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modify 1:1 比对方式

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王庆刚
2024-12-05 10:23:03 +08:00
parent 8bbee310ba
commit 1e6c5deee4
18 changed files with 728 additions and 398 deletions
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contrast/one2one_contrast.py
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@ -11,7 +11,7 @@ Created on Fri Aug 30 17:53:03 2024
标准特征提取，并保存至文件夹 stdFeaturePath 中，
也可在运行过程中根据与购物事件集合 barcodes 交集执行
2. 1:1 比对性能测试，
func: one2one_eval(resultPath)
func: one2one_eval(similPath)
(1) 求购物事件和标准特征级 Barcode 交集，构造 evtDict、stdDict
(2) 构造扫 A 放 A、扫 A 放 B 组合，mergePairs = AA_list + AB_list
(3) 循环计算 mergePairs 中元素 "(A, A) 或 (A, B)" 相似度;
@ -32,6 +32,7 @@ import os
import sys
import random
import pickle
import json
# import torch
import time
# import json
@ -47,10 +48,12 @@ from datetime import datetime
# from feat_inference import inference_image
sys.path.append(r"D:\DetectTracking")
from tracking.utils.read_data import extract_data, read_tracking_output, read_one2one_simi, read_deletedBarcode_file
from config import config as conf
from genfeats import model_init, genfeatures, stdfeat_infer
from tracking.utils.read_data import extract_data, read_tracking_output, read_similar, read_deletedBarcode_file
from tracking.utils.plotting import Annotator, colors
from feat_extract.config import config as conf
from feat_extract.inference import FeatsInterface
from utils.event import Event
from genfeats import gen_bcd_features
IMG_FORMAT = ['.bmp', '.jpg', '.jpeg', '.png']
@ -107,6 +110,10 @@ def creat_shopping_event(eventPath):
evtType = 'other'
'''================ 1. 构造事件描述字典，暂定 9 items ==============='''
event = {}
event['barcode'] = barcode
event['type'] = evtType
@ -118,7 +125,8 @@ def creat_shopping_event(eventPath):
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['one2one_simi'] = None
event['one2one'] = None
event['one2n'] = None
event['feats_select'] = np.empty((0, 256), dtype=np.float64)
@ -145,8 +153,9 @@ def creat_shopping_event(eventPath):
event['front_feats'] = tracking_output_feats
if dataname.find("process.data")==0:
simiDict = read_one2one_simi(datapath)
event['one2one_simi'] = simiDict
simiDict = read_similar(datapath)
event['one2one'] = simiDict['one2one']
event['one2n'] = simiDict['one2n']
if len(event['back_boxes'])==0 or len(event['front_boxes'])==0:
@ -215,6 +224,52 @@ def creat_shopping_event(eventPath):
return event
def plot_save_image(event, savepath):
cameras = ('front', 'back')
for camera in cameras:
if camera == 'front':
boxes = event.front_trackerboxes
imgpaths = event.front_imgpaths
else:
boxes = event.back_trackerboxes
imgpaths = event.back_imgpaths
def array2list(bboxes):
'''[x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]'''
frame_ids = bboxes[:, 7].astype(int)
fID = np.unique(bboxes[:, 7].astype(int))
fboxes = []
for f_id in fID:
idx = np.where(frame_ids==f_id)[0]
box = bboxes[idx, :]
fboxes.append((f_id, box))
return fboxes
fboxes = array2list(boxes)
for fid, fbox in fboxes:
imgpath = imgpaths[int(fid-1)]
image = cv2.imread(imgpath)
annotator = Annotator(image.copy(), line_width=2)
for i, *xyxy, tid, score, cls, fid, bid in enumerate(fbox):
