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

20241217

Browse Source
This commit is contained in:
王庆刚
2024-12-17 17:32:09 +08:00
parent afd033b965
commit 39f94c7bd4
11 changed files with 768 additions and 250 deletions
Download Patch File Download Diff File Expand all files Collapse all files

186
contrast/one2one_contrast.py
View File

@ -84,82 +84,84 @@ 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 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):
@ -296,7 +298,11 @@ def one2one_simi():
if not os.path.exists(pairpath):
os.makedirs(pairpath)
try:
save_event_subimg(event, pairpath)
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)
@ -304,10 +310,16 @@ def one2one_simi():
if not os.path.exists(img_path):
os.makedirs(img_path)
try:
plot_save_image(event, img_path)
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:
@ -353,17 +365,16 @@ def one2one_simi():
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
@ -436,8 +447,11 @@ def gen_eventdict(sourcePath, saveimg=True):
errEvents = []
k = 0
for source_path in sourcePath:
bname = os.path.basename(source_path)
evtpath, bname = os.path.split(source_path)
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")
if os.path.isfile(pickpath): continue
@ -451,9 +465,9 @@ def gen_eventdict(sourcePath, saveimg=True):
errEvents.append(source_path)
print(e)
# k += 1
# if k==10:
# break
k += 1
if k==1:
break
errfile = os.path.join(eventDataPath, f'error_events.txt')
with open(errfile, 'w', encoding='utf-8') as f: