modify 1:1 比对方式
This commit is contained in:
王庆刚
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@ -62,7 +62,7 @@ class Config:
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# test_val = "./data/test_data_100"
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# test_model = "checkpoints/best_resnet18_v11.pth"
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test_model = "checkpoints/zhanting_cls22_v11.pth"
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test_model = "checkpoints/zhanting_res_801.pth"
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@ -136,6 +136,8 @@ def stdfeat_infer(imgPath, featPath, bcdSet=None):
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continue
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featpath = os.path.join(featPath, f"{bcd}.pickle")
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# if os.path.isfile(featpath):
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# continue
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stdbDict = {}
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t1 = time.time()
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@ -11,7 +11,7 @@ Created on Fri Aug 30 17:53:03 2024
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标准特征提取，并保存至文件夹 stdFeaturePath 中，
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也可在运行过程中根据与购物事件集合 barcodes 交集执行
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2. 1:1 比对性能测试，
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func: one2one_eval(resultPath)
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func: one2one_eval(similPath)
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(1) 求购物事件和标准特征级 Barcode 交集，构造 evtDict、stdDict
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(2) 构造扫 A 放 A、扫 A 放 B 组合，mergePairs = AA_list + AB_list
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(3) 循环计算 mergePairs 中元素 "(A, A) 或 (A, B)" 相似度;
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@ -32,6 +32,7 @@ import os
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import sys
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import random
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import pickle
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import json
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# import torch
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import time
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# import json
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@ -47,10 +48,12 @@ from datetime import datetime
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# from feat_inference import inference_image
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sys.path.append(r"D:\DetectTracking")
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from tracking.utils.read_data import extract_data, read_tracking_output, read_one2one_simi, read_deletedBarcode_file
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from config import config as conf
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from genfeats import model_init, genfeatures, stdfeat_infer
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from tracking.utils.read_data import extract_data, read_tracking_output, read_similar, read_deletedBarcode_file
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from tracking.utils.plotting import Annotator, colors
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from feat_extract.config import config as conf
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from feat_extract.inference import FeatsInterface
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from utils.event import Event
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from genfeats import gen_bcd_features
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IMG_FORMAT = ['.bmp', '.jpg', '.jpeg', '.png']
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@ -107,6 +110,10 @@ def creat_shopping_event(eventPath):
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evtType = 'other'
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'''================ 1. 构造事件描述字典，暂定 9 items ==============='''
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event = {}
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event['barcode'] = barcode
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event['type'] = evtType
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@ -118,7 +125,8 @@ def creat_shopping_event(eventPath):
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event['back_feats'] = np.empty((0, 256), dtype=np.float64)
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event['front_feats'] = np.empty((0, 256), dtype=np.float64)
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event['feats_compose'] = np.empty((0, 256), dtype=np.float64)
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event['one2one_simi'] = None
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event['one2one'] = None
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event['one2n'] = None
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event['feats_select'] = np.empty((0, 256), dtype=np.float64)
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@ -145,8 +153,9 @@ def creat_shopping_event(eventPath):
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event['front_feats'] = tracking_output_feats
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if dataname.find("process.data")==0:
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simiDict = read_one2one_simi(datapath)
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event['one2one_simi'] = simiDict
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simiDict = read_similar(datapath)
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event['one2one'] = simiDict['one2one']
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event['one2n'] = simiDict['one2n']
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if len(event['back_boxes'])==0 or len(event['front_boxes'])==0:
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@ -215,6 +224,52 @@ def creat_shopping_event(eventPath):
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return event
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def plot_save_image(event, savepath):
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cameras = ('front', 'back')
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for camera in cameras:
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if camera == 'front':
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boxes = event.front_trackerboxes
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imgpaths = event.front_imgpaths
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else:
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boxes = event.back_trackerboxes
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imgpaths = event.back_imgpaths
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def array2list(bboxes):
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'''[x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]'''
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frame_ids = bboxes[:, 7].astype(int)
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fID = np.unique(bboxes[:, 7].astype(int))
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fboxes = []
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for f_id in fID:
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idx = np.where(frame_ids==f_id)[0]
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box = bboxes[idx, :]
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fboxes.append((f_id, box))
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return fboxes
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fboxes = array2list(boxes)
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for fid, fbox in fboxes:
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imgpath = imgpaths[int(fid-1)]
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image = cv2.imread(imgpath)
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annotator = Annotator(image.copy(), line_width=2)
