From 09e92d63b3f0bec952f59aa67e5a3d3f920bfd0f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E7=8E=8B=E5=BA=86=E5=88=9A?= Date: Wed, 11 Sep 2024 17:37:32 +0800 Subject: [PATCH] modify the 1:1 contrast code --- .gitignore | 1 + contrast/20240905_185407.pickle | Bin 11401 -> 0 bytes contrast/20240905_185642.pickle | Bin 11431 -> 0 bytes contrast/__pycache__/test_ori.cpython-39.pyc | Bin 10249 -> 10273 bytes ...ontrast_one2one.py => one2one_contrast.py} | 412 ++++++++++++------ contrast/one2one_onsite.py | 109 +++++ contrast/result/pickle/20240909_164035.txt | 174 ++++++++ .../result/pickle/20240909_164035_ft16.txt | 174 ++++++++ .../result/pickle/20240909_164035_uint8.txt | 174 ++++++++ .../record/20240907_142813.txt} | 120 ++--- .../result/record/20240907_142813_ft16.txt | 124 ++++++ .../record/20240907_171156.txt} | 114 ++--- .../result/record/20240907_171156_ft16.txt | 124 ++++++ contrast/result/record/20240909_085740.txt | 124 ++++++ .../result/record/20240909_085740_ft16.txt | 124 ++++++ .../result/record/20240909_085740_uint8.txt | 124 ++++++ contrast/result/record/20240909_094404.txt | 124 ++++++ .../result/record/20240909_094404_ft16.txt | 124 ++++++ .../result/record/20240909_094404_uint8.txt | 124 ++++++ .../record/20240909_094945.txt} | 118 ++--- .../result/record/20240909_094945_ft16.txt | 124 ++++++ .../result/record/20240909_094945_uint8.txt | 124 ++++++ contrast/result/record/20240909_152012.txt | 174 ++++++++ .../result/record/20240909_152012_ft16.txt | 174 ++++++++ .../result/record/20240909_152012_uint8.txt | 174 ++++++++ contrast/result/record/20240909_163328.txt | 174 ++++++++ .../result/record/20240909_163328_ft16.txt | 174 ++++++++ .../result/record/20240909_163328_uint8.txt | 174 ++++++++ contrast/test_ori.py | 2 +- contrast/write_feature_json.py | 174 ++++++++ track_reid.py | 7 +- tracking/time_test.py | 4 +- tracking/utils/videot.py | 5 +- 33 files changed, 3559 insertions(+), 313 deletions(-) delete mode 100644 contrast/20240905_185407.pickle delete mode 100644 contrast/20240905_185642.pickle rename contrast/{contrast_one2one.py => one2one_contrast.py} (63%) create mode 100644 contrast/one2one_onsite.py create mode 100644 contrast/result/pickle/20240909_164035.txt create mode 100644 contrast/result/pickle/20240909_164035_ft16.txt create mode 100644 contrast/result/pickle/20240909_164035_uint8.txt rename contrast/{20240905_185642.txt => result/record/20240907_142813.txt} (51%) create mode 100644 contrast/result/record/20240907_142813_ft16.txt rename contrast/{20240905_185407.txt => result/record/20240907_171156.txt} (53%) create mode 100644 contrast/result/record/20240907_171156_ft16.txt create mode 100644 contrast/result/record/20240909_085740.txt create mode 100644 contrast/result/record/20240909_085740_ft16.txt create mode 100644 contrast/result/record/20240909_085740_uint8.txt create mode 100644 contrast/result/record/20240909_094404.txt create mode 100644 contrast/result/record/20240909_094404_ft16.txt create mode 100644 contrast/result/record/20240909_094404_uint8.txt rename contrast/{results.txt => result/record/20240909_094945.txt} (52%) create mode 100644 contrast/result/record/20240909_094945_ft16.txt create mode 100644 contrast/result/record/20240909_094945_uint8.txt create mode 100644 contrast/result/record/20240909_152012.txt create mode 100644 contrast/result/record/20240909_152012_ft16.txt create mode 100644 contrast/result/record/20240909_152012_uint8.txt create mode 100644 contrast/result/record/20240909_163328.txt create mode 100644 contrast/result/record/20240909_163328_ft16.txt create mode 100644 contrast/result/record/20240909_163328_uint8.txt create mode 100644 contrast/write_feature_json.py diff --git a/.gitignore b/.gitignore index 7cba0ed..f9e4e3c 100644 --- a/.gitignore +++ b/.gitignore @@ -23,6 +23,7 @@ *.rar *.pkl +*.pickle *.npy *.csv diff --git a/contrast/20240905_185407.pickle b/contrast/20240905_185407.pickle deleted file mode 100644 index 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z7`0}p8_ul%iL+Wb2Mg<4{A1|5HC@tGO;=@&M!?