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王庆刚
2024-11-04 18:06:52 +08:00
parent dfb2272a15
commit 5ecc1285d4
41 changed files with 2552 additions and 440 deletions
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tracking/dotrack/__pycache__/dotracks.cpython-39.pyc
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tracking/dotrack/__pycache__/dotracks_back.cpython-39.pyc
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tracking/dotrack/__pycache__/track_back.cpython-39.pyc
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tracking/dotrack/__pycache__/track_front.cpython-39.pyc
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tracking/dotrack/dotracks.py
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@ -59,21 +59,21 @@ class ShoppingCart:
@property
def incart(self):
img = cv2.imread(str(curpath/'cart_tempt/back_incart.png'), cv2.IMREAD_GRAYSCALE)
img = cv2.imread(str(parpath/'shopcart/cart_tempt/incart.png'), cv2.IMREAD_GRAYSCALE)
ret, binary = cv2.threshold(img, 250, 255, cv2.THRESH_BINARY)
return binary
@property
def outcart(self):
img = cv2.imread(str(curpath/'cart_tempt/back_outcart.png'), cv2.IMREAD_GRAYSCALE)
img = cv2.imread(str(parpath/'shopcart/cart_tempt/outcart.png'), cv2.IMREAD_GRAYSCALE)
ret, binary = cv2.threshold(img, 250, 255, cv2.THRESH_BINARY)
return binary
@property
def cartedge(self):
img = cv2.imread(str(curpath/'cart_tempt/back_cartedge.png'), cv2.IMREAD_GRAYSCALE)
img = cv2.imread(str(parpath/'shopcart/cart_tempt/cartedge.png'), cv2.IMREAD_GRAYSCALE)
ret, binary = cv2.threshold(img, 250, 255, cv2.THRESH_BINARY)
return binary
@ -520,8 +520,7 @@ class doTracks:
mergedTracks.append(cur_list)
return mergedTracks
@staticmethod
def join_tracks(tlista, tlistb):
"""Combine two lists of stracks into a single one."""
@ -541,6 +540,93 @@ class doTracks:
def sub_tracks(tlista, tlistb):
track_ids_b = {t.tid for t in tlistb}
return [t for t in tlista if t.tid not in track_ids_b]
def array2frame(self, bboxes):
frameID = np.sort(np.unique(bboxes[:, 7].astype(int)))
fboxes = []
for fid in frameID:
idx = np.where(bboxes[:, 7] == fid)[0]
box = bboxes[idx, :]
fboxes.append(box)
return fboxes
def isintrude(self):
'''
boxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
0 1 2 3 4 5 6 7 8
'''
OverlapNum = 3
bboxes = self.bboxes.astype(np.int64)
fboxes = self.array2frame(bboxes)
incart = cv2.bitwise_not(self.incart)
sum_incart = np.zeros(incart.shape, dtype=np.int64)
for fid, boxes in enumerate(fboxes):
for i in range(len(boxes)):
x1, y1, x2, y2 = boxes[i, 0:4]
sum_incart[y1:y2, x1:x2] += 1
sumincart = np.zeros(sum_incart.shape, dtype=np.uint8)
idx255 = np.where(sum_incart >= OverlapNum)
sumincart[idx255] = 255
idxnzr = np.where(sum_incart!=0)
base = np.zeros(sum_incart.shape, dtype=np.uint8)
base[idxnzr] = 255
contours_sum, _ = cv2.findContours(sumincart, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours_base, _ = cv2.findContours(base, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
have_existed, invasion = [], []
for k, ct_temp in enumerate(contours_base):
tmp1 = np.zeros(sum_incart.shape, dtype=np.uint8)
cv2.drawContours(tmp1, [ct_temp], -1, 255, cv2.FILLED)
# 确定轮廓的包含关系
for ct_sum in contours_sum:
tmp2 = np.zeros(sum_incart.shape, dtype=np.uint8)
cv2.drawContours(tmp2, [ct_sum], -1, 255, cv2.FILLED)
tmp = cv2.bitwise_and(tmp1, tmp2)
if np.count_nonzero(tmp) == np.count_nonzero(tmp2):
have_existed.append(k)
inIdx = [i for i in range(len(contours_base)) if i not in have_existed]
invasion = np.zeros(sum_incart.shape, dtype=np.uint8)
for i in inIdx:
cv2.drawContours(invasion, [contours_base[i]], -1, 255, cv2.FILLED)
cv2.imwrite("./result/intrude/invasion.png", invasion)
Intrude = True if len(inIdx)>=1 else False
print(f"is intruded: {Intrude}")
return Intrude

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tracking/dotrack/dotracks_back.py
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@ -5,8 +5,15 @@ Created on Mon Mar 4 18:36:31 2024
@author: ym
"""
import numpy as np
import cv2
from tracking.utils.mergetrack import track_equal_track
from scipy.spatial.distance import cdist
from pathlib import Path
curpath = Path(__file__).resolve().parents[0]
curpath = Path(curpath)
parpath = curpath.parent
from .dotracks import doTracks, ShoppingCart
from .track_back import backTrack
@ -18,10 +25,26 @@ class doBackTracks(doTracks):
self.tracks = [backTrack(b, f) for b, f in zip(self.lboxes, self.lfeats)]
# self.similar_dict = self.similarity()
# self.similar_dict = self.similarity()
# self.shopcart = ShoppingCart(bboxes)
self.incart = self.getincart()
def getincart(self):
img1 = cv2.imread(str(parpath/'shopcart/cart_tempt/incart.png'), cv2.IMREAD_GRAYSCALE)
img2 = cv2.imread(str(parpath/'shopcart/cart_tempt/cartedge.png'), cv2.IMREAD_GRAYSCALE)
ret, binary1 = cv2.threshold(img1, 250, 255, cv2.THRESH_BINARY)
ret, binary2 = cv2.threshold(img2, 250, 255, cv2.THRESH_BINARY)
binary = cv2.bitwise_or(binary1, binary2)
return binary
self.shopcart = ShoppingCart(bboxes)
def classify(self):
'''功能：对 tracks 中元素分类 '''

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tracking/dotrack/dotracks_front.py
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@ -4,7 +4,13 @@ Created on Mon Mar 4 18:38:20 2024
@author: ym
"""
import cv2
import numpy as np
from pathlib import Path
curpath = Path(__file__).resolve().parents[0]
curpath = Path(curpath)
parpath = curpath.parent
# from tracking.utils.mergetrack import track_equal_track
from .dotracks import doTracks
from .track_front import frontTrack
@ -16,6 +22,14 @@ class doFrontTracks(doTracks):
# self.tracks = [frontTrack(b) for b in self.lboxes]
self.tracks = [frontTrack(b, f) for b, f in zip(self.lboxes, self.lfeats)]
self.incart = self.getincart()
def getincart(self):
img = cv2.imread(str(parpath/'shopcart/cart_tempt/incart_ftmp.png'), cv2.IMREAD_GRAYSCALE)
ret, binary = cv2.threshold(img, 250, 255, cv2.THRESH_BINARY)
return binary
def classify(self):
'''功能：对 tracks 中元素分类 '''

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tracking/dotrack/track_back.py
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@ -49,7 +49,7 @@ class backTrack(Track):
self.incartrates = incartrates'''
self.compute_ious_feat()
# self.PCA()
def isimgborder(self, BoundPixel=10, BoundThresh=0.3):

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tracking/dotrack/track_front.py
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@ -5,9 +5,15 @@ Created on Mon Mar 4 18:33:01 2024
@author: ym
"""
import numpy as np
from sklearn.cluster import KMeans
import cv2
# from sklearn.cluster import KMeans
from .dotracks import MoveState, Track
