contrast performance evaluatation have done!
This commit is contained in:
王庆刚
@ -33,150 +33,176 @@ import numpy as np
|
||||
import cv2
|
||||
import os
|
||||
import sys
|
||||
|
||||
import random
|
||||
import pickle
|
||||
import torch
|
||||
import time
|
||||
import json
|
||||
from config import config as conf
|
||||
from model import resnet18
|
||||
from inference import load_contrast_model
|
||||
from inference import featurize
|
||||
from pathlib import Path
|
||||
from scipy.spatial.distance import cdist
|
||||
import matplotlib.pyplot as plt
|
||||
import shutil
|
||||
from datetime import datetime
|
||||
|
||||
# Vit版resnet, 和现场特征不一致，需将resnet_vit中文件提出
|
||||
# from config import config as conf
|
||||
# from model import resnet18
|
||||
# from inference import load_contrast_model
|
||||
# from inference import featurize
|
||||
# embedding_size = conf.embedding_size
|
||||
# img_size = conf.img_size
|
||||
# device = conf.device
|
||||
# model = load_contrast_model()
|
||||
|
||||
sys.path.append(r"D:\DetectTracking")
|
||||
from tracking.utils.read_data import extract_data, read_tracking_output, read_deletedBarcode_file
|
||||
|
||||
from config import config as conf
|
||||
from model import resnet18 as resnet18
|
||||
from test_ori import inference_image
|
||||
|
||||
|
||||
IMG_FORMAT = ['.bmp', '.jpg', '.jpeg', '.png']
|
||||
model = resnet18().to(conf.device)
|
||||
|
||||
model = load_contrast_model()
|
||||
print('load model {} '.format(conf.testbackbone))
|
||||
# model = nn.DataParallel(model).to(conf.device)
|
||||
model.load_state_dict(torch.load(conf.test_model, map_location=conf.device))
|
||||
model.eval()
|
||||
|
||||
def creat_shopping_event(basePath, savePath, subimgPath=False):
|
||||
eventList = []
|
||||
|
||||
def creat_shopping_event(eventPath, subimgPath=False):
|
||||
'''构造放入商品事件字典，这些事件需满足条件：
|
||||
1) 前后摄至少有一条轨迹输出
|
||||
2) 保存有帧图像，以便裁剪出 boxe 子图
|
||||
'''
|
||||
# filename = "20240723-155413_6904406215720"
|
||||
|
||||
'''一、构造放入商品事件列表'''
|
||||
k = 0
|
||||
for filename in os.listdir(basePath):
|
||||
# filename = "20240723-155413_6904406215720"
|
||||
|
||||
'''filename下为一次购物事件'''
|
||||
filepath = os.path.join(basePath, filename)
|
||||
'''filename下为一次购物事件'''
|
||||
eventName = os.path.basename(eventPath)
|
||||
|
||||
'''================ 0. 检查 filename 及 filepath 正确性和有效性 ================'''
|
||||
nmlist = filename.split('_')
|
||||
if filename.find('2024')<0 or len(nmlist)!=2 or len(nmlist[0])!=15 or len(nmlist[1])<11:
|
||||
continue
|
||||
if not os.path.isdir(filepath): continue
|
||||
'''================ 0. 检查 filename 及 eventPath 正确性和有效性 ================'''
|
||||
nmlist = eventName.split('_')
|
||||
if eventName.find('2024')<0 or len(nmlist)!=2 or len(nmlist[0])!=15 or len(nmlist[1])<11:
|
||||
return
|
||||
if not os.path.isdir(eventPath):
|
||||
return
|
||||
|
||||
|
||||
|
||||
'''================ 1. 构造事件描述字典，暂定 9 items ==============='''
|
||||
event = {}
|
||||
event['barcode'] = eventName.split('_')[1]
|
||||
event['type'] = 'input'
|
||||
event['filepath'] = eventPath
|
||||
event['back_imgpaths'] = []
|
||||
event['front_imgpaths'] = []
|
||||
event['back_boxes'] = np.empty((0, 9), dtype=np.float64)
|
||||
event['front_boxes'] = np.empty((0, 9), dtype=np.float64)
|
||||
event['back_feats'] = np.empty((0, 256), dtype=np.float64)
|
||||
event['front_feats'] = np.empty((0, 256), dtype=np.float64)
|
||||
event['feats_compose'] = np.empty((0, 256), dtype=np.float64)
|
||||
# event['feats_select'] = np.empty((0, 256), dtype=np.float64)
|
||||
|
||||
|
||||
'''================= 2. 读取 data 文件 ============================='''
|
||||
for dataname in os.listdir(eventPath):
|
||||
# filename = '1_track.data'
|
||||
datapath = os.path.join(eventPath, dataname)
|
||||
if not os.path.isfile(datapath): continue
|
||||
|
||||
'''================ 1. 构造事件描述字典，暂定 9 items ==============='''
|
||||
event = {}
|
||||
event['barcode'] = nmlist[1]
|
||||
event['type'] = 'input'
|
||||
event['filepath'] = filepath
|
||||
event['back_imgpaths'] = []
|
||||
event['front_imgpaths'] = []
|
||||
event['back_boxes'] = np.empty((0, 9), dtype=np.float64)
|
||||
event['front_boxes'] = np.empty((0, 9), dtype=np.float64)
|
||||
event['back_feats'] = np.empty((0, 256), dtype=np.float64)
|
||||
event['front_feats'] = np.empty((0, 256), dtype=np.float64)
|
||||
event['feats_compose'] = np.empty((0, 256), dtype=np.float64)
|
||||
# event['feats_select'] = np.empty((0, 256), dtype=np.float64)
|
||||
|
||||
|
||||
'''================= 2. 读取 data 文件 ============================='''
|
||||
for dataname in os.listdir(filepath):
|
||||
# filename = '1_track.data'
|
||||
datapath = os.path.join(filepath, dataname)
|
||||
if not os.path.isfile(datapath): continue
|
||||
|
||||
CamerType = dataname.split('_')[0]
|
||||
''' 2.1 读取 0/1_track.data 中数据，暂不考虑'''
|
||||
# if dataname.find("_track.data")>0:
|
||||
# bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, tracking_feat_dict = extract_data(datapath)
|
||||
CamerType = dataname.split('_')[0]
|
||||
''' 2.1 读取 0/1_track.data 中数据，暂不考虑'''
|
||||
# if dataname.find("_track.data")>0:
|
||||
# bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, tracking_feat_dict = extract_data(datapath)
|
||||
|
||||
''' 2.2 读取 0/1_tracking_output.data 中数据'''
|
||||
if dataname.find("_tracking_output.data")>0:
|
||||
tracking_output_boxes, tracking_output_feats = read_tracking_output(datapath)
|
||||
if len(tracking_output_boxes) != len(tracking_output_feats): continue
|
||||
if CamerType == '0':
|
||||
event['back_boxes'] = tracking_output_boxes
|
||||
event['back_feats'] = tracking_output_feats
|
||||
elif CamerType == '1':
|
||||
event['front_boxes'] = tracking_output_boxes
|
||||
event['front_feats'] = tracking_output_feats
|
||||
|
||||
'''2.3 事件的特征表征方式: 特征选择、特征集成'''
|
||||
bk_feats = event['back_feats']
|
||||
ft_feats = event['front_feats']
|
||||
|
||||
'''2.3.1 特征集成'''
|
||||
feats_compose = np.empty((0, 256), dtype=np.float64)
|
||||
if len(ft_feats):
|
||||
feats_compose = np.concatenate((feats_compose, ft_feats), axis=0)
|
||||
if len(bk_feats):
|
||||
feats_compose = np.concatenate((feats_compose, bk_feats), axis=0)
|
||||
event['feats_compose'] = feats_compose
|
||||
|
||||
'''2.3.1 特征选择'''
|
||||
if len(ft_feats):
|
||||
event['feats_select'] = ft_feats
|
||||
|
||||
pickpath = os.path.join(savePath, f"{filename}.pickle")
|
||||
with open(pickpath, 'wb') as f:
|
||||
pickle.dump(event, f)
|
||||
print(f"Event: {filename}")
|
||||
|
||||
if subimgPath==False:
|
||||
eventList.append(event)
|
||||
continue
|
||||
|
||||
'''================ 2. 读取图像文件地址，并按照帧ID排序 ============='''
|
||||
frontImgs, frontFid = [], []
|
||||
backImgs, backFid = [], []
|
||||
for imgname in os.listdir(filepath):
|
||||
name, ext = os.path.splitext(imgname)
|
||||
if ext not in IMG_FORMAT or name.find('frameId')<0: continue
|
||||
|
||||
CamerType = name.split('_')[0]
|
||||
frameId = int(name.split('_')[3])
|
||||
imgpath = os.path.join(filepath, imgname)
|
||||
''' 2.2 读取 0/1_tracking_output.data 中数据'''
|
||||
if dataname.find("_tracking_output.data")>0:
|
||||
tracking_output_boxes, tracking_output_feats = read_tracking_output(datapath)
|
||||
if len(tracking_output_boxes) != len(tracking_output_feats): continue
|
||||
if CamerType == '0':
|
||||
backImgs.append(imgpath)
