839057168", + "4025839057182", + "4210201121428", + "4255612500073", + "4260290260010", + "4260290260034", + "4260290260041", + "4528428195680", + "4528428198148", + "4528428198209", + "4550516475961", + "4600080298647", + "4710068971179", + "4710068972176", + "4710199037010", + "4710199080566", + "4710199085707", + "4710199087701", 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+from test_logic import create_milvus, img2feature +from config import config as conf +from img_data import library_imgs, temp_imgs + +def createMainMilvus(imgs_dict): ##imgs->{barcode1:[img1_1...img1_n], barcode2:[img2_1...img2_n]} + barcode_list, imgs_list = img2feature(imgs_dict) + mainMilvus = create_milvus('main_features', conf.host, conf.port, barcode_list, imgs_list) + return mainMilvus +def createTempMilvus(imgs_dict): ##imgs->{barcode1:[img1_1...img1_n], barcode2:[img2_1...img2_n]} + barcode_list, imgs_list = img2feature(imgs_dict) + tempMilvus = create_milvus('temp_features', conf.host, conf.port, barcode_list, imgs_list) + return tempMilvus + +if __name__ == "__main__": + createMainMilvus(library_imgs) + # createTempMilvus(temp_imgs) \ No newline at end of file diff --git a/contrast/model/__init__.py b/contrast/model/__init__.py new file mode 100644 index 0000000..367b7dc --- /dev/null +++ b/contrast/model/__init__.py @@ -0,0 +1,2 @@ +from .resnet_pre import resnet18 +from 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PyTorch. + +See the paper "Inverted Residuals and Linear Bottlenecks: +Mobile Networks for Classification, Detection and Segmentation" for more details. +''' +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +from tools.config import config as conf + + +class hswish(nn.Module): + def forward(self, x): + out = x * F.relu6(x + 3, inplace=True) / 6 + return out + + +class hsigmoid(nn.Module): + def forward(self, x): + out = F.relu6(x + 3, inplace=True) / 6 + return out + + +class SeModule(nn.Module): + def __init__(self, in_size, reduction=4): + super(SeModule, self).__init__() + self.se = nn.Sequential( + nn.AdaptiveAvgPool2d(1), + nn.Conv2d(in_size, in_size // reduction, kernel_size=1, stride=1, padding=0, bias=False), + nn.BatchNorm2d(in_size // reduction), + nn.ReLU(inplace=True), + nn.Conv2d(in_size // reduction, in_size, kernel_size=1, stride=1, padding=0, bias=False), + nn.BatchNorm2d(in_size), + hsigmoid() + ) + + def forward(self, x): + return x * self.se(x) + + +class Block(nn.Module): + '''expand + depthwise + pointwise''' + def __init__(self, kernel_size, in_size, expand_size, out_size, nolinear, semodule, stride): + super(Block, self).__init__() + self.stride = stride + self.se = semodule + + self.conv1 = nn.Conv2d(in_size, expand_size, kernel_size=1, stride=1, padding=0, bias=False) + self.bn1 = nn.BatchNorm2d(expand_size) + self.nolinear1 = nolinear + self.conv2 = nn.Conv2d(expand_size, expand_size, kernel_size=kernel_size, stride=stride, padding=kernel_size//2, groups=expand_size, bias=False) + self.bn2 = nn.BatchNorm2d(expand_size) + self.nolinear2 = nolinear + self.conv3 = nn.Conv2d(expand_size, out_size, kernel_size=1, stride=1, padding=0, bias=False) + self.bn3 = nn.BatchNorm2d(out_size) + + self.shortcut = nn.Sequential() + if stride == 1 and in_size != out_size: + self.shortcut = nn.Sequential( + nn.Conv2d(in_size, out_size, kernel_size=1, stride=1, padding=0, bias=False), + nn.BatchNorm2d(out_size), + ) + + def forward(self, x): + out = self.nolinear1(self.bn1(self.conv1(x))) + out = self.nolinear2(self.bn2(self.conv2(out))) + out = self.bn3(self.conv3(out)) + if self.se != None: + out = self.se(out) + out = out + self.shortcut(x) if self.stride==1 else out + return out + + +class MobileNetV3_Large(nn.Module): + def __init__(self, num_classes=conf.embedding_size): + super(MobileNetV3_Large, self).__init__() + self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=2, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(16) + self.hs1 = hswish() + + self.bneck = nn.Sequential( + Block(3, 16, 16, 16, nn.ReLU(inplace=True), None, 1), + Block(3, 16, 64, 24, nn.ReLU(inplace=True), None, 2), + Block(3, 24, 72, 24, nn.ReLU(inplace=True), None, 1), + Block(5, 24, 72, 40, nn.ReLU(inplace=True), SeModule(40), 2), + Block(5, 40, 120, 40, nn.ReLU(inplace=True), SeModule(40), 1), + Block(5, 40, 120, 40, nn.ReLU(inplace=True), SeModule(40), 1), + Block(3, 40, 240, 80, hswish(), None, 2), + Block(3, 80, 200, 80, hswish(), None, 1), + Block(3, 80, 184, 80, hswish(), None, 1), + Block(3, 80, 184, 80, hswish(), None, 1), + Block(3, 80, 480, 112, hswish(), SeModule(112), 1), + Block(3, 112, 672, 112, hswish(), SeModule(112), 1), + Block(5, 112, 672, 160, hswish(), SeModule(160), 1), + Block(5, 160, 672, 160, hswish(), SeModule(160), 2), + Block(5, 160, 960, 160, hswish(), SeModule(160), 1), + ) + + + self.conv2 = nn.Conv2d(160, 960, kernel_size=1, stride=1, padding=0, bias=False) + self.bn2 = nn.BatchNorm2d(960) + self.hs2 = hswish() + self.linear3 = nn.Linear(960, 1280) + self.bn3 = nn.BatchNorm1d(1280) + self.hs3 = hswish() + self.linear4 = nn.Linear(1280, num_classes) + self.init_params() + + def init_params(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + init.kaiming_normal_(m.weight, mode='fan_out') + if m.bias is not None: + init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm2d): + init.constant_(m.weight, 1) + init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + init.normal_(m.weight, std=0.001) + if m.bias is not None: + init.constant_(m.bias, 0) + + def forward(self, x): + out = self.hs1(self.bn1(self.conv1(x))) + out = self.bneck(out) + out = self.hs2(self.bn2(self.conv2(out))) + out = F.avg_pool2d(out, conf.img_size // 32) + out = out.view(out.size(0), -1) + out = self.hs3(self.bn3(self.linear3(out))) + out = self.linear4(out) + return out + + + +class MobileNetV3_Small(nn.Module): + def __init__(self, num_classes=conf.embedding_size): + super(MobileNetV3_Small, self).__init__() + self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=2, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(16) + self.hs1 = hswish() + + self.bneck = nn.Sequential( + Block(3, 16, 16, 16, nn.ReLU(inplace=True), SeModule(16), 2), + Block(3, 16, 72, 24, nn.ReLU(inplace=True), None, 2), + Block(3, 24, 88, 24, nn.ReLU(inplace=True), None, 1), + Block(5, 24, 96, 40, hswish(), SeModule(40), 2), + Block(5, 40, 240, 40, hswish(), SeModule(40), 1), + Block(5, 40, 240, 40, hswish(), SeModule(40), 1), + Block(5, 40, 120, 48, hswish(), SeModule(48), 1), + Block(5, 48, 144, 48, hswish(), SeModule(48), 1), + Block(5, 48, 288, 96, hswish(), SeModule(96), 2), + Block(5, 96, 576, 96, hswish(), SeModule(96), 1), + Block(5, 96, 576, 96, hswish(), SeModule(96), 1), + ) + + + self.conv2 = nn.Conv2d(96, 576, kernel_size=1, stride=1, padding=0, bias=False) + self.bn2 = nn.BatchNorm2d(576) + self.hs2 = hswish() + self.linear3 = nn.Linear(576, 1280) + self.bn3 = nn.BatchNorm1d(1280) + self.hs3 = hswish() + self.linear4 = nn.Linear(1280, num_classes) + self.init_params() + + def init_params(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + init.kaiming_normal_(m.weight, mode='fan_out') + if m.bias is not None: + init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm2d): + init.constant_(m.weight, 1) + init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + init.normal_(m.weight, std=0.001) + if m.bias is not None: + init.constant_(m.bias, 0) + + def forward(self, x): + out = self.hs1(self.bn1(self.conv1(x))) + out = self.bneck(out) + out = self.hs2(self.bn2(self.conv2(out))) + out = F.avg_pool2d(out, conf.img_size // 32) + out = out.view(out.size(0), -1) + + out = self.hs3(self.bn3(self.linear3(out))) + out = self.linear4(out) + return out + + + +def test(): + net = MobileNetV3_Small() + x = torch.randn(2,3,224,224) + y = net(x) + print(y.size()) + +# test() \ No newline at end of file diff --git a/contrast/model/resnet_pre.py b/contrast/model/resnet_pre.py new file mode 100644 index 0000000..83b3068 --- /dev/null +++ b/contrast/model/resnet_pre.py @@ -0,0 +1,462 @@ +import torch +import torch.nn as nn +from tools.config import config as conf + +try: + from torch.hub import load_state_dict_from_url +except ImportError: + from torch.utils.model_zoo import load_url as load_state_dict_from_url +# from .utils import load_state_dict_from_url + +__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', + 'resnet152', 'resnext50_32x4d', 'resnext101_32x8d', + 'wide_resnet50_2', 'wide_resnet101_2'] + +model_urls = { + 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', + 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', + 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', + 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', + 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', + 'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth', + 'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth', + 'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth', + 'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth', +} + + +def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=dilation, groups=groups, bias=False, dilation=dilation) + +def conv1x1(in_planes, out_planes, stride=1): + """1x1 convolution""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) + + +class SpatialAttention(nn.Module): + def __init__(self, kernel_size=7): + super(SpatialAttention, self).__init__() + + assert kernel_size in (3, 7), 'kernel size must be 3 or 7' + padding = 3 if kernel_size == 7 else 1 + + self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False) + self.sigmoid = nn.Sigmoid() + + def forward(self, x): + avg_out = torch.mean(x, dim=1, keepdim=True) + max_out, _ = torch.max(x, dim=1, keepdim=True) + x = torch.cat([avg_out, max_out], dim=1) + x = self.conv1(x) + return self.sigmoid(x) + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, + base_width=64, dilation=1, norm_layer=None, cam=False, bam=False): + super(BasicBlock, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + if groups != 1 or base_width != 64: + raise ValueError('BasicBlock only supports groups=1 and base_width=64') + if dilation > 1: + raise NotImplementedError("Dilation > 1 not supported in BasicBlock") + self.cam = cam + self.bam = bam + # Both self.conv1 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = norm_layer(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = norm_layer(planes) + self.downsample = downsample + self.stride = stride + if self.cam: + if planes == 64: + self.globalAvgPool = nn.AvgPool2d(56, stride=1) + elif planes == 128: + self.globalAvgPool = nn.AvgPool2d(28, stride=1) + elif planes == 256: + self.globalAvgPool = nn.AvgPool2d(14, stride=1) + elif planes == 512: + self.globalAvgPool = nn.AvgPool2d(7, stride=1) + + self.fc1 = nn.Linear(in_features=planes, out_features=round(planes / 16)) + self.fc2 = nn.Linear(in_features=round(planes / 16), out_features=planes) + self.sigmod = nn.Sigmoid() + if self.bam: + self.bam = SpatialAttention() + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + identity = self.downsample(x) + + if self.cam: + ori_out = self.globalAvgPool(out) + out = out.view(out.size(0), -1) + out = self.fc1(out) + out = self.relu(out) + out = self.fc2(out) + out = self.sigmod(out) + out = out.view(out.size(0), out.size(-1), 1, 1) + out = out * ori_out + + if self.bam: + out = out*self.bam(out) + + out += identity + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2) + # while original implementation places the stride at the first 1x1 convolution(self.conv1) + # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385. + # This variant is also known as ResNet V1.5 and improves accuracy according to + # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch. + + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, + base_width=64, dilation=1, norm_layer=None, cam=False, bam=False): + super(Bottleneck, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + width = int(planes * (base_width / 64.)) * groups + self.cam = cam + self.bam = bam + # Both self.conv2 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv1x1(inplanes, width) + self.bn1 = norm_layer(width) + self.conv2 = conv3x3(width, width, stride, groups, dilation) + self.bn2 = norm_layer(width) + self.conv3 = conv1x1(width, planes * self.expansion) + self.bn3 = norm_layer(planes * self.expansion) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + if self.cam: + if planes == 64: + self.globalAvgPool = nn.AvgPool2d(56, stride=1) + elif planes == 128: + self.globalAvgPool = nn.AvgPool2d(28, stride=1) + elif planes == 256: + self.globalAvgPool = nn.AvgPool2d(14, stride=1) + elif planes == 512: + self.globalAvgPool = nn.AvgPool2d(7, stride=1) + + self.fc1 = nn.Linear(planes * self.expansion, round(planes / 4)) + self.fc2 = nn.Linear(round(planes / 4), planes * self.expansion) + self.sigmod = nn.Sigmoid() + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + identity = self.downsample(x) + + if self.cam: + ori_out = self.globalAvgPool(out) + out = out.view(out.size(0), -1) + out = self.fc1(out) + out = self.relu(out) + out = self.fc2(out) + out = self.sigmod(out) + out = out.view(out.size(0), out.size(-1), 1, 1) + out = out * ori_out + out += identity + out = self.relu(out) + return out + + +class ResNet(nn.Module): + + def __init__(self, block, layers, num_classes=conf.embedding_size, zero_init_residual=False, + groups=1, width_per_group=64, replace_stride_with_dilation=None, + norm_layer=None, scale=0.75): + super(ResNet, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + self._norm_layer = norm_layer + + self.inplanes = 64 + self.dilation = 1 + if replace_stride_with_dilation is None: + # each element in the tuple indicates if we should replace + # the 2x2 stride with a dilated convolution instead + replace_stride_with_dilation = [False, False, False] + if len(replace_stride_with_dilation) != 3: + raise ValueError("replace_stride_with_dilation should be None " + "or a 3-element tuple, got {}".format(replace_stride_with_dilation)) + self.groups = groups + self.base_width = width_per_group + self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = norm_layer(self.inplanes) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, int(64*scale), layers[0]) + self.layer2 = self._make_layer(block, int(128*scale), layers[1], stride=2, + dilate=replace_stride_with_dilation[0]) + self.layer3 = self._make_layer(block, int(256*scale), layers[2], stride=2, + dilate=replace_stride_with_dilation[1]) + self.layer4 = self._make_layer(block, int(512*scale), layers[3], stride=2, + dilate=replace_stride_with_dilation[2]) + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.fc = nn.Linear(int(512 * block.expansion*scale), num_classes) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + # Zero-initialize the last BN in each residual branch, + # so that the residual branch starts with zeros, and each residual block