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for i, *xyxy, tid, score, cls, fid, bid in enumerate(fbox):
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label = f'{int(id), int(cls)}'
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if tid >=0 and cls==0:
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color = colors(int(cls), True)
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elif tid >=0 and cls!=0:
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color = colors(int(id), True)
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else:
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color = colors(19, True) # 19为调色板的最后一个元素
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annotator.box_label(xyxy, label, color=color)
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im0 = annotator.result()
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spath = os.path.join(savepath, Path(imgpath).name)
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cv2.imwrite(spath, im0)
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def save_event_subimg(event, savepath):
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'''
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功能: 保存一次购物事件的轨迹子图
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@ -224,160 +279,92 @@ def save_event_subimg(event, savepath):
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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
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'''
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cameras = ('front', 'back')
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k = 0
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for camera in cameras:
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if camera == 'front':
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boxes = event['front_boxes']
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imgpaths = event['front_imgpaths']
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boxes = event.front_boxes
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imgpaths = event.front_imgpaths
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else:
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boxes = event['back_boxes']
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imgpaths = event['back_imgpaths']
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boxes = event.back_boxes
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imgpaths = event.back_imgpaths
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for i, box in enumerate(boxes):
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x1, y1, x2, y2, tid, score, cls, fid, bid = box
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imgpath = imgpaths[i]
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imgpath = imgpaths[int(fid-1)]
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image = cv2.imread(imgpath)
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subimg = image[int(y1/2):int(y2/2), int(x1/2):int(x2/2), :]
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camerType, timeTamp, _, frameID = os.path.basename(imgpath).split('.')[0].split('_')
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subimgName = f"{k}_cam-{camerType}_tid-{int(tid)}_fid-({int(fid)}, {frameID}).png"
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subimgName = f"cam{camerType}_{i}_tid{int(tid)}_fid({int(fid)}, {frameID}).png"
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spath = os.path.join(savepath, subimgName)
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cv2.imwrite(spath, subimg)
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k += 1
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# basename = os.path.basename(event['filepath'])
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print(f"Image saved: {os.path.basename(event['filepath'])}")
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print(f"Image saved: {os.path.basename(event.eventpath)}")
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def one2one_eval(resultPath):
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# stdBarcode = [p.stem for p in Path(stdFeaturePath).iterdir() if p.is_file() and p.suffix=='.pickle']
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stdBarcode = [p.stem for p in Path(stdBarcodePath).iterdir() if p.is_file() and p.suffix=='.pickle']
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'''购物事件列表，该列表中的 Barcode 存在于标准的 stdBarcode 内'''
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evtList = [(p.stem, p.stem.split('_')[-1]) for p in Path(eventFeatPath).iterdir()
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if p.is_file()
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and p.suffix=='.pickle'
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and (len(p.stem.split('_'))==2 or len(p.stem.split('_'))==3)
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and p.stem.split('_')[-1].isdigit()
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and p.stem.split('_')[-1] in stdBarcode
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]
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barcodes = set([bcd for _, bcd in evtList])
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'''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'''
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stdfeat_infer(stdBarcodePath, stdFeaturePath, barcodes)
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'''========= 构建用于比对的标准特征字典 ============='''
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stdDict = {}
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for barcode in barcodes:
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stdpath = os.path.join(stdFeaturePath, barcode+'.pickle')
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with open(stdpath, 'rb') as f:
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stddata = pickle.load(f)
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stdDict[barcode] = stddata
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'''========= 构建用于比对的操作事件字典 ============='''
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evtDict = {}
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for event, barcode in evtList:
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evtpath = os.path.join(eventFeatPath, event+'.pickle')
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with open(evtpath, 'rb') as f:
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evtdata = pickle.load(f)
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evtDict[event] = evtdata
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'''===== 构造 3 个事件对: 扫 A 放 A, 扫 A 放 B, 合并 ===================='''
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AA_list = [(event, barcode, "same") for event, barcode in evtList]
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AB_list = []
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for event, barcode in evtList:
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dset = list(barcodes.symmetric_difference(set([barcode])))
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idx = random.randint(0, len(dset)-1)
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AB_list.append((event, dset[idx], "diff"))
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mergePairs = AA_list + AB_list
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'''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'''
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rltdata, rltdata_ft16, rltdata_ft16_ = [], [], []
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for evt, stdbcd, label in mergePairs:
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event = evtDict[evt]
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## 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
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pairpath = os.path.join(subimgPath, f"{evt}")
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if not os.path.exists(pairpath):
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os.makedirs(pairpath)
