lichen/detecttracking
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

pipeline.py 等更新

Browse Source
This commit is contained in:
王庆刚
2025-01-21 18:22:56 +08:00
parent bfe7bc0fd5
commit 64248b1557
15 changed files with 106 additions and 77 deletions
Download Patch File Download Diff File Expand all files Collapse all files

BIN
contrast/__pycache__/event_test.cpython-39.pyc
View File

Binary file not shown.

3
contrast/event_test.py
View File

@ -169,9 +169,6 @@ def calc_simil(event, stdfeat):
Similar = None # 在event.front_feats和event.back_feats同时为空时
return Similar
def simi_matrix():

BIN
contrast/feat_extract/__pycache__/config.cpython-39.pyc
View File

Binary file not shown.

BIN
contrast/feat_extract/__pycache__/inference.cpython-39.pyc
View File

Binary file not shown.

3
contrast/feat_extract/config.py
View File

@ -61,8 +61,9 @@ class Config:
test_val = "D:/比对/cl"
# test_val = "./data/test_data_100"
test_model = "checkpoints/best_resnet18_v12.pth"
# test_model = "checkpoints/best_resnet18_v12.pth"
# test_model = "checkpoints/zhanting_res_801.pth"
test_model = "checkpoints/zhanting_res_abroad_8021.pth"

23
contrast/feat_extract/inference.py
View File

@ -61,8 +61,17 @@ class FeatsInterface:
batch_patches = []
patches = []
for i, img in enumerate(images):
img = img.copy()
patch = self.transform(img)
img = img.copy()
## 对 img 进行补黑边，生成新的图像new_img
width, height = img.size
new_size = max(width, height)
new_img = Image.new("RGB", (new_size, new_size), (0, 0, 0))
paste_x = (new_size - width) // 2
paste_y = (new_size - height) // 2
new_img.paste(img, (paste_x, paste_y))
patch = self.transform(new_img)
if str(self.device) != "cpu":
patch = patch.to(device=self.device).half()
else:
@ -107,10 +116,12 @@ class FeatsInterface:
patch = self.transform(img1)
# patch = patch.to(device=self.device).half()
if str(self.device) != "cpu":
patch = patch.to(device=self.device).half()
else:
patch = patch.to(device=self.device)
# if str(self.device) != "cpu":
# patch = patch.to(device=self.device).half()
# patch = patch.to(device=self.device)
# else:
# patch = patch.to(device=self.device)
patch = patch.to(device=self.device)
patches.append(patch)
if (d + 1) % self.batch_size == 0:

14
contrast/genfeats.py
View File

@ -158,6 +158,8 @@ def stdfeat_infer(imgPath, featPath, bcdSet=None):
feature /= np.linalg.norm(feature, axis=1)[:, None]
feature_ft32 = feature.astype(np.float32)
# float16
feature_ft16 = feature.astype(np.float16)
feature_ft16 /= np.linalg.norm(feature_ft16, axis=1)[:, None]
@ -173,7 +175,7 @@ def stdfeat_infer(imgPath, featPath, bcdSet=None):
##================== float32
stdbDict["barcode"] = barcode
stdbDict["imgpaths"] = imgpaths
stdbDict["feats_ft32"] = feature
stdbDict["feats_ft32"] = feature_ft32
stdbDict["feats_ft16"] = feature_ft16
stdbDict["feats_uint8"] = feature_uint8
@ -202,9 +204,13 @@ def gen_bcd_features(imgpath, bcdpath, featpath, bcdSet=None):
stdfeat_infer(bcdpath, featpath, bcdSet)
def main():
imgpath = 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"
bcdpath = r"D:\exhibition\dataset\bcdpath"
featpath = r"D:\exhibition\dataset\feats"
imgpath = r"\\192.168.1.28\share\数据\已完成数据\展厅数据\v2.0_abroad\比对数据\all_base_二筛"
bcdpath = r"D:\exhibition\dataset\bcdpath_abroad"
featpath = r"D:\exhibition\dataset\feats_abroad"
if not os.path.exists(bcdpath):
os.makedirs(bcdpath)
if not os.path.exists(featpath):
os.makedirs(featpath)
gen_bcd_features(imgpath, bcdpath, featpath)

