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王庆刚
2024-09-02 11:50:08 +08:00
parent 5109400a57
commit 0cc36ba920
34 changed files with 1401 additions and 275 deletions
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tracking/module_analysis.py
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@ -25,110 +25,14 @@ from tracking.utils.drawtracks import plot_frameID_y2, draw_all_trajectories
from tracking.utils.read_data import extract_data, read_deletedBarcode_file, read_tracking_output
from contrast_analysis import contrast_analysis
from tracking.utils.annotator import TrackAnnotator
W, H = 1024, 1280
Mode = 'front' #'back'
ImgFormat = ['.jpg', '.jpeg', '.png', '.bmp']
def video2imgs(path):
vpath = os.path.join(path, "videos")
k = 0
have = False
for filename in os.listdir(vpath):
file, ext = os.path.splitext(filename)
imgdir = os.path.join(path, file)
if os.path.exists(imgdir):
continue
else:
os.mkdir(imgdir)
vfile = os.path.join(vpath, filename)
cap = cv2.VideoCapture(vfile)
i = 0
while True:
ret, frame = cap.read()
if not ret:
break
i += 1
imgp = os.path.join(imgdir, file+f"_{i}.png")
cv2.imwrite(imgp, frame)
print(filename+f": {i}")
cap.release()
k+=1
if k==1000:
break
def draw_boxes():
datapath = r'D:\datasets\ym\videos_test\20240530\1_tracker_inout(1).data'
VideosData = read_tracker_input(datapath)
bboxes = VideosData[0][0]
ffeats = VideosData[0][1]
videopath = r"D:\datasets\ym\videos_test\20240530\134458234-1cd970cf-f8b9-4e80-9c2e-7ca3eec83b81-1_seek0.10415589124891511.mp4"
cap = cv2.VideoCapture(videopath)
i = 0
while True:
ret, frame = cap.read()
if not ret:
break
annotator = Annotator(frame.copy(), line_width=3)
boxes = bboxes[i]
for *xyxy, conf, cls in reversed(boxes):
label = f'{int(cls)}: {conf:.2f}'
color = colors(int(cls), True)
annotator.box_label(xyxy, label, color=color)
img = annotator.result()
imgpath = r"D:\datasets\ym\videos_test\20240530\result\int8_front\{}.png".format(i+1)
cv2.imwrite(imgpath, img)
print(f"Output: {i}")
i += 1
cap.release()
def read_imgs(imgspath, CamerType):
imgs, frmIDs = [], []
for filename in os.listdir(imgspath):
file, ext = os.path.splitext(filename)
flist = file.split('_')
if len(flist)==4 and ext in ImgFormat:
camID, frmID = flist[0], int(flist[-1])
imgpath = os.path.join(imgspath, filename)
img = cv2.imread(imgpath)
if camID==CamerType:
imgs.append(img)
frmIDs.append(frmID)
if len(frmIDs):
indice = np.argsort(np.array(frmIDs))
imgs = [imgs[i] for i in indice]
return imgs
pass
'''调用tracking()函数，利用本地跟踪算法获取各目标轨迹，可以比较本地跟踪算法与现场跟踪算法的区别。'''
def init_tracker(tracker_yaml = None, bs=1):
"""
Initialize tracker for object tracking during prediction.
@ -177,38 +81,45 @@ def tracking(bboxes, ffeats):
return TrackBoxes, TracksDict
def read_imgs(imgspath, CamerType):
'''
inputs:
imgspath；序列图像地址
CamerType：相机类型，0：后摄，1：前摄
outputs：
imgs：图像序列
功能：
根据CamerType类型读取imgspath文件夹中的图像，并根据帧索引进行排序。
do_tracking()中调用该函数，实现（1）读取imgs并绘制各目标轨迹框；（2）获取subimgs
'''
imgs, frmIDs = [], []
for filename in os.listdir(imgspath):
file, ext = os.path.splitext(filename)
flist = file.split('_')
if len(flist)==4 and ext in ImgFormat:
camID, frmID = flist[0], int(flist[-1])
imgpath = os.path.join(imgspath, filename)
img = cv2.imread(imgpath)
if camID==CamerType:
imgs.append(img)
frmIDs.append(frmID)
def do_tracker_tracking(fpath, save_dir):
bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, tracking_feat_dict = extract_data(fpath)
tboxes, feats_dict = tracking(bboxes, ffeats)
CamerType = os.path.basename(fpath).split('_')[0]
dirname = os.path.split(os.path.split(fpath)[0])[1]
if CamerType == '1':
vts = doFrontTracks(tboxes, feats_dict)
vts.classify()
if len(frmIDs):
indice = np.argsort(np.array(frmIDs))
imgs = [imgs[i] for i in indice]
plt = plot_frameID_y2(vts)
plt.savefig('front_y2.png')
# plt.close()
elif CamerType == '0':
vts = doBackTracks(tboxes, feats_dict)
vts.classify()
filename = dirname+'_' + CamerType
edgeline = cv2.imread("./shopcart/cart_tempt/edgeline.png")
draw_all_trajectories(vts, edgeline, save_dir, filename)
else:
print("Please check data file!")
