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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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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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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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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##============================================ 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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##================================================================= 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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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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'''****************** 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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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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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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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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##========================================================= 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
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rppath_ft16 = os.path.join(similPath, f'{evt}_ft16.pickle')
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with open(rppath_ft16, 'wb') as f:
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pickle.dump(rltdata_ft16, f)
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|
||||
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]
|
||||
@ -398,12 +385,12 @@ 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]
|
||||
@ -411,29 +398,132 @@ def one2one_eval(resultPath):
|
||||
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(eventFeatPath, f"{eventName}.pickle")
|
||||
if os.path.isfile(pickpath):
|
||||
continue
|
||||
eventPath = os.path.join(datePath, eventName)
|
||||
pickpath = os.path.join(eventDataPath, f"{bname}.pickle")
|
||||
if os.path.isfile(pickpath): continue
|
||||
|
||||
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 bname != "20241129-100321-a9dae9e3-7db5-4e31-959c-d7dfc228923e_6972636670213":
|
||||
# continue
|
||||
|
||||
# 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)
|
||||
|
||||
# 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
|
||||
|
||||
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])
|
||||
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"
|
||||
# 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\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\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 = [], []
|
||||
|
||||
@ -120,7 +122,10 @@ 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')
|
||||
@ -128,6 +133,8 @@ def one2one_pr(paths):
|
||||
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:
|
||||
@ -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
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
74
pipeline.py
74
pipeline.py
@ -5,11 +5,19 @@ Created on Sun Sep 29 08:59:21 2024
|
||||
@author: ym
|
||||
"""
|
||||
import os
|
||||
import sys
|
||||
import cv2
|
||||
import pickle
|
||||
import argparse
|
||||
import numpy as np
|
||||
from pathlib import Path
|
||||
from track_reid import parse_opt, yolo_resnet_tracker
|
||||
from track_reid import parse_opt
|
||||
from track_reid import yolo_resnet_tracker
|
||||
# FILE = Path(__file__).resolve()
|
||||
# ROOT = FILE.parents[0] # YOLOv5 root directory
|
||||
# if str(ROOT) not in sys.path:
|
||||
# sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
# ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
||||
|
||||
from tracking.dotrack.dotracks_back import doBackTracks
|
||||
from tracking.dotrack.dotracks_front import doFrontTracks
|
||||
@ -35,38 +43,27 @@ def get_interbcd_inputenents():
|
||||
|
||||
return input_enents
|
||||
|
||||
def pipeline(eventpath, stdfeat_path=None, SourceType = "image"):
|
||||
'''
|
||||
inputs:
|
||||
eventpath: 事件文件夹
|
||||
stdfeat_path: 标准特征文件地址
|
||||
outputs:
|
||||
|
||||
'''
|
||||
# SourceType = "image" # image
|
||||
# eventpath = r"\\192.168.1.28\share\测试_202406\0918\images1\20240918-110822-1bc3902e-5a8e-4e23-8eca-fb3f02738551_6938314601726"
|
||||
|
||||
savepath = r"D:\contrast\detect"
|
||||
|
||||
opt = parse_opt()
|
||||
optdict = vars(opt)
|
||||
optdict["project"] = savepath
|
||||
|
||||
eventname = os.path.basename(eventpath)
|
||||
# barcode = eventname.split('_')[-1]
|
||||
|
||||
|
||||
def pipeline(
|
||||
eventpath,
|
||||
savepath,
|
||||
SourceType = "image", # video
|
||||
stdfeat_path = None
|
||||
):
|
||||
if SourceType == "video":
|
||||
vpaths = get_video_pairs(eventpath)
|
||||
elif SourceType == "image":
|
||||
vpaths = get_image_pairs(eventpath)
|
||||
|
||||
|
||||
'''======== 函数 yolo_resnet_tracker() 的参数字典 ========'''
|
||||
opt = parse_opt()
|
||||
optdict = vars(opt)
|
||||
optdict["is_save_img"] = True
|
||||
optdict["is_save_video"] = True
|
||||
|
||||
event_tracks = []
|
||||
for vpath in vpaths:
|
||||
'''事件结果文件夹'''
|
||||
save_dir_event = Path(savepath) / Path(eventname)
|
||||
save_dir_event = Path(savepath) / Path(os.path.basename(eventpath))
|
||||
if isinstance(vpath, list):
|
||||
save_dir_video = save_dir_event / Path("images")
|
||||
else:
|
||||
@ -78,8 +75,7 @@ def pipeline(eventpath, stdfeat_path=None, SourceType = "image"):
|
||||
'''Yolo + Resnet + Tracker'''
|
||||
optdict["source"] = vpath
|
||||
optdict["save_dir"] = save_dir_video
|
||||
optdict["is_save_img"] = True
|
||||
optdict["is_save_video"] = True
|
||||
|
||||
|
||||
tracksdict = yolo_resnet_tracker(**optdict)
|
||||
|
||||
@ -138,6 +134,7 @@ def pipeline(eventpath, stdfeat_path=None, SourceType = "image"):
|
||||
|
||||
|
||||
'''前后摄轨迹选择'''
|
||||
|
||||
if stdfeat_path is not None:
|
||||
with open(stdfeat_path, 'rb') as f:
|
||||
featDict = pickle.load(f)
|
||||
@ -171,21 +168,28 @@ def main_loop():
|
||||
stdfeat_path = os.path.join(bcdpath, f"{bcd}.pickle")
|
||||
input_enents.append((event_path, stdfeat_path))
|
||||
|
||||
|
||||
parmDict = {}
|
||||
parmDict["SourceType"] = "image"
|
||||
parmDict["savepath"] = r"D:\contrast\detect"
|
||||
for eventpath, stdfeat_path in input_enents:
|
||||
pipeline(eventpath, stdfeat_path, SourceType)
|
||||
parmDict["eventpath"] = eventpath
|
||||
parmDict["stdfeat_path"] = stdfeat_path
|
||||
|
||||
pipeline(**parmDict)
|
||||
|
||||
|
||||
def main():
|
||||
eventpath = r"D:\datasets\ym\exhibition\175836"
|
||||
'''
|
||||
函数：pipeline()，遍历事件文件夹，选择类型 image 或 video,
|
||||
'''
|
||||
parmDict = {}
|
||||
parmDict["eventpath"] = 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\20241121-144855-dce94b09-1100-43f1-92e8-33a1b538b159_6924743915848_6924743915848"
|
||||
|
||||
eventpath = 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\20241121-144855-dce94b09-1100-43f1-92e8-33a1b538b159_6924743915848_6924743915848"
|
||||
|
||||
SourceType = 'image'
|
||||
stdfeat_path = None
|
||||
|
||||
pipeline(eventpath, stdfeat_path, SourceType)
|
||||
parmDict["savepath"] = r"D:\contrast\detect"
|
||||
parmDict["SourceType"] = "image" # video, image
|
||||
parmDict["stdfeat_path"] = None
|
||||
|
||||
pipeline(**parmDict)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
@ -207,11 +207,6 @@ def state_measure(periods, weights, hands, spath=None):
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
'''序列索引号， 相机类型，时间戳, 单摄状态1、单摄状态2、CV综合状态、综合数据类型、综合状态
|
||||
0 1 2 3 4 5 6 7
|
||||
'''
|
||||
@ -428,8 +423,8 @@ def run_tracking(trackboxes, MotionSlice):
|
||||
|
||||
|
||||
def show_seri():
|
||||
datapath = r"\\192.168.1.28\share\realtime\eventdata\1731316835560"
|
||||
savedir = r"D:\DetectTracking\realtime"
|
||||
datapath = r"\\192.168.1.28\share\个人文件\wqg\realtime\eventdata\1731316835560"
|
||||
savedir = r"D:\DetectTracking\realtime\1"
|
||||
|
||||
|
||||
imgdir = datapath.split('\\')[-2] + "_" + datapath.split('\\')[-1]
|
||||
@ -475,7 +470,7 @@ def show_seri():
|
||||
|
||||
|
||||
def main():
|
||||
runyolo()
|
||||
# runyolo()
|
||||
|
||||
show_seri()
|
||||
|
||||
|
||||
156
track_reid.py
156
track_reid.py
@ -64,6 +64,9 @@ from contrast.feat_extract.config import config as conf
|
||||
from contrast.feat_extract.inference import FeatsInterface
|
||||
ReIDEncoder = FeatsInterface(conf)
|
||||
|
||||
IMG_FORMATS = '.bmp', '.dng', '.jpeg', '.jpg', '.mpo', '.png', '.tif', '.tiff', '.webp', '.pfm' # include image suffixes
|
||||
VID_FORMATS = '.asf', '.avi', '.gif', '.m4v', '.mkv', '.mov', '.mp4', '.mpeg', '.mpg', '.ts', '.wmv' # include video suffixes
|
||||
|
||||
# from tracking.trackers.reid.reid_interface import ReIDInterface
|
||||
# from tracking.trackers.reid.config import config as ReIDConfig
|
||||
# ReIDEncoder = ReIDInterface(ReIDConfig)
|
||||
@ -141,6 +144,9 @@ def yolo_resnet_tracker(
|
||||
name='exp', # save results to project/name
|
||||
save_dir = '',
|
||||
|
||||
is_save_img = False,
|
||||
is_save_video = True,
|
||||
|
||||
tracker_yaml = "./tracking/trackers/cfg/botsort.yaml",
|
||||
imgsz=(640, 640), # inference size (height, width)
|
||||
conf_thres=0.25, # confidence threshold
|
||||
@ -153,18 +159,15 @@ def yolo_resnet_tracker(
|
||||
save_csv=False, # save results in CSV format
|
||||
save_conf=False, # save confidences in --save-txt labels
|
||||
save_crop=False, # save cropped prediction boxes
|
||||
|
||||
nosave=False, # do not save images/videos
|
||||
is_save_img = False,
|
||||
is_save_video = True,
|
||||
|
||||
update=False, # update all models
|
||||
exist_ok=False, # existing project/name ok, do not increment
|
||||
|
||||
classes=None, # filter by class: --class 0, or --class 0 2 3
|
||||
agnostic_nms=False, # class-agnostic NMS
|
||||
augment=False, # augmented inference
|
||||
visualize=False, # visualize features
|
||||
update=False, # update all models
|
||||
exist_ok=False, # existing project/name ok, do not increment
|
||||
|
||||
line_thickness=3, # bounding box thickness (pixels)
|
||||
hide_labels=False, # hide labels
|
||||
hide_conf=False, # hide confidencesL
|
||||
@ -180,6 +183,8 @@ def yolo_resnet_tracker(
|
||||
stride, names, pt = model.stride, model.names, model.pt
|
||||
imgsz = check_img_size(imgsz, s=stride) # check image size
|
||||
|
||||
|
||||
|
||||
# Dataloader
|
||||
bs = 1 # batch_size
|
||||
|
||||
@ -203,8 +208,8 @@ def yolo_resnet_tracker(
|
||||
|
||||
# Inference
|
||||
with dt[1]:
|
||||
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
|
||||
pred = model(im, augment=augment, visualize=visualize)
|
||||
visualize = increment_path(project / Path(path).stem, mkdir=True) if visualize else False
|
||||
pred = model(im, augment=augment, visualize=False)
|
||||
|
||||
# NMS
|
||||
with dt[2]:
|
||||
@ -273,19 +278,27 @@ def yolo_resnet_tracker(
|
||||
annotator.box_label(xyxy, label, color=color)
|
||||
|
||||
'''====== Save results (image and video) ======'''
|
||||
save_path = str(save_dir / Path(path).name) # 带有后缀名
|
||||
# save_path = str(save_dir / Path(path).name) # 带有后缀名
|
||||
im0 = annotator.result()
|
||||
if is_save_img:
|
||||
save_path_img, ext = os.path.splitext(save_path)
|
||||
save_path_img = str(save_dir / Path(path).stem)
|
||||
if dataset.mode == 'image':
|
||||
imgpath = save_path_img + ".png"
|
||||
else:
|
||||
imgpath = save_path_img + f"_{frameId}.png"
|
||||
cv2.imwrite(Path(imgpath), im0)
|
||||
|
||||
if dataset.mode == 'video' and is_save_video:
|
||||
if vid_path[i] != save_path: # new video
|
||||
vid_path[i] = save_path
|
||||
# if dataset.mode == 'video' and is_save_video:
|
||||
|
||||
if is_save_video:
|
||||
if dataset.mode == 'video':
|
||||
vdieo_path = str(save_dir / Path(path).stem) + '.mp4' # 带有后缀名
|
||||
else:
|
||||
videoname = str(Path(path).stem).split('_')[0] + '.mp4'
|
||||
vdieo_path = str(save_dir / videoname)
|
||||
|
||||
if vid_path[i] != vdieo_path: # new video
|
||||
vid_path[i] = vdieo_path
|
||||
if isinstance(vid_writer[i], cv2.VideoWriter):
|
||||
vid_writer[i].release() # release previous video writer
|
||||
if vid_cap: # video
|
||||
@ -293,9 +306,9 @@ def yolo_resnet_tracker(
|
||||
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
else: # stream
|
||||
fps, w, h = 30, im0.shape[1], im0.shape[0]
|
||||
save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
|
||||
vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
|
||||
fps, w, h = 25, im0.shape[1], im0.shape[0]
|
||||
vdieo_path = str(Path(vdieo_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
|
||||
vid_writer[i] = cv2.VideoWriter(vdieo_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
|
||||
vid_writer[i].write(im0)
|
||||
|
||||
# Print time (inference-only)
|
||||
@ -344,6 +357,9 @@ def run(
|
||||
vid_stride=1, # video frame-rate stride
|
||||
data=ROOT / 'data/coco128.yaml', # dataset.yaml path
|
||||
):
|
||||
'''
|
||||
source: 视频文件或图像列表
|
||||
'''
|
||||
source = str(source)
|
||||
# filename = os.path.split(source)[-1]
|
||||
|
||||
@ -356,6 +372,9 @@ def run(
|
||||
source = check_file(source) # download
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# spth = source.split('\\')[-2] + "_" + Path(source).stem
|
||||
save_dir = Path(project) / Path(source.split('\\')[-2] + "_" + str(Path(source).stem))
|
||||
# save_dir = Path(project) / Path(source).stem
|
||||
@ -440,8 +459,7 @@ def run(
|
||||
|
||||
|
||||
|
||||
p = Path(p) # to Path
|
||||
save_path = str(save_dir / p.name) # im.jpg
|
||||
|
||||
s += '%gx%g ' % im.shape[2:] # print string
|
||||
|
||||
# im0_ant = im0.copy()
|
||||
@ -552,28 +570,33 @@ def run(
|
||||
|
||||
# Save results (image and video with tracking)
|
||||
im0 = annotator.result()
|
||||
save_path_img, ext = os.path.splitext(save_path)
|
||||
|
||||
p = Path(p) # to Path
|
||||
save_path = str(save_dir / p.name) # im.jpg
|
||||
if save_img:
|
||||
save_path_img, ext = os.path.splitext(save_path)
|
||||
if dataset.mode == 'image':
|
||||
imgpath = save_path_img + f"_{dataset}.png"
|
||||
imgpath = save_path_img + ".png"
|
||||
else:
|
||||
imgpath = save_path_img + f"_{frameId}.png"
|
||||
|
||||
cv2.imwrite(Path(imgpath), im0)
|
||||
|
||||
if vid_path[i] != save_path: # new video
|
||||
vid_path[i] = save_path
|
||||
if isinstance(vid_writer[i], cv2.VideoWriter):
|
||||
vid_writer[i].release() # release previous video writer
|
||||
if vid_cap: # video
|
||||
fps = vid_cap.get(cv2.CAP_PROP_FPS)
|
||||
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
else: # stream
|
||||
fps, w, h = 30, im0.shape[1], im0.shape[0]
|
||||
save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
|
||||
vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
|
||||
vid_writer[i].write(im0)
|
||||
if dataset.mode == 'video':
|
||||
|
||||
if vid_path[i] != save_path: # new video
|
||||
vid_path[i] = save_path
|
||||
if isinstance(vid_writer[i], cv2.VideoWriter):
|
||||
vid_writer[i].release() # release previous video writer
|
||||
if vid_cap: # video
|
||||
fps = vid_cap.get(cv2.CAP_PROP_FPS)
|
||||
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
else: # stream
|
||||
fps, w, h = 30, im0.shape[1], im0.shape[0]
|
||||
save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
|
||||
vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
|
||||
vid_writer[i].write(im0)
|
||||
|
||||
|
||||
# Print time (inference-only)
|
||||
LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
|
||||
@ -672,40 +695,25 @@ def parse_opt():
|
||||
print_args(vars(opt))
|
||||
return opt
|
||||
|
||||
def find_files_in_nested_dirs(root_dir):
|
||||
def find_video_imgs(root_dir):
|
||||
all_files = []
|
||||
extensions = ['.mp4']
|
||||
for dirpath, dirnames, filenames in os.walk(root_dir):
|
||||
for filename in filenames:
|
||||
file, ext = os.path.splitext(filename)
|
||||
if ext in extensions:
|
||||
if ext in IMG_FORMATS + VID_FORMATS:
|
||||
all_files.append(os.path.join(dirpath, filename))
|
||||
return all_files
|
||||
|
||||
print('=======')
|
||||
|
||||
def main(opt):
|
||||
|
||||
def main():
|
||||
'''
|
||||
run(): 单张图像或单个视频文件的推理，不支持图像序列，
|
||||
'''
|
||||
|
||||
check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))
|
||||
optdict = vars(opt)
|
||||
|
||||
p = r"D:\datasets\ym"
|
||||
p = r"D:\exhibition\images\153112511_0_seek_105.mp4"
|
||||
|
||||
optdict["project"] = r"D:\exhibition\result"
|
||||
|
||||
files = []
|
||||
if os.path.isdir(p):
|
||||
files.extend(sorted(glob.glob(os.path.join(p, '*.*'))))
|
||||
optdict["source"] = files
|
||||
elif os.path.isfile(p):
|
||||
optdict["source"] = p
|
||||
|
||||
run(**optdict)
|
||||
|
||||
|
||||
def main_loop(opt):
|
||||
check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))
|
||||
|
||||
opt = parse_opt()
|
||||
optdict = vars(opt)
|
||||
|
||||
# p = r"D:\datasets\ym\永辉测试数据_比对"
|
||||
@ -714,28 +722,22 @@ def main_loop(opt):
|
||||
# p = r"D:\datasets\ym\实验室测试"
|
||||
# p = r"D:\datasets\ym\永辉双摄视频\新建文件夹"
|
||||
# p = r"\\192.168.1.28\share\测试_202406\0723\0723_2\20240723-112522_"
|
||||
p = r"D:\datasets\ym\联华中环"
|
||||
# p = r"D:\datasets\ym\联华中环"
|
||||
p = r"D:\exhibition\images\153112511_0_seek_105.mp4"
|
||||
# p = r"D:\exhibition\images\image"
|
||||
|
||||
k = 0
|
||||
|
||||
optdict["project"] = r"D:\exhibition\result"
|
||||
if os.path.isdir(p):
|
||||
files = find_files_in_nested_dirs(p)
|
||||
|
||||
# files = [r"D:\datasets\ym\广告板遮挡测试\8\6926636301004_20240508-175300_back_addGood_70f754088050_215_17327712807.mp4",
|
||||
# r"D:\datasets\ym\videos\标记视频\test_20240402-173935_6920152400975_back_174037372.mp4",
|
||||
# r"D:\datasets\ym\videos\标记视频\test_20240402-173935_6920152400975_front_174037379.mp4",
|
||||
# r"D:\datasets\ym\广告板遮挡测试\8\2500441577966_20240508-175946_front_addGood_70f75407b7ae_155_17788571404.mp4"
|
||||
# ]
|
||||
|
||||
# files = [r"\\192.168.1.28\share\测试_202406\0723\0723_2\20240723-095838_\1_seek_193.mp4"]
|
||||
|
||||
|
||||
files = find_video_imgs(p)
|
||||
k = 0
|
||||
for file in files:
|
||||
optdict["source"] = file
|
||||
run(**optdict)
|
||||
|
||||
# k += 1
|
||||
# if k == 10:
|
||||
# break
|
||||
k += 1
|
||||
if k == 1:
|
||||
break
|
||||
elif os.path.isfile(p):
|
||||
optdict["source"] = p
|
||||
run(**optdict)
|
||||
@ -744,10 +746,8 @@ def main_loop(opt):
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
opt = parse_opt()
|
||||
|
||||
main(opt)
|
||||
# main_loop(opt)
|
||||
main()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Binary file not shown.
@ -35,9 +35,6 @@ def find_samebox_in_array(arr, target):
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def extract_data(datapath):
|
||||
'''
|
||||
0/1_track.data 数据读取
|
||||
@ -71,7 +68,7 @@ def extract_data(datapath):
|
||||
|
||||
boxes, feats, tboxes, tfeats = [], [], [], []
|
||||
|
||||
if line.find("box:") >= 0 and line.find("output_box:") < 0:
|
||||
if line.find("box:") >= 0 and line.find("output_box:")<0 and line.find("out_boxes")<0:
|
||||
box = line[line.find("box:") + 4:].strip()
|
||||
# if len(box)==6:
|
||||
boxes.append(str_to_float_arr(box))
|
||||
@ -122,7 +119,9 @@ def extract_data(datapath):
|
||||
for line in lines:
|
||||
line = line.strip() # 去除行尾的换行符和可能的空白字符
|
||||
if not line: # 跳过空行
|
||||
tracking_flag = False
|
||||
continue
|
||||
|
||||
if tracking_flag:
|
||||
if line.find("tracking_") >= 0:
|
||||
tracking_flag = False
|
||||
@ -177,7 +176,9 @@ def read_tracking_output(filepath):
|
||||
if data.size == 256:
|
||||
feats.append(data)
|
||||
|
||||
assert(len(feats)==len(boxes)), f"{filepath}, len(feats)!=len(boxes)"
|
||||
|
||||
if len(feats) != len(boxes):
|
||||
return np.array([]), np.array([])
|
||||
|
||||
return np.array(boxes), np.array(feats)
|
||||
|
||||
@ -331,7 +332,6 @@ def read_similar(filePath):
|
||||
line = line[:-1]
|
||||
Dict = {}
|
||||
|
||||
|
||||
if not line:
|
||||
if len(one2one_list): SimiDict['one2one'] = one2one_list
|
||||
if len(one2n_list): SimiDict['one2n'] = one2n_list
|
||||
|
||||
15
说明文档.txt
15
说明文档.txt
@ -38,7 +38,7 @@
|
||||
需分 2 步运行模块:
|
||||
(1) runyolo()
|
||||
|
||||
该模块调用 imgs_inference.py 中模块 run_yolo
|
||||
该模块调用 imgs_inference.py 中模块 run_yolo, 该模块重新定义了类 LoadImages, 对图像进行了旋转。
|
||||
后续工作：
|
||||
1). 将run_yolo模块与track_redi.yolo_resnet_tracker模块合并
|
||||
2). 图像文件名标准化
|
||||
@ -126,14 +126,14 @@
|
||||
|
||||
|
||||
./contrast
|
||||
feat_similar.py
|
||||
seqfeat_compare.py
|
||||
similarity_compare_sequence(root_dir)
|
||||
inputs:
|
||||
root_dir：文件夹，包含"subimgs"字段，对该文件夹中的相邻图像进行相似度比较
|
||||
silimarity_compare()
|
||||
功能：对imgpaths文件夹中的图像进行相似度比较
|
||||
|
||||
feat_select.py
|
||||
input_getout_compare.py
|
||||
creatd_deletedBarcode_front(filepath)
|
||||
(1) 基于 deletedBarcode.txt, 构造取出事件和相应的放入事件，构成列表并更新这些列表。
|
||||
MatchList = [(getout_event, InputList), ...]
|
||||
@ -145,6 +145,9 @@
|
||||
precision_compare(filepath, savepath)
|
||||
读取 deletedBarcode.txt 和 deletedBarcodeTest.txt 中的数据，进行相似度比较
|
||||
|
||||
stdfeat_analys()
|
||||
|
||||
|
||||
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, ...]}
|
||||
@ -207,6 +210,7 @@
|
||||
|
||||
|
||||
one2one_contrast.py
|
||||
已修改，未更新。
|
||||
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, ...]}
|
||||
@ -283,6 +287,11 @@
|
||||
(3) featpath：调用 inference_image(), 对每一个barcode，生成字典并进行存储
|
||||
|
||||
|
||||
time_devide.py
|
||||
runyolo()
|
||||
执行 imgs_inference.py 中的 run_yolo()模块，该模块重新定义了类 LoadImages, 对图像进行了旋转。
|
||||
|
||||
|
||||
show_seri()
|
||||
|
||||
|
||||
Reference in New Issue
Block a user