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save_event_subimg(event, pairpath)
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## 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
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stdImgpath = stdDict[stdbcd]["imgpaths"]
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pstdpath = os.path.join(subimgPath, f"{stdbcd}")
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if not os.path.exists(pstdpath):
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os.makedirs(pstdpath)
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ii = 1
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for filepath in stdImgpath:
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stdpath = os.path.join(pstdpath, f"{stdbcd}_{ii}.png")
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shutil.copy2(filepath, stdpath)
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ii += 1
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##============================================ float32
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stdfeat = stdDict[stdbcd]["feats"]
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evtfeat = event["feats_compose"]
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matrix = 1 - cdist(stdfeat, evtfeat, 'cosine')
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simi_mean = np.mean(matrix)
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simi_max = np.max(matrix)
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stdfeatm = np.mean(stdfeat, axis=0, keepdims=True)
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evtfeatm = np.mean(evtfeat, axis=0, keepdims=True)
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simi_mfeat = 1- np.maximum(0.0, cdist(stdfeatm, evtfeatm, 'cosine'))
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rltdata.append((label, stdbcd, evt, simi_mean, simi_max, simi_mfeat[0,0]))
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##============================================ float16
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stdfeat_ft16 = stdfeat.astype(np.float16)
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evtfeat_ft16 = evtfeat.astype(np.float16)
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stdfeat_ft16 /= np.linalg.norm(stdfeat_ft16, axis=1)[:, None]
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evtfeat_ft16 /= np.linalg.norm(evtfeat_ft16, axis=1)[:, None]
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matrix_ft16 = 1 - cdist(stdfeat_ft16, evtfeat_ft16, 'cosine')
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simi_mean_ft16 = np.mean(matrix_ft16)
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simi_max_ft16 = np.max(matrix_ft16)
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stdfeatm_ft16 = np.mean(stdfeat_ft16, axis=0, keepdims=True)
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evtfeatm_ft16 = np.mean(evtfeat_ft16, axis=0, keepdims=True)
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simi_mfeat_ft16 = 1- np.maximum(0.0, cdist(stdfeatm_ft16, evtfeatm_ft16, 'cosine'))
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rltdata_ft16.append((label, stdbcd, evt, simi_mean_ft16, simi_max_ft16, simi_mfeat_ft16[0,0]))
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'''****************** uint8 is ok!!!!!! ******************'''
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##============================================ uint8
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# stdfeat_uint8, stdfeat_ft16_ = ft16_to_uint8(stdfeat_ft16)
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# evtfeat_uint8, evtfeat_ft16_ = ft16_to_uint8(evtfeat_ft16)
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stdfeat_uint8 = (stdfeat_ft16*128).astype(np.int8)
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evtfeat_uint8 = (evtfeat_ft16*128).astype(np.int8)
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stdfeat_ft16_ = stdfeat_uint8.astype(np.float16)/128
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evtfeat_ft16_ = evtfeat_uint8.astype(np.float16)/128
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absdiff = np.linalg.norm(stdfeat_ft16_ - stdfeat) / stdfeat.size
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matrix_ft16_ = 1 - cdist(stdfeat_ft16_, evtfeat_ft16_, 'cosine')
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simi_mean_ft16_ = np.mean(matrix_ft16_)
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simi_max_ft16_ = np.max(matrix_ft16_)
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stdfeatm_ft16_ = np.mean(stdfeat_ft16_, axis=0, keepdims=True)
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evtfeatm_ft16_ = np.mean(evtfeat_ft16_, axis=0, keepdims=True)
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simi_mfeat_ft16_ = 1- np.maximum(0.0, cdist(stdfeatm_ft16_, evtfeatm_ft16_, 'cosine'))
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rltdata_ft16_.append((label, stdbcd, evt, simi_mean_ft16_, simi_max_ft16_, simi_mfeat_ft16_[0,0]))
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def data_precision_compare(stdfeat, evtfeat, evtMessage, save=True):
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evt, stdbcd, label = evtMessage
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rltdata, rltdata_ft16, rltdata_ft16_ = [], [], []
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matrix = 1 - cdist(stdfeat, evtfeat, 'cosine')
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simi_mean = np.mean(matrix)
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simi_max = np.max(matrix)
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stdfeatm = np.mean(stdfeat, axis=0, keepdims=True)
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evtfeatm = np.mean(evtfeat, axis=0, keepdims=True)
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simi_mfeat = 1- np.maximum(0.0, cdist(stdfeatm, evtfeatm, 'cosine'))
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rltdata = [label, stdbcd, evt, simi_mean, simi_max, simi_mfeat[0,0]]
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tm = datetime.fromtimestamp(time.time()).strftime('%Y%m%d_%H%M%S')
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##================================================ save as float32,
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rppath = os.path.join(resultPath, f'{tm}.pickle')
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##================================================================= float16
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stdfeat_ft16 = stdfeat.astype(np.float16)
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evtfeat_ft16 = evtfeat.astype(np.float16)
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stdfeat_ft16 /= np.linalg.norm(stdfeat_ft16, axis=1)[:, None]
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evtfeat_ft16 /= np.linalg.norm(evtfeat_ft16, axis=1)[:, None]
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matrix_ft16 = 1 - cdist(stdfeat_ft16, evtfeat_ft16, 'cosine')
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simi_mean_ft16 = np.mean(matrix_ft16)
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simi_max_ft16 = np.max(matrix_ft16)
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stdfeatm_ft16 = np.mean(stdfeat_ft16, axis=0, keepdims=True)
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evtfeatm_ft16 = np.mean(evtfeat_ft16, axis=0, keepdims=True)
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simi_mfeat_ft16 = 1- np.maximum(0.0, cdist(stdfeatm_ft16, evtfeatm_ft16, 'cosine'))
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rltdata_ft16 = [label, stdbcd, evt, simi_mean_ft16, simi_max_ft16, simi_mfeat_ft16[0,0]]
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'''****************** uint8 is ok!!!!!! ******************'''
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##=================================================================== uint8
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# stdfeat_uint8, stdfeat_ft16_ = ft16_to_uint8(stdfeat_ft16)
