542 lines
21 KiB
Python
542 lines
21 KiB
Python
# -*- coding: utf-8 -*-
|
||
"""
|
||
Created on Sun Sep 29 08:59:21 2024
|
||
|
||
@author: ym
|
||
"""
|
||
import os
|
||
# import sys
|
||
import cv2
|
||
import pickle
|
||
import numpy as np
|
||
from pathlib import Path
|
||
from scipy.spatial.distance import cdist
|
||
from track_reid import yolo_resnet_tracker, yolov10_resnet_tracker
|
||
|
||
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 utils.getsource import get_image_pairs, get_video_pairs
|
||
from tracking.utils.read_data import read_similar, get_process_csv_data
|
||
|
||
from openpyxl import Workbook, load_workbook
|
||
|
||
|
||
def save_subimgs(imgdict, boxes, spath, ctype, featdict = None):
|
||
'''
|
||
当前 box 特征和该轨迹前一个 box 特征的相似度，可用于和跟踪序列中的相似度进行比较
|
||
'''
|
||
boxes = boxes[np.argsort(boxes[:, 7])]
|
||
for i in range(len(boxes)):
|
||
simi = None
|
||
tid, fid, bid = int(boxes[i, 4]), int(boxes[i, 7]), int(boxes[i, 8])
|
||
|
||
if i>0:
|
||
_, fid0, bid0 = int(boxes[i-1, 4]), int(boxes[i-1, 7]), int(boxes[i-1, 8])
|
||
if f"{fid0}_{bid0}" in featdict.keys() and f"{fid}_{bid}" in featdict.keys():
|
||
feat0 = featdict[f"{fid0}_{bid0}"]
|
||
feat1 = featdict[f"{fid}_{bid}"]
|
||
simi = 1 - np.maximum(0.0, cdist(feat0[None, :], feat1[None, :], "cosine"))[0][0]
|
||
|
||
img = imgdict[f"{fid}_{bid}"]
|
||
imgpath = spath / f"{ctype}_tid{tid}-{fid}-{bid}.png"
|
||
if simi is not None:
|
||
imgpath = spath / f"{ctype}_tid{tid}-{fid}-{bid}_sim{simi:.2f}.png"
|
||
|
||
cv2.imwrite(imgpath, img)
|
||
|
||
|
||
def save_subimgs_1(imgdict, boxes, spath, ctype, simidict = None):
|
||
'''
|
||
当前 box 特征和该轨迹 smooth_feat 特征的相似度, yolo_resnet_tracker 函数中，
|
||
采用该方式记录特征相似度
|
||
'''
|
||
for i in range(len(boxes)):
|
||
tid, fid, bid = int(boxes[i, 4]), int(boxes[i, 7]), int(boxes[i, 8])
|
||
|
||
key = f"{fid}_{bid}"
|
||
img = imgdict[key]
|
||
imgpath = spath / f"{ctype}_tid{tid}-{fid}-{bid}.png"
|
||
if simidict is not None and key in simidict.keys():
|
||
imgpath = spath / f"{ctype}_tid{tid}-{fid}-{bid}_sim{simidict[key]:.2f}.png"
|
||
|
||
cv2.imwrite(imgpath, img)
|
||
|
||
def show_result(eventpath, event_tracks, yrtDict, savepath_pipe):
|
||
'''保存 Tracking 输出的运动轨迹子图，并记录相似度'''
|
||
|
||
savepath_pipe_subimgs = savepath_pipe / Path("subimgs")
|
||
if not savepath_pipe_subimgs.exists():
|
||
savepath_pipe_subimgs.mkdir(parents=True, exist_ok=True)
|
||
|
||
|
||
for CamerType, vts in event_tracks:
|
||
if len(vts.tracks)==0: continue
|
||
if CamerType == 'front':
|
||
# yolos = ShoppingDict["frontCamera"]["yoloResnetTracker"]
|
||
|
||
yolos = yrtDict["frontyrt"]
|
||
ctype = 1
|
||
if CamerType == 'back':
|
||
# yolos = ShoppingDict["backCamera"]["yoloResnetTracker"]
|
||
|
||
yolos = yrtDict["backyrt"]
|
||
ctype = 0
|
||
|
||
imgdict, featdict, simidict = {}, {}, {}
|
||
for y in yolos:
|
||
imgdict.update(y["imgs"])
|
||
featdict.update(y["feats"])
