359 lines
13 KiB
Python
359 lines
13 KiB
Python
# -*- 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
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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 pipeline(
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eventpath,
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savepath,
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SourceType,
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weights,
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YoloVersion="V5"
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):
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'''
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eventpath: 单个事件的存储路径
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'''
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optdict = {}
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optdict["weights"] = weights
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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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event_tracks = []
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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'''
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if not savepath:
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savepath = Path(__file__).resolve().parents[0] / "events_result"
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savepath_pipeline = Path(savepath) / Path("Yolos_Tracking") / evtname
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"""ShoppingDict pickle 文件保存地址 """
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savepath_spdict = Path(savepath) / "ShoppingDict_pkfile"
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if not savepath_spdict.exists():
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savepath_spdict.mkdir(parents=True, exist_ok=True)
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pf_path = Path(savepath_spdict) / Path(str(evtname)+".pickle")
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# if pf_path.exists():
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# print(f"Pickle file have saved: {evtname}.pickle")
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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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yrtDict = {}
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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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for vpath in vpaths:
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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. 事件结果存储文件夹 ================='''
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if isinstance(vpath, list):
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savepath_pipeline_imgs = savepath_pipeline / Path("images")
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else:
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savepath_pipeline_imgs = savepath_pipeline / Path(str(Path(vpath).stem))
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if not savepath_pipeline_imgs.exists():
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savepath_pipeline_imgs.mkdir(parents=True, exist_ok=True)
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savepath_pipeline_subimgs = savepath_pipeline / Path("subimgs")
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if not savepath_pipeline_subimgs.exists():
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savepath_pipeline_subimgs.mkdir(parents=True, exist_ok=True)
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'''================= 3. Yolo + Resnet + Tracker ================='''
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optdict["source"] = vpath
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optdict["save_dir"] = savepath_pipeline_imgs
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optdict["is_save_img"] = True
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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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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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# 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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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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yrtDict["frontyrt"] = yrtOut
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'''========================== 保存模块 ================================='''
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'''(1) 保存 ShoppingDict 事件'''
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with open(str(pf_path), 'wb') as f:
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pickle.dump(ShoppingDict, f)
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'''(2) 保存 Tracking 输出的运动轨迹子图，并记录相似度'''
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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_pipeline_subimgs, ctype, featdict)
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else:
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save_subimgs(imgdict, track.slt_boxes, savepath_pipeline_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_pipeline, CamerType, draw5p=True)
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illus[0] = img_tracking
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plt = plot_frameID_y2(vts)
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plt.savefig(os.path.join(savepath_pipeline, "front_y2.png"))
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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_pipeline, 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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trajpath = os.path.join(savepath_pipeline, "trajectory.png")
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cv2.imwrite(trajpath, img_cat)
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def execute_pipeline(evtdir = r"D:\datasets\ym\后台数据\unzip",
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source_type = "video", # video, image,
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save_path = r"D:\work\result_pipeline",
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yolo_ver = "V10", # V10, V5
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weight_yolo_v5 = r'./ckpts/best_cls10_0906.pt' ,
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weight_yolo_v10 = r'./ckpts/best_v10s_width0375_1205.pt',
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k=0
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):
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'''
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运行函数 pipeline()，遍历事件文件夹，每个文件夹是一个事件
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'''
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parmDict = {}
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parmDict["SourceType"] = source_type
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parmDict["savepath"] = save_path
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parmDict["YoloVersion"] = yolo_ver
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if parmDict["YoloVersion"] == "V5":
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parmDict["weights"] = weight_yolo_v5
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elif parmDict["YoloVersion"] == "V10":
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parmDict["weights"] = weight_yolo_v10
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evtdir = Path(evtdir)
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errEvents = []
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for item in evtdir.iterdir():
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if item.is_dir():
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item = evtdir/Path("20250310-175352-741")
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parmDict["eventpath"] = item
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pipeline(**parmDict)
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# try:
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# pipeline(**parmDict)
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# except Exception as e:
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# errEvents.append(str(item))
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k+=1
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if k==1:
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break
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errfile = os.path.join(parmDict["savepath"], 'error_events.txt')
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with open(errfile, 'w', encoding='utf-8') as f:
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for line in errEvents:
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f.write(line + '\n')
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if __name__ == "__main__":
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execute_pipeline()
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# spath_v10 = r"D:\work\result_pipeline_v10"
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# spath_v5 = r"D:\work\result_pipeline_v5"
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# execute_pipeline(save_path=spath_v10, yolo_ver="V10")
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# execute_pipeline(save_path=spath_v5, yolo_ver="V5")
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datapath = r'/home/wqg/dataset/test_dataset/base_dataset/single_event/source/'
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savepath = r'/home/wqg/dataset/pipeline/contrast/single_event_V5'
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execute_pipeline(evtdir = datapath,
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DataType = "raw", # raw, pkl
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kk=1,
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source_type = "video", # video, image,
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save_path = savepath,
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yolo_ver = "V10", # V10, V5
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weight_yolo_v5 = r'./ckpts/best_cls10_0906.pt' ,
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weight_yolo_v10 = r'./ckpts/best_v10s_width0375_1205.pt',
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saveimages = False
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)
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