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# evtfeat_uint8, evtfeat_ft16_ = ft16_to_uint8(evtfeat_ft16)
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stdfeat_uint8 = (stdfeat_ft16*128).astype(np.int8)
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evtfeat_uint8 = (evtfeat_ft16*128).astype(np.int8)
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stdfeat_ft16_ = stdfeat_uint8.astype(np.float16)/128
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evtfeat_ft16_ = evtfeat_uint8.astype(np.float16)/128
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absdiff = np.linalg.norm(stdfeat_ft16_ - stdfeat) / stdfeat.size
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matrix_ft16_ = 1 - cdist(stdfeat_ft16_, evtfeat_ft16_, 'cosine')
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simi_mean_ft16_ = np.mean(matrix_ft16_)
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simi_max_ft16_ = np.max(matrix_ft16_)
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stdfeatm_ft16_ = np.mean(stdfeat_ft16_, axis=0, keepdims=True)
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evtfeatm_ft16_ = np.mean(evtfeat_ft16_, axis=0, keepdims=True)
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simi_mfeat_ft16_ = 1- np.maximum(0.0, cdist(stdfeatm_ft16_, evtfeatm_ft16_, 'cosine'))
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rltdata_ft16_ = [label, stdbcd, evt, simi_mean_ft16_, simi_max_ft16_, simi_mfeat_ft16_[0,0]]
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if not save:
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return
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##========================================================= save as float32
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rppath = os.path.join(similPath, f'{evt}_ft32.pickle')
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with open(rppath, 'wb') as f:
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pickle.dump(rltdata, f)
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rtpath = os.path.join(resultPath, f'{tm}.txt')
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rtpath = os.path.join(similPath, f'{evt}_ft32.txt')
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with open(rtpath, 'w', encoding='utf-8') as f:
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for result in rltdata:
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part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result]
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@ -385,12 +372,12 @@ def one2one_eval(resultPath):
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f.write(line + '\n')
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##================================================ save as float16,
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rppath_ft16 = os.path.join(resultPath, f'{tm}_ft16.pickle')
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##========================================================= save as float16
|
||||
rppath_ft16 = os.path.join(similPath, f'{evt}_ft16.pickle')
|
||||
with open(rppath_ft16, 'wb') as f:
|
||||
pickle.dump(rltdata_ft16, f)
|
||||
|
||||
rtpath_ft16 = os.path.join(resultPath, f'{tm}_ft16.txt')
|
||||
rtpath_ft16 = os.path.join(similPath, f'{evt}_ft16.txt')
|
||||
with open(rtpath_ft16, 'w', encoding='utf-8') as f:
|
||||
for result in rltdata_ft16:
|
||||
part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result]
|
||||
@ -398,42 +385,145 @@ def one2one_eval(resultPath):
|
||||
f.write(line + '\n')
|
||||
|
||||
|
||||
##================================================ save as uint8,
|
||||
rppath_uint8 = os.path.join(resultPath, f'{tm}_uint8.pickle')
|
||||
##=========================================================== save as uint8
|
||||
rppath_uint8 = os.path.join(similPath, f'{evt}_uint8.pickle')
|
||||
with open(rppath_uint8, 'wb') as f:
|
||||
pickle.dump(rltdata_ft16_, f)
|
||||
|
||||
rtpath_uint8 = os.path.join(resultPath, f'{tm}_uint8.txt')
|
||||
rtpath_uint8 = os.path.join(similPath, f'{evt}_uint8.txt')
|
||||
with open(rtpath_uint8, 'w', encoding='utf-8') as f:
|
||||
for result in rltdata_ft16_:
|
||||
part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result]
|
||||
line = ', '.join(part)
|
||||
f.write(line + '\n')
|
||||
|
||||
|
||||
def one2one_simi():
|
||||
'''
|
||||
stdFeaturePath: 标准特征集地址
|
||||
eventDataPath: Event对象地址
|
||||
'''
|
||||
|
||||
stdBarcode = [p.stem for p in Path(stdFeaturePath).iterdir() if p.is_file() and p.suffix=='.pickle']
|
||||
|
||||
'''======1. 购物事件列表，该列表中的 Barcode 存在于标准的 stdBarcode 内 ==='''
|
||||
evtList = [(p.stem, p.stem.split('_')[-1]) for p in Path(eventDataPath).iterdir()
|
||||
if p.is_file()
|
||||
and p.suffix=='.pickle'
|
||||
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])
|
||||
|
||||
'''======2. 构建用于比对的标准特征字典 ============='''
|
||||
stdDict = {}
|
||||
for barcode in barcodes:
|
||||
stdpath = os.path.join(stdFeaturePath, barcode+'.pickle')
|
||||
with open(stdpath, 'rb') as f:
|
||||
stddata = pickle.load(f)
|
||||
stdDict[barcode] = stddata
|
||||
|
||||
|
||||
'''======3. 构建用于比对的操作事件字典 ============='''
|
||||
evtDict = {}
|
||||
for evtname, barcode in evtList:
|
||||
evtpath = os.path.join(eventDataPath, evtname+'.pickle')
|
||||
with open(evtpath, 'rb') as f:
|
||||
evtdata = pickle.load(f)
|
||||
evtDict[evtname] = evtdata
|
||||
|
||||
|
||||
'''======4.1 事件轨迹子图保存 ======================'''
|
||||
error_event = []
|
||||
for evtname, event in evtDict.items():
|
||||
pairpath = os.path.join(subimgPath, f"{evtname}")
|
||||
if not os.path.exists(pairpath):
|
||||
os.makedirs(pairpath)
|
||||
try:
|
||||
save_event_subimg(event, pairpath)
|
||||
except Exception as e:
|
||||
error_event.append(evtname)
|
||||
|
||||
img_path = os.path.join(imagePath, f"{evtname}")
|
||||
if not os.path.exists(img_path):
|
||||
os.makedirs(img_path)
|
||||
try:
|
||||
plot_save_image(event, img_path)
|
||||
except Exception as e:
|
||||
error_event.append(evtname)
|
||||
|
||||
|
||||
errfile = os.path.join(subimgPath, f'error_event.txt')
|
||||
with open(errfile, 'w', encoding='utf-8') as f:
|
||||
for line in error_event:
|
||||
f.write(line + '\n')
|
||||
|
||||
|
||||
'''======4.2 barcode 标准图像保存 =================='''
|
||||
# for stdbcd in barcodes:
|
||||
# stdImgpath = stdDict[stdbcd]["imgpaths"]
|
||||
# pstdpath = os.path.join(subimgPath, f"{stdbcd}")
|
||||
# if not os.path.exists(pstdpath):
|
||||
# os.makedirs(pstdpath)
|
||||
# ii = 1
|
||||
# for filepath in stdImgpath:
|
||||
# stdpath = os.path.join(pstdpath, f"{stdbcd}_{ii}.png")
|
||||
# shutil.copy2(filepath, stdpath)
|
||||
# ii += 1
|
||||
|
||||
'''======5 构造 3 个事件对: 扫 A 放 A, 扫 A 放 B, 合并 ===================='''
|
||||
AA_list = [(evtname, barcode, "same") for evtname, barcode in evtList]
|
||||
AB_list = []
|
||||
for evtname, barcode in evtList:
|
||||
dset = list(barcodes.symmetric_difference(set([barcode])))
|
||||
if len(dset):
|
||||
idx = random.randint(0, len(dset)-1)
|
||||
AB_list.append((evtname, dset[idx], "diff"))
|
||||
|
||||
mergePairs = AA_list + AB_list
|
||||
|
||||
'''======6 计算事件、标准特征集相似度 =================='''
|
||||
rltdata = []
|
||||
for i in range(len(mergePairs)):
|
||||
evtname, stdbcd, label = mergePairs[i]
|
||||
event = evtDict[evtname]
|
||||
|
||||
##============================================ float32
|
||||
stdfeat = stdDict[stdbcd]["feats_ft32"]
|
||||
evtfeat = event.feats_compose
|
||||
|
||||
if len(evtfeat)==0: continue
|
||||
|
||||
matrix = 1 - cdist(stdfeat, evtfeat, 'cosine')
|
||||
matrix[matrix < 0] = 0
|
||||
|
||||
|
||||
simi_mean = np.mean(matrix)
|
||||
simi_max = np.max(matrix)
|
||||
stdfeatm = np.mean(stdfeat, axis=0, keepdims=True)
|
||||
evtfeatm = np.mean(evtfeat, axis=0, keepdims=True)
|
||||
simi_mfeat = 1- np.maximum(0.0, cdist(stdfeatm, evtfeatm, 'cosine'))
|
||||
rltdata.append((label, stdbcd, evtname, simi_mean, simi_max, simi_mfeat[0,0]))
|
||||
|
||||
'''================ float32、16、int8 精度比较与存储 ============='''
|
||||
# data_precision_compare(stdfeat, evtfeat, mergePairs[i], save=True)
|
||||
|
||||
print("func: one2one_eval(), have finished!")
|
||||
|
||||
return rltdata
|
||||
|
||||
|
||||
|
||||
|
||||
def compute_precise_recall(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)
|
||||
|
||||
def compute_precise_recall(rltdata):
|
||||
Same, Cross = [], []
|
||||
for label, stdbcd, evt, simi_mean, simi_max, simi_mft in results:
|
||||
for label, stdbcd, evtname, simi_mean, simi_max, simi_mft in rltdata:
|
||||
if label == "same":
|
||||
Same.append(simi_mean)
|
||||
if label == "diff":
|
||||
Cross.append(simi_mean)
|
||||
|
||||
|
||||
|
||||
Same = np.array(Same)
|
||||
Cross = np.array(Cross)
|
||||
TPFN = len(Same)
|
||||
@ -480,115 +570,135 @@ def compute_precise_recall(pickpath):
|
||||
ax.set_xlabel(f"Same Num: {TPFN}, Cross Num: {TNFP}")
|
||||
ax.legend()
|
||||
plt.show()
|
||||
plt.savefig(f'./result/{file}_pr.png') # svg, png, pdf
|
||||
|
||||
rltpath = os.path.join(similPath, 'pr.png')
|
||||
plt.savefig(rltpath) # svg, png, pdf
|
||||
|
||||
|
||||
|
||||
def gen_eventdict(eventDatePath, saveimg=True):
|
||||
def gen_eventdict(sourcePath, saveimg=True):
|
||||
eventList = []
|
||||
# k = 0
|
||||
for datePath in eventDatePath:
|
||||
for eventName in os.listdir(datePath):
|
||||
errEvents = []
|
||||
k = 0
|
||||
for source_path in sourcePath:
|
||||
bname = os.path.basename(source_path)
|
||||
|
||||
pickpath = os.path.join(eventDataPath, f"{bname}.pickle")
|
||||
if os.path.isfile(pickpath): continue
|
||||
|
||||
# if bname != "20241129-100321-a9dae9e3-7db5-4e31-959c-d7dfc228923e_6972636670213":
|
||||
# continue
|
||||
|
||||
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!")
|
||||
|
||||
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!")
|
||||
# if saveimg and eventDict:
|
||||
# basename = os.path.basename(eventDict['filepath'])
|
||||
# savepath = os.path.join(subimgPath, basename)
|
||||
# if not os.path.exists(savepath):
|
||||
# os.makedirs(savepath)
|
||||
# save_event_subimg(eventDict, savepath)
|
||||
|
||||
try:
|
||||
event = Event(source_path)
|
||||
eventList.append(event)
|
||||
with open(pickpath, 'wb') as f:
|
||||
pickle.dump(event, f)
|
||||
print(bname)
|
||||
except Exception as e:
|
||||
errEvents.append(source_path)
|
||||
print(e)
|
||||
|
||||
# k += 1
|
||||
# if k==10:
|
||||
# break
|
||||
|
||||
# k += 1
|
||||
# if k==1:
|
||||
# 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)
|
||||
errfile = os.path.join(eventDataPath, f'error_events.txt')
|
||||
with open(errfile, 'w', encoding='utf-8') as f:
|
||||
for line in errEvents:
|
||||
f.write(line + '\n')
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
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:
|
||||
bcdList, event_spath = [], []
|
||||
for evtpath in eventSourcePath:
|
||||
for evtname in os.listdir(evtpath):
|
||||
evt = evtname.split('_')
|
||||
dirpath = os.path.join(evtpath, evtname)
|
||||
if os.path.isfile(dirpath): continue
|
||||
if len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10:
|
||||
bcdList.append(evt[-1])
|
||||
|
||||
bcdList.append(evt[-1])
|
||||
event_spath.append(os.path.join(evtpath, evtname))
|
||||
|
||||
bcdSet = set(bcdList)
|
||||
|
||||
|
||||
|
||||
|
||||
model = model_init(conf)
|
||||
|
||||
'''==== 1. 生成标准特征集, 只需运行一次 ==============='''
|
||||
genfeatures(model, stdSamplePath, stdBarcodePath, stdFeaturePath, bcdSet)
|
||||
'''==== 1. 生成标准特征集, 只需运行一次, 在 genfeats.py 中实现 ==========='''
|
||||
# gen_bcd_features(stdSamplePath, stdBarcodePath, stdFeaturePath, bcdSet)
|
||||
print("stdFeats have generated and saved!")
|
||||
|
||||
|
||||
'''==== 2. 生成事件字典, 只需运行一次 ==============='''
|
||||
|
||||
gen_eventdict(eventDatePath)
|
||||
gen_eventdict(event_spath)
|
||||
print("eventList have generated and saved!")
