499 lines
18 KiB
Python
499 lines
18 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Sat Jul ** 14:07:25 2024
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现场测试精度、召回率分析程序，是 feat_select.py 的简化版，
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但支持循环计算，并输出总的pr曲线
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@author: ym
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"""
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import os.path
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import shutil
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import numpy as np
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import matplotlib.pyplot as plt
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import cv2
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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.read_data import extract_data, read_deletedBarcode_file, read_tracking_output, read_returnGoods_file
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from tracking.utils.plotting import draw_tracking_boxes, get_subimgs
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from contrast.utils.tools import showHist, show_recall_prec, compute_recall_precision
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# =============================================================================
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# def read_tracking_output(filepath):
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# boxes = []
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# feats = []
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# with open(filepath, 'r', encoding='utf-8') as file:
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# for line in file:
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# line = line.strip() # 去除行尾的换行符和可能的空白字符
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#
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# if not line:
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# continue
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#
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# if line.endswith(','):
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# line = line[:-1]
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#
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# data = np.array([float(x) for x in line.split(",")])
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# if data.size == 9:
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# boxes.append(data)
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# if data.size == 256:
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# feats.append(data)
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#
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# return np.array(boxes), np.array(feats)
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# =============================================================================
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def read_tracking_imgs(imgspath):
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'''
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input:
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imgspath：该路径中的图像为Yolo算法的输入图像，640x512
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output：
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imgs_0：后摄图像，根据 frameId 进行了排序
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imgs_1：前摄图像，根据 frameId 进行了排序
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'''
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imgs_0, frmIDs_0, imgs_1, frmIDs_1 = [], [], [], []
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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==".jpg":
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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=='0':
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imgs_0.append(img)
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frmIDs_0.append(frmID)
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if camID=='1':
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imgs_1.append(img)
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frmIDs_1.append(frmID)
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if len(frmIDs_0):
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indice = np.argsort(np.array(frmIDs_0))
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imgs_0 = [imgs_0[i] for i in indice ]
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if len(frmIDs_1):
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indice = np.argsort(np.array(frmIDs_1))
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imgs_1 = [imgs_1[i] for i in indice ]
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return imgs_0, imgs_1
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# =============================================================================
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# def draw_tracking_boxes(imgs, tracks):
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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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# 关键：imgs中的次序和 track 中的 fid 对应
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# '''
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# subimgs = []
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# for *xyxy, tid, conf, cls, fid, bid in tracks:
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# label = f'id:{int(tid)}_{int(cls)}_{conf:.2f}'
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#
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# annotator = Annotator(imgs[int(fid-1)].copy())
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# if 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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#
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# pt2 = [p/2 for p in xyxy]
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# annotator.box_label(pt2, label, color=color)
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# img0 = annotator.result()
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#
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# subimgs.append(img0)
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#
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# return subimgs
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# =============================================================================
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def get_contrast_paths(pair, basepath):
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assert(len(pair)==2 or len(pair)==3), "pair: seqdir, delete, barcodes"
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getout_fold = pair[0] # 取出操作对应的文件夹
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relvt_barcode = pair[1] # 取出操作对应放入操作的 Barcode
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if len(pair)==3:
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error_match = pair[2] # 取出操作错误匹配的 Barcode
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else:
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error_match = ''
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getoutpath, inputpath, errorpath = '', '', ''
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day, hms = getout_fold.strip('_').split('-')
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input_folds, times = [], []
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errmatch_folds, errmatch_times = [], []
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for pathname in os.listdir(basepath):
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if pathname.endswith('_'): continue
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if os.path.isfile(os.path.join(basepath, pathname)):continue
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infold = pathname.split('_')
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if len(infold)!=2: continue
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day1, hms1 = infold[0].split('-')
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if day1==day and infold[1]==relvt_barcode and int(hms1)<int(hms):
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input_folds.append(pathname)
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times.append(int(hms1))
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if day1==day and len(error_match) and infold[1]==error_match and int(hms1)<int(hms):
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errmatch_folds.append(pathname)
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errmatch_times.append(int(hms1))
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''' 根据时间排序，选择离取出操作最近时间的文件夹，
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作为取出操作应正确匹配的放入操作所对应的文件夹 '''
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if len(input_folds):
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indice = np.argsort(np.array(times))