label = f'{int(id), int(cls)}'
if tid >=0 and cls==0:
color = colors(int(cls), True)
elif tid >=0 and cls!=0:
color = colors(int(id), True)
else:
color = colors(19, True) # 19为调色板的最后一个元素
annotator.box_label(xyxy, label, color=color)
im0 = annotator.result()
spath = os.path.join(savepath, Path(imgpath).name)
cv2.imwrite(spath, im0)
def save_event_subimg(event, savepath):
'''
功能: 保存一次购物事件的轨迹子图
@ -224,160 +279,92 @@ def save_event_subimg(event, savepath):
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']
boxes = event.front_boxes
imgpaths = event.front_imgpaths
else:
boxes = event['back_boxes']
imgpaths = event['back_imgpaths']
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]
imgpath = imgpaths[int(fid-1)]
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"
subimgName = f"cam{camerType}_{i}_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'])}")
print(f"Image saved: {os.path.basename(event.eventpath)}")
def one2one_eval(resultPath):
# stdBarcode = [p.stem for p in Path(stdFeaturePath).iterdir() if p.is_file() and p.suffix=='.pickle']
stdBarcode = [p.stem for p in Path(stdBarcodePath).iterdir() if p.is_file() and p.suffix=='.pickle']
'''购物事件列表，该列表中的 Barcode 存在于标准的 stdBarcode 内'''
evtList = [(p.stem, p.stem.split('_')[-1]) for p in Path(eventFeatPath).iterdir()
if p.is_file()
and p.suffix=='.pickle'
and (len(p.stem.split('_'))==2 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])
'''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(stdBarcodePath, stdFeaturePath, barcodes)
'''========= 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
'''========= 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'''
rltdata, rltdata_ft16, rltdata_ft16_ = [], [], []
for evt, stdbcd, label in mergePairs:
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 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
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
##============================================ float32
stdfeat = stdDict[stdbcd]["feats"]
evtfeat = event["feats_compose"]
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'))
rltdata.append((label, stdbcd, evt, simi_mean, simi_max, simi_mfeat[0,0]))
##============================================ float16
stdfeat_ft16 = stdfeat.astype(np.float16)
evtfeat_ft16 = evtfeat.astype(np.float16)
stdfeat_ft16 /= np.linalg.norm(stdfeat_ft16, axis=1)[:, None]
evtfeat_ft16 /= np.linalg.norm(evtfeat_ft16, axis=1)[:, None]
matrix_ft16 = 1 - cdist(stdfeat_ft16, evtfeat_ft16, 'cosine')
simi_mean_ft16 = np.mean(matrix_ft16)
simi_max_ft16 = np.max(matrix_ft16)
stdfeatm_ft16 = np.mean(stdfeat_ft16, axis=0, keepdims=True)
evtfeatm_ft16 = np.mean(evtfeat_ft16, axis=0, keepdims=True)
simi_mfeat_ft16 = 1- np.maximum(0.0, cdist(stdfeatm_ft16, evtfeatm_ft16, 'cosine'))
rltdata_ft16.append((label, stdbcd, evt, simi_mean_ft16, simi_max_ft16, simi_mfeat_ft16[0,0]))
'''****************** uint8 is ok!!!!!! ******************'''
##============================================ uint8
# stdfeat_uint8, stdfeat_ft16_ = ft16_to_uint8(stdfeat_ft16)
# evtfeat_uint8, evtfeat_ft16_ = ft16_to_uint8(evtfeat_ft16)
stdfeat_uint8 = (stdfeat_ft16*128).astype(np.int8)
evtfeat_uint8 = (evtfeat_ft16*128).astype(np.int8)
stdfeat_ft16_ = stdfeat_uint8.astype(np.float16)/128
evtfeat_ft16_ = evtfeat_uint8.astype(np.float16)/128
absdiff = np.linalg.norm(stdfeat_ft16_ - stdfeat) / stdfeat.size
matrix_ft16_ = 1 - cdist(stdfeat_ft16_, evtfeat_ft16_, 'cosine')
simi_mean_ft16_ = np.mean(matrix_ft16_)
simi_max_ft16_ = np.max(matrix_ft16_)
stdfeatm_ft16_ = np.mean(stdfeat_ft16_, axis=0, keepdims=True)
evtfeatm_ft16_ = np.mean(evtfeat_ft16_, axis=0, keepdims=True)
simi_mfeat_ft16_ = 1- np.maximum(0.0, cdist(stdfeatm_ft16_, evtfeatm_ft16_, 'cosine'))
rltdata_ft16_.append((label, stdbcd, evt, simi_mean_ft16_, simi_max_ft16_, simi_mfeat_ft16_[0,0]))
def data_precision_compare(stdfeat, evtfeat, evtMessage, save=True):
evt, stdbcd, label = evtMessage
rltdata, rltdata_ft16, rltdata_ft16_ = [], [], []
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'))
rltdata = [label, stdbcd, evt, simi_mean, simi_max, simi_mfeat[0,0]]
tm = datetime.fromtimestamp(time.time()).strftime('%Y%m%d_%H%M%S')
##================================================ save as float32,
rppath = os.path.join(resultPath, f'{tm}.pickle')
##================================================================= float16
stdfeat_ft16 = stdfeat.astype(np.float16)
evtfeat_ft16 = evtfeat.astype(np.float16)
stdfeat_ft16 /= np.linalg.norm(stdfeat_ft16, axis=1)[:, None]
evtfeat_ft16 /= np.linalg.norm(evtfeat_ft16, axis=1)[:, None]
matrix_ft16 = 1 - cdist(stdfeat_ft16, evtfeat_ft16, 'cosine')
simi_mean_ft16 = np.mean(matrix_ft16)
simi_max_ft16 = np.max(matrix_ft16)
stdfeatm_ft16 = np.mean(stdfeat_ft16, axis=0, keepdims=True)
evtfeatm_ft16 = np.mean(evtfeat_ft16, axis=0, keepdims=True)
simi_mfeat_ft16 = 1- np.maximum(0.0, cdist(stdfeatm_ft16, evtfeatm_ft16, 'cosine'))
rltdata_ft16 = [label, stdbcd, evt, simi_mean_ft16, simi_max_ft16, simi_mfeat_ft16[0,0]]
'''****************** uint8 is ok!!!!!! ******************'''
##=================================================================== uint8
# stdfeat_uint8, stdfeat_ft16_ = ft16_to_uint8(stdfeat_ft16)
# evtfeat_uint8, evtfeat_ft16_ = ft16_to_uint8(evtfeat_ft16)
stdfeat_uint8 = (stdfeat_ft16*128).astype(np.int8)
evtfeat_uint8 = (evtfeat_ft16*128).astype(np.int8)
stdfeat_ft16_ = stdfeat_uint8.astype(np.float16)/128
evtfeat_ft16_ = evtfeat_uint8.astype(np.float16)/128
absdiff = np.linalg.norm(stdfeat_ft16_ - stdfeat) / stdfeat.size
matrix_ft16_ = 1 - cdist(stdfeat_ft16_, evtfeat_ft16_, 'cosine')
simi_mean_ft16_ = np.mean(matrix_ft16_)
simi_max_ft16_ = np.max(matrix_ft16_)
stdfeatm_ft16_ = np.mean(stdfeat_ft16_, axis=0, keepdims=True)
evtfeatm_ft16_ = np.mean(evtfeat_ft16_, axis=0, keepdims=True)
simi_mfeat_ft16_ = 1- np.maximum(0.0, cdist(stdfeatm_ft16_, evtfeatm_ft16_, 'cosine'))
rltdata_ft16_ = [label, stdbcd, evt, simi_mean_ft16_, simi_max_ft16_, simi_mfeat_ft16_[0,0]]
if not save:
return
##========================================================= save as float32
rppath = os.path.join(similPath, f'{evt}_ft32.pickle')
with open(rppath, 'wb') as f:
pickle.dump(rltdata, f)
rtpath = os.path.join(resultPath, f'{tm}.txt')
rtpath = os.path.join(similPath, f'{evt}_ft32.txt')
with open(rtpath, 'w', encoding='utf-8') as f:
for result in rltdata:
part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result]
@ -385,12 +372,12 @@ def one2one_eval(resultPath):