behaves like an identity. + # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 + if zero_init_residual: + for m in self.modules(): + if isinstance(m, Bottleneck): + nn.init.constant_(m.bn3.weight, 0) + elif isinstance(m, BasicBlock): + nn.init.constant_(m.bn2.weight, 0) + + def _make_layer(self, block, planes, blocks, stride=1, dilate=False): + norm_layer = self._norm_layer + downsample = None + previous_dilation = self.dilation + if dilate: + self.dilation *= stride + stride = 1 + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + conv1x1(self.inplanes, planes * block.expansion, stride), + norm_layer(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample, self.groups, + self.base_width, previous_dilation, norm_layer)) + self.inplanes = planes * block.expansion + for _ in range(1, blocks): + layers.append(block(self.inplanes, planes, groups=self.groups, + base_width=self.base_width, dilation=self.dilation, + norm_layer=norm_layer)) + return nn.Sequential(*layers) + + def _forward_impl(self, x): + # See note [TorchScript super()] + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + # print('poolBefore', x.shape) + x = self.avgpool(x) + # print('poolAfter', x.shape) + x = torch.flatten(x, 1) + # print('fcBefore',x.shape) + x = self.fc(x) + + # print('fcAfter',x.shape) + + return x + + def forward(self, x): + return self._forward_impl(x) + + +# def _resnet(arch, block, layers, pretrained, progress, **kwargs): +# model = ResNet(block, layers, **kwargs) +# if pretrained: +# state_dict = load_state_dict_from_url(model_urls[arch], +# progress=progress) +# model.load_state_dict(state_dict, strict=False) +# return model +def _resnet(arch, block, layers, pretrained, progress, **kwargs): + model = ResNet(block, layers, **kwargs) + if pretrained: + state_dict = load_state_dict_from_url(model_urls[arch], + progress=progress) + + src_state_dict = state_dict + target_state_dict = model.state_dict() + skip_keys = [] + # skip mismatch size tensors in case of pretraining + for k in src_state_dict.keys(): + if k not in target_state_dict: + continue + if src_state_dict[k].size() != target_state_dict[k].size(): + skip_keys.append(k) + for k in skip_keys: + del src_state_dict[k] + missing_keys, unexpected_keys = model.load_state_dict(src_state_dict, strict=False) + + return model + + +def resnet14(pretrained=True, progress=True, **kwargs): + r"""ResNet-14 model from + `"Deep Residual Learning for Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet18', BasicBlock, [2, 1, 1, 2], pretrained, progress, + **kwargs) + + +def resnet18(pretrained=True, progress=True, **kwargs): + r"""ResNet-18 model from + `"Deep Residual Learning for Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress, + **kwargs) + + +def resnet34(pretrained=False, progress=True, **kwargs): + r"""ResNet-34 model from + `"Deep Residual Learning for Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress, + **kwargs) + + +def resnet50(pretrained=False, progress=True, **kwargs): + r"""ResNet-50 model from + `"Deep Residual Learning for Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress, + **kwargs) + + +def resnet101(pretrained=False, progress=True, **kwargs): + r"""ResNet-101 model from + `"Deep Residual Learning for Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress, + **kwargs) + + +def resnet152(pretrained=False, progress=True, **kwargs): + r"""ResNet-152 model from + `"Deep Residual Learning for Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress, + **kwargs) + + +def resnext50_32x4d(pretrained=False, progress=True, **kwargs): + r"""ResNeXt-50 32x4d model from + `"Aggregated Residual Transformation for Deep Neural Networks" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs['groups'] = 32 + kwargs['width_per_group'] = 4 + return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3], + pretrained, progress, **kwargs) + + +def resnext101_32x8d(pretrained=False, progress=True, **kwargs): + r"""ResNeXt-101 32x8d model from + `"Aggregated Residual Transformation for Deep Neural Networks" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs['groups'] = 32 + kwargs['width_per_group'] = 8 + return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3], + pretrained, progress, **kwargs) + + +def wide_resnet50_2(pretrained=False, progress=True, **kwargs): + r"""Wide ResNet-50-2 model from + `"Wide Residual Networks" `_ + + The model is the same as ResNet except for the bottleneck number of channels + which is twice larger in every block. The number of channels in outer 1x1 + convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 + channels, and in Wide ResNet-50-2 has 2048-1024-2048. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs['width_per_group'] = 64 * 2 + return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3], + pretrained, progress, **kwargs) + + +def wide_resnet101_2(pretrained=False, progress=True, **kwargs): + r"""Wide ResNet-101-2 model from + `"Wide Residual Networks" `_ + + The model is the same as ResNet except for the bottleneck number of channels + which is twice larger in every block. The number of channels in outer 1x1 + convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 + channels, and in Wide ResNet-50-2 has 2048-1024-2048. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs['width_per_group'] = 64 * 2 + return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3], + pretrained, progress, **kwargs) diff --git a/contrast/search.py b/contrast/search.py new file mode 100644 index 0000000..3a522f9 --- /dev/null +++ b/contrast/search.py @@ -0,0 +1,101 @@ +import pdb + + +class ImgSearch(): + def __init__(self): + self.search_params = { + "metric_type": "COSINE", + } + + def get_max(self, a, b): + if a > b: + return a + else: + return b + + def check_keys(self, dict1_last, dict2): + for key2 in list(dict2.keys()): + if key2 in list(dict1_last.keys()): + value = self.get_max(dict1_last[key2], dict2[key2]) + dict1_last[key2] = value + else: + dict1_last[key2] = dict2[key2] + return dict1_last + def result_analysis(self, result, top1_flag=False): + result_dict = dict() ## 将同一barcode所有图片比对结果保存到该字典 + for hits in result: + for hit in hits: + if not hit.id in result_dict: ## barcode（hit.id）不在结果字典中 + result_dict.update({hit.id: round(hit.distance, 2)}) + else: ## 将同一barcode相似度保存较高的 + distance = result_dict.get(hit.id) + distance_new = self.get_max(distance, hit.distance) + result_dict.update({hit.id: round(distance_new, 2)}) + if top1_flag: + return result_dict + else: + ## 将所有barcode相似度结果排序存储 + if len(result_dict) > 10: + result_sort_dict = dict(sorted(result_dict.items(), key=lambda x: x[1], reverse=True)[:10]) + else: + result_sort_dict = dict(sorted(result_dict.items(), key=lambda x: x[1], reverse=True)) + return result_sort_dict + def result_update(self, temp_result, last_result): + temp_keys = list(temp_result.keys()) + last_keys = list(last_result.keys()) + for ke in temp_keys: + temp_value = temp_result[ke] + if ke in last_keys: ## track_id1的结果和track_id2的结果有公共barcode，track_id2中barcode相似度高才更新 + last_value = last_result[ke] + if temp_value > last_value: + last_result.update({ke: temp_value}) + else: ## track_id1的结果和track_id2的结果无公共barcode + last_result.update({ke: temp_value}) + return last_result + def mainSearch10(self, mainMilvus, queBarIdList, queueFeatures): ###queueBarIdList->传入的box barcode-track_Id + result_last = dict() + for i in range(len(queBarIdList)): + vectorsSearch = queueFeatures[i] + result = mainMilvus.search(vectorsSearch, anns_field='embeddings', param=self.search_params, limit=10) + result_sort_dic = self.result_analysis(result) + result_last.update({queBarIdList[i]: result_sort_dic}) + return result_last + + def tempSearch(self, tempMilvus, queueList, queueFeatures, barIdList, tempbarId): + newBarList = [] + ### tempbarId格式->[macID_barcode_trackId1,..., macID_barcode_trackIdn] + for bar in tempbarId: ### 找出barIdList和tempbarId中共有的barcode + if len(bar.split('_')) == 3: + mac_barcode = bar.split('_')[0] + '_' + bar.split('_')[1] + if mac_barcode in barIdList: + newBarList.append(bar) ## newBarList格式->[macID_barcode_trackId1,..., macID_barcode_trackIdm] + if len(newBarList) == 0: + return {} + else: + expr = f"pk in {newBarList}" + result_last = dict() + for i in range(len(queueList)): + vectorsSearch = queueFeatures[i] + result = tempMilvus.search(vectorsSearch, anns_field='embeddings', expr=expr, param=self.search_params, + limit=len(newBarList)) + result_sort_dic = self.result_analysis(result) + result_last.update({queueList[i]: result_sort_dic}) + return result_last + + def mainSearch1(self, mainMilvus, queBarIdList, queFeatures): ###queueBarIdList->传入的box macID_barcode_trackId + result_last = dict() + for i in range(len(queBarIdList)): + pk_barcode = queBarIdList[i].split('_')[1] #### 解析barcode 查询图片名称为macID_barcode_trackId + vectorsSearch = queFeatures[i] + result = mainMilvus.search(vectorsSearch, anns_field='embeddings', expr=f"pk=='{pk_barcode}'", + param=self.search_params, limit=1) + result_dic = self.result_analysis(result, top1_flag=True) + if (len(result_dic) != 0) and (len(result_last) != 0): + result_last = self.result_update(result_dic, result_last) + else: + result_last.update({key: value for key, value in result_dic.items()}) + if len(result_last) == 0: + pk_barcode = queBarIdList[0].split('_')[1] + result_last.update({pk_barcode: 0}) + return result_last + diff --git a/contrast/test_logic.py b/contrast/test_logic.py new file mode 100644 index 0000000..faa4019 --- /dev/null +++ b/contrast/test_logic.py @@ -0,0 +1,317 @@ +# -*- coding: utf-8 -*- +import pdb +import random +import json +import time + +import torch +from PIL import Image + +from contrast.model import resnet18, MobileNetV3_Large +# import pymilvus +# from pymilvus import ( +# connections, +# utility, +# FieldSchema, CollectionSchema, DataType, +# Collection, +# Milvus +# ) +# from config import config as conf +from contrast.search import ImgSearch +from contrast.img_data import queueImgs_add +import sys +from threading import Thread + + +sys.path.append('../tools') +from tools.config import cfg as conf +from tools.config import gvalue + + + +def test_preprocess(images: list, actionModel) -> torch.Tensor: + res = [] + for img in images: + # print(img) + try: + im = conf.test_transform(img) if actionModel else conf.test_transform(Image.open(img)) + res.append(im) + except: + continue + data = torch.stack(res) + return data + + +def inference(images, model, actionModel): + data = test_preprocess(images, actionModel) + if torch.cuda.is_available(): + data = data.to(conf.device) + features = model(data) + return features + + +def group_image(images, batch=64) -> list: + """Group image paths by batch size""" + size = len(images) + res = [] + for i in range(0, size, batch): + end = min(batch + i, size) + res.append(images[i:end]) + return res + + +def barcode_state(barcodeIDList): + with open('contrast/main_barcodes.json', 'r') as file: + data = json.load(file) + main_barcode = list(data.values())[0] + barIdList_true = [] + barIdList_false = [] + for barId in barcodeIDList: + bar = barId.split('_')[1] + if bar in main_barcode: + barIdList_true.append(barId) + else: + barIdList_false.append(barId) + return barIdList_true, barIdList_false + + +def getFeatureList(barList, imgList, model, actionModel): + featList = [[] for i in range(len(barList))] + for index, feat in enumerate(imgList): + groups = group_image(feat) + for group in groups: + feat_tensor = inference(group, model, actionModel) + for fe in feat_tensor: + if fe.device == 'cpu': + fe_np = fe.squeeze().detach().numpy() + else: + fe_np = fe.squeeze().detach().cpu().numpy() + featList[index].append(fe_np) + return featList + + +def img2feature(imgs_dict, model, actionModel, barcode_flag): + if not len(imgs_dict) > 0: + raise ValueError("Tracking fail no images files provided") + queBarIdList = list(imgs_dict.keys()) + if barcode_flag: + # # ========判断barcode是否在特征库============ + queBarIdList_t, barIdList_f = barcode_state(queBarIdList) + queFeatList_t = [] + if len(queBarIdList_t) == 0: + print(f"All barcodes are not in the main_library: {barIdList_f}") + return queBarIdList_t, queFeatList_t + else: + if len(barIdList_f) > 0: ## 将不在barcode库中的barcode及图片删除 + print(f"These barcodes are not in the main_library: {barIdList_f}") + for bar_f in barIdList_f: + del imgs_dict[bar_f] + queImgList_t = list(imgs_dict.values()) + queFeatList_t = getFeatureList(queBarIdList_t, queImgList_t, model, actionModel) + return queBarIdList_t, queFeatList_t + else: + queImgsList = list(imgs_dict.values()) + queFeatList = getFeatureList(queBarIdList, queImgsList, model, actionModel) + return queBarIdList, queFeatList + + + +# def create_milvus(collection_name, host, port, barcode_list, features): +# # 1. connect to Milvus +# fmt = "\n=== {:30} ===\n" +# connections.connect('default', host=host, port=port) # 连接到 Milvus 服务器 +# has = utility.has_collection(collection_name) ##检查collection_name是否存在milvus中 +# print(f"Does collection {collection_name} exist in Milvus: {has}") +# # if has: ## 删除collection_name的库 +# # utility.drop_collection(collection_name) +# +# # 2. create colllection +# fields = [ +# FieldSchema(name="pk", dtype=DataType.VARCHAR, is_primary=True, auto_id=False, max_length=100), ###图片路径 +# FieldSchema(name="embeddings", dtype=DataType.FLOAT_VECTOR, dim=256) +# ] +# schema = CollectionSchema(fields) +# print(fmt.format(f"Create collection {collection_name}")) +# hello_milvus = Collection(collection_name, schema, consistency_level="Strong") +# # 3. insert data +# for i in range(len(features)): +# entities = [ +# # provide the pk field because `auto_id` is set to False +# [barcode_list[i]] * len(features[i]), ## 图片维度和向量维度需匹配 每个向量都生成一个barcode +# features[i], +# ] +# print(fmt.format("Start inserting entities")) +# insert_result = hello_milvus.insert(entities) +# hello_milvus.flush() +# print(f"Number of entities in {collection_name}: {hello_milvus.num_entities}") # check the num_entities +# return hello_milvus + + +# def load_collection(collection_name): +# collection = Collection(collection_name) +# # collection.release() ### 将collection从加载状态变成未加载 +# # collection.drop_index() ### 删除索引 +# +# index_params = { +# "index_type": "IVF_FLAT", +# # "index_type": "IVF_SQ8", +# # "index_type": "GPU_IVF_FLAT", +# "metric_type": "COSINE", +# "params": { +# "nlist": 10000 +# } +# } +# #### 准确率低 +# # index_params = { +# # "index_type": "IVF_PQ", +# # "metric_type": "COSINE", +# # "params": { +# # "nlist": 99, +# # "m": 2, +# # "nbits": 8 +# # } +# # } +# collection.create_index( +# field_name="embeddings", +# index_params=index_params, +# index_name="SQ8" +# ) +# collection.load() +# return collection + + +# def similarity(queImgsDict, add_flag, barcode_flag, main_milvus, model, barcode_list, actionModel): +# searchImg = ImgSearch() ## 相似度比较 +# # 将输入图片加入临时库 +# if add_flag: +# if actionModel: +# queBarIdList, queBarIdFeatures = img2feature(dict(list(queImgsDict.items())[2:-2]), model, actionModel, barcode_flag) +# else: +# queBarIdList, queBarIdFeatures = img2feature(dict(list(queImgsDict.items())[:-2]), model, actionModel, barcode_flag) +# +# if barcode_flag: ### 加购 有barcode -> 输出top10和top1 +# if len(queBarIdList) == 0: +# top10, top1 = {}, {} +# else: +# for bar in queBarIdList: +# # gvalue.tempLibList.append(bar) ## 临时特征库key值为macID_barcode_trackID +# if gvalue.tempLibLists.get(gvalue.mac_id) is not None: +# gvalue.tempLibLists[gvalue.mac_id] += [bar] ## 临时特征库key值为macID_barcode_trackID +# else: +# gvalue.tempLibLists[gvalue.mac_id] = [bar] +# # 存入临时特征库 +# # create_milvus('temp_features', conf.host, conf.port, queBarIdList, queBarIdFeatures) +# +# thread = Thread(target=create_milvus, kwargs={'collection_name': 'temp_features', +# 'host': conf.host, +# 'port': conf.port, +# 'barcode_list': queBarIdList, +# 'features': queBarIdFeatures}) +# thread.start() +# start1 = time.time() +# top10 = searchImg.mainSearch10(main_milvus, queBarIdList, queBarIdFeatures) +# start2 = time.time() +# print('search top10 time>>>> {}'.format(start2-start1)) +# top1 = searchImg.mainSearch1(main_milvus, queBarIdList, queBarIdFeatures) +# start3 = time.time() +# print('search top1 time>>>>> {}'.format(start3-start2)) +# return top10, top1, gvalue.tempLibLists +# else: # 加购 无barcode -> 输出top10 +# # 无barcode时，生成随机数作为字典key值 +# queBarIdList_rand = [] +# for i in range(len(queBarIdList)): +# random_number = ''.join(random.choices('0123456789', k=10)) +# queBarIdList_rand.append(str(random_number)) +# # gvalue.tempLibList.append(str(random_number)) +# if gvalue.tempLibLists.get(gvalue.mac_id) is not None: +# gvalue.tempLibLists[gvalue.mac_id] += [str(random_number)] ## 临时特征库key值为macID_barcode_trackID +# else: +# gvalue.tempLibLists[gvalue.mac_id] = [str(random_number)] +# # create_milvus('temp_features', conf.host, conf.port, queBarIdList_rand, queBarIdFeatures) +# thread = Thread(target=create_milvus, kwargs={'collection_name': 'temp_features', +# 'host': conf.host, +# 'port': conf.port, +# 'barcode_list': queBarIdList_rand, +# 'features': queBarIdFeatures}) +# thread.start() +# top10 = searchImg.mainSearch10(main_milvus, queBarIdList, queBarIdFeatures) +# # print(f'top10: {top10}') +# return top10, gvalue.tempLibLists +# else: # 退购 -> 输出top10和topn +# if gvalue.tempLibLists.get(gvalue.mac_id) is None: +# gvalue.tempLibList = [] +# else: +# gvalue.tempLibList = gvalue.tempLibLists[gvalue.mac_id] +# ## 加载临时特征库 +# tempMilvusName = "temp_features" +# has = utility.has_collection(tempMilvusName) +# print(f"Does collection {tempMilvusName} exist in Milvus: {has}") +# tempMilvus = load_collection(tempMilvusName) +# print(f"Number of entities in {tempMilvusName}: {tempMilvus.num_entities}") +# if actionModel: +# barcode_list = barcode_list +# else: +# barcode_list = queueImgs_add['barcode_list'] +# if actionModel: +# queBarIdList, queBarIdFeatures = img2feature(dict(list(queImgsDict.items())[2:-1]), model, actionModel, barcode_flag) +# else: +# queBarIdList, queBarIdFeatures = img2feature(dict(list(queImgsDict.items())[:-3]), model, actionModel, barcode_flag) +# if barcode_flag: +# if len(queBarIdList) == 0: +# top10, top1, top_n = {}, {}, {} +# else: +# start1 = time.time() +# top1 = searchImg.mainSearch1(main_milvus, queBarIdList, queBarIdFeatures) +# start2 = time.time() +# print('search top1 time>>>> {}'.format(start2 - start1)) +# top10 = searchImg.mainSearch10(main_milvus, queBarIdList, queBarIdFeatures) +# start3 = time.time() +# print('search top10 time>>>> {}'.format(start3 - start2)) +# top_n = searchImg.tempSearch(tempMilvus, queBarIdList, queBarIdFeatures, barcode_list, gvalue.tempLibList) +# # print(f'top10: {top10}, top1: {top1}, topn: {top_n}') +# return top10, top1, top_n +# else: +# top10 = searchImg.mainSearch10(main_milvus, queBarIdList, queBarIdFeatures) +# top_n = searchImg.tempSearch(tempMilvus, queBarIdList, queBarIdFeatures, barcode_list, gvalue.tempLibList) +# # print(f'top10: {top10}, topn: {top_n}') +# return top10, top_n + + +def similarity_interface(dataCollection): + queImgsDict = dataCollection.queImgsDict + add_flag = dataCollection.add_flag + barcode_flag = dataCollection.barcode_flag + main_milvus = dataCollection.mainMilvus + #tempLibList = dataCollection.tempLibList + model = dataCollection.model + actionModel = dataCollection.actionModel + barcode_list = dataCollection.barcode_list + #return similarity(queImgsDict, add_flag, barcode_flag, main_milvus, tempLibList, model, barcode_list, actionModel) + return 0 + + + +if __name__ == '__main__': + pass + + # connections.connect('default', host=conf.host, port=conf.port) + # # 加载主特征库 + # mainMilvusName = "main_features" + # has = utility.has_collection(mainMilvusName) + # print(f"Does collection {mainMilvusName} exist in Milvus: {has}") + # mainMilvus = Collection(mainMilvusName) + # mainMilvus.load() + # model = initModel() + # # queueImgs_add queueImgs_back 分别为加购和退购时的入参 + # add_flag = queueImgs_add['add_flag'] + # barcode_flag = queueImgs_add['barcode_flag'] + # tempLibList = [] # 临时特征库的barcodeId_list + # # tempLibList = ['3500610085338_01', '4260290263776_01'] ##test + # if add_flag: + # if barcode_flag: # 加购 有barcode -> 输出top10和top1 + # top10, top1, tempLibList = similarity(queueImgs_add, add_flag, barcode_flag, mainMilvus, tempLibList, model) + # print(f"top10: {top10}\ntop1: {top1}") + # else: # 加购 无barcode -> 输出top10 + # top10, tempLibList = similarity(queueImgs_add, add_flag, barcode_flag, mainMilvus, tempLibList, model) + # else: # 退购 -> 输出top10和topn + # top10, topn = similarity(queueImgs_back, add_flag, barcode_flag, mainMilvus, tempLibList, model) diff --git a/contrast/utils.py b/contrast/utils.py new file mode 100644 index 0000000..78f3994 --- /dev/null +++ b/contrast/utils.py @@ -0,0 +1,64 @@ +"""Train List 训练列表 +格式： +ImagePath Label + +示例: +/data/WebFace/0124920/003.jpg 10572 +/data/WebFace/0124920/012.jpg 10572 +/data/WebFace/0124920/020.jpg 10572 +""" + +import os +import os.path as osp +from imutils import paths + +def generate_list(images_directory, saved_name=None): + """生成数据列表 + Args: + images_directory: 人脸数据目录，通常包含多个子文件夹。如 + WebFace和LFW的格式 + Returns: + data_list: [<路径> <标签>] + """ + subdirs = os.listdir(images_directory) + num_ids = len(subdirs) + data_list = [] + for i in range(num_ids): + subdir = osp.join(images_directory, subdirs[i]) + files = os.listdir(subdir) + paths = [osp.join(subdir, file) for file in files] + # 添加ID作为其人脸标签 + paths_with_Id = [f"{p} {i}\n" for p in paths] + data_list.extend(paths_with_Id) + + if saved_name: + with open(saved_name, 'w', encoding='utf-8') as f: + f.writelines(data_list) + return data_list + +def transform_clean_list(webface_directory, cleaned_list_path): + """转换webface的干净列表格式 + Args: + webface_directory: WebFace数据目录 + cleaned_list_path: cleaned_list.txt路径 + Returns: + cleaned_list: 转换后的数据列表 + """ + with open(cleaned_list_path, encoding='utf-8') as f: + cleaned_list = f.readlines() + cleaned_list = [p.replace('\\', '/') for p in cleaned_list] + cleaned_list = [osp.join(webface_directory, p) for p in cleaned_list] + return cleaned_list + +def remove_dirty_image(webface_directory, cleaned_list): + cleaned_list = set([c.split()[0] for c in cleaned_list]) + for p in paths.list_images(webface_directory): + if p not in cleaned_list: + print(f"remove {p}") + os.remove(p) + +if __name__ == '__main__': + data = '/data/CASIA-WebFace/' + lst = '/data/cleaned_list.txt' + cleaned_list = transform_clean_list(data, lst) + remove_dirty_image(data, cleaned_list) \ No newline at end of file diff --git a/dealdata.py b/dealdata.py new file mode 100644 index 0000000..e01f771 --- /dev/null +++ b/dealdata.py @@ -0,0 +1,160 @@ +import pickle +import os + +os.environ["OMP_NUM_THREADS"] = '1' +from sklearn.decomposition import PCA +import numpy as np +import matplotlib.pyplot as plt +from sklearn.cluster import KMeans +from sklearn import metrics + +from sklearn.metrics import silhouette_score, calinski_harabasz_score, davies_bouldin_score +import shutil +import math + +distance_lists = [] +all_distance_lists = [] + + +def showImg(newx): + plt.scatter(newx[:, 0], newx[:, 1]) + plt.show() + + +def showHistogram(data): + fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(10, 8)) + bins = np.arange(0, 1.5, 0.1) + axs[0, 0].hist(data[0], bins=bins, edgecolor='black') + axs[0, 0].set_title('silhouette') + axs[0, 0].set_xlabel('Similarity') + axs[0, 0].set_ylabel('Frequency') + axs[0, 0].legend(labels=['频次']) + axs[0, 1].hist(data[1], bins=bins, edgecolor='black') + axs[0, 1].set_title('calinski') + axs[0, 1].set_xlabel('Similarity') + axs[0, 1].set_ylabel('Frequency') + axs[0, 1].legend(labels=['频次']) + axs[1, 0].hist(data[2], bins=bins, edgecolor='black') + axs[1, 0].set_title('davies') + axs[1, 0].set_xlabel('Similarity') + axs[1, 0].set_ylabel('Frequency') + axs[1, 0].legend(labels=['频次']) + axs[1, 1].hist(data[3], bins=bins, edgecolor='black') + axs[1, 1].set_title('inertia') + axs[1, 1].set_xlabel('Similarity') + axs[1, 1].set_ylabel('Frequency') + axs[1, 1].legend(labels=['频次']) + # 显示图形 + plt.tight_layout() + plt.savefig('multiple_histograms_in_ranges_non_pca.png') + plt.show() + + +def get_pca_data(data): + pca = PCA(n_components=16) + newx = pca.fit_transform(data) + # print(pca.explained_variance_ratio_) + return newx + + +def cosine_similarity(vec1, vec2): + # dot_product = sum(a * b for a, b in zip(vec1, vec2)) + # norm_vec1 = math.sqrt(sum(x ** 2 for x in vec1)) + # norm_vec2 = math.sqrt(sum(x ** 2 for x in vec2)) + # return dot_product / (norm_vec1 * norm_vec2) + vec1 = np.array(vec1) + vec2 = np.array(vec2) + cos_sim = vec1.dot(vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2)) + return cos_sim + +def get_kclusters(data, k, dicts, standard='silhouette'): + Kmeans = KMeans(n_clusters=k, n_init='auto') + y_pred = Kmeans.fit_predict(data) + if standard == 'silhouette': + s_score = silhouette_score(data, y_pred, metric='euclidean') + dicts[s_score] = [y_pred, Kmeans.cluster_centers_] + elif standard == 'calinski': + c_score = calinski_harabasz_score(data, y_pred) + dicts[c_score] = [y_pred, Kmeans.cluster_centers_] + elif standard == 'davies': + d_score = davies_bouldin_score(data, y_pred) + dicts[d_score] = [y_pred, Kmeans.cluster_centers_] + elif standard == 'inertia': + i_score = Kmeans.inertia_ + dicts[i_score] = [y_pred, Kmeans.cluster_centers_] + return dicts + + +def get_keams(data, standard='silhouette'): + global distance_lists + distance_list = [] + dicts = {} + for k in range(2, 10): + dicts = get_kclusters(data, k, dicts, standard) + if standard == 'silhouette' or standard == 'calinski': + max_key = max(dicts.keys()) + value = dicts[max_key] + elif standard == 'davies' or standard == 'inertia': + max_key = min(dicts.keys()) + value = dicts[max_key] + + for num, i in enumerate(value[0]): + distance_list.append(abs(cosine_similarity(value[1][i], data[num]))) + distance_lists += distance_list + return distance_lists + +def move_file(labels, y_pred): + for label, y_label in zip(labels, y_pred): + print('label >>> {}'.format(label)) + if not os.path.isdir(os.sep.join(['data', label.split('_')[0], str(y_label)])): + os.mkdir(os.sep.join(['data', label.split('_')[0], str(y_label)])) + try: + shutil.move(os.sep.join(['data', label.split('_')[0], label]), + os.sep.join(['data', label.split('_')[0], str(y_label), label])) + except Exception as e: + print(e) + +def move_video(root_pth='data_test'): # 数据准备 + for Dirs in os.listdir(root_pth): + for root, dirs, files in os.walk(os.sep.join([root_pth, Dirs])): + if len(dirs) > 0: + for dir in dirs: + filespth = os.sep.join([root_pth, Dirs, dir]) + for file in os.listdir(filespth): + if file.endswith('.jpg'): + oldpth = os.sep.join([filespth, file]) + newpth = os.sep.join([root_pth, Dirs, file]) + shutil.move(oldpth, newpth) + +def main(): + global distance_lists + for standard in ['silhouette', 'calinski', 'davies', 'inertia']: + with open('data_test/data.pkl', 'rb') as f: + data = pickle.load(f) + for keys, values in data.items(): + all_data, labels = [], [] + temp = None + for value in values: + for key1, value1 in value.items(): + # print('key1 >>>> {}'.format(key1)) + if temp is None: + labels.append(key1) + temp = value1 + else: + labels.append(key1) + temp = np.vstack((temp, value1)) + try: + # temp = get_pca_data(temp) + get_keams(temp, standard) + # move_file(labels, y_pred) + except Exception as e: + print(e) + f.close() + all_distance_lists.append(distance_lists) + distance_lists = [] + showHistogram(all_distance_lists) + + +if __name__ == '__main__': + main() + # move_video() diff --git a/image_quality.py b/image_quality.py new file mode 100644 index 0000000..36e5f65 --- /dev/null +++ b/image_quality.py @@ -0,0 +1,104 @@ +import os + +import numpy as np + +from ytracking.track_ import * +from tools.Interface import AiInterface, AiClass +from tools.operate_usearch import create_base_index, search_in_index +from tools.initModel import models +from imgcompare import get_feature_list, compute_similarity_matrix +import pickle +models.initModel() +ai_obj = AiClass() + + +def get_img_lists(pth): + imglist, imglists = [], [] + for root, dirs, files in os.walk(pth): + if not any(dirs): + for file in files: + if file.endswith('.jpg'): + imglist.append(os.sep.join([root, file])) + imglists.append(imglist) + imglist = [] + return imglists + +def get_standard_image(cosine_similarities, similarity_threshold=0.6): + """ + :param cosine_similarities: + :return: + """ + target_indexs = [] + max_similarity = {} + mask = (cosine_similarities > similarity_threshold) + counts = mask.sum(axis=1) + for key in range(counts.shape[0]): + max_similarity[key] = counts[key] + sorted_dict_desc = dict(sorted(max_similarity.items(), key=lambda item: item[1], reverse=True)) + keys = list(sorted_dict_desc.keys()) + while len(keys) > 10: + target_indexs.append(keys[0]) + single_line = cosine_similarities[keys[0], :] + rows = np.where((single_line > similarity_threshold)) + if len(rows[0]) < 2: + break + for row in rows[0]: + try: + keys.remove(row) + except Exception as e: + continue + # print(target_indexs) + return target_indexs + +def create_feature_library(pth, save_index_name, index_file_pth=None): + target_feature_lists, target_barcode_lists = [], [] + imglists = get_img_lists(pth) + for imglist in imglists: + feature_list = get_feature_list(imglist, False) + cosine_similarities = compute_similarity_matrix(feature_list) + target_indexs = get_standard_image(cosine_similarities) + target_feature_lists.append([feature_list[i] for i in target_indexs]) + target_barcode_lists.append([os.path.basename(imglist[i]).split('_')[0] for i in target_indexs]) + create_base_index(save_index_name=save_index_name, + barcodes=target_barcode_lists, + features=target_feature_lists, + index_file_pth=index_file_pth) + with