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This commit is contained in:
王庆刚
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contrast/__pycache__/genfeats.cpython-39.pyc
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contrast/__pycache__/genfeats.cpython-39.pyc
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@ -16,7 +16,7 @@ from tracking.utils.read_data import extract_data, read_deletedBarcode_file, rea
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# from tracking.dotrack.dotracks import Track
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from one2n_contrast import compute_recall_precision, show_recall_prec
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from one2n_contrast import performance_evaluate
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from one2n_contrast import performance_evaluate, one2n_return, one2n_deleted
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def compute_similar(feat1, feat2):
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@ -27,7 +27,7 @@ model.load_state_dict(torch.load(model_path, map_location=conf.device))
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model.eval()
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print('load model {} '.format(conf.testbackbone))
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def get_std_barcodeDict(bcdpath, savepath):
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def get_std_barcodeDict(bcdpath, savepath, bcdSet):
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'''
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inputs:
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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
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@ -42,10 +42,14 @@ def get_std_barcodeDict(bcdpath, savepath):
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'''读取数据集中 barcode 列表'''
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stdBarcodeList = []
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for filename in os.listdir(bcdpath):
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filepath = os.path.join(bcdpath, filename)
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# filepath = os.path.join(bcdpath, filename)
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# if not os.path.isdir(filepath) or not filename.isdigit() or len(filename)<8:
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# continue
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stdBarcodeList.append(filename)
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if bcdSet is None:
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stdBarcodeList.append(filename)
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elif filename in bcdSet:
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stdBarcodeList.append(filename)
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bcdPaths = [(barcode, os.path.join(bcdpath, barcode)) for barcode in stdBarcodeList]
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@ -184,18 +188,15 @@ def stdfeat_infer(imgPath, featPath, bcdSet=None):
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def genfeatures(imgpath, bcdpath, featpath):
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def genfeatures(imgpath, bcdpath, featpath, bcdSet=None):
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''' 生成标准特征集 '''
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'''1. 提取 imgpath 中样本地址，生成字典{barcode: [imgpath1, imgpath1, ...]}
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并存储于: bcdpath, 格式为 barcode.pickle'''
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get_std_barcodeDict(imgpath, bcdpath, bcdSet)
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get_std_barcodeDict(imgpath, bcdpath)
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stdfeat_infer(bcdpath, featpath, bcdSet=None)
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'''2. 特征提取，并保存至文件夹 featpath 中，也根据 bcdSet 交集执行'''
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stdfeat_infer(bcdpath, featpath, bcdSet)
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print(f"Features have generated, saved in: {featpath}")
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def main():
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imgpath = r"\\192.168.1.28\share\展厅barcode数据\整理\zhantingBase"
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bcdpath = r"D:\exhibition\dataset\bcdpath"
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@ -21,7 +21,7 @@ import sys
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sys.path.append(r"D:\DetectTracking")
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from tracking.utils.plotting import Annotator, colors
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from tracking.utils.read_data import extract_data, read_deletedBarcode_file, read_tracking_output, read_returnGoods_file
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from tracking.utils.plotting import draw_tracking_boxes
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from tracking.utils.plotting import draw_tracking_boxes, get_subimgs
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from contrast.utils.tools import showHist, show_recall_prec, compute_recall_precision
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@ -148,13 +148,18 @@ def get_contrast_paths(pair, basepath):
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if len(input_folds):
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indice = np.argsort(np.array(times))
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input_fold = input_folds[indice[-1]]
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inputpath = os.path.join(basepath, input_fold)
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'''取出操作错误匹配的放入操作对应的文件夹'''
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if len(errmatch_folds):
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indice = np.argsort(np.array(errmatch_times))
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errmatch_fold = errmatch_folds[indice[-1]]
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errorpath = os.path.join(basepath, errmatch_fold)
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'''放入事件文件夹地址、取出事件文件夹地址'''
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getoutpath = os.path.join(basepath, getout_fold)
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@ -163,33 +168,33 @@ def get_contrast_paths(pair, basepath):
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return getoutpath, inputpath, errorpath
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def save_tracking_imgpairs(pair, basepath, savepath):
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def save_tracking_imgpairs(pairs, savepath):
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'''
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basepath: 原始测试数据文件夹的路径
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pairs: 匹配事件对
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savepath: 保存的目标文件夹
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'''
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def get_event_path(evtpath):
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basepath, eventname = os.path.split(evtpath)
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evt_path = ''
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for filename in os.listdir(basepath):
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if filename.find(eventname)==0:
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evt_path = os.path.join(basepath, filename)
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break
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return evt_path
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getoutpath, inputpath, errorpath = get_contrast_paths(pair, basepath)
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if len(inputpath)==0:
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return
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getoutpath = get_event_path(pairs[0])
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inputpath = get_event_path(pairs[1])
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if len(pairs) == 3:
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errorpath = get_event_path(pairs[2])
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else:
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errorpath = ''
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'''==== 读取放入、取出事件对应的 Yolo输入的前后摄图像，0：后摄，1：前摄 ===='''
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'''==== 读取放入、取出事件对应的 tracking 输出：boxes, feats ===='''
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if len(inputpath):
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imgs_input_0, imgs_input_1 = read_tracking_imgs(inputpath)
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input_data_0 = os.path.join(inputpath, '0_tracking_output.data')
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input_data_1 = os.path.join(inputpath, '1_tracking_output.data')
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boxes_input_0, feats_input_0 = read_tracking_output(input_data_0)
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boxes_input_1, feats_input_1 = read_tracking_output(input_data_1)
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ImgsInput_0 = draw_tracking_boxes(imgs_input_0, boxes_input_0)
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ImgsInput_1 = draw_tracking_boxes(imgs_input_1, boxes_input_1)
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''' 1. 读取放入、取出事件对应的 Yolo输入的前后摄图像，0：后摄，1：前摄
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2. 读取放入、取出事件对应的 tracking 输出：boxes, feats
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3. boxes绘制并保存图像序列
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4. 截取并保存轨迹子图
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'''
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if len(getoutpath):
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imgs_getout_0, imgs_getout_1 = read_tracking_imgs(getoutpath)
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@ -199,9 +204,28 @@ def save_tracking_imgpairs(pair, basepath, savepath):
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boxes_output_1, feats_output_1 = read_tracking_output(getout_data_1)
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ImgsGetout_0 = draw_tracking_boxes(imgs_getout_0, boxes_output_0)
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ImgsGetout_1 = draw_tracking_boxes(imgs_getout_1, boxes_output_1)
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if len(errorpath):
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SubimgsGetout_0 = get_subimgs(imgs_getout_0, boxes_output_0)
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SubimgsGetout_1 = get_subimgs(imgs_getout_1, boxes_output_1)
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savedir = os.path.basename(getoutpath)
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if len(inputpath):
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imgs_input_0, imgs_input_1 = read_tracking_imgs(inputpath)
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input_data_0 = os.path.join(inputpath, '0_tracking_output.data')
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input_data_1 = os.path.join(inputpath, '1_tracking_output.data')
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boxes_input_0, feats_input_0 = read_tracking_output(input_data_0)
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boxes_input_1, feats_input_1 = read_tracking_output(input_data_1)
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ImgsInput_0 = draw_tracking_boxes(imgs_input_0, boxes_input_0)
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ImgsInput_1 = draw_tracking_boxes(imgs_input_1, boxes_input_1)
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SubimgsInput_0 = get_subimgs(imgs_input_0, boxes_input_0)
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SubimgsInput_1 = get_subimgs(imgs_input_1, boxes_input_1)
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savedir = savedir + '+' + os.path.basename(inputpath)
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if len(errorpath):
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imgs_error_0, imgs_error_1 = read_tracking_imgs(errorpath)
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error_data_0 = os.path.join(errorpath, '0_tracking_output.data')
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@ -211,37 +235,61 @@ def save_tracking_imgpairs(pair, basepath, savepath):
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ImgsError_0 = draw_tracking_boxes(imgs_error_0, boxes_error_0)
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ImgsError_1 = draw_tracking_boxes(imgs_error_1, boxes_error_1)
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SubimgsError_0 = get_subimgs(imgs_error_0, boxes_error_0)
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SubimgsError_1 = get_subimgs(imgs_error_0, boxes_error_0)
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savedir = pair[0] + pair[1]
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if len(errorpath):
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savedir = savedir + '_' + errorpath.split('_')[-1]
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foldname = os.path.join(savepath, 'imgpairs', savedir)
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if not os.path.exists(foldname):
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os.makedirs(foldname)
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for i, img in enumerate(ImgsInput_0):
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imgpath = os.path.join(foldname, f'input_0_{i}.png')
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cv2.imwrite(imgpath, img)
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for i, img in enumerate(ImgsInput_1):
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imgpath = os.path.join(foldname, f'input_1_{i}.png')
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cv2.imwrite(imgpath, img)
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for i, img in enumerate(ImgsGetout_0):
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imgpath = os.path.join(foldname, f'getout_0_{i}.png')
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cv2.imwrite(imgpath, img)
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for i, img in enumerate(ImgsGetout_1):
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imgpath = os.path.join(foldname, f'getout_1_{i}.png')
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cv2.imwrite(imgpath, img)
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savedir = savedir + '+' + os.path.basename(errorpath)
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for i, img in enumerate(ImgsError_0):
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imgpath = os.path.join(foldname, f'errMatch_0_{i}.png')
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''' savepath\pairs\savedir\eventpairs\保存画框后的图像序列 '''
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entpairs = os.path.join(savepath, 'pairs', savedir, 'eventpairs')
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if not os.path.exists(entpairs):
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os.makedirs(entpairs)
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for fid, img in ImgsInput_0:
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imgpath = os.path.join(entpairs, f'input_0_{fid}.png')
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cv2.imwrite(imgpath, img)
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for i, img in enumerate(ImgsError_1):
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imgpath = os.path.join(foldname, f'errMatch_1_{i}.png')
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for fid, img in ImgsInput_1:
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imgpath = os.path.join(entpairs, f'input_1_{fid}.png')
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cv2.imwrite(imgpath, img)
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for fid, img in ImgsGetout_0:
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imgpath = os.path.join(entpairs, f'getout_0_{fid}.png')
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cv2.imwrite(imgpath, img)
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for fid, img in ImgsGetout_1:
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imgpath = os.path.join(entpairs, f'getout_1_{fid}.png')
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cv2.imwrite(imgpath, img)
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if 'ImgsError_0' in vars() and 'ImgsError_1' in vars():
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for fid, img in ImgsError_0:
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imgpath = os.path.join(entpairs, f'errMatch_0_{fid}.png')
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cv2.imwrite(imgpath, img)
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for fid, img in ImgsError_1:
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imgpath = os.path.join(entpairs, f'errMatch_1_{fid}.png')
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cv2.imwrite(imgpath, img)
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''' savepath\pairs\savedir\subimgpairs\保存轨迹子图 '''
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subimgpairs = os.path.join(savepath, 'pairs', savedir, 'subimgpairs')
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if not os.path.exists(subimgpairs):
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os.makedirs(subimgpairs)
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for fid, bid, img in SubimgsGetout_0:
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imgpath = os.path.join(subimgpairs, f'getout_0_{fid}_{bid}.png')
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cv2.imwrite(imgpath, img)
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for fid, bid, img in SubimgsGetout_1:
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imgpath = os.path.join(subimgpairs, f'getout_1_{fid}_{bid}.png')
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cv2.imwrite(imgpath, img)
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for fid, bid, img in SubimgsInput_0:
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imgpath = os.path.join(subimgpairs, f'input_0_{fid}_{bid}.png')
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cv2.imwrite(imgpath, img)
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for fid, bid, img in SubimgsInput_1:
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imgpath = os.path.join(subimgpairs, f'input_1_{fid}_{bid}.png')
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cv2.imwrite(imgpath, img)
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if 'SubimgsError_0' in vars() and 'SubimgsError_1' in vars():
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for fid, bid, img in SubimgsError_0:
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imgpath = os.path.join(subimgpairs, f'errMatch_0_{fid}_{bid}.png')
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cv2.imwrite(imgpath, img)
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for fid, bid, img in SubimgsError_1:
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imgpath = os.path.join(subimgpairs, f'errMatch_1_{fid}_{bid}.png')
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cv2.imwrite(imgpath, img)
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def one2n_old(all_list):
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def one2n_deleted(all_list):
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corrpairs, errpairs, correct_similarity, err_similarity = [], [], [], []
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for s_list in all_list:
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seqdir = s_list['SeqDir'].strip()
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@ -277,8 +325,9 @@ def one2n_old(all_list):
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def one2n_new(all_list):
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corrpairs, correct_similarity, errpairs, err_similarity = [], [], [], []
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def one2n_return(all_list, basepath):
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corrpairs, corr_similarity, errpairs, err_similarity = [], [], [], []
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for s_list in all_list:
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seqdir = s_list['SeqDir'].strip()
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delete = s_list['Deleted'].strip()
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@ -305,7 +354,7 @@ def one2n_new(all_list):
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matched_barcode = barcodes[index]
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if matched_barcode == delete:
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corrpairs.append((seqdir, events[index]))
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correct_similarity.append(max(similarity))
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corr_similarity.append(max(similarity))
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else:
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idx = [i for i, name in enumerate(events) if name.split('_')[-1] == delete]
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idxmax, simimax = -1, -1
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@ -314,49 +363,80 @@ def one2n_new(all_list):
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if similarity[k] > simimax:
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idxmax = k
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simimax = similarity[k]
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errpairs.append((seqdir, events[idxmax], events[index]))
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if idxmax>-1:
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input_event = events[idxmax]
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else:
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input_event = ''
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errpairs.append((seqdir, input_event, events[index]))
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err_similarity.append(max(similarity))
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return errpairs, corrpairs, err_similarity, correct_similarity
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return corrpairs, errpairs, corr_similarity, err_similarity
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# def contrast_analysis(del_barcode_file, basepath, savepath, saveimgs=False):
