# -*- coding: utf-8 -*- """ Created on Fri Aug 30 17:53:03 2024 功能：1:1比对性能测试程序 1. 基于标准特征集所对应的原始图像样本，生成标准特征集并保存。 func: generate_event_and_stdfeatures(): (1) get_std_barcodeDict(stdSamplePath, stdBarcodePath) 提取 stdSamplePath 中样本地址，生成字典{barcode: [imgpath1, imgpath1, ...]} 并存储为 pickle 文件，barcode.pickle''' (2) stdfeat_infer(stdBarcodePath, stdFeaturePath, bcdSet=None) 标准特征提取，并保存至文件夹 stdFeaturePath 中， 也可在运行过程中根据与购物事件集合 barcodes 交集执行 2. 1:1 比对性能测试， func: contrast_performance_evaluate(resultPath) (1) 求购物事件和标准特征级 Barcode 交集，构造 evtDict、stdDict (2) 构造扫 A 放 A、扫 A 放 B 组合，mergePairs = AA_list + AB_list (3) 循环计算 mergePairs 中元素 "(A, A) 或 (A, B)" 相似度; 对于未保存的轨迹图像或标准 barcode 图像，保存图像 (4) 保存计算结果 3. precise、recall等指标计算 func: compute_precise_recall(pickpath) @author: ym """ import numpy as np import cv2 import os import sys import random import pickle 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 # Vit版resnet, 和现场特征不一致，需将resnet_vit中文件提出 # from config import config as conf # from model import resnet18 # from inference import load_contrast_model # from inference import featurize # embedding_size = conf.embedding_size # img_size = conf.img_size # device = conf.device # model = load_contrast_model() sys.path.append(r"D:\DetectTracking") from tracking.utils.read_data import extract_data, read_tracking_output, read_deletedBarcode_file from config import config as conf from model import resnet18 as resnet18 from feat_inference import inference_image IMG_FORMAT = ['.bmp', '.jpg', '.jpeg', '.png'] ''' 共6个地址： (1) stdSamplePath: 用于生成比对标准特征集的原始图像地址 (2) stdBarcodePath: 比对标准特征集原始图像地址的pickle文件存储，{barcode: [imgpath1, imgpath1, ...]} (3) stdFeaturePath: 比对标准特征集特征存储地址 (4) eventFeatPath: 用于1:1比对的购物事件特征存储地址、对应子图存储地址 (5) subimgPath: 1:1比对购物事件轨迹、标准barcode所对应的 subimgs 存储地址 (6) resultPath: 1:1比对结果存储地址 ''' stdSamplePath = r"\\192.168.1.28\share\已标注数据备份\对比数据\barcode\barcode_500_1979_已清洗" stdBarcodePath = r"\\192.168.1.28\share\测试_202406\contrast\std_barcodes_2192" stdFeaturePath = r"\\192.168.1.28\share\测试_202406\contrast\std_features_ft32" eventFeatPath = r"\\192.168.1.28\share\测试_202406\contrast\events" subimgPath = r'\\192.168.1.28\share\测试_202406\contrast\subimgs' resultPath = r"D:\DetectTracking\contrast\result\pickle" if not os.path.exists(resultPath): os.makedirs(resultPath) ##============ load resnet mdoel model = resnet18().to(conf.device) # model = nn.DataParallel(model).to(conf.device) model.load_state_dict(torch.load(conf.test_model, map_location=conf.device)) model.eval() print('load model {} '.format(conf.testbackbone)) def creat_shopping_event(eventPath, subimgPath=False): '''构造放入商品事件字典，这些事件需满足条件： 1) 前后摄至少有一条轨迹输出 2) 保存有帧图像，以便裁剪出 boxe 子图 ''' # filename = "20240723-155413_6904406215720" '''filename下为一次购物事件''' eventName = os.path.basename(eventPath) '''================ 0. 检查 filename 及 eventPath 正确性和有效性 ================''' nmlist = eventName.split('_') # if eventName.find('2024')<0 or len(nmlist)!=2 or len(nmlist[0])!=15 or len(nmlist[1])<11: # return if eventName.find('2024')<0 or len(nmlist)!=2 or len(nmlist[1])<11: return if not os.path.isdir(eventPath): return '''================ 1. 构造事件描述字典，暂定 9 items ===============''' event = {} event['barcode'] = eventName.split('_')[1] event['type'] = 'input' event['filepath'] = eventPath event['back_imgpaths'] = [] event['front_imgpaths'] = [] event['back_boxes'] = np.empty((0, 9), dtype=np.float64) event['front_boxes'] = np.empty((0, 9), dtype=np.float64) event['back_feats'] = np.empty((0, 256), dtype=np.float64) event['front_feats'] = np.empty((0, 256), dtype=np.float64) event['feats_compose'] = np.empty((0, 256), dtype=np.float64) # event['feats_select'] = np.empty((0, 256), dtype=np.float64) '''================= 2. 