open('search_library/target_barcode_lists.pkl', 'wb') as f: + pickle.dump(target_barcode_lists, f) + +def search_top_in_index(test_image_pth, index_name): #1:N + s_barcode, s_similarity = [], [] + img_lists = [os.sep.join([test_image_pth, name]) for name in os.listdir(test_image_pth)] + feature_lists = get_feature_list(img_lists, False) + for feature in feature_lists: + result = search_in_index(query=np.array(feature), index_name=index_name) + s_barcode.append(result.keys) + s_similarity.append(1-result.distances) + s_barcode = np.array(s_barcode) + s_similarity = np.array(s_similarity) + return s_barcode, s_similarity + +def search_one_in_index(test_image_pth, index_name): # 1:1 + barcodes = [int(os.path.basename(name).split('_')[0]) for name in os.listdir(test_image_pth)] + barcodes = list(set(barcodes)) + # barcodes = ['6934364805640'] + img_lists = [os.sep.join([test_image_pth, name]) for name in os.listdir(test_image_pth)] + feature_lists = get_feature_list(img_lists, False) + result = search_in_index(barcode=barcodes, + query=feature_lists, + index_name=index_name, + temp_index=False) + print(feature_lists) + + +if __name__ == '__main__': + pth = 'imageQualityData/test_data' + save_index_name = 'search_library/test_index_10_simple_0717.usearch' + create_feature_library(pth, + save_index_name=save_index_name) + + # test_images_pth = 'D:/Project/ieemoo/image_quality_assessment/imageQualityData/test_images' + # # index_name = 'D:/Project/ieemoo/image_quality_assessment/search_library/test_index_10_normal_0717.usearch' + # index_name = 'D:/Project/ieemoo/image_quality_assessment/search_library/test_index_10_simple_0717.usearch' + # # search_top_in_index(test_images_pth, index_name) + # search_one_in_index(test_images_pth, index_name) \ No newline at end of file diff --git a/imgcompare.py b/imgcompare.py new file mode 100644 index 0000000..6839dd1 --- /dev/null +++ b/imgcompare.py @@ -0,0 +1,313 @@ +import os.path +import shutil + +import numpy as np + +from ytracking.track_ import * +from contrast.test_logic import group_image, inference +from tools.Interface import AiInterface, AiClass +from tools.config import cfg, gvalue + +from tools.initModel import models +from scipy.spatial.distance import cdist + +from dealdata import get_keams +import matplotlib.pyplot as plt +from contrast.model.resnet_pre import resnet18 +from prettytable import PrettyTable +from sklearn.cluster import KMeans + +models.initModel() +ai_obj = AiClass() + + +def showComprehensiveHistogram(data, title): + bins = np.arange(0, 1.01, 0.1) + plt.hist(data, bins, edgecolor='black') + plt.title(title) + plt.xlabel('Similarity') + plt.ylabel('Frequency') + # plt.show() + plt.savefig(title + '.png') + + +def showHistogram(): + fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(10, 8)) + bins = np.arange(0, 1.01, 0.1) + axs[0, 0].hist(gvalue.back_return_similarity, bins=bins, edgecolor='black') + axs[0, 0].set_title('back_return_similarity') + axs[0, 0].set_xlabel('Similarity') + axs[0, 0].set_ylabel('Frequency') + axs[0, 0].legend(labels=['back_return_similarity']) + + axs[0, 1].hist(gvalue.back_add_similarity, bins=bins, edgecolor='black') + axs[0, 1].set_title('back_add_similarity') + axs[0, 1].set_xlabel('Similarity') + axs[0, 1].set_ylabel('Frequency') + axs[0, 1].legend(labels=['back_add_similarity']) + + axs[1, 0].hist(gvalue.front_return_similarity, bins=bins, edgecolor='black') + axs[1, 0].set_title('front_return_similarity') + axs[1, 0].set_xlabel('Similarity') + axs[1, 0].set_ylabel('Frequency') + axs[1, 0].legend(labels=['front_return_similarity']) + + axs[1, 1].hist(gvalue.front_add_similarity, bins=bins, edgecolor='black') + axs[1, 1].set_title('front_add_similarity') + axs[1, 1].set_xlabel('Similarity') + axs[1, 1].set_ylabel('Frequency') + axs[1, 1].legend(labels=['front_add_similarity']) + # 显示图形 + plt.tight_layout() + plt.savefig('multiple_histograms.png') + plt.close(fig) + # plt.show() + + +def showgrid(): + y_back_return = get_count_number(gvalue.back_return_similarity) + y_back_add = get_count_number(gvalue.back_add_similarity) + y_front_return = get_count_number(gvalue.front_return_similarity) + y_front_add = get_count_number(gvalue.front_add_similarity) + y_comprehensive = get_count_number(gvalue.comprehensive_similarity) + x = np.linspace(start=0.1, stop=1.0, num=10, endpoint=True).tolist() + plt.figure(figsize=(10, 6)) + plt.plot(x, y_back_return, color='red', label='back_return') + plt.plot(x, y_back_add, color='blue', label='back_add') + plt.plot(x, y_front_return, color='green', label='front_return') + plt.plot(x, y_front_add, color='purple', label='front_add') + plt.plot(x, y_comprehensive, color='orange', label='comprehensive') + plt.legend() + plt.xlabel('Similarity') + plt.ylabel('Frequency') + plt.grid(True, linestyle='--', alpha=0.5) + plt.savefig('multiple_grid.png') + plt.close() + + +def showtable(): # 在指定相似度下离群点统计 + temp_lists = [get_count_number(gvalue.back_return_similarity), + get_count_number(gvalue.back_add_similarity), + get_count_number(gvalue.front_return_similarity), + get_count_number(gvalue.front_add_similarity), + get_count_number(gvalue.comprehensive_similarity)] + rows = [] + table = PrettyTable() + tablename = ['back_return', 'back_add', 'front_return', 'front_add', 'comprehensive'] + table.field_names = ['name', '0.1', '0.2', '0.3', '0.4', '0.5', '0.6', '0.7', '0.8', '0.9', '1.0'] + for List, name in zip(temp_lists, tablename): + o_data = [round(data / List[-1], 3) for data in List] + o_data.insert(0, name) + rows.append(o_data) + # print(rows) + table.add_rows(rows) + print(table) + + +def compute_similarity_matrix(featurelists): + """计算图片之间的余弦相似度矩阵""" + # 计算所有向量对之间的余弦相似度 + cosine_similarities = 1 - cdist(featurelists, featurelists, metric='cosine') + cosine_similarities = np.around(cosine_similarities, decimals=3) + return cosine_similarities + + +def remove_empty_folders(root_dir): + for foldername, subfolders, files in os.walk(root_dir): + if not subfolders and not files: # 如果当前文件夹无子文件夹且无文件 + print(f"Removing empty folder: {foldername}") + try: + shutil.rmtree(foldername) # 删除空文件夹 + except Exception as e: + print(f"Error removing folder {foldername}: {e}") + + +def cosine_similarity(vec_mean, vecs, k=False, y_pred=None): # 余弦角相似度 + all_similarity = [] + if not k: + vec_mean = np.array(vec_mean) + for ovec in vecs: + ovec = np.array(ovec) + cos_sim = ovec.dot(vec_mean) / (np.linalg.norm(vec_mean) * np.linalg.norm(ovec)) + all_similarity.append(cos_sim) + else: + for nu, ks in enumerate(y_pred): + ovec = np.array(vecs[nu]) + vecmean = np.array(vec_mean[ks]) + cos_sim = ovec.dot(vecmean) / (np.linalg.norm(vecmean) * np.linalg.norm(ovec)) + all_similarity.append(cos_sim) + # print(all_similarity) + return all_similarity + + +def get_count_number(numbers): + count_less = [] + thresholds = np.linspace(start=0.1, stop=1.0, num=10, endpoint=True).tolist() + for threshold in thresholds: + count_less.append(sum(map(lambda x: x < threshold, numbers))) + print(count_less) + return count_less + + +def shuntVideo_imgs(obj: AiInterface, rootpth, vpth, ): # 制作单trackid下的相似度矩阵 + videospth = os.sep.join([rootpth, vpth]) + for videoname in os.listdir(videospth): + if videoname.endswith('mp4'): + cameraId = '0' if videoname.split('_')[2] == 'back' else '1' + videopth = os.sep.join([videospth, videoname]) + save_imgs_dir = os.sep.join([rootpth, 'images', videoname.split('.')