449 lines
15 KiB
Python
449 lines
15 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Thu May 30 14:03:03 2024
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现场测试性能分析
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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 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, draw_tracking_boxes
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from tracking.utils import Boxes, IterableSimpleNamespace, yaml_load
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from tracking.trackers import BOTSORT, BYTETracker
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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 tracking.utils.read_data import extract_data, read_deletedBarcode_file, read_tracking_output
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from contrast_analysis import contrast_analysis
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from tracking.utils.annotator import TrackAnnotator
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W, H = 1024, 1280
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Mode = 'front' #'back'
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ImgFormat = ['.jpg', '.jpeg', '.png', '.bmp']
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def video2imgs(path):
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vpath = os.path.join(path, "videos")
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k = 0
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have = False
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for filename in os.listdir(vpath):
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file, ext = os.path.splitext(filename)
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imgdir = os.path.join(path, file)
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if os.path.exists(imgdir):
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continue
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else:
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os.mkdir(imgdir)
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vfile = os.path.join(vpath, filename)
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cap = cv2.VideoCapture(vfile)
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i = 0
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while True:
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ret, frame = cap.read()
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if not ret:
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break
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i += 1
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imgp = os.path.join(imgdir, file+f"_{i}.png")
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cv2.imwrite(imgp, frame)
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print(filename+f": {i}")
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cap.release()
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k+=1
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if k==1000:
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break
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def draw_boxes():
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datapath = r'D:\datasets\ym\videos_test\20240530\1_tracker_inout(1).data'
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VideosData = read_tracker_input(datapath)
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bboxes = VideosData[0][0]
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ffeats = VideosData[0][1]
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videopath = r"D:\datasets\ym\videos_test\20240530\134458234-1cd970cf-f8b9-4e80-9c2e-7ca3eec83b81-1_seek0.10415589124891511.mp4"
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cap = cv2.VideoCapture(videopath)
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i = 0
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while True:
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ret, frame = cap.read()
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if not ret:
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break
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annotator = Annotator(frame.copy(), line_width=3)
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boxes = bboxes[i]
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for *xyxy, conf, cls in reversed(boxes):
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label = f'{int(cls)}: {conf:.2f}'
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color = colors(int(cls), True)
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annotator.box_label(xyxy, label, color=color)
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img = annotator.result()
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imgpath = r"D:\datasets\ym\videos_test\20240530\result\int8_front\{}.png".format(i+1)
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cv2.imwrite(imgpath, img)
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print(f"Output: {i}")
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i += 1
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cap.release()
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def read_imgs(imgspath, CamerType):
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imgs, frmIDs = [], []
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for filename in os.listdir(imgspath):
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file, ext = os.path.splitext(filename)
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flist = file.split('_')
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if len(flist)==4 and ext in ImgFormat:
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camID, frmID = flist[0], int(flist[-1])
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imgpath = os.path.join(imgspath, filename)
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img = cv2.imread(imgpath)
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if camID==CamerType:
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imgs.append(img)
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frmIDs.append(frmID)
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if len(frmIDs):
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indice = np.argsort(np.array(frmIDs))
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imgs = [imgs[i] for i in indice]
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return imgs
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pass
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def init_tracker(tracker_yaml = None, bs=1):
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"""
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Initialize tracker for object tracking during prediction.
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"""
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TRACKER_MAP = {'bytetrack': BYTETracker, 'botsort': BOTSORT}
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cfg = IterableSimpleNamespace(**yaml_load(tracker_yaml))
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tracker = TRACKER_MAP[cfg.tracker_type](args=cfg, frame_rate=30)
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return tracker
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def tracking(bboxes, ffeats):
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tracker_yaml = r"./trackers/cfg/botsort.yaml"
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tracker = init_tracker(tracker_yaml)
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TrackBoxes = np.empty((0, 9), dtype = np.float32)
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TracksDict = {}
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'''========================== 执行跟踪处理 ============================='''
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# dets 与 feats 应保持严格对应
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for dets, feats in zip(bboxes, ffeats):
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det_tracking = Boxes(dets).cpu().numpy()
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tracks = tracker.update(det_tracking, features=feats)
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'''tracks: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
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0 1 2 3 4 5 6 7 8
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这里，frame_index 也可以用视频的 帧ID 代替, box_index 保持不变
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'''
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if len(tracks):
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TrackBoxes = np.concatenate([TrackBoxes, tracks], axis=0)
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FeatDict = {}
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for track in tracks:
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tid = int(track[8])
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FeatDict.update({tid: feats[tid, :]})
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frameID = tracks[0, 7]
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# print(f"frameID: {int(frameID)}")
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assert len(tracks) == len(FeatDict), f"Please check the func: tracker.update() at frameID({int(frameID)})"
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TracksDict[f"frame_{int(frameID)}"] = {"feats":FeatDict}
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return TrackBoxes, TracksDict
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def do_tracker_tracking(fpath, save_dir):
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bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, tracking_feat_dict = extract_data(fpath)
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tboxes, feats_dict = tracking(bboxes, ffeats)
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CamerType = os.path.basename(fpath).split('_')[0]
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dirname = os.path.split(os.path.split(fpath)[0])[1]
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if CamerType == '1':
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vts = doFrontTracks(tboxes, feats_dict)
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vts.classify()
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plt = plot_frameID_y2(vts)
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plt.savefig('front_y2.png')
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# plt.close()
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elif CamerType == '0':
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vts = doBackTracks(tboxes, feats_dict)
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vts.classify()
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filename = dirname+'_' + CamerType
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edgeline = cv2.imread("./shopcart/cart_tempt/edgeline.png")
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draw_all_trajectories(vts, edgeline, save_dir, filename)
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else:
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print("Please check data file!")