return imgs
def do_tracking(fpath, savedir, event_name='images'):
'''
fpath: 算法各模块输出的data文件地址，匹配；
savedir: 对 fpath 各模块输出的复现；
分析具体视频时，需指定 fpath 和 savedir
args:
fpath: 算法各模块输出的data文件地址，匹配；
savedir: 对 fpath 各模块输出的复现；
分析具体视频时，需指定 fpath 和 savedir
outputs:
img_tracking：目标跟踪轨迹、本地轨迹分析算法的轨迹对比图
abimg：现场轨迹分析算法、轨迹选择输出的对比图
'''
# fpath = r'D:\contrast\dataset\1_to_n\709\20240709-102758_6971558612189\1_track.data'
# savedir = r'D:\contrast\dataset\result\20240709-102843_6958770005357_6971558612189\error_6971558612189'
@ -231,8 +142,10 @@ def do_tracking(fpath, savedir, event_name='images'):
bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, tracking_feat_dict = extract_data(fpath)
tracking_output_boxes, _ = read_tracking_output(tracking_output_path)
'''1.2 利用本地跟踪算法生成各商品轨迹'''
# trackerboxes, tracker_feat_dict = tracking(bboxes, ffeats)
'''1.2 分别构造 2 个文件夹，(1) 存储画框后的图像; (2) 运动轨迹对应的 boxes子图'''
'''1.3 分别构造 2 个文件夹，(1) 存储画框后的图像; (2) 运动轨迹对应的 boxes子图'''
save_dir = os.path.join(savedir, event_name)
subimg_dir = os.path.join(savedir, event_name + '_subimgs')
if not os.path.exists(save_dir):
@ -241,8 +154,6 @@ def do_tracking(fpath, savedir, event_name='images'):
os.makedirs(subimg_dir)
'''2. 执行轨迹分析, 保存轨迹分析前后的对比图示'''
traj_graphic = event_name + '_' + CamerType
if CamerType == '1':
@ -344,24 +255,30 @@ def do_tracking(fpath, savedir, event_name='images'):
def tracking_simulate(eventpath, savepath):
'''args:
eventpath: 时间文件夹
eventpath: 事件文件夹
savepath: 存储文件夹
遍历eventpath
'''
'''1. 获取事件名'''
event_names = os.path.basename(eventpath).strip().split('_')
if len(event_names)==2 and len(event_names[1])>=8:
enent_name = event_names[1]
elif len(event_names)==2 and len(event_names[1])==0:
enent_name = event_names[0]
else:
return
# =============================================================================
# '''1. 获取事件名'''
# event_names = os.path.basename(eventpath).strip().split('_')
# if len(event_names)==2 and len(event_names[1])>=8:
# enent_name = event_names[1]
# elif len(event_names)==2 and len(event_names[1])==0:
# enent_name = event_names[0]
# else:
# return
# =============================================================================
enent_name = os.path.basename(eventpath)[:15]
'''2. 依次读取 0/1_track.data 中数据，进行仿真'''
illu_tracking, illu_select = [], []
for filename in os.listdir(eventpath):
# filename = '1_track.data'
if filename.find("track.data") <= 0: continue
if filename.find("track.data") < 0: continue
fpath = os.path.join(eventpath, filename)
if not os.path.isfile(fpath): continue
@ -451,7 +368,7 @@ def main_loop():
'''2. 循环执行操作事件：取出、放入、错误匹配'''
for eventpath in tuple_paths:
try:
tracking_simulate(eventpath, savepath)
tracking_simulate(eventpath, savepath)
except Exception as e:
print(f'Error! {eventpath}, {e}')
@ -462,29 +379,29 @@ def main_loop():
def main():
'''
eventpath: data文件地址，该 data 文件包括 Pipeline 各模块输出
savepath: 包含二级目录，一级目录为轨迹图像;二级目录为与data文件对应的序列图像存储地址。
eventPaths: data文件地址，该 data 文件包括 Pipeline 各模块输出
SavePath: 包含二级目录，一级目录为轨迹图像;二级目录为与data文件对应的序列图像存储地址。
'''
EventPaths = r'\\192.168.1.28\share\测试_202406\0723\0723_2'
SavePath = r'D:\contrast\dataset\result'
eventPaths = r'\\192.168.1.28\share\测试_202406\0723\0723_3'
savePath = r'D:\contrast\dataset\result'
k=0
for pathname in os.listdir(EventPaths):
# pathname = "20240723-094731_6903148242797"
eventpath = os.path.join(EventPaths, pathname)
savepath = os.path.join(SavePath, pathname)
for pathname in os.listdir(eventPaths):
pathname = "20240723-163121_6925282237668"
eventpath = os.path.join(eventPaths, pathname)
savepath = os.path.join(savePath, pathname)
if not os.path.exists(savepath):
os.makedirs(savepath)
# tracking_simulate(eventpath, savepath)
try:
tracking_simulate(eventpath, savepath)
except Exception as e:
print(f'Error! {eventpath}, {e}')
tracking_simulate(eventpath, savepath)
# try:
# tracking_simulate(eventpath, savepath)
# except Exception as e:
# print(f'Error! {eventpath}, {e}')
# k += 1
# if k==10:
# break
k += 1
if k==1:
break
if __name__ == "__main__":