&;YlGG4Ls-Td8EfVy@zolfI2?QQKGww|uA#ET&!X!=53XFeyqT0N=y|#0%RTounH^9oM<8F1ts1;APo6vIo-658OFZ ze#Ci__oOjI0zu(pc^etcudBF3N@ebN2o1rL$}NHy<3U)e+}bjVzEdw@!8lcPAtLuG z5k`-5SF*6v14=BYP@`|5B^Jiu=*mfQHUE8OBdP5~=NO_Ju?EqDxQg&Xb5)z`BFb_O zy;zNgn+M$%E8%t3PLhTdt`VEjr4juVnn)&Jx5`X}Da@v! zsd~6#j7yK5u12RGk%ec~?IZ)gs`~{W8ineSn(gF4{%Or;Gm#;^wqLl3*)7P`R+4#G zQ+w`z<7q8hW?1L>;$4Inw7{oTyDjuzaU({pM#M#Ub_TCkFTRGb{T#>F9jK!4W?eUl zfV*y?tqYAT;tt|2VmqP;VVuiRu5?RO9#W$N(ZYrfVhD&KaIW|4ynu2T*Of9QdE#tD zjnWi)BZ%{eaSl&CUEmK%h$Uo|GWrmMM=-cPeO?j{^;3oQOT_^>sz@d6G|rfA?o-3M zSsr3i@gcw0$LaI>C2N-wlp~Q7{y6)qeM(po{Zt96OpV8kXxS^Kmi9R1dD56AdI>*$ z8F2+cCSj_6ku1XD4P(ppx(qio|7Sz#{B*;F6VF>WCqli{7&R}= 0: + simi_dict["SeqDir"] = value + if label.isdigit() and len(label) >= 8: + + simi_max, simi_min = value.strip(',').split('.') + simi_dict["barcode"] = label + simi_dict["simi_max"] = float(simi_max) / 1000 + simi_dict["simi_min"] = float(simi_min) / 1000 + + if len(simi_dict): simiList.append(simi_dict) + + return simiList + +def main(): + filepath = r"\\192.168.1.28\share\测试_202406\0910\images\OneToOneCompare.txt" + + simiList = read_one2one_data(filepath) + simimax, simimean = [], [] + small = [] + for simidict in simiList: + simimax.append(simidict["simi_max"]) + simimean.append(simidict["simi_min"]) + if simidict["simi_max"]<0.6: + small.append(simidict) + + + simimax = np.array(simimax) + simimean = np.array(simimean) + + + + TPFN_max = len(simimax) + TPFN_mean = len(simimean) + + + + fig, axs = plt.subplots(2, 1) + axs[0].hist(simimax, bins=60, edgecolor='black') + axs[0].set_xlim([-0.2, 1]) + axs[0].set_title(f'Same Barcode, Num: {TPFN_max}') + axs[1].hist(simimean, bins=60, edgecolor='black') + axs[1].set_xlim([-0.2, 1]) + axs[1].set_title(f'Cross Barcode, Num: {TPFN_mean}') + # plt.savefig(f'./result/{file}_hist.png') # svg, png, pdf + + + + Recall_Pos = [] + Thresh = np.linspace(-0.2, 1, 100) + for th in Thresh: + TP = np.sum(simimax > th) + Recall_Pos.append(TP/TPFN_max) + + fig, ax = plt.subplots() + ax.plot(Thresh, Recall_Pos, 'b', label='Recall_Pos: TP/TPFN') + ax.set_xlim([0, 1]) + ax.set_ylim([0, 1]) + ax.grid(True) + ax.set_title('Positive recall') + ax.set_xlabel(f"Num: {TPFN_max}") + ax.legend() + plt.show() + # plt.savefig(f'./result/{file}_pr.png') # svg, png, pdf + + print("Have done!") + + + + + + + + + + + + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/contrast/result/pickle/20240909_164035.txt b/contrast/result/pickle/20240909_164035.txt new file mode 100644 index 0000000..dc77bfe --- /dev/null +++ b/contrast/result/pickle/20240909_164035.txt @@ -0,0 +1,174 @@ +same, 6954256905319, 20240715-144627_6954256905319, 0.208, 0.746, 0.604 +same, 6902265589617, 20240719-143553_6902265589617, 0.296, 0.719, 0.561 +same, 6930364600025, 20240719-143603_6930364600025, 0.301, 0.772, 0.666 +same, 6924509902839, 20240719-143630_6924509902839, 0.176, 0.660, 0.435 +same, 6902007115524, 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contrast/20240905_185642.txt rename to contrast/result/record/20240907_142813.txt index dda7cbf..ac84cad 100644 --- a/contrast/20240905_185642.txt +++ b/contrast/result/record/20240907_142813.txt @@ -60,65 +60,65 @@ same, 6924187828544, 20240723-112505_6924187828544, 0.281, 0.875, 0.573 same, 6974158894528, 20240723-155423_6974158894528, 0.433, 0.890, 0.788 same, 6970399922518, 20240723-160227_6970399922518, 