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def do_tracking(fpath, savedir):
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'''
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fpath: 算法各模块输出的data文件地址，匹配；
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savedir: 对 fpath 各模块输出的复现；
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分析具体视频时，需指定 fpath 和 savedir
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'''
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# fpath = r'D:\contrast\dataset\1_to_n\709\20240709-102758_6971558612189\1_track.data'
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# savedir = r'D:\contrast\dataset\result\20240709-102843_6958770005357_6971558612189\error_6971558612189'
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'''1.1 构造 0/1_tracking_output.data 文件地址，读取文件数据'''
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imgpath, dfname = os.path.split(fpath)
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CamerType = dfname.split('_')[0]
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tracking_output_path = os.path.join(imgpath, CamerType + '_tracking_output.data')
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if not os.path.isfile(fpath):
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print(f"Can't find {dfname} file!")
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return
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if not os.path.isfile(tracking_output_path):
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print(f"Can't find {CamerType}_tracking_output.data file!")
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return
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bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, tracking_feat_dict = extract_data(fpath)
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tracking_output_boxes, _ = read_tracking_output(tracking_output_path)
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'''1.2 分别构造 2 个文件夹，(1) 存储画框后的图像; (2) 运动轨迹对应的 boxes子图'''
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save_dir, basename = os.path.split(savedir)
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if not os.path.exists(savedir):
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os.makedirs(savedir)
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subimg_dir = os.path.join(save_dir, basename.split('_')[0] + '_subimgs')
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if not os.path.exists(subimg_dir):
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os.makedirs(subimg_dir)
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'''1.3 读取 fpath 中 track.data 文件对应的图像 imgs '''
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imgs = read_imgs(imgpath, CamerType)
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'''2. 执行轨迹分析, 保存轨迹分析前后的对比图示'''
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traj_graphic = basename + '_' + CamerType
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if CamerType == '1':
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vts = doFrontTracks(trackerboxes, tracker_feat_dict)
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vts.classify()
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plt = plot_frameID_y2(vts)
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# ftpath = os.path.join(save_dir, f"{traj_graphic}_front_y2.png")
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# plt.savefig(ftpath)
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plt.close()
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elif CamerType == '0':
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vts = doBackTracks(trackerboxes, tracker_feat_dict)
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vts.classify()
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edgeline = cv2.imread("./shopcart/cart_tempt/edgeline.png")
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img = draw_all_trajectories(vts, edgeline, save_dir, traj_graphic)
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# imgpth = os.path.join(save_dir, f"{traj_graphic}_.png")
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# cv2.imwrite(str(imgpth), img)
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else:
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print("Please check data file!")
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'''3 tracking() 算法输出后多轨迹选择问题分析'''
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if CamerType == '1':
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aline = cv2.imread("./shopcart/cart_tempt/board_ftmp_line.png")
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elif CamerType == '0':
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aline = cv2.imread("./shopcart/cart_tempt/edgeline.png")
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else:
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print("Please check data file!")