|
||||
backFid.append(frameId)
|
||||
if CamerType == '1':
|
||||
frontImgs.append(imgpath)
|
||||
frontFid.append(frameId)
|
||||
|
||||
frontIdx = np.argsort(np.array(frontFid))
|
||||
backIdx = np.argsort(np.array(backFid))
|
||||
|
||||
'''2.1 生成依据帧 ID 排序的前后摄图像地址列表'''
|
||||
frontImgs = [frontImgs[i] for i in frontIdx]
|
||||
backImgs = [backImgs[i] for i in backIdx]
|
||||
event['back_boxes'] = tracking_output_boxes
|
||||
event['back_feats'] = tracking_output_feats
|
||||
elif CamerType == '1':
|
||||
event['front_boxes'] = tracking_output_boxes
|
||||
event['front_feats'] = tracking_output_feats
|
||||
|
||||
|
||||
if len(event['back_boxes'])==0 or len(event['front_boxes'])==0:
|
||||
return None
|
||||
|
||||
'''2.3 事件的特征表征方式: 特征选择、特征集成'''
|
||||
bk_feats = event['back_feats']
|
||||
ft_feats = event['front_feats']
|
||||
|
||||
'''2.3.1 特征集成'''
|
||||
feats_compose = np.empty((0, 256), dtype=np.float64)
|
||||
if len(ft_feats):
|
||||
feats_compose = np.concatenate((feats_compose, ft_feats), axis=0)
|
||||
if len(bk_feats):
|
||||
feats_compose = np.concatenate((feats_compose, bk_feats), axis=0)
|
||||
event['feats_compose'] = feats_compose
|
||||
|
||||
'''2.3.1 特征选择'''
|
||||
if len(ft_feats):
|
||||
event['feats_select'] = ft_feats
|
||||
|
||||
'''2.2 将前、后摄图像路径添加至事件字典'''
|
||||
bfid = event['back_boxes'][:, 7].astype(np.int64)
|
||||
ffid = event['front_boxes'][:, 7].astype(np.int64)
|
||||
if len(bfid) and max(bfid) <= len(backImgs):
|
||||
event['back_imgpaths'] = [backImgs[i-1] for i in bfid]
|
||||
if len(ffid) and max(ffid) <= len(frontImgs):
|
||||
event['front_imgpaths'] = [frontImgs[i-1] for i in ffid]
|
||||
# pickpath = os.path.join(savePath, f"{filename}.pickle")
|
||||
# with open(pickpath, 'wb') as f:
|
||||
# pickle.dump(event, f)
|
||||
# print(f"Event: {filename}")
|
||||
|
||||
# if subimgPath==False:
|
||||
# eventList.append(event)
|
||||
# continue
|
||||
|
||||
|
||||
'''================ 3. 判断当前事件有效性，并添加至事件列表 =========='''
|
||||
condt1 = len(event['back_imgpaths'])==0 or len(event['front_imgpaths'])==0
|
||||
condt2 = len(event['front_feats'])==0 and len(event['back_feats'])==0
|
||||
if condt1 or condt2:
|
||||
print(f" Error, condt1: {condt1}, condt2: {condt2}")
|
||||
continue
|
||||
'''================ 2. 读取图像文件地址，并按照帧ID排序 ============='''
|
||||
frontImgs, frontFid = [], []
|
||||
backImgs, backFid = [], []
|
||||
for imgname in os.listdir(eventPath):
|
||||
name, ext = os.path.splitext(imgname)
|
||||
if ext not in IMG_FORMAT or name.find('frameId')<0: continue
|
||||
|
||||
eventList.append(event)
|
||||
|
||||
# k += 1
|
||||
# if k==1:
|
||||
# continue
|
||||
|
||||
'''一、构造放入商品事件列表，暂不处理'''
|
||||
CamerType = name.split('_')[0]
|
||||
frameId = int(name.split('_')[3])
|
||||
imgpath = os.path.join(eventPath, imgname)
|
||||
if CamerType == '0':
|
||||
backImgs.append(imgpath)
|
||||
backFid.append(frameId)
|
||||
if CamerType == '1':
|
||||
frontImgs.append(imgpath)
|
||||
frontFid.append(frameId)
|
||||
|
||||
frontIdx = np.argsort(np.array(frontFid))
|
||||
backIdx = np.argsort(np.array(backFid))
|
||||
|
||||
'''2.1 生成依据帧 ID 排序的前后摄图像地址列表'''
|
||||
frontImgs = [frontImgs[i] for i in frontIdx]
|
||||
backImgs = [backImgs[i] for i in backIdx]
|
||||
|
||||
'''2.2 将前、后摄图像路径添加至事件字典'''
|
||||
|
||||
|
||||
bfid = event['back_boxes'][:, 7].astype(np.int64)
|
||||
ffid = event['front_boxes'][:, 7].astype(np.int64)
|
||||
if len(bfid) and max(bfid) <= len(backImgs):
|
||||
event['back_imgpaths'] = [backImgs[i-1] for i in bfid]
|
||||
if len(ffid) and max(ffid) <= len(frontImgs):
|
||||
event['front_imgpaths'] = [frontImgs[i-1] for i in ffid]
|
||||
|
||||