|
||
simidict.update(y["featsimi"])
|
||
|
||
for track in vts.Residual:
|
||
if isinstance(track, np.ndarray):
|
||
save_subimgs(imgdict, track, savepath_pipe_subimgs, ctype, featdict)
|
||
else:
|
||
save_subimgs(imgdict, track.slt_boxes, savepath_pipe_subimgs, ctype, featdict)
|
||
|
||
'''(3) 轨迹显示与保存'''
|
||
illus = [None, None]
|
||
for CamerType, vts in event_tracks:
|
||
if len(vts.tracks)==0: continue
|
||
|
||
if CamerType == 'front':
|
||
edgeline = cv2.imread("./tracking/shopcart/cart_tempt/board_ftmp_line.png")
|
||
|
||
h, w = edgeline.shape[:2]
|
||
# nh, nw = h//2, w//2
|
||
# edgeline = cv2.resize(edgeline, (nw, nh), interpolation=cv2.INTER_AREA)
|
||
|
||
img_tracking = draw_all_trajectories(vts, edgeline, savepath_pipe, CamerType, draw5p=True)
|
||
illus[0] = img_tracking
|
||
|
||
plt = plot_frameID_y2(vts)
|
||
'''==========yj callbackdata========='''
|
||
plt.savefig(os.path.join(eventpath, "front_y2.png"))
|
||
'''========================================'''
|
||
|
||
if CamerType == 'back':
|
||
edgeline = cv2.imread("./tracking/shopcart/cart_tempt/edgeline.png")
|
||
|
||
h, w = edgeline.shape[:2]
|
||
# nh, nw = h//2, w//2
|
||
# edgeline = cv2.resize(edgeline, (nw, nh), interpolation=cv2.INTER_AREA)
|
||
|
||
img_tracking = draw_all_trajectories(vts, edgeline, savepath_pipe, CamerType, draw5p=True)
|
||
illus[1] = img_tracking
|
||
|
||
illus = [im for im in illus if im is not None]
|
||
if len(illus):
|
||
img_cat = np.concatenate(illus, axis = 1)
|
||
if len(illus)==2:
|
||
H, W = img_cat.shape[:2]
|
||
cv2.line(img_cat, (int(W/2), 0), (int(W/2), int(H)), (128, 128, 255), 3)
|
||
|
||
'''==========yj callbackdata========='''
|
||
trajpath = os.path.join(eventpath, "trajectory.png")
|
||
# trajpath = os.path.join(savepath_pipe, "trajectory.png")
|
||
'''======================================'''
|
||
cv2.imwrite(trajpath, img_cat)
|
||
|
||
|
||
|
||
|
||
def pipeline(dict_data,
|
||
pickle_exist,
|
||
eventpath,
|
||
SourceType,
|
||
weights,
|
||
DataType = "raw", #raw, pkl: images or videos, pkl, pickle file
|
||
YoloVersion="V5",
|
||
savepath = None,
|
||
saveimages = True,
|
||
|
||
):
|
||
|
||
## 构造购物事件字典
|
||
evtname = Path(eventpath).stem
|
||
barcode = evtname.split('_')[-1] if len(evtname.split('_'))>=2 \
|
||
and len(evtname.split('_')[-1])>=8 \
|
||
and evtname.split('_')[-1].isdigit() else ''
|
||
|
||
'''事件结果存储文件夹: savepath_pipe, savepath_pkl'''
|
||
if not savepath:
|
||
savepath = Path(__file__).resolve().parents[0] / "events_result"
|
||
savepath_pipe = Path(savepath) / Path("yolos_tracking") / evtname
|
||
|
||
|
||
savepath_pkl = Path(savepath) / "shopping_pkl"
|
||
if not savepath_pkl.exists():
|
||
savepath_pkl.mkdir(parents=True, exist_ok=True)
|
||
pklpath = Path(savepath_pkl) / Path(str(evtname)+".pickle")
|
||
|
||
|
||
|
||
yrt_out = []
|
||
if DataType == "raw":
|
||
if not pickle_exist:
|
||