f.write(line + '\n')
##================================================ save as float16,
rppath_ft16 = os.path.join(resultPath, f'{tm}_ft16.pickle')
##========================================================= save as float16
rppath_ft16 = os.path.join(similPath, f'{evt}_ft16.pickle')
with open(rppath_ft16, 'wb') as f:
pickle.dump(rltdata_ft16, f)
rtpath_ft16 = os.path.join(resultPath, f'{tm}_ft16.txt')
rtpath_ft16 = os.path.join(similPath, f'{evt}_ft16.txt')
with open(rtpath_ft16, 'w', encoding='utf-8') as f:
for result in rltdata_ft16:
part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result]
@ -398,42 +385,145 @@ def one2one_eval(resultPath):
f.write(line + '\n')
##================================================ save as uint8,
rppath_uint8 = os.path.join(resultPath, f'{tm}_uint8.pickle')
##=========================================================== save as uint8
rppath_uint8 = os.path.join(similPath, f'{evt}_uint8.pickle')
with open(rppath_uint8, 'wb') as f:
pickle.dump(rltdata_ft16_, f)
rtpath_uint8 = os.path.join(resultPath, f'{tm}_uint8.txt')
rtpath_uint8 = os.path.join(similPath, f'{evt}_uint8.txt')
with open(rtpath_uint8, 'w', encoding='utf-8') as f:
for result in rltdata_ft16_:
part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result]
line = ', '.join(part)
f.write(line + '\n')
def one2one_simi():
'''
stdFeaturePath: 标准特征集地址
eventDataPath: Event对象地址
'''
stdBarcode = [p.stem for p in Path(stdFeaturePath).iterdir() if p.is_file() and p.suffix=='.pickle']
'''======1. 购物事件列表，该列表中的 Barcode 存在于标准的 stdBarcode 内 ==='''
evtList = [(p.stem, p.stem.split('_')[-1]) for p in Path(eventDataPath).iterdir()
if p.is_file()
and p.suffix=='.pickle'
and (len(p.stem.split('_'))==2 or len(p.stem.split('_'))==3)
and p.stem.split('_')[-1].isdigit()
and p.stem.split('_')[-1] in stdBarcode
]
barcodes = set([bcd for _, bcd in evtList])
'''======2. 构建用于比对的标准特征字典 ============='''
stdDict = {}
for barcode in barcodes:
stdpath = os.path.join(stdFeaturePath, barcode+'.pickle')
with open(stdpath, 'rb') as f:
stddata = pickle.load(f)
stdDict[barcode] = stddata
'''======3. 构建用于比对的操作事件字典 ============='''
evtDict = {}
for evtname, barcode in evtList:
evtpath = os.path.join(eventDataPath, evtname+'.pickle')
with open(evtpath, 'rb') as f:
evtdata = pickle.load(f)
evtDict[evtname] = evtdata
'''======4.1 事件轨迹子图保存 ======================'''
error_event = []
for evtname, event in evtDict.items():
pairpath = os.path.join(subimgPath, f"{evtname}")
if not os.path.exists(pairpath):
os.makedirs(pairpath)
try:
save_event_subimg(event, pairpath)
except Exception as e:
error_event.append(evtname)
img_path = os.path.join(imagePath, f"{evtname}")
if not os.path.exists(img_path):
os.makedirs(img_path)
try:
plot_save_image(event, img_path)
except Exception as e:
error_event.append(evtname)
errfile = os.path.join(subimgPath, f'error_event.txt')
with open(errfile, 'w', encoding='utf-8') as f:
for line in error_event:
f.write(line + '\n')
'''======4.2 barcode 标准图像保存 =================='''
