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20241218

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王庆刚
2024-12-18 17:35:24 +08:00
parent 39f94c7bd4
commit dac3b3f2b6
8 changed files with 862 additions and 787 deletions
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176
contrast/one2one_contrast.py
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@ -52,7 +52,7 @@ from tracking.utils.read_data import extract_data, read_tracking_output, read_si
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 ShoppingEvent
from utils.event import ShoppingEvent, save_data
from genfeats import gen_bcd_features
@ -84,86 +84,6 @@ def ft16_to_uint8(arr_ft16):
return arr_uint8, arr_ft16_
# =============================================================================
# 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
# 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')
# 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[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"cam{camerType}_{i}_tid{int(tid)}_fid({int(fid)}, {frameID}).png"
# spath = os.path.join(savepath, subimgName)
#
# cv2.imwrite(spath, subimg)
# # basename = os.path.basename(event['filepath'])
# print(f"Image saved: {os.path.basename(event.eventpath)}")
# =============================================================================
def data_precision_compare(stdfeat, evtfeat, evtMessage, save=True):
evt, stdbcd, label = evtMessage
rltdata, rltdata_ft16, rltdata_ft16_ = [], [], []
@ -289,43 +209,7 @@ def one2one_simi():
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:
subimgpairs = event.save_event_subimg(pairpath)
for subimgName, subimg in subimgpairs:
spath = os.path.join(pairpath, subimgName)
cv2.imwrite(spath, subimg)
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:
imgpairs = event.plot_save_image(img_path)
for imgname, img in imgpairs:
spath = os.path.join(img_path, imgname)
cv2.imwrite(spath, img)
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:
@ -440,16 +324,13 @@ def compute_precise_recall(rltdata):
rltpath = os.path.join(similPath, 'pr.png')
plt.savefig(rltpath) # svg, png, pdf
def gen_eventdict(sourcePath, saveimg=True):
eventList = []
errEvents = []
k = 0
k, errEvents = 0, []
for source_path in sourcePath:
evtpath, bname = os.path.split(source_path)
bname = r"20241126-135911-bdf91cf9-3e9a-426d-94e8-ddf92238e175_6923555210479"
# bname = r"20241126-135911-bdf91cf9-3e9a-426d-94e8-ddf92238e175_6923555210479"
source_path = os.path.join(evtpath, bname)
pickpath = os.path.join(eventDataPath, f"{bname}.pickle")
@ -457,7 +338,8 @@ def gen_eventdict(sourcePath, saveimg=True):
try:
event = ShoppingEvent(source_path, stype="data")
eventList.append(event)
# save_data(event, resultPath)
with open(pickpath, 'wb') as f:
pickle.dump(event, f)
print(bname)
@ -465,11 +347,12 @@ def gen_eventdict(sourcePath, saveimg=True):
errEvents.append(source_path)
print(e)
k += 1
if k==1:
break
# k += 1
# if k==1:
# break
errfile = os.path.join(eventDataPath, f'error_events.txt')
errfile = os.path.join(resultPath, 'error_events.txt')
with open(errfile, 'w', encoding='utf-8') as f:
for line in errEvents:
f.write(line + '\n')
@ -477,6 +360,8 @@ def gen_eventdict(sourcePath, saveimg=True):
def test_one2one():
'''==== 0. 生成事件列表和对应的 Barcodes列表 ==========='''
bcdList, event_spath = [], []
for evtpath in eventSourcePath:
for evtname in os.listdir(evtpath):
@ -486,9 +371,9 @@ def test_one2one():
if len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10:
bcdList.append(evt[-1])
event_spath.append(os.path.join(evtpath, evtname))
bcdSet = set(bcdList)
'''==== 1. 生成标准特征集, 只需运行一次, 在 genfeats.py 中实现 ==========='''
'''==== 1. 生成标准特征集, 只需运行一次, 在 genfeats.py 中实现 ==========='''
# bcdSet = set(bcdList)
# gen_bcd_features(stdSamplePath, stdBarcodePath, stdFeaturePath, bcdSet)
print("stdFeats have generated and saved!")
@ -511,40 +396,25 @@ if __name__ == '__main__':
(3) stdFeaturePath: 比对标准特征集特征存储地址
(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)
(6) eventDataPath: 用于1:1比对的购物事件存储地址，在resultPath下
(7) similPath: 1:1比对结果存储地址(事件级)，在resultPath下
'''
# 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"]
eventSourcePath = [r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\images"]
resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\result"
'''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")
eventDataPath = os.path.join(resultPath, "evtobjs")
similPath = os.path.join(resultPath, "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)