0.078, 0.564, 0.215 same, 6974158894528, 20240723-160942_6974158894528, 0.417, 0.895, 0.832 -diff, 6972219890632, 20240715-144627_6954256905319, 0.045, 0.441, 0.100 -diff, 4897073991658, 20240719-143603_6930364600025, 0.030, 0.305, 0.060 -diff, 6934024590268, 20240719-143630_6924509902839, -0.003, 0.922, -0.011 -diff, 6920382723257, 20240719-143729_6902007115524, 0.064, 0.471, 0.140 -diff, 6902890022558, 20240719-143807_6930628801472, 0.025, 0.560, 0.083 -diff, 6930364600001, 20240719-144335_6908355010450, 0.024, 0.445, 0.063 -diff, 6924509902839, 20240719-144454_6924509902754, 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-0.060, 0.365, -0.119 diff --git a/contrast/test_ori.py b/contrast/test_ori.py index 7333a96..a92c1ed 100644 --- a/contrast/test_ori.py +++ b/contrast/test_ori.py @@ -118,7 +118,7 @@ def inference_image(images: list, transform, net, device, bs=16, embedding_size= patches = torch.stack(patches, dim=0) batch_patches.append(patches) - features = np.zeros((0, embedding_size)) + features = np.zeros((0, embedding_size), dtype=np.float32) for patches in batch_patches: pred = net(patches) pred[torch.isinf(pred)] = 1.0 diff --git a/contrast/write_feature_json.py b/contrast/write_feature_json.py new file mode 100644 index 0000000..ba8a350 --- /dev/null +++ b/contrast/write_feature_json.py @@ -0,0 +1,174 @@ +import json +import os +import pickle +import numpy as np +from config import config as conf +# from img_data import library_imgs, temp_imgs, main_library_imgs, main_imgs_2 +# from test_logic import initModel,getFeatureList +from model import resnet18 +import torch +from PIL import Image + +device = conf.device + + +def initModel(): + model = resnet18().to(device) + model.load_state_dict(torch.load(conf.test_model, map_location=conf.device)) + model.eval() + return model + + +from PIL import Image + + +def convert_rgba_to_rgb(image_path, output_path=None): + """ + 将给定路径的4通道PNG图像转换为3通道，并保存到指定输出路径。 + + :param image_path: 输入图像的路径 + :param output_path: 转换后的图像保存路径 + """ + # 打开图像 + img = Image.open(image_path) + # 转换图像模式从RGBA到RGB + # .convert('RGB')会丢弃Alpha通道并转换为纯RGB图像 + if img.mode == 'RGBA': + # 转换为RGB模式 + img_rgb = img.convert('RGB') + # 保存转换后的图像 + img_rgb.save(image_path) + print(f"Image converted from RGBA to RGB and saved to {image_path}") + # else: + # # 如果已经是RGB或其他模式，直接保存 + # img.save(image_path) + # print(f"Image already in {img.mode} mode, saved to {image_path}") + + +def test_preprocess(images: list, actionModel=False) -> torch.Tensor: + res = [] + for img in images: + try: + print(img) + 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=False): + 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 getFeatureList(barList, imgList, model): + 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) + 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 get_files(folder): + file_dict = {} + cnt = 0 + # barcode_list = ['6944649700065', '6924743915848', '6920459905012', '6901285991219', '6924882406269'] + for root, dirs, files in os.walk(folder): + + folder_name = os.path.basename(root) # 获取当前文件夹名称 + print(folder_name) + # with open('main_barcode.txt','a') as f: + # f.write(folder_name + '\n') + + # if len(dirs) == 0 and len(files) > 0 and folder_name in barcode_list: # 如果该文件夹没有子文件夹且有文件 + if len(dirs) == 0 and len(files) > 0: # 如果该文件夹没有子文件夹且有文件 + + file_names = [os.path.join(root, file) for file in files] # 获取所有文件名 + for file_name in file_names: + try: + convert_rgba_to_rgb(file_name) + except: + file_names.remove(file_name) + cnt += len(file_names) + file_dict[folder_name] = file_names + print(cnt) + + return file_dict + +def normalize(queFeatList): + for num1 in range(len(queFeatList)): + for num2 in