from pathlib import Path
curpath = Path(__file__).resolve().parents[0]
curpath = Path(curpath)
parpath = curpath.parent
class frontTrack(Track):
# boxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
@ -36,9 +42,6 @@ class frontTrack(Track):
self.HAND_STATIC_THRESH = 100
self.CART_POSIT_0 = 430
self.CART_POSIT_1 = 620
def is_left_or_right_cornpoint(self):
''' 基于 all(boxes)，

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tracking/module_analysis.py
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@ -22,9 +22,9 @@ from tracking.dotrack.dotracks_back import doBackTracks
from tracking.dotrack.dotracks_front import doFrontTracks
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 tracking.utils.read_data import extract_data, read_deletedBarcode_file, read_tracking_output, read_returnGoods_file
from contrast_analysis import contrast_analysis
from contrast.one2n_contrast import get_relative_paths, one2n_new, read_returnGoods_file
from tracking.utils.annotator import TrackAnnotator
W, H = 1024, 1280
@ -32,6 +32,7 @@ Mode = 'front' #'back'
ImgFormat = ['.jpg', '.jpeg', '.png', '.bmp']
'''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):
"""
@ -143,7 +144,7 @@ def do_tracking(fpath, savedir, event_name='images'):
# trackerboxes, tracker_feat_dict = tracking(bboxes, ffeats)
'''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)
save_dir = os.path.join(savedir, event_name + '_images')
subimg_dir = os.path.join(savedir, event_name + '_subimgs')
if not os.path.exists(save_dir):
os.makedirs(save_dir)
@ -237,14 +238,15 @@ def do_tracking(fpath, savedir, event_name='images'):
subimg_path = os.path.join(subimg_dir, f'{CamerType}_tid{int(tid)}_{int(fid)}_{int(bid)}.png' )
cv2.imwrite(subimg_path, subimg)
# for track in tracking_output_boxes:
# for *xyxy, tid, conf, cls, fid, bid in track:
# img = imgs[int(fid-1)]
# x1, y1, x2, y2 = int(xyxy[0]/2), int(xyxy[1]/2), int(xyxy[2]/2), int(xyxy[3]/2)
# subimg = img[y1:y2, x1:x2]
for track in tracking_output_boxes:
for *xyxy, tid, conf, cls, fid, bid in track:
img = imgs[int(fid-1)]
x1, y1, x2, y2 = int(xyxy[0]/2), int(xyxy[1]/2), int(xyxy[2]/2), int(xyxy[3]/2)
subimg = img[y1:y2, x1:x2]
# subimg_path = os.path.join(subimg_dir, f'{CamerType}_tid{int(tid)}_{int(fid-1)}_{int(bid)}_x.png' )
# cv2.imwrite(subimg_path, subimg)
subimg_path = os.path.join(subimg_dir, f'x_{CamerType}_tid{int(tid)}_{int(fid)}_{int(bid)}.png' )
cv2.imwrite(subimg_path, subimg)
return img_tracking, abimg
@ -267,9 +269,13 @@ def tracking_simulate(eventpath, savepath):
# else:
# return
# =============================================================================
bname = os.path.basename(eventpath)
idx = bname.find('2024')
enent_name = bname[idx:(idx+15)]
enent_name = os.path.basename(eventpath)
## only for simplify the filename
idx = enent_name.find('2024')
if idx>=0:
enent_name = enent_name[idx:(idx+15)]
'''2. 依次读取 0/1_track.data 中数据，进行仿真'''
illu_tracking, illu_select = [], []
@ -308,27 +314,34 @@ def tracking_simulate(eventpath, savepath):
else:
Img_t = None
'''3.1 单独另存保存完好的 8 轨迹图'''
basepath, _ = os.path.split(savepath)
trajpath = os.path.join(basepath, 'trajs')
if not os.path.exists(trajpath):
os.makedirs(trajpath)
traj_path = os.path.join(trajpath, enent_name+'.png')
imgpath_tracking = os.path.join(savepath, enent_name + '_ing.png')
imgpath_select = os.path.join(savepath, enent_name + '_slt.png')
imgpath_ts = os.path.join(savepath, enent_name + '_ts.png')
'''3.1 保存输出轨迹图，若tracking、select的shape相同，则合并输出，否则单独输出'''
imgpath_tracking = os.path.join(savepath, enent_name + '_tracking.png')
imgpath_select = os.path.join(savepath, enent_name + '_select.png')
imgpath_ts = os.path.join(savepath, enent_name + '_tracking_select.png')
if Img_t is not None and Img_s is not None and np.all(Img_s.shape==Img_t.shape):
Img_ts = np.concatenate((Img_t, Img_s), axis = 1)
H, W = Img_ts.shape[:2]
cv2.line(Img_ts, (int(W/2), 0), (int(W/2), int(H)), (0, 0, 255), 4)
cv2.imwrite(imgpath_ts, Img_ts)
cv2.imwrite(traj_path, Img_ts)
else:
if Img_s: cv2.imwrite(imgpath_select, Img_s) # 不会执行到该处
if Img_t: cv2.imwrite(imgpath_tracking, Img_t) # 不会执行到该处
Img_ts = None
'''3.2 单独另存保存完好的 8 轨迹图'''
if Img_ts is not None:
basepath, _ = os.path.split(savepath)
trajpath = os.path.join(basepath, 'trajs')
if not os.path.exists(trajpath):
os.makedirs(trajpath)
traj_path = os.path.join(trajpath, enent_name+'.png')
cv2.imwrite(traj_path, Img_ts)
return Img_ts
@ -336,14 +349,30 @@ def tracking_simulate(eventpath, savepath):
# warnings.simplefilter("error", category=np.VisibleDeprecationWarning)
def main_loop():
del_barcode_file = r'\\192.168.1.28\share\测试_202406\0723\0723_3\deletedBarcode.txt'
basepath = r'\\192.168.1.28\share\测试_202406\0723\0723_3' # 测试数据文件夹地址
SavePath = r'D:\contrast\dataset\resultx' # 结果保存地址
# prefix = ["getout_", "input_", "error_"]
# del_barcode_file = r'\\192.168.1.28\share\测试_202406\1030\images\returnGoods.txt'
# basepath = r'\\192.168.1.28\share\测试_202406\1030\images' # 测试数据文件夹地址
'''获取性能测试数据相关路径'''
relative_paths = contrast_analysis(del_barcode_file, basepath, SavePath)
SavePath = r'D:\contrast\dataset\resultx' # 结果保存地址
saveimgs = True
if os.path.basename(del_barcode_file).find('deletedBarcode'):
relative_paths = get_relative_paths(del_barcode_file, basepath, SavePath, saveimgs)
elif os.path.basename(del_barcode_file).find('returnGoods'):
blist = read_returnGoods_file(del_barcode_file)
errpairs, corrpairs, err_similarity, correct_similarity = one2n_new(blist)
relative_paths = []
for getoutevent, inputevent, errevent in errpairs:
relative_paths.append(os.path.join(basepath, getoutevent))
relative_paths.append(os.path.join(basepath, inputevent))
relative_paths.append(os.path.join(basepath, errevent))
# prefix = ["getout_", "input_", "error_"]
'''开始循环执行每次测试过任务'''
k = 0
for tuple_paths in relative_paths:
@ -383,12 +412,12 @@ def main():
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_3'
eventPaths = r"D:\DetectTracking\tracking\images"
eventPaths = r'D:\datasets\ym\exhibition\识别错'
savePath = r'D:\contrast\dataset\result'
k=0
for pathname in os.listdir(eventPaths):
pathname = "20240925-142635-3e3cb61a-8bbe-45f2-aed7-a40de7f2d624_6924743924161"
pathname = "放入薯片识别为辣条"
eventpath = os.path.join(eventPaths, pathname)
savepath = os.path.join(savePath, pathname)
@ -396,6 +425,7 @@ def main():
os.makedirs(savepath)
tracking_simulate(eventpath, savepath)
# try:
# tracking_simulate(eventpath, savepath)
# except Exception as e:

18
tracking/tracking_test.py
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@ -85,10 +85,8 @@ def have_tracked():
k = 0
gt = Profile()
for filename in os.listdir(trackdict):
# filename = 'test_20240402-173935_6920152400975_back_174037372.pkl'
# filename = '6907149227609_20240508-174733_back_returnGood_70f754088050_425_17327712807.pkl'
# filename = '6907149227609_20240508-174733_front_returnGood_70f754088050_425_17327712807.pkl'
filename = '153112511_0_seek_105.pkl'
file, ext = os.path.splitext(filename)
filepath = os.path.join(trackdict, filename)
TracksDict = np.load(filepath, allow_pickle=True)
@ -97,6 +95,9 @@ def have_tracked():
with gt:
if filename.find("front") >= 0:
vts = doFrontTracks(bboxes, TracksDict)
Intrude = vts.isintrude()
vts.classify()
save_subimgs(vts, file, TracksDict)
@ -113,6 +114,9 @@ def have_tracked():
else:
vts = doBackTracks(bboxes, TracksDict)
Intrude = vts.isintrude()
vts.classify()
alltracks.append(vts)
@ -124,9 +128,9 @@ def have_tracked():
cv2.imwrite(str(trackpath), img_tracking)
print(file+f" need time: {gt.dt:.2f}s")
# k += 1
# if k==1:
# break
k += 1
if k==1:
break
if len(alltracks):
drawFeatures(alltracks, save_dir)

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tracking/utils/__pycache__/read_data.cpython-39.pyc
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tracking/utils/drawtracks.py
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@ -7,11 +7,13 @@ Created on Mon Jan 15 15:26:38 2024
import numpy as np
import cv2
import os
from pathlib import Path
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from utils.annotator import TrackAnnotator
from utils.plotting import colors
from pathlib import Path
from tracking.utils.annotator import TrackAnnotator
from tracking.utils.plotting import colors
def plot_frameID_y2(vts):

133
tracking/utils/read_data.py
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@ -10,10 +10,10 @@ import numpy as np
import re
import os
from collections import OrderedDict
import warnings
import matplotlib.pyplot as plt
def str_to_float_arr(s):
# 移除字符串末尾的逗号（如果存在）
if s.endswith(','):
@ -31,7 +31,9 @@ def find_samebox_in_array(arr, target):
return i
return -1
import warnings
def extract_data(datapath):
@ -41,30 +43,26 @@ def extract_data(datapath):
trackerfeats = np.empty((0, 256), dtype=np.float64)
boxes, feats, tboxes, tfeats = [], [], [], []
timestamps, frameIds = [], []
with open(datapath, 'r', encoding='utf-8') as lines:
for line in lines:
line = line.strip() # 去除行尾的换行符和可能的空白字符
if not line: # 跳过空行
continue
if line.find("CameraId")>=0:
if len(boxes): bboxes.append(np.array(boxes))
if len(feats): ffeats.append(np.array(feats))
# with warnings.catch_warnings(record=True) as w:
# if len(boxes): bboxes.append(np.array(boxes))
# if len(feats): ffeats.append(np.array(feats))
# if w:
# print(f"捕获到 {len(w)} 个警告:")
# for warning in w:
# print(f"警告类型: {warning.category}")
# print(f"警告消息: {warning.message}")
# print(f"警告发生的地方: {warning.filename}:{warning.lineno}")
if len(tboxes):
if len(tboxes):
trackerboxes = np.concatenate((trackerboxes, np.array(tboxes)))
if len(tfeats):
trackerfeats = np.concatenate((trackerfeats, np.array(tfeats)))
timestamp, frameId = [int(ln.split(":")[1]) for ln in line.split(",")[1:]]
timestamps.append(timestamp)
frameIds.append(frameId)
boxes, feats, tboxes, tfeats = [], [], [], []
@ -103,6 +101,9 @@ def extract_data(datapath):
assert(len(trackerboxes)==len(trackerfeats)), "Error at tracker output!"