|
||||
|
||||
|
||||
'''==== 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
|
||||
pickpath = os.path.join(resultPath, filename)
|
||||
compute_precise_recall(pickpath)
|
||||
|
||||
|
||||
rltdata = one2one_simi()
|
||||
compute_precise_recall(rltdata)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
'''
|
||||
共6个地址：
|
||||
共7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
|
||||
(4) eventSourcePath: 事件地址
|
||||
(5) resultPath: 结果存储地址
|
||||
(6) eventDataPath: 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
|
||||
(7) subimgPath: 1:1比对购物事件轨迹、标准barcode所对应的 subimgs 存储地址
|
||||
(8) similPath: 1:1比对结果存储地址(事件级)
|
||||
'''
|
||||
|
||||
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)
|
||||
# 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"
|
||||
# eventDataPath = r"\\192.168.1.28\share\测试_202406\contrast\events"
|
||||
# subimgPath = r'\\192.168.1.28\share\测试_202406\contrast\subimgs'
|
||||
# similPath = r"D:\DetectTracking\contrast\result\pickle"
|
||||
# eventSourcePath = [r'\\192.168.1.28\share\测试_202406\1101\images']
|
||||
|
||||
stdSamplePath = r"\\192.168.1.28\share\数据\已完成数据\展厅数据\v1.0\比对数据\整理\zhantingBase"
|
||||
stdBarcodePath = r"D:\exhibition\dataset\bcdpath"
|
||||
stdFeaturePath = r"D:\exhibition\dataset\feats"
|
||||
resultPath = r"D:\exhibition\result\events"
|
||||
# eventSourcePath = [r'D:\exhibition\images\20241202']
|
||||
# eventSourcePath = [r"\\192.168.1.28\share\测试视频数据以及日志\各模块测试记录\展厅测试\1129_展厅模型v801测试组测试"]
|
||||
eventSourcePath = [r"\\192.168.1.28\share\测试视频数据以及日志\各模块测试记录\展厅测试\1126_展厅模型v801测试"]
|
||||
|
||||
|
||||
|
||||
'''定义当前事件存储地址及生成相应文件件'''
|
||||
eventDataPath = os.path.join(resultPath, "1126", "evtobjs")
|
||||
subimgPath = os.path.join(resultPath, "1126", "subimgs")
|
||||
imagePath = os.path.join(resultPath, "1126", "image")
|
||||
similPath = os.path.join(resultPath, "1126", "simidata")
|
||||
|
||||
if not os.path.exists(eventDataPath):
|
||||
os.makedirs(eventDataPath)
|
||||
if not os.path.exists(subimgPath):
|
||||
os.makedirs(subimgPath)
|
||||
if not os.path.exists(imagePath):
|
||||
os.makedirs(imagePath)
|
||||
if not os.path.exists(similPath):
|
||||
os.makedirs(similPath)
|
||||
|
||||
test_one2one()
|
||||
|
||||
|
||||
@ -106,7 +106,9 @@ def test_compare():
|
||||
|
||||
def one2one_pr(paths):
|
||||
paths = Path(paths)
|
||||
evtpaths = [p for p in paths.iterdir() if p.is_dir() and len(p.name.split('_'))>=2]
|
||||
|
||||
# evtpaths = [p for p in paths.iterdir() if p.is_dir() and len(p.name.split('_'))>=2]
|
||||
evtpaths = [p for p in paths.iterdir() if p.is_dir()]
|
||||
|
||||
events, similars = [], []
|
||||
|
||||
@ -120,14 +122,19 @@ def one2one_pr(paths):
|
||||
##===================================== 应用于1:n
|
||||
tpevents, fnevents, fpevents, tnevents = [], [], [], []
|
||||
tpsimi, fnsimi, tnsimi, fpsimi = [], [], [], []
|
||||
|
||||
other_event, other_simi = [], []
|
||||
|
||||
##===================================== barcodes总数、比对错误事件
|
||||
bcdList, one2onePath = [], []
|
||||
for path in evtpaths:
|
||||
barcode = path.stem.split('_')[-1]
|
||||
datapath = path.joinpath('process.data')
|
||||
|
||||
if not barcode.isdigit() or len(barcode)<10: continue
|
||||
if not datapath.is_file(): continue
|
||||
|
||||
|
||||
bcdList.append(barcode)
|
||||
|
||||
try:
|
||||
SimiDict = read_similar(datapath)
|
||||
except Exception as e:
|
||||
@ -150,13 +157,17 @@ def one2one_pr(paths):
|
||||
|
||||
one2oneAA.extend(simAA)
|
||||
one2oneAB.extend(simAB)
|
||||
|
||||
one2onePath.append(path.stem)
|
||||
|
||||
##===================================== 以下应用适用于展厅 1：N
|
||||
max_idx = similars.index(max(similars))
|
||||
max_sim = similars[max_idx]
|
||||
# max_bcd = barcodes[max_idx]
|
||||
|
||||
if path.stem.find('100321')>0:
|
||||
print("hhh")
|
||||
|
||||
|
||||
for i in range(len(one2one)):
|
||||
bcd, simi = barcodes[i], similars[i]
|
||||
if bcd==barcode and simi==max_sim:
|
||||
@ -172,7 +183,7 @@ def one2one_pr(paths):
|
||||
fp_simi.append(simi)
|
||||
fp_events.append(path.stem)
|
||||
|
||||
|
||||
|
||||
##===================================== 以下应用适用1:n
|
||||
events, evt_barcodes, evt_similars, evt_types = [], [], [], []
|
||||
for dt in one2n:
|
||||
@ -197,9 +208,13 @@ def one2one_pr(paths):
|
||||
elif bcd!=barcode and simi!=maxsim:
|
||||
tnsimi.append(simi)
|
||||
tnevents.append(path.stem)
|
||||
else:
|
||||
elif bcd!=barcode and simi==maxsim:
|
||||
fpsimi.append(simi)
|
||||
fpevents.append(path.stem)
|
||||
else:
|
||||
other_simi.append(simi)
|
||||
other_event.append(path.stem)
|
||||
|
||||
|
||||
'''命名规则:
|
||||
1:1 1:n 1:N
|
||||
@ -228,9 +243,12 @@ def one2one_pr(paths):
|
||||
FN_ = sum(np.array(one2oneAA) < th)
|
||||
TN_ = sum(np.array(one2oneAB) < th)
|
||||
PPrecise_.append(TP_/(TP_+FP_+1e-6))
|
||||
PRecall_.append(TP_/(TP_+FN_+1e-6))
|
||||
# PRecall_.append(TP_/(TP_+FN_+1e-6))
|
||||
PRecall_.append(TP_/(len(one2oneAA)+1e-6))
|
||||
|
||||
NPrecise_.append(TN_/(TN_+FN_+1e-6))
|
||||
NRecall_.append(TN_/(TN_+FP_+1e-6))
|
||||
# NRecall_.append(TN_/(TN_+FP_+1e-6))
|
||||
NRecall_.append(TN_/(len(one2oneAB)+1e-6))
|
||||
|
||||
'''============================= 1:n'''
|
||||
TP = sum(np.array(tpsimi) >= th)
|
||||
@ -238,9 +256,12 @@ def one2one_pr(paths):
|
||||
FN = sum(np.array(fnsimi) < th)
|
||||
TN = sum(np.array(tnsimi) < th)
|
||||
PPrecise.append(TP/(TP+FP+1e-6))
|
||||
PRecall.append(TP/(TP+FN+1e-6))
|
||||
# PRecall.append(TP/(TP+FN+1e-6))
|
||||
PRecall.append(TP/(len(tpsimi)+len(fnsimi)+1e-6))
|
||||
|
||||
NPrecise.append(TN/(TN+FN+1e-6))
|
||||
NRecall.append(TN/(TN+FP+1e-6))
|
||||
# NRecall.append(TN/(TN+FP+1e-6))
|
||||
NRecall.append(TN/(len(tnsimi)+len(fpsimi)+1e-6))
|
||||
|
||||
|
||||
'''============================= 1:N 展厅'''
|
||||
@ -249,9 +270,12 @@ def one2one_pr(paths):
|
||||
FNX = sum(np.array(fn_simi) < th)
|
||||
TNX = sum(np.array(tn_simi) < th)
|
||||
PPreciseX.append(TPX/(TPX+FPX+1e-6))
|
||||
PRecallX.append(TPX/(TPX+FNX+1e-6))
|
||||
# PRecallX.append(TPX/(TPX+FNX+1e-6))
|
||||
PRecallX.append(TPX/(len(tp_simi)+len(fn_simi)+1e-6))
|
||||
|
||||
NPreciseX.append(TNX/(TNX+FNX+1e-6))
|
||||
NRecallX.append(TNX/(TNX+FPX+1e-6))
|
||||
# NRecallX.append(TNX/(TNX+FPX+1e-6))
|
||||
NRecallX.append(TNX/(len(tn_simi)+len(fp_simi)+1e-6))
|
||||
|
||||
'''============================= 1:1 曲线'''
|
||||
fig, ax = plt.subplots()
|
||||
@ -262,8 +286,8 @@ def one2one_pr(paths):
|
||||
ax.set_xlim([0, 1])
|
||||
ax.set_ylim([0, 1])
|
||||
ax.grid(True)
|
||||
ax.set_title('Precise & Recall')
|
||||
ax.set_xlabel(f"Num: {len(evtpaths)}")
|
||||
ax.set_title('1:1 Precise & Recall')
|
||||
ax.set_xlabel(f"Event Num: {len(one2oneAA)}")
|
||||
ax.legend()
|
||||
plt.show()
|
||||
|
||||
@ -286,8 +310,8 @@ def one2one_pr(paths):
|
||||
ax.set_xlim([0, 1])
|
||||
ax.set_ylim([0, 1])
|
||||
ax.grid(True)
|
||||
ax.set_title('Precise & Recall')
|
||||
ax.set_xlabel(f"Num: {len(evtpaths)}")
|
||||
ax.set_title('1:n Precise & Recall')
|
||||
ax.set_xlabel(f"Event Num: {len(one2oneAA)}")
|
||||
ax.legend()
|
||||
plt.show()
|
||||
|
||||
@ -317,8 +341,8 @@ def one2one_pr(paths):
|
||||
ax.set_xlim([0, 1])
|
||||
ax.set_ylim([0, 1])
|
||||
ax.grid(True)
|
||||
ax.set_title('Precise & Recall')
|
||||
ax.set_xlabel(f"Num: {len(evtpaths)}")
|
||||
ax.set_title('1:N Precise & Recall')
|
||||
ax.set_xlabel(f"Event Num: {len(one2oneAA)}")
|
||||
ax.legend()
|
||||
plt.show()
|
||||
|
||||
@ -338,16 +362,23 @@ def one2one_pr(paths):
|
||||
axes[1, 1].set_title('FN')
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
# bcdSet = set(bcdList)
|
||||
# one2nErrFile = str(paths.joinpath("one_2_Small_n_Error.txt"))
|
||||
# with open(one2nErrFile, "w") as file:
|
||||
# for item in fnevents:
|
||||
# file.write(item + "\n")
|
||||
|
||||
# one2NErrFile = str(paths.joinpath("one_2_Big_N_Error.txt"))
|
||||
# with open(one2NErrFile, "w") as file:
|
||||
# for item in fn_events:
|
||||
# file.write(item + "\n")
|
||||
|
||||
print('Done!')
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
evtpaths = r"\\192.168.1.28\share\测试视频数据以及日志\各模块测试记录\展厅测试\1120_展厅模型v801测试\扫A放A"
|
||||
evtpaths = r"\\192.168.1.28\share\测试视频数据以及日志\各模块测试记录\展厅测试\1129_展厅模型v801测试组测试"
|
||||
one2one_pr(evtpaths)
|
||||
|
||||
|
||||
|
||||
BIN
contrast/utils/__pycache__/event.cpython-39.pyc
Normal file
BIN
contrast/utils/__pycache__/event.cpython-39.pyc
Normal file
Binary file not shown.
