diff --git a/__pycache__/track_reid.cpython-39.pyc b/__pycache__/track_reid.cpython-39.pyc index e5287f5..d4143c9 100644 Binary files a/__pycache__/track_reid.cpython-39.pyc and b/__pycache__/track_reid.cpython-39.pyc differ diff --git a/contrast/__pycache__/event_test.cpython-39.pyc b/contrast/__pycache__/event_test.cpython-39.pyc new file mode 100644 index 0000000..0118aa4 Binary files /dev/null and b/contrast/__pycache__/event_test.cpython-39.pyc differ diff --git a/contrast/event_test.py b/contrast/event_test.py index 1c150f8..449349a 100644 --- a/contrast/event_test.py +++ b/contrast/event_test.py @@ -6,10 +6,39 @@ Created on Mon Dec 16 18:56:18 2024 """ import os import cv2 - +import json +import numpy as np +import matplotlib.pyplot as plt +from matplotlib import rcParams +from matplotlib.font_manager import FontProperties +from scipy.spatial.distance import cdist from utils.event import ShoppingEvent, save_data -def main(): + + +rcParams['font.sans-serif'] = ['SimHei'] # 用黑体显示中文 +rcParams['axes.unicode_minus'] = False # 正确显示负号 + + +'''*********** USearch ***********''' +def read_usearch(): + stdFeaturePath = r"D:\contrast\stdlib\v11_test.json" + stdBarcode = [] + stdlib = {} + with open(stdFeaturePath, 'r', encoding='utf-8') as f: + data = json.load(f) + for dic in data['total']: + barcode = dic['key'] + feature = np.array(dic['value']) + stdBarcode.append(barcode) + stdlib[barcode] = feature + + return stdlib + +def get_eventlist(): + ''' + 读取一次测试中的错误事件 + ''' evtpaths = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\images" text1 = "one2n_Error.txt" text2 = "one2SN_Error.txt" @@ -24,9 +53,16 @@ def main(): if line: fpath=os.path.join(evtpaths, line) events.append(fpath) - - + events = list(set(events)) + + return events + +def single_event(): + + events = get_eventlist() + + '''定义当前事件存储地址及生成相应文件件''' resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\result\single_event" @@ -35,6 +71,291 @@ def main(): save_data(event, resultPath) print(event.evtname) + + + +def get_topk_percent(data, k): + """ + 获取数据中最大的 k% 的元素 + """ + # 将数据转换为 NumPy 数组 + if isinstance(data, list): + data = np.array(data) + + percentile = np.percentile(data, 100-k) + top_k_percent = data[data >= percentile] + + return top_k_percent +def cluster(data, thresh=0.15): + # data = np.array([0.1, 0.13, 0.7, 0.2, 0.8, 0.52, 0.3, 0.7, 0.85, 0.58]) + # data = np.array([0.1, 0.13, 0.2, 0.3]) + # data = np.array([0.1]) + + if isinstance(data, list): + data = np.array(data) + + data1 = np.sort(data) + cluter, Cluters, = [data1[0]], [] + for i in range(1, len(data1)): + if data1[i] - data1[i-1]< thresh: + cluter.append(data1[i]) + else: + Cluters.append(cluter) + cluter = [data1[i]] + Cluters.append(cluter) + + clt_center = [] + for clt in Cluters: + ## 是否应该在此处限制一个聚类中的最小轨迹样本数，应该将该因素放在轨迹分析中 + # if len(clt)>=3: + # clt_center.append(np.mean(clt)) + clt_center.append(np.mean(clt)) + + # print(clt_center) + + return clt_center + +def calc_simil(event, stdfeat): + + def calsiml(feat1, feat2): + '''轨迹样本和标准特征集样本相似度的选择策略''' + matrix = 1 - cdist(feat1, feat2, 'cosine') + simi_max = [] + for i in range(len(matrix)): + sim = np.mean(get_topk_percent(matrix[i, :], 75)) + simi_max.append(sim) + cltc_max = cluster(simi_max) + Simi = max(cltc_max) + + ## cltc_max为空属于编程考虑不周，应予以排查解决 + # if len(cltc_max): + # Simi = max(cltc_max) + # else: + # Simi = 0 #不应该走到该处 + + + return Simi + + + front_boxes = np.empty((0, 9), dtype=np.float64) ##和类doTracks兼容 + front_feats = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容 + for i in range(len(event.front_boxes)): + front_boxes = np.concatenate((front_boxes, event.front_boxes[i]), axis=0) + front_feats = np.concatenate((front_feats, event.front_feats[i]), axis=0) + + back_boxes = np.empty((0, 9), dtype=np.float64) ##和类doTracks兼容 + back_feats = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容 + for i in range(len(event.back_boxes)): + back_boxes = np.concatenate((back_boxes, event.back_boxes[i]), axis=0) + back_feats = np.concatenate((back_feats, event.back_feats[i]), axis=0) + + if len(front_feats): + front_simi = calsiml(front_feats, stdfeat) + if len(back_feats): + back_simi = calsiml(back_feats, stdfeat) + + '''前后摄相似度融合策略''' + if len(front_feats) and len(back_feats): + diff_simi = abs(front_simi - back_simi) + if diff_simi>0.15: + Similar = max([front_simi, back_simi]) + else: + Similar = (front_simi+back_simi)/2 + elif len(front_feats) and len(back_feats)==0: + Similar = front_simi + elif len(front_feats)==0 and len(back_feats): + Similar = back_simi + else: + Similar = None # 在event.front_feats和event.back_feats同时为空时 + + return Similar + + + + + + + + + + + + + + + +def simi_matrix(): + resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\result\single_event" + + stdlib = read_usearch() + events = get_eventlist() + for evtpath in events: + evtname = os.path.basename(evtpath) + _, barcode = evtname.split("_") + + # 生成事件与相应标准特征集 + event = ShoppingEvent(evtpath) + stdfeat = stdlib[barcode] + + Similar = calc_simil(event, stdfeat) + + # 构造 boxes 子图存储路径 + subimgpath = os.path.join(resultPath, f"{event.evtname}", "subimg") + if not os.path.exists(subimgpath): + os.makedirs(subimgpath) + histpath = os.path.join(resultPath, "simi_hist") + if not os.path.exists(histpath): + os.makedirs(histpath) + + + + mean_values, max_values = [], [] + cameras = ('front', 'back') + fig, ax = plt.subplots(2, 3, figsize=(16, 9), dpi=100) + kpercent = 25 + for camera in cameras: + boxes = np.empty((0, 9), dtype=np.float64) ##和类doTracks兼容 + evtfeat = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容 + if camera == 'front': + for i in range(len(event.front_boxes)): + boxes = np.concatenate((boxes, event.front_boxes[i]), axis=0) + evtfeat = np.concatenate((evtfeat, event.front_feats[i]), axis=0) + imgpaths = event.front_imgpaths + + else: + for i in range(len(event.back_boxes)): + boxes = np.concatenate((boxes, event.back_boxes[i]), axis=0) + evtfeat = np.concatenate((evtfeat, event.back_feats[i]), axis=0) + imgpaths = event.back_imgpaths + + assert len(boxes)==len(evtfeat), f"Please check the Event: {evtname}" + if len(boxes)==0: continue + print(evtname) + + matrix = 1 - cdist(evtfeat, stdfeat, 'cosine') + simi_1d = matrix.flatten() + simi_mean = np.mean(matrix, axis=1) + # simi_max = np.max(matrix, axis=1) + + '''以相似度矩阵每一行最大的 k% 的相似度做均值计算''' + simi_max = [] + for i in range(len(matrix)): + sim = np.mean(get_topk_percent(matrix[i, :], kpercent)) + simi_max.append(sim) + + + mean_values.append(np.mean(matrix)) + max_values.append(np.mean(simi_max)) + + diff_max_mean = np.mean(simi_max) - np.mean(matrix) + + '''相似度统计特性图示''' + k =0 + if camera == 'front': k = 1 + + '''********************* 相似度全体数据 *********************''' + ax[k, 0].hist(simi_1d, bins=60, range=(-0.2, 1), edgecolor='black') + ax[k, 0].set_xlim([-0.2, 1]) + ax[k, 0].set_title(camera) + + _, y_max = ax[k, 0].get_ylim() # 获取y轴范围 + '''相似度变动范围''' + ax[k, 0].text(-0.1, 0.15*y_max, f"rng:{max(simi_1d)-min(simi_1d):.3f}", fontsize=18, color='b') + + '''********************* 均值********************************''' + ax[k, 1].hist(simi_mean, bins=24, range=(-0.2, 1), edgecolor='black') + ax[k, 1].set_xlim([-0.2, 1]) + ax[k, 1].set_title("mean") + _, y_max = ax[k, 1].get_ylim() # 获取y轴范围 + '''相似度变动范围''' + ax[k, 1].text(-0.1, 0.15*y_max, f"rng:{max(simi_mean)-min(simi_mean):.3f}", fontsize=18, color='b') + + + '''********************* 最大值 ******************************''' + ax[k, 2].hist(simi_max, bins=24, range=(-0.2, 1), edgecolor='black') + ax[k, 2].set_xlim([-0.2, 1]) + ax[k, 2].set_title("max") + _, y_max = ax[k, 2].get_ylim() # 获取y轴范围 + '''相似度变动范围''' + ax[k, 2].text(-0.1, 0.15*y_max, f"rng:{max(simi_max)-min(simi_max):.3f}", fontsize=18, color='b') + + + '''绘制聚类中心''' + cltc_mean = cluster(simi_mean) + for value in cltc_mean: + ax[k, 1].axvline(x=value, color='m', linestyle='--', linewidth=3) + + cltc_max = cluster(simi_max) + for value in cltc_max: + ax[k, 2].axvline(x=value, color='m', linestyle='--', linewidth=3) + + '''绘制相似度均值与最大值均值''' + ax[k, 1].axvline(x=np.mean(matrix), color='r', linestyle='-', linewidth=3) + ax[k, 2].axvline(x=np.mean(simi_max), color='g', linestyle='-', linewidth=3) + + '''绘制相似度最大值均值 - 均值''' + _, y_max = ax[k, 2].get_ylim() # 获取y轴范围 + ax[k, 2].text(-0.1, 0.05*y_max, f"g-r={diff_max_mean:.3f}", fontsize=18, color='m') + + plt.show() + + # for i, box in enumerate(boxes): + # x1, y1, x2, y2, tid, score, cls, fid, bid = box + # imgpath = imgpaths[int(fid-1)] + # image = cv2.imread(imgpath) + # subimg = image[int(y1/2):int(y2/2), int(x1/2):int(x2/2), :] + # camerType, timeTamp, _, frameID = os.path.basename(imgpath).split('.')