51
contrast/one2n_contrast.py
View File

@ -92,9 +92,9 @@ def simi_calc(event, o2nevt, typee=None):
feat2 = o2nevt.front_feats
'''自定义事件特征选择'''
if typee==3:
feat1 = event.feats_compose
feat2 = o2nevt.feats_compose
if typee==3 and len(event.feats_compose) and len(o2nevt.feats_compose):
feat1 = [event.feats_compose]
feat2 = [o2nevt.feats_compose]
if len(feat1) and len(feat2):
@ -109,15 +109,18 @@ def one2n_pr(evtDicts, pattern=1):
'''
pattern:
1: process.data 中记录的相似度
2: 根据 process.data 中标记的 type 选择特征计算
3: 以其它方式选择特征计算
2: 根据 process.data 中标记的 type 选择特征计算相似度
3: 以其它方式选择特征计算相似度
'''
tpevents, fnevents, fpevents, tnevents = [], [], [], []
tpsimi, fnsimi, tnsimi, fpsimi = [], [], [], []
errorFile_one2n = []
one2nFile, errorFile_one2n = [], []
for evtname, event in evtDicts.items():
evt_names, evt_barcodes, evt_similars, evt_types = [], [], [], []
if len(event.barcode)==0:
continue
for ndict in event.one2n:
nname = ndict["event"]
@ -149,6 +152,13 @@ def one2n_pr(evtDicts, pattern=1):
continue
evt_similars.append(simival)
## process.data的oneTon的各项中，均不包括当前事件的barcode
if event.barcode not in evt_barcodes:
errorFile_one2n.append(evtname)
continue
else:
one2nFile.append(evtname)
if len(evt_names)==len(evt_barcodes) and len(evt_barcodes)==len(evt_similars) \
and len(evt_similars)==len(evt_types) and len(evt_names)>0:
@ -166,7 +176,7 @@ def one2n_pr(evtDicts, pattern=1):
elif bcd!=event.barcode and simi!=maxsim:
tnsimi.append(simi)
tnevents.append(evtname)
elif bcd!=event.barcode and simi==maxsim and event.barcode in evt_barcodes:
elif bcd!=event.barcode and simi==maxsim:
fpsimi.append(simi)
fpevents.append(evtname)
else:
@ -187,7 +197,7 @@ def one2n_pr(evtDicts, pattern=1):
TN = sum(np.array(tnsimi) < th)
PPrecise.append(TP/(TP+FP+1e-6))
PRecall.append(TP/(len(tpsimi)+len(fnsimi)+1e-6))
PRecall.append(TP/(len(one2nFile)+1e-6))
NPrecise.append(TN/(TN+FN+1e-6))
NRecall.append(TN/(len(tnsimi)+len(fpsimi)+1e-6))
@ -202,23 +212,23 @@ def one2n_pr(evtDicts, pattern=1):
ax.set_ylim([0, 1])
ax.grid(True)
ax.set_title('1:n Precise & Recall')
ax.set_xlabel(f"Event Num: {len(tpsimi)+len(fnsimi)}")
ax.set_xlabel(f"Event Num: {len(one2nFile)}")
ax.legend()
plt.show()
## ============================= 1:n 直方图'''
fig, axes = plt.subplots(2, 2)
axes[0, 0].hist(tpsimi, bins=60, range=(-0.2, 1), edgecolor='black')
axes[0, 0].set_xlim([-0.2, 1])
axes[0, 0].set_title('TP')
axes[0, 0].set_title(f'TP: {len(tpsimi)}')
axes[0, 1].hist(fpsimi, bins=60, range=(-0.2, 1), edgecolor='black')
axes[0, 1].set_xlim([-0.2, 1])
axes[0, 1].set_title('FP')
axes[0, 1].set_title(f'FP: {len(fpsimi)}')
axes[1, 0].hist(tnsimi, bins=60, range=(-0.2, 1), edgecolor='black')
axes[1, 0].set_xlim([-0.2, 1])
axes[1, 0].set_title('TN')
axes[1, 0].set_title(f'TN: {len(tnsimi)}')
axes[1, 1].hist(fnsimi, bins=60, range=(-0.2, 1), edgecolor='black')
axes[1, 1].set_xlim([-0.2, 1])
axes[1, 1].set_title('FN')
axes[1, 1].set_title(f'FN: {len(fnsimi)}')
plt.show()
return fpevents
@ -226,14 +236,14 @@ def one2n_pr(evtDicts, pattern=1):
def main():
'''1. 生成事件字典并保存至 eventDataPath, 只需运行一次 '''
init_eventdict(eventSourcePath, stype="source")
init_eventdict(eventSourcePath, stype="data")
'''2. 读取事件字典 '''
evtDicts = read_eventdict(eventDataPath)
'''3. 1:n 比对事件评估 '''
fpevents = one2n_pr(evtDicts, pattern=3)
fpevents = one2n_pr(evtDicts, pattern=1)
fpErrFile = str(Path(resultPath).joinpath("one2n_fp_Error.txt"))
with open(fpErrFile, "w") as file:
@ -243,15 +253,16 @@ def main():
if __name__ == '__main__':
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\result\ShoppingDict_pkfile"
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\contrast"
eventSourcePath = r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\比对数据"
resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\testing"
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(similPath):
os.makedirs(similPath)
# similPath = os.path.join(resultPath, "simidata")
# if not os.path.exists(similPath):
# os.makedirs(similPath)
main()

