545 lines
21 KiB
Python
545 lines
21 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Tue Nov 26 17:35:05 2024
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@author: ym
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"""
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import os
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import cv2
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import pickle
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import numpy as np
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from pathlib import Path
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import sys
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sys.path.append(r"D:\DetectTracking")
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from tracking.utils.plotting import Annotator, colors
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from tracking.utils.drawtracks import drawTrack
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from tracking.utils.read_data import extract_data, read_tracking_output, read_similar
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from tracking.utils.read_data import extract_data_realtime, read_tracking_output_realtime
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# import platform
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# import pathlib
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# plt = platform.system()
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IMG_FORMAT = ['.bmp', '.jpg', '.jpeg', '.png']
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VID_FORMAT = ['.mp4', '.avi']
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def save_data(event, resultPath=None):
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'''事件轨迹子图保存'''
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if resultPath is None:
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resultPath = os.path.dirname(os.path.abspath(__file__))
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subimgpath = os.path.join(resultPath, f"{event.evtname}", "subimg")
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imgspath = os.path.join(resultPath, f"{event.evtname}", "imgs")
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if not os.path.exists(subimgpath):
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os.makedirs(subimgpath)
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if not os.path.exists(imgspath):
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os.makedirs(imgspath)
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##(2) 保存轨迹中的子图
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subimgpairs = event.save_event_subimg(subimgpath)
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for subimgName, subimg in subimgpairs:
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spath = os.path.join(subimgpath, subimgName)
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cv2.imwrite(spath, subimg)
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##(3) 保存序列图像
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imgpairs = event.plot_save_image(imgspath)
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for imgname, img in imgpairs:
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spath = os.path.join(imgspath, imgname)
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cv2.imwrite(spath, img)
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##(4) 保存轨迹散点图
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img_cat = event.draw_tracks()
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trajpath = os.path.join(resultPath, "trajectory")
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if not os.path.exists(trajpath):
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os.makedirs(trajpath)
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traj_imgpath = os.path.join(trajpath, event.evtname+".png")
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cv2.imwrite(traj_imgpath, img_cat)
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def array2list(bboxes):
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'''
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将 bboxes 变换为 track 列表
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bboxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
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Return：
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lboxes：列表，列表中元素具有同一 track_id，x1y1x2y2 格式
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[x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
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'''
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lboxes = []
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if len(bboxes)==0:
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return []
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trackID = np.unique(bboxes[:, 4].astype(int))
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track_ids = bboxes[:, 4].astype(int)
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for t_id in trackID:
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idx = np.where(track_ids == t_id)[0]
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box = bboxes[idx, :]
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lboxes.append(box)
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return lboxes
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class ShoppingEvent:
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def __init__(self, eventpath, stype="data"):
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'''stype: str, 'source', 'data', 'realtime', 共三种
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source: 前后摄视频经 pipeline 生成的文件
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data: 基于事件切分的原 data 文件版本
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realtime: 全实时生成的 data 文件
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'''
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self.eventpath = eventpath
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self.evtname = str(Path(eventpath).stem)
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self.barcode = ''
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self.evtType = ''
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'''=========== path of image and video =========== '''
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self.back_videopath = ''
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self.front_videopath = ''
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self.back_imgpaths = []
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self.front_imgpaths = []
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'''=========== process.data ==============================='''
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self.one2one = None
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self.one2n = None
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self.one2SN = None
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'''=========== 0/1_track.data ============================='''
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self.back_yolobboxes = []
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self.back_yolofeats = []
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self.back_trackerboxes = np.empty((0, 9), dtype=np.float64) ##和类doTracks兼容
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self.back_trackerfeats = {}
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self.back_trackingboxes = []
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self.back_trackingfeats = []
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self.front_yolobboxes = []
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self.front_yolofeats = []
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self.front_trackerboxes = np.empty((0, 9), dtype=np.float64) ##和类doTracks兼容
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self.front_trackerfeats = {}
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self.front_trackingboxes = []
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self.front_trackingfeats = []
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'''=========== 0/1_tracking_output.data ==================='''
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self.back_boxes = []
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self.back_feats = []
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self.front_boxes = []
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self.front_feats = []
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if stype=="data":
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self.from_datafile(eventpath)
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if stype=="realtime":
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self.from_realtime_datafile(eventpath)
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if stype=="source":
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self.from_source_pkl(eventpath)
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self.feats_select = np.empty((0, 256), dtype=np.float64)
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self.feats_compose = np.empty((0, 256), dtype=np.float64)
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self.select_feats()
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self.compose_feats()
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# if stype=="image":
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# self.from_image(eventpath)
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def kerndata(self, ShoppingDict, camtype="backCamera"):
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'''
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camtype: str, "backCamera" or "frontCamera"
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'''
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yoloboxes, resfeats = [], []
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trackerboxes = np.empty((0, 9), dtype=np.float64)
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trackefeats = {}
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trackingboxes, trackingfeats = [], []
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frameDictList = ShoppingDict[camtype]["yoloResnetTracker"]
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for frameDict in frameDictList:
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yoloboxes.append(frameDict["bboxes"])
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tboxes = frameDict["tboxes"]
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trackefeats.update(frameDict["feats"])
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trackerboxes = np.concatenate((trackerboxes, np.array(tboxes)), axis=0)
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Residual = ShoppingDict[camtype]["tracking"].Residual
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for track in Residual:
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trackingboxes.append(track.boxes)
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trackingfeats.append(track.features)
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kdata = (yoloboxes, resfeats, trackerboxes, trackefeats, trackingboxes, trackingfeats)
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tracking_out_boxes, tracking_out_feats = [], []
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Confirmed = ShoppingDict[camtype]["tracking"].Confirmed
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for track in Confirmed:
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tracking_out_boxes.append(track.boxes)
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tracking_out_feats.append(track.features)
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outdata = (tracking_out_boxes, tracking_out_feats)
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return kdata, outdata
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def from_source_pkl(self, eventpath):
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# if plt == 'Windows':
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# pathlib.PosixPath = pathlib.WindowsPath
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with open(eventpath, 'rb') as f:
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ShoppingDict = pickle.load(f)
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self.eventpath = ShoppingDict["eventPath"]
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self.evtname = ShoppingDict["eventName"]
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self.barcode = ShoppingDict["barcode"]
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if len(ShoppingDict["one2n"]):
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self.one2n = ShoppingDict["one2n"]
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'''=========== path of image and video =========== '''
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self.back_videopath = ShoppingDict["backCamera"]["videoPath"]
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self.front_videopath = ShoppingDict["frontCamera"]["videoPath"]
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self.back_imgpaths = ShoppingDict["backCamera"]["imagePaths"]
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self.front_imgpaths = ShoppingDict["frontCamera"]["imagePaths"]
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'''===========对应于 0/1_track.data ============================='''
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backdata, back_outdata = self.kerndata(ShoppingDict, "backCamera")
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frontdata, front_outdata = self.kerndata(ShoppingDict, "frontCamera")
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self.back_yolobboxes = backdata[0]
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self.back_yolofeats = backdata[1]
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self.back_trackerboxes = backdata[2]
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self.back_trackerfeats = [3]
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self.back_trackingboxes = [4]
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self.back_trackingfeats = [5]
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self.front_yolobboxes = frontdata[0]
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self.front_yolofeats = frontdata[1]
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self.front_trackerboxes = frontdata[2]
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self.front_trackerfeats = frontdata[3]
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self.front_trackingboxes = frontdata[4]
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self.front_trackingfeats = frontdata[5]
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'''===========对应于 0/1_tracking_output.data ============================='''
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self.back_boxes = back_outdata[0]
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self.back_feats = back_outdata[1]
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self.front_boxes = front_outdata[0]
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self.front_feats = front_outdata[1]
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def from_datafile(self, eventpath):
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evtList = self.evtname.split('_')
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if len(evtList)>=2 and len(evtList[-1])>=10 and evtList[-1].isdigit():
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self.barcode = evtList[-1]
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if len(evtList)==3 and evtList[-1]== evtList[-2]:
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self.evtType = 'input'
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else:
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self.evtType = 'other'
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'''================ path of image ============='''
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frontImgs, frontFid = [], []
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backImgs, backFid = [], []
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for imgname in os.listdir(eventpath):
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name, ext = os.path.splitext(imgname)
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if ext not in IMG_FORMAT or name.find('frameId') < 0: continue
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if len(name.split('_')) != 3 and not name.split('_')[3].isdigit(): continue
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CamerType = name.split('_')[0]
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frameId = int(name.split('_')[3])
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imgpath = os.path.join(eventpath, imgname)
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if CamerType == '0':
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backImgs.append(imgpath)
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backFid.append(frameId)
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if CamerType == '1':
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frontImgs.append(imgpath)
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frontFid.append(frameId)
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## 生成依据帧 ID 排序的前后摄图像地址列表
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frontIdx = np.argsort(np.array(frontFid))
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backIdx = np.argsort(np.array(backFid))
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self.front_imgpaths = [frontImgs[i] for i in frontIdx]
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self.back_imgpaths = [backImgs[i] for i in backIdx]
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'''================ path of video ============='''
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for vidname in os.listdir(eventpath):
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name, ext = os.path.splitext(vidname)
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if ext not in VID_FORMAT: continue
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vidpath = os.path.join(eventpath, vidname)
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CamerType = name.split('_')[0]
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if CamerType == '0':
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self.back_videopath = vidpath
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if CamerType == '1':
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self.front_videopath = vidpath
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'''================ process.data ============='''
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procpath = Path(eventpath).joinpath('process.data')
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if procpath.is_file():
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SimiDict = read_similar(procpath)
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self.one2one = SimiDict['one2one']
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self.one2n = SimiDict['one2n']
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self.one2SN = SimiDict['one2SN']
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'''=========== 0/1_track.data & 0/1_tracking_output.data ======='''
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for dataname in os.listdir(eventpath):
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datapath = os.path.join(eventpath, dataname)
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if not os.path.isfile(datapath): continue
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CamerType = dataname.split('_')[0]
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'''========== 0/1_track.data =========='''
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if dataname.find("_track.data")>0:
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bboxes, ffeats, trackerboxes, trackerfeats, trackingboxes, trackingfeats = extract_data(datapath)
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if CamerType == '0':
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self.back_yolobboxes = bboxes
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self.back_yolofeats = ffeats
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self.back_trackerboxes = trackerboxes
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self.back_trackerfeats = trackerfeats
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self.back_trackingboxes = trackingboxes
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self.back_trackingfeats = trackingfeats
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if CamerType == '1':
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self.front_yolobboxes = bboxes
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self.front_yolofeats = ffeats
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self.front_trackerboxes = trackerboxes
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self.front_trackerfeats = trackerfeats
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self.front_trackingboxes = trackingboxes
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self.front_trackingfeats = trackingfeats
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'''========== 0/1_tracking_output.data =========='''
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if dataname.find("_tracking_output.data")>0:
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tracking_output_boxes, tracking_output_feats = read_tracking_output(datapath)
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if CamerType == '0':
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self.back_boxes = tracking_output_boxes
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self.back_feats = tracking_output_feats
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elif CamerType == '1':
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self.front_boxes = tracking_output_boxes
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self.front_feats = tracking_output_feats
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def from_realtime_datafile(self, eventpath):
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evtList = self.evtname.split('_')
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if len(evtList)>=2 and len(evtList[-1])>=10 and evtList[-1].isdigit():
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self.barcode = evtList[-1]
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if len(evtList)==3 and evtList[-1]== evtList[-2]:
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self.evtType = 'input'
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else:
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self.evtType = 'other'
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'''================ path of video ============='''
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for vidname in os.listdir(eventpath):
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name, ext = os.path.splitext(vidname)
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if ext not in VID_FORMAT: continue
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vidpath = os.path.join(eventpath, vidname)
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CamerType = name.split('_')[0]
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if CamerType == '0':
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self.back_videopath = vidpath
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if CamerType == '1':
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self.front_videopath = vidpath
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'''================ process.data ============='''
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procpath = Path(eventpath).joinpath('process.data')
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if procpath.is_file():
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SimiDict = read_similar(procpath)
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self.one2one = SimiDict['one2one']
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self.one2n = SimiDict['one2n']
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self.one2SN = SimiDict['one2SN']
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'''=========== 0/1_track.data & 0/1_tracking_output.data ======='''
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for dataname in os.listdir(eventpath):
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datapath = os.path.join(eventpath, dataname)
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if not os.path.isfile(datapath): continue
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CamerType = dataname.split('_')[0]
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'''========== 0/1_track.data =========='''
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if dataname.find("_tracker.data")>0:
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trackerboxes, trackerfeats = extract_data_realtime(datapath)
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if CamerType == '0':
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self.back_trackerboxes = trackerboxes
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self.back_trackerfeats = trackerfeats
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if CamerType == '1':
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self.front_trackerboxes = trackerboxes
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self.front_trackerfeats = trackerfeats
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'''========== 0/1_tracking_output.data =========='''
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if dataname.find("_tracking_output.data")>0:
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trackingboxes, trackingfeats, tracking_outboxes, tracking_outfeats = read_tracking_output_realtime(datapath)
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if CamerType == '0':
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self.back_trackingboxes = trackingboxes
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self.back_trackingfeats = trackingfeats
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self.back_boxes = tracking_outboxes
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self.back_feats = tracking_outfeats
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elif CamerType == '1':
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self.front_trackingboxes = trackingboxes
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self.front_trackingfeats = trackingfeats
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self.front_boxes = tracking_outboxes
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self.front_feats = tracking_outfeats
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def compose_feats(self):
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'''事件的特征集成'''
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feats_compose = np.empty((0, 256), dtype=np.float64)
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if len(self.front_feats):
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for feat in self.front_feats:
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feats_compose = np.concatenate((feats_compose, feat), axis=0)
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if len(self.back_feats):
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for feat in self.back_feats:
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feats_compose = np.concatenate((feats_compose, feat), axis=0)
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self.feats_compose = feats_compose
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def select_feats(self):
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'''事件的特征选择'''
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if len(self.front_feats):
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self.feats_select = self.front_feats[0]
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elif len(self.back_feats):
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self.feats_select = self.back_feats[0]
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def plot_save_image(self, savepath):
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def array2list(bboxes):
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'''[x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]'''
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frame_ids = bboxes[:, 7].astype(int)
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fID = np.unique(bboxes[:, 7].astype(int))
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fboxes = []
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for f_id in fID:
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idx = np.where(frame_ids==f_id)[0]
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box = bboxes[idx, :]
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fboxes.append((f_id, box))
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return fboxes
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imgpairs = []
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cameras = ('front', 'back')
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for camera in cameras:
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if camera == 'front':
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boxes = self.front_trackerboxes
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imgpaths = self.front_imgpaths
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else:
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boxes = self.back_trackerboxes
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imgpaths = self.back_imgpaths
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fboxes = array2list(boxes)
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for fid, fbox in fboxes:
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imgpath = imgpaths[int(fid-1)]
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image = cv2.imread(imgpath)
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annotator = Annotator(image.copy(), line_width=2)
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for i, box in enumerate(fbox):
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x1, y1, x2, y2, tid, score, cls, fid, bid = box
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label = f'{int(tid), int(cls)}'
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if tid >=0 and cls==0:
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color = colors(int(cls), True)
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elif tid >=0 and cls!=0:
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color = colors(int(tid), True)
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else:
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color = colors(19, True) # 19为调色板的最后一个元素
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xyxy = (x1/2, y1/2, x2/2, y2/2)
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annotator.box_label(xyxy, label, color=color)
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im0 = annotator.result()
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imgpairs.append((Path(imgpath).name, im0))
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# spath = os.path.join(savepath, Path(imgpath).name)
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# cv2.imwrite(spath, im0)
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return imgpairs
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def save_event_subimg(self, savepath):
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'''
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功能: 保存一次购物事件的轨迹子图
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9 items: barcode, type, filepath, back_imgpaths, front_imgpaths,
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back_boxes, front_boxes, back_feats, front_feats,
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feats_compose, feats_select
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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
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'''
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imgpairs = []
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cameras = ('front', 'back')
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for camera in cameras:
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boxes = np.empty((0, 9), dtype=np.float64) ##和类doTracks兼容
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if camera == 'front':
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for b in self.front_boxes:
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boxes = np.concatenate((boxes, b), axis=0)
|
||
imgpaths = self.front_imgpaths
|
||
else:
|
||
for b in self.back_boxes:
|
||
boxes = np.concatenate((boxes, b), axis=0)
|
||
imgpaths = self.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"
|
||
|
||
imgpairs.append((subimgName, subimg))
|
||
|
||
# spath = os.path.join(savepath, subimgName)
|
||
|
||
# cv2.imwrite(spath, subimg)
|
||
return imgpairs
|
||
# basename = os.path.basename(event['filepath'])
|
||
print(f"Image saved: {os.path.basename(self.eventpath)}")
|
||
|
||
def draw_tracks(self):
|
||
front_edge = cv2.imread(r"D:\DetectTracking\tracking\shopcart\cart_tempt\board_ftmp_line.png")
|
||
back_edge = cv2.imread(r"D:\DetectTracking\tracking\shopcart\cart_tempt\edgeline.png")
|
||
|
||
front_trackerboxes = array2list(self.front_trackerboxes)
|
||
back_trackerboxes = array2list(self.back_trackerboxes)
|
||
|
||
# img1, img2 = edgeline.copy(), edgeline.copy()
|
||
img1 = drawTrack(front_trackerboxes, front_edge.copy())
|
||
img2 = drawTrack(self.front_trackingboxes, front_edge.copy())
|
||
|
||
img3 = drawTrack(back_trackerboxes, back_edge.copy())
|
||
img4 = drawTrack(self.back_trackingboxes, back_edge.copy())
|
||
|
||
|
||
|
||
imgcat1 = np.concatenate((img1, img2), axis = 1)
|
||
H, W = imgcat1.shape[:2]
|
||
cv2.line(imgcat1, (int(W/2), 0), (int(W/2), H), (128, 255, 128), 2)
|
||
|
||
imgcat2 = np.concatenate((img3, img4), axis = 1)
|
||
H, W = imgcat2.shape[:2]
|
||
cv2.line(imgcat2, (int(W/2), 0), (int(W/2), H), (128, 255, 128), 2)
|
||
|
||
|
||
illus = [imgcat1, imgcat2]
|
||
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)
|
||
|
||
return img_cat
|
||
|
||
|
||
|
||
|
||
|
||
def main():
|
||
# pklpath = r"D:\DetectTracking\evtresult\images2\ShoppingDict.pkl"
|
||
# evt = ShoppingEvent(pklpath, stype='source')
|
||
|
||
evtpath = 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\20241209-160248-08edd5f6-1806-45ad-babf-7a4dd11cea60_6973226721445"
|
||
evt = ShoppingEvent(evtpath, stype='data')
|
||
|
||
img_cat = evt.draw_tracks()
|
||
|
||
cv2.imwrite("a.png", img_cat)
|
||
|
||
|
||
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|
||
# main1()
|
||
|
||
|
||
|
||
|
||
|
||
|