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def get_relative_paths(del_barcode_file, basepath, savepath, saveimgs=False):
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'''
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del_barcode_file:
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deletedBarcode.txt 格式的 1:n 数据结果文件
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returnGoods.txt格式数据文件不需要调用该函数，one2n_old() 函数返回的 errpairs
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中元素为三元元组（取出，放入, 错误匹配）
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'''
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def test_rpath_deleted():
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'''deletedBarcode.txt 格式的 1:n 数据结果文件, returnGoods.txt格式数据文件不需要调用该函数'''
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del_bfile = r'\\192.168.1.28\share\测试_202406\709\deletedBarcode.txt'
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basepath = r'\\192.168.1.28\share\测试_202406\709'
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savepath = r'D:\DetectTracking\contrast\result'
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saveimgs = True
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relative_paths = []
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'''1. 读取 deletedBarcode 文件 '''
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all_list = read_deletedBarcode_file(del_barcode_file)
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all_list = read_deletedBarcode_file(del_bfile)
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'''2. 算法性能评估，并输出 (取出，删除, 错误匹配) 对 '''
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errpairs, corrpairs, _, _ = one2n_old(all_list)
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corrpairs, errpairs, _, _ = one2n_deleted(all_list)
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'''3. 构造事件组合(取出，放入并删除, 错误匹配) 对应路径 '''
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for errpair in errpairs:
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GetoutPath, InputPath, ErrorPath = get_contrast_paths(errpair, basepath)
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relative_paths.append((GetoutPath, InputPath, ErrorPath))
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pairs = (GetoutPath, InputPath, ErrorPath)
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relative_paths.append(pairs)
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print(InputPath)
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'''3. 获取 (取出，放入并删除, 错误匹配) 对应路径，保存相应轨迹图像'''
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if saveimgs:
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save_tracking_imgpairs(errpair, basepath, savepath)
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if saveimgs:
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save_tracking_imgpairs(pairs, savepath)
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return relative_paths
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def one2n_test():
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fpath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\other'
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fpath = r'\\192.168.1.28\share\测试_202406\1030\images'
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def test_rpath_return():
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return_bfile = r'\\192.168.1.28\share\测试_202406\1101\images\returnGoods.txt'
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basepath = r'\\192.168.1.28\share\测试_202406\1101\images'
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savepath = r'D:\DetectTracking\contrast\result'
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all_list = read_returnGoods_file(return_bfile)
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corrpairs, errpairs, _, _ = one2n_return(all_list, basepath)
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for corrpair in corrpairs:
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GetoutPath = os.path.join(basepath, corrpair[0])
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InputPath = os.path.join(basepath, corrpair[1])
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pairs = (GetoutPath, InputPath)
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save_tracking_imgpairs(pairs, savepath)
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for errpair in errpairs:
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GetoutPath = os.path.join(basepath, errpair[0])
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InputPath = os.path.join(basepath, errpair[1])
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ErrorPath = os.path.join(basepath, errpair[2])
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pairs = (GetoutPath, InputPath, ErrorPath)
|
||||
save_tracking_imgpairs(pairs, savepath)
|
||||
|
||||
|
||||
def test_one2n():
|
||||
'''
|
||||
1:n 性能测试
|
||||
兼容 2 种 txt 文件格式：returnGoods.txt, deletedBarcode.txt
|
||||
fpath: 文件路径、或文件夹，其中包含多个 txt 文件
|
||||
savepath: pr曲线保存路径
|
||||
'''
|
||||
# fpath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\other' # deletedBarcode.txt
|
||||
fpath = r'\\192.168.1.28\share\测试_202406\returnGoods\all' # returnGoods.txt
|
||||
savepath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\illustration'
|
||||
if not os.path.exists(savepath):
|
||||
os.mkdir(savepath)
|
||||
|
||||
if os.path.isdir(fpath):
|
||||
filepaths = [os.path.join(fpath, f) for f in os.listdir(fpath)
|
||||
@ -366,37 +446,27 @@ def one2n_test():
|
||||
filepaths = [fpath]
|
||||
else:
|
||||
return
|
||||
|
||||
|
||||
FileFormat = {}
|
||||
|
||||
if not os.path.exists(savepath):
|
||||
os.mkdir(savepath)
|
||||
|
||||
BarLists, blists = {}, []
|
||||
for pth in filepaths:
|
||||
file = str(Path(pth).stem)
|
||||
if file.find('deletedBarcode')>=0:
|
||||
FileFormat[file] = 'deletedBarcode'
|
||||
blist = read_deletedBarcode_file(pth)
|
||||
elif file.find('returnGoods')>=0:
|
||||
FileFormat[file] = 'returnGoods'
|
||||
if file.find('returnGoods')>=0:
|
||||
blist = read_returnGoods_file(pth)
|
||||
else:
|
||||
return
|
||||
|
||||
|
||||
|
||||
BarLists.update({file: blist})
|
||||
blists.extend(blist)
|
||||
|
||||
BarLists.update({file: blist})
|
||||
BarLists.update({"Total": blists})
|
||||
|
||||
if len(blists): BarLists.update({"Total": blists})
|
||||
for file, blist in BarLists.items():
|
||||
if FileFormat[file] == 'deletedBarcode':
|
||||
_, _, err_similarity, correct_similarity = one2n_old(blist)
|
||||
elif FileFormat[file] == 'returnGoods':
|
||||
_, _, err_similarity, correct_similarity = one2n_new(blist)
|
||||
else:
|
||||
_, _, err_similarity, correct_similarity = one2n_old(blist)
|
||||
|
||||
if all(b['filetype']=="deletedBarcode" for b in blist):
|
||||
_, _, correct_similarity, err_similarity = one2n_deleted(blist)
|
||||
if all(b['filetype']=="returnGoods" for b in blists):
|
||||
_, _, correct_similarity, err_similarity = one2n_return(blist)
|
||||
|
||||
recall, prec, ths = compute_recall_precision(err_similarity, correct_similarity)
|
||||
|
||||
@ -413,51 +483,16 @@ def one2n_test():
|
||||
|
||||
|
||||
|
||||
|
||||
def test_getreltpath():
|
||||
'''
|
||||
适用于：deletedBarcode.txt，不适用于：returnGoods.txt
|
||||
'''
|
||||
|
||||
del_barcode_file = r'\\192.168.1.28\share\测试_202406\709\deletedBarcode.txt'
|
||||
basepath = r'\\192.168.1.28\share\测试_202406\709'
|
||||
|
||||
# del_barcode_file = r'\\192.168.1.28\share\测试_202406\1030\images\returnGoods.txt'
|
||||
# basepath = r'\\192.168.1.28\share\测试_202406\1030\images'
|
||||
|
||||
savepath = r'D:\contrast\dataset\result'
|
||||
saveimgs = True
|
||||
try:
|
||||
relative_path = get_relative_paths(del_barcode_file, basepath, savepath, saveimgs)
|
||||
except Exception as e:
|
||||
print(f'Error Type: {e}')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
one2n_test()
|
||||
|
||||
# test_getreltpath()
|
||||
|
||||
# test_one2n()
|
||||
test_rpath_return() # returnGoods.txt
|
||||
test_rpath_deleted() # deleteBarcode.txt
|
||||
|
||||
|
||||
# try:
|
||||
# test_rpath_return()
|
||||
# test_rpath_deleted()
|
||||
# except Exception as e:
|
||||
# print(e)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -11,7 +11,7 @@ Created on Fri Aug 30 17:53:03 2024
|
||||
标准特征提取，并保存至文件夹 stdFeaturePath 中，
|
||||
也可在运行过程中根据与购物事件集合 barcodes 交集执行
|
||||
2. 1:1 比对性能测试，
|
||||
func: contrast_performance_evaluate(resultPath)
|
||||
func: one2one_eval(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;
|
||||
@ -32,86 +32,83 @@ import os
|
||||
import sys
|
||||
import random
|
||||
import pickle
|
||||
import torch
|
||||
# import torch
|
||||
import time
|
||||
import json
|
||||
# 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 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()
|
||||
# from model import resnet18 as resnet18
|
||||
# from feat_inference import inference_image
|
||||
|
||||
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
|
||||
from tracking.utils.read_data import extract_data, read_tracking_output, read_one2one_simi, read_deletedBarcode_file
|
||||
|
||||
from genfeats import genfeatures, stdfeat_infer
|
||||
|
||||
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):
|
||||
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 creat_shopping_event(eventPath):
|
||||
'''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:
|
||||
'''evtName 为一次购物事件'''
|
||||
evtName = os.path.basename(eventPath)
|
||||
evtList = evtName.split('_')
|
||||
|
||||
'''================ 0. 检查 evtName 及 eventPath 正确性和有效性 ================'''
|
||||
if evtName.find('2024')<0 and len(evtList[0])!=15:
|
||||
return
|
||||
if not os.path.isdir(eventPath):
|
||||
return
|
||||
|
||||
if len(evtList)==1 or (len(evtList)==2 and len(evtList[1])==0):
|
||||
barcode = ''
|
||||
else:
|
||||
barcode = evtList[-1]
|
||||
|
||||
if len(evtList)==3 and evtList[-1]== evtList[-2]:
|
||||
evtType = 'input'
|
||||
else:
|
||||
evtType = 'other'
|
||||
|
||||
'''================ 1. 构造事件描述字典，暂定 9 items ==============='''
|
||||
event = {}
|
||||
event['barcode'] = eventName.split('_')[1]
|
||||
event['type'] = 'input'
|
||||
event['barcode'] = barcode
|
||||
event['type'] = evtType
|
||||
event['filepath'] = eventPath
|
||||
event['back_imgpaths'] = []
|
||||
event['front_imgpaths'] = []
|
||||
@ -120,7 +117,8 @@ def creat_shopping_event(eventPath, subimgPath=False):
|
||||
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)
|
||||
event['one2one_simi'] = None
|
||||
event['feats_select'] = np.empty((0, 256), dtype=np.float64)
|
||||
|
||||
|
||||
'''================= 2. 读取 data 文件 ============================='''
|
||||
@ -144,8 +142,12 @@ def creat_shopping_event(eventPath, subimgPath=False):
|
||||
elif CamerType == '1':
|
||||
event['front_boxes'] = tracking_output_boxes
|
||||
event['front_feats'] = tracking_output_feats
|
||||
|
||||
|
||||
|
||||
if dataname.find("process.data")==0:
|
||||
simiDict = read_one2one_simi(datapath)
|
||||
event['one2one_simi'] = simiDict
|
||||
|
||||
|
||||
if len(event['back_boxes'])==0 or len(event['front_boxes'])==0:
|
||||
return None
|
||||
|
||||
@ -165,16 +167,8 @@ def creat_shopping_event(eventPath, subimgPath=False):
|
||||
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 ============='''
|
||||
'''================ 3. 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):
|
||||
@ -194,11 +188,11 @@ def creat_shopping_event(eventPath, subimgPath=False):
|
||||
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'''
|
||||
'''3.1 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'''
|
||||
'''3.2 将前、后摄图像路径添加至事件字典'''
|
||||
|
||||
|
||||
bfid = event['back_boxes'][:, 7].astype(np.int64)
|
||||
@ -209,101 +203,16 @@ def creat_shopping_event(eventPath, subimgPath=False):
|
||||
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 =========='''
|
||||
'''================ 4. 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}")
|
||||
print(f"Event: {evtName}, 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):
|
||||
'''
|
||||
@ -340,131 +249,20 @@ def save_event_subimg(event, savepath):
|
||||
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):
|
||||
def one2one_eval(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()
|
||||
'''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 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
|
||||
and (len(p.stem.split('_'))==2 or len(p.stem.split('_'))==3)
|
||||
and p.stem.split('_')[-1].isdigit()
|
||||
and p.stem.split('_')[-1] in stdBarcode
|
||||
]
|
||||
|
||||
barcodes = set([bcd for _, bcd in evtList])
|
||||
@ -612,7 +410,7 @@ def contrast_performance_evaluate(resultPath):
|
||||
f.write(line + '\n')
|
||||
|
||||
|
||||
print("func: contrast_performance_evaluate(), have finished!")
|
||||
print("func: one2one_eval(), have finished!")
|
||||
|
||||
|
||||
|
||||
@ -684,44 +482,16 @@ def compute_precise_recall(pickpath):
|
||||
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
|
||||
]
|
||||
def gen_eventdict(eventDatePath, saveimg=True):
|
||||
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)
|
||||
@ -736,52 +506,61 @@ def generate_event_and_stdfeatures():
|
||||
# break
|
||||
|
||||
## 保存轨迹中 boxes 子图
|
||||
if not saveimg:
|
||||
return
|
||||
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)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def test_one2one():
|
||||
eventDatePath = [r'\\192.168.1.28\share\测试_202406\1101\images',
|
||||
# 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
|
||||
]
|
||||
bcdList = []
|
||||
for evtpath in eventDatePath:
|
||||
for evtname in os.listdir(evtpath):
|
||||
evt = evtname.split('_')
|
||||
if len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10:
|
||||
bcdList.append(evt[-1])
|
||||
|
||||
bcdSet = set(bcdList)
|
||||
|
||||
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)
|
||||
|
||||
'''==== 1. 生成标准特征集, 只需运行一次 ==============='''
|
||||
genfeatures(stdSamplePath, stdBarcodePath, stdFeaturePath, bcdSet)
|
||||
print("stdFeats have generated and saved!")
|
||||
|
||||
|
||||
arrDistNorm = np.linalg.norm(arr_ft16_ - arr_ft16) / arr_ft16_.size
|
||||
|
||||
|
||||
return arr_uint8, arr_ft16_
|
||||
'''==== 2. 生成事件字典, 只需运行一次 ==============='''
|
||||
|
||||
gen_eventdict(eventDatePath)
|
||||
print("eventList have generated and saved!")
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def main():
|
||||
# generate_event_and_stdfeatures()
|
||||
|
||||
contrast_performance_evaluate(resultPath)
|
||||
'''==== 3. 1:1性能评估 ==============='''
|
||||
one2one_eval(resultPath)
|
||||
for filename in os.listdir(resultPath):
|
||||
if filename.find('.pickle') < 0: continue
|
||||
if filename.find('0911') < 0: continue
|
||||
@ -789,63 +568,29 @@ def main():
|
||||
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()
|
||||
'''
|
||||
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)
|
||||
|
||||
test_one2one()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -17,7 +17,6 @@ import copy
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
from imgs_inference import run_yolo
|
||||
|
||||
from event_time_specify import devide_motion_state#, state_measure
|
||||
from tracking.utils.read_data import read_seneor
|
||||
|
||||
|
||||
Binary file not shown.