读取 data 文件 =============================''' for dataname in os.listdir(eventPath): # filename = '1_track.data' datapath = os.path.join(eventPath, dataname) if not os.path.isfile(datapath): continue CamerType = dataname.split('_')[0] ''' 2.1 读取 0/1_track.data 中数据，暂不考虑''' # if dataname.find("_track.data")>0: # bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, tracking_feat_dict = extract_data(datapath) ''' 2.2 读取 0/1_tracking_output.data 中数据''' if dataname.find("_tracking_output.data")>0: tracking_output_boxes, tracking_output_feats = read_tracking_output(datapath) if len(tracking_output_boxes) != len(tracking_output_feats): continue if CamerType == '0': event['back_boxes'] = tracking_output_boxes event['back_feats'] = tracking_output_feats elif CamerType == '1': event['front_boxes'] = tracking_output_boxes event['front_feats'] = tracking_output_feats if len(event['back_boxes'])==0 or len(event['front_boxes'])==0: return None '''2.3 事件的特征表征方式: 特征选择、特征集成''' bk_feats = event['back_feats'] ft_feats = event['front_feats'] '''2.3.1 特征集成''' feats_compose = np.empty((0, 256), dtype=np.float64) if len(ft_feats): feats_compose = np.concatenate((feats_compose, ft_feats), axis=0) if len(bk_feats): feats_compose = np.concatenate((feats_compose, bk_feats), axis=0) event['feats_compose'] = feats_compose '''2.3.1 特征选择''' if len(ft_feats): event['feats_select'] = ft_feats # pickpath = os.path.join(savePath, f"{filename}.pickle") # with open(pickpath, 'wb') as f: # pickle.dump(event, f) # print(f"Event: {filename}") # if subimgPath==False: # eventList.append(event) # continue '''================ 2. 读取图像文件地址，并按照帧ID排序 =============''' frontImgs, frontFid = [], [] backImgs, backFid = [], [] for imgname in os.listdir(eventPath): name, ext = os.path.splitext(imgname) if ext not in IMG_FORMAT or name.find('frameId')<0: continue CamerType = name.split('_')[0] frameId = int(name.split('_')[3]) imgpath = os.path.join(eventPath, imgname) if CamerType == '0': backImgs.append(imgpath) backFid.append(frameId) if CamerType == '1': frontImgs.append(imgpath) frontFid.append(frameId) frontIdx = np.argsort(np.array(frontFid)) backIdx = np.argsort(np.array(backFid)) '''2.1 生成依据帧 ID 排序的前后摄图像地址列表''' frontImgs = [frontImgs[i] for i in frontIdx] backImgs = [backImgs[i] for i in backIdx] '''2.2 将前、后摄图像路径添加至事件字典''' bfid = event['back_boxes'][:, 7].astype(np.int64) ffid = event['front_boxes'][:, 7].astype(np.int64) if len(bfid) and max(bfid) <= len(backImgs): event['back_imgpaths'] = [backImgs[i-1] for i in bfid] if len(ffid) and max(ffid) <= len(frontImgs): event['front_imgpaths'] = [frontImgs[i-1] for i in ffid] '''================ 3. 判断当前事件有效性，并添加至事件列表 ==========''' condt1 = len(event['back_imgpaths'])==0 or len(event['front_imgpaths'])==0 condt2 = len(event['front_feats'])==0 and len(event['back_feats'])==0 if condt1 or condt2: print(f"Event: {eventName}, Error, condt1: {condt1}, condt2: {condt2}") return None '''构造放入商品事件列表，暂不处理''' # delepath = os.path.join(basePath, 'deletedBarcode.txt') # bcdList = read_deletedBarcode_file(delepath) # for slist in bcdList: # getoutFold = slist['SeqDir'].strip() # getoutPath = os.path.join(basePath, getoutFold) # '''取出事件文件夹不存在，跳出循环''' # if not os.path.exists(getoutPath) and not os.path.isdir(getoutPath): # continue # ''' 生成取出事件字典 ''' # event = {} # event['barcode'] = slist['Deleted'].strip() # event['type'] = 'getout' # event['basePath'] = getoutPath return event def get_std_barcodeDict(bcdpath, savepath): ''' inputs: bcdpath: 已清洗的barcode样本图像，如果barcode下有'base'文件夹，只选用该文件夹下图像 (default = r'\\192.168.1.28\share\已标注数据备份\对比数据\barcode\barcode_1771') 功能： 生成并保存只有一个key值的字典 {barcode: [imgpath1, imgpath1, ...]