[0].split('_') + # subimgName = f"cam{camerType}_{i}_tid{int(tid)}_fid({int(fid)}, {frameID})_{simi_mean[i]:.3f}.png" + # imgpairs.append((subimgName, subimg)) + # spath = os.path.join(subimgpath, subimgName) + # cv2.imwrite(spath, subimg) + + # oldname = f"cam{camerType}_{i}_tid{int(tid)}_fid({int(fid)}, {frameID}).png" + # oldpath = os.path.join(subimgpath, oldname) + # if os.path.exists(oldpath): + # os.remove(oldpath) + + + if len(mean_values)==2: + mean_diff = abs(mean_values[1]-mean_values[0]) + ax[0, 1].set_title(f"mean diff: {mean_diff:.3f}") + if len(max_values)==2: + max_values = abs(max_values[1]-max_values[0]) + ax[0, 2].set_title(f"max diff: {max_values:.3f}") + try: + fig.suptitle(f"Similar: {Similar:.3f}", fontsize=16) + except Exception as e: + print(e) + print(f"Similar: {Similar}") + pltpath = os.path.join(subimgpath, f"hist_max_{kpercent}%_.png") + plt.savefig(pltpath) + + pltpath1 = os.path.join(histpath, f"{evtname}_.png") + plt.savefig(pltpath1) + + + plt.close() + + + + + + +def main(): + + simi_matrix() + + + + + + + + + + + + @@ -42,3 +363,14 @@ def main(): if __name__ == "__main__": main() + # cluster() + + + + + + + + + + diff --git a/contrast/feat_extract/__pycache__/config.cpython-39.pyc b/contrast/feat_extract/__pycache__/config.cpython-39.pyc index 3792f4c..11b46cc 100644 Binary files a/contrast/feat_extract/__pycache__/config.cpython-39.pyc and b/contrast/feat_extract/__pycache__/config.cpython-39.pyc differ diff --git a/contrast/feat_extract/config.py b/contrast/feat_extract/config.py index 9e9c6b3..463ec75 100644 --- a/contrast/feat_extract/config.py +++ b/contrast/feat_extract/config.py @@ -61,8 +61,8 @@ class Config: test_val = "D:/比对/cl" # test_val = "./data/test_data_100" - # test_model = "checkpoints/best_resnet18_v11.pth" - test_model = "checkpoints/zhanting_res_801.pth" + test_model = "checkpoints/best_resnet18_v11.pth" + # test_model = "checkpoints/zhanting_res_801.pth" diff --git a/contrast/one2n_contrast.py b/contrast/one2n_contrast.py index 5c32ea2..e04571c 100644 --- a/contrast/one2n_contrast.py +++ b/contrast/one2n_contrast.py @@ -13,25 +13,23 @@ from scipy.spatial.distance import cdist from utils.event import ShoppingEvent -def gen_eventdict(sourcePath, stype="data"): +def init_eventdict(sourcePath, stype="data"): '''stype: str, 'source': 由 videos 或 images 生成的 pickle 文件 'data': 从 data 文件中读取的现场运行数据 ''' k, errEvents = 0, [] - for source_path in sourcePath: - evtpath, bname = os.path.split(source_path) - + for bname in os.listdir(sourcePath): # bname = r"20241126-135911-bdf91cf9-3e9a-426d-94e8-ddf92238e175_6923555210479" - source_path = os.path.join(evtpath, bname) - + + source_path = os.path.join(sourcePath, bname) + if not os.path.isdir(source_path): continue + pickpath = os.path.join(eventDataPath, f"{bname}.pickle") if os.path.isfile(pickpath): continue - try: event = ShoppingEvent(source_path, stype) - # save_data(event, resultPath) with open(pickpath, 'wb') as f: pickle.dump(event, f) @@ -44,7 +42,7 @@ def gen_eventdict(sourcePath, stype="data"): # break errfile = os.path.join(resultPath, 'error_events.txt') - with open(errfile, 'w', encoding='utf-8') as f: + with open(errfile, 'a', encoding='utf-8') as f: for line in errEvents: f.write(line + '\n') @@ -61,6 +59,44 @@ def read_eventdict(eventDataPath): return evtDict + +def simi_calc(event, o2nevt, typee=None): + if typee == "11": + boxes1 = event.front_boxes + boxes2 = o2nevt.front_boxes + + feat1 = event.front_feats + feat2 = o2nevt.front_feats + if typee == "10": + boxes1 = event.front_boxes + boxes2 = o2nevt.back_boxes + + feat1 = event.front_feats + feat2 = o2nevt.back_feats + if typee == "00": + boxes1 = event.back_boxes + boxes2 = o2nevt.back_boxes + + feat1 = event.back_feats + feat2 = o2nevt.back_feats + if typee == "01": + boxes1 = event.back_boxes + boxes2 = o2nevt.front_boxes + + feat1 = event.back_feats + feat2 = o2nevt.front_feats + + if len(feat1) and len(feat2): + matrix = 1 - cdist(feat1[0], feat2[0], 'cosine') + simi = np.mean(matrix) + else: + simi = None + + + + return simi + + def one2n_pr(evtDicts): @@ -81,45 +117,22 @@ def one2n_pr(evtDicts): o2nevt = o2n_evt[0] else: continue - if typee == "11": - boxes1 = event.front_trackingboxes - boxes2 = o2nevt.front_trackingboxes - - feat1 = event.front_trackingfeats - feat2 = o2nevt.front_trackingfeats - if typee == "10": - boxes1 = event.front_trackingboxes - boxes2 = o2nevt.back_trackingboxes - - feat1 = event.front_trackingfeats - feat2 = o2nevt.back_trackingfeats - if typee == "00": - boxes1 = event.back_trackingboxes - boxes2 = o2nevt.back_trackingboxes - - feat1 = event.back_trackingfeats - feat2 = o2nevt.back_trackingfeats - if typee == "01": - boxes1 = event.back_trackingboxes - boxes2 = o2nevt.front_trackingboxes - - feat1 = event.back_trackingfeats - feat2 = o2nevt.front_trackingfeats - - matrix = 1 - cdist(feat1[0], feat2[0], 'cosine') - simi_mean = np.mean(matrix) - simi_max = np.max(matrix) + simival = simi_calc(event, o2nevt, typee) + if simival==None: + continue + evt_names.append(nname) evt_barcodes.append(barcode) - evt_similars.append(simi_mean) + evt_similars.append(simival) evt_types.append(typee) if len(evt_names)==len(evt_barcodes) and len(evt_barcodes)==len(evt_similars) \ and len(evt_similars)==len(evt_types) and len(evt_names)>0: - maxsim = evt_similars[evt_similars.index(max(evt_similars))] + # maxsim = evt_similars[evt_similars.index(max(evt_similars))] + maxsim = max(evt_similars) for i in range(len(evt_names)): bcd, simi = evt_barcodes[i], evt_similars[i] @@ -173,16 +186,16 @@ def one2n_pr(evtDicts): plt.show() ## ============================= 1:n 直方图''' fig, axes = plt.subplots(2, 2) - axes[0, 0].hist(tpsimi, bins=60, edgecolor='black') + axes[0, 0].hist(tpsimi, bins=60, range=(-0.2, 1), edgecolor='black') axes[0, 0].set_xlim([-0.2, 1]) axes[0, 0].set_title('TP') - axes[0, 1].hist(fpsimi, bins=60, edgecolor='black') + axes[0, 1].hist(fpsimi, bins=60, range=(-0.2, 1), edgecolor='black') axes[0, 1].set_xlim([-0.2, 1]) axes[0, 1].set_title('FP') - axes[1, 0].hist(tnsimi, bins=60, edgecolor='black') + axes[1, 0].hist(tnsimi, bins=60, range=(-0.2, 1), edgecolor='black') axes[1, 0].set_xlim([-0.2, 1]) axes[1, 0].set_title('TN') - axes[1, 1].hist(fnsimi, bins=60, edgecolor='black') + axes[1, 1].hist(fnsimi, bins=60, range=(-0.2, 1), edgecolor='black') axes[1, 1].set_xlim([-0.2, 1]) axes[1, 1].set_title('FN') plt.show() @@ -192,16 +205,16 @@ def one2n_pr(evtDicts): def main(): '''1. 生成事件字典并保存至 eventDataPath, 只需运行一次 ''' - # gen_eventdict(sourcePath) + # init_eventdict(eventSourcePath) - '''2. 读取时间字典 ''' + '''2. 读取事件字典 ''' evtDicts = read_eventdict(eventDataPath) '''3. 1:n 比对事件评估 ''' fpevents = one2n_pr(evtDicts) - fpErrFile = str(Path(resultPath).joinpath("one2n_Error.txt")) + fpErrFile = str(Path(resultPath).joinpath("one2n_fp_Error.txt")) with open(fpErrFile, "w") as file: for item in fpevents: file.write(item + "\n") @@ -214,8 +227,7 @@ def main(): if __name__ == '__main__': - - eventSourcePath = [r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\images"] + eventSourcePath = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\images" resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\result" eventDataPath = os.path.join(resultPath, "evtobjs") diff --git a/contrast/one2one_contrast.py b/contrast/one2one_contrast.py index 78349b1..3ee7eb9 100644 --- a/contrast/one2one_contrast.py +++ b/contrast/one2one_contrast.py @@ -11,7 +11,7 @@ Created on Fri Aug 30 17:53:03 2024 标准特征提取，并保存至文件夹 stdFeaturePath 中， 也可在运行过程中根据与购物事件集合 barcodes 交集执行 2. 1:1 比对性能测试， - func: one2one_eval(similPath) + func: one2one_simi() (1) 求购物事件和标准特征级 Barcode 交集，构造 evtDict、stdDict (2) 构造扫 A 放 A、扫 A 放 B 组合，mergePairs = AA_list + AB_list (3) 循环计算 mergePairs 中元素 "(A, A) 或 (A, B)" 相似度; @@ -20,7 +20,7 @@ Created on Fri Aug 30 17:53:03 2024 3. precise、recall等指标计算 - func: compute_precise_recall(pickpath) + func: compute_one2one_pr(pickpath) @author: ym @@ -33,6 +33,9 @@ import sys import random import pickle import json +import random +import copy +import sys # import torch import time # import json @@ -54,6 +57,7 @@ from feat_extract.config import config as conf from feat_extract.inference import FeatsInterface from utils.event import ShoppingEvent, save_data from genfeats import gen_bcd_features +from event_test import calc_simil @@ -175,14 +179,53 @@ def data_precision_compare(stdfeat, evtfeat, evtMessage, save=True): f.write(line + '\n') -def one2one_simi(): + + +def simi_calc(event, stdfeat): + evtfeat = event.feats_compose + if isinstance(event.feats_select, list): + if len(event.feats_select) and len(event.feats_select[0]): + evtfeat = event.feats_select[0] + else: + return None, None, None + else: + evtfeat = event.feats_select + + if len(evtfeat)==0 or len(stdfeat)==0: + return None, None, None + + matrix = 1 - cdist(evtfeat, stdfeat, 'cosine') + matrix[matrix < 0] = 0 + + simi_mean = np.mean(matrix) + simi_max = np.max(matrix) + stdfeatm = np.mean(stdfeat, axis=0, keepdims=True) + evtfeatm = np.mean(evtfeat, axis=0, keepdims=True) + simi_mfeat = 1- np.maximum(0.0, cdist(stdfeatm, evtfeatm, 'cosine')) + + return simi_mean, simi_max, simi_mfeat[0,0] + + +def build_std_evt_dict(): ''' stdFeaturePath: 标准特征集地址 eventDataPath: Event对象地址 ''' - stdBarcode = [p.stem for p in Path(stdFeaturePath).iterdir() if p.is_file() and p.suffix=='.pickle'] + # stdBarcode = [p.stem for p in Path(stdFeaturePath).iterdir() if p.is_file() and p.suffix=='.json'] + '''*********** USearch ***********''' + stdFeaturePath = r"D:\contrast\stdlib\v11_test.json" + stdBarcode = [] + stdlib = {} + with open(stdFeaturePath, 'r', encoding='utf-8') as f: + data = json.load(f) + for dic in data['total']: + barcode = dic['key'] + feature = np.array(dic['value']) + stdBarcode.append(barcode) + stdlib[barcode] = feature + '''======1. 购物事件列表，该列表中的 Barcode 存在于标准的 stdBarcode 内 ===''' evtList = [(p.stem, p.stem.split('_')[-1]) for p in Path(eventDataPath).iterdir() if p.is_file() @@ -192,16 +235,21 @@ def one2one_simi(): and p.stem.split('_')[-1] in stdBarcode ] barcodes = set([bcd for _, bcd in evtList]) - + '''======2. 构建用于比对的标准特征字典 =============''' + # stdDict = {} + # for barcode in barcodes: + # stdpath = os.path.join(stdFeaturePath, barcode+'.json') + # with open(stdpath, 'r', encoding='utf-8') as f: + # stddata = json.load(f) + # feat = np.array(stddata["value"]) + # stdDict[barcode] = feat + + '''*********** USearch ***********''' stdDict = {} for barcode in barcodes: - stdpath = os.path.join(stdFeaturePath, barcode+'.pickle') - with open(stdpath, 'rb') as f: - stddata = pickle.load(f) - stdDict[barcode] = stddata - - + stdDict[barcode] = stdlib[barcode] + '''======3. 构建用于比对的操作事件字典 =============''' evtDict = {} for evtname, barcode in evtList: @@ -209,21 +257,123 @@ def one2one_simi(): with open(evtpath, 'rb') as f: evtdata = pickle.load(f) evtDict[evtname] = evtdata + + return evtList, evtDict, stdDict + +def one2SN_pr(evtList, evtDict, stdDict): + + std_barcodes = set([bcd for _, bcd in evtList]) + + + tp_events, fn_events, fp_events, tn_events = [], [], [], [] + tp_simi, fn_simi, tn_simi, fp_simi = [], [], [], [] + errorFile_one2SN = [] + + SN = 9 + for evtname, barcode in evtList: + bcd_selected = [barcode] + + dset = list(std_barcodes - set([barcode])) + if len(dset) > SN: + random.shuffle(dset) + bcd_selected.extend(dset[:SN]) + else: + bcd_selected.extend(dset) + + event = evtDict[evtname] + ## 无轨迹判断 + if len(event.front_feats)+len(event.back_feats)==0: + print(evtname) + continue + + barcodes, similars = [], [] + for stdbcd in bcd_selected: + stdfeat = stdDict[stdbcd] + # simi_mean, simi_max, simi_mfeat = simi_calc(event, stdfeat) + simi_mean = calc_simil(event, stdfeat) + + ## 在event.front_feats和event.back_feats同时为空时，此处不需要保护 + # if simi_mean==None: + # continue + + barcodes.append(stdbcd) + similars.append(simi_mean) + + ## 此处不需要保护 + # if len(similars)==0: + # print(evtname) + # continue + + max_idx = similars.index(max(similars)) + max_sim = similars[max_idx] + for i in range(len(barcodes)): + bcd, simi = barcodes[i], similars[i] + if bcd==barcode and simi==max_sim: + tp_simi.append(simi) + tp_events.append(evtname) + elif bcd==barcode and simi!=max_sim: + fn_simi.append(simi) + fn_events.append(evtname) + elif bcd!=barcode and simi!=max_sim: + tn_simi.append(simi) + tn_events.append(evtname) + elif bcd!=barcode and simi==max_sim and barcode in barcodes: + fp_simi.append(simi) + fp_events.append(evtname) + else: + errorFile_one2SN.append(evtname) + + PPreciseX, PRecallX = [], [] + NPreciseX, NRecallX = [], [] + Thresh = np.linspace(-0.2, 1, 100) + for th in Thresh: + '''适用于 (Precise, Recall) 计算方式：多个相似度计算并排序，barcode相等且排名第一为 TP ''' + '''===================================== 1:SN ''' + TPX = sum(np.array(tp_simi) >= th) + FPX = sum(np.array(fp_simi) >= th) + FNX = sum(np.array(fn_simi) < th) + TNX = sum(np.array(tn_simi) < th) + PPreciseX.append(TPX/(TPX+FPX+1e-6)) + PRecallX.append(TPX/(len(tp_simi)+len(fn_simi)+1e-6)) + + NPreciseX.append(TNX/(TNX+FNX+1e-6)) + NRecallX.append(TNX/(len(tn_simi)+len(fp_simi)+1e-6)) + + fig, ax = plt.subplots() + ax.plot(Thresh, PPreciseX, 'r', label='Precise_Pos: TP/TPFP') + ax.plot(Thresh, PRecallX, 'b', label='Recall_Pos: TP/TPFN') + ax.plot(Thresh, NPreciseX, 'g', label='Precise_Neg: TN/TNFP') + ax.plot(Thresh, NRecallX, 'c', label='Recall_Neg: TN/TNFN') + ax.set_xlim([0, 1]) + ax.set_ylim([0, 1]) + ax.grid(True) + ax.set_title('1:SN Precise & Recall') + ax.set_xlabel(f"Event Num: {len(evtList)}") + ax.legend() + plt.show() + ## ============================= 1:N 展厅 直方图''' + fig, axes = plt.subplots(2, 2) + axes[0, 0].hist(tp_simi, bins=60, range=(-0.2, 1), edgecolor='black') + axes[0, 0].set_xlim([-0.2, 1]) + axes[0, 0].set_title(f'TP({len(tp_simi)})') + axes[0, 1].hist(fp_simi, bins=60, range=(-0.2, 1), edgecolor='black') + axes[0, 1].set_xlim([-0.2, 1]) + axes[0, 1].set_title(f'FP({len(fp_simi)})') + axes[1, 0].hist(tn_simi, bins=60, range=(-0.2, 1), edgecolor='black') + axes[1, 0].set_xlim([-0.2, 1]) + axes[1, 0].set_title(f'TN({len(tn_simi)})') + axes[1, 1].hist(fn_simi, bins=60, range=(-0.2, 1), edgecolor='black') + axes[1, 1].set_xlim([-0.2, 1]) + axes[1, 1].set_title(f'FN({len(fn_simi)})') + plt.show() - '''======4.2 barcode 标准图像保存 ==================''' - # for stdbcd in barcodes: - # stdImgpath = stdDict[stdbcd]["imgpaths"] - # pstdpath = os.path.join(subimgPath, f"{stdbcd}") - # if not os.path.exists(pstdpath): - # os.makedirs(pstdpath) - # ii = 1 - # for filepath in stdImgpath: - # stdpath = os.path.join(pstdpath, f"{stdbcd}_{ii}.png") - # shutil.copy2(filepath, stdpath) - # ii += 1 - '''======5 构造 3 个事件对: 扫 A 放 A, 扫 A 放 B, 合并 ====================''' + +def one2one_simi(evtList, evtDict, stdDict): + + barcodes = set([bcd for _, bcd in evtList]) + '''======1 构造 3 个事件对: 扫 A 放 A, 扫 A 放 B, 合并 ====================''' AA_list = [(evtname, barcode, "same") for evtname, barcode in evtList] AB_list = [] for evtname, barcode in evtList: @@ -234,45 +384,36 @@ def one2one_simi(): mergePairs = AA_list + AB_list - '''======6 计算事件、标准特征集相似度 ==================''' + '''======2 计算事件、标准特征集相似度 ==================''' rltdata = [] for i in range(len(mergePairs)): evtname, stdbcd, label = mergePairs[i] event = evtDict[evtname] - - ##============================================ float32 - stdfeat = stdDict[stdbcd]["feats_ft32"] - - evtfeat = event.feats_compose - if len(evtfeat)==0: continue - - matrix = 1 - cdist(stdfeat, evtfeat, 'cosine') - matrix[matrix < 0] = 0 - - simi_mean = np.mean(matrix) - simi_max = np.max(matrix) - stdfeatm = np.mean(stdfeat, axis=0, keepdims=True) - evtfeatm = np.mean(evtfeat, axis=0, keepdims=True) - simi_mfeat = 1- np.maximum(0.0, cdist(stdfeatm, evtfeatm, 'cosine')) - rltdata.append((label, stdbcd, evtname, simi_mean, simi_max, simi_mfeat[0,0])) + if len(event.feats_compose)==0: continue + + stdfeat = stdDict[stdbcd] # float32 + + simi_mean, simi_max, simi_mfeat = simi_calc(event, stdfeat) + if simi_mean is None: + continue + + rltdata.append((label, stdbcd, evtname, simi_mean, simi_max, simi_mfeat)) '''================ float32、16、int8 精度比较与存储 =============''' # data_precision_compare(stdfeat, evtfeat, mergePairs[i], save=True) - print("func: one2one_eval(), have finished!") + return rltdata - - -def compute_precise_recall(rltdata): +def one2one_pr(rltdata): Same, Cross = [], [] for label, stdbcd, evtname, simi_mean, simi_max, simi_mft in rltdata: if label == "same": - Same.append(simi_mean) + Same.append(simi_max) if label == "diff": - Cross.append(simi_mean) + Cross.append(simi_max) Same = np.array(Same) Cross = np.array(Cross) @@ -280,11 +421,11 @@ def compute_precise_recall(rltdata): TNFP = len(Cross) # fig, axs = plt.subplots(2, 1) - # axs[0].hist(Same, bins=60, edgecolor='black') + # axs[0].hist(Same, bins=60, range=(-0.2, 1), edgecolor='black') # axs[0].set_xlim([-0.2, 1]) # axs[0].set_title(f'Same Barcode, Num: {TPFN}') - # axs[1].hist(Cross, bins=60, edgecolor='black') + # axs[1].hist(Cross, bins=60, range=(-0.2, 1), edgecolor='black') # axs[1].set_xlim([-0.2, 1]) # axs[1].set_title(f'Cross Barcode, Num: {TNFP}') # plt.savefig(f'./result/{file}_hist.png') # svg, png, pdf @@ -324,6 +465,23 @@ def compute_precise_recall(rltdata): rltpath = os.path.join(similPath, 'pr.png') plt.savefig(rltpath) # svg, png, pdf + + fig, axes = plt.subplots(2,1) + axes[0].hist(Same, bins=60, range=(-0.2, 1), edgecolor='black') + axes[0].set_xlim([-0.2, 1]) + axes[0].set_title(f'TP({len(Same)})') + + axes[1].hist(Cross, bins=60, range=(-0.2, 1), edgecolor='black') + axes[1].set_xlim([-0.2, 1]) + axes[1].set_title(f'TN({len(Cross)})') + + rltpath = os.path.join(similPath, 'hist.png') + plt.savefig(rltpath) + + + plt.show() + + def gen_eventdict(sourcePath, saveimg=True): k, errEvents = 0, [] @@ -358,9 +516,7 @@ def gen_eventdict(sourcePath, saveimg=True): f.write(line + '\n') - -def test_one2one(): - +def init_std_evt_dict(): '''==== 0. 生成事件列表和对应的 Barcodes列表 ===========''' bcdList, event_spath = [], [] for evtpath in eventSourcePath: @@ -383,10 +539,33 @@ def test_one2one(): print("eventList have generated and saved!") - '''==== 3. 1:1性能评估 ===============''' - rltdata = one2one_simi() - compute_precise_recall(rltdata) + + +def test_one2one(): + '''1:1性能评估''' + # 1. 只需运行一次，生成事件字典和相应的标准特征库字典 + # init_std_evt_dict() + + # 2. 基于事件barcode集和标准库barcode交集构造事件集合 + evtList, evtDict, stdDict = build_std_evt_dict() + + rltdata = one2one_simi(evtList, evtDict, stdDict) + + one2one_pr(rltdata) + + +def test_one2SN(): + '''1:SN性能评估''' + + # 1. 只需运行一次，生成事件字典和相应的标准特征库字典 + # init_std_evt_dict() + + # 2. 事件barcode集和标准库barcode求交集 + evtList, evtDict, stdDict = build_std_evt_dict() + + one2SN_pr(evtList, evtDict, stdDict) + if __name__ == '__main__': ''' @@ -402,7 +581,7 @@ if __name__ == '__main__': stdSamplePath = r"\\192.168.1.28\share\数据\已完成数据\展厅数据\v1.0\比对数据\整理\zhantingBase" stdBarcodePath = r"D:\exhibition\dataset\bcdpath" - stdFeaturePath = r"D:\exhibition\dataset\feats" + stdFeaturePath = r"\\192.168.1.28\share\数据\已完成数据\比对数据\barcode\all_totalBarocde\features_json\v11_barcode_11592" # eventSourcePath = [r'D:\exhibition\images\20241202'] # eventSourcePath = [r"\\192.168.1.28\share\测试视频数据以及日志\各模块测试记录\展厅测试\1129_展厅模型v801测试组测试"] @@ -419,6 +598,8 @@ if __name__ == '__main__': os.makedirs(similPath) test_one2one() + + # test_one2SN() diff --git a/contrast/onsite_contrast_pr.py b/contrast/onsite_contrast_pr.py index b26005f..5786d93 100644 --- a/contrast/onsite_contrast_pr.py +++ b/contrast/onsite_contrast_pr.py @@ -107,9 +107,7 @@ def test_compare(): def contrast_pr(paths): ''' 1:1 - - ''' - + ''' paths = Path(paths) evtpaths = [] @@ -117,25 +115,19 @@ def contrast_pr(paths): condt1 = p.is_dir() condt2 = len(p.name.split('_'))>=2 condt3 = len(p.name.split('_')[-1])>8 - condt4 = p.name.split('_')[-1].isdigit() - + condt4 = p.name.split('_')[-1].isdigit() if condt1 and condt2 and condt3 and condt4: evtpaths.append(p) - - - - + # evtpaths = [p for p in paths.iterdir() if p.is_dir() and len(p.name.split('_'))>=2 and len(p.name.split('_')[-1])>8] # evtpaths = [p for p in paths.iterdir() if p.is_dir()] events, similars = [], [] - ##===================================== 扫A放A, 扫A放B场景() one2oneAA, one2oneAB = [], [] one2SNAA, one2SNAB = [], [] ##===================================== 应用于 1：1 - _tp_events, _fn_events, _fp_events, _tn_events = [], [], [], [] _tp_simi, _fn_simi, _tn_simi, _fp_simi = [], [], [], [] @@ -406,9 +398,12 @@ def contrast_pr(paths): axes[0].hist(np.array(one2SNAA), bins=60, edgecolor='black') axes[0].set_xlim([-0.2, 1]) axes[0].set_title('AA') + axes[0].set_xlabel(f"Event Num: {len(one2SNAA)}") + axes[1].hist(np.array(one2SNAB), bins=60, edgecolor='black') axes[1].set_xlim([-0.2, 1]) axes[1].set_title('BB') + axes[1].set_xlabel(f"Event Num: {len(one2SNAB)}") plt.show() ''''3. ============================= 1:SN 曲线''' @@ -428,16 +423,16 @@ def contrast_pr(paths): fig, axes = plt.subplots(2, 2) axes[0, 0].hist(tp_simi, bins=60, edgecolor='black') axes[0, 0].set_xlim([-0.2, 1]) - axes[0, 0].set_title('TP') + axes[0, 0].set_title(f'TP({len(tp_simi)})') axes[0, 1].hist(fp_simi, bins=60, edgecolor='black') axes[0, 1].set_xlim([-0.2, 1]) - axes[0, 1].set_title('FP') + axes[0, 1].set_title(f'FP({len(fp_simi)})') axes[1, 0].hist(tn_simi, bins=60, edgecolor='black') axes[1, 0].set_xlim([-0.2, 1]) - axes[1, 0].set_title('TN') + axes[1, 0].set_title(f'TN({len(tn_simi)})') axes[1, 1].hist(fn_simi, bins=60, edgecolor='black') axes[1, 1].set_xlim([-0.2, 1]) - axes[1, 1].set_title('FN') + axes[1, 1].set_title(f'FN({len(fn_simi)})') plt.show() @@ -458,16 +453,16 @@ def contrast_pr(paths): fig, axes = plt.subplots(2, 2) axes[0, 0].hist(tpsimi, bins=60, edgecolor='black') axes[0, 0].set_xlim([-0.2, 1]) - axes[0, 0].set_title('TP') + axes[0, 0].set_title(f'TP({len(tpsimi)})') axes[0, 1].hist(fpsimi, bins=60, edgecolor='black') axes[0, 1].set_xlim([-0.2, 1]) - axes[0, 1].set_title('FP') + axes[0, 1].set_title(f'FP({len(fpsimi)})') axes[1, 0].hist(tnsimi, bins=60, edgecolor='black') axes[1, 0].set_xlim([-0.2, 1]) - axes[1, 0].set_title('TN') + axes[1, 0].set_title(f'TN({len(tnsimi)})') axes[1, 1].hist(fnsimi, bins=60, edgecolor='black') axes[1, 1].set_xlim([-0.2, 1]) - axes[1, 1].set_title('FN') + axes[1, 1].set_title(f'FN({len(fnsimi)})') plt.show() @@ -500,7 +495,7 @@ def contrast_pr(paths): if __name__ == "__main__": - evtpaths = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\images" + evtpaths = r"D:\全实时\source_data\2024122416" contrast_pr(evtpaths) diff --git a/contrast/select_subimgs.py b/contrast/select_subimgs.py new file mode 100644 index 0000000..cfbcf62 --- /dev/null +++ b/contrast/select_subimgs.py @@ -0,0 +1,117 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Dec 23 13:58:13 2024 + +writting for selectting std subimgs to Wuhuaqi + +@author: ym +""" + +import os +import time +# import torch +import pickle +# import json +import numpy as np +from PIL import Image +import matplotlib.pyplot as plt +from scipy.spatial.distance import cdist +from feat_extract.config import config as conf +# from model import resnet18 as resnet18 +from feat_extract.inference import FeatsInterface #, inference_image + + +IMG_FORMAT = ['.bmp', '.jpg', '.jpeg', '.png'] + + + +def gen_features(imgpath): + + Encoder = FeatsInterface(conf) + imgs, imgnames = [], [] + for filename in os.listdir(imgpath): + file, ext = os.path.splitext(filename) + if ext not in IMG_FORMAT: continue + + fpath = os.path.join(imgpath, filename) + img = Image.open(fpath) + + imgs.append(img) + + filelist = file.split("_") + newname = "_".join([filelist[0],filelist[1], filelist[2], filelist[-3], filelist[-2], filelist[-1]]) + # imgnames.append(newname) + imgnames.append(file) + + features = Encoder.inference(imgs) + features /= np.linalg.norm(features, axis=1)[:, None] + + return features, imgnames + +def top_p_percent_indices(matrix, p): + """ + Finds the indices of the top p% largest elements in a 2D matrix. + Args: + matrix (np.ndarray): A 2D NumPy array. + p: int, 0-100 + Returns: + List[Tuple[int, int]]: A list of indices (row, column) for the top 10% largest elements. + """ + # Flatten the matrix + flat_matrix = matrix.flatten() + + # Calculate the threshold for the top 10% + num_elements = len(flat_matrix) + threshold_index = int(num_elements * 0.01*p) # Top 10% + threshold_index = max(1, threshold_index) # Ensure at least one element is considered + threshold_value = np.partition(flat_matrix, -threshold_index)[-threshold_index] + + # Create a mask for elements >= threshold + mask = matrix >= threshold_value + + # Get the indices of elements that satisfy the mask + indices = np.argwhere(mask) + + return list(map(tuple, indices)) + + +def main(): + imgpath = r"\\192.168.1.28\share\数据\已完成数据\展厅数据\v1.0\比对数据\整理\zhantingBase\6923555210479" + + feats, imgnames = gen_features(imgpath) + n = len(feats) + + matrix = 1 - cdist(feats, feats, 'cosine') + + nmatrix = np.array([[matrix[i][j] for j in range(n) if i != j] for i in range(n)]) + + top_p_large_index = top_p_percent_indices(nmatrix, 1) + top_p_small_index = top_p_percent_indices(-1*nmatrix, 1) + + simi_mean = np.mean(nmatrix, axis=1) + max_simi = np.max(nmatrix) + max_index = np.where(nmatrix==max_simi) + min_simi = np.min(nmatrix) + min_index = np.where(nmatrix==min_simi) + + fig, ax = plt.subplots() + + simils = [matrix[i][j] for j in range(n) for i in range(n) if j>i] + ax.hist(simils, bins=60, range=(-0.2, 1), edgecolor='black') + ax.set_xlim([-0.2, 1]) + ax.set_title("Similarity") + + + print("done!") + + + + + + + + + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/contrast/utils/__pycache__/event.cpython-39.pyc b/contrast/utils/__pycache__/event.cpython-39.pyc index 3ee59c8..8d46fdf 100644 Binary files a/contrast/utils/__pycache__/event.cpython-39.pyc and b/contrast/utils/__pycache__/event.cpython-39.pyc differ diff --git a/contrast/utils/event.py b/contrast/utils/event.py index 541a2ab..beda525 100644 --- a/contrast/utils/event.py +++ b/contrast/utils/event.py @@ -15,6 +15,7 @@ sys.path.append(r"D:\DetectTracking") from tracking.utils.plotting import Annotator, colors from tracking.utils.drawtracks import drawTrack from tracking.utils.read_data import extract_data, read_tracking_output, read_similar +from tracking.utils.read_data import extract_data_realtime, read_tracking_output_realtime IMG_FORMAT = ['.bmp', '.jpg', '.jpeg', '.png'] VID_FORMAT = ['.mp4', '.avi'] @@ -117,10 +118,12 @@ class ShoppingEvent: if stype=="data": self.from_datafile(eventpath) + if stype=="realtime": + self.from_realtime_datafile(eventpath) if stype=="source": self.from_source_pkl(eventpath) - self.feats_select = [] + self.feats_select = np.empty((0, 256), dtype=np.float64) self.feats_compose = np.empty((0, 256), dtype=np.float64) self.select_feats() self.compose_feats() @@ -289,6 +292,66 @@ class ShoppingEvent: elif CamerType == '1': self.front_boxes = tracking_output_boxes self.front_feats = tracking_output_feats + + def from_realtime_datafile(self, eventpath): + # evtList = self.evtname.split('_') + # if len(evtList)>=2 and len(evtList[-1])>=10 and evtList[-1].isdigit(): + # self.barcode = evtList[-1] + # if len(evtList)==3 and evtList[-1]== evtList[-2]: + # self.evtType = 'input' + # else: + # self.evtType = 'other' + + '''================ path of video =============''' + for vidname in os.listdir(eventpath): + name, ext = os.path.splitext(vidname) + if ext not in VID_FORMAT: continue + vidpath = os.path.join(eventpath, vidname) + + CamerType = name.split('_')[0] + if CamerType == '0': + self.back_videopath = vidpath + if CamerType == '1': + self.front_videopath = vidpath + + '''================ process.data =============''' + procpath = Path(eventpath).joinpath('process.data') + if procpath.is_file(): + SimiDict = read_similar(procpath) + self.one2one = SimiDict['one2one'] + self.one2n = SimiDict['one2n'] + self.one2SN = SimiDict['one2SN'] + + '''=========== 0/1_track.data & 0/1_tracking_output.data =======''' + for dataname in os.listdir(eventpath): + datapath = os.path.join(eventpath, dataname) + if not os.path.isfile(datapath): continue + CamerType = dataname.split('_')[0] + '''========== 0/1_track.data ==========''' + if dataname.find("_track.data")>0: + trackerboxes, trackerfeats = extract_data_realtime(datapath) + if CamerType == '0': + self.back_trackerboxes = trackerboxes + self.back_trackerfeats = trackerfeats + + if CamerType == '1': + self.front_trackerboxes = trackerboxes + self.front_trackerfeats = trackerfeats + '''========== 0/1_tracking_output.data ==========''' + if dataname.find("_tracking_output.data")>0: + trackingboxes, trackingfeats, tracking_outboxes, tracking_outfeats = read_tracking_output_realtime(datapath) + if CamerType == '0': + self.back_trackingboxes = trackingboxes + self.back_trackingfeats = trackingfeats + self.back_boxes = tracking_outboxes + self.back_feats = tracking_outfeats + elif CamerType == '1': + self.front_trackingboxes = trackingboxes + self.front_trackingfeats = trackingfeats + self.front_boxes = tracking_outboxes + self.front_feats = tracking_outfeats + + @@ -305,11 +368,10 @@ class ShoppingEvent: def select_feats(self): '''事件的特征选择''' - self.feats_select = [] if len(self.front_feats): - self.feats_select = self.front_feats + self.feats_select = self.front_feats[0] elif len(self.back_feats): - self.feats_select = self.back_feats + self.feats_select = self.back_feats[0] def plot_save_image(self, savepath): diff --git a/pipeline.py b/pipeline.py index f3ef177..cf9adad 100644 --- a/pipeline.py +++ b/pipeline.py @@ -120,9 +120,7 @@ def pipeline( save_dir_video = save_dir_event / Path(str(Path(vpath).stem)) if not save_dir_video.exists(): save_dir_video.mkdir(parents=True, exist_ok=True) - - - + '''Yolo + Resnet + Tracker''' optdict["source"] = vpath @@ -212,15 +210,7 @@ def pipeline( trajpath = os.path.join(save_dir_event, "traj.png") cv2.imwrite(trajpath, img_cat) - - - - '''前后摄轨迹选择''' - - if stdfeat_path is not None: - with open(stdfeat_path, 'rb') as f: - featDict = pickle.load(f) - + def main_loop(): bcdpath = r"\\192.168.1.28\share\测试_202406\contrast\std_barcodes_2192" @@ -264,12 +254,12 @@ def main(): ''' 函数：pipeline()，遍历事件文件夹，选择类型 image 或 video, ''' - evtdir = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\images" + evtdir = r"D:\全实时\source_data\2024122416" evtdir = Path(evtdir) parmDict = {} - parmDict["savepath"] = r"D:\contrast\detect\pipeline" - parmDict["SourceType"] = "image" # video, image + parmDict["savepath"] = r"D:\全实时\result\pipeline" + parmDict["SourceType"] = "video" # video, image parmDict["stdfeat_path"] = None k = 0 @@ -278,16 +268,16 @@ def main(): if item.is_dir(): # item = r"D:\exhibition\images\images2\images2" parmDict["eventpath"] = item - pipeline(**parmDict) + # pipeline(**parmDict) - # try: - # pipeline(**parmDict) - # except Exception as e: - # errEvents.append(str(item)) + try: + pipeline(**parmDict) + except Exception as e: + errEvents.append(str(item)) - k+=1 - if k==1: - break + # k+=1 + # if k==1: + # break errfile = os.path.join(parmDict["savepath"], f'error_events.txt') with open(errfile, 'w', encoding='utf-8') as f: diff --git a/event_time_specify.py b/realtime/event_time_specify.py similarity index 100% rename from event_time_specify.py rename to realtime/event_time_specify.py diff --git a/realtime/full_realtime.py b/realtime/full_realtime.py new file mode 100644 index 0000000..018a1c5 --- /dev/null +++ b/realtime/full_realtime.py @@ -0,0 +1,137 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Dec 25 09:17:32 2024 + +@author: ym +""" +import os +import sys +import numpy as np +import matplotlib.pyplot as plt +from datetime import datetime +from contrast.utils.event import ShoppingEvent + +sys.path.append(r"D:\DetectTracking") +from tracking.utils.read_data import read_weight_sensor, extract_data_realtime, read_tracking_output_realtime +from tracking.utils.read_data import read_process + +def time_std2stamp(custom_time): + date_part = custom_time.split('-')[0] + time_part = custom_time.split('-')[1] + ms_part = int(custom_time.split('-')[2]) + standard_time = f"{date_part} {time_part[:2]}:{time_part[2:4]}:{time_part[4:]}" + dt = datetime.strptime(standard_time, "%Y%m%d %H:%M:%S") + timestamp = int(dt.timestamp() * 1000) + ms_part + return timestamp + +def time_stamp2std(timestamp): + if isinstance(timestamp, float) or isinstance(timestamp, str): + timestamp = int(timestamp) + ms = timestamp%1000 + times = timestamp//1000 + std_time = datetime.fromtimestamp(times) + stdtime = std_time.strftime("%Y%m%d-%H%M%S") + '-' +str(ms) + return stdtime + +def get_timeduring_weight(procpath): + eventStart, eventEnd, weightValue = None, None, None + if os.path.isfile(procpath): + timeDict = read_process(procpath) + if "eventStart" in timeDict.keys(): + eventStart = timeDict["eventStart"] + if "eventEnd" in timeDict.keys(): + eventEnd = timeDict["eventEnd"] + if "weightValue" in timeDict.keys(): + weightValue = timeDict["weightValue"] + return eventStart, eventEnd, weightValue + +def event_devide(wpath): + ''' + 基于重力时序数据 _weight.data 进行事件切分 + ''' + # wpath = r'D:\全实时\source_data\2024122416\20241224-162658370_weight.data' + + tpath, _ = os.path.split(wpath) + + wsdata = read_weight_sensor(wpath) + times, weights = wsdata[:, 0], wsdata[:, 1] + Start, End = times[0], times[-1] + + evtpaths, evtTimeWeight = [], [] + for filename in os.listdir(tpath): + filelist = filename.split('_') + custom_time = filelist[0] + evtpath = os.path.join(tpath, filename) + if os.path.isdir(evtpath): + stamp = time_std2stamp(custom_time) + if stamp >= Start and stamp <= End: + evtpaths.append(evtpath) + + for evtpath in evtpaths: + evtname = os.path.basename(evtpath) + event = ShoppingEvent(evtpath, stype = "realtime") + # try: + # event = ShoppingEvent(evtpath, stype = "realtime") + # except Exception as e: + # print(f"Error is: {e}", evtname) + + '''读取事件的起止时间、重力变化值''' + propath = os.path.join(evtpath, "process.data") + evtStart, evtEnd, wgtValue = get_timeduring_weight(propath) + evtTimeWeight.append((evtStart, evtEnd, wgtValue)) + + + '''重力变化曲线、事件起止区间''' + fig, ax1 = plt.subplots(figsize=(16, 9), dpi=100) + ax1.plot(times-Start, weights, 'bo-', linewidth=1, markersize=3) + ax1.set_title('Weight (gram)') + for t0, t1, w in evtTimeWeight: + min_diff = float('inf') + index = None + for i, t in enumerate(times): + diff = abs(t0 - t) + if diff < min_diff: + min_diff = diff + index = i + w0 = weights[index] + w1 = w0 + w + ax1.plot((t0, t0) - Start, (w0, w1), 'r*-', linewidth=1, markersize=6) + ax1.plot((t1, t1) - Start, (w0, w1), 'r*-', linewidth=1, markersize=6) + ax1.plot((t0, t1) - Start, (w1, w1), 'r*-', linewidth=1, markersize=6) + ax1.grid(True) + plt.show() + + return plt + + + + + + + +def main(): + tpath = r"D:\全实时\source_data\2024122416" + rltpath = r"D:\全实时\result" + for filename in os.listdir(tpath): + bname = filename.split("_")[0] + if filename.find("_weight.data") <= 0: + continue + wpath = os.path.join(tpath, filename) + + plt = event_devide(wpath) + + plt.savefig(os.path.join(rltpath, f'{bname}.png' )) # svg, png, pdf + + print(filename) + + + + + + + + print("Done!") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/time_devide.py b/realtime/time_devide.py similarity index 98% rename from time_devide.py rename to realtime/time_devide.py index 1836112..fa8f45b 100644 --- a/time_devide.py +++ b/realtime/time_devide.py @@ -17,7 +17,7 @@ import copy import matplotlib.pyplot as plt from imgs_inference import run_yolo from event_time_specify import devide_motion_state#, state_measure -from tracking.utils.read_data import read_seneor +from tracking.utils.read_data import read_weight_sensor # IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm' # include image suffixes # VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv' # include video suffixes @@ -450,10 +450,10 @@ def show_seri(): '''===============读取重力信号数据===================''' seneorfile = os.path.join(datapath, 'sensor.txt') - WeightDict = read_seneor(seneorfile) + weights = read_weight_sensor(seneorfile) - weights = [(float(t), w) for t, w in WeightDict.items()] - weights = np.array(weights) + # weights = [(float(t), w) for t, w in WeightDict.items()] + # weights = np.array(weights) '''===============重力、图像信息融合===================''' diff --git a/track_reid.py b/track_reid.py index 06c33e1..72d886d 100644 --- a/track_reid.py +++ b/track_reid.py @@ -214,15 +214,16 @@ def yolo_resnet_tracker( # NMS with dt[2]: pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det) - + + if dataset.mode == "video": + frameId = dataset.frame + else: + frameId = dataset.count # Process predictions for i, det in enumerate(pred): # per image im0 = im0s.copy() - - s += '%gx%g ' % im.shape[2:] # print string - annotator = Annotator(im0.copy(), line_width=line_thickness, example=str(names)) - + s += '%gx%g ' % im.shape[2:] # print string if len(det): # Rescale boxes from img_size to im0 size det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() @@ -235,46 +236,39 @@ def yolo_resnet_tracker( ''' det_tracking = Boxes(det, im0.shape).cpu().numpy() tracks = tracker.update(det_tracking, im0) - if len(tracks) == 0: - continue - if dataset.mode == "video": - frameId = dataset.frame - else: - frameId = dataset.count - - tracks[:, 7] = frameId - - # trackerBoxes = np.concatenate([trackerBoxes, tracks], axis=0) - '''================== 1. 存储 dets/subimgs/features Dict =============''' - imgs, features = ReIDEncoder.inference(im0, tracks) - featdict = {} - for ii, bid in enumerate(tracks[:, 8]): - featdict.update({f"{int(frameId)}_{int(bid)}": features[ii, :]}) # [f"feat_{int(bid)}"] = features[i, :] - - frameDict = {"path": path, - "fid": int(frameId), - "bboxes": det, - "tboxes": tracks, - "imgs": imgs, - "feats": featdict} - yoloResnetTracker.append(frameDict) - - # imgs, features = inference_image(im0, tracks) - # TrackerFeats = np.concatenate([TrackerFeats, features], axis=0) - - '''================== 2. 提取手势位置 ===================''' - for *xyxy, id, conf, cls, fid, bid in reversed(tracks): - name = ('' if id==-1 else f'id:{int(id)} ') + names[int(cls)] - label = None if hide_labels else (name if hide_conf else f'{name} {conf:.2f}') - - if id >=0 and cls==0: - color = colors(int(cls), True) - elif id >=0 and cls!=0: - color = colors(int(id), True) - else: - color = colors(19, True) # 19为调色板的最后一个元素 - annotator.box_label(xyxy, label, color=color) + if len(tracks) > 0: + tracks[:, 7] = frameId + # trackerBoxes = np.concatenate([trackerBoxes, tracks], axis=0) + '''================== 1. 存储 dets/subimgs/features Dict =============''' + imgs, features = ReIDEncoder.inference(im0, tracks) + featdict = {} + for ii, bid in enumerate(tracks[:, 8]): + featdict.update({f"{int(frameId)}_{int(bid)}": features[ii, :]}) # [f"feat_{int(bid)}"] = features[i, :] + + frameDict = {"path": path, + "fid": int(frameId), + "bboxes": det, + "tboxes": tracks, + "imgs": imgs, + "feats": featdict} + yoloResnetTracker.append(frameDict) + + # imgs, features = inference_image(im0, tracks) + # TrackerFeats = np.concatenate([TrackerFeats, features], axis=0) + + '''================== 2. 提取手势位置 ===================''' + for *xyxy, id, conf, cls, fid, bid in reversed(tracks): + name = ('' if id==-1 else f'id:{int(id)} ') + names[int(cls)] + label = None if hide_labels else (name if hide_conf else f'{name} {conf:.2f}') + + if id >=0 and cls==0: + color = colors(int(cls), True) + elif id >=0 and cls!=0: + color = colors(int(id), True) + else: + color = colors(19, True) # 19为调色板的最后一个元素 + annotator.box_label(xyxy, label, color=color) '''====== Save results (image and video) ======''' # save_path = str(save_dir / Path(path).name) # 带有后缀名 @@ -719,11 +713,13 @@ def main(): # p = r"D:\datasets\ym\永辉双摄视频\新建文件夹" # p = r"\\192.168.1.28\share\测试_202406\0723\0723_2\20240723-112522_" # p = r"D:\datasets\ym\联华中环" - p = r"D:\exhibition\images\153112511_0_seek_105.mp4" + # p = r"D:\exhibition\images\153112511_0_seek_105.mp4" # p = r"D:\exhibition\images\image" - - optdict["project"] = r"D:\exhibition\result" + p = r"\\192.168.1.28\share\数据\原始数据\小物品数据\视频\82654976401_20241213-143457_front_addGood_5478c9a53bbe_40_17700000001.mp4" + optdict["project"] = r"D:\小物品入侵检测\result" + + # optdict["project"] = r"D:\exhibition\result" if os.path.isdir(p): files = find_video_imgs(p) k = 0 diff --git a/tracking/utils/__pycache__/read_data.cpython-39.pyc b/tracking/utils/__pycache__/read_data.cpython-39.pyc index 00f91e8..b60e5fe 100644 Binary files a/tracking/utils/__pycache__/read_data.cpython-39.pyc and b/tracking/utils/__pycache__/read_data.cpython-39.pyc differ diff --git a/tracking/utils/read_data.py b/tracking/utils/read_data.py index 8996f72..a30d2fe 100644 --- a/tracking/utils/read_data.py +++ b/tracking/utils/read_data.py @@ -33,6 +33,22 @@ def find_samebox_in_array(arr, target): return i return -1 +def array2list(boxes, feats): + '''boxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]''' + + trackID = np.unique(boxes[:, 4].astype(int)) + + track_ids = boxes[:, 4].astype(int) + lboxes = [] + for t_id in trackID: + idx = np.where(track_ids == t_id)[0] + box = boxes[idx, :] + feat = feats[idx, :] + + assert len(set(box[:, 7])) == len(box), "Please check!!!" + lboxes.append(box) + + return lboxes def extract_data(datapath): ''' @@ -174,6 +190,79 @@ def extract_data(datapath): # return bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, trackingfeats return bboxes, ffeats, trackerboxes, tracker_feats, trackingboxes, trackingfeats + + + +def extract_data_realtime(datapath): + boxes, feats = [], [] + tracker_feats = {} + with open(datapath, 'r', encoding='utf-8') as lines: + for line in lines: + line = line.strip() # 去除行尾的换行符和可能的空白字符 + if not line: # 跳过空行 + continue + + if line.endswith(','): + line = line[:-1] + ftlist = [float(x) for x in line.split()] + if len(ftlist) != 265: continue + boxes.append(ftlist[:9]) + feats.append(ftlist[9:]) + + trackerboxes = np.array(boxes) + trackerfeats = np.array(feats) + + if len(trackerboxes)==0 or len(trackerboxes) != len(trackerfeats): + return np.array([]), {} + + frmIDs = np.sort(np.unique(trackerboxes[:, 7].astype(int))) + for fid in frmIDs: + idx = np.where(trackerboxes[:, 7] == fid)[0] + box = trackerboxes[idx, :] + feat = trackerfeats[idx, :] + + for i in range(len(box)): + f, b = int(box[i, 7]), int(box[i, 8]) + tracker_feats.update({f"{f}_{b}": feat[i, :]}) + return trackerboxes, tracker_feats + + +def read_tracking_output_realtime(datapath): + + trackingboxes, trackingfeats = [], [] + tracking_outboxes, tracking_outfeats = [], [] + with open(datapath, 'r', encoding='utf-8') as lines: + boxes, feats = [], [] + Flag = False + for line in lines: + line = line.strip() # 去除行尾的换行符和可能的空白字符 + if not line: # 跳过空行 + continue + if line.endswith(','): + line = line[:-1] + + ftlist = [float(x) for x in line.split()] + if len(ftlist) != 265: continue + + Flag = all(elem == 0 for elem in ftlist) + if Flag: + trackingboxes.append(np.array(boxes)) + trackingfeats.append(np.array(feats)) + boxes, feats = [], [] + continue + + boxes.append(ftlist[:9]) + feats.append(ftlist[9:]) + if len(boxes): + trackingboxes.append(np.array(boxes)) + trackingfeats.append(np.array(feats)) + if len(trackingboxes): + tracking_outboxes = trackingboxes[:1] + tracking_outfeats = trackingfeats[:1] + + return trackingboxes, trackingfeats, tracking_outboxes, tracking_outfeats + + def read_tracking_output(filepath): ''' 0/1_tracking_output.data 数据读取 @@ -182,7 +271,7 @@ def read_tracking_output(filepath): boxes = [] feats = [] if not os.path.isfile(filepath): - return np.array(boxes), np.array(feats) + return boxes, feats with open(filepath, 'r', encoding='utf-8') as file: for line in file: @@ -201,10 +290,173 @@ def read_tracking_output(filepath): feats.append(data) - if len(feats) != len(boxes): - return [np.array([])], [np.array([])] + if len(feats) != len(boxes) or len(boxes)==0: + return [], [] return [np.array(boxes)], [np.array(feats)] + + +def read_process(filePath): + timeDict = {} + with open(filePath, 'r', encoding='utf-8') as f: + lines = f.readlines() + clines = [line.strip().replace("'", '').replace('"', '') for line in lines] + for i, line in enumerate(clines): + line = line.strip() + if line.endswith(','): + line = line[:-1] + if not line: continue + + lnList = line.split(":") + if line.find("eventStart")>=0: + timeDict["eventStart"] = int(lnList[-1]) + + if line.find("eventEnd")>=0: + timeDict["eventEnd"] = int(lnList[-1]) + + if line.find("weightValue")>=0: + timeDict["weightValue"] = int(lnList[-1]) + return timeDict + + +def read_similar(filePath): + SimiDict = {} + SimiDict['one2one'] = [] + SimiDict['one2SN'] = [] + SimiDict['one2n'] = [] + + with open(filePath, 'r', encoding='utf-8') as f: + lines = f.readlines() + clean_lines = [line.strip().replace("'", '').replace('"', '') for line in lines] + one2one_list, one2SN_list, one2n_list = [], [], [] + + Flag_1to1, Flag_1toSN, Flag_1ton = False, False, False + for i, line in enumerate(clean_lines): + line = line.strip() + if line.endswith(','): + line = line[:-1] + Dict = {} + + if not line: + if len(one2one_list): SimiDict['one2one'] = one2one_list + if len(one2SN_list): SimiDict['one2SN'] = one2SN_list + if len(one2n_list): SimiDict['one2n'] = one2n_list + + + one2one_list, one2SN_list, one2n_list = [], [], [] + Flag_1to1, Flag_1toSN, Flag_1ton = False, False, False + continue + + if line.find('oneToOne')>=0: + Flag_1to1, Flag_1toSN, Flag_1ton = True, False,False + continue + if line.find('oneToSN')>=0: + Flag_1to1, Flag_1toSN, Flag_1ton = False, True, False + continue + if line.find('oneTon')>=0: + Flag_1to1, Flag_1toSN, Flag_1ton = False, False, True + continue + + + + if Flag_1to1: + barcode = line.split(',')[0].strip() + value = line.split(',')[1].split(':')[1].strip() + Dict['barcode'] = barcode + Dict['similar'] = float(value) + one2one_list.append(Dict) + continue + + if Flag_1toSN: + barcode = line.split(',')[0].strip() + value = line.split(',')[1].split(':')[1].strip() + Dict['barcode'] = barcode + Dict['similar'] = float(value) + one2SN_list.append(Dict) + continue + + if Flag_1ton: + label = line.split(':')[0].strip() + value = line.split(':')[1].strip() + + bcd = label.split('_')[-1] + if len(bcd)<8: continue + + Dict['event'] = label + Dict['barcode'] = bcd + Dict['similar'] = float(value.split(',')[0]) + Dict['type'] = value.split('=')[-1] + one2n_list.append(Dict) + + if len(one2one_list): SimiDict['one2one'] = one2one_list + if len(one2n_list): SimiDict['one2n'] = one2n_list + if len(one2SN_list): SimiDict['one2SN'] = one2SN_list + + return SimiDict + +def read_weight_sensor(filepath): + WeightDict = OrderedDict() + with open(filepath, 'r', encoding='utf-8') as f: + lines = f.readlines() + clean_lines = [line.strip().replace("'", '').replace('"', '') for line in lines] + for i, line in enumerate(clean_lines): + line = line.strip() + + line = line.strip() + + if line.find(':') < 0: continue + if line.find("Weight") >= 0: + label = "Weight" + continue + + + keyword = line.split(':')[0] + value = line.split(':')[1] + + if label == "Weight": + vdata = [float(s) for s in value.split(',') if len(s)] + WeightDict[keyword] = vdata[-1] + + + weights = [(float(t), w) for t, w in WeightDict.items()] + weights = np.array(weights).astype(np.int64) + + return weights + +def read_weight_timeConsuming(filePth): + WeightDict, SensorDict, ProcessTimeDict = OrderedDict(), OrderedDict(), OrderedDict() + + with open(filePth, 'r', encoding='utf-8') as f: + lines = f.readlines() + # label = '' + for i, line in enumerate(lines): + line = line.strip() + + if line.find(':') < 0: continue + if line.find("Weight") >= 0: + label = "Weight" + continue + if line.find("Sensor") >= 0: + label = "Sensor" + continue + if line.find("processTime") >= 0: + label = "ProcessTime" + continue + + keyword = line.split(':')[0] + value = line.split(':')[1] + + if label == "Weight": + WeightDict[keyword] = float(value.strip(',')) + if label == "Sensor": + SensorDict[keyword] = [float(s) for s in value.split(',') if len(s)] + if label == "ProcessTime": + ProcessTimeDict[keyword] = float(value.strip(',')) + + # print("Done!") + return WeightDict, SensorDict, ProcessTimeDict + + def read_deletedBarcode_file(filePath): @@ -320,132 +572,7 @@ def read_returnGoods_file(filePath): -def read_seneor(filepath): - WeightDict = OrderedDict() - with open(filepath, 'r', encoding='utf-8') as f: - lines = f.readlines() - clean_lines = [line.strip().replace("'", '').replace('"', '') for line in lines] - for i, line in enumerate(clean_lines): - line = line.strip() - - keyword = line.split(':')[0] - value = line.split(':')[1] - - vdata = [float(s) for s in value.split(',') if len(s)] - - WeightDict[keyword] = vdata[-1] - - return WeightDict -def read_similar(filePath): - SimiDict = {} - SimiDict['one2one'] = [] - SimiDict['one2SN'] = [] - SimiDict['one2n'] = [] - - with open(filePath, 'r', encoding='utf-8') as f: - lines = f.readlines() - clean_lines = [line.strip().replace("'", '').replace('"', '') for line in lines] - one2one_list, one2SN_list, one2n_list = [], [], [] - - Flag_1to1, Flag_1toSN, Flag_1ton = False, False, False - for i, line in enumerate(clean_lines): - line = line.strip() - if line.endswith(','): - line = line[:-1] - Dict = {} - - if not line: - if len(one2one_list): SimiDict['one2one'] = one2one_list - if len(one2SN_list): SimiDict['one2SN'] = one2SN_list - if len(one2n_list): SimiDict['one2n'] = one2n_list - - - one2one_list, one2SN_list, one2n_list = [], [], [] - Flag_1to1, Flag_1toSN, Flag_1ton = False, False, False - continue - - if line.find('oneToOne')>=0: - Flag_1to1, Flag_1toSN, Flag_1ton = True, False,False - continue - if line.find('oneToSN')>=0: - Flag_1to1, Flag_1toSN, Flag_1ton = False, True, False - continue - if line.find('oneTon')>=0: - Flag_1to1, Flag_1toSN, Flag_1ton = False, False, True - continue - - - - if Flag_1to1: - barcode = line.split(',')[0].strip() - value = line.split(',')[1].split(':')[1].strip() - Dict['barcode'] = barcode - Dict['similar'] = float(value) - one2one_list.append(Dict) - continue - - if Flag_1toSN: - barcode = line.split(',')[0].strip() - value = line.split(',')[1].split(':')[1].strip() - Dict['barcode'] = barcode - Dict['similar'] = float(value) - one2SN_list.append(Dict) - continue - - if Flag_1ton: - label = line.split(':')[0].strip() - value = line.split(':')[1].strip() - - bcd = label.split('_')[-1] - if len(bcd)<8: continue - - Dict['event'] = label - Dict['barcode'] = bcd - Dict['similar'] = float(value.split(',')[0]) - Dict['type'] = value.split('=')[-1] - one2n_list.append(Dict) - - if len(one2one_list): SimiDict['one2one'] = one2one_list - if len(one2n_list): SimiDict['one2n'] = one2n_list - if len(one2SN_list): SimiDict['one2SN'] = one2SN_list - - return SimiDict - - - -def read_weight_timeConsuming(filePth): - WeightDict, SensorDict, ProcessTimeDict = OrderedDict(), OrderedDict(), OrderedDict() - - with open(filePth, 'r', encoding='utf-8') as f: - lines = f.readlines() - # label = '' - for i, line in enumerate(lines): - line = line.strip() - - if line.find(':') < 0: continue - if line.find("Weight") >= 0: - label = "Weight" - continue - if line.find("Sensor") >= 0: - label = "Sensor" - continue - if line.find("processTime") >= 0: - label = "ProcessTime" - continue - - keyword = line.split(':')[0] - value = line.split(':')[1] - - if label == "Weight": - WeightDict[keyword] = float(value.strip(',')) - if label == "Sensor": - SensorDict[keyword] = [float(s) for s in value.split(',') if len(s)] - if label == "ProcessTime": - ProcessTimeDict[keyword] = float(value.strip(',')) - - # print("Done!") - return WeightDict, SensorDict, ProcessTimeDict def plot_sensor_curve(WeightDict, SensorDict, ProcessTimeDict): diff --git a/tracking/utils/videot.py b/tracking/utils/videot.py index ca349b6..1895024 100644 --- a/tracking/utils/videot.py +++ b/tracking/utils/videot.py @@ -80,12 +80,12 @@ def videosave(bboxes, videopath="100_1688009697927.mp4"): cap.release() def main(): - videopath = r'D:\datasets\ym' - savepath = r'D:\datasets\ym' + videopath = r'D:\videos' + savepath = r'D:\videos' # video2imgs(videopath, savepath) k = 0 for filename in os.listdir(videopath): - filename = "20240929-155533.ts" + # filename = "20240929-155533.ts" file, ext = os.path.splitext(filename) if ext not in VideoFormat: @@ -100,9 +100,9 @@ def main(): videof = os.path.join(videopath, filename) video2imgs(videof, imgdir) - k += 1 - if k == 1: - break + # k += 1 + # if k == 1: + # break diff --git a/说明文档.txt b/说明文档.txt index 5e2cfcc..a47428f 100644 --- a/说明文档.txt +++ b/说明文档.txt @@ -27,6 +27,8 @@ 4. 整体流程仿真 pipeline.py SourceType: "image", "video", yolo+resent+tracker模块输入数据类型 + + 保存为 pickle 文件，该文件可用于 ShoppingEvent 对象构建。 @@ -181,63 +183,79 @@ test_one2one() - (1) 生成标准特征集, 只需运行一次 - genfeatures() - (2) 生成事件字典, 只需运行一次 - gen_eventdict(eventDatePath, saveimg) - 参数: - eventDatePath: 事件集列表，其中每个元素均为事件的集合; - saveimg: 是否保存事件子图 - + 1:1比对 + (1) 初始化事件和标准特征集, 只需运行一次 + init_std_evt_dict() + (2) 事件barcode集和标准库barcode求交集 + build_std_evt_dict() (3) 1:1性能评估 + one2one_simi() (4) 计算PR曲线 + one2one_pr() + test_one2SN() + 1:SN比对 + (1) 初始化事件和标准特征集, 只需运行一次 + init_std_evt_dict() + (2) 事件barcode集和标准库barcode求交集 + build_std_evt__dict() + (3) 1:SN性能评估 + one2SN_pr() - - creat_shopping_event(eventPath, subimgPath=False) - 构造一次购物事件字典, 共12个关键字。 + + init_std_evt_dict() + 生成标准特征集与事件对象并存储，并存储相关数据，只需运行一次 - save_event_subimg(event, savepath) - 保存一次购物事件的子图 + (1) Barcode图像进行特征推理并保存 + gen_bcd_features(stdSamplePath, stdBarcodePath, stdFeaturePath, bcdSet) + + (2) 基于data文件或pipeline输出的pickle文件生成 ShoppingEvent 对象 + gen_eventdict(eventDatePath, saveimg) + + build_std_evt_dict() + 基于事件barcode集和标准库barcode交集构造用于评估性能的事件集合。 + + simi_calc() + 计算事件和标准特征集的相似度 + + data_precision_compare() + 不同数据精度下的性能比较 + - one2one_eval() - - - - compute_precise_recall() - - - - int8_to_ft16() ft16_to_uint8() one2n_contrast.py - 执行1:n共需要3步： - '''1. 生成事件字典并保存至 eventDataPath, 只需运行一次 ''' - # gen_eventdict(sourcePath) - - '''2. 读取时间字典 ''' - evtDicts = read_eventdict(eventDataPath) - - - '''3. 1:n 比对事件评估 ''' - - - - + 执行1:n共需要3步，分别对应3个函数： + (1) 生成事件字典并保存至 eventDataPath, 只需运行一次 + (2) 读取事件字典 + (3) 1:n 比对事件评估 - - - - + (1) gen_eventdict(sourcePath) + + + (2) read_eventdict() + inputs： + eventDataPath: 类 ShoppingEvent 对象存储地址，该对象由函数 gen_eventdict() 生成。 + + output： + evtDicts + + + (3) one2n_pr() + inputs： + evtDicts + + output： + fpevents：FP事件地址 + + (4) simi_calc() + 计算两个事件的相似度 - - one2n_contrast_old.py (disused) test_one2n() @@ -290,13 +308,6 @@ 获得 1:n 情况下正确或匹配事件对（取出事件、放入事件、错误匹配事件） 匹配事件分析, 实现函数：save_tracking_imgpairs() - - - - - - - time_devide.py runyolo()