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Path(__file__).resolve().parents[0] / "events_result" - savepath_pipe = Path(savepath) / Path("yolos_tracking") / evtname + savepath_pipeline = Path(savepath) / Path("Yolos_Tracking") / evtname - savepath_pkl = Path(savepath) / "shopping_pkl" - if not savepath_pkl.exists(): - savepath_pkl.mkdir(parents=True, exist_ok=True) - pklpath = Path(savepath_pkl) / Path(str(evtname)+".pickle") - yrtDict = {} - - yrt_out = [] - if DataType == "raw": - ### 不重复执行已经过yolo-resnet-tracker - # if pklpath.exists(): - # print(f"Pickle file have saved: {evtname}.pickle") - # return + """ShoppingDict pickle 文件保存地址 """ + savepath_spdict = Path(savepath) / "ShoppingDict_pkfile" + if not savepath_spdict.exists(): + savepath_spdict.mkdir(parents=True, exist_ok=True) + pf_path = Path(savepath_spdict) / Path(str(evtname)+".pickle") - if SourceType == "video": - vpaths = get_video_pairs(eventpath) - elif SourceType == "image": - vpaths = get_image_pairs(eventpath) - - - - for vpath in vpaths: - '''================= 2. 事件结果存储文件夹 =================''' - - - if isinstance(vpath, list): - savepath_pipe_imgs = savepath_pipe / Path("images") - else: - savepath_pipe_imgs = savepath_pipe / Path(str(Path(vpath).stem)) - - if not savepath_pipe_imgs.exists(): - savepath_pipe_imgs.mkdir(parents=True, exist_ok=True) - - optdict = {} - optdict["weights"] = weights - optdict["source"] = vpath - optdict["save_dir"] = savepath_pipe_imgs - optdict["is_save_img"] = saveimages - optdict["is_save_video"] = True - - - if YoloVersion == "V5": - yrtOut = yolo_resnet_tracker(**optdict) - elif YoloVersion == "V10": - yrtOut = yolov10_resnet_tracker(**optdict) - - yrt_out.append((vpath, yrtOut)) - - elif DataType == "pkl": - pass - - else: - return - - + # if pf_path.exists(): + # print(f"Pickle file have saved: {evtname}.pickle") + # return '''====================== 构造 ShoppingDict 模块 =======================''' ShoppingDict = {"eventPath": eventpath, @@ -241,13 +112,16 @@ def pipeline(eventpath, "backCamera": {}, "one2n": [] # } + yrtDict = {} + + procpath = Path(eventpath).joinpath('process.data') if procpath.is_file(): SimiDict = read_similar(procpath) ShoppingDict["one2n"] = SimiDict['one2n'] - event_tracks = [] - for vpath, yrtOut in yrt_out: + + for vpath in vpaths: '''================= 1. 构造相机事件字典 =================''' CameraEvent = {"cameraType": '', # "front", "back" "videoPath": '', @@ -266,10 +140,34 @@ def pipeline(eventpath, CameraEvent["cameraType"] = "back" if bname.split('_')[0] == "1" or bname.find('front')>=0: CameraEvent["cameraType"] = "front" + + '''================= 2. 事件结果存储文件夹 =================''' + if isinstance(vpath, list): + savepath_pipeline_imgs = savepath_pipeline / Path("images") + else: + savepath_pipeline_imgs = savepath_pipeline / Path(str(Path(vpath).stem)) + if not savepath_pipeline_imgs.exists(): + savepath_pipeline_imgs.mkdir(parents=True, exist_ok=True) - '''2种保存方式: (1) save images, (2) no save images''' - ### (1) save images + savepath_pipeline_subimgs = savepath_pipeline / Path("subimgs") + if not savepath_pipeline_subimgs.exists(): + savepath_pipeline_subimgs.mkdir(parents=True, exist_ok=True) + + + '''================= 3. Yolo + Resnet + Tracker =================''' + optdict["source"] = vpath + optdict["save_dir"] = savepath_pipeline_imgs + optdict["is_save_img"] = True + optdict["is_save_video"] = True + + + if YoloVersion == "V5": + yrtOut = yolo_resnet_tracker(**optdict) + elif YoloVersion == "V10": + yrtOut = yolov10_resnet_tracker(**optdict) + + yrtOut_save = [] for frdict in yrtOut: fr_dict = {} @@ -279,7 +177,6 @@ def pipeline(eventpath, yrtOut_save.append(fr_dict) CameraEvent["yoloResnetTracker"] = yrtOut_save - ### (2) no save images # CameraEvent["yoloResnetTracker"] = yrtOut '''================= 4. tracking =================''' @@ -322,58 +219,108 @@ def pipeline(eventpath, yrtDict["frontyrt"] = yrtOut '''========================== 保存模块 =================================''' - # 保存 ShoppingDict - with open(str(pklpath), 'wb') as f: + '''(1) 保存 ShoppingDict 事件''' + with open(str(pf_path), 'wb') as f: pickle.dump(ShoppingDict, f) + + '''(2) 保存 Tracking 输出的运动轨迹子图，并记录相似度''' + for CamerType, vts in event_tracks: + if len(vts.tracks)==0: continue + if CamerType == 'front': + # yolos = ShoppingDict["frontCamera"]["yoloResnetTracker"] + + yolos = yrtDict["frontyrt"] + ctype = 1 + if CamerType == 'back': + # yolos = ShoppingDict["backCamera"]["yoloResnetTracker"] + + yolos = yrtDict["backyrt"] + ctype = 0 + + imgdict, featdict, simidict = {}, {}, {} + for y in yolos: + imgdict.update(y["imgs"]) + featdict.update(y["feats"]) + simidict.update(y["featsimi"]) + + for track in vts.Residual: + if isinstance(track, np.ndarray): + save_subimgs(imgdict, track, savepath_pipeline_subimgs, ctype, featdict) + else: + save_subimgs(imgdict, track.slt_boxes, savepath_pipeline_subimgs, ctype, featdict) + + '''(3) 轨迹显示与保存''' + illus = [None, None] + for CamerType, vts in event_tracks: + if len(vts.tracks)==0: continue + + if CamerType == 'front': + edgeline = cv2.imread("./tracking/shopcart/cart_tempt/board_ftmp_line.png") + + h, w = edgeline.shape[:2] + # nh, nw = h//2, w//2 + # edgeline = cv2.resize(edgeline, (nw, nh), interpolation=cv2.INTER_AREA) + + img_tracking = draw_all_trajectories(vts, edgeline, savepath_pipeline, CamerType, draw5p=True) + illus[0] = img_tracking + + plt = plot_frameID_y2(vts) + plt.savefig(os.path.join(savepath_pipeline, "front_y2.png")) + + if CamerType == 'back': + edgeline = cv2.imread("./tracking/shopcart/cart_tempt/edgeline.png") + + h, w = edgeline.shape[:2] + # nh, nw = h//2, w//2 + # edgeline = cv2.resize(edgeline, (nw, nh), interpolation=cv2.INTER_AREA) + + img_tracking = draw_all_trajectories(vts, edgeline, savepath_pipeline, CamerType, draw5p=True) + illus[1] = img_tracking - # 绘制并保存轨迹图 - show_result(event_tracks, yrtDict, savepath_pipe) - - + illus = [im for im in illus if im is not None] + if len(illus): + img_cat = np.concatenate(illus, axis = 1) + if len(illus)==2: + H, W = img_cat.shape[:2] + cv2.line(img_cat, (int(W/2), 0), (int(W/2), int(H)), (128, 128, 255), 3) + + trajpath = os.path.join(savepath_pipeline, "trajectory.png") + cv2.imwrite(trajpath, img_cat) def execute_pipeline(evtdir = r"D:\datasets\ym\后台数据\unzip", - DataType = "raw", # raw, pkl + source_type = "video", # video, image, save_path = r"D:\work\result_pipeline", - kk=1, - source_type = "video", # video, image, yolo_ver = "V10", # V10, V5 + weight_yolo_v5 = r'./ckpts/best_cls10_0906.pt' , weight_yolo_v10 = r'./ckpts/best_v10s_width0375_1205.pt', - saveimages = True + k=0 ): ''' 运行函数 pipeline()，遍历事件文件夹，每个文件夹是一个事件 ''' parmDict = {} - parmDict["DataType"] = DataType - parmDict["savepath"] = save_path parmDict["SourceType"] = source_type - + parmDict["savepath"] = save_path parmDict["YoloVersion"] = yolo_ver if parmDict["YoloVersion"] == "V5": parmDict["weights"] = weight_yolo_v5 elif parmDict["YoloVersion"] == "V10": parmDict["weights"] = weight_yolo_v10 - - parmDict["saveimages"] = saveimages - evtdir = Path(evtdir) errEvents = [] - k = 0 for item in evtdir.iterdir(): if item.is_dir(): item = evtdir/Path("20250310-175352-741") parmDict["eventpath"] = item - pipeline(**parmDict) # try: # pipeline(**parmDict) # except Exception as e: # errEvents.append(str(item)) - k+=1 - if kk is not None and k==kk: + if k==1: break errfile = os.path.join(parmDict["savepath"], 'error_events.txt') @@ -389,6 +336,23 @@ if __name__ == "__main__": # execute_pipeline(save_path=spath_v10, yolo_ver="V10") # execute_pipeline(save_path=spath_v5, yolo_ver="V5") + datapath = r'/home/wqg/dataset/test_dataset/base_dataset/single_event/source/' + savepath = r'/home/wqg/dataset/pipeline/contrast/single_event_V5' + + + + + execute_pipeline(evtdir = datapath, + DataType = "raw", # raw, pkl + kk=1, + source_type = "video", # video, image, + save_path = savepath, + yolo_ver = "V10", # V10, V5 + weight_yolo_v5 = r'./ckpts/best_cls10_0906.pt' , + weight_yolo_v10 = r'./ckpts/best_v10s_width0375_1205.pt', + saveimages = False + ) + diff --git a/contrast/__pycache__/__init__.cpython-312.pyc b/contrast/__pycache__/__init__.cpython-312.pyc index 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rcParams['axes.unicode_minus'] = False # 正确显示负号 - '''*********** USearch ***********''' def read_usearch(): stdFeaturePath = r"D:\contrast\stdlib\v11_test.json" @@ -35,13 +37,12 @@ def read_usearch(): return stdlib -def get_eventlist(): +def get_eventlist_errortxt(evtpaths): ''' 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" + ''' + text1 = "one_2_Small_n_Error.txt" + text2 = "one_2_Big_N_Error.txt" events = [] text = (text1, text2) for txt in text: @@ -53,16 +54,16 @@ def get_eventlist(): if line: fpath=os.path.join(evtpaths, line) events.append(fpath) + + events = list(set(events)) return events -def single_event(): - - events = get_eventlist() - - +def save_eventdata(): + evtpaths = r"/home/wqg/dataset/test_dataset/performence_dataset/" + events = get_eventlist_errortxt(evtpaths) '''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" @@ -74,121 +75,148 @@ def single_event(): -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) +# 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] +# 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]) +# 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) +# 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) +# 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)) +# 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) +# # print(clt_center) - return clt_center +# return clt_center -def calc_simil(event, stdfeat): - '''事件与标准库的对比策略 - 该比对策略是否可以拓展到事件与事件的比对？ - ''' +# def calsimi_vs_stdfeat_new(event, stdfeat): +# '''事件与标准库的对比策略 +# 该比对策略是否可以拓展到事件与事件的比对？ +# ''' - def calsiml(feat1, feat2, topkp=75, cluth=0.15): - '''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, :], topkp)) - simi_max.append(sim) - cltc_max = cluster(simi_max, cluth) - Simi = max(cltc_max) +# def calsiml(feat1, feat2, topkp=75, cluth=0.15): +# '''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, :], topkp)) +# simi_max.append(sim) +# cltc_max = cluster(simi_max, cluth) +# 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 +# ## 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 +# 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) +# 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) +# 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) +# 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 - '''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" + evtpaths = r"/home/wqg/dataset/pipeline/contrast/single_event_V10/evtobjs/" - stdlib = read_usearch() - events = get_eventlist() - for evtpath in events: - evtname = os.path.basename(evtpath) - _, barcode = evtname.split("_") + stdfeatPath = r"/home/wqg/dataset/test_dataset/total_barcode/features_json/v11_barcode_0304/" + resultPath = r"/home/wqg/dataset/performence_dataset/result/" + + evt_paths, bcdSet = get_evtList(evtpaths) + + ## read std features + stdDict={} + evtDict = {} + for barcode in bcdSet: + stdpath = os.path.join(stdfeatPath, f"{barcode}.json") + if not os.path.isfile(stdpath): + continue - # 生成事件与相应标准特征集 - event = ShoppingEvent(evtpath) - stdfeat = stdlib[barcode] + with open(stdpath, 'r', encoding='utf-8') as f: + stddata = json.load(f) + feat = np.array(stddata["value"]) + stdDict[barcode] = feat + + for evtpath in evt_paths: + barcode = Path(evtpath).stem.split("_")[-1] + + if barcode not in stdDict.keys(): + continue - Similar = calc_simil(event, stdfeat) + # try: + # with open(evtpath, 'rb') as f: + # evtdata = pickle.load(f) + # except Exception as e: + # print(evtname) + + with open(evtpath, 'rb') as f: + event = pickle.load(f) + + stdfeat = stdDict[barcode] + + Similar = calsimi_vs_stdfeat_new(event, stdfeat) # 构造 boxes 子图存储路径 subimgpath = os.path.join(resultPath, f"{event.evtname}", "subimg") @@ -217,9 +245,9 @@ def simi_matrix(): 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}" + assert len(boxes)==len(evtfeat), f"Please check the Event: {event.evtname}" if len(boxes)==0: continue - print(evtname) + print(event.evtname) matrix = 1 - cdist(evtfeat, stdfeat, 'cosine') simi_1d = matrix.flatten() @@ -309,8 +337,8 @@ def simi_matrix(): 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}") + max_diff = abs(max_values[1]-max_values[0]) + ax[0, 2].set_title(f"max diff: {max_diff:.3f}") try: fig.suptitle(f"Similar: {Similar:.3f}", fontsize=16) except Exception as e: @@ -319,19 +347,14 @@ def simi_matrix(): pltpath = os.path.join(subimgpath, f"hist_max_{kpercent}%_.png") plt.savefig(pltpath) - pltpath1 = os.path.join(histpath, f"{evtname}_.png") + 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if os.path.isfile(pickpath): continue - - # event = ShoppingEvent(source_path, stype) - try: - event = ShoppingEvent(source_path, stype) - with open(pickpath, 'wb') as f: - pickle.dump(event, f) - print(evtname) - except Exception as e: - errEvents.append(source_path) - print(f"Error: {evtname}, {e}") - # k += 1 - # if k==1: - # break - - errfile = Path(eventDataPath).parent / 'error_events.txt' - with open(str(errfile), 'a', encoding='utf-8') as f: - for line in errEvents: - f.write(line + '\n') +from utils.tools import init_eventDict def read_eventdict(eventDataPath): evtDict = {} diff --git a/contrast/one2one_contrast.py b/contrast/one2one_contrast.py index fdce5f1..2e79b27 100644 --- a/contrast/one2one_contrast.py +++ b/contrast/one2one_contrast.py @@ -27,188 +27,24 @@ Created on Fri Aug 30 17:53:03 2024 """ import numpy as np -import cv2 import os import sys import random import pickle import json -import random -import copy -import sys -# import torch -import time -# import json + from pathlib import Path -from scipy.spatial.distance import cdist import matplotlib.pyplot as plt -import shutil -from datetime import datetime -# from openpyxl import load_workbook, Workbook -# from config import config as conf -# from model import resnet18 as resnet18 -# from feat_inference import inference_image +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) -sys.path.append(r"D:\DetectTracking") -from tracking.utils.read_data import extract_data, read_tracking_output, read_similar, read_deletedBarcode_file -from tracking.utils.plotting import Annotator, colors -from feat_extract.config import config as conf -from feat_extract.inference import FeatsInterface -from utils.event import ShoppingEvent, save_data +from utils.calsimi import calsimi_vs_stdfeat, calsimi_vs_stdfeat_new +from utils.tools import get_evtList, init_eventDict +from utils.databits import data_precision_compare from genfeats import gen_bcd_features -from event_test import calc_simil -from one2n_contrast import init_eventDict - - - - - -def int8_to_ft16(arr_uint8, amin, amax): - arr_ft16 = (arr_uint8 / 255 * (amax-amin) + amin).astype(np.float16) - - return arr_ft16 - -def ft16_to_uint8(arr_ft16): - # pickpath = r"\\192.168.1.28\share\测试_202406\contrast\std_features_ft32vsft16\6902265587712_ft16.pickle" - - # with open(pickpath, 'rb') as f: - # edict = pickle.load(f) - - # arr_ft16 = edict['feats'] - - amin = np.min(arr_ft16) - amax = np.max(arr_ft16) - arr_ft255 = (arr_ft16 - amin) * 255 / (amax-amin) - arr_uint8 = arr_ft255.astype(np.uint8) - - arr_ft16_ = int8_to_ft16(arr_uint8, amin, amax) - - arrDistNorm = np.linalg.norm(arr_ft16_ - arr_ft16) / arr_ft16_.size - - return arr_uint8, arr_ft16_ - - -def data_precision_compare(stdfeat, evtfeat, evtMessage, save=True): - evt, stdbcd, label = evtMessage - rltdata, rltdata_ft16, rltdata_ft16_ = [], [], [] - - matrix = 1 - cdist(stdfeat, evtfeat, 'cosine') - 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 = [label, stdbcd, evt, simi_mean, simi_max, simi_mfeat[0,0]] - - - ##================================================================= float16 - stdfeat_ft16 = stdfeat.astype(np.float16) - evtfeat_ft16 = evtfeat.astype(np.float16) - stdfeat_ft16 /= np.linalg.norm(stdfeat_ft16, axis=1)[:, None] - evtfeat_ft16 /= np.linalg.norm(evtfeat_ft16, axis=1)[:, None] - - - matrix_ft16 = 1 - cdist(stdfeat_ft16, evtfeat_ft16, 'cosine') - simi_mean_ft16 = np.mean(matrix_ft16) - simi_max_ft16 = np.max(matrix_ft16) - stdfeatm_ft16 = np.mean(stdfeat_ft16, axis=0, keepdims=True) - evtfeatm_ft16 = np.mean(evtfeat_ft16, axis=0, keepdims=True) - simi_mfeat_ft16 = 1- np.maximum(0.0, cdist(stdfeatm_ft16, evtfeatm_ft16, 'cosine')) - rltdata_ft16 = [label, stdbcd, evt, simi_mean_ft16, simi_max_ft16, simi_mfeat_ft16[0,0]] - - '''****************** uint8 is ok!!!!!! ******************''' - ##=================================================================== uint8 - # stdfeat_uint8, stdfeat_ft16_ = ft16_to_uint8(stdfeat_ft16) - # evtfeat_uint8, evtfeat_ft16_ = ft16_to_uint8(evtfeat_ft16) - - stdfeat_uint8 = (stdfeat_ft16*128).astype(np.int8) - evtfeat_uint8 = (evtfeat_ft16*128).astype(np.int8) - stdfeat_ft16_ = stdfeat_uint8.astype(np.float16)/128 - evtfeat_ft16_ = evtfeat_uint8.astype(np.float16)/128 - - absdiff = np.linalg.norm(stdfeat_ft16_ - stdfeat) / stdfeat.size - - matrix_ft16_ = 1 - cdist(stdfeat_ft16_, evtfeat_ft16_, 'cosine') - simi_mean_ft16_ = np.mean(matrix_ft16_) - simi_max_ft16_ = np.max(matrix_ft16_) - stdfeatm_ft16_ = np.mean(stdfeat_ft16_, axis=0, keepdims=True) - evtfeatm_ft16_ = np.mean(evtfeat_ft16_, axis=0, keepdims=True) - simi_mfeat_ft16_ = 1- np.maximum(0.0, cdist(stdfeatm_ft16_, evtfeatm_ft16_, 'cosine')) - rltdata_ft16_ = [label, stdbcd, evt, simi_mean_ft16_, simi_max_ft16_, simi_mfeat_ft16_[0,0]] - - if not save: - return - - - ##========================================================= save as float32 - rppath = os.path.join(similPath, f'{evt}_ft32.pickle') - with open(rppath, 'wb') as f: - pickle.dump(rltdata, f) - - rtpath = os.path.join(similPath, f'{evt}_ft32.txt') - with open(rtpath, 'w', encoding='utf-8') as f: - for result in rltdata: - part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result] - line = ', '.join(part) - f.write(line + '\n') - - - ##========================================================= save as float16 - rppath_ft16 = os.path.join(similPath, f'{evt}_ft16.pickle') - with open(rppath_ft16, 'wb') as f: - pickle.dump(rltdata_ft16, f) - - rtpath_ft16 = os.path.join(similPath, f'{evt}_ft16.txt') - with open(rtpath_ft16, 'w', encoding='utf-8') as f: - for result in rltdata_ft16: - part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result] - line = ', '.join(part) - f.write(line + '\n') - - - ##=========================================================== save as uint8 - rppath_uint8 = os.path.join(similPath, f'{evt}_uint8.pickle') - with open(rppath_uint8, 'wb') as f: - pickle.dump(rltdata_ft16_, f) - - rtpath_uint8 = os.path.join(similPath, f'{evt}_uint8.txt') - with open(rtpath_uint8, 'w', encoding='utf-8') as f: - for result in rltdata_ft16_: - part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result] - line = ', '.join(part) - f.write(line + '\n') - - - - -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 - - - evtfeat /= np.linalg.norm(evtfeat, axis=1)[:, None] - stdfeat /= np.linalg.norm(stdfeat, axis=1)[:, 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(): @@ -218,18 +54,6 @@ def build_std_evt_dict(): ''' stdBarcode = [p.stem for p in Path(stdFeaturePath).iterdir() if p.is_file() and (p.suffix=='.json' or p.suffix=='.pickle')] - - '''*********** 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() @@ -259,10 +83,7 @@ def build_std_evt_dict(): stddata = pickle.load(f) feat = stddata["feats_ft32"] stdDict[barcode] = feat - - - - + '''*********** USearch ***********''' # stdDict = {} # for barcode in barcodes: @@ -282,7 +103,7 @@ def build_std_evt_dict(): return evtList, evtDict, stdDict -def one2SN_pr(evtList, evtDict, stdDict): +def one2SN_pr(evtList, evtDict, stdDict, simType="simple"): std_barcodes = set([bcd for _, bcd in evtList]) @@ -312,8 +133,14 @@ def one2SN_pr(evtList, evtDict, stdDict): 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) + + if simType=="typea": + simi_mean, simi_max, simi_mfeat = calsimi_vs_stdfeat(event, stdfeat) + elif simType=="typeb": + pass + else: + simi_mean, simi_1, simi_2 = calsimi_vs_stdfeat_new(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: @@ -376,6 +203,10 @@ def one2SN_pr(evtList, evtDict, stdDict): ax.set_xlabel(f"Event Num: {len(tp_events) + len(fn_events)}") ax.legend() plt.show() + + rltpath = os.path.join(similPath, f'pr_1toSN_{simType}.png') + plt.savefig(rltpath) + ## ============================= 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') @@ -391,11 +222,14 @@ def one2SN_pr(evtList, evtDict, stdDict): axes[1, 1].set_xlim([-0.2, 1]) axes[1, 1].set_title(f'FN({len(fn_simi)})') plt.show() + + rltpath = os.path.join(similPath, f'hist_1toSN_{simType}.png') + plt.savefig(rltpath) -def one2one_simi(evtList, evtDict, stdDict): +def one2one_simi(evtList, evtDict, stdDict, simType): 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 ====================''' @@ -421,31 +255,50 @@ def one2one_simi(evtList, evtDict, stdDict): continue stdfeat = stdDict[stdbcd] # float32 - - simi_mean, simi_max, simi_mfeat = simi_calc(event, stdfeat) + + if simType=="typea": + simi_mean, simi_1, simi_2 = calsimi_vs_stdfeat_new(event, stdfeat) + elif simType=="typeb": + pass + else: + simi_mean, simi_1, simi_2 = calsimi_vs_stdfeat(event, stdfeat) + if simi_mean is None: continue - rltdata.append((label, stdbcd, evtname, simi_mean, simi_max, simi_mfeat)) + rltdata.append((label, stdbcd, evtname, simi_mean, simi_1, simi_2)) '''================ float32、16、int8 精度比较与存储 =============''' - # data_precision_compare(stdfeat, evtfeat, mergePairs[i], save=True) + # data_precision_compare(stdfeat, evtfeat, mergePairs[i], similPath, save=True) errorFile_one2one = list(set(errorFile_one2one)) return rltdata, errorFile_one2one -def one2one_pr(evtList, evtDict, stdDict): +def one2one_pr(evtList, evtDict, stdDict, simType="simple"): - rltdata, errorFile_one2one = one2one_simi(evtList, evtDict, stdDict) + rltdata, errorFile_one2one = one2one_simi(evtList, evtDict, stdDict, simType) Same, Cross = [], [] + for label, stdbcd, evtname, simi_mean, simi_max, simi_mft in rltdata: - if label == "same": + if simType=="simple" and label == "same": Same.append(simi_max) - if label == "diff": + if simType=="simple" and label == "diff": Cross.append(simi_max) + + if simType=="typea" and label == "same": + Same.append(simi_mean) + if simType=="typea" and label == "diff": + Cross.append(simi_mean) + + + # for label, stdbcd, evtname, simi_mean, simi_max, simi_mft in rltdata: + # if label == "same": + # Same.append(simi_mean) + # if label == "diff": + # Cross.append(simi_mean) Same = np.array(Same) Cross = np.array(Cross) @@ -508,7 +361,7 @@ def one2one_pr(evtList, evtDict, stdDict): ax.legend() plt.show() - rltpath = os.path.join(similPath, 'pr.png') + rltpath = os.path.join(similPath, f'pr_1to1_{simType}.png') plt.savefig(rltpath) # svg, png, pdf @@ -521,7 +374,7 @@ def one2one_pr(evtList, evtDict, stdDict): axes[1].set_xlim([-0.2, 1]) axes[1].set_title(f'TN({len(Cross)})') - rltpath = os.path.join(similPath, 'hist.png') + rltpath = os.path.join(similPath, f'hist_1to1_{simType}.png') plt.savefig(rltpath) @@ -529,158 +382,25 @@ def one2one_pr(evtList, evtDict, stdDict): -def gen_eventdict(sourcePath, saveimg=True): - k, errEvents = 0, [] - for source_path in sourcePath: - evtpath, bname = os.path.split(source_path) - - ## 兼容事件的两种情况：文件夹 和 Yolo-Resnet-Tracker 的输出 - if os.path.isfile(source_path): - bname, ext = os.path.splitext(bname) - # evt = bname.split("_") - - evt = bname.split('_') - condt = len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10 - if not condt: continue - # 如果已完成事件生成，则不执行 - pickpath = os.path.join(eventDataPath, f"{bname}.pickle") - if os.path.isfile(pickpath): continue - - try: - event = ShoppingEvent(source_path, stype=source_type) - # save_data(event, resultPath) - - with open(pickpath, 'wb') as f: - pickle.dump(event, f) - print(bname) - except Exception as e: - errEvents.append(source_path) - print(e) - - # k += 1 - # if k==1: - # break - - errfile = os.path.join(resultPath, 'error_events.txt') - # with open(errfile, 'w', encoding='utf-8') as f: - # for line in errEvents: - # f.write(line + '\n') - - -# def init_std_evt_dict(): -# '''==== 0. 生成事件列表和对应的 Barcodes列表 ===========''' -# bcdList, event_spath = [], [] -# for evtname in os.listdir(eventSourcePath): -# bname, ext = os.path.splitext(evtname) - -# ## 处理事件的两种情况：文件夹 和 Yolo-Resnet-Tracker 的输出 -# fpath = os.path.join(eventSourcePath, evtname) -# if os.path.isfile(fpath) and (ext==".pkl" or ext==".pickle"): -# evt = bname.split('_') -# elif os.path.isdir(fpath): -# evt = evtname.split('_') -# else: -# continue - -# if len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10: -# bcdList.append(evt[-1]) -# event_spath.append(fpath) - -# '''==== 1. 生成标准特征集, 只需运行一次, 在 genfeats.py 中实现 ===========''' -# bcdSet = set(bcdList) -# gen_bcd_features(stdSamplePath, stdBarcodePath, stdFeaturePath, bcdSet) -# print("stdFeats have generated and saved!") - -# '''==== 2. 生成事件字典, 只需运行一次 ===============''' -# gen_eventdict(event_spath) -# print("eventList have generated and saved!") - -def get_evtList(): - - '''==== 0. 生成事件列表和对应的 Barcodes 集合 ===========''' - bcdList, evtpaths = [], [] - for evtname in os.listdir(eventSourcePath): - bname, ext = os.path.splitext(evtname) - - ## 处理事件的两种情况：文件夹 和 Yolo-Resnet-Tracker 的输出 - fpath = os.path.join(eventSourcePath, evtname) - if os.path.isfile(fpath) and (ext==".pkl" or ext==".pickle"): - evt = bname.split('_') - elif os.path.isdir(fpath): - evt = evtname.split('_') - else: - continue - - if len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10: - bcdList.append(evt[-1]) - evtpaths.append(fpath) - - bcdSet = set(bcdList) - - return evtpaths, bcdSet - - - -# def init_stdDict(): -# evtpaths, bcdSet = get_evtList() -# gen_bcd_features(stdSamplePath, stdBarcodePath, stdFeaturePath, bcdSet) -# print("stdFeats have generated and saved!") - - -# def init_evtDict(): -# '''==== 0. 生成事件列表和对应的 Barcodes列表 ===========''' -# bcdList, event_spath = [], [] -# for evtname in os.listdir(eventSourcePath): -# bname, ext = os.path.splitext(evtname) - -# ## 处理事件的两种情况：文件夹 和 Yolo-Resnet-Tracker 的输出 -# fpath = os.path.join(eventSourcePath, evtname) -# if os.path.isfile(fpath) and (ext==".pkl" or ext==".pickle"): -# evt = bname.split('_') -# elif os.path.isdir(fpath): -# evt = evtname.split('_') -# else: -# continue - -# if len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10: -# bcdList.append(evt[-1]) -# event_spath.append(fpath) - -# '''==== 2. 生成事件字典, 只需运行一次 ===============''' -# gen_eventdict(event_spath) -# print("eventList have generated and saved!") - - - - - -def test_one2one_one2SN(): +def test_one2one_one2SN(simType): '''1:1性能评估''' - # evtpaths, bcdSet = get_evtList() + # evtpaths, bcdSet = get_evtList(eventSourcePath) '''=== 1. 只需运行一次，生成事件对应的标准特征库字典,如已生成，无需运行 ====''' # gen_bcd_features(stdSamplePath, stdBarcodePath, stdFeaturePath, eventSourcePath) '''==== 2. 生成事件字典, 只需运行一次 ====================''' - - # date_ = ['2025-3-4_1', '2025-3-5_1', '2025-3-5_2'] - # for dt in date_: - # evtpaths = os.path.join(eventSourcePath, dt) - # init_eventDict(evtpaths, eventDataPath, source_type) - - init_eventDict(eventSourcePath, eventDataPath, source_type) - - + # init_eventDict(eventSourcePath, eventDataPath, source_type) - '''==== 2. 基于事件barcode集和标准库barcode交集构造事件集合 =========''' + '''==== 3. 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() - one2one_pr(evtList, evtDict, stdDict) + one2one_pr(evtList, evtDict, stdDict, simType) - one2SN_pr(evtList, evtDict, stdDict) + one2SN_pr(evtList, evtDict, stdDict, simType) if __name__ == '__main__': ''' @@ -694,21 +414,10 @@ if __name__ == '__main__': (7) similPath: 1:1比对结果存储地址(事件级)，在resultPath下 ''' - # 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"\\192.168.1.28\share\数据\已完成数据\比对数据\barcode\all_totalBarocde\features_json\v11_barcode_11592" + stdSamplePath = "/home/wqg/dataset/total_barcode/totalBarcode" + stdBarcodePath = "/home/wqg/dataset/total_barcode/bcdpath" + stdFeaturePath = "/home/wqg/dataset/test_dataset/total_barcode/features_json/v11_barcode_0304/" - # 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" - - # stdSamplePath = r"\\192.168.1.28\share\数据\已完成数据\展厅数据\v2.0_abroad\比对数据\all_base_二筛" - # stdBarcodePath = r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\比对测试数据20250121_testing\bcdpath" - # stdFeaturePath = r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\比对测试数据20250121_testing\stdfeats" - - stdSamplePath = r"\\192.168.1.28\share\数据\已完成数据\比对数据\barcode\all_totalBarocde\totalBarcode" - stdBarcodePath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\testing\bcdpath" - 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_0304" - if not os.path.exists(stdBarcodePath): os.makedirs(stdBarcodePath) if not os.path.exists(stdFeaturePath): @@ -719,18 +428,24 @@ if __name__ == '__main__': "data": 基于事件切分的原 data 文件版本 "realtime": 全实时生成的 data 文件 ''' - source_type = 'realtime' # 'source', 'data', 'realtime' - eventSourcePath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\V12\基准数据集\2025-3-4_1" - resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\testing" + source_type = 'source' # 'source', 'data', 'realtime' + simType = "typea" # "simple", "typea", "typeb" - eventDataPath = os.path.join(resultPath, "evtobjs_0304_1") - similPath = os.path.join(resultPath, "simidata_0304_1") + evttype = "single_event_V10" + # evttype = "single_event_V5" + # evttype = "performence_V10" + # evttype = "performence_V5" + eventSourcePath = "/home/wqg/dataset/pipeline/yrt/{}/shopping_pkl".format(evttype) + + resultPath = "/home/wqg/dataset/pipeline/contrast/{}".format(evttype) + eventDataPath = os.path.join(resultPath, "evtobjs") + similPath = os.path.join(resultPath, "simidata") if not os.path.exists(eventDataPath): os.makedirs(eventDataPath) if not os.path.exists(similPath): os.makedirs(similPath) - test_one2one_one2SN() + test_one2one_one2SN(simType) diff --git a/contrast/onsite_contrast_pr.py b/contrast/onsite_contrast_pr.py index e228bf6..ffd744a 100644 --- a/contrast/onsite_contrast_pr.py +++ b/contrast/onsite_contrast_pr.py @@ -16,7 +16,11 @@ from pathlib import Path import matplotlib.pyplot as plt import sys -sys.path.append(r"D:\DetectTracking") +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) + from tracking.utils.read_data import read_similar def read_one2one_data(filepath): @@ -531,21 +535,23 @@ def contrast_pr(evtPaths): # bcdSet = set(bcdList) - one2nErrFile = os.path.join(evtPaths, "one_2_Small_n_Error.txt") - with open(one2nErrFile, "w") as file: - for item in fnevents: - file.write(item + "\n") + + + # one2nErrFile = os.path.join(evtPaths, "one_2_Small_n_Error.txt") + # with open(one2nErrFile, "w") as file: + # for item in fnevents: + # file.write(item + "\n") - one2NErrFile = os.path.join(evtPaths, "one_2_Big_N_Error.txt") - with open(one2NErrFile, "w") as file: - for item in fn_events: - file.write(item + "\n") + # one2NErrFile = os.path.join(evtPaths, "one_2_Big_N_Error.txt") + # with open(one2NErrFile, "w") as file: + # for item in fn_events: + # file.write(item + "\n") print('Done!') if __name__ == "__main__": - evtpaths = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\V12\2025-3-3" + evtpaths = r"/home/wqg/dataset/test_base_dataset/single_event/source" contrast_pr(evtpaths) diff --git a/contrast/trail2trail.py b/contrast/trail2trail.py new file mode 100644 index 0000000..cbd364a --- /dev/null +++ b/contrast/trail2trail.py @@ -0,0 +1,172 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" + 取出再放回场景下商品轨迹特征比对方式与性能分析 + +Created on Tue Apr 1 17:17:47 2025 +@author: wqg +""" +import os +import pickle +import random +import numpy as np +from pathlib import Path +import matplotlib.pyplot as plt +from scipy.spatial.distance import cdist +from utils.calsimi import calsiml, calsimi_vs_evts + +def read_eventdict(evtpaths): + evtDict = {} + for filename in os.listdir(evtpaths): + evtname, ext = os.path.splitext(filename) + if ext != ".pickle": continue + + evtpath = os.path.join(evtpaths, filename) + with open(evtpath, 'rb') as f: + evtdata = pickle.load(f) + evtDict[evtname] = evtdata + + + return evtDict + + + +def compute_show_pr(Same, Cross): + TPFN = len(Same) + TNFP = len(Cross) + + Recall_Pos, Recall_Neg = [], [] + Precision_Pos, Precision_Neg = [], [] + Correct = [] + Thresh = np.linspace(-0.2, 1, 100) + for th in Thresh: + TP = np.sum(Same >= th) + FN = np.sum(Same < th) + # FN = TPFN - TP + + TN = np.sum(Cross < th) + FP = np.sum(Cross >= th) + # FP = TNFP - TN + + + Precision_Pos.append(TP/(TP+FP+1e-6)) + Precision_Neg.append(TN/(TN+FN+1e-6)) + Recall_Pos.append(TP/(TP+FN+1e-6)) + Recall_Neg.append(TN/(TN+FP+1e-6)) + + # Recall_Pos.append(TP/TPFN) + # Recall_Neg.append(TN/TNFP) + + + Correct.append((TN+TP)/(TPFN+TNFP)) + + fig, ax = plt.subplots() + + ax.plot(Thresh, Precision_Pos, 'r', label='Precision_Pos: TP/(TP+FP)') + ax.plot(Thresh, Recall_Pos, 'b', label='Recall_Pos: TP/TPFN') + ax.plot(Thresh, Recall_Neg, 'g', label='Recall_Neg: TN/TNFP') + ax.plot(Thresh, Correct, 'c', label='Correct: (TN+TP)/(TPFN+TNFP)') + ax.plot(Thresh, Precision_Neg, 'm', label='Precision_Neg: TN/(TN+FN)') + + ax.set_xlim([0, 1]) + ax.set_ylim([0, 1]) + + ax.set_xticks(np.arange(0, 1, 0.1)) + ax.set_yticks(np.arange(0, 1, 0.1)) + ax.grid(True, linestyle='--') + + ax.set_title('PrecisePos & PreciseNeg') + ax.set_xlabel(f"Same Num: {TPFN}, Cross Num: {TNFP}") + ax.legend() + plt.show() + + # rltpath = os.path.join(similPath, f'pr_1to1_{simType}.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, f'hist_1to1_{simType}.png') + # plt.savefig(rltpath) + + + plt.show() + + + +def trail_to_trail(evtpaths, rltpaths): + # select the method type of how to calculate the feat similarity of trail + simType = 2 + + ##1. read all the ShoppingEvent object in the dir 'evtpaths' + evtDicts = read_eventdict(evtpaths) + + ##2. Combine event object with the same barcode + barcodes, evtpairDict = [], {} + for k in evtDicts.keys(): + evt = k.split('_') + condt = len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10 + if not condt: continue + + barcode = evt[-1] + if barcode not in evtpairDict.keys(): + evtpairDict[barcode] = [] + barcodes.append(barcode) + + evtpairDict[barcode].append(evtDicts[k]) + barcodes = set(barcodes) + + AA_list, AB_list = [], [] + for barcode in evtpairDict.keys(): + events = evtpairDict[barcode] + if len(events)>1: + evta, evtb = random.sample(events, 2) + AA_list.append((evta, evtb, "same")) + + evtc = random.sample(events, 1)[0] + + dset = list(barcodes.symmetric_difference(set([barcode]))) + bcd = random.sample(dset, 1)[0] + evtd = random.sample(evtpairDict[bcd], 1)[0] + AB_list.append((evtc, evtd, "diff")) + + mergePairs = AA_list + AB_list + + ##3. calculate the similar of two event: evta, evtb + new_pirs = [] + for evta, evtb, label in mergePairs: + similar = calsimi_vs_evts(evta, evtb, simType) + if similar is None: + continue + new_pirs.append((label, round(similar, 3), evta.evtname[:15], evtb.evtname[:15])) + + ##4. compute PR and showing + Same = np.array([s for label, s, _, _ in new_pirs if label=="same"]) + Cross = np.array([s for label, s, _, _ in new_pirs if label=="diff"]) + compute_show_pr(Same, Cross) + + +def main(): + evttypes = ["single_event_V10", "single_event_V5", "performence_V10", "performence_V5"] + # evttypes = ["single_event_V10"] + + for evttype in evttypes: + evtpaths = "/home/wqg/dataset/pipeline/contrast/{}/evtobjs/".format(evttype) + rltpaths = "/home/wqg/dataset/pipeline/yrt/{}/yolos_tracking".format(evttype) + + trail_to_trail(evtpaths, rltpaths) + + + +if __name__ == '__main__': + main() + + + \ No newline at end of file diff --git a/contrast/utils/__pycache__/__init__.cpython-312.pyc b/contrast/utils/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 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scipy.spatial.distance import cdist + + +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 calsiml(feat1, feat2, topkp=75, cluth=0.15): + '''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, :], topkp)) + simi_max.append(sim) + cltc_max = cluster(simi_max, cluth) + 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 + + +def calsimi_vs_stdfeat_new(event, stdfeat): + '''事件与标准库的对比策略 + 该比对策略是否可以拓展到事件与事件的比对？ + ''' + 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) + + front_simi, back_simi = None, None + 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, front_simi, back_simi + + + + + +def calsimi_vs_stdfeat(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 + + + evtfeat /= np.linalg.norm(evtfeat, axis=1)[:, None] + stdfeat /= np.linalg.norm(stdfeat, axis=1)[:, 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 calsimi_vs_evts(evta, evtb, simType=1): + if simType==1: + if len(evta.feats_compose) and len(evtb.feats_compose): + feata = evta.feats_compose + featb = evtb.feats_compose + matrix = 1 - cdist(feata, featb, 'cosine') + similar = np.mean(matrix) + else: + similar = None + return similar + + if simType==2: + if len(evta.feats_compose) and len(evtb.feats_compose): + feata = evta.feats_compose + featb = evtb.feats_compose + matrix = 1 - cdist(feata, featb, 'cosine') + similar = np.max(matrix) + else: + similar = None + return similar + + if simType==3: + if len(evta.feats_compose) and len(evtb.feats_compose): + feata = evta.feats_compose + featb = evtb.feats_compose + similar = calsiml(feata, featb) + else: + similar = None + return similar + + + ##1. the front feats of evta, evtb + fr_feata = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容 + for i in range(len(evta.front_feats)): + fr_feata = np.concatenate((fr_feata, evta.front_feats[i]), axis=0) + + fr_featb = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容 + for i in range(len(evtb.front_feats)): + fr_featb = np.concatenate((fr_featb, evtb.front_feats[i]), axis=0) + + ##2. the back feats of evta, evtb + bk_feata = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容 + for i in range(len(evta.back_feats)): + bk_feata = np.concatenate((bk_feata, evta.back_feats[i]), axis=0) + + bk_featb = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容 + for i in range(len(evtb.back_feats)): + bk_featb = np.concatenate((bk_featb, evtb.back_feats[i]), axis=0) + + + front_simi, back_simi = None, None + if len(fr_feata) and len(fr_featb): + front_simi = calsiml(fr_feata, fr_featb) + + if len(bk_feata) and len(bk_featb): + back_simi = calsiml(bk_feata, bk_featb) + + '''前后摄相似度融合策略''' + if front_simi is not None and back_simi is not None: + 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 front_simi is not None and back_simi is None: + similar = front_simi + elif front_simi is None and back_simi is not None: + similar = back_simi + else: + similar = None # 在event.front_feats和event.back_feats同时为空时 + + return similar + + diff --git a/contrast/utils/databits.py b/contrast/utils/databits.py new file mode 100644 index 0000000..9d59e2b --- /dev/null +++ b/contrast/utils/databits.py @@ -0,0 +1,127 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Apr 1 16:43:04 2025 + +@author: wqg +""" +import os +import pickle +import numpy as np +from scipy.spatial.distance import cdist + + +def int8_to_ft16(arr_uint8, amin, amax): + arr_ft16 = (arr_uint8 / 255 * (amax-amin) + amin).astype(np.float16) + + return arr_ft16 + +def ft16_to_uint8(arr_ft16): + # pickpath = r"\\192.168.1.28\share\测试_202406\contrast\std_features_ft32vsft16\6902265587712_ft16.pickle" + + # with open(pickpath, 'rb') as f: + # edict = pickle.load(f) + + # arr_ft16 = edict['feats'] + + amin = np.min(arr_ft16) + amax = np.max(arr_ft16) + arr_ft255 = (arr_ft16 - amin) * 255 / (amax-amin) + arr_uint8 = arr_ft255.astype(np.uint8) + + arr_ft16_ = int8_to_ft16(arr_uint8, amin, amax) + + arrDistNorm = np.linalg.norm(arr_ft16_ - arr_ft16) / arr_ft16_.size + + return arr_uint8, arr_ft16_ + + +def data_precision_compare(stdfeat, evtfeat, evtMessage, similPath='', save=True): + evt, stdbcd, label = evtMessage + rltdata, rltdata_ft16, rltdata_ft16_ = [], [], [] + + matrix = 1 - cdist(stdfeat, evtfeat, 'cosine') + 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 = [label, stdbcd, evt, simi_mean, simi_max, simi_mfeat[0,0]] + + + ##================================================================= float16 + stdfeat_ft16 = stdfeat.astype(np.float16) + evtfeat_ft16 = evtfeat.astype(np.float16) + stdfeat_ft16 /= np.linalg.norm(stdfeat_ft16, axis=1)[:, None] + evtfeat_ft16 /= np.linalg.norm(evtfeat_ft16, axis=1)[:, None] + + + matrix_ft16 = 1 - cdist(stdfeat_ft16, evtfeat_ft16, 'cosine') + simi_mean_ft16 = np.mean(matrix_ft16) + simi_max_ft16 = np.max(matrix_ft16) + stdfeatm_ft16 = np.mean(stdfeat_ft16, axis=0, keepdims=True) + evtfeatm_ft16 = np.mean(evtfeat_ft16, axis=0, keepdims=True) + simi_mfeat_ft16 = 1- np.maximum(0.0, cdist(stdfeatm_ft16, evtfeatm_ft16, 'cosine')) + rltdata_ft16 = [label, stdbcd, evt, simi_mean_ft16, simi_max_ft16, simi_mfeat_ft16[0,0]] + + '''****************** uint8 is ok!!!!!! ******************''' + ##=================================================================== uint8 + # stdfeat_uint8, stdfeat_ft16_ = ft16_to_uint8(stdfeat_ft16) + # evtfeat_uint8, evtfeat_ft16_ = ft16_to_uint8(evtfeat_ft16) + + stdfeat_uint8 = (stdfeat_ft16*128).astype(np.int8) + evtfeat_uint8 = (evtfeat_ft16*128).astype(np.int8) + stdfeat_ft16_ = stdfeat_uint8.astype(np.float16)/128 + evtfeat_ft16_ = evtfeat_uint8.astype(np.float16)/128 + + absdiff = np.linalg.norm(stdfeat_ft16_ - stdfeat) / stdfeat.size + + matrix_ft16_ = 1 - cdist(stdfeat_ft16_, evtfeat_ft16_, 'cosine') + simi_mean_ft16_ = np.mean(matrix_ft16_) + simi_max_ft16_ = np.max(matrix_ft16_) + stdfeatm_ft16_ = np.mean(stdfeat_ft16_, axis=0, keepdims=True) + evtfeatm_ft16_ = np.mean(evtfeat_ft16_, axis=0, keepdims=True) + simi_mfeat_ft16_ = 1- np.maximum(0.0, cdist(stdfeatm_ft16_, evtfeatm_ft16_, 'cosine')) + rltdata_ft16_ = [label, stdbcd, evt, simi_mean_ft16_, simi_max_ft16_, simi_mfeat_ft16_[0,0]] + + if not save: + return + + + ##========================================================= save as float32 + rppath = os.path.join(similPath, f'{evt}_ft32.pickle') + with open(rppath, 'wb') as f: + pickle.dump(rltdata, f) + + rtpath = os.path.join(similPath, f'{evt}_ft32.txt') + with open(rtpath, 'w', encoding='utf-8') as f: + for result in rltdata: + part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result] + line = ', '.join(part) + f.write(line + '\n') + + + ##========================================================= save as float16 + rppath_ft16 = os.path.join(similPath, f'{evt}_ft16.pickle') + with open(rppath_ft16, 'wb') as f: + pickle.dump(rltdata_ft16, f) + + rtpath_ft16 = os.path.join(similPath, f'{evt}_ft16.txt') + with open(rtpath_ft16, 'w', encoding='utf-8') as f: + for result in rltdata_ft16: + part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result] + line = ', '.join(part) + f.write(line + '\n') + + + ##=========================================================== save as uint8 + rppath_uint8 = os.path.join(similPath, f'{evt}_uint8.pickle') + with open(rppath_uint8, 'wb') as f: + pickle.dump(rltdata_ft16_, f) + + rtpath_uint8 = os.path.join(similPath, f'{evt}_uint8.txt') + with open(rtpath_uint8, 'w', encoding='utf-8') as f: + for result in rltdata_ft16_: + part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result] + line = ', '.join(part) + f.write(line + '\n') \ No newline at end of file diff --git a/contrast/utils/event.py b/contrast/utils/event.py index e65ff1d..02df620 100644 --- a/contrast/utils/event.py +++ b/contrast/utils/event.py @@ -5,19 +5,25 @@ Created on Tue Nov 26 17:35:05 2024 @author: ym """ import os +import sys import cv2 import pickle import numpy as np from pathlib import Path -import sys -sys.path.append(r"D:\DetectTracking") +FILE = Path(__file__).resolve() +ROOT = FILE.parents[2] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) + 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 + + # import platform # import pathlib # plt = platform.system() diff --git a/contrast/utils/tools.py b/contrast/utils/tools.py index 7abdb5e..f3214e7 100644 --- a/contrast/utils/tools.py +++ b/contrast/utils/tools.py @@ -4,8 +4,81 @@ Created on Thu Oct 31 15:17:01 2024 @author: ym """ +import os import numpy as np +import pickle +from pathlib import Path import matplotlib.pyplot as plt +from .event import ShoppingEvent + +def init_eventDict(sourcePath, eventDataPath, 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 + "realtime": 全实时数据，从 data 文件中读取的现场运行数据 + + sourcePath:事件文件夹，事件类型包含2种： + (1) pipeline生成的 pickle 文件 + (2) 直接采集的事件文件夹 + ''' + k, errEvents = 0, [] + for evtname in os.listdir(sourcePath): + bname, ext = os.path.splitext(evtname) + source_path = os.path.join(sourcePath, evtname) + + if stype=="source" and ext not in ['.pkl', '.pickle']: continue + if stype=="data" and os.path.isfile(source_path): continue + if stype=="realtime" and os.path.isfile(source_path): continue + + evt = bname.split('_') + condt = len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10 + if not condt: continue + + pickpath = os.path.join(eventDataPath, f"{bname}.pickle") + if os.path.isfile(pickpath): continue + + # event = ShoppingEvent(source_path, stype) + try: + event = ShoppingEvent(source_path, stype) + with open(pickpath, 'wb') as f: + pickle.dump(event, f) + print(evtname) + except Exception as e: + errEvents.append(source_path) + print(f"Error: {evtname}, {e}") + # k += 1 + # if k==1: + # break + + errfile = Path(eventDataPath).parent / 'error_events.txt' + with open(str(errfile), 'a', encoding='utf-8') as f: + for line in errEvents: + f.write(line + '\n') + + +def get_evtList(evtpath): + '''==== 0. 生成事件列表和对应的 Barcodes 集合 ===========''' + bcdList, evtpaths = [], [] + for evtname in os.listdir(evtpath): + bname, ext = os.path.splitext(evtname) + + ## 处理事件的两种情况：文件夹 和 Yolo-Resnet-Tracker 的输出 + fpath = os.path.join(evtpath, evtname) + if os.path.isfile(fpath) and (ext==".pkl" or ext==".pickle"): + evt = bname.split('_') + elif os.path.isdir(fpath): + evt = evtname.split('_') + else: + continue + + if len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10: + bcdList.append(evt[-1]) + evtpaths.append(fpath) + + bcdSet = set(bcdList) + + return evtpaths, bcdSet diff --git a/execute_pipeline.py b/execute_pipeline.py index 6e9fa4f..39ed7ae 100644 --- a/execute_pipeline.py +++ b/execute_pipeline.py @@ -5,27 +5,37 @@ Created on Fri Mar 28 11:35:28 2025 @author: ym """ -from pipeline_01 import execute_pipeline +from pipeline import execute_pipeline -execute_pipeline(evtdir = r"D:\datasets\ym\后台数据\unzip", - DataType = "raw", # raw, pkl - kk=1, - source_type = "video", # video, image, - save_path = r"D:\work\result_pipeline_V5", - yolo_ver = "V5", # V10, V5 - weight_yolo_v5 = r'./ckpts/best_cls10_0906.pt' , - weight_yolo_v10 = r'./ckpts/best_v10s_width0375_1205.pt', - saveimages = False - ) +def execute(datapath, savepath_v5, savepath_v10): + execute_pipeline(evtdir = datapath, + DataType = "raw", # raw, pkl + kk=None, + source_type = "video", # video, image, + save_path = savepath_v5, + yolo_ver = "V5", # V10, V5 + weight_yolo_v5 = r'./ckpts/best_cls10_0906.pt' , + weight_yolo_v10 = r'./ckpts/best_v10s_width0375_1205.pt', + saveimages = False + ) + execute_pipeline(evtdir = datapath, + DataType = "raw", # raw, pkl + kk=None, + source_type = "video", # video, image, + save_path = savepath_v10, + yolo_ver = "V10", # V10, V5 + weight_yolo_v5 = r'./ckpts/best_cls10_0906.pt' , + weight_yolo_v10 = r'./ckpts/best_v10s_width0375_1205.pt', + saveimages = False + ) + +datapath = r'/home/wqg/dataset/test_dataset/base_dataset/single_event/source/' +savepath_v5 = r'/home/wqg/dataset/pipeline/contrast/single_event_V5' +savepath_v10 = r'/home/wqg/dataset/pipeline/contrast/single_event_V10' +execute(datapath, savepath_v5, savepath_v10) -execute_pipeline(evtdir = r"D:\datasets\ym\后台数据\unzip", - DataType = "raw", # raw, pkl - kk=1, - source_type = "video", # video, image, - save_path = r"D:\work\result_pipeline_V10", - yolo_ver = "V10", # V10, V5 - weight_yolo_v5 = r'./ckpts/best_cls10_0906.pt' , - weight_yolo_v10 = r'./ckpts/best_v10s_width0375_1205.pt', - saveimages = False - ) \ No newline at end of file +datapath = r'/home/wqg/dataset/test_performence_dataset/' +savepath_v5 = r'/home/wqg/dataset/pipeline/contrast/performence_V5' +savepath_v10 = r'/home/wqg/dataset/pipeline/contrast/performence_V10' +execute(datapath, savepath_v5, savepath_v10) diff --git a/hands/__pycache__/hand_inference.cpython-312.pyc b/hands/__pycache__/hand_inference.cpython-312.pyc index bf54fe94e2d0c1fd3fb5299bd284268dc89f1c7c..35e1f47bac8707a16b87ce0a4bbb75767d3146d3 100644 GIT binary patch delta 21 bcmdm_y-AzrG%qg~0}!|cKTF@pvrH5KLE{D# delta 21 bcmdm_y-AzrG%qg~0}#}@ElS(SvrH5KL8Arv diff --git a/models/__pycache__/__init__.cpython-312.pyc b/models/__pycache__/__init__.cpython-312.pyc index 84d0122c4e9e366a9debdd62f5f413a7a19c5393..0c0f867ce767b9009456304e451a4a25770c6db8 100644 GIT binary patch delta 20 acmeBS>|x|N&CAQh00eHq&(bIIGyniEy#*Nn delta 20 acmeBS>|x|N&CAQh00gyei_#|YGyniEm<0X+ diff --git a/models/__pycache__/common.cpython-312.pyc b/models/__pycache__/common.cpython-312.pyc index c3173a8cd9fc81ad26a347f81efef5bb93666c07..b614dff12601299961fcea3cde615c1fccfeff79 100644 GIT binary patch delta 26 gcmey~2L84Z;EG-Ms$egmoqigf6?ryK#<%c31 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a/models/__pycache__/yolo.cpython-312.pyc b/models/__pycache__/yolo.cpython-312.pyc index 9a3919f6971ad6877af2734e78083c8647c2c49c..072f2b09edab1cda751eda4b972e1788bb4edb84 100644 GIT binary patch delta 23 dcmbR9oN>-`MxN8Wyj%=G;1>KWeIw6=VgO%12dV%7 delta 23 dcmbR9oN>-`MxN8Wyj%=GQ0ulRZ6nWwVgO$M2af;% diff --git a/pipeline.py b/pipeline.py index 8f63a4f..afdc6f0 100644 --- a/pipeline.py +++ b/pipeline.py @@ -60,48 +60,158 @@ def save_subimgs_1(imgdict, boxes, spath, ctype, simidict = None): cv2.imwrite(imgpath, img) +def show_result(event_tracks, yrtDict, savepath_pipe): + '''保存 Tracking 输出的运动轨迹子图，并记录相似度''' + + savepath_pipe_subimgs = savepath_pipe / Path("subimgs") + if not savepath_pipe_subimgs.exists(): + savepath_pipe_subimgs.mkdir(parents=True, exist_ok=True) + + + + + for CamerType, vts in event_tracks: + if len(vts.tracks)==0: continue + if CamerType == 'front': + # yolos = ShoppingDict["frontCamera"]["yoloResnetTracker"] + + yolos = yrtDict["frontyrt"] + ctype = 1 + if CamerType == 'back': + # yolos = ShoppingDict["backCamera"]["yoloResnetTracker"] + + yolos = yrtDict["backyrt"] + ctype = 0 + + imgdict, featdict, simidict = {}, {}, {} + for y in yolos: + imgdict.update(y["imgs"]) + featdict.update(y["feats"]) + simidict.update(y["featsimi"]) -def pipeline( - eventpath, - savepath, - SourceType, - weights, - YoloVersion="V5" - ): - ''' - eventpath: 单个事件的存储路径 + for track in vts.Residual: + if isinstance(track, np.ndarray): + save_subimgs(imgdict, track, savepath_pipe_subimgs, ctype, featdict) + else: + save_subimgs(imgdict, track.slt_boxes, savepath_pipe_subimgs, ctype, featdict) + + '''(3) 轨迹显示与保存''' + illus = [None, None] + for CamerType, vts in event_tracks: + if len(vts.tracks)==0: continue + + if CamerType == 'front': + edgeline = cv2.imread("./tracking/shopcart/cart_tempt/board_ftmp_line.png") + + h, w = edgeline.shape[:2] + # nh, nw = h//2, w//2 + # edgeline = cv2.resize(edgeline, (nw, nh), interpolation=cv2.INTER_AREA) + + img_tracking = draw_all_trajectories(vts, edgeline, savepath_pipe, CamerType, draw5p=True) + illus[0] = img_tracking + + plt = plot_frameID_y2(vts) + plt.savefig(os.path.join(savepath_pipe, "front_y2.png")) + + if CamerType == 'back': + edgeline = cv2.imread("./tracking/shopcart/cart_tempt/edgeline.png") + + h, w = edgeline.shape[:2] + # nh, nw = h//2, w//2 + # edgeline = cv2.resize(edgeline, (nw, nh), interpolation=cv2.INTER_AREA) + + img_tracking = draw_all_trajectories(vts, edgeline, savepath_pipe, CamerType, draw5p=True) + illus[1] = img_tracking - ''' - optdict = {} - optdict["weights"] = weights - - if SourceType == "video": - vpaths = get_video_pairs(eventpath) - elif SourceType == "image": - vpaths = get_image_pairs(eventpath) - event_tracks = [] + illus = [im for im in illus if im is not None] + if len(illus): + img_cat = np.concatenate(illus, axis = 1) + if len(illus)==2: + H, W = img_cat.shape[:2] + cv2.line(img_cat, (int(W/2), 0), (int(W/2), int(H)), (128, 128, 255), 3) + + trajpath = os.path.join(savepath_pipe, "trajectory.png") + cv2.imwrite(trajpath, img_cat) + + + +def pipeline(eventpath, + SourceType, + weights, + DataType = "raw", #raw, pkl: images or videos, pkl, pickle file + YoloVersion="V5", + savepath = None, + saveimages = True + ): + ## 构造购物事件字典 evtname = Path(eventpath).stem barcode = evtname.split('_')[-1] if len(evtname.split('_'))>=2 \ and len(evtname.split('_')[-1])>=8 \ and evtname.split('_')[-1].isdigit() else '' - '''事件结果存储文件夹''' + + '''事件结果存储文件夹: savepath_pipe, savepath_pkl''' if not savepath: savepath = Path(__file__).resolve().parents[0] / "events_result" + savepath_pipe = Path(savepath) / Path("yolos_tracking") / evtname - savepath_pipeline = Path(savepath) / Path("Yolos_Tracking") / evtname + savepath_pkl = Path(savepath) / "shopping_pkl" + if not savepath_pkl.exists(): + savepath_pkl.mkdir(parents=True, exist_ok=True) + pklpath = Path(savepath_pkl) / Path(str(evtname)+".pickle") - """ShoppingDict pickle 文件保存地址 """ - savepath_spdict = Path(savepath) / "ShoppingDict_pkfile" - if not savepath_spdict.exists(): - savepath_spdict.mkdir(parents=True, exist_ok=True) - pf_path = Path(savepath_spdict) / Path(str(evtname)+".pickle") + + + yrt_out = [] + if DataType == "raw": + ### 不重复执行已经过yolo-resnet-tracker + if pklpath.exists(): + print(f"Pickle file have saved: {evtname}.pickle") + return - # if pf_path.exists(): - # print(f"Pickle file have saved: {evtname}.pickle") - # return + if SourceType == "video": + vpaths = get_video_pairs(eventpath) + elif SourceType == "image": + vpaths = get_image_pairs(eventpath) + + + + for vpath in vpaths: + '''================= 2. 事件结果存储文件夹 =================''' + + + if isinstance(vpath, list): + savepath_pipe_imgs = savepath_pipe / Path("images") + else: + savepath_pipe_imgs = savepath_pipe / Path(str(Path(vpath).stem)) + + if not savepath_pipe_imgs.exists(): + savepath_pipe_imgs.mkdir(parents=True, exist_ok=True) + + optdict = {} + optdict["weights"] = weights + optdict["source"] = vpath + optdict["save_dir"] = savepath_pipe_imgs + optdict["is_save_img"] = saveimages + optdict["is_save_video"] = True + + + if YoloVersion == "V5": + yrtOut = yolo_resnet_tracker(**optdict) + elif YoloVersion == "V10": + yrtOut = yolov10_resnet_tracker(**optdict) + + yrt_out.append((vpath, yrtOut)) + + elif DataType == "pkl": + pass + + else: + return + + '''====================== 构造 ShoppingDict 模块 =======================''' ShoppingDict = {"eventPath": eventpath, @@ -112,16 +222,14 @@ def pipeline( "backCamera": {}, "one2n": [] # } - yrtDict = {} - - procpath = Path(eventpath).joinpath('process.data') if procpath.is_file(): SimiDict = read_similar(procpath) ShoppingDict["one2n"] = SimiDict['one2n'] - - for vpath in vpaths: + yrtDict = {} + event_tracks = [] + for vpath, yrtOut in yrt_out: '''================= 1. 构造相机事件字典 =================''' CameraEvent = {"cameraType": '', # "front", "back" "videoPath": '', @@ -140,34 +248,10 @@ def pipeline( CameraEvent["cameraType"] = "back" if bname.split('_')[0] == "1" or bname.find('front')>=0: CameraEvent["cameraType"] = "front" - - '''================= 2. 事件结果存储文件夹 =================''' - if isinstance(vpath, list): - savepath_pipeline_imgs = savepath_pipeline / Path("images") - else: - savepath_pipeline_imgs = savepath_pipeline / Path(str(Path(vpath).stem)) - if not savepath_pipeline_imgs.exists(): - savepath_pipeline_imgs.mkdir(parents=True, exist_ok=True) - savepath_pipeline_subimgs = savepath_pipeline / Path("subimgs") - if not savepath_pipeline_subimgs.exists(): - savepath_pipeline_subimgs.mkdir(parents=True, exist_ok=True) - - - '''================= 3. Yolo + Resnet + Tracker =================''' - optdict["source"] = vpath - optdict["save_dir"] = savepath_pipeline_imgs - optdict["is_save_img"] = True - optdict["is_save_video"] = True - - - if YoloVersion == "V5": - yrtOut = yolo_resnet_tracker(**optdict) - elif YoloVersion == "V10": - yrtOut = yolov10_resnet_tracker(**optdict) - - + '''2种保存方式: (1) no save subimg, (2) save img''' + ###(1) save images yrtOut_save = [] for frdict in yrtOut: fr_dict = {} @@ -177,6 +261,7 @@ def pipeline( yrtOut_save.append(fr_dict) CameraEvent["yoloResnetTracker"] = yrtOut_save + ###(2) no save images # CameraEvent["yoloResnetTracker"] = yrtOut '''================= 4. tracking =================''' @@ -219,108 +304,58 @@ def pipeline( yrtDict["frontyrt"] = yrtOut '''========================== 保存模块 =================================''' - '''(1) 保存 ShoppingDict 事件''' - with open(str(pf_path), 'wb') as f: + # 保存 ShoppingDict + with open(str(pklpath), 'wb') as f: pickle.dump(ShoppingDict, f) - - '''(2) 保存 Tracking 输出的运动轨迹子图，并记录相似度''' - for CamerType, vts in event_tracks: - if len(vts.tracks)==0: continue - if CamerType == 'front': - # yolos = ShoppingDict["frontCamera"]["yoloResnetTracker"] - - yolos = yrtDict["frontyrt"] - ctype = 1 - if CamerType == 'back': - # yolos = ShoppingDict["backCamera"]["yoloResnetTracker"] - - yolos = yrtDict["backyrt"] - ctype = 0 - - imgdict, featdict, simidict = {}, {}, {} - for y in yolos: - imgdict.update(y["imgs"]) - featdict.update(y["feats"]) - simidict.update(y["featsimi"]) - - for track in vts.Residual: - if isinstance(track, np.ndarray): - save_subimgs(imgdict, track, savepath_pipeline_subimgs, ctype, featdict) - else: - save_subimgs(imgdict, track.slt_boxes, savepath_pipeline_subimgs, ctype, featdict) - - '''(3) 轨迹显示与保存''' - illus = [None, None] - for CamerType, vts in event_tracks: - if len(vts.tracks)==0: continue - - if CamerType == 'front': - edgeline = cv2.imread("./tracking/shopcart/cart_tempt/board_ftmp_line.png") - - h, w = edgeline.shape[:2] - # nh, nw = h//2, w//2 - # edgeline = cv2.resize(edgeline, (nw, nh), interpolation=cv2.INTER_AREA) - - img_tracking = draw_all_trajectories(vts, edgeline, savepath_pipeline, CamerType, draw5p=True) - illus[0] = img_tracking - - plt = plot_frameID_y2(vts) - plt.savefig(os.path.join(savepath_pipeline, "front_y2.png")) - - if CamerType == 'back': - edgeline = cv2.imread("./tracking/shopcart/cart_tempt/edgeline.png") - - h, w = edgeline.shape[:2] - # nh, nw = h//2, w//2 - # edgeline = cv2.resize(edgeline, (nw, nh), interpolation=cv2.INTER_AREA) - - img_tracking = draw_all_trajectories(vts, edgeline, savepath_pipeline, CamerType, draw5p=True) - illus[1] = img_tracking - illus = [im for im in illus if im is not None] - if len(illus): - img_cat = np.concatenate(illus, axis = 1) - if len(illus)==2: - H, W = img_cat.shape[:2] - cv2.line(img_cat, (int(W/2), 0), (int(W/2), int(H)), (128, 128, 255), 3) - - trajpath = os.path.join(savepath_pipeline, "trajectory.png") - cv2.imwrite(trajpath, img_cat) + # 绘制并保存轨迹图 + show_result(event_tracks, yrtDict, savepath_pipe) + + def execute_pipeline(evtdir = r"D:\datasets\ym\后台数据\unzip", - source_type = "video", # video, image, + DataType = "raw", # raw, pkl save_path = r"D:\work\result_pipeline", + kk=1, + source_type = "video", # video, image, yolo_ver = "V10", # V10, V5 - weight_yolo_v5 = r'./ckpts/best_cls10_0906.pt' , weight_yolo_v10 = r'./ckpts/best_v10s_width0375_1205.pt', - k=0 + saveimages = True ): ''' 运行函数 pipeline()，遍历事件文件夹，每个文件夹是一个事件 ''' parmDict = {} - parmDict["SourceType"] = source_type + parmDict["DataType"] = DataType parmDict["savepath"] = save_path + parmDict["SourceType"] = source_type + parmDict["YoloVersion"] = yolo_ver if parmDict["YoloVersion"] == "V5": parmDict["weights"] = weight_yolo_v5 elif parmDict["YoloVersion"] == "V10": parmDict["weights"] = weight_yolo_v10 + + parmDict["saveimages"] = saveimages + evtdir = Path(evtdir) errEvents = [] + k = 0 for item in evtdir.iterdir(): if item.is_dir(): - item = evtdir/Path("20250310-175352-741") + # item = evtdir/Path("20241212-171505-f0afe929-fdfe-4efa-94d0-2fa748d65fbb_6907992518930") parmDict["eventpath"] = item pipeline(**parmDict) + # try: # pipeline(**parmDict) # except Exception as e: # errEvents.append(str(item)) + k+=1 - if k==1: + if kk is not None and k==kk: break errfile = os.path.join(parmDict["savepath"], 'error_events.txt') @@ -329,12 +364,20 @@ def execute_pipeline(evtdir = r"D:\datasets\ym\后台数据\unzip", f.write(line + '\n') if __name__ == "__main__": - execute_pipeline() + datapath = r'/home/wqg/dataset/test_dataset/base_dataset/single_event/source/' + savepath = r'/home/wqg/dataset/pipeline/test_result/single_event_V10' + + execute_pipeline(evtdir = datapath, + DataType = "raw", # raw, pkl + kk=1, + source_type = "video", # video, image, + save_path = savepath, + yolo_ver = "V10", # V10, V5 + weight_yolo_v5 = r'./ckpts/best_cls10_0906.pt' , + weight_yolo_v10 = r'./ckpts/best_v10s_width0375_1205.pt', + saveimages = False + ) - # spath_v10 = r"D:\work\result_pipeline_v10" - # spath_v5 = r"D:\work\result_pipeline_v5" - # execute_pipeline(save_path=spath_v10, yolo_ver="V10") - # execute_pipeline(save_path=spath_v5, yolo_ver="V5") diff --git a/realtime/__pycache__/event_time_specify.cpython-312.pyc b/realtime/__pycache__/event_time_specify.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0719985d581210d5ea93a7a0f2a3cc0b7a9db0bc GIT binary patch literal 13250 zcmdU0dsG|OnV*qHLT?EWZ)^+mvK+t&FfTi9u>k{#?YMTF*umD383Dq)JTsWbjvBd5 zYLSpwBu){DyOtBTkrSsT-RzdlNw-d$-L%`?Q^_Jlov?e@Q{$LF_JrE)>1qGj{qBrL zQdHrjIlFt#UXJeE_xJ4T^|P8VTI{CkV`7Bz6oV zaZ=KcAH(^t$(tXE-)48JCz16IH4`l-4v>2U9e=|C4_vQ(NZmc~j;V|9b6s%?&Z+hdov{n45X*t4N$aTH(bLOV z8OMNq&(I(}G5|%pA|vI=k%N!8d+!7#nSGS~8_4FsH4CIYCdYUQIu_*Ml_jBra#qf>m3@fZyP<` zvyJ3g#Y9-O4LH+}II_)t#y%)8$4U>|yByu)6~p74fo3QxGh~f6O6j3Vv}ouS@Lbq( zisIbF*$GeKQ%{C9T6dS5a<{tiH91yN`uby+AM+o&(iuvty4>l*FKqXwzEtn)bUhSE 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np.zeros((fnum, 2), dtype=np.int64) frameState = np.concatenate((frameTstamp, state), axis = 1).astype(np.int64) handState = np.concatenate((frameTstamp, state), axis = 1).astype(np.int64) + + + if fnum < width: + return frameState, handState + mtrackFid = {} handFid = {} '''frameState 标记由图像判断的购物车状态：0: 静止，1: 运动''' for idx in range(width, fnum+1): idx0 = idx-width + # if idx == 40: + # print("123") + lboxes = np.concatenate(fboxes[idx0:idx], axis = 0) md = MoveDetect(lboxes) md.classify() diff --git a/realtime/intrude_detect.py b/realtime/intrude_detect.py new file mode 100644 index 0000000..d9561b1 --- /dev/null +++ b/realtime/intrude_detect.py @@ -0,0 +1,420 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Apr 8 10:07:17 2025 + +@author: wqg +""" + +import csv +import os +import platform +import sys +import pickle +import cv2 +import numpy as np +from pathlib import Path +import matplotlib.pyplot as plt +import matplotlib.pyplot as plt +from typing import List, Tuple +from scipy.spatial.distance import cdist +from scipy.spatial import ConvexHull +from shapely.geometry import Point, Polygon + +##################################################### for method: run_yrt() +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] +if str(ROOT) not in sys.path: + sys.path.insert(0, str(ROOT)) + +from track_reid import yolov10_resnet_tracker +from event_time_specify import devide_motion_state + + +def cross(o: Tuple[float, float], a: Tuple[float, float], b: Tuple[float, float]) -> float: + """ 计算向量 OA × OB 的叉积 """ + return (a[0] - o[0]) * (b[1] - o[1]) - (a[1] - o[1]) * (b[0] - o[0]) + +def compute_convex_hull(points: List[Tuple[float, float]]) -> List[Tuple[float, float]]: + """ 使用 Andrew's Monotone Chain 算法求二维点集的凸包 """ + points = sorted(set(points)) # 排序并去重 + if len(points) <= 1: + return points + + lower = [] + for p in points: + while len(lower) >= 2 and cross(lower[-2], lower[-1], p) <= 0: + lower.pop() + lower.append(p) + + upper = [] + for p in reversed(points): + while len(upper) >= 2 and cross(upper[-2], upper[-1], p) <= 0: + upper.pop() + upper.append(p) + + # 去掉重复的连接点 + return lower[:-1] + upper[:-1] + +def is_point_in_convex_hull(point: Tuple[float, float], hull: List[Tuple[float, float]]) -> bool: + """ 判断一个点是否在凸包（含边界）内 """ + n = len(hull) + if n < 3: + # 对于点或线段，直接判断是否共线或在线段上 + if n == 1: + return point == hull[0] + if n == 2: + a, b = hull + return abs(cross(a, b, point)) < 1e-10 and min(a[0], b[0]) <= point[0] <= max(a[0], b[0]) and min(a[1], b[1]) <= point[1] <= max(a[1], b[1]) + return False + + for i in range(n): + a = hull[i] + b = hull[(i + 1) % n] + if cross(a, b, point) < -1e-10: # 必须全部在左边或边上 + return False + return True + + + +def plot_convex_hull(points: List[Tuple[float, float]], hull: List[Tuple[float, float]], test_points: List[Tuple[float, float]] = None): + x_all, y_all = zip(*points) + fig, ax = plt.subplots() + + ax.set_xlim(0, 1024) + ax.set_ylim(1280, 0) + + ax.plot(x_all, y_all, 'o', label='Points') + + # 凸包闭环线 + hull_loop = hull + [hull[0]] + hx, hy = zip(*hull_loop) + ax.plot(hx, hy, 'r-', linewidth=2, label='Convex Hull') + + # 如果有测试点 + if test_points: + for pt in test_points: + color = 'green' if is_point_in_convex_hull(pt, hull) else 'black' + ax.plot(pt[0], pt[1], 's', color=color, markersize=8) + ax.text(pt[0] + 0.05, pt[1], f'{pt}', fontsize=9) + + ax.legend() + ax.grid(True) + plt.title("Convex Hull Visualization") + plt.show() + + +def convex_scipy(): + points = np.array([ + [0, 0], + [2, 0], + [1, 1], + [2, 2], + [0, 2], + [1, 0.5]]) + hull = ConvexHull(points) + + # 凸包顶点的索引 + print("凸包顶点索引:{}".format(hull.vertices)) + print("凸包顶点坐标:") + for i in hull.vertices: + print(points[i]) + + + # 将凸包坐标构造成 Polygon + hull_points = points[hull.vertices] + polygon = Polygon(hull_points) + + # 判断一个点是否在凸包内 + p = Point(1, 1) # 示例点 + print("是否在凸包内:", polygon.contains(p)) # True or False + + +def test_convex(): + # 测试数据 + sample_points = [(0, 0), (1, 1), (2, 2), (2, 0), (0, 2), (1, 0.5)] + convex_hull = compute_convex_hull(sample_points) + + # 测试点在凸包内 + test_point_inside = (1, 1) + test_point_outside = (3, 3) + test_point_on_edge = (1, 0) + + inside = is_point_in_convex_hull(test_point_inside, convex_hull) + outside = is_point_in_convex_hull(test_point_outside, convex_hull) + on_edge = is_point_in_convex_hull(test_point_on_edge, convex_hull) + + convex_hull, inside, outside, on_edge + + # 展示图像 + plot_convex_hull(sample_points, convex_hull, [test_point_inside, test_point_outside, test_point_on_edge]) + +def array2frame(tboxes): + "tboxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]" + idx = np.where(tboxes[:, 6] != 0)[0] + bboxes = tboxes[idx, :] + frameID = np.sort(np.unique(bboxes[:, 7].astype(int))) + fboxes = [] + for fid in frameID: + idx = np.where(bboxes[:, 7] == fid)[0] + box = bboxes[idx, :] + fboxes.append(box) + return fboxes + +def convex_based(tboxes, width, TH=40): + fboxes = array2frame(tboxes) + fnum = len(fboxes) + + fids = np.array([i+1 for i in range(fnum)])[:, np.newaxis] + state = np.zeros((fnum, 1), dtype=np.int64) + frameState = np.concatenate((fids, state), axis = 1).astype(np.int64) + if fnum < width: + return frameState + + for idx1 in range(width, fnum+1): + idx0 = idx1 - width + idx = idx1 - width//2 - 1 + + iboxes = fboxes[:idx] + cboxes = fboxes[idx][:, 0:4] + + cur_xy = np.zeros((len(cboxes), 2)) + cur_xy[:, 0] = (fboxes[idx][:, 0]+fboxes[idx][:, 2])/2 + cur_xy[:, 1] = (fboxes[idx][:, 1]+fboxes[idx][:, 3])/2 + for i in range(width//2): + x1, y1, x2, y2 = iboxes[i][:, 0], iboxes[i][:, 1], iboxes[i][:, 2], iboxes[i][:, 3] + + boxes = np.array([(x1, y1), (x1, y2), (x2, y1), (x2, y2)]).transpose(0, 2, 1).reshape(-1, 2) + box1 = [(x, y) for x, y in boxes] + convex_hull = compute_convex_hull(box1) + + for pt in cur_xy: + inside = is_point_in_convex_hull(pt, convex_hull) + + if not inside: + break + if not inside: + break + + # Based on the distance between the four corners of the current frame boxes + # and adjacent frame boxes + iboxes = fboxes[idx0:idx] + fboxes[idx+1:idx1] + cboxes = fboxes[idx][:, 0:4] + cx1, cy1, cx2, cy2 = cboxes[:, 0], cboxes[:, 1], cboxes[:, 2], cboxes[:, 3] + cxy = np.array([(cx1, cy1), (cx1, cy2), (cx2, cy1), (cx2, cy2)]).transpose(0, 2, 1).reshape(-1, 2) + + iiboxes = np.concatenate(iboxes, axis=0) + ix1, iy1, ix2, iy2 = iiboxes[:, 0], iiboxes[:, 1], iiboxes[:, 2], iiboxes[:, 3] + ixy = np.array([(ix1, iy1), (ix1, iy2), (ix2, iy1), (ix2, iy2)]).transpose(0, 2, 1).reshape(-1, 2) + + Dist = cdist(cxy, ixy).round(2) + max_dist = np.max(np.min(Dist, axis=1)) + if max_dist > TH and not inside: + frameState[idx, 1] = 1 + # plot_convex_hull(boxes, convex_hull, [pt]) + frameState[idx, 1] = 1 + + return frameState + + +def single_point(tboxes, width, TH=60): + """width: window width, >=2""" + + + fboxes = array2frame(tboxes) + fnum = len(fboxes) + + fids = np.array([i+1 for i in range(fnum)])[:, np.newaxis] + state = np.zeros((fnum, 1), dtype=np.int64) + frameState = np.concatenate((fids, state), axis = 1).astype(np.int64) + + + if fnum < width: + return frameState + + for idx1 in range(width, fnum+1): + idx0 = idx1 - width + idx = idx1 - width//2 - 1 + + iboxe1 = fboxes[idx0:idx] + iboxe2 = fboxes[idx+1:idx1] + iboxes = fboxes[idx0:idx] + fboxes[idx+1:idx1] + + cboxes = fboxes[idx][:, 0:4] + cur_xy = np.zeros((len(cboxes), 2)) + cur_xy[:, 0] = (fboxes[idx][:, 0]+fboxes[idx][:, 2])/2 + cur_xy[:, 1] = (fboxes[idx][:, 1]+fboxes[idx][:, 3])/2 + Dist = np.empty((len(cboxes), 0)) + for i in range(width-1): + boxes = iboxes[i][:, 0:4] + + box_xy = np.zeros((len(boxes), 2)) + box_xy[:, 0] = (boxes[:, 0]+boxes[:, 2])/2 + box_xy[:, 1] = (boxes[:, 1]+boxes[:, 3])/2 + dist2 = cdist(cur_xy, box_xy).round(2) + + Dist = np.concatenate((Dist, dist2), axis=1) + + max_dist = np.max(np.min(Dist, axis=1)) + + if max_dist > TH: + frameState[idx, 1] = 1 + + return frameState + + + +def intrude(): + pkpath = Path("/home/wqg/dataset/small-goods/pkfiles") + savepath = Path("/home/wqg/dataset/small-goods/illustration_convex") + + if not savepath.exists(): + savepath.mkdir(parents=True, exist_ok=True) + + err_trail, err_single, err_all = [], [], [] + num = 0 + for pth in pkpath.iterdir(): + # item = r"69042386_20250407-145737_front_returnGood_b82d28427666_15_17700000001.pickle" + # pth = pkpath/item + + with open(str(pth), 'rb') as f: + yrt = pickle.load(f) + + evtname = pth.stem + + bboxes = [] + trackerboxes = np.empty((0, 10), dtype=np.float64) + for frameDict in yrt: + boxes = frameDict["bboxes"] + tboxes = frameDict["tboxes"] + tboxes = np.concatenate((tboxes, tboxes[:,7][:, None]), axis=1) + + bboxes.append(boxes) + + trackerboxes = np.concatenate((trackerboxes, np.array(tboxes)), axis=0) + + '''single-points based for intrusion detection''' + # wd =5 + # fstate1 = single_point(trackerboxes, wd) + + '''convex-based ''' + width = 5 + fstate = convex_based(trackerboxes, width, TH=60) + + # fstate = np.zeros(fstate1.shape) + # fstate[:, 0] = fstate1[:, 0] + # fstate[:, 1] = fstate1[:, 1] * fstate2[:, 1] + + '''trajectory based for intrusion detection + period: 0 1 2 3 + fid timestamp(fid) 基于滑动窗的tid扩展 滑动窗覆盖的运动区间 + ''' + win_width = 12 + period, handState = devide_motion_state(trackerboxes, win_width) + + num += 1 + if np.all(period[:,2:4]==0): + err_trail.append(evtname) + if np.all(fstate[:,1]==0): + err_single.append(evtname) + if np.all(period[:,2:4]==0) and np.all(fstate[:,1]==0): + err_all.append(evtname) + + fig, (ax1, ax2) = plt.subplots(2, 1) + ax1.plot(period[:, 1], period[:, 2], 'bo-', linewidth=1, markersize=4) + ax1.plot(period[:, 1], period[:, 3], 'rx-', linewidth=1, markersize=8) + + ax2.plot(fstate[:, 0], fstate[:, 1], 'rx-', linewidth=1, markersize=8) + plt.savefig(os.path.join(str(savepath), f"{evtname}.png")) + + + plt.close() + # if num==1: + # break + + rate_trail = 1 - len(err_trail)/num + rate_single = 1 - len(err_single)/num + rate_all = 1 - len(err_all)/num + + print(f"rate_trail: {rate_trail}") + print(f"rate_single: {rate_single}") + print(f"rate_all: {rate_all}") + + txtpath = savepath.parents[0] / "error.txt" + with open(str(txtpath), "w") as f: + f.write(f"rate_trail: {rate_trail}" + "\n") + f.write(f"rate_single: {rate_single}" + "\n") + f.write(f"rate_all: {rate_all}" + "\n") + + f.write("\n" + "err_trail" + "\n") + for line in err_trail: + f.write(line + "\n") + + f.write("\n" + "err_single" + "\n") + for line in err_single: + f.write(line + "\n") + + f.write("\n" + "err_all" + "\n") + for line in err_all: + f.write(line + "\n") + print("Done!") + + + + +def run_yrt(): + datapath = Path("/home/wqg/dataset/small-goods/videos/") + savepath = Path("/home/wqg/dataset/small-goods/result/") + pkpath = Path("/home/wqg/dataset/small-goods/pkfiles/") + + if not savepath.exists(): + savepath.mkdir(parents=True, exist_ok=True) + if not pkpath.exists(): + pkpath.mkdir(parents=True, exist_ok=True) + + + optdict = {} + optdict["weights"] = ROOT / 'ckpts/best_v10s_width0375_1205.pt' + optdict["is_save_img"] = False + optdict["is_save_video"] = True + + k = 0 + for pth in datapath.iterdir(): + item = "69042386_20250407-145819_back_returnGood_b82d28427666_15_17700000001.mp4" + pth = pth.parents[0] /item + + optdict["source"] = pth + optdict["save_dir"] = savepath + + # try: + yrtOut = yolov10_resnet_tracker(**optdict) + + pkpath_ = pkpath / f"{Path(pth).stem}.pickle" + with open(str(pkpath_), 'wb') as f: + pickle.dump(yrtOut, f) + + k += 1 + if k==1: + break + # except Exception as e: + # print("abc") + + + +if __name__ == '__main__': + # run_yrt() + + intrude() + + # test_convex() + + + + + + + + + + + diff --git a/realtime/time_devide.py b/realtime/time_devide.py index 2daaed9..d30bb1f 100644 --- a/realtime/time_devide.py +++ b/realtime/time_devide.py @@ -19,7 +19,18 @@ from collections import OrderedDict from event_time_specify import devide_motion_state #, state_measure import sys -sys.path.append(r"D:\DetectTracking") + + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH + + + + + + from imgs_inference import run_yolo from tracking.utils.read_data import read_weight_sensor diff --git a/track_reid.py b/track_reid.py index 06d158f..9dd39ee 100644 --- a/track_reid.py +++ b/track_reid.py @@ -128,6 +128,8 @@ def init_trackers(tracker_yaml = None, bs=1): """ # tracker_yaml = r"./tracking/trackers/cfg/botsort.yaml" + tracker_yaml = str(tracker_yaml) + TRACKER_MAP = {'bytetrack': BYTETracker, 'botsort': BOTSORT} cfg = IterableSimpleNamespace(**yaml_load(tracker_yaml)) @@ -149,7 +151,7 @@ def yolov10_resnet_tracker( is_save_img = True, is_save_video = True, - tracker_yaml = "./tracking/trackers/cfg/botsort.yaml", + tracker_yaml = ROOT / "tracking/trackers/cfg/botsort.yaml", line_thickness=3, # bounding box thickness (pixels) hide_labels=False, # hide labels ): @@ -157,7 +159,7 @@ def yolov10_resnet_tracker( ## load a custom model model = YOLOv10(weights) - custom = {"conf": 0.25, "batch": 1, "save": False, "mode": "predict"} + custom = {"conf": 0.1, "batch": 1, "save": False, "mode": "predict"} kwargs = {"save": True, "imgsz": 640, "conf": 0.1} args = {**model.overrides, **custom, **kwargs} predictor = model.task_map[model.task]["predictor"](overrides=args, _callbacks=model.callbacks) @@ -294,7 +296,7 @@ def yolo_resnet_tracker( is_save_img = True, is_save_video = True, - tracker_yaml = "./tracking/trackers/cfg/botsort.yaml", + tracker_yaml = ROOT / "tracking/trackers/cfg/botsort.yaml", imgsz=(640, 640), # inference size (height, width) conf_thres=0.25, # confidence threshold iou_thres=0.45, # NMS IOU threshold @@ -359,6 +361,7 @@ def yolo_resnet_tracker( # Process predictions for i, det in enumerate(pred): # per image im0 = im0s.copy() + annotator = Annotator(im0.copy(), line_width=line_thickness, example=str(names)) s += '%gx%g ' % im.shape[2:] # print string if len(det): @@ -438,7 +441,7 @@ def yolo_resnet_tracker( if dataset.mode == 'image': imgpath = save_path_img + ".png" else: - imgpath = save_path_img + f"_{frameId}.png" + imgpath = save_path_img + f"_{frameId}.png" cv2.imwrite(Path(imgpath), im0) # if dataset.mode == 'video' and is_save_video: @@ -461,6 +464,7 @@ def yolo_resnet_tracker( else: # stream fps, w, h = 25, im0.shape[1], im0.shape[0] ## for image rotating in dataloader.LoadImages.__next__() + w, h = im0.shape[1], im0.shape[0] vdieo_path = str(Path(vdieo_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos @@ -484,7 +488,7 @@ def run( project=ROOT / 'runs/detect', # save results to project/name name='exp', # save results to project/name - tracker_yaml = "./tracking/trackers/cfg/botsort.yaml", + tracker_yaml = ROOT / "tracking/trackers/cfg/botsort.yaml", imgsz=(640, 640), # inference size (height, width) conf_thres=0.25, # confidence threshold iou_thres=0.45, # NMS IOU threshold diff --git a/tracking/__pycache__/__init__.cpython-312.pyc 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b/tracking/dotrack/dotracks_back.py @@ -7,9 +7,19 @@ Created on Mon Mar 4 18:36:31 2024 import numpy as np import cv2 import copy -from tracking.utils.mergetrack import track_equal_track -from scipy.spatial.distance import cdist +import sys from pathlib import Path + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[2] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) + +from tracking.utils.mergetrack import track_equal_track + + +from scipy.spatial.distance import cdist + curpath = Path(__file__).resolve().parents[0] curpath = Path(curpath) parpath = curpath.parent diff --git a/tracking/trackers/__pycache__/__init__.cpython-312.pyc b/tracking/trackers/__pycache__/__init__.cpython-312.pyc index e01f8017f26f28a400a7e361e0c4ad6ab7230967..b32cfce441d3ddf60b18eacd3bef57c58ce2616b 100644 GIT binary patch delta 21 bcmX@fbdrhZG%qg~0}w<7KTF@pW6KBtKOY5$ delta 21 bcmX@fbdrhZG%qg~0}#}@ElS(SW6KBtK0gIm diff --git 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bcmX>Vc`B0UG%qg~0}#}?ElS(Svrh*AOoj%v diff --git a/tracking/tracking_pipeline.py b/tracking/tracking_pipeline.py index 4e2b9d7..54354b2 100644 --- a/tracking/tracking_pipeline.py +++ b/tracking/tracking_pipeline.py @@ -11,170 +11,222 @@ import pickle import numpy as np from pathlib import Path from scipy.spatial.distance import cdist +import copy -from .dotrack.dotracks_back import doBackTracks -from .dotrack.dotracks_front import doFrontTracks -from .utils.drawtracks import plot_frameID_y2, draw_all_trajectories -from .utils.read_data import read_similar +from dotrack.dotracks_back import doBackTracks +from dotrack.dotracks_front import doFrontTracks +from utils.drawtracks import plot_frameID_y2, draw_all_trajectories +from utils.read_data import read_similar +def get_trail(ShoppingDict, ppath): + + evtname = ShoppingDict["eventName"] + + back_yrt = ShoppingDict["backCamera"]["yoloResnetTracker"] + front_yrt = ShoppingDict["frontCamera"]["yoloResnetTracker"] + + back_vts = ShoppingDict["frontCamera"]["tracking"] + front_vts = ShoppingDict["backCamera"]["tracking"] + + + event_tracks = [("back", back_yrt, back_vts), ("front", front_yrt, front_vts)] + + + savepath = ppath / "alltrail" + if not savepath.exists(): + savepath.mkdir() + + savepath = str(savepath) + evtime = evtname[:15] + + illus = [None, None] + for camera_type, yrtOut, vts in event_tracks: + if len(vts.Residual)==1: continue + + if camera_type == 'front': + edgeline = cv2.imread("./shopcart/cart_tempt/board_ftmp_line.png") + + img_tracking = draw_all_trajectories(vts, edgeline, savepath, camera_type, draw5p=False) + illus[0] = img_tracking - -class CameraEvent_: - def __init__(self): - self.cameraType = '', # "front", "back" - self.videoPath = '', - self.imagePaths = [], - self.yoloResnetTracker =[], - self.tracking = None, - -class ShoppingEvent_: - def __init__(self): - self.eventPath = '' - self.eventName = '' - self.barcode = '' - self.eventType = '', # "input", "output", "other" - self.frontCamera = None - self.backCamera = None - self.one2n = [] + plt = plot_frameID_y2(vts) + plt.savefig(os.path.join(savepath, f"{evtime}_front.png")) + + if camera_type == 'back': + edgeline = cv2.imread("./shopcart/cart_tempt/edgeline.png") + + img_tracking = draw_all_trajectories(vts, edgeline, savepath, camera_type, draw5p=False) + illus[1] = img_tracking + + illus = [im for im in illus if im is not None] + if len(illus): + img_cat = np.concatenate(illus, axis = 1) + if len(illus)==2: + H, W = img_cat.shape[:2] + cv2.line(img_cat, (int(W/2), 0), (int(W/2), int(H)), (128, 128, 255), 3) + + trajpath = os.path.join(savepath, f"{evtime}.png") + cv2.imwrite(trajpath, img_cat) + + return evtime + + return None + + -def main(): + + + +def track_opt(ShoppingDict, ppath): ''' 将一个对象读取，修改其中一个属性 ''' + evtname = ShoppingDict["eventName"] + shopping = copy.deepcopy(ShoppingDict) - evt_pkfile = 'path.pickle' - with open(evt_pkfile, 'rb') as f: - ShoppingDict = pickle.load(f) - - savepath = "" - + ## only need to init item: tracking for each Camera + shopping["frontCamera"]["tracking"] = [] + shopping["backCamera"]["tracking"] = [] + back_camera = ShoppingDict["backCamera"]["cameraType"] back_yrt = ShoppingDict["backCamera"]["yoloResnetTracker"] front_camera = ShoppingDict["frontCamera"]["cameraType"] front_yrt = ShoppingDict["frontCamera"]["yoloResnetTracker"] yrts = [(back_camera, back_yrt), (front_camera, front_yrt)] - - shopping_event = ShoppingEvent_() - shopping_event.eventPath = ShoppingDict["eventPath"] - shopping_event.eventName = ShoppingDict["eventName"] - shopping_event.barcode = ShoppingDict["barcode"] - - yrtDict = {} event_tracks = [] - for camera_type, yrtOut in yrts: - ''' - inputs: - yrtOut - camera_type - outputs: - CameraEvent - ''' - - camera_event = CameraEvent_() - - - - '''================= 4. tracking =================''' + errtrail = '' + for camera_type, yrtOut in yrts: + '''================= 1. tracking =================''' '''(1) 生成用于 tracking 模块的 boxes、feats''' - bboxes = np.empty((0, 6), dtype=np.float64) + # bboxes = np.empty((0, 6), dtype=np.float64) trackerboxes = np.empty((0, 9), dtype=np.float64) trackefeats = {} for frameDict in yrtOut: tboxes = frameDict["tboxes"] ffeats = frameDict["feats"] - boxes = frameDict["bboxes"] - bboxes = np.concatenate((bboxes, np.array(boxes)), axis=0) + # boxes = frameDict["bboxes"] + # bboxes = np.concatenate((bboxes, np.array(boxes)), axis=0) trackerboxes = np.concatenate((trackerboxes, np.array(tboxes)), axis=0) for i in range(len(tboxes)): fid, bid = int(tboxes[i, 7]), int(tboxes[i, 8]) trackefeats.update({f"{fid}_{bid}": ffeats[f"{fid}_{bid}"]}) - '''(2) tracking, 后摄''' - if CameraEvent["cameraType"] == "back": + if camera_type == "back": vts = doBackTracks(trackerboxes, trackefeats) vts.classify() - event_tracks.append(("back", vts)) + shopping["backCamera"]["tracking"] = vts - - - camera_event.camera_type = camera_type - camera_event.yoloResnetTracker = yrtOut - camera_event.tracking = vts - camera_event.videoPath = ShoppingDict["backCamera"]["videoPath"] - camera_event.imagePaths = ShoppingDict["backCamera"]["imagePaths"] - shopping_event.backCamera = camera_event - - yrtDict["backyrt"] = yrtOut - - '''(2) tracking, 前摄''' - if CameraEvent["cameraType"] == "front": + if len(vts.Residual)!=1: + errtrail = evtname + + '''(3) tracking, 前摄''' + if camera_type == "front": vts = doFrontTracks(trackerboxes, trackefeats) vts.classify() - event_tracks.append(("front", vts)) + shopping["frontCamera"]["tracking"] = vts - camera_event.camera_type = camera_type - camera_event.yoloResnetTracker = yrtOut - camera_event.tracking = vts - camera_event.videoPath = ShoppingDict["frontCamera"]["videoPath"] - camera_event.imagePaths = ShoppingDict["frontCamera"]["imagePaths"] - shopping_event.backCamera = camera_event - - yrtDict["frontyrt"] = yrtOut + if len(vts.Residual)!=1: + errtrail = evtname + + event_tracks.append((camera_type, yrtOut, vts)) + + pckpath = ppath / "track_optim" + if not pckpath.exists(): + pckpath.mkdir() + + fpath = pckpath / "{}_new.pickle".format(evtname) + with open(str(fpath), 'wb') as f: + pickle.dump(shopping, f) + - name = Path(evt_pkfile).stem - pf_path = os.path.join(savepath, name+"_new.pickle") - with open(str(pf_path), 'wb') as f: - pickle.dump(shopping_event, f) - - + savepath = ppath / "yolos_tracking" / evtname illus = [None, None] - for CamerType, vts in event_tracks: + for camera_type, yrtOut, vts in event_tracks: if len(vts.tracks)==0: continue - if CamerType == 'front': - edgeline = cv2.imread("./tracking/shopcart/cart_tempt/board_ftmp_line.png") + if camera_type == 'front': + edgeline = cv2.imread("./shopcart/cart_tempt/board_ftmp_line.png") - h, w = edgeline.shape[:2] - # nh, nw = h//2, w//2 - # edgeline = cv2.resize(edgeline, (nw, nh), interpolation=cv2.INTER_AREA) - - img_tracking = draw_all_trajectories(vts, edgeline, savepath_pipeline, CamerType, draw5p=True) + img_tracking = draw_all_trajectories(vts, edgeline, savepath, camera_type, draw5p=False) illus[0] = img_tracking plt = plot_frameID_y2(vts) - plt.savefig(os.path.join(savepath_pipeline, "front_y2.png")) + plt.savefig(os.path.join(savepath, "front_y2_new.png")) - if CamerType == 'back': - edgeline = cv2.imread("./tracking/shopcart/cart_tempt/edgeline.png") - - h, w = edgeline.shape[:2] - # nh, nw = h//2, w//2 - # edgeline = cv2.resize(edgeline, (nw, nh), interpolation=cv2.INTER_AREA) - - img_tracking = draw_all_trajectories(vts, edgeline, savepath_pipeline, CamerType, draw5p=True) + if camera_type == 'back': + edgeline = cv2.imread("./shopcart/cart_tempt/edgeline.png") + + img_tracking = draw_all_trajectories(vts, edgeline, savepath, camera_type, draw5p=False) illus[1] = img_tracking + + illus = [im for im in illus if im is not None] + if len(illus): + img_cat = np.concatenate(illus, axis = 1) + if len(illus)==2: + H, W = img_cat.shape[:2] + cv2.line(img_cat, (int(W/2), 0), (int(W/2), int(H)), (128, 128, 255), 3) + + trajpath = os.path.join(savepath, "trajectory_new.png") + cv2.imwrite(trajpath, img_cat) + + return errtrail - - - - - - - +def main(): + # evttypes = ["single_event_V10", "single_event_V5", "performence_V10", "performence_V5"] + evttypes = ["single_event_V10"] + + k = 0 + error_trail = [] + for evttype in evttypes: + ppath = Path("/home/wqg/dataset/pipeline/yrt/{}".format(evttype)) + + pkpath = ppath / "shopping_pkl" + for fp in pkpath.iterdir(): + # fp = pkpath / "{}.pickle".format("20250305-152917-635_6970209860221_6970209860221") + print(fp) + + if fp.suffix != '.pickle': continue + with open(str(fp), 'rb') as f: + ShoppingDict = pickle.load(f) + + # errtrail = track_opt(ShoppingDict, ppath) + # error_trail.append(errtrail) + + errtrail = get_trail(ShoppingDict, ppath) + if errtrail is not None: + error_trail.append(errtrail) + + # k+=1 + # if k==100: + # break + + errfile = ppath / 'error_trail.txt' + with open(errfile, 'w', encoding='utf-8') as f: + for line in 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--- a/ultralytics/data/loaders.py +++ b/ultralytics/data/loaders.py @@ -19,6 +19,8 @@ from ultralytics.data.utils import IMG_FORMATS, VID_FORMATS from ultralytics.utils import LOGGER, is_colab, is_kaggle, ops from ultralytics.utils.checks import check_requirements +import subprocess +import json @dataclass class SourceTypes: @@ -340,6 +342,12 @@ class LoadImagesAndVideos: if success: success, im0 = self.cap.retrieve() + ##====================== + '''判断视频是否含旋转信息''' + rotation = self.get_rotation(path) + if rotation == 270: + im0 = cv2.rotate(im0, cv2.ROTATE_90_COUNTERCLOCKWISE) + ###====================== if success: self.frame += 1 paths.append(path) @@ -355,6 +363,7 @@ class LoadImagesAndVideos: self.cap.release() if self.count < self.nf: self._new_video(self.files[self.count]) + else: self.mode = "image" im0 = cv2.imread(path) # BGR @@ -378,6 +387,23 @@ class LoadImagesAndVideos: raise FileNotFoundError(f"Failed to open video {path}") self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride) + def get_rotation(self, filename): + cmd = [ + "ffprobe", # 注意是 ffprobe，不是 ffmpeg + "-v", "error", + "-select_streams", "v:0", + "-show_entries", "stream_tags=rotate", + "-of", "json", + filename + ] + result = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE) + if result.returncode == 0: + metadata = json.loads(result.stdout) + rotation = metadata.get("streams", [{}])[0].get("tags", {}).get("rotate", 0) + return int(rotation) + else: + return 0 + def __len__(self): """Returns the number of batches in the object.""" return math.ceil(self.nf / self.bs) # number of files diff --git a/ultralytics/engine/__pycache__/__init__.cpython-312.pyc b/ultralytics/engine/__pycache__/__init__.cpython-312.pyc index fdaad91bf92f6653ca07841d4a17e1af075d45e6..b4f938429d06bf261acc4de0b4825802f45b6046 100644 GIT binary patch delta 48 zcmbQqID?V!Vv&V CG7oS7 delta 49 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