不运行pipline,直接解析process.data
This commit is contained in:
jiajie555
542
process_addWrite.py
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542
process_addWrite.py
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# -*- coding: utf-8 -*-
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"""
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Created on Sun Sep 29 08:59:21 2024
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@author: ym
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"""
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import os
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# import sys
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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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from scipy.spatial.distance import cdist
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from track_reid import yolo_resnet_tracker, yolov10_resnet_tracker
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from tracking.dotrack.dotracks_back import doBackTracks
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from tracking.dotrack.dotracks_front import doFrontTracks
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from tracking.utils.drawtracks import plot_frameID_y2, draw_all_trajectories
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from utils.getsource import get_image_pairs, get_video_pairs
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from tracking.utils.read_data import read_similar, get_process_csv_data
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from openpyxl import Workbook, load_workbook
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def save_subimgs(imgdict, boxes, spath, ctype, featdict = None):
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'''
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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
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'''
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boxes = boxes[np.argsort(boxes[:, 7])]
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for i in range(len(boxes)):
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simi = None
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tid, fid, bid = int(boxes[i, 4]), int(boxes[i, 7]), int(boxes[i, 8])
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if i>0:
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_, fid0, bid0 = int(boxes[i-1, 4]), int(boxes[i-1, 7]), int(boxes[i-1, 8])
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if f"{fid0}_{bid0}" in featdict.keys() and f"{fid}_{bid}" in featdict.keys():
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feat0 = featdict[f"{fid0}_{bid0}"]
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feat1 = featdict[f"{fid}_{bid}"]
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simi = 1 - np.maximum(0.0, cdist(feat0[None, :], feat1[None, :], "cosine"))[0][0]
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img = imgdict[f"{fid}_{bid}"]
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imgpath = spath / f"{ctype}_tid{tid}-{fid}-{bid}.png"
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if simi is not None:
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imgpath = spath / f"{ctype}_tid{tid}-{fid}-{bid}_sim{simi:.2f}.png"
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cv2.imwrite(imgpath, img)
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def save_subimgs_1(imgdict, boxes, spath, ctype, simidict = None):
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'''
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当前 box 特征和该轨迹 smooth_feat 特征的相似度, yolo_resnet_tracker 函数中，
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采用该方式记录特征相似度
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'''
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for i in range(len(boxes)):
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tid, fid, bid = int(boxes[i, 4]), int(boxes[i, 7]), int(boxes[i, 8])
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key = f"{fid}_{bid}"
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img = imgdict[key]
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imgpath = spath / f"{ctype}_tid{tid}-{fid}-{bid}.png"
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if simidict is not None and key in simidict.keys():
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imgpath = spath / f"{ctype}_tid{tid}-{fid}-{bid}_sim{simidict[key]:.2f}.png"
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cv2.imwrite(imgpath, img)
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def show_result(eventpath, event_tracks, yrtDict, savepath_pipe):
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'''保存 Tracking 输出的运动轨迹子图，并记录相似度'''
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savepath_pipe_subimgs = savepath_pipe / Path("subimgs")
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if not savepath_pipe_subimgs.exists():
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savepath_pipe_subimgs.mkdir(parents=True, exist_ok=True)
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for CamerType, vts in event_tracks:
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if len(vts.tracks)==0: continue
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if CamerType == 'front':
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# yolos = ShoppingDict["frontCamera"]["yoloResnetTracker"]
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yolos = yrtDict["frontyrt"]
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ctype = 1
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if CamerType == 'back':
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# yolos = ShoppingDict["backCamera"]["yoloResnetTracker"]
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yolos = yrtDict["backyrt"]
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ctype = 0
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imgdict, featdict, simidict = {}, {}, {}
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for y in yolos:
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imgdict.update(y["imgs"])
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featdict.update(y["feats"])
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simidict.update(y["featsimi"])
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for track in vts.Residual:
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if isinstance(track, np.ndarray):
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save_subimgs(imgdict, track, savepath_pipe_subimgs, ctype, featdict)
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else:
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save_subimgs(imgdict, track.slt_boxes, savepath_pipe_subimgs, ctype, featdict)
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'''(3) 轨迹显示与保存'''
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illus = [None, None]
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for CamerType, vts in event_tracks:
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if len(vts.tracks)==0: continue
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if CamerType == 'front':
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edgeline = cv2.imread("./tracking/shopcart/cart_tempt/board_ftmp_line.png")