tracker_feat_dict = {}
tracker_feat_dict["timestamps"] = timestamps
tracker_feat_dict["frameIds"] = frameIds
for i in range(len(trackerboxes)):
tid, fid, bid = int(trackerboxes[i, 4]), int(trackerboxes[i, 7]), int(trackerboxes[i, 8])
if f"frame_{fid}" not in tracker_feat_dict:
@ -169,8 +170,8 @@ def read_tracking_output(filepath):
return np.array(boxes), np.array(feats)
def read_deletedBarcode_file(filePth):
with open(filePth, 'r', encoding='utf-8') as f:
def read_deletedBarcode_file(filePath):
with open(filePath, 'r', encoding='utf-8') as f:
lines = f.readlines()
split_flag, all_list = False, []
@ -179,6 +180,9 @@ def read_deletedBarcode_file(filePth):
clean_lines = [line.strip().replace("'", '').replace('"', '') for line in lines]
for i, line in enumerate(clean_lines):
if line.endswith(','):
line = line[:-1]
stripped_line = line.strip()
if not stripped_line:
if len(barcode_list): dict['barcode'] = barcode_list
@ -210,11 +214,106 @@ def read_deletedBarcode_file(filePth):
return all_list
def read_returnGoods_file(filePath):
'''
20241030开始，原 deletedBarcode.txt 中数据格式修改为 returnGoods.txt，读数方式随之变化
'''
with open(filePath, 'r', encoding='utf-8') as f:
lines = f.readlines()
clean_lines = [line.strip().replace("'", '').replace('"', '') for line in lines]
all_list = []
split_flag, dict = False, {}
barcode_list, similarity_list = [], []
event_list, type_list = [], []
for i, line in enumerate(clean_lines):
stripped_line = line.strip()
if line.endswith(','):
line = line[:-1]
if not stripped_line:
if len(barcode_list): dict['barcode'] = barcode_list
if len(similarity_list): dict['similarity'] = similarity_list
if len(event_list): dict['event'] = event_list
if len(type_list): dict['type'] = type_list
if len(dict) and dict['SeqDir'].find('*')<0:
all_list.append(dict)
split_flag, dict = False, {}
barcode_list, similarity_list = [], []
event_list, type_list = [], []
continue
if line.find(':')<0: continue
if line.find('1:n')==0: continue
label = line.split(':')[0].strip()
value = line.split(':')[1].strip()
if label == 'SeqDir':
dict['SeqDir'] = value
dict['Deleted'] = value.split('_')[-1]
if label == 'List':
split_flag = True
continue
if split_flag:
event_list.append(label)
barcode_list.append(label.split('_')[-1])
similarity_list.append(value.split(',')[0])
type_list.append(value.split('=')[-1])
if len(barcode_list): dict['barcode'] = barcode_list
if len(similarity_list): dict['similarity'] = similarity_list
if len(event_list): dict['event'] = event_list
if len(type_list): dict['type'] = type_list
if len(dict) and dict['SeqDir'].find('*')<0:
all_list.append(dict)
return all_list
def read_seneor(filepath):
WeightDict = OrderedDict()
with open(filepath, 'r', encoding='utf-8') as f:
lines = f.readlines()
for i, line in enumerate(lines):
line = line.strip()
keyword = line.split(':')[0]
value = line.split(':')[1]
vdata = [float(s) for s in value.split(',') if len(s)]
WeightDict[keyword] = vdata[-1]
return WeightDict
def read_weight_timeConsuming(filePth):
WeightDict, SensorDict, ProcessTimeDict = OrderedDict(), OrderedDict(), OrderedDict()
with open(filePth, 'r', encoding='utf-8') as f:
lines = f.readlines()
# label = ''
for i, line in enumerate(lines):
line = line.strip()