}， savepath: 字典存储地址，文件名格式：barcode.pickle ''' # savepath = r'\\192.168.1.28\share\测试_202406\contrast\std_barcodes' '''读取数据集中 barcode 列表''' stdBarcodeList = [] for filename in os.listdir(bcdpath): filepath = os.path.join(bcdpath, filename) # if not os.path.isdir(filepath) or not filename.isdigit() or len(filename)<8: # continue stdBarcodeList.append(filename) bcdPaths = [(barcode, os.path.join(bcdpath, barcode)) for barcode in stdBarcodeList] '''遍历数据集，针对每一个barcode，生成并保存字典{barcode: [imgpath1, imgpath1, ...]}''' k = 0 errbarcodes = [] for barcode, bpath in bcdPaths: pickpath = os.path.join(savepath, f"{barcode}.pickle") if os.path.isfile(pickpath): continue stdBarcodeDict = {} stdBarcodeDict[barcode] = [] for root, dirs, files in os.walk(bpath): imgpaths = [] if "base" in dirs: broot = os.path.join(root, "base") for imgname in os.listdir(broot): imgpath = os.path.join(broot, imgname) file, ext = os.path.splitext(imgpath) if ext not in IMG_FORMAT: continue imgpaths.append(imgpath) stdBarcodeDict[barcode].extend(imgpaths) break else: for imgname in files: imgpath = os.path.join(root, imgname) _, ext = os.path.splitext(imgpath) if ext not in IMG_FORMAT: continue imgpaths.append(imgpath) stdBarcodeDict[barcode].extend(imgpaths) pickpath = os.path.join(savepath, f"{barcode}.pickle") with open(pickpath, 'wb') as f: pickle.dump(stdBarcodeDict, f) print(f"Barcode: {barcode}") # k += 1 # if k == 10: # break print(f"Len of errbarcodes: {len(errbarcodes)}") return def save_event_subimg(event, savepath): ''' 功能: 保存一次购物事件的轨迹子图 9 items: barcode, type, filepath, back_imgpaths, front_imgpaths, back_boxes, front_boxes, back_feats, front_feats, feats_compose, feats_select 子图保存次序：先前摄、后后摄，以 k 为编号，和 "feats_compose" 中次序相同 ''' cameras = ('front', 'back') k = 0 for camera in cameras: if camera == 'front': boxes = event['front_boxes'] imgpaths = event['front_imgpaths'] else: boxes = event['back_boxes'] imgpaths = event['back_imgpaths'] for i, box in enumerate(boxes): x1, y1, x2, y2, tid, score, cls, fid, bid = box imgpath = imgpaths[i] image = cv2.imread(imgpath) subimg = image[int(y1/2):int(y2/2), int(x1/2):int(x2/2), :] camerType, timeTamp, _, frameID = os.path.basename(imgpath).split('.')[0].split('_') subimgName = f"{k}_cam-{camerType}_tid-{int(tid)}_fid-({int(fid)}, {frameID}).png" spath = os.path.join(savepath, subimgName) cv2.imwrite(spath, subimg) k += 1 # basename = os.path.basename(event['filepath']) print(f"Image saved: {os.path.basename(event['filepath'])}") def batch_inference(imgpaths, batch): size = len(imgpaths) groups = [] for i in range(0, size, batch): end = min(batch + i, size) groups.append(imgpaths[i: end]) features = [] for group in groups: feature = featurize(group, conf.test_transform, model, conf.device) features.append(feature) features = np.concatenate(features, axis=0) return features def stdfeat_infer(imgPath, featPath, bcdSet=None): ''' inputs: imgPath: 该文件夹下的 pickle 文件格式 {barcode: [imgpath1, imgpath1, ...]