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h, w = edgeline.shape[:2]
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# nh, nw = h//2, w//2
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# edgeline = cv2.resize(edgeline, (nw, nh), interpolation=cv2.INTER_AREA)
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img_tracking = draw_all_trajectories(vts, edgeline, savepath_pipe, CamerType, draw5p=True)
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illus[0] = img_tracking
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plt = plot_frameID_y2(vts)
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'''==========yj callbackdata========='''
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plt.savefig(os.path.join(eventpath, "front_y2.png"))
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'''========================================'''
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if CamerType == 'back':
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edgeline = cv2.imread("./tracking/shopcart/cart_tempt/edgeline.png")
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h, w = edgeline.shape[:2]
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# nh, nw = h//2, w//2
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# edgeline = cv2.resize(edgeline, (nw, nh), interpolation=cv2.INTER_AREA)
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img_tracking = draw_all_trajectories(vts, edgeline, savepath_pipe, CamerType, draw5p=True)
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illus[1] = img_tracking
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illus = [im for im in illus if im is not None]
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if len(illus):
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img_cat = np.concatenate(illus, axis = 1)
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if len(illus)==2:
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H, W = img_cat.shape[:2]
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cv2.line(img_cat, (int(W/2), 0), (int(W/2), int(H)), (128, 128, 255), 3)
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'''==========yj callbackdata========='''
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trajpath = os.path.join(eventpath, "trajectory.png")
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# trajpath = os.path.join(savepath_pipe, "trajectory.png")
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'''======================================'''
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cv2.imwrite(trajpath, img_cat)
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def pipeline(dict_data,
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pickle_exist,
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eventpath,
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SourceType,
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weights,
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DataType = "raw", #raw, pkl: images or videos, pkl, pickle file
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YoloVersion="V5",
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savepath = None,
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saveimages = True,
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):
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## 构造购物事件字典
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evtname = Path(eventpath).stem
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barcode = evtname.split('_')[-1] if len(evtname.split('_'))>=2 \
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and len(evtname.split('_')[-1])>=8 \
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and evtname.split('_')[-1].isdigit() else ''
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'''事件结果存储文件夹: savepath_pipe, savepath_pkl'''
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if not savepath:
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savepath = Path(__file__).resolve().parents[0] / "events_result"
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savepath_pipe = Path(savepath) / Path("yolos_tracking") / evtname
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savepath_pkl = Path(savepath) / "shopping_pkl"
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if not savepath_pkl.exists():
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savepath_pkl.mkdir(parents=True, exist_ok=True)
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pklpath = Path(savepath_pkl) / Path(str(evtname)+".pickle")
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yrt_out = []
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if DataType == "raw":
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if not pickle_exist:
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### 不重复执行已经过yolo-resnet-tracker
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if pklpath.exists():
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print(f"Pickle file have saved: {evtname}.pickle")
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return
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if SourceType == "video":
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vpaths = get_video_pairs(eventpath)
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elif SourceType == "image":
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vpaths = get_image_pairs(eventpath)
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for vpath in vpaths:
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'''================= 2. 事件结果存储文件夹 ================='''
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if isinstance(vpath, list):
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savepath_pipe_imgs = savepath_pipe / Path("images")
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else:
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savepath_pipe_imgs = savepath_pipe / Path(str(Path(vpath).stem))
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if not savepath_pipe_imgs.exists():
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savepath_pipe_imgs.mkdir(parents=True, exist_ok=True)
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optdict = {}
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optdict["weights"] = weights
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optdict["source"] = vpath
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optdict["save_dir"] = savepath_pipe_imgs
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optdict["is_save_img"] = saveimages
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optdict["is_save_video"] = True
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if YoloVersion == "V5":
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yrtOut = yolo_resnet_tracker(**optdict)
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elif YoloVersion == "V10":
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yrtOut = yolov10_resnet_tracker(**optdict)
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yrt_out.append((vpath, yrtOut))
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elif DataType == "pkl":
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pass
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else:
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return
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'''====================== 构造 ShoppingDict 模块 ======================='''
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ShoppingDict = {"eventPath": eventpath,
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"eventName": evtname,
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"barcode": barcode,
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"eventType": '', # "input", "output", "other"
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"frontCamera": {},
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"backCamera": {},
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"one2n": [] #
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}
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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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ShoppingDict["one2n"] = SimiDict['one2n']
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yrtDict = {}
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event_tracks = []
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for vpath, yrtOut in yrt_out:
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'''================= 1. 构造相机事件字典 ================='''
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CameraEvent = {"cameraType": '', # "front", "back"
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"videoPath": '',
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"imagePaths": [],
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"yoloResnetTracker": [],
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"tracking": [],
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}
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if isinstance(vpath, list):
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CameraEvent["imagePaths"] = vpath
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bname = os.path.basename(vpath[0])
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if not isinstance(vpath, list):
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CameraEvent["videoPath"] = vpath
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bname = os.path.basename(vpath).split('.')[0]
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if bname.split('_')[0] == "0" or bname.find('back')>=0:
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CameraEvent["cameraType"] = "back"
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if bname.split('_')[0] == "1" or bname.find('front')>=0:
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CameraEvent["cameraType"] = "front"
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'''2种保存方式: (1) no save subimg, (2) save img'''
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###(1) save images
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yrtOut_save = []
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for frdict in yrtOut:
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fr_dict = {}
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for k, v in frdict.items():
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if k != "imgs":
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fr_dict[k]=v
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yrtOut_save.append(fr_dict)
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CameraEvent["yoloResnetTracker"] = yrtOut_save
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###(2) no save images
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# CameraEvent["yoloResnetTracker"] = yrtOut
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'''================= 4. tracking ================='''
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'''(1) 生成用于 tracking 模块的 boxes、feats'''
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bboxes = np.empty((0, 6), dtype=np.float64)
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trackerboxes = np.empty((0, 9), dtype=np.float64)
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trackefeats = {}
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for frameDict in yrtOut:
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tboxes = frameDict["tboxes"]
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ffeats = frameDict["feats"]
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boxes = frameDict["bboxes"]
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bboxes = np.concatenate((bboxes, np.array(boxes)), axis=0)
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trackerboxes = np.concatenate((trackerboxes, np.array(tboxes)), axis=0)
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for i in range(len(tboxes)):
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fid, bid = int(tboxes[i, 7]), int(tboxes[i, 8])
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trackefeats.update({f"{fid}_{bid}": ffeats[f"{fid}_{bid}"]})
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'''(2) tracking, 后摄'''
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if CameraEvent["cameraType"] == "back":
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vts = doBackTracks(trackerboxes, trackefeats)
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vts.classify()
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event_tracks.append(("back", vts))
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CameraEvent["tracking"] = vts
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ShoppingDict["backCamera"] = CameraEvent
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'''====yj callbackdata======='''
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back_cnts = len(vts.Residual)
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dict_data['后摄轨迹数'] = back_cnts
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print(f"back_cnts: {back_cnts}")
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'''=============================='''
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yrtDict["backyrt"] = yrtOut
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'''(2) tracking, 前摄'''
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if CameraEvent["cameraType"] == "front":
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vts = doFrontTracks(trackerboxes, trackefeats)
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vts.classify()
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event_tracks.append(("front", vts))
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CameraEvent["tracking"] = vts
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ShoppingDict["frontCamera"] = CameraEvent
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'''====yj callbackdata======='''
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front_cnts = len(vts.Residual)
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dict_data['前摄轨迹数'] = front_cnts
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print(f"front_cnts: {front_cnts}")
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'''=============================='''
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yrtDict["frontyrt"] = yrtOut
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'''========================== 保存模块 ================================='''
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# 保存 ShoppingDict
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with open(str(pklpath), 'wb') as f:
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pickle.dump(ShoppingDict, f)
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# 绘制并保存轨迹图
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show_result(eventpath, event_tracks, yrtDict, savepath_pipe)
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return dict_data
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class WriteExcel:
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def is_excel(self, input_excel):
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# 若文件存在，加载工作簿
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wb = load_workbook(input_excel)
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sheet_name = wb.sheetnames[0] ##默认回传分析表只有一个sheet
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# 获取活动工作表
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ws = wb.active
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sheet = wb[sheet_name]
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##确定新增列的位置
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# new_col_index = sheet.max_column
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return wb, ws, sheet
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def init_excel(self, input_excel, output_excel, headers, max_col=13):
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if os.path.exists(output_excel):
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wb, ws, sheet = self.is_excel(output_excel)
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return wb, ws, sheet
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elif os.path.exists(input_excel):
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wb, ws, sheet = self.is_excel(input_excel)
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self.add_header(wb, sheet, max_col, headers, output_excel)
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return wb, ws, sheet
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else:
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raise FileNotFoundError(f"文件 '{input_excel}' 不存在")
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'''在已有excel文件中新增列标题'''
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def add_header(self, wb, sheet, column, headers, file_name):
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write_data = {}
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sub_data = {}
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for i, head in enumerate(headers):
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k = column + i + 1
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sub_data[k] = head
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write_data[1] = sub_data
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self.add_data_to_excel(wb, sheet, write_data, file_name)
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def add_data_to_excel(self, wb, sheet, write_data, file_name):
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'''
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示例写入数据，格式为 {行号: {列号: 值}}
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write_data = {
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5: {11: 1, 12: 2, 13: 3}
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}
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'''
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# 写入指定行列的数据
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for row_num, col_data in write_data.items():
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for col_num, value in col_data.items():
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# print('row_num', row_num, 'col_num', col_num, 'value', value)
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sheet.cell(row=row_num, column=col_num, value=str(value))
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wb.save(file_name)
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def get_simiTitle_index(self, ws, title_name='事件名'):
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'''获取excel文件中标题名称与追加内容相同部分的列索引
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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'''
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# 获取列标题
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headers = [cell.value for cell in ws[1]]
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# # 找到“事件名”所在的列索引
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event_name_index = headers.index(title_name) if title_name in headers else None
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if event_name_index is None:
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print("未找到标题为'事件名'的列")
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return
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else:
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return event_name_index
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def get_event_row(self, sheet, event_name_index, event_name):
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'''获得当前事件名event_name在excel文件中所在的行索引'''
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row_index = 0
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for row, content in enumerate(sheet.iter_rows(min_row=2, values_only=True)):
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# print('row', row, content[event_name_index])
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if content[event_name_index] == event_name:
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row_index = row + 2 ### 默认只有一行标题，若有两行标题则需改为+3
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# print('row_index', row_index)
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break
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return row_index
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def write_simi_add(self, wb, ws, sheet, max_col, evtname, dict_data, headers, file_name):
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'''
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在已有excel文件中追加内容
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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
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'''
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event_index = self.get_simiTitle_index(ws)
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if event_index is not None:
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print('evtname', evtname)
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row_index = self.get_event_row(sheet, event_index, evtname)
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if row_index > 0:
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sub_dict = {}
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print('headers', headers)
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for i, header in enumerate(headers):
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col_index = max_col + i + 1
|
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# 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
|
||||
)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user