|
||||
'''================ 3. 判断当前事件有效性，并添加至事件列表 =========='''
|
||||
condt1 = len(event['back_imgpaths'])==0 or len(event['front_imgpaths'])==0
|
||||
condt2 = len(event['front_feats'])==0 and len(event['back_feats'])==0
|
||||
|
||||
if condt1 or condt2:
|
||||
print(f"Event: {eventName}, Error, condt1: {condt1}, condt2: {condt2}")
|
||||
return None
|
||||
|
||||
|
||||
|
||||
|
||||
'''构造放入商品事件列表，暂不处理'''
|
||||
# delepath = os.path.join(basePath, 'deletedBarcode.txt')
|
||||
# bcdList = read_deletedBarcode_file(delepath)
|
||||
# for slist in bcdList:
|
||||
@ -192,9 +218,8 @@ def creat_shopping_event(basePath, savePath, subimgPath=False):
|
||||
# event['barcode'] = slist['Deleted'].strip()
|
||||
# event['type'] = 'getout'
|
||||
# event['basePath'] = getoutPath
|
||||
|
||||
|
||||
return eventList
|
||||
return event
|
||||
|
||||
def get_std_barcodeDict(bcdpath, bpath):
|
||||
'''
|
||||
@ -206,7 +231,7 @@ def get_std_barcodeDict(bcdpath, bpath):
|
||||
bpath: 字典存储地址
|
||||
'''
|
||||
|
||||
# bpath = r'\\192.168.1.28\share\测试_202406\contrast\barcodes'
|
||||
# bpath = r'\\192.168.1.28\share\测试_202406\contrast\std_barcodes'
|
||||
|
||||
'''读取数据集中 barcode 列表'''
|
||||
stdBlist = []
|
||||
@ -255,61 +280,41 @@ def get_std_barcodeDict(bcdpath, bpath):
|
||||
|
||||
return
|
||||
|
||||
|
||||
def extract_save_trajture_subimgs(shoppingEventPath, shoppingFeatPath, subimgPath):
|
||||
'''用于保存一次购物事件的轨迹图像子图'''
|
||||
|
||||
shoppingFeatPath = r"\\192.168.1.28\share\测试_202406\contrast\events"
|
||||
subimgPath = r'\\192.168.1.28\share\测试_202406\contrast\subimgs'
|
||||
|
||||
|
||||
eventList = creat_shopping_event(shoppingEventPath, shoppingFeatPath, subimgPath=True)
|
||||
print("======= eventList have generated and features have saved! =======")
|
||||
|
||||
|
||||
barcodeDict = {}
|
||||
for event in eventList:
|
||||
'''9 items: barcode, type, filepath, back_imgpaths, front_imgpaths,
|
||||
back_boxes, front_boxes, back_feats, front_feats
|
||||
'''
|
||||
if len(event['feats_select']):
|
||||
event_feats = event['feats_select']
|
||||
elif len(event['back_feats']):
|
||||
event_feats = event['back_feats']
|
||||
def save_event_subimg(event, savepath):
|
||||
'''
|
||||
功能: 保存一次购物事件的轨迹子图
|
||||
9 items: barcode, type, filepath, back_imgpaths, front_imgpaths,
|
||||
back_boxes, front_boxes, back_feats, front_feats,
|
||||
feats_compose, feats_select
|
||||
子图保存次序：先前摄、后后摄，以 k 为编号，和 "feats_compose" 中次序相同
|
||||
'''
|
||||
cameras = ('front', 'back')
|
||||
k = 0
|
||||
for camera in cameras:
|
||||
if camera == 'front':
|
||||
boxes = event['front_boxes']
|
||||
imgpaths = event['front_imgpaths']
|
||||
else:
|
||||
continue
|
||||
boxes = event['back_boxes']
|
||||
imgpaths = event['back_imgpaths']
|
||||
|
||||
|
||||
'''保存一次购物事件的轨迹子图'''
|
||||
basename = os.path.basename(event['filepath'])
|
||||
spath = os.path.join(subimgPath, basename)
|
||||
if not os.path.exists(spath):
|
||||
os.makedirs(spath)
|
||||
cameras = ('front', 'back')
|
||||
for camera in cameras:
|
||||
if camera == 'front':
|
||||
boxes = event['front_boxes']
|
||||
imgpaths = event['front_imgpaths']
|
||||
else:
|
||||
boxes = event['back_boxes']
|
||||
imgpaths = event['back_imgpaths']
|
||||
|
||||
for i, box in enumerate(boxes):
|
||||
x1, y1, x2, y2, tid, score, cls, fid, bid = box
|
||||
|
||||
imgpath = imgpaths[i]
|
||||
image = cv2.imread(imgpath)
|
||||
subimg = image[int(y1/2):int(y2/2), int(x1/2):int(x2/2), :]
|
||||
|
||||
camerType, timeTamp, _, frameID = os.path.basename(imgpath).split('.')[0].split('_')
|
||||
subimgName = f"{camerType}_{tid}_fid({fid}, {frameID}).png"
|
||||
subimgPath = os.path.join(spath, subimgName)
|
||||
|
||||
cv2.imwrite(subimgPath, subimg)
|
||||
|
||||
print(f"Image saved: {basename}")
|
||||
for i, box in enumerate(boxes):
|
||||
x1, y1, x2, y2, tid, score, cls, fid, bid = box
|
||||
|
||||
|
||||
imgpath = imgpaths[i]
|
||||
image = cv2.imread(imgpath)
|
||||
subimg = image[int(y1/2):int(y2/2), int(x1/2):int(x2/2), :]
|
||||
|
||||
camerType, timeTamp, _, frameID = os.path.basename(imgpath).split('.')[0].split('_')
|
||||
subimgName = f"{k}_cam-{camerType}_tid-{int(tid)}_fid-({int(fid)}, {frameID}).png"
|
||||
spath = os.path.join(savepath, subimgName)
|
||||
|
||||
cv2.imwrite(spath, subimg)
|
||||
k += 1
|
||||
# basename = os.path.basename(event['filepath'])
|
||||
print(f"Image saved: {os.path.basename(event['filepath'])}")
|
||||
|
||||
|
||||
|
||||
def batch_inference(imgpaths, batch):
|
||||
size = len(imgpaths)
|
||||
@ -326,7 +331,7 @@ def batch_inference(imgpaths, batch):
|
||||
|
||||
return features
|
||||
|
||||
def stdfeat_infer(imgPath, featPath):
|
||||
def stdfeat_infer(imgPath, featPath, bcdSet=None):
|
||||
'''
|
||||
inputs:
|
||||
imgPath: 该文件夹下的 pickle 文件格式 {barcode: [imgpath1, imgpath1, ...]}
|
||||
@ -337,11 +342,15 @@ def stdfeat_infer(imgPath, featPath):
|
||||
|
||||
'''
|
||||
|
||||
# imgPath = r"\\192.168.1.28\share\测试_202406\contrast\barcodes"
|
||||
# featPath = r"\\192.168.1.28\share\测试_202406\contrast\features"
|
||||
# imgPath = r"\\192.168.1.28\share\测试_202406\contrast\std_barcodes"
|
||||
# featPath = r"\\192.168.1.28\share\测试_202406\contrast\std_features"
|
||||
stdBarcodeDict = {}
|
||||
k = 0
|
||||
for filename in os.listdir(imgPath):
|
||||
bcd, ext = os.path.splitext(filename)
|
||||
if bcdSet is not None and bcd not in bcdSet:
|
||||
continue
|
||||
|
||||
filepath = os.path.join(imgPath, filename)
|
||||
|
||||
stdbDict = {}
|
||||
@ -351,12 +360,21 @@ def stdfeat_infer(imgPath, featPath):
|
||||
with open(filepath, 'rb') as f:
|
||||
bpDict = pickle.load(f)
|
||||
for barcode, imgpaths in bpDict.items():
|
||||
feature = batch_inference(imgpaths, 8)
|
||||
# feature = batch_inference(imgpaths, 8) #from vit distilled model of Lichen
|
||||
feature = inference_image(imgpaths, conf.test_transform, model, conf.device)
|
||||
|
||||
feature /= np.linalg.norm(feature, axis=1)[:, None]
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error accured at: {filename}, with Exception is: {e}")
|
||||
|
||||
'''================ 保存单个barcode特征 ================'''
|
||||
stdbDict[barcode] = feature
|
||||
stdbDict["barcode"] = barcode
|
||||
stdbDict["imgpaths"] = imgpaths
|
||||
stdbDict["feats"] = feature
|
||||
|
||||
|
||||
|
||||
pkpath = os.path.join(featPath, f"{barcode}.pickle")
|
||||
with open(pkpath, 'wb') as f:
|
||||
pickle.dump(stdbDict, f)
|
||||
@ -364,25 +382,210 @@ def stdfeat_infer(imgPath, featPath):
|
||||
stdBarcodeDict[barcode] = feature
|
||||
t2 = time.time()
|
||||
print(f"Barcode: {barcode}, need time: {t2-t1:.1f} secs")
|
||||
k += 1
|
||||
if k == 10:
|
||||
break
|
||||
# k += 1
|
||||
# if k == 10:
|
||||
# break
|
||||
|
||||
pickpath = os.path.join(featPath, f"barcode_features_{k}.pickle")
|
||||
with open(pickpath, 'wb') as f:
|
||||
pickle.dump(stdBarcodeDict, f)
|
||||
|
||||
def contrast_performance_evaluate():
|
||||
eventFeatPath = r"\\192.168.1.28\share\测试_202406\contrast\events"
|
||||
stdFeatPath = r"\\192.168.1.28\share\测试_202406\contrast\features"
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def generate_event_and_standard_features():
|
||||
|
||||
def contrast_performance_evaluate():
|
||||
eventFeatPath = r"\\192.168.1.28\share\测试_202406\contrast\events"
|
||||
stdBcdPath = r"\\192.168.1.28\share\测试_202406\contrast\std_barcodes"
|
||||
|
||||
stdFeatPath = r"\\192.168.1.28\share\测试_202406\contrast\std_features"
|
||||
|
||||
subimgPath = r"\\192.168.1.28\share\测试_202406\contrast\subimgs"
|
||||
|
||||
|
||||
# stdBarcode = [p.stem for p in Path(stdFeatPath).iterdir() if p.is_file() and p.suffix=='.pickle']
|
||||
stdBarcode = [p.stem for p in Path(stdBcdPath).iterdir() if p.is_file() and p.suffix=='.pickle']
|
||||
|
||||
|
||||
'''购物事件列表，该列表中的 Barcode 存在于标准的 stdBarcode 内'''
|
||||
# evtList = [(p.stem, p.stem.split('_')[1]) for p in Path(eventFeatPath).iterdir()
|
||||
# if p.is_file()
|
||||
# and p.suffix=='.pickle'
|
||||
# and len(p.stem.split('_'))==2
|
||||
# and p.stem.split('_')[1].isdigit()
|
||||
# and p.stem.split('_')[1] in stdBarcode
|
||||
# ]
|
||||
|
||||
evtList = [(p.stem, p.stem.split('_')[1]) for p in Path(eventFeatPath).iterdir()
|
||||
if p.is_file()
|
||||
and p.suffix=='.pickle'
|
||||
and len(p.stem.split('_'))==2
|
||||
and p.stem.split('_')[1].isdigit()
|
||||
and p.stem.split('_')[1] in stdBarcode
|
||||
]
|
||||
|
||||
barcodes = set([bcd for _, bcd in evtList])
|
||||
|
||||
'''标准特征集图像样本经特征提取并保存，运行一次后无需再运行'''
|
||||
# stdfeat_infer(stdBcdPath, stdFeatPath, barcodes)
|
||||
|
||||
'''========= 构建用于比对的标准特征字典 ============='''
|
||||
stdDict = {}
|
||||
for barcode in barcodes:
|
||||
stdpath = os.path.join(stdFeatPath, barcode+'.pickle')
|
||||
with open(stdpath, 'rb') as f:
|
||||
stddata = pickle.load(f)
|
||||
stdDict[barcode] = stddata
|
||||
|
||||
'''========= 构建用于比对的操作事件字典 ============='''
|
||||
evtDict = {}
|
||||
for event, barcode in evtList:
|
||||
evtpath = os.path.join(eventFeatPath, event+'.pickle')
|
||||
with open(evtpath, 'rb') as f:
|
||||
evtdata = pickle.load(f)
|
||||
evtDict[event] = evtdata
|
||||
|
||||
|
||||
'''===== 构造 3 个事件对: 扫 A 放 A, 扫 A 放 B, 合并 ===================='''
|
||||
AA_list = [(event, barcode, "same") for event, barcode in evtList]
|
||||
AB_list = []
|
||||
for event, barcode in evtList:
|
||||
dset = list(barcodes.symmetric_difference(set([barcode])))
|
||||
idx = random.randint(0, len(dset)-1)
|
||||
AB_list.append((event, dset[idx], "diff"))
|
||||
|
||||
mergePairs = AA_list + AB_list
|
||||
|
||||
'''读取事件、标准特征文件中数据，以 AA_list 和 AB_list 中关键字为 key 生成字典'''
|
||||
results = []
|
||||
for evt, stdbcd, label in mergePairs:
|
||||
## 标准特征字典的构造方式不合适，需改进，不能用具体的barcode做key值
|
||||
|
||||
|
||||
event = evtDict[evt]
|
||||
|
||||
|
||||
## 判断是否存在轨迹图像文件夹，不存在则创建文件夹并保存轨迹图像
|
||||
pairpath = os.path.join(subimgPath, f"{evt}")
|
||||
if not os.path.exists(pairpath):
|
||||
os.makedirs(pairpath)
|
||||
save_event_subimg(event, pairpath)
|
||||
|
||||
## 判断是否存在 barcode 样本集图像文件夹，不存在则创建文件夹并存储 barcode 样本集图像
|
||||
stdImgpath = stdDict[stdbcd]["imgpaths"]
|
||||
pstdpath = os.path.join(subimgPath, f"{stdbcd}")
|
||||
if not os.path.exists(pstdpath):
|
||||
os.makedirs(pstdpath)
|
||||
ii = 1
|
||||
for filepath in stdImgpath:
|
||||
stdpath = os.path.join(pstdpath, f"{stdbcd}_{ii}.png")
|
||||
shutil.copy2(filepath, stdpath)
|
||||
ii += 1
|
||||
|
||||
|
||||
stdfeat = stdDict[stdbcd]["feats"]
|
||||
evtfeat = event["feats_compose"]
|
||||
|
||||
# 比较特征归一化前后的相似度变化情况
|
||||
|
||||
matrix = 1 - cdist(stdfeat, evtfeat, 'cosine')
|
||||
simi_mean = np.mean(matrix)
|
||||
simi_max = np.max(matrix)
|
||||
|
||||
stdfeatm = np.mean(stdfeat, axis=0, keepdims=True)
|
||||
evtfeatm = np.mean(evtfeat, axis=0, keepdims=True)
|
||||
simi_mfeat = 1- np.maximum(0.0, cdist(stdfeatm, evtfeatm, 'cosine'))
|
||||
|
||||
results.append((label, stdbcd, evt, simi_mean, simi_max, simi_mfeat[0,0]))
|
||||
|
||||
print("contrast performance evaluate have done!")
|
||||
|
||||
tm = datetime.fromtimestamp(time.time()).strftime('%Y%m%d_%H%M%S')
|
||||
with open(f'{tm}.pickle', 'wb') as f:
|
||||
pickle.dump(results, f)
|
||||
|
||||
with open(f'{tm}.txt', 'w', encoding='utf-8') as f:
|
||||
for result in results:
|
||||
part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result]
|
||||
line = ', '.join(part)
|
||||
f.write(line + '\n')
|
||||
|
||||
def compute_contrast_accuracy(pickpath):
|
||||
|
||||
pickfile = os.path.basename(pickpath)
|
||||
file, _ = os.path.splitext(pickfile)
|
||||
|
||||
# tm = datetime.fromtimestamp(time.time()).strftime('%Y%m%d_%H%M%S')
|
||||
|
||||
|
||||
with open(pickpath, 'rb') as f:
|
||||
results = pickle.load(f)
|
||||
|
||||
Same, Cross = [], []
|
||||
for label, stdbcd, evt, simi_mean, simi_max, simi_mft in results:
|
||||
if label == "same":
|
||||
Same.append(simi_max)
|
||||
if label == "diff":
|
||||
Cross.append(simi_max)
|
||||
|
||||
|
||||
Same = np.array(Same)
|
||||
Cross = np.array(Cross)
|
||||
TPFN = len(Same)
|
||||
TNFP = len(Cross)
|
||||
|
||||
fig, axs = plt.subplots(2, 1)
|
||||
axs[0].hist(Same, bins=60, edgecolor='black')
|
||||
axs[0].set_xlim([-0.2, 1])
|
||||
axs[0].set_title(f'Same Barcode, Num: {TPFN}')
|
||||
|
||||
axs[1].hist(Cross, bins=60, edgecolor='black')
|
||||
axs[1].set_xlim([-0.2, 1])
|
||||
axs[1].set_title(f'Cross Barcode, Num: {TNFP}')
|
||||
plt.savefig(f'./result/{file}_hist.png') # svg, png, pdf
|
||||
|
||||
|
||||
Recall_Pos, Recall_Neg = [], []
|
||||
Precision_Pos, Precision_Neg = [], []
|
||||
Correct = []
|
||||
Thresh = np.linspace(-0.2, 1, 100)
|
||||
for th in Thresh:
|
||||
TP = np.sum(Same > th)
|
||||
FN = TPFN - TP
|
||||
TN = np.sum(Cross < th)
|
||||
FP = TNFP - TN
|
||||
|
||||
Recall_Pos.append(TP/TPFN)
|
||||
Recall_Neg.append(TN/TNFP)
|
||||
Precision_Pos.append(TP/(TP+FP+1e-6))
|
||||
Precision_Neg.append(TN/(TN+FN+1e-6))
|
||||
Correct.append((TN+TP)/(TPFN+TNFP))
|
||||
|
||||
fig, ax = plt.subplots()
|
||||
ax.plot(Thresh, Correct, 'r', label='Correct: (TN+TP)/(TPFN+TNFP)')
|
||||
ax.plot(Thresh, Recall_Pos, 'b', label='Recall_Pos: TP/TPFN')
|
||||
ax.plot(Thresh, Recall_Neg, 'g', label='Recall_Neg: TN/TNFP')
|
||||
ax.plot(Thresh, Precision_Pos, 'c', label='Precision_Pos: TP/(TP+FP)')
|
||||
ax.plot(Thresh, Precision_Neg, 'm', label='Precision_Neg: TN/(TN+FN)')
|
||||
|
||||
ax.set_xlim([0, 1])
|
||||
ax.set_ylim([0, 1])
|
||||
ax.grid(True)
|
||||
ax.set_title('PrecisePos & PreciseNeg')
|
||||
ax.set_xlabel(f"Same Num: {TPFN}, Cross Num: {TNFP}")
|
||||
ax.legend()
|
||||
plt.show()
|
||||
plt.savefig(f'./result/{file}_pr.png') # svg, png, pdf
|
||||
|
||||
print("Haved done!!!")
|
||||
|
||||
|
||||
|
||||
def generate_event_and_stdfeatures():
|
||||
stdSamplePath = r"\\192.168.1.28\share\已标注数据备份\对比数据\barcode\barcode_1771"
|
||||
stdBarcodePath = r"\\192.168.1.28\share\测试_202406\contrast\barcodes"
|
||||
stdFeaturePath = r"\\192.168.1.28\share\测试_202406\contrast\features"
|
||||
stdBarcodePath = r"\\192.168.1.28\share\测试_202406\contrast\std_barcodes"
|
||||
stdFeaturePath = r"\\192.168.1.28\share\测试_202406\contrast\std_features"
|
||||
|
||||
'''=========================== 1. 生成标准特征集 ========================'''
|
||||
'''1.1 提取并保存标准特征库原始图像文件地址字典'''
|
||||
@ -390,46 +593,78 @@ def generate_event_and_standard_features():
|
||||
# print("standard imgpath have extracted and saved")
|
||||
|
||||
|
||||
'''1.2 特征提取，并保存至文件夹 stdFeaturePath 中'''
|
||||
stdfeat_infer(stdBarcodePath, stdFeaturePath)
|
||||
'''1.2 特征提取，并保存至文件夹 stdFeaturePath 中，也可在运行过程中根据barcodes交集执行'''
|
||||
# stdfeat_infer(stdBarcodePath, stdFeaturePath, bcdSet=None)
|
||||
# print("standard features have generated!")
|
||||
|
||||
|
||||
'''=========================== 2. 提取并存储事件特征 ========================'''
|
||||
shoppingEventPath = [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']
|
||||
shoppingFeatPath = r"\\192.168.1.28\share\测试_202406\contrast\events"
|
||||
# for sPath in shoppingEventPath:
|
||||
# eventList = creat_shopping_event(sPath, shoppingFeatPath)
|
||||
eventDatePath = [# 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
|
||||
]
|
||||
eventFeatPath = r"\\192.168.1.28\share\测试_202406\contrast\events"
|
||||
subimgPath = r'\\192.168.1.28\share\测试_202406\contrast\subimgs'
|
||||
|
||||
eventList = []
|
||||
# k = 0
|
||||
for datePath in eventDatePath:
|
||||
for eventName in os.listdir(datePath):
|
||||
pickpath = os.path.join(eventFeatPath, f"{eventName}.pickle")
|
||||
if os.path.isfile(pickpath):
|
||||
continue
|
||||
|
||||
eventPath = os.path.join(datePath, eventName)
|
||||
|
||||
eventDict = creat_shopping_event(eventPath)
|
||||
if eventDict:
|
||||
eventList.append(eventDict)
|
||||
with open(pickpath, 'wb') as f:
|
||||
pickle.dump(eventDict, f)
|
||||
print(f"Event: {eventName}, have saved!")
|
||||
|
||||
# k += 1
|
||||
# if k==1:
|
||||
# break
|
||||
|
||||
## 保存轨迹中 boxes 子图
|
||||
# for event in eventList:
|
||||
# basename = os.path.basename(event['filepath'])
|
||||
# savepath = os.path.join(subimgPath, basename)
|
||||
# if not os.path.exists(savepath):
|
||||
# os.makedirs(savepath)
|
||||
# save_event_subimg(event, savepath)
|
||||
|
||||
print("eventList have generated and features have saved!")
|
||||
|
||||
|
||||
|
||||
|
||||
def shopping_event_test():
|
||||
fplist = [#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'
|
||||
]
|
||||
|
||||
shoppingFeatPath = r"\\192.168.1.28\share\测试_202406\contrast\events"
|
||||
subimgPath = r'\\192.168.1.28\share\测试_202406\contrast\subimgs'
|
||||
|
||||
for filepath in fplist:
|
||||
'''用于保存一次购物事件的轨迹轨迹特征、及对应的图像子图'''
|
||||
extract_save_trajture_subimgs(filepath, shoppingFeatPath, subimgPath)
|
||||
|
||||
|
||||
|
||||
|
||||
def main():
|
||||
generate_event_and_standard_features()
|
||||
# shopping_event_test()
|
||||
# generate_event_and_stdfeatures()
|
||||
|
||||
contrast_performance_evaluate()
|
||||
|
||||
ppath = r"D:\DetectTracking\contrast"
|
||||
for filename in os.listdir(ppath):
|
||||
if filename.find('.pickle') < 0:
|
||||
continue
|
||||
|
||||
pickpath = os.path.join(ppath, filename)
|
||||
compute_contrast_accuracy(pickpath)
|
||||
|
||||
pass
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
Reference in New Issue
Block a user