### 不重复执行已经过yolo-resnet-tracker
|
||
if pklpath.exists():
|
||
print(f"Pickle file have saved: {evtname}.pickle")
|
||
return
|
||
|
||
if SourceType == "video":
|
||
vpaths = get_video_pairs(eventpath)
|
||
elif SourceType == "image":
|
||
vpaths = get_image_pairs(eventpath)
|
||
|
||
|
||
|
||
for vpath in vpaths:
|
||
'''================= 2. 事件结果存储文件夹 ================='''
|
||
|
||
|
||
if isinstance(vpath, list):
|
||
savepath_pipe_imgs = savepath_pipe / Path("images")
|
||
else:
|
||
savepath_pipe_imgs = savepath_pipe / Path(str(Path(vpath).stem))
|
||
|
||
if not savepath_pipe_imgs.exists():
|
||
savepath_pipe_imgs.mkdir(parents=True, exist_ok=True)
|
||
|
||
optdict = {}
|
||
optdict["weights"] = weights
|
||
optdict["source"] = vpath
|
||
optdict["save_dir"] = savepath_pipe_imgs
|
||
|
||
optdict["is_save_img"] = saveimages
|
||
optdict["is_save_video"] = True
|
||
|
||
|
||
if YoloVersion == "V5":
|
||
yrtOut = yolo_resnet_tracker(**optdict)
|
||
elif YoloVersion == "V10":
|
||
yrtOut = yolov10_resnet_tracker(**optdict)
|
||
|
||
yrt_out.append((vpath, yrtOut))
|
||
|
||
elif DataType == "pkl":
|
||
pass
|
||
|
||
else:
|
||
return
|
||
|
||
|
||
|
||
'''====================== 构造 ShoppingDict 模块 ======================='''
|
||
ShoppingDict = {"eventPath": eventpath,
|
||
"eventName": evtname,
|
||
"barcode": barcode,
|
||
"eventType": '', # "input", "output", "other"
|
||
"frontCamera": {},
|
||
"backCamera": {},
|
||
"one2n": [] #
|
||
}
|
||
procpath = Path(eventpath).joinpath('process.data')
|
||
if procpath.is_file():
|
||
SimiDict = read_similar(procpath)
|
||
ShoppingDict["one2n"] = SimiDict['one2n']
|
||
|
||
yrtDict = {}
|
||
event_tracks = []
|
||
for vpath, yrtOut in yrt_out:
|
||
'''================= 1. 构造相机事件字典 ================='''
|
||
CameraEvent = {"cameraType": '', # "front", "back"
|
||
"videoPath": '',
|
||
"imagePaths": [],
|
||
"yoloResnetTracker": [],
|
||
"tracking": [],
|
||
}
|
||
|
||
if isinstance(vpath, list):
|
||
CameraEvent["imagePaths"] = vpath
|
||
bname = os.path.basename(vpath[0])
|
||
if not isinstance(vpath, list):
|
||
CameraEvent["videoPath"] = vpath
|
||
bname = os.path.basename(vpath).split('.')[0]
|
||
if bname.split('_')[0] == "0" or bname.find('back')>=0:
|
||
CameraEvent["cameraType"] = "back"
|
||
if bname.split('_')[0] == "1" or bname.find('front')>=0:
|
||
CameraEvent["cameraType"] = "front"
|
||
|
||
|
||
'''2种保存方式: (1) no save subimg, (2) save img'''
|
||
###(1) save images
|
||
yrtOut_save = []
|
||
for frdict in yrtOut:
|
||
fr_dict = {}
|
||
for k, v in frdict.items():
|
||
if k != "imgs":
|
||
fr_dict[k]=v
|
||
yrtOut_save.append(fr_dict)
|
||
CameraEvent["yoloResnetTracker"] = yrtOut_save
|
||
|
||
###(2) no save images
|
||
# CameraEvent["yoloResnetTracker"] = yrtOut
|
||
|
||
'''================= 4. tracking ================='''
|
||
'''(1) 生成用于 tracking 模块的 boxes、feats'''
|
||