179
contrast/utils/event.py
Normal file
179
contrast/utils/event.py
Normal file
@ -0,0 +1,179 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Tue Nov 26 17:35:05 2024
|
||||
|
||||
@author: ym
|
||||
"""
|
||||
import os
|
||||
import numpy as np
|
||||
from pathlib import Path
|
||||
|
||||
import sys
|
||||
sys.path.append(r"D:\DetectTracking")
|
||||
from tracking.utils.read_data import extract_data, read_tracking_output, read_similar
|
||||
|
||||
IMG_FORMAT = ['.bmp', '.jpg', '.jpeg', '.png']
|
||||
VID_FORMAT = ['.mp4', '.avi']
|
||||
|
||||
class Event:
|
||||
def __init__(self, eventpath, stype="data"):
|
||||
'''stype: str, 'video', 'image', 'data', '''
|
||||
|
||||
self.eventpath = eventpath
|
||||
self.evtname = str(Path(eventpath).stem)
|
||||
self.barcode = ''
|
||||
self.evtType = ''
|
||||
|
||||
'''=========== path of image and video =========== '''
|
||||
self.back_videopath = ''
|
||||
self.front_videopath = ''
|
||||
self.back_imgpaths = []
|
||||
self.front_imgpaths = []
|
||||
|
||||
'''=========== process.data ==============================='''
|
||||
self.one2one = None
|
||||
self.one2n = None
|
||||
|
||||
'''=========== 0/1_track.data ============================='''
|
||||
self.back_yolobboxes = np.empty((0, 6), dtype=np.float64)
|
||||
self.back_yolofeats = np.empty((0, 256), dtype=np.float64)
|
||||
self.back_trackerboxes = np.empty((0, 9), dtype=np.float64)
|
||||
self.back_trackerfeats = np.empty((0, 256), dtype=np.float64)
|
||||
self.back_trackingboxes = np.empty((0, 9), dtype=np.float64)
|
||||
self.back_trackingfeats = np.empty((0, 256), dtype=np.float64)
|
||||
|
||||
self.front_yolobboxes = np.empty((0, 6), dtype=np.float64)
|
||||
self.front_yolofeats = np.empty((0, 256), dtype=np.float64)
|
||||
self.front_trackerboxes = np.empty((0, 9), dtype=np.float64)
|
||||
self.front_trackerfeats = np.empty((0, 256), dtype=np.float64)
|
||||
self.front_trackingboxes = np.empty((0, 9), dtype=np.float64)
|
||||
self.front_trackingfeats = np.empty((0, 256), dtype=np.float64)
|
||||
|
||||
'''=========== 0/1_tracking_output.data ==================='''
|
||||
self.back_boxes = np.empty((0, 9), dtype=np.float64)
|
||||
self.front_boxes = np.empty((0, 9), dtype=np.float64)
|
||||
self.back_feats = np.empty((0, 256), dtype=np.float64)
|
||||
self.front_feats = np.empty((0, 256), dtype=np.float64)
|
||||
self.feats_compose = np.empty((0, 256), dtype=np.float64)
|
||||
self.feats_select = np.empty((0, 256), dtype=np.float64)
|
||||
|
||||
if stype=="data":
|
||||
self.from_datafile(eventpath)
|
||||
|
||||
if stype=="video":
|
||||
self.from_video(eventpath)
|
||||
|
||||
if stype=="image":
|
||||
self.from_image(eventpath)
|
||||
|
||||
def from_datafile(self, eventpath):
|
||||
evtList = self.evtname.split('_')
|
||||
if len(evtList)>=2 and len(evtList[-1])>=10 and evtList[-1].isdigit():
|
||||
self.barcode = evtList[-1]
|
||||
if len(evtList)==3 and evtList[-1]== evtList[-2]:
|
||||
self.evtType = 'input'
|
||||
else:
|
||||
self.evtType = 'other'
|
||||
|
||||
'''================ path of image ============='''
|
||||
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
|
||||
if len(name.split('_')) != 3 and not name.split('_')[3].isdigit(): 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)
|
||||
## 生成依据帧 ID 排序的前后摄图像地址列表
|
||||
frontIdx = np.argsort(np.array(frontFid))
|
||||
backIdx = np.argsort(np.array(backFid))
|
||||
self.front_imgpaths = [frontImgs[i] for i in frontIdx]
|
||||
self.back_imgpaths = [backImgs[i] for i in backIdx]
|
||||
|
||||
|
||||
'''================ path of video ============='''
|
||||
for vidname in os.listdir(eventpath):
|
||||
name, ext = os.path.splitext(vidname)
|
||||
if ext not in VID_FORMAT: continue
|
||||
vidpath = os.path.join(eventpath, vidname)
|
||||
|
||||
CamerType = name.split('_')[0]
|
||||
if CamerType == '0':
|
||||
self.back_videopath = vidpath
|
||||
if CamerType == '1':
|
||||
self.front_videopath = vidpath
|
||||
|
||||
'''================ process.data ============='''
|
||||
procpath = Path(eventpath).joinpath('process.data')
|
||||
if procpath.is_file():
|
||||
SimiDict = read_similar(procpath)
|
||||
self.one2one = SimiDict['one2one']
|
||||
self.one2n = SimiDict['one2n']
|
||||
|
||||
|
||||
'''=========== 0/1_track.data & 0/1_tracking_output.data ======='''
|
||||
for dataname in os.listdir(eventpath):
|
||||
datapath = os.path.join(eventpath, dataname)
|
||||
if not os.path.isfile(datapath): continue
|
||||
CamerType = dataname.split('_')[0]
|
||||
|
||||
'''========== 0/1_track.data =========='''
|
||||
if dataname.find("_track.data")>0:
|
||||
bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, tracking_feat_dict = extract_data(datapath)
|
||||
if CamerType == '0':
|
||||
self.back_yolobboxes = bboxes
|
||||
self.back_yolofeats = ffeats
|
||||
self.back_trackerboxes = trackerboxes
|
||||
self.back_trackerfeats = tracker_feat_dict
|
||||
self.back_trackingboxes = trackingboxes
|
||||
self.back_trackingfeats = tracking_feat_dict
|
||||
if CamerType == '1':
|
||||
self.front_yolobboxes = bboxes
|
||||
self.front_yolofeats = ffeats
|
||||
self.front_trackerboxes = trackerboxes
|
||||
self.front_trackerfeats = tracker_feat_dict
|
||||
self.front_trackingboxes = trackingboxes
|
||||
self.front_trackingfeats = tracking_feat_dict
|
||||
|
||||
'''========== 0/1_tracking_output.data =========='''
|
||||
if dataname.find("_tracking_output.data")>0:
|
||||
tracking_output_boxes, tracking_output_feats = read_tracking_output(datapath)
|
||||
if CamerType == '0':
|
||||
self.back_boxes = tracking_output_boxes
|
||||
self.back_feats = tracking_output_feats
|
||||
elif CamerType == '1':
|
||||
self.front_boxes = tracking_output_boxes
|
||||
self.front_feats = tracking_output_feats
|
||||
self.select_feat()
|
||||
self.compose_feats()
|
||||
|
||||
|
||||
def compose_feats(self):
|
||||
'''事件的特征集成'''
|
||||
feats_compose = np.empty((0, 256), dtype=np.float64)
|
||||
if len(self.front_feats):
|
||||
feats_compose = np.concatenate((feats_compose, self.front_feats), axis=0)
|
||||
if len(self.back_feats):
|
||||
feats_compose = np.concatenate((feats_compose, self.back_feats), axis=0)
|
||||
self.feats_compose = feats_compose
|
||||
|
||||
def select_feats(self):
|
||||
'''事件的特征选择'''
|
||||
if len(self.front_feats):
|
||||
self.feats_select = self.front_feats
|
||||
else:
|
||||
self.feats_select = self.back_feats
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user