Binary file not shown.
@ -285,6 +285,38 @@ def boxing_img(det, img, line_width=3):
|
||||
|
||||
return imgx
|
||||
|
||||
def array2list(bboxes):
|
||||
track_fids = np.unique(bboxes[:, 7].astype(int))
|
||||
track_fids.sort()
|
||||
|
||||
lboxes = []
|
||||
for f_id in track_fids:
|
||||
# print(f"The ID is: {t_id}")
|
||||
idx = np.where(bboxes[:, 7] == f_id)[0]
|
||||
box = bboxes[idx, :]
|
||||
lboxes.append(box)
|
||||
|
||||
assert len(set(box[:, 4])) == len(box), "Please check!!!"
|
||||
|
||||
return lboxes
|
||||
|
||||
|
||||
def get_subimgs(imgs, tracks, scale=2):
|
||||
bboxes = []
|
||||
if len(tracks):
|
||||
bboxes = array2list(tracks)
|
||||
|
||||
subimgs = []
|
||||
for i, boxes in enumerate(bboxes):
|
||||
fid = int(boxes[0, 7])
|
||||
|
||||
for *xyxy, tid, conf, cls, fid, bid in boxes:
|
||||
pt2 = [p/scale for p in xyxy]
|
||||
x1, y1, x2, y2 = (int(pt2[0]), int(pt2[1])), (int(pt2[2]), int(pt2[3]))
|
||||
subimgs.append((int(fid), int(bid), imgs[fid-1][y1:y2, x1:x2]))
|
||||
|
||||
return subimgs
|
||||
|
||||
def draw_tracking_boxes(imgs, tracks, scale=2):
|
||||
'''需要确保 imgs 覆盖tracks中的帧ID数
|
||||
tracks: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
|
||||
@ -295,27 +327,15 @@ def draw_tracking_boxes(imgs, tracks, scale=2):
|
||||
|
||||
'''
|
||||
|
||||
def array2list(bboxes):
|
||||
track_fids = np.unique(bboxes[:, 7].astype(int))
|
||||
track_fids.sort()
|
||||
|
||||
lboxes = []
|
||||
for f_id in track_fids:
|
||||
# print(f"The ID is: {t_id}")
|
||||
idx = np.where(bboxes[:, 7] == f_id)[0]
|
||||
box = bboxes[idx, :]
|
||||
lboxes.append(box)
|
||||
|
||||
assert len(set(box[:, 4])) == len(box), "Please check!!!"
|
||||
|
||||
return lboxes
|
||||
|
||||
bboxes = array2list(tracks)
|
||||
|
||||
bboxes = []
|
||||
if len(tracks):
|
||||
bboxes = array2list(tracks)
|
||||
|
||||
# if len(bboxes)!=len(imgs):
|
||||
# return False, imgs
|
||||
|
||||
subimgs = []
|
||||
annimgs = []
|
||||
for i, boxes in enumerate(bboxes):
|
||||
fid = int(boxes[0, 7])
|
||||
annotator = Annotator(imgs[fid-1].copy())
|
||||
@ -331,11 +351,11 @@ def draw_tracking_boxes(imgs, tracks, scale=2):
|
||||
|
||||
pt2 = [p/scale for p in xyxy]
|
||||
annotator.box_label(pt2, label, color=color)
|
||||
|
||||
|
||||
img = annotator.result()
|
||||
subimgs.append((fid, img))
|
||||
annimgs.append((int(fid), img))
|
||||
|
||||
return subimgs
|
||||
return annimgs
|
||||
|
||||
|
||||
|
||||
|
||||
@ -37,6 +37,9 @@ def find_samebox_in_array(arr, target):
|
||||
|
||||
|
||||
def extract_data(datapath):
|
||||
'''
|
||||
0/1_track.data 数据读取
|
||||
'''
|
||||
bboxes, ffeats = [], []
|
||||
|
||||
trackerboxes = np.empty((0, 9), dtype=np.float64)
|
||||
@ -147,8 +150,15 @@ def extract_data(datapath):
|
||||
return bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, tracking_feat_dict
|
||||
|
||||
def read_tracking_output(filepath):
|
||||
'''
|
||||
0/1_tracking_output.data 数据读取
|
||||
'''
|
||||
|
||||
boxes = []
|
||||
feats = []
|
||||
if not os.path.isfile(filepath):
|
||||
return np.array(boxes), np.array(feats)
|
||||
|
||||
with open(filepath, 'r', encoding='utf-8') as file:
|
||||
for line in file:
|
||||
line = line.strip() # 去除行尾的换行符和可能的空白字符
|
||||
@ -176,7 +186,6 @@ def read_deletedBarcode_file(filePath):
|
||||
|
||||
split_flag, all_list = False, []
|
||||
dict, barcode_list, similarity_list = {}, [], []
|
||||
|
||||
clean_lines = [line.strip().replace("'", '').replace('"', '') for line in lines]
|
||||
|
||||
for i, line in enumerate(clean_lines):
|
||||
@ -199,6 +208,7 @@ def read_deletedBarcode_file(filePath):
|
||||
|
||||
if label == 'SeqDir':
|
||||
dict['SeqDir'] = value
|
||||
dict['filetype'] = "deletedBarcode"
|
||||
if label == 'Deleted':
|
||||
dict['Deleted'] = value
|
||||
if label == 'List':
|
||||
@ -259,15 +269,19 @@ def read_returnGoods_file(filePath):
|
||||
if label == 'SeqDir':
|
||||
dict['SeqDir'] = value
|
||||
dict['Deleted'] = value.split('_')[-1]
|
||||
dict['filetype'] = "returnGoods"
|
||||
if label == 'List':