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bline = aline.copy()
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annotator = TrackAnnotator(aline, line_width=2)
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for track in trackingboxes:
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annotator.plotting_track(track)
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aline = annotator.result()
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annotator = TrackAnnotator(bline, line_width=2)
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if not isinstance(tracking_output_boxes, list):
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tracking_output_boxes = [tracking_output_boxes]
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for track in tracking_output_boxes:
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annotator.plotting_track(track)
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bline = annotator.result()
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abimg = np.concatenate((aline, bline), axis = 1)
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abH, abW = abimg.shape[:2]
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cv2.line(abimg, (int(abW/2), 0), (int(abW/2), abH), (128, 255, 128), 2)
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# algpath = os.path.join(save_dir, f"{traj_graphic}_alg.png")
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# cv2.imwrite(str(algpath), abimg)
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'''4. 画框后的图像和子图保存，若imgs数与tracker中fid数不匹配，只保存原图，不保存子图'''
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'''4.1 imgs数 < trackerboxes 的 max(fid)，返回原图'''
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if len(imgs) < np.max(trackerboxes[:,7]):
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for i in range(len(imgs)):
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img_savepath = os.path.join(savedir, CamerType + "_" + f"{i}.png")
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cv2.imwrite(img_savepath, imgs[i])
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print(f"fpath: {fpath}, len(imgs) = {len(imgs)} < Tracker max(fid) = {int(np.max(trackerboxes[:,7]))}, 无法匹配画框")
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return
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'''4.2 在 imgs 上画框并保存'''
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imgs_dw = draw_tracking_boxes(imgs, trackerboxes)
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for fid, img in imgs_dw:
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img_savepath = os.path.join(savedir, CamerType + "_fid_" + f"{fid}.png")
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cv2.imwrite(img_savepath, img)
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# =============================================================================
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# '''4.3.1 保存轨迹分析对应的子图'''
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# for track in vts.Residual:
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# for *xyxy, tid, conf, cls, fid, bid in track.boxes:
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# img = imgs[int(fid-1)]
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# x1, y1, x2, y2 = int(xyxy[0]/2), int(xyxy[1]/2), int(xyxy[2]/2), int(xyxy[3]/2)
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# subimg = img[y1:y2, x1:x2]
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#
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# subimg_path = os.path.join(subimg_dir, f'{CamerType}_tid{int(tid)}_{int(fid-1)}_{int(bid)}.png' )
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# cv2.imwrite(subimg_path, subimg)
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# =============================================================================
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'''4.3.2 保存轨迹选择对应的子图'''
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for track in tracking_output_boxes:
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for *xyxy, tid, conf, cls, fid, bid in track:
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img = imgs[int(fid-1)]
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x1, y1, x2, y2 = int(xyxy[0]/2), int(xyxy[1]/2), int(xyxy[2]/2), int(xyxy[3]/2)
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subimg = img[y1:y2, x1:x2]
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subimg_path = os.path.join(subimg_dir, f'{CamerType}_tid{int(tid)}_{int(fid-1)}_{int(bid)}.png' )
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cv2.imwrite(subimg_path, subimg)
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return abimg
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def main_loop():
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del_barcode_file = r'\\192.168.1.28\share\测试_202406\0723\0723_2\deletedBarcode.txt'
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basepath = r'\\192.168.1.28\share\测试_202406\0723\0723_2' # 测试数据文件夹地址
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SavePath = r'D:\contrast\dataset\resultx' # 结果保存地址
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prefix = ["getout_", "input_", "error_"]
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'''获取性能测试数据相关路径'''
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relative_paths = contrast_analysis(del_barcode_file, basepath, SavePath)
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'''开始循环执行每次测试过任务'''
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k = 0
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for tuple_paths in relative_paths:
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'''生成文件夹存储结果图像的文件夹'''
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namedirs = []
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for data_path in tuple_paths:
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base_name = os.path.basename(data_path).strip().split('_')
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if len(base_name[-1]):
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name = base_name[-1]
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else:
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name = base_name[0]
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namedirs.append(name)
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sdir = "_".join(namedirs)
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savepath = os.path.join(SavePath, sdir)
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if not os.path.exists(savepath):
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os.makedirs(savepath)
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for path in tuple_paths:
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'''============= 分别指定指定存储、读取对应的文件夹 ============='''
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# if sdir.find('094631_6904724022444_6976075000082') < 0: continue
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# if path.find('094631_') < 0: continue
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imgs = []
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for filename in os.listdir(path):
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# filename = '1_track.data'
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fpath = os.path.join(path, filename)
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if os.path.isfile(fpath) and filename.find("track.data")>0:
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enent_name = ''
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'''构建结果保存文件名前缀'''
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for i, name in enumerate(namedirs):
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if fpath.find(name)>0:
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enent_name = prefix[i] + name
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break
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spath = os.path.join(savepath, enent_name)
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# abimg = do_tracking(fpath, spath)
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# imgs.append(abimg)
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try:
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abimg = do_tracking(fpath, spath)
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imgs.append(abimg)
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if len(imgs) == 2:
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Img = np.concatenate((imgs[0], imgs[1]), axis = 0)
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H, W = Img.shape[:2]
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cv2.line(Img, (0, int(H/2)), (int(W), int(H/2)), (128, 255, 128), 2)
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else:
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Img = imgs[0]
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imgpath = os.path.join(savepath, enent_name + '_alg.png')
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cv2.imwrite(imgpath, Img)
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except Exception as e:
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print(f'Error! {fpath}, {e}')
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# k +=1
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# if k==1:
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# break
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def main():
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'''
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fpath: data文件地址，该 data 文件包括 Pipeline 各模块输出
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save_dir：需包含二级目录，其中一级目录为轨迹图像；
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二级目录为与data文件对应的序列图像存储地址。
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'''
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fpath = r'\\192.168.1.28\share\测试_202406\0723\0723_1\20240723-101506_6906839615771\1_track.data'
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save_dir = r'D:\contrast\dataset\result\20240723-101506_\images'
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do_tracking(fpath, save_dir)
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if __name__ == "__main__":
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main_loop()
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# main()
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# try:
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# main_loop()
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# except Exception as e:
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# print(f'Error: {e}')
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