bboxes = np.empty((0, 6), dtype=np.float64)
|
||
trackerboxes = np.empty((0, 9), dtype=np.float64)
|
||
trackefeats = {}
|
||
for frameDict in yrtOut:
|
||
tboxes = frameDict["tboxes"]
|
||
ffeats = frameDict["feats"]
|
||
|
||
boxes = frameDict["bboxes"]
|
||
bboxes = np.concatenate((bboxes, np.array(boxes)), axis=0)
|
||
trackerboxes = np.concatenate((trackerboxes, np.array(tboxes)), axis=0)
|
||
for i in range(len(tboxes)):
|
||
fid, bid = int(tboxes[i, 7]), int(tboxes[i, 8])
|
||
trackefeats.update({f"{fid}_{bid}": ffeats[f"{fid}_{bid}"]})
|
||
|
||
|
||
'''(2) tracking, 后摄'''
|
||
if CameraEvent["cameraType"] == "back":
|
||
vts = doBackTracks(trackerboxes, trackefeats)
|
||
vts.classify()
|
||
event_tracks.append(("back", vts))
|
||
|
||
CameraEvent["tracking"] = vts
|
||
ShoppingDict["backCamera"] = CameraEvent
|
||
|
||
'''====yj callbackdata======='''
|
||
back_cnts = len(vts.Residual)
|
||
dict_data['后摄轨迹数'] = back_cnts
|
||
print(f"back_cnts: {back_cnts}")
|
||
'''=============================='''
|
||
|
||
yrtDict["backyrt"] = yrtOut
|
||
|
||
'''(2) tracking, 前摄'''
|
||
if CameraEvent["cameraType"] == "front":
|
||
vts = doFrontTracks(trackerboxes, trackefeats)
|
||
vts.classify()
|
||
event_tracks.append(("front", vts))
|
||
|
||
CameraEvent["tracking"] = vts
|
||
ShoppingDict["frontCamera"] = CameraEvent
|
||
|
||
'''====yj callbackdata======='''
|
||
front_cnts = len(vts.Residual)
|
||
dict_data['前摄轨迹数'] = front_cnts
|
||
print(f"front_cnts: {front_cnts}")
|
||
'''=============================='''
|
||
|
||
yrtDict["frontyrt"] = yrtOut
|
||
|
||
'''========================== 保存模块 ================================='''
|
||
|
||
# 保存 ShoppingDict
|
||
with open(str(pklpath), 'wb') as f:
|
||
pickle.dump(ShoppingDict, f)
|
||
|
||
# 绘制并保存轨迹图
|
||
show_result(eventpath, event_tracks, yrtDict, savepath_pipe)
|
||
return dict_data
|
||
|
||
class WriteExcel:
|
||
|
||
def is_excel(self, input_excel):
|
||
|
||
# 若文件存在，加载工作簿
|
||
wb = load_workbook(input_excel)
|
||
sheet_name = wb.sheetnames[0] ##默认回传分析表只有一个sheet
|
||
# 获取活动工作表
|
||
ws = wb.active
|
||
sheet = wb[sheet_name]
|
||
##确定新增列的位置
|
||
# new_col_index = sheet.max_column
|
||
return wb, ws, sheet
|
||
|
||
def init_excel(self, input_excel, output_excel, headers, max_col=13):
|
||
if os.path.exists(output_excel):
|
||
wb, ws, sheet = self.is_excel(output_excel)
|
||
return wb, ws, sheet
|
||
elif os.path.exists(input_excel):
|
||
wb, ws, sheet = self.is_excel(input_excel)
|
||
self.add_header(wb, sheet, max_col, headers, output_excel)
|
||
return wb, ws, sheet
|
||
else:
|
||
raise FileNotFoundError(f"文件 '{input_excel}' 不存在")
|
||
|
||
'''在已有excel文件中新增列标题'''
|
||
def add_header(self, wb, sheet, column, headers, file_name):
|
||
|
||
write_data = {}
|
||
sub_data = {}
|
||
for i, head in enumerate(headers):
|
||
k = column + i + 1
|
||
sub_data[k] = head
|
||
write_data[1] = sub_data
|
||
self.add_data_to_excel(wb, sheet, write_data, file_name)
|
||
|
||
def add_data_to_excel(self, wb, sheet, write_data, file_name):
|
||
'''
|
||
示例写入数据，格式为 {行号: {列号: 值}}
|
||
write_data = {
|
||
5: {11: 1, 12: 2, 13: 3}
|
||
}
|
||
'''
|
||
# 写入指定行列的数据
|
||
for row_num, col_data in write_data.items():
|
||
for col_num, value in col_data.items():
|
||
# print('row_num', row_num, 'col_num', col_num, 'value', value)
|
||
sheet.cell(row=row_num, column=col_num, value=str(value))
|
||
wb.save(file_name)
|
||
|
||
def get_simiTitle_index(self, ws, title_name='事件名'):
|
||
'''获取excel文件中标题名称与追加内容相同部分的列索引
|
||
例如：默认以"事件名"为基准，追加统一事件名下不同组成信息'''
|
||
# 获取列标题
|
||
headers = [cell.value for cell in ws[1]]
|
||
# # 找到“事件名”所在的列索引
|
||
event_name_index = headers.index(title_name) if title_name in headers else None
|
||
if event_name_index is None:
|
||
print("未找到标题为'事件名'的列")
|
||
return
|
||
else:
|
||
return event_name_index
|
||
|
||
def get_event_row(self, sheet, event_name_index, event_name):
|
||
'''获得当前事件名event_name在excel文件中所在的行索引'''
|
||
row_index = 0
|
||
for row, content in enumerate(sheet.iter_rows(min_row=2, values_only=True)):
|
||
# print('row', row, content[event_name_index])
|
||
if content[event_name_index] == event_name:
|
||
row_index = row + 2 ### 默认只有一行标题，若有两行标题则需改为+3
|
||
# print('row_index', row_index)
|
||
break
|
||
return row_index
|
||
def write_simi_add(self, wb, ws, sheet, max_col, evtname, dict_data, headers, file_name):
|
||
'''
|
||
在已有excel文件中追加内容
|
||
找出事件名所在行索引和原excel最大列索引，在原excel最大列索引后指定行写入新内容，内容顺序与新增headers顺序一致
|
||
'''
|
||
event_index = self.get_simiTitle_index(ws)
|
||
if event_index is not None:
|
||
print('evtname', evtname)
|
||
row_index = self.get_event_row(sheet, event_index, evtname)
|
||
if row_index > 0:
|
||
sub_dict = {}
|
||
print('headers', headers)
|
||
for i, header in enumerate(headers):
|
||
col_index = max_col + i + 1
|
||
# print('list(dict_data.keys())', list(dict_data.keys()))
|
||
if header in list(dict_data.keys()):
|
||
sub_dict[col_index] = dict_data[header]
|
||
else:
|
||
sub_dict[col_index] = ''
|
||
write_data = {row_index: sub_dict}
|
||
self.add_data_to_excel(wb, sheet, write_data, file_name)
|
||
|
||
print("=========save excel===========")
|
||
else:
|
||
raise Exception(f"未找到事件名:{evtname}")
|
||
else:
|
||
raise Exception("未找到标题为'事件名'的列")
|
||
|
||
|
||
def execute_pipeline(evtdir = r"D:\datasets\ym\后台数据\unzip",
|
||
DataType = "raw", # raw, pkl
|
||
save_path = r"D:\work\result_pipeline",
|
||
kk=1,
|
||
source_type = "video", # video, image,
|
||
yolo_ver = "V10", # V10, V5
|
||
weight_yolo_v5 = r'./ckpts/best_cls10_0906.pt' ,
|
||
weight_yolo_v10 = r'./ckpts/best_v10s_width0375_1205.pt',
|
||
saveimages = True,
|
||
max_col = 12,
|
||
track_txt = ''
|
||
):
|
||
'''
|
||
运行函数 pipeline()，遍历事件文件夹，每个文件夹是一个事件
|
||
'''
|
||
parmDict = {}
|
||
parmDict["DataType"] = DataType
|
||
parmDict["savepath"] = save_path
|
||
parmDict["SourceType"] = source_type
|
||
|
||
parmDict["YoloVersion"] = yolo_ver
|
||
if parmDict["YoloVersion"] == "V5":
|
||
parmDict["weights"] = weight_yolo_v5
|
||
elif parmDict["YoloVersion"] == "V10":
|
||
parmDict["weights"] = weight_yolo_v10
|
||
|
||
parmDict["saveimages"] = saveimages
|
||
|
||
|
||
evtdir = Path(evtdir)
|
||
errEvents = []
|
||
# k = 0
|
||
'''=========change callbackdata=============='''
|
||
csv_name = 'data.csv'
|
||
xlsx_name = '现场回传数据分析表.xlsx'
|
||
output_name = '现场回传数据分析表_all.xlsx'
|
||
# headers = ['algroStartToEnd', 'one2one', 'one2SN', 'one2n', '前摄轨迹数', '后摄轨迹数']
|
||
headers = ['algroStartToEnd', 'one2one', 'one2SN', 'one2n']
|
||
|
||
|
||
excelWriter = WriteExcel() ## 实例化excel对象
|
||
|
||
for name in evtdir.iterdir(): ##人名
|
||
for date_file in name.iterdir(): ##2025-01-13
|
||
# try:
|
||
xlsx_data = os.path.join(date_file, xlsx_name)
|
||
csv_data = os.path.join(date_file, csv_name)
|
||
excel_name = os.path.join(date_file, output_name)
|
||
wb, ws, sheet = excelWriter.init_excel(xlsx_data, excel_name, headers, max_col)
|
||
if csv_data == '':
|
||
with open('no_datacsv.txt', 'a') as f:
|
||
f.write(str(date_file) + '\n')
|
||
|
||
|
||
for item in date_file.iterdir():
|
||
# dict_data = {}
|
||
if item.is_dir():
|
||
# item = evtdir/Path("20241212-171505-f0afe929-fdfe-4efa-94d0-2fa748d65fbb_6907992518930")
|
||
parmDict["eventpath"] = item
|
||
event_name = str(item.name)
|
||
dict_data = get_process_csv_data(csv_data, item)
|
||
print('dict_data', dict_data)
|
||
# dict_data_all = pipeline(dict_data, pickle_exist, **parmDict)
|
||
if dict_data is not None: #已保存pickle文件的事件返回为None
|
||
# print('dict_data_all', dict_data_all)
|
||
excelWriter.write_simi_add(wb, ws, sheet, max_col, event_name, dict_data, headers, excel_name)
|
||
|
||
# except Exception as e:
|
||
# with open('process_error.txt', 'a') as f:
|
||
# f.write(str(date_file) + ':' + str(e) + '\n')
|
||
|
||
|
||
|
||
if __name__ == "__main__":
|
||
# datapath = '/home/yujia/yj/gpu_code/callback_data_test/'
|
||
# datapath = '/home/yujia/yj/gpu_code/callback_data_test_0417/'
|
||
# savepath = '/home/yujia/yj/gpu_code/result_0417_v10/'
|
||
|
||
datapath = '/shareData/data/temp_data/tengXunCloud_data/code_test_0427/'
|
||
# savepath = '/shareData/data/temp_data/tengXunCloud_data/code_test/pipline_result/' ##保存pipline结果路径
|
||
|
||
max_col = 12 ##excel表格列索引从0开始，从这列开始写入代码解析内容
|
||
# track_txt = '轨迹数为空.txt'
|
||
track_txt = '' ##第一次跑pipline
|
||
|
||
execute_pipeline(evtdir=datapath,
|
||
DataType = "raw", # raw, pkl
|
||
kk=1,
|
||
source_type = "video", # video, image,
|
||
save_path = '',
|
||
yolo_ver = "V5", # V10, V5 ##20250401之前使用V5 ressnet使用resv10
|
||
weight_yolo_v5 = './ckpts/best_cls10_0906.pt' ,
|
||
weight_yolo_v10 = './ckpts/best_v10s_width0375_1205.pt',
|
||
saveimages = False,
|
||
max_col = max_col,
|
||
track_txt = track_txt
|
||
)
|
||
|
||
|
||
|
||
|
||
|
||
|