range(len(queFeatList[num1])): + queFeatList[num1][num2] = queFeatList[num1][num2] / np.linalg.norm(queFeatList[num1][num2]) + return queFeatList +def img2feature(imgs_dict, model, barcode_flag): + if not len(imgs_dict) > 0: + raise ValueError("No imgs files provided") + queBarIdList = list(imgs_dict.keys()) + queImgsList = list(imgs_dict.values()) + queFeatList = getFeatureList(queBarIdList, queImgsList, model) + queFeatList = normalize(queFeatList) + return queBarIdList, queFeatList + + +def createFeatureDict(imgs_dict, model, + barcode_flag=False): ##imgs->{barcode1:[img1_1...img1_n], barcode2:[img2_1...img2_n]} + dicts_all = {} + value_list = [] + barcode_list, imgs_list = img2feature(imgs_dict, model, barcode_flag=False) + for i in range(len(barcode_list)): + dicts = {} + + imgs_list_ = [] + for j in range(len(imgs_list[i])): + imgs_list_.append(imgs_list[i][j].tolist()) + # with open('feature.txt','a') as f: + # f.write(str(imgs_list[i][j].tolist())+'\n') + + dicts['key'] = barcode_list[i] + dicts['value'] = imgs_list_ + value_list.append(dicts) + dicts_all['total'] = value_list + print('dicts_all', dicts_all) + with open('data_0909.json', 'a') as json_file: + json.dump(dicts_all, json_file) + + +def read_pkl_file(file_path): + with open(file_path, 'rb') as file: + data = pickle.load(file) + return data + + +if __name__ == "__main__": + ###将图片名称和模型推理特征向量字典存为json文件 + img_path = 'data/2000_train/base' + imgs_dict = get_files(img_path) + # print('imgs_dict', imgs_dict) + model = initModel() + createFeatureDict(imgs_dict, model, barcode_flag=False) + ###======================================================= + # ## =========pkl转json================ + # contents = read_pkl_file('dicts_list_1887.pkl') + # print(contents) + # with open('data_1887.json', 'w') as json_file: + # json.dump(contents, json_file) diff --git a/track_reid.py b/track_reid.py index d2ef1de..e282218 100644 --- a/track_reid.py +++ b/track_reid.py @@ -302,11 +302,8 @@ def run( # if feat is not None: # feat_curr[ii] = feat - - - - ## ================================================================ writed by WQG - + + ## ================================================================ writed by WQG '''tracks: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index] 0 1 2 3 4 5 6 7 8 这里，frame_index 也可以用视频的 帧ID 代替, box_index 保持不变 diff --git a/tracking/time_test.py b/tracking/time_test.py index d1c374d..cab8c8e 100644 --- a/tracking/time_test.py +++ b/tracking/time_test.py @@ -37,10 +37,10 @@ def main(): t6 = ProcessTimeDict['YoloResnetTrackerEnd'] # Yolo、Resnet、tracker执行结束时间 t7 = ProcessTimeDict['trackingEnd'] # 轨迹分析结束时间 t8 = ProcessTimeDict['contrastEnd'] # 比对结束时间 - t9 = ProcessTimeDict['algroStartToEnd'] # 算法从开始至结束时间 + t9 = ProcessTimeDict['algroStartToEnd'] # 算法总执行时间 t10 = ProcessTimeDict['weightstablityToEnd'] # 重力稳定至算法结束时间 t11 = ProcessTimeDict['frameEndToEnd'] # 最后一帧图像至算法结束时间 - + TimeConsuming.append((t1, t2, t3, t4, t5, wv, t6, t7, t8, t9, t10, t11)) DayHMS.append(datename) except Exception as e: diff --git a/tracking/utils/videot.py b/tracking/utils/videot.py index 15ae6e2..a6b9b91 100644 --- a/tracking/utils/videot.py +++ b/tracking/utils/videot.py @@ -81,11 +81,12 @@ def videosave(bboxes, videopath="100_1688009697927.mp4"): def main(): videopath = r'\\192.168.1.28\share\测试_202406\0822\A_1724314806144' - savepath = r'D:\badvideo' + videopath = r'D:\videos' + savepath = r'D:\videos' # video2imgs(videopath, savepath) k = 0 for filename in os.listdir(videopath): - filename = "20240822-163506_88e6409d-f19b-4e97-9f01-b3fde259cbff.ts" + # filename = "20240822-163506_88e6409d-f19b-4e97-9f01-b3fde259cbff.ts" file, ext = os.path.splitext(filename) if ext not in VideoFormat: