773 lines
27 KiB
Python
773 lines
27 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Tue Apr 16 11:51:07 2024
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@author: ym
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"""
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import cv2
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import os
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import numpy as np
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# import time
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import pickle
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import json
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# import matplotlib.pyplot as plt
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import pandas as pd
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import shutil
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import random
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import math
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from scipy.spatial.distance import cdist
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import matplotlib.pyplot as plt
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from pathlib import Path
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from utils.gen import Profile
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from dotrack.dotracks_back import doBackTracks
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from dotrack.dotracks_front import doFrontTracks
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from utils.drawtracks import plot_frameID_y2, draw_all_trajectories
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# from utils.drawtracks import draw5points, drawTrack, drawtracefeat, drawFeatures
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# from datetime import datetime
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from utils.mergetrack import readDict
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def get_img_filename(imgpath = r'./matching/images/' ):
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imgexts = ['.png', '.jpg', '.jpeg']
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ImgFileList = []
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for root, dirs, files in os.walk(imgpath):
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ImgList = []
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for file in files:
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_, ext = os.path.splitext(file)
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if ext in imgexts:
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ImgFileList.append(os.path.join(root, file))
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return ImgFileList
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def calculate_similarity_track(similarmode = 'other'):
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'''
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similarmode:
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'mean'
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'other'
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'''
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ContrastDict = np.load('./matching/featdata/imgs_feats_data_refined.pkl', allow_pickle=True)
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print(f"The Num of imgsample: {len(ContrastDict)}")
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''''================= 构造空字典 MatchObjDict ================='''
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def splitkey(key):
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videoname = key.split('_')
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BarCode = videoname[0]
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SampleTime = videoname[1].split('-')[1]
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CameraType = videoname[2]
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ActionType = videoname[3]
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TrackID = '_'.join(key.split('_')[7:])
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return BarCode, SampleTime, CameraType, ActionType, TrackID
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MatchObjList = []
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CameraList = []
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for key in ContrastDict.keys():
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BarCode, SampleTime, CameraType, ActionType, FeatureID = splitkey(key)
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MatchObjList.append('_'.join([BarCode, SampleTime, ActionType]))
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CameraList.append(CameraType)
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# MatchObjSet = set(MatchObjList)
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# CameraSet = set(CameraList)
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objects = list(set(MatchObjList))
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cameras = list(set(CameraList))
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assert len(cameras) == 2, "The num of cameras is error!"
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MatchObjDict = {}
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for obj in objects:
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CameraDict = {}
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for camera in cameras:
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CameraDict[camera] = {}
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MatchObjDict[obj] = CameraDict
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for key, value in ContrastDict.items():
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BarCode, SampleTime, CameraType, ActionType, FeatureID = splitkey(key)
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MatchObj = '_'.join([BarCode, SampleTime, ActionType])
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vdict = {}
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if FeatureID not in MatchObjDict[MatchObj][CameraType]:
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vdict[FeatureID] = value['feature']
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MatchObjDict[MatchObj][CameraType].update(vdict)
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print(f"The Num of MatchObjDict: {len(MatchObjDict)}")
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# MatchKeys = [key for key in MatchObjDict.keys()]
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num = len(objects)
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GtMatrix = np.zeros((num, num), dtype=np.float32)
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Similarity = np.zeros((num, num), dtype=np.float32)
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InterMatrix = np.zeros((num, num), dtype=np.float32) # 类间
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IntraMatrix = np.zeros((num, num), dtype=np.float32) # 类内
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'''生成GT矩阵: GtMatrix, IntraMatrix, InterMatrix'''
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for i, obi in enumerate(objects):
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barcode_i = obi.split('_')[0]
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for j, obj in enumerate(objects):
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barcode_j = obj.split('_')[0]
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if barcode_i == barcode_j:
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GtMatrix[i, j] = 1
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if i!=j: IntraMatrix[i, j] = 1
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else:
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GtMatrix[i, j] = 0
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InterMatrix[i, j] = 1
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'''生成相似度矩阵: Similarity '''
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ObjFeatList = []
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for i, obi in enumerate(objects):
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obidict = MatchObjDict[obi]
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camlist = []
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for camera in obidict.keys():
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featlist = []
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for fid in obidict[camera].keys():
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featlist.append(MatchObjDict[obi][camera][fid])
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camlist.append(featlist)
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ObjFeatList.append(camlist)
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Similarity_1 = Similarity.copy()
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for i in range(len(objects)):
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obi = ObjFeatList[i]
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for j in range(len(objects)):
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obj = ObjFeatList[j]
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simival = []
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for ii in range(len(obi)):
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if len(obi[ii])==0: continue
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feat_ii = np.asarray(obi[ii])
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for jj in range(len(obj)):
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if len(obj[jj])==0: continue
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feat_jj = np.asarray(obj[jj])
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if similarmode == 'mean':
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featii = np.mean(feat_ii, axis=0)
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featjj = np.mean(feat_jj, axis=0)
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try:
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matrix = 1- np.maximum(0.0, cdist(featii[None, :], featjj[None, :], 'cosine'))
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except Exception as e:
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print(f'error is {e.__class__.__name__}')
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else:
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matrix = 1- np.maximum(0.0, cdist(feat_ii, feat_jj, 'cosine'))
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simival.append(np.max(matrix))
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if len(simival)==0: continue
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Similarity[i, j] = max(simival)
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# feat_i = np.empty((0, 256), dtype = np.float32)
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# feat_j = np.empty((0, 256), dtype = np.float32)
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# for ii in range(len(obi)):
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# feat_ii = np.asarray(obi[ii])
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# feat_i = np.concatenate((feat_i, feat_ii), axis=0)
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# for jj in range(len(obj)):
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# feat_jj = np.asarray(obi[jj])
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# feat_j = np.concatenate((feat_j, feat_jj), axis=0)
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# if similarmode == 'mean':
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# feati = np.mean(feat_i, axis=0)
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# featj = np.mean(feat_j, axis=0)
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# matrix = 1- np.maximum(0.0, cdist(feati[None, :], featj[None, :], 'cosine'))
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# else:
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# matrix = 1- np.maximum(0.0, cdist(feat_i, feat_j, 'cosine'))
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# Similarity_1[i, j] = np.max(matrix)
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SimiDict = {'keys': objects, 'GtMatrix': GtMatrix, 'Similarity': Similarity,
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'IntraMatrix':IntraMatrix, 'InterMatrix':InterMatrix}
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with open(r"./matching/featdata/MatchDict_track.pkl", "wb") as f:
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pickle.dump(SimiDict, f)
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# SimiDict_1 = {'keys': objects, 'GtMatrix': GtMatrix, 'Similarity':Similarity_1,
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# 'IntraMatrix':IntraMatrix, 'InterMatrix':InterMatrix}
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# with open(r"./matching/featdata/MatchDict_track_1.pkl", "wb") as f:
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# pickle.dump(SimiDict_1, f)
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df_GtMatrix = pd.DataFrame(data=GtMatrix, columns = objects, index = objects)
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df_GtMatrix.to_csv('./matching/featdata/GtMatrix_track.csv',index=True)
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df_similarity = pd.DataFrame(data=Similarity, columns = objects, index = objects)
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df_similarity.to_csv('./matching/featdata/Similarity_track.csv',index=True)
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# df_similarity_1 = pd.DataFrame(data=Similarity_1, columns = objects, index = objects)
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# df_similarity_1.to_csv('./matching/featdata/Similarity_track_1.csv',index=True)
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print("Done!!!!")
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# SimilarMode = ['mean', 'max']
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def calculate_similarity(similarmode = 'mean'):
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ContrastDict = np.load('./matching/featdata/imgs_feats_data_noplane.pkl', allow_pickle=True)
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print(f"The Num of imgsample: {len(ContrastDict)}")
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FrontBackMerged = True
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TracKeys = {}
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for key, value in ContrastDict.items():
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feature = value['feature']
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videoname = key.split('_')[:7]
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BarCode = videoname[0]
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SampleTime = videoname[1].split('-')[1]
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CameraType = videoname[2]
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ActionType = videoname[3]
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TrackID = key.split('_')[7]
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if FrontBackMerged:
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TracKey = '_'.join([BarCode, SampleTime, ActionType])
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else:
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TracKey = '_'.join([BarCode, SampleTime, CameraType, ActionType])
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if TracKey in TracKeys:
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TracKeys[TracKey].append(feature)
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else:
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TracKeys[TracKey] = []
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TracKeys[TracKey].append(feature)
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'''===== 生成GT矩阵: Similarity、GtMatrix、IntraMatrix、InterMatrix ====='''
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num = len(TracKeys)
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keys = [key for key in TracKeys.keys()]
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GtMatrix = np.zeros((num, num), dtype=np.float32)
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Similarity = np.zeros((num, num), dtype=np.float32)
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InterMatrix = np.zeros((num, num), dtype=np.float32) # 类间
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IntraMatrix = np.zeros((num, num), dtype=np.float32) # 类内
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for i, key_i in enumerate(keys):
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barcode_i = key_i.split('_')[0]
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feat_i = np.asarray(TracKeys[key_i], dtype=np.float32)
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for j, key_j in enumerate(keys):
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barcode_j = key_j.split('_')[0]
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feat_j = np.asarray(TracKeys[key_j], dtype=np.float32)
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if similarmode == 'mean':
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feati = np.mean(feat_i, axis=0)
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featj = np.mean(feat_j, axis=0)
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matrix = 1- np.maximum(0.0, cdist(feati[None, :], featj[None, :], 'cosine'))
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else:
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matrix = 1- np.maximum(0.0, cdist(feat_i, feat_j, 'cosine'))
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Similarity[i, j] = np.max(matrix)
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if barcode_i == barcode_j:
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GtMatrix[i, j] = 1
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if i!=j: IntraMatrix[i, j] = 1
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else:
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GtMatrix[i, j] = 0
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InterMatrix[i, j] = 1
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# =============================================================================
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# '''生成相似度矩阵: Similarity '''
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# for i, key_i in enumerate(keys):
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# feat_i = np.asarray(TracKeys[key_i], dtype=np.float32)
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# for j, key_j in enumerate(keys):
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# feat_j = np.asarray(TracKeys[key_j], dtype=np.float32)
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#
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# if similarmode == 'mean':
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# feati = np.mean(feat_i, axis=0)
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# featj = np.mean(feat_j, axis=0)
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# matrix = 1- np.maximum(0.0, cdist(feati[None, :], featj[None, :], 'cosine'))
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# else:
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# matrix = 1- np.maximum(0.0, cdist(feat_i, feat_j, 'cosine'))
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# Similarity[i, j] = np.max(matrix)
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# =============================================================================
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MatchDict = {'keys': keys, 'GtMatrix':GtMatrix, 'Similarity':Similarity,
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'IntraMatrix':IntraMatrix, 'InterMatrix':InterMatrix}
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with open(r"./matching/featdata/MatchDict_noplane.pkl", "wb") as f:
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pickle.dump(MatchDict, f)
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df_GtMatrix = pd.DataFrame(data=GtMatrix, columns = keys, index = keys)
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df_GtMatrix.to_csv('./matching/featdata/GtMatrix_noplane.csv',index=True)
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df_similarity = pd.DataFrame(data=Similarity, columns = keys, index = keys)
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df_similarity.to_csv('./matching/featdata/Similarity_noplane.csv',index=True)
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def sortN_matching(filename = r'./matching/featdata/MatchDict.pkl'):
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SimilarDict = np.load(filename, allow_pickle=True)
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'''********** keys的顺序与Similarity中行列值索引一一对应 **********'''
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keys = SimilarDict['keys']
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Similarity = SimilarDict['Similarity']
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'''1. 将时间根据 Barcode 归并，并确保每个 Barcode 下至少两个事件'''
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BarcodeDict1 = {}
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for i, key in enumerate(keys):
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barcode = key.split('_')[0]
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if barcode not in BarcodeDict1.keys():
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BarcodeDict1[barcode] = []
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BarcodeDict1[barcode].append(i)
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BarcodeDict = {}
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BarcodeList = []
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for barcode, value in BarcodeDict1.items():
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if len(value) < 2: continue
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BarcodeDict[barcode] = value
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BarcodeList.append(barcode)
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BarcodeList = list(set(BarcodeList))
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'''实验参数设定
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N: 任意选取的 Barcode 数
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R：重复实验次数，每次从同一 Barcode 下随机选取2个事件，分别归入加购、退购集合
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Thresh：相似度阈值
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'''
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N = 10
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if N > len(BarcodeList):
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N = math.ceil(len(BarcodeList)/2)
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R = 20
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Thresh = np.linspace(0.1, 1, 100)
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# Thresh = np.linspace(0.601, 0.7, 100)
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Recall, Precision = [], []
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for th in Thresh:
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recall = np.zeros((1, R), dtype=np.float32)
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precision = np.zeros((1, R), dtype=np.float32)
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for rep in range(R):
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BarcodeSelect = random.sample(BarcodeList, N)
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AddDict = {}
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TakeoutDict = {}
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for barcode in BarcodeSelect:
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barlist = BarcodeDict[barcode]
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if len(barlist) < 2:continue
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selected = random.sample(barlist, 2)
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AddDict[barcode] = selected[0]
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TakeoutDict[barcode] = selected[1]
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OrderMatrix = np.zeros((N, N), dtype=np.float32)
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GTMatrix = np.zeros((N, N), dtype=np.float32)
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MatchMatrix_1 = np.zeros((N, N), dtype=np.float32)
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MatchMatrix_2 = np.zeros((N, N), dtype=np.float32)
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i = 0
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for keyi in BarcodeSelect:
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ii = TakeoutDict[keyi]
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j = 0
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for keyj in BarcodeSelect:
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jj = AddDict[keyj]
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OrderMatrix[i, j] = Similarity[int(ii), int(jj)]
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if keyi == keyj:
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GTMatrix[i, j] = 1
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j += 1
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i += 1
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max_indices = np.argmax(OrderMatrix, axis = 1)
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for i in range(N):
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MatchMatrix_1[i, max_indices[i]] = 1
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similar = OrderMatrix[i, max_indices[i]]
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if similar > th:
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MatchMatrix_2[i, max_indices[i]] = 1
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GT_indices = np.where(GTMatrix == 1)
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FNTP = MatchMatrix_2[GT_indices]
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pred_indices = np.where(MatchMatrix_2 == 1)
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TP = np.sum(FNTP==1)
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FN = np.sum(FNTP==0)
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FPTP = GTMatrix[pred_indices]
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FP = np.sum(FPTP == 0)
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# assert TP == np.sum(FPTP == 0), "Please Check Errors!!!"
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recall[0, rep] = TP/(TP+FN)
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precision[0, rep] = TP/(TP+FP+1e-3) # 阈值太大时可能TP、FP都为0，
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Recall.append(recall)
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Precision.append(precision)
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Recall = np.asarray(Recall).reshape([len(Thresh),-1])
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Precision = np.asarray(Precision).reshape([len(Thresh),-1])
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reclmean = np.sum(Recall, axis=1) / (np.count_nonzero(Recall, axis=1) + 1e-3)
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precmean = np.sum(Precision, axis=1) / (np.count_nonzero(Precision, axis=1) + 1e-3)
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print("Done!!!!!")
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# th1, recl = [c[0] for c in Recall], [c[1] for c in Recall]
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# th2, prep = [c[0] for c in Precision], [c[1] for c in Precision]
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recl = [r for r in reclmean]
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prep = [p for p in precmean]
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'''================= Precision & Recall ================='''
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fig, ax = plt.subplots()
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ax.plot(Thresh, recl, 'g', label='Recall = TP/(TP+FN)')
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ax.plot(Thresh, prep, 'r', label='PrecisePos = TP/(TP+FP)')
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# ax.set_xlim([0, 1])
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# ax.set_ylim([0, 1])
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ax.grid(True)
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ax.set_title('Precision & Recall')
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ax.legend()
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plt.show()
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def match_evaluate(filename = r'./matching/featdata/MatchDict.pkl'):
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SimiDict = np.load(filename, allow_pickle=True)
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keys = SimiDict['keys']
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GtMatrix = SimiDict['GtMatrix']
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Similarity = SimiDict['Similarity']
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IntraMatrix = SimiDict['IntraMatrix']
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InterMatrix = SimiDict['InterMatrix']
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BarcodeList = []
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for key in keys:
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BarcodeList.append(key.split('_')[0])
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BarcodeList = list(set(BarcodeList))
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IntraRows, IntraCols = np.nonzero(IntraMatrix)
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InterRows, InterCols = np.nonzero(InterMatrix)
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IntraN, InterN = len(IntraRows), len(InterRows)
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assert IntraN <= InterN, "类内大于类间数，样本不平衡"
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InterNSelect = IntraN
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Thresh = np.linspace(0.1, 1, 100)
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# Thresh = np.linspace(0.2, 0.4, 11)
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Correct = []
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PrecisePos = []
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PreciseNeg = []
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Recall = []
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CorrectMatries = []
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for th in Thresh:
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MatchMatrix = Similarity > th
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CorrectMatrix = MatchMatrix == GtMatrix
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CorrectMatries.append(CorrectMatrix)
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nn = np.random.permutation(np.arange(InterN))[:InterNSelect]
|
||
InterRowsSelect, InterColsSelect = InterRows[nn], InterCols[nn]
|
||
|
||
IntraCorrMatrix = CorrectMatrix[IntraRows, IntraCols]
|
||
InterCorrMatrix = CorrectMatrix[InterRowsSelect, InterColsSelect]
|
||
|
||
TP = np.sum(IntraCorrMatrix)
|
||
TN = np.sum(InterCorrMatrix)
|
||
FN = IntraN - TP
|
||
FP = InterNSelect - TN
|
||
|
||
if TP+FP > 0:
|
||
PrecisePos.append((th, TP/(TP+FP)))
|
||
if TN+FN > 0:
|
||
PreciseNeg.append((th, TN/(TN+FN)))
|
||
if TP+FN > 0:
|
||
Recall.append((th, TP/(TP+FN)))
|
||
|
||
if TP+TN+FP+FN > 0:
|
||
Correct.append((th, (TP+TN)/(TP+TN+FP+FN)))
|
||
# print(f'Th: {th}')
|
||
# print(f'TP:{TP}, FP:{FP}, TN:{TN}, FN:{FN}')
|
||
|
||
|
||
CorrectMatries = np.asarray(CorrectMatries)
|
||
|
||
'''====================== 分析错误原因 =========================='''
|
||
'''
|
||
keys两种构成方式，其中的元素来自于：MatchingDict
|
||
BarCode, SampleTime, ActionType #不考虑摄像头类型（前后摄）
|
||
BarCode, SampleTime, CameraType, ActionType# 考虑摄像头类型（前后摄）
|
||
为了便于显示，在图像文件名中，将 ActionType 进行了缩写，匹配时取 [:3]
|
||
"addGood" --------> "add"
|
||
"returnGood" --------> "return"
|
||
'''
|
||
##============= 获取图像存储位置,可以通过 keys 检索到对应的图像文件
|
||
imgpath = r'./matching/images/'
|
||
ImgFileList = get_img_filename(imgpath)
|
||
|
||
rowx, colx = np.where(CorrectMatries[66,:,:] == False)
|
||
rows, cols = [], []
|
||
for i in range(len(rowx)):
|
||
ri, ci = rowx[i], colx[i]
|
||
if ci > ri:
|
||
rows.append(ri)
|
||
cols.append(ci)
|
||
|
||
|
||
KeysError = [(keys[rows[i]], keys[cols[i]]) for i in range(len(rows))]
|
||
SimiScore = [Similarity[rows[i], cols[i]] for i in range(len(rows))]
|
||
|
||
|
||
for i, keykey in enumerate(KeysError):
|
||
key1, key2 = keykey
|
||
sscore = SimiScore[i]
|
||
|
||
kt1, kt2 = key1.split('_'), key2.split('_')
|
||
|
||
if len(kt1)==3 and len(kt2)==3:
|
||
file1 = [f for f in ImgFileList if kt1[0] in f and kt1[1] in f and kt1[2][:3] in f]
|
||
file2 = [f for f in ImgFileList if kt2[0] in f and kt2[1] in f and kt2[2][:3] in f]
|
||
|
||
elif len(kt1)==4 and len(kt1)==4:
|
||
file1 = [f for f in ImgFileList if kt1[0] in f and kt1[1] in f and kt1[2] in f and kt1[3][:3] in f]
|
||
file2 = [f for f in ImgFileList if kt2[0] in f and kt2[1] in f and kt2[2] in f and kt2[3][:3] in f]
|
||
else:
|
||
pass
|
||
|
||
if len(file1)==0 or len(file2)==0:
|
||
continue
|
||
|
||
if kt1[0] == kt2[0]:
|
||
gt = "same"
|
||
else:
|
||
gt = "diff"
|
||
|
||
path = Path(f'./matching/results/{i}_{gt}_{sscore:.2f}')
|
||
if path.exists() and path.is_dir():
|
||
shutil.rmtree(path)
|
||
path1, path2 = path.joinpath(key1), path.joinpath(key2)
|
||
|
||
path1.mkdir(parents=True, exist_ok=True)
|
||
path2.mkdir(parents=True, exist_ok=True)
|
||
for file in file1:
|
||
shutil.copy2(file, path1)
|
||
for file in file2:
|
||
shutil.copy2(file, path2)
|
||
|
||
if i==99:
|
||
break
|
||
|
||
th1, corr = [c[0] for c in Correct], [c[1] for c in Correct]
|
||
th2, recl = [c[0] for c in Recall], [c[1] for c in Recall]
|
||
th3, prep = [c[0] for c in PrecisePos], [c[1] for c in PrecisePos]
|
||
th4, pren = [c[0] for c in PreciseNeg], [c[1] for c in PreciseNeg]
|
||
|
||
'''================= Correct ==================='''
|
||
fig, ax = plt.subplots()
|
||
ax.plot(th1, corr, 'b', label='Correct = (TP+TN)/(TP+TN+FP+FN)')
|
||
max_corr = max(corr)
|
||
max_index = corr.index(max_corr)
|
||
max_thresh = th1[max_index]
|
||
ax.plot([0, max_thresh], [max_corr, max_corr], 'r--')
|
||
ax.plot([max_thresh, max_thresh], [0, max_corr], 'r--')
|
||
ax.set_xlim([0, 1])
|
||
ax.set_ylim([0, 1])
|
||
ax.grid(True)
|
||
ax.set_title('Correct')
|
||
ax.legend()
|
||
plt.show()
|
||
|
||
|
||
'''================= PrecisePos & PreciseNeg & Recall ================='''
|
||
fig, ax = plt.subplots()
|
||
ax.plot(th2, recl, 'g', label='Recall = TP/(TP+FN)')
|
||
ax.plot(th3, prep, 'c', label='PrecisePos = TP/(TP+FP)')
|
||
ax.plot(th4, pren, 'm', label='PreciseNeg = TN/(TN+FN)')
|
||
|
||
ax.set_xlim([0, 1])
|
||
ax.set_ylim([0, 1])
|
||
ax.grid(True)
|
||
ax.set_title('PrecisePos & PreciseNeg')
|
||
ax.legend()
|
||
plt.show()
|
||
|
||
|
||
def have_tracked():
|
||
featdir = r"./data/trackfeats"
|
||
trackdir = r"./data/tracks"
|
||
|
||
# =============================================================================
|
||
# FileList = []
|
||
# with open(r'./matching/视频分类/单.txt', 'r') as file:
|
||
# lines = file.readlines()
|
||
# for line in lines:
|
||
# file = line.split('.')[0]
|
||
# FileList.append(file)
|
||
# FileList = list(set(FileList))
|
||
# =============================================================================
|
||
|
||
MatchingDict = {}
|
||
k, gt = 0, Profile()
|
||
for filename in os.listdir(featdir):
|
||
file, ext = os.path.splitext(filename)
|
||
|
||
# if file not in FileList: continue
|
||
if file.find('20240508')<0: continue
|
||
if file.find('17327712807')<0: continue
|
||
|
||
trackpath = os.path.join(trackdir, file + ".npy")
|
||
featpath = os.path.join(featdir, filename)
|
||
|
||
bboxes = np.load(trackpath)
|
||
features_dict = np.load(featpath, allow_pickle=True)
|
||
with gt:
|
||
if filename.find("front") >= 0:
|
||
vts = doFrontTracks(bboxes, features_dict)
|
||
vts.classify()
|
||
|
||
plt = plot_frameID_y2(vts)
|
||
|
||
savedir = save_dir.joinpath(f'{file}_y2.png')
|
||
|
||
plt.savefig(savedir)
|
||
plt.close()
|
||
elif filename.find("back") >= 0:
|
||
vts = doBackTracks(bboxes, features_dict)
|
||
vts.classify()
|
||
|
||
edgeline = cv2.imread("./shopcart/cart_tempt/edgeline.png")
|
||
draw_all_trajectories(vts, edgeline, save_dir, filename)
|
||
print(file+f" need time: {gt.dt:.2f}s")
|
||
|
||
elements = file.split('_')
|
||
assert len(elements) == 7, f"{filename} fields num: {len(elements)}"
|
||
BarCode = elements[0]
|
||
|
||
## ====================================== 只用于在images文件夹下保存图片
|
||
SampleTime = elements[1].split('-')[1]
|
||
CameraType = elements[2]
|
||
if elements[3]=="addGood":
|
||
ActionType = "add"
|
||
elif elements[3]=="returnGood":
|
||
ActionType = "return"
|
||
else:
|
||
ActionType = "x"
|
||
|
||
subimg_dir = Path(f'./matching/images/{BarCode}_{SampleTime}_{ActionType}/')
|
||
if not subimg_dir.exists():
|
||
subimg_dir.mkdir(parents=True, exist_ok=True)
|
||
|
||
# boxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
|
||
# 0, 1, 2, 3, 4, 5, 6, 7, 8
|
||
for track in vts.Residual:
|
||
boxes = track.boxes
|
||
for i in range(boxes.shape[0]):
|
||
box = boxes[i, :]
|
||
tid, fid, bid = int(box[4]), int(box[7]), int(box[8])
|
||
|
||
feat_dict = features_dict[fid]
|
||
feature = feat_dict[bid]
|
||
img = feat_dict[f'{bid}_img']
|
||
|
||
sub_img_file = subimg_dir.joinpath(f"{BarCode}_{SampleTime}_{CameraType}_{ActionType}_{tid}_{fid}_{bid}.png")
|
||
cv2.imwrite(str(sub_img_file), img)
|
||
|
||
condict = {f"{file}_{tid}_{fid}_{bid}": {'img': img, 'feature': feature}}
|
||
|
||
MatchingDict.update(condict)
|
||
# k += 1
|
||
# if k == 100:
|
||
# break
|
||
|
||
featpath = Path('./matching/featdata/')
|
||
if not featpath.exists():
|
||
featpath.mkdir(parents=True, exist_ok=True)
|
||
|
||
featdata = featpath.joinpath('imgs_feats_data_noplane.pkl')
|
||
with open(featdata, 'wb') as file:
|
||
pickle.dump(MatchingDict, file)
|
||
|
||
def imgsample_cleaning():
|
||
ContrastDict = np.load('./matching/featdata/imgs_feats_data.pkl', allow_pickle=True)
|
||
print(f"The Num of imgsample: {len(ContrastDict)}")
|
||
|
||
|
||
MatchingDict_refined = {}
|
||
for filename, value in ContrastDict.items():
|
||
elements = filename.split('_')
|
||
|
||
|
||
tid = elements[7]
|
||
fid = elements[8]
|
||
bid = elements[9]
|
||
|
||
BarCode = elements[0]
|
||
SampleTime = elements[1].split('-')[1]
|
||
CameraType = elements[2]
|
||
if elements[3]=="addGood":
|
||
ActionType = "add"
|
||
elif elements[3]=="returnGood":
|
||
ActionType = "return"
|
||
else:
|
||
ActionType = "x"
|
||
refimgdir = f'.\matching\images_refined\{BarCode}_{SampleTime}_{ActionType}'
|
||
|
||
file = '_'.join(elements[0:7])
|
||
if os.path.exists(refimgdir) and os.path.isdir(refimgdir):
|
||
imgpath = os.path.join(refimgdir, f"{BarCode}_{SampleTime}_{CameraType}_{ActionType}_{tid}_{fid}_{bid}.png")
|
||
if os.path.isfile(imgpath):
|
||
condict = {f"{file}_{tid}_{fid}_{bid}": value}
|
||
|
||
MatchingDict_refined.update(condict)
|
||
|
||
featpath = Path('./matching/featdata/')
|
||
if not featpath.exists():
|
||
featpath.mkdir(parents=True, exist_ok=True)
|
||
|
||
featdata = featpath.joinpath('imgs_feats_data_refined.pkl')
|
||
with open(featdata, 'wb') as file:
|
||
pickle.dump(MatchingDict_refined, file)
|
||
|
||
print(f"The Num of ContrastDict: {len(ContrastDict)}")
|
||
print(f"The Num of MatchingDict_refined: {len(MatchingDict_refined)}")
|
||
print(f"The Num of cleaned img: {len(ContrastDict)} - {len(MatchingDict_refined)}")
|
||
|
||
|
||
def main():
|
||
'''1. 提取运动商品轨迹'''
|
||
# have_tracked()
|
||
|
||
'''2. 清除一次事件中包含多件商品的事件'''
|
||
# imgsample_cleaning()
|
||
|
||
'''3.1 计算事件间相似度: 将 front、back 的所有 track 特征合并'''
|
||
# calculate_similarity()
|
||
|
||
'''3.2 计算事件间相似度: 考虑前后摄的不同组合，或 track 间的不同组合'''
|
||
# calculate_similarity_track()
|
||
|
||
|
||
'''4.1 事件间匹配的总体性能评估'''
|
||
filename = r'./matching/featdata/MatchDict_plane.pkl'
|
||
match_evaluate(filename)
|
||
|
||
filename = r'./matching/featdata/MatchDict_noplane.pkl'
|
||
match_evaluate(filename)
|
||
|
||
'''4.2 模拟实际场景，任选N件作为一组作为加购，取出其中一件时的性能评估'''
|
||
# filename = r'./matching/featdata/MatchDict_refined.pkl'
|
||
# sortN_matching(filename)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
save_dir = Path(f'./result/')
|
||
main()
|
||
|
||
|
||
|