13
contrast/one2one_contrast.py
View File

@ -626,12 +626,13 @@ if __name__ == '__main__':
# 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"]
stdSamplePath = r"\\192.168.1.28\share\数据\已完成数据\比对数据\barcode\all_totalBarocde\totalBarcode"
stdBarcodePath = r"D:\全实时\source_data\bcdpath"
stdFeaturePath = r"D:\全实时\source_data\stdfeats"
stdSamplePath = r"\\192.168.1.28\share\数据\已完成数据\展厅数据\v2.0_abroad\比对数据\all_base_二筛"
stdBarcodePath = r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\testing\bcdpath"
stdFeaturePath = r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\testing\stdfeats"
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\result\ShoppingDict_pkfile"]
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\contrast"
eventSourcePath = [r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\比对数据"]
resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\testing\evtobjs"
eventDataPath = os.path.join(resultPath, "evtobjs")
similPath = os.path.join(resultPath, "simidata")
if not os.path.exists(eventDataPath):
@ -639,7 +640,7 @@ if __name__ == '__main__':
if not os.path.exists(similPath):
os.makedirs(similPath)
# test_one2one()
test_one2one()
test_one2SN()

2
contrast/onsite_contrast_pr.py
View File

@ -495,7 +495,7 @@ def contrast_pr(paths):
if __name__ == "__main__":
evtpaths = r"D:\全实时\source_data\2024122416"
evtpaths = 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"
contrast_pr(evtpaths)

BIN
contrast/utils/__pycache__/event.cpython-39.pyc
View File

Binary file not shown.

8
contrast/utils/event.py
View File

@ -202,10 +202,10 @@ class ShoppingEvent:
self.front_trackingfeats = frontdata[5]
'''===========对应于 0/1_tracking_output.data ============================='''
self.back_boxes = back_outdata[0]
self.back_feats = back_outdata[1]
self.front_boxes = front_outdata[0]
self.front_feats = front_outdata[1]
self.back_boxes = back_outdata
self.back_feats = back_outdata
self.front_boxes = front_outdata
self.front_feats = front_outdata
def from_datafile(self, eventpath):