# for stdbcd in barcodes:
# stdImgpath = stdDict[stdbcd]["imgpaths"]
# pstdpath = os.path.join(subimgPath, f"{stdbcd}")
# if not os.path.exists(pstdpath):
# os.makedirs(pstdpath)
# ii = 1
# for filepath in stdImgpath:
# stdpath = os.path.join(pstdpath, f"{stdbcd}_{ii}.png")
# shutil.copy2(filepath, stdpath)
# ii += 1
'''======5 构造 3 个事件对: 扫 A 放 A, 扫 A 放 B, 合并 ===================='''
AA_list = [(evtname, barcode, "same") for evtname, barcode in evtList]
AB_list = []
for evtname, barcode in evtList:
dset = list(barcodes.symmetric_difference(set([barcode])))
if len(dset):
idx = random.randint(0, len(dset)-1)
AB_list.append((evtname, dset[idx], "diff"))
mergePairs = AA_list + AB_list
'''======6 计算事件、标准特征集相似度 =================='''
rltdata = []
for i in range(len(mergePairs)):
evtname, stdbcd, label = mergePairs[i]
event = evtDict[evtname]
##============================================ float32
stdfeat = stdDict[stdbcd]["feats_ft32"]
evtfeat = event.feats_compose
if len(evtfeat)==0: continue
matrix = 1 - cdist(stdfeat, evtfeat, 'cosine')
matrix[matrix < 0] = 0
simi_mean = np.mean(matrix)
simi_max = np.max(matrix)
stdfeatm = np.mean(stdfeat, axis=0, keepdims=True)
evtfeatm = np.mean(evtfeat, axis=0, keepdims=True)
simi_mfeat = 1- np.maximum(0.0, cdist(stdfeatm, evtfeatm, 'cosine'))
rltdata.append((label, stdbcd, evtname, simi_mean, simi_max, simi_mfeat[0,0]))
'''================ float32、16、int8 精度比较与存储 ============='''
# data_precision_compare(stdfeat, evtfeat, mergePairs[i], save=True)
print("func: one2one_eval(), have finished!")
return rltdata
def compute_precise_recall(pickpath):
pickfile = os.path.basename(pickpath)
file, ext = os.path.splitext(pickfile)
if ext != '.pickle': return
if file.find('ft16') < 0: return
with open(pickpath, 'rb') as f:
results = pickle.load(f)
def compute_precise_recall(rltdata):
Same, Cross = [], []
for label, stdbcd, evt, simi_mean, simi_max, simi_mft in results:
for label, stdbcd, evtname, simi_mean, simi_max, simi_mft in rltdata:
if label == "same":
Same.append(simi_mean)
if label == "diff":
Cross.append(simi_mean)
Same = np.array(Same)
Cross = np.array(Cross)
TPFN = len(Same)
@ -480,115 +570,135 @@ def compute_precise_recall(pickpath):
ax.set_xlabel(f"Same Num: {TPFN}, Cross Num: {TNFP}")
ax.legend()
plt.show()
plt.savefig(f'./result/{file}_pr.png') # svg, png, pdf
rltpath = os.path.join(similPath, 'pr.png')
plt.savefig(rltpath) # svg, png, pdf
def gen_eventdict(eventDatePath, saveimg=True):
def gen_eventdict(sourcePath, saveimg=True):
eventList = []
# k = 0
for datePath in eventDatePath:
for eventName in os.listdir(datePath):
errEvents = []
k = 0
for source_path in sourcePath:
bname = os.path.basename(source_path)
pickpath = os.path.join(eventDataPath, f"{bname}.pickle")
if os.path.isfile(pickpath): continue
# if bname != "20241129-100321-a9dae9e3-7db5-4e31-959c-d7dfc228923e_6972636670213":
# continue
pickpath = os.path.join(eventFeatPath, f"{eventName}.pickle")
if os.path.isfile(pickpath):
continue
eventPath = os.path.join(datePath, eventName)
# eventDict = creat_shopping_event(eventPath)
# if eventDict:
# eventList.append(eventDict)
# with open(pickpath, 'wb') as f:
# pickle.dump(eventDict, f)
# print(f"Event: {eventName}, have saved!")
eventDict = creat_shopping_event(eventPath)
if eventDict:
eventList.append(eventDict)
with open(pickpath, 'wb') as f:
pickle.dump(eventDict, f)
print(f"Event: {eventName}, have saved!")
# if saveimg and eventDict:
# basename = os.path.basename(eventDict['filepath'])
# savepath = os.path.join(subimgPath, basename)
# if not os.path.exists(savepath):
# os.makedirs(savepath)
# save_event_subimg(eventDict, savepath)
try:
event = Event(source_path)
eventList.append(event)
with open(pickpath, 'wb') as f:
pickle.dump(event, f)
print(bname)
except Exception as e:
errEvents.append(source_path)
print(e)
# k += 1
# if k==10:
# break
# k += 1
# if k==1:
# break
## 保存轨迹中 boxes 子图
if not saveimg:
return
for event in eventList:
basename = os.path.basename(event['filepath'])
savepath = os.path.join(subimgPath, basename)
if not os.path.exists(savepath):
os.makedirs(savepath)
save_event_subimg(event, savepath)
errfile = os.path.join(eventDataPath, f'error_events.txt')
with open(errfile, 'w', encoding='utf-8') as f:
for line in errEvents:
f.write(line + '\n')
def test_one2one():
eventDatePath = [r'\\192.168.1.28\share\测试_202406\1101\images',
# r'\\192.168.1.28\share\测试_202406\0910\images',
# r'\\192.168.1.28\share\测试_202406\0723\0723_1',
# r'\\192.168.1.28\share\测试_202406\0723\0723_2',
# r'\\192.168.1.28\share\测试_202406\0723\0723_3',
# r'\\192.168.1.28\share\测试_202406\0722\0722_01',
# r'\\192.168.1.28\share\测试_202406\0722\0722_02'
# r'\\192.168.1.28\share\测试_202406\0719\719_3',
# r'\\192.168.1.28\share\测试_202406\0716\0716_1',
# r'\\192.168.1.28\share\测试_202406\0716\0716_2',
# r'\\192.168.1.28\share\测试_202406\0716\0716_3',
# r'\\192.168.1.28\share\测试_202406\0712\0712_1', # 无帧图像
# r'\\192.168.1.28\share\测试_202406\0712\0712_2', # 无帧图像
]
bcdList = []
for evtpath in eventDatePath:
bcdList, event_spath = [], []
for evtpath in eventSourcePath:
for evtname in os.listdir(evtpath):
evt = evtname.split('_')
dirpath = os.path.join(evtpath, evtname)
if os.path.isfile(dirpath): continue
if len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10:
bcdList.append(evt[-1])
bcdList.append(evt[-1])
event_spath.append(os.path.join(evtpath, evtname))
bcdSet = set(bcdList)
model = model_init(conf)
'''==== 1. 生成标准特征集, 只需运行一次 ==============='''
genfeatures(model, stdSamplePath, stdBarcodePath, stdFeaturePath, bcdSet)
'''==== 1. 生成标准特征集, 只需运行一次, 在 genfeats.py 中实现 ==========='''
# gen_bcd_features(stdSamplePath, stdBarcodePath, stdFeaturePath, bcdSet)
print("stdFeats have generated and saved!")
'''==== 2. 生成事件字典, 只需运行一次 ==============='''
gen_eventdict(eventDatePath)
gen_eventdict(event_spath)
print("eventList have generated and saved!")
'''==== 3. 1:1性能评估 ==============='''
one2one_eval(resultPath)
for filename in os.listdir(resultPath):
if filename.find('.pickle') < 0: continue
if filename.find('0911') < 0: continue
pickpath = os.path.join(resultPath, filename)
compute_precise_recall(pickpath)
rltdata = one2one_simi()
compute_precise_recall(rltdata)
if __name__ == '__main__':
'''
共6个地址：
共7个地址：
(1) stdSamplePath: 用于生成比对标准特征集的原始图像地址
(2) stdBarcodePath: 比对标准特征集原始图像地址的pickle文件存储，{barcode: [imgpath1, imgpath1, ...]}
(3) stdFeaturePath: 比对标准特征集特征存储地址
(4) eventFeatPath: 用于1:1比对的购物事件特征存储地址、对应子图存储地址
(5) subimgPath: 1:1比对购物事件轨迹、标准barcode所对应的 subimgs 存储地址
(6) resultPath: 1:1比对结果存储地址
(4) eventSourcePath: 事件地址
(5) resultPath: 结果存储地址
(6) eventDataPath: 用于1:1比对的购物事件特征存储地址、对应子图存储地址
(7) subimgPath: 1:1比对购物事件轨迹、标准barcode所对应的 subimgs 存储地址
(8) similPath: 1:1比对结果存储地址(事件级)
'''
stdSamplePath = r"\\192.168.1.28\share\已标注数据备份\对比数据\barcode\barcode_500_1979_已清洗"
stdBarcodePath = r"\\192.168.1.28\share\测试_202406\contrast\std_barcodes_2192"
stdFeaturePath = r"\\192.168.1.28\share\测试_202406\contrast\std_features_ft32"
eventFeatPath = r"\\192.168.1.28\share\测试_202406\contrast\events"
subimgPath = r'\\192.168.1.28\share\测试_202406\contrast\subimgs'
resultPath = r"D:\DetectTracking\contrast\result\pickle"
if not os.path.exists(resultPath):
os.makedirs(resultPath)
# stdSamplePath = r"\\192.168.1.28\share\已标注数据备份\对比数据\barcode\barcode_500_1979_已清洗"
# stdBarcodePath = r"\\192.168.1.28\share\测试_202406\contrast\std_barcodes_2192"
# stdFeaturePath = r"\\192.168.1.28\share\测试_202406\contrast\std_features_ft32"
# eventDataPath = r"\\192.168.1.28\share\测试_202406\contrast\events"
# subimgPath = r'\\192.168.1.28\share\测试_202406\contrast\subimgs'
# similPath = r"D:\DetectTracking\contrast\result\pickle"
# eventSourcePath = [r'\\192.168.1.28\share\测试_202406\1101\images']
stdSamplePath = r"\\192.168.1.28\share\数据\已完成数据\展厅数据\v1.0\比对数据\整理\zhantingBase"
stdBarcodePath = r"D:\exhibition\dataset\bcdpath"
stdFeaturePath = r"D:\exhibition\dataset\feats"
resultPath = r"D:\exhibition\result\events"
# eventSourcePath = [r'D:\exhibition\images\20241202']
# eventSourcePath = [r"\\192.168.1.28\share\测试视频数据以及日志\各模块测试记录\展厅测试\1129_展厅模型v801测试组测试"]
eventSourcePath = [r"\\192.168.1.28\share\测试视频数据以及日志\各模块测试记录\展厅测试\1126_展厅模型v801测试"]
'''定义当前事件存储地址及生成相应文件件'''
eventDataPath = os.path.join(resultPath, "1126", "evtobjs")
subimgPath = os.path.join(resultPath, "1126", "subimgs")
imagePath = os.path.join(resultPath, "1126", "image")
similPath = os.path.join(resultPath, "1126", "simidata")
if not os.path.exists(eventDataPath):
os.makedirs(eventDataPath)
if not os.path.exists(subimgPath):
os.makedirs(subimgPath)
if not os.path.exists(imagePath):
os.makedirs(imagePath)
if not os.path.exists(similPath):
os.makedirs(similPath)
test_one2one()