|
||||
split_flag = True
|
||||
continue
|
||||
if split_flag:
|
||||
bcd = label.split('_')[-1]
|
||||
# event_list.append(label + '_' + bcd)
|
||||
event_list.append(label)
|
||||
barcode_list.append(label.split('_')[-1])
|
||||
barcode_list.append(bcd)
|
||||
similarity_list.append(value.split(',')[0])
|
||||
type_list.append(value.split('=')[-1])
|
||||
|
||||
|
||||
if len(barcode_list): dict['barcode'] = barcode_list
|
||||
if len(similarity_list): dict['similarity'] = similarity_list
|
||||
if len(event_list): dict['event'] = event_list
|
||||
@ -279,33 +293,51 @@ def read_returnGoods_file(filePath):
|
||||
|
||||
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# def read_seneor(filepath):
|
||||
# WeightDict = OrderedDict()
|
||||
# with open(filepath, 'r', encoding='utf-8') as f:
|
||||
# lines = f.readlines()
|
||||
# for i, line in enumerate(lines):
|
||||
# line = line.strip()
|
||||
#
|
||||
# keyword = line.split(':')[0]
|
||||
# value = line.split(':')[1]
|
||||
#
|
||||
# vdata = [float(s) for s in value.split(',') if len(s)]
|
||||
#
|
||||
# WeightDict[keyword] = vdata[-1]
|
||||
#
|
||||
# return WeightDict
|
||||
# =============================================================================
|
||||
|
||||
def read_one2one_simi(filePath):
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def read_seneor(filepath):
|
||||
WeightDict = OrderedDict()
|
||||
with open(filepath, 'r', encoding='utf-8') as f:
|
||||
SimiDict = {}
|
||||
with open(filePath, 'r', encoding='utf-8') as f:
|
||||
lines = f.readlines()
|
||||
flag = False
|
||||
for i, line in enumerate(lines):
|
||||
line = line.strip()
|
||||
if line.find('barcode:')<0 and not flag:
|
||||
continue
|
||||
if line.find('barcode:')==0 :
|
||||
flag = True
|
||||
continue
|
||||
|
||||
keyword = line.split(':')[0]
|
||||
value = line.split(':')[1]
|
||||
|
||||
vdata = [float(s) for s in value.split(',') if len(s)]
|
||||
|
||||
WeightDict[keyword] = vdata[-1]
|
||||
|
||||
return WeightDict
|
||||
# if line.endswith(','):
|
||||
# line = line[:-1]
|
||||
if flag:
|
||||
barcode = line.split(',')[0].strip()
|
||||
value = line.split(',')[1].split(':')[1].strip()
|
||||
SimiDict[barcode] = float(value)
|
||||
|
||||
if flag and not line:
|
||||
flag = False
|
||||
|
||||
return SimiDict
|
||||
|
||||
|
||||
|
||||
def read_weight_timeConsuming(filePth):
|
||||
@ -362,15 +394,14 @@ def plot_sensor_curve(WeightDict, SensorDict, ProcessTimeDict):
|
||||
nw = len(wdata)
|
||||
assert(nw) >= 8, "The num of weight data is less than 8!"
|
||||
|
||||
i1, i2 = 0, 7
|
||||
while i2 < nw:
|
||||
data = wdata[i1:(i2+1)]
|
||||
max(data) - min(data)
|
||||
|
||||
if i2<7:
|
||||
i1 = 0
|
||||
else:
|
||||
i1 = i2-windth
|
||||
# i1, i2 = 0, 7
|
||||
# while i2 < nw:
|
||||
# data = wdata[i1:(i2+1)]
|
||||
# max(data) - min(data)
|
||||
# if i2<7:
|
||||
# i1 = 0
|
||||
# else:
|
||||
# i1 = i2-windth
|
||||
|
||||
min_t = min(wtime + stime)
|
||||
wtime = [t-min_t for t in wtime]
|
||||
@ -405,15 +436,12 @@ def plot_sensor_curve(WeightDict, SensorDict, ProcessTimeDict):
|
||||
|
||||
|
||||
|
||||
def main(file_path):
|
||||
def test_process(file_path):
|
||||
WeightDict, SensorDict, ProcessTimeDict = read_weight_timeConsuming(file_path)
|
||||
plot_sensor_curve(WeightDict, SensorDict, ProcessTimeDict)
|
||||
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
|
||||
def main():
|
||||
files_path = r'\\192.168.1.28\share\测试_202406\0814\0814\20240814-102227-62264578-a720-4eb9-b95e-cb8be009aa98_null'
|
||||
k = 0
|
||||
for filename in os.listdir(files_path):
|
||||
@ -424,42 +452,21 @@ if __name__ == "__main__":
|
||||
extract_data(file_path)
|
||||
|
||||
if os.path.isfile(file_path) and filename.find("process.data")>=0:
|
||||
main(file_path)
|
||||
test_process(file_path)
|
||||
|
||||
k += 1
|
||||
if k == 1:
|
||||
break
|
||||
|
||||
|
||||
|
||||
# print("Done")
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def main1():
|
||||