[0]]) + if not os.path.exists(save_imgs_dir): + os.makedirs(save_imgs_dir) + track_boxes, features_dict, frame_id_img = run(models, source=videopth) + allimages, trackIdList = obj.getTrackingBox(track_boxes, + features_dict, + cameraId, + frame_id_img, + save_imgs_dir) + featList = get_feature_list(allimages) + cosine_similarities = compute_similarity_matrix(featList) + print(len(cosine_similarities)) + print(cosine_similarities) + + +def get_feature_list(allimages, actionModel=True): + featList = [] + groups = group_image(allimages, batch=64) + if not actionModel: + groups = [groups] + for group in groups: + for img in group: + feat_tensor = inference(img, models.similarityModel, actionModel) + for fe in feat_tensor: + if fe.device == 'cpu': + fe_np = fe.squeeze().detach().numpy() + else: + fe_np = fe.squeeze().detach().cpu().numpy() + featList.append(fe_np) + return featList + + +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 + remove_empty_folders(imgs_pth) + for imgdirs in os.listdir(imgs_pth): + imgpth = [] + for img in os.listdir(os.sep.join([imgs_pth, imgdirs])): + imgpth.append(os.sep.join([imgs_pth, imgdirs, img])) + featList = get_feature_list(imgpth, actionModel) + # assert all(len(lst) == len(featList[0]) for lst in featList) + if len(featList) < k: + continue + featList = np.array(featList) + Kmeans = KMeans(n_clusters=k) + y_pred = Kmeans.fit_predict(featList) + ores = cosine_similarity(Kmeans.cluster_centers_, featList, k=True, y_pred=y_pred) + if 'back_return' in imgdirs: + gvalue.back_return_similarity += ores + elif 'back_add' in imgdirs: + gvalue.back_add_similarity += ores + elif 'front_return' in imgdirs: + gvalue.front_return_similarity += ores + elif 'front_add' in imgdirs: + gvalue.front_add_similarity += ores + gvalue.comprehensive_similarity += ores + showtable() # 离群点表格 + + +def average_similarity(imgs_pth, actionModel=False): # 平均向量与每个图片的相似度 + remove_empty_folders(imgs_pth) + for imgdirs in os.listdir(imgs_pth): + imgpth = [] + if len(os.listdir(os.sep.join([imgs_pth, imgdirs]))) < 10: + continue + for img in os.listdir(os.sep.join([imgs_pth, imgdirs])): + imgpth.append(os.sep.join([imgs_pth, imgdirs, img])) + featList = get_feature_list(imgpth, actionModel) + assert all(len(lst) == len(featList[0]) for lst in featList) + vec_mean = [sum(column) / len(featList) for column in zip(*featList)] + ores = cosine_similarity(vec_mean, featList) + if 'back_return' in imgdirs: + gvalue.back_return_similarity += ores + elif 'back_add' in imgdirs: + gvalue.back_add_similarity += ores + elif 'front_return' in imgdirs: + gvalue.front_return_similarity += ores + elif 'front_add' in imgdirs: + gvalue.front_add_similarity += ores + gvalue.comprehensive_similarity += ores + showHistogram() # 绘制直方图 + showgrid() # 绘制折线图 + showtable() # 离群点表格 + showComprehensiveHistogram(gvalue.comprehensive_similarity, 'comprehensive_similarity') + + +def barcode_similarity(rootpths): + for dir in os.listdir(rootpths): + if dir == 'barcode_similarity': + continue + new_dir = os.sep.join([rootpths, 'barcode_similarity', dir]) + if not os.path.exists(new_dir): + os.makedirs(new_dir) + else: + continue + rootpth = os.sep.join([rootpths, dir]) # 6934660520292 + imgs_pth = [os.sep.join([rootpth, name]) for name in os.listdir(rootpth)] + featList = get_feature_list(imgs_pth, False) + cosine_similarities = compute_similarity_matrix(featList) + num = 0 + for i in range(cosine_similarities.shape[0]): + cols = np.where(cosine_similarities[i, :] > 0.5)[0] + if len(cols) > num: + num = len(cols) + max_cols = cols + imgPth = [os.sep.join([rootpth, imgName]) for imgName in [os.listdir(rootpth)[i] for i in max_cols]] + for img in imgPth: + try: + shutil.copy(img, new_dir) + except Exception as e: + print(e) + continue + # shutil.copy(img, new_dir) + # print(imgPth) + # print(featList) + # print(imgs_pth) + + +def compare_two_img(img1, img2): + img1_feature = get_feature_list(img1, False)[0] + img2_feature = get_feature_list(img2, False)[0] + cos_sim = img1_feature.dot(img2_feature) / (np.linalg.norm(img1_feature) * np.linalg.norm(img2_feature)) + print(cos_sim) + + +if __name__ == '__main__': + rootpth = 'Single_purchase_data' + ''' + 制作单trackid下的相似度矩阵 + ''' + # vpth = 'test_video' + # shuntVideo_imgs(ai_obj, rootpth, vpth) + + ''' + 平均向量与每个图片的相似度 + ''' + imgs_pth = os.sep.join([rootpth, 'images']) + average_similarity(imgs_pth) + + ''' + k值聚类中心向量与每个图片的相似度 + ''' + # imgs_pth = os.sep.join([rootpth, 'images']) + # k_similarity(imgs_pth, k=3) + + ''' + 计算筛选单个barcode相似度集中最多的图片 + ''' + # rootpths = 'data_test' + # barcode_similarity(rootpths) + + ''' + 对比两张图的相似度 + ''' + # img1 = ['C:/Users/HP/Desktop/maskBackImg.jpg'] + # img2 = ['C:/Users/HP/Desktop/frontImgMask.jpg'] + # compare_two_img(img1, img2) diff --git a/manualSortingTools.py b/manualSortingTools.py new file mode 100644 index 0000000..94d3aba --- /dev/null +++ b/manualSortingTools.py @@ -0,0 +1,77 @@ +import os.path +import shutil + + +from ytracking.track_ import * +from contrast.test_logic import group_image, inference +from tools.Interface import AiInterface, AiClass + +from tools.initModel import models +import matplotlib.pyplot as plt +from sklearn.cluster import KMeans + +models.initModel() +ai_obj = AiClass() +distance_lists = [] +all_distance_lists = [] + + +def showImg(newx): + plt.scatter(newx[:, 0], newx[:, 1]) + plt.show() + + +def cosine_similarity(vec1, vec2): + vec1 = np.array(vec1) + vec2 = np.array(vec2) + cos_sim = vec1.dot(vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2)) + return cos_sim + + +def get_kclusters(data, k): + Kmeans = KMeans(n_clusters=k, n_init='auto') + y_pred = Kmeans.fit_predict(data) + return y_pred + + +def move_file(pth, dirs, labels, y_pred): + for label, y_label in zip(labels, y_pred): + if not os.path.isdir(os.sep.join([pth, dirs, str(y_label)])): + os.mkdir(os.sep.join([pth, dirs, str(y_label)])) + try: + shutil.move(os.sep.join([pth, dirs, label]), + os.sep.join([pth, dirs, str(y_label), label])) + except Exception as e: + print(e) + + +def createDic(pth, dirs): + all_dics = {} + imgs = os.sep.join([pth, dirs]) + for img in os.listdir(imgs): + imgPth = os.sep.join([imgs, img]) + feat_tensor = inference([imgPth], models.similarityModel, actionModel=False) + all_dics[img] = feat_tensor + return all_dics + + +def main(pth, dirs): + global distance_lists + allDics = createDic(pth, dirs) + labels = [] + temp = None + for key, value in allDics.items(): + value = value.cpu().detach().numpy() + labels.append(key) + if temp is None: + temp = value + else: + temp = np.vstack((temp, value)) + y_pred = get_kclusters(temp, 3) + move_file(pth, dirs, labels, y_pred) + + +if __name__ == '__main__': + pth = 'C:/Users/HP/Desktop/40084' + dirs = '1' + main(pth, dirs) diff --git a/multiple_grid.png b/multiple_grid.png new file mode 100644 index 0000000000000000000000000000000000000000..37fdf924383b3a0edccdabeb8376cb81705e7c22 GIT binary patch literal 61537 zcmeFZc{JAT`z}oLAexknrKAw4WXMpdB%!Db8A4IWJkO0Pp+X{4Wk^CuW-3$W%A6_l zJZGNX^Ei*=IIsI>WF*$CqF+TpL9s^i zs2!|BU2$Bp6h?#z-?%v!&9X(l!l9}G&-qdN! zX#2ZKvCCcv5c0ZMy;D1+I`2S@tdH-_#8<&vx3YdIoSD+nOn2j2^jRcxz(JH*m{D}t z$EQ2fp4X(vzM=9uFIDE@V+%jR`CXk9zyJH#(&T#!KMH4VaI?<;G%Y>|} zZ3XDVxl`(7l$Gi4-@jj_e^AzWyQs{>PHp+(`#zqAngKl;2-%e6ucVm|U98 zIo{dXIpT11W{<_87cXAi##N?WRqXSW?bmN~q zdK6@OCu?IqKksuBnZjVZ4RO2MgIV)eJr>F@kzM}ro;CTZR=;PG^WQ9Kjon*zgxdL* 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