} featPath: imgPath图像对应特征的存储地址 功能: 对 imgPath中图像进行特征提取，生成只有一个key值的字典， {barcode: features}，features.shape=(nsample, 256)，并保存至 featPath 中 ''' # imgPath = r"\\192.168.1.28\share\测试_202406\contrast\std_barcodes" # featPath = r"\\192.168.1.28\share\测试_202406\contrast\std_features" stdBarcodeDict = {} stdBarcodeDict_ft16 = {} '''4处同名: (1)barcode原始图像文件夹; (2)imgPath中的 .pickle 文件名、该pickle文件中字典的key值''' k = 0 for filename in os.listdir(imgPath): bcd, ext = os.path.splitext(filename) pkpath = os.path.join(featPath, f"{bcd}.pickle") if os.path.isfile(pkpath): continue if bcdSet is not None and bcd not in bcdSet: continue filepath = os.path.join(imgPath, filename) stdbDict = {} stdbDict_ft16 = {} stdbDict_uint8 = {} t1 = time.time() try: with open(filepath, 'rb') as f: bpDict = pickle.load(f) for barcode, imgpaths in bpDict.items(): # feature = batch_inference(imgpaths, 8) #from vit distilled model of LiChen feature = inference_image(imgpaths, conf.test_transform, model, conf.device) feature /= np.linalg.norm(feature, axis=1)[:, None] # float16 feature_ft16 = feature.astype(np.float16) feature_ft16 /= np.linalg.norm(feature_ft16, axis=1)[:, None] # uint8, 两种策略，1) 精度损失小, 2) 计算复杂度小 # feature_uint8, _ = ft16_to_uint8(feature_ft16) feature_uint8 = (feature_ft16*128).astype(np.int8) except Exception as e: print(f"Error accured at: {filename}, with Exception is: {e}") '''================ 保存单个barcode特征 ================''' ##================== float32 stdbDict["barcode"] = barcode stdbDict["imgpaths"] = imgpaths stdbDict["feats_ft32"] = feature stdbDict["feats_ft16"] = feature_ft16 stdbDict["feats_uint8"] = feature_uint8 with open(pkpath, 'wb') as f: pickle.dump(stdbDict, f) stdBarcodeDict[barcode] = feature stdBarcodeDict_ft16[barcode] = feature_ft16 t2 = time.time() print(f"Barcode: {barcode}, need time: {t2-t1:.1f} secs") # k += 1 # if k == 10: # break ##================== float32 # pickpath = os.path.join(featPath, f"barcode_features_{k}.pickle") # with open(pickpath, 'wb') as f: # pickle.dump(stdBarcodeDict, f) ##================== float16 # pickpath_ft16 = os.path.join(featPath, f"barcode_features_ft16_{k}.pickle") # with open(pickpath_ft16, 'wb') as f: # pickle.dump(stdBarcodeDict_ft16, f) return def contrast_performance_evaluate(resultPath): # stdBarcode = [p.stem for p in Path(stdFeaturePath).iterdir() if p.is_file() and p.suffix=='.pickle'] stdBarcode = [p.stem for p in Path(stdBarcodePath).iterdir() if p.is_file() and p.suffix=='.pickle'] '''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(eventFeatPath).iterdir() # if p.is_file() # and p.suffix=='.pickle' # and len(p.stem.split('_'))==2 # and p.stem.split('_')[1].isdigit() # and p.stem.split('_')[1] in stdBarcode # ] evtList = [(p.stem, p.stem.split('_')[1]) for p in Path(eventFeatPath).iterdir() if p.is_file() and str(p).find('240910')>0 and p.suffix=='.pickle' and len(p.stem.split('_'))==2 and p.stem.split('_')[1].isdigit() and p.stem.split('_')[1] in stdBarcode ] barcodes = set([bcd for _, bcd in evtList]) '''标准特征集图像样本经特征提取并保存，运行一次后无需再运行''' stdfeat_infer(stdBarcodePath, stdFeaturePath, barcodes) '''========= 构建用于比对的标准特征字典 =============''' stdDict = {} for barcode in barcodes: stdpath = os.path.join(stdFeaturePath, barcode+'.pickle') with open(stdpath, 'rb') as f: stddata = pickle.load(f) stdDict[barcode] = stddata '''========= 构建用于比对的操作事件字典 =============''' evtDict = {} for event, barcode in evtList: evtpath = os.path.join(eventFeatPath, event+'.pickle') with open(evtpath, 'rb') as f: evtdata = pickle.load(f) evtDict[event] = evtdata '''===== 构造 3 个事件对: 扫 A 放 A, 扫 A 放 B, 合并 ====================''' AA_list = [(event, barcode, "same") for event, barcode in evtList] AB_list = [] for event, barcode in evtList: dset = list(barcodes.symmetric_difference(set([barcode]))) idx = random.randint(0, len(dset)-1) AB_list.append((event, dset[idx], "diff")) mergePairs = AA_list + AB_list '''读取事件、标准特征文件中数据，以 AA_list 和 AB_list 中关键字为 key 生成字典''' rltdata, rltdata_ft16, rltdata_ft16_ = [], [], [] for evt, stdbcd, label in mergePairs: event = evtDict[evt] ## 判断是否存在轨迹图像文件夹，不存在则创建文件夹并保存轨迹图像 pairpath = os.path.join(subimgPath, f"{evt}") if not os.path.exists(pairpath): os.makedirs(pairpath) save_event_subimg(event, pairpath) ## 判断是否存在 barcode 标准样本集图像文件夹，不存在则创建文件夹并存储 barcode 样本集图像 stdImgpath = stdDict[stdbcd]["imgpaths"] pstdpath = os.path.join(subimgPath, f"{stdbcd}") if not os.path.exists(pstdpath): os.makedirs(pstdpath) ii = 1 for filepath in stdImgpath: stdpath = os.path.join(pstdpath, f"{stdbcd}_{ii}.png") shutil.copy2(filepath, stdpath) ii += 1 ##============================================ float32 stdfeat = stdDict[stdbcd]["feats"] evtfeat = event["feats_compose"] 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.append((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.append((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_.append((label, stdbcd, evt, simi_mean_ft16_, simi_max_ft16_, simi_mfeat_ft16_[0,0])) tm = datetime.fromtimestamp(time.time()).strftime('%Y%m%d_%H%M%S') ##================================================ save as float32, rppath = os.path.join(resultPath, f'{tm}.pickle') with open(rppath, 'wb') as f: pickle.dump(rltdata, f) rtpath = os.path.join(resultPath, f'{tm}.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(resultPath, f'{tm}_ft16.pickle') with open(rppath_ft16, 'wb') as f: pickle.dump(rltdata_ft16, f) rtpath_ft16 = os.path.join(resultPath, f'{tm}_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(resultPath, f'{tm}_uint8.pickle') with open(rppath_uint8, 'wb') as f: pickle.dump(rltdata_ft16_, f) rtpath_uint8 = os.path.join(resultPath, f'{tm}_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') print("func: contrast_performance_evaluate(), have finished!") def compute_precise_recall(pickpath): pickfile = os.path.basename(pickpath) file, ext = os.path.splitext(pickfile) if ext != '.pickle': return if file.find('ft16') < 0: return with open(pickpath, 'rb') as f: results = pickle.load(f) Same, Cross = [], [] for label, stdbcd, evt, simi_mean, simi_max, simi_mft in results: if label == "same": Same.append(simi_mean) if label == "diff": Cross.append(simi_mean) Same = np.array(Same) Cross = np.array(Cross) TPFN = len(Same) TNFP = len(Cross) # fig, axs = plt.subplots(2, 1) # axs[0].hist(Same, bins=60, edgecolor='black') # axs[0].set_xlim([-0.2, 1]) # axs[0].set_title(f'Same Barcode, Num: {TPFN}') # axs[1].hist(Cross, bins=60, edgecolor='black') # axs[1].set_xlim([-0.2, 1]) # axs[1].set_title(f'Cross Barcode, Num: {TNFP}') # plt.savefig(f'./result/{file}_hist.png') # svg, png, pdf 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 = TPFN - TP TN = np.sum(Cross < th) FP = TNFP - TN Recall_Pos.append(TP/TPFN) Recall_Neg.append(TN/TNFP) Precision_Pos.append(TP/(TP+FP+1e-6)) Precision_Neg.append(TN/(TN+FN+1e-6)) Correct.append((TN+TP)/(TPFN+TNFP)) fig, ax = plt.subplots() ax.plot(Thresh, Correct, 'r', label='Correct: (TN+TP)/(TPFN+TNFP)') 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, Precision_Pos, 'c', label='Precision_Pos: TP/(TP+FP)') ax.plot(Thresh, Precision_Neg, 'm', label='Precision_Neg: TN/(TN+FN)') ax.set_xlim([0, 1]) ax.set_ylim([0, 1]) ax.grid(True) ax.set_title('PrecisePos & PreciseNeg') ax.set_xlabel(f"Same Num: {TPFN}, Cross Num: {TNFP}") ax.legend() plt.show() plt.savefig(f'./result/{file}_pr.png') # svg, png, pdf def generate_event_and_stdfeatures(): '''=========================== 1. 生成标准特征集 ========================''' '''1.1 提取 stdSamplePath 中样本地址，生成字典{barcode: [imgpath1, imgpath1, ...]} 并存储为 pickle 文件，barcode.pickle''' # get_std_barcodeDict(stdSamplePath, stdBarcodePath) # print("standard imgpath have extracted and saved") '''1.2 特征提取，并保存至文件夹 stdFeaturePath 中，也可在运行过程中根据 barcodes 交集执行''' # stdfeat_infer(stdBarcodePath, stdFeaturePath, bcdSet=None) # print("standard features have generated!") '''=========================== 2. 提取并存储事件特征 ========================''' eventDatePath = [r'\\192.168.1.28\share\测试_202406\0910\images', # r'\\192.168.1.28\share\测试_202406\0723\0723_1', # r'\\192.168.1.28\share\测试_202406\0723\0723_2', # r'\\192.168.1.28\share\测试_202406\0723\0723_3', # r'\\192.168.1.28\share\测试_202406\0722\0722_01', # r'\\192.168.1.28\share\测试_202406\0722\0722_02' # r'\\192.168.1.28\share\测试_202406\0719\719_3', # r'\\192.168.1.28\share\测试_202406\0716\0716_1', # r'\\192.168.1.28\share\测试_202406\0716\0716_2', # r'\\192.168.1.28\share\测试_202406\0716\0716_3', # r'\\192.168.1.28\share\测试_202406\0712\0712_1', # 无帧图像 # r'\\192.168.1.28\share\测试_202406\0712\0712_2', # 无帧图像 ] eventList = [] # k = 0 for datePath in eventDatePath: for eventName in os.listdir(datePath): pickpath = os.path.join(eventFeatPath, f"{eventName}.pickle") if os.path.isfile(pickpath): continue eventPath = os.path.join(datePath, eventName) eventDict = creat_shopping_event(eventPath) if eventDict: eventList.append(eventDict) with open(pickpath, 'wb') as f: pickle.dump(eventDict, f) print(f"Event: {eventName}, have saved!") # k += 1 # if k==1: # break ## 保存轨迹中 boxes 子图 for event in eventList: basename = os.path.basename(event['filepath']) savepath = os.path.join(subimgPath, basename) if not os.path.exists(savepath): os.makedirs(savepath) save_event_subimg(event, savepath) print("eventList have generated and features have saved!") 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 main(): # generate_event_and_stdfeatures() contrast_performance_evaluate(resultPath) for filename in os.listdir(resultPath): if filename.find('.pickle') < 0: continue if filename.find('0911') < 0: continue pickpath = os.path.join(resultPath, filename) compute_precise_recall(pickpath) def main_std(): std_sample_path = r"\\192.168.1.28\share\已标注数据备份\对比数据\barcode\barcode_500_2192_已清洗" std_barcode_path = r"\\192.168.1.28\share\测试_202406\contrast\std_barcodes_2192" std_feature_path = r"\\192.168.1.28\share\测试_202406\contrast\std_features_2192_ft32vsft16" get_std_barcodeDict(std_sample_path, std_barcode_path) stdfeat_infer(std_barcode_path, std_feature_path, bcdSet=None) # fileList = [] # for filename in os.listdir(std_barcode_path): # filepath = os.path.join(std_barcode_path, filename) # with open(filepath, 'rb') as f: # bpDict = pickle.load(f) # for v in bpDict.values(): # fileList.append(len(v)) # print("done") if __name__ == '__main__': # main() main_std()