fpath = r'\\192.168.1.28\share\测试_202406\1101\images\20241101-140456-44dc75b5-c406-4cb2-8317-c4660bb727a3_6922130101355_6922130101355\process.data'
|
||||
simidct = read_one2one_simi(fpath)
|
||||
print(simidct)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# main()
|
||||
main1()
|
||||
|
||||
|
||||
|
||||
|
||||
129
说明文档.txt
129
说明文档.txt
@ -144,15 +144,124 @@
|
||||
|
||||
precision_compare(filepath, savepath)
|
||||
读取 deletedBarcode.txt 和 deletedBarcodeTest.txt 中的数据，进行相似度比较
|
||||
|
||||
|
||||
genfeats.py
|
||||
get_std_barcodeDict(bcdpath, savepath)
|
||||
功能: 生成并保存只有一个key值的字典 {barcode: [imgpath1, imgpath1, ...]}
|
||||
|
||||
stdfeat_infer(imgPath, featPath, bcdSet=None)
|
||||
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
|
||||
|
||||
|
||||
one2n_contrast.py
|
||||
1:n 比对，读取 deletedBarcode.txt，实现现场测试评估。
|
||||
main():
|
||||
循环读取不同文件夹中的 deletedBarcode.txt，合并评估。
|
||||
main1():
|
||||
指定deletedBarcode.txt进行1:n性能评估
|
||||
test_one2n()
|
||||
1:n 现场测试性能评估，输出 PR 曲线
|
||||
兼容 2 种 txt 文件格式：returnGoods.txt, deletedBarcode.txt,
|
||||
分别对应不同的文件读取函数：
|
||||
- read_deletedBarcode_file()
|
||||
- read_returnGoods_file()
|
||||
|
||||
one2n_return(all_list)
|
||||
输入：从returnGoods.txt读取的数据
|
||||
输出：
|
||||
corrpairs：(取出事件, 正确匹配的放入事件)
|
||||
errpairs：(取出事件, 放入事件, 错误匹配的放入事件)
|
||||
corr_similarity: (正确匹配时的相似度)
|
||||
err_similarity: (错误匹配时的相似度)
|
||||
|
||||
|
||||
one2n_deleted(all_list)
|
||||
输入: 从deletedBarcode.txt读取的数据
|
||||
输出：
|
||||
corrpairs：(取出事件, 取出的barcode)
|
||||
errpairs：(取出事件, 取出的barcode, 错误匹配的barcode)
|
||||
corr_similarity: (正确匹配时的相似度)
|
||||
err_similarity: (错误匹配时的相似度)
|
||||
|
||||
save_tracking_imgpairs(pairs, savepath)
|
||||
输入：
|
||||
pairs：匹配时间对，len(2)=2 or 3, 对应正确匹配与错误匹配
|
||||
savepath：结果保存地址，其中图像文件的命名为：取出事件 + 放入事件 + 错误匹配时间
|
||||
子函数 get_event_path(), 扫码放入的对齐名
|
||||
对于 returnGoods.txt, 放入事件的事件名和对应的文件夹名不一致，需要对齐
|
||||
|
||||
test_rpath_deleted()
|
||||
功能：
|
||||
针对 eletedBarcode.txt 格式的 1:n 数据结果文件
|
||||
获得 1:n 情况下正确或匹配事件对（取出事件、放入事件、错误匹配事件）
|
||||
匹配事件分析, 实现函数：save_tracking_imgpairs()
|
||||
重要参数：
|
||||
del_barcode_file:
|
||||
basepath: 对应事件路径
|
||||
savepath: 存储路径, 是函数 save_tracking_imgpairs() 的输入
|
||||
saveimgs: Ture, False, 是否保存错误匹配的事件对
|
||||
|
||||
get_contrast_paths()
|
||||
针对 eletedBarcode.txt 格式的 1:n 数据结果文件,返回三元时间元组（getoutpath, inputpath, errorpath）
|
||||
|
||||
test_rpath_return()
|
||||
针对 returnGoods.txt 格式 1:n 数据文件，不需要调用函数get_contrast_paths()
|
||||
获得 1:n 情况下正确或匹配事件对（取出事件、放入事件、错误匹配事件）
|
||||
匹配事件分析, 实现函数：save_tracking_imgpairs()
|
||||
|
||||
|
||||
one2one_contrast.py
|
||||
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
|
||||
|
||||
(1), (2), (3): 保存标准特征集向量，只需运行一次
|
||||
(4): 保存测试的事件字典，只需运行一次
|
||||
|
||||
|
||||
test_one2one()
|
||||
(1) 生成标准特征集, 只需运行一次
|
||||
genfeatures()
|
||||
(2) 生成事件字典, 只需运行一次
|
||||
gen_eventdict(eventDatePath, saveimg)
|
||||
参数:
|
||||
eventDatePath: 事件集列表，其中每个元素均为事件的集合;
|
||||
saveimg: 是否保存事件子图
|
||||
|
||||
(3) 1:1性能评估
|
||||
(4) 计算PR曲线
|
||||
|
||||
|
||||
|
||||
creat_shopping_event(eventPath, subimgPath=False)
|
||||
构造一次购物事件字典, 共12个关键字。
|
||||
|
||||
save_event_subimg(event, savepath)
|
||||
保存一次购物事件的子图
|
||||
|
||||
|
||||
one2one_eval()
|
||||
|
||||
|
||||
|
||||
compute_precise_recall()
|
||||
|
||||
|
||||
|
||||
|
||||
int8_to_ft16()
|
||||
|
||||
|
||||
ft16_to_uint8()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
one2one_onsite.py
|
||||
现场试验输出数据的 1:1 性能评估；
|
||||
适用于202410前数据保存版本的，需调用 OneToOneCompare.txt
|
||||
@ -161,11 +270,13 @@
|
||||
std_sample_path：图像样本的存储地址
|
||||
std_barcode_path：对 std_sample_path 中文件列表进行遍历，形成{barcode: 图像样本地址}形式字典并进行存储
|
||||
std_feature_path：调用 inference_image(), 对每一个barcode，生成字典并进行存储
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
genfeats.py
|
||||
genfeatures(imgpath, bcdpath, featpath)
|
||||
功能：生成标准特征向量
|
||||
功能：生成标准特征向量的字典, 并保存为: barcode.pickle
|
||||
keys: barcode, imgpaths, feats_ft32, feats_ft16, feats_uint8
|
||||
参数：
|
||||
(1) imgpath：图像样本的存储地址
|
||||
(2) bcdpath：对 imgpath 中文件列表进行遍历，形成{barcode: 图像样本地址}形式字典并进行存储
|
||||
|
||||
Reference in New Issue
Block a user