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input_fold = input_folds[indice[-1]]
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inputpath = os.path.join(basepath, input_fold)
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'''取出操作错误匹配的放入操作对应的文件夹'''
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if len(errmatch_folds):
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indice = np.argsort(np.array(errmatch_times))
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errmatch_fold = errmatch_folds[indice[-1]]
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errorpath = os.path.join(basepath, errmatch_fold)
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'''放入事件文件夹地址、取出事件文件夹地址'''
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getoutpath = os.path.join(basepath, getout_fold)
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return getoutpath, inputpath, errorpath
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def save_tracking_imgpairs(pairs, savepath):
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'''
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pairs: 匹配事件对
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savepath: 保存的目标文件夹
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'''
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def get_event_path(evtpath):
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basepath, eventname = os.path.split(evtpath)
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evt_path = ''
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for filename in os.listdir(basepath):
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if filename.find(eventname)==0:
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evt_path = os.path.join(basepath, filename)
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break
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return evt_path
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getoutpath = get_event_path(pairs[0])
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inputpath = get_event_path(pairs[1])
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if len(pairs) == 3:
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errorpath = get_event_path(pairs[2])
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else:
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errorpath = ''
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''' 1. 读取放入、取出事件对应的 Yolo输入的前后摄图像，0：后摄，1：前摄
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2. 读取放入、取出事件对应的 tracking 输出：boxes, feats
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3. boxes绘制并保存图像序列
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4. 截取并保存轨迹子图
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'''
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if len(getoutpath):
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imgs_getout_0, imgs_getout_1 = read_tracking_imgs(getoutpath)
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getout_data_0 = os.path.join(getoutpath, '0_tracking_output.data')
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getout_data_1 = os.path.join(getoutpath, '1_tracking_output.data')
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boxes_output_0, feats_output_0 = read_tracking_output(getout_data_0)
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boxes_output_1, feats_output_1 = read_tracking_output(getout_data_1)
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ImgsGetout_0 = draw_tracking_boxes(imgs_getout_0, boxes_output_0)
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ImgsGetout_1 = draw_tracking_boxes(imgs_getout_1, boxes_output_1)
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SubimgsGetout_0 = get_subimgs(imgs_getout_0, boxes_output_0)
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SubimgsGetout_1 = get_subimgs(imgs_getout_1, boxes_output_1)
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savedir = os.path.basename(getoutpath)
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if len(inputpath):
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imgs_input_0, imgs_input_1 = read_tracking_imgs(inputpath)
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input_data_0 = os.path.join(inputpath, '0_tracking_output.data')
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input_data_1 = os.path.join(inputpath, '1_tracking_output.data')
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boxes_input_0, feats_input_0 = read_tracking_output(input_data_0)
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boxes_input_1, feats_input_1 = read_tracking_output(input_data_1)
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ImgsInput_0 = draw_tracking_boxes(imgs_input_0, boxes_input_0)
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ImgsInput_1 = draw_tracking_boxes(imgs_input_1, boxes_input_1)
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SubimgsInput_0 = get_subimgs(imgs_input_0, boxes_input_0)
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SubimgsInput_1 = get_subimgs(imgs_input_1, boxes_input_1)
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savedir = savedir + '+' + os.path.basename(inputpath)
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if len(errorpath):
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imgs_error_0, imgs_error_1 = read_tracking_imgs(errorpath)
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error_data_0 = os.path.join(errorpath, '0_tracking_output.data')
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error_data_1 = os.path.join(errorpath, '1_tracking_output.data')
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boxes_error_0, feats_error_0 = read_tracking_output(error_data_0)
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boxes_error_1, feats_error_1 = read_tracking_output(error_data_1)
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ImgsError_0 = draw_tracking_boxes(imgs_error_0, boxes_error_0)
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ImgsError_1 = draw_tracking_boxes(imgs_error_1, boxes_error_1)
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SubimgsError_0 = get_subimgs(imgs_error_0, boxes_error_0)
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SubimgsError_1 = get_subimgs(imgs_error_0, boxes_error_0)
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savedir = savedir + '+' + os.path.basename(errorpath)
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''' savepath\pairs\savedir\eventpairs\保存画框后的图像序列 '''
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entpairs = os.path.join(savepath, 'pairs', savedir, 'eventpairs')
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if not os.path.exists(entpairs):
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os.makedirs(entpairs)
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for fid, img in ImgsInput_0:
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imgpath = os.path.join(entpairs, f'input_0_{fid}.png')
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cv2.imwrite(imgpath, img)
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for fid, img in ImgsInput_1:
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imgpath = os.path.join(entpairs, f'input_1_{fid}.png')
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cv2.imwrite(imgpath, img)
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for fid, img in ImgsGetout_0:
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imgpath = os.path.join(entpairs, f'getout_0_{fid}.png')
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cv2.imwrite(imgpath, img)
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for fid, img in ImgsGetout_1:
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imgpath = os.path.join(entpairs, f'getout_1_{fid}.png')
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cv2.imwrite(imgpath, img)
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if 'ImgsError_0' in vars() and 'ImgsError_1' in vars():
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for fid, img in ImgsError_0:
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imgpath = os.path.join(entpairs, f'errMatch_0_{fid}.png')
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cv2.imwrite(imgpath, img)
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for fid, img in ImgsError_1:
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imgpath = os.path.join(entpairs, f'errMatch_1_{fid}.png')
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cv2.imwrite(imgpath, img)
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''' savepath\pairs\savedir\subimgpairs\保存轨迹子图 '''
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subimgpairs = os.path.join(savepath, 'pairs', savedir, 'subimgpairs')
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if not os.path.exists(subimgpairs):
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os.makedirs(subimgpairs)
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for fid, bid, img in SubimgsGetout_0:
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imgpath = os.path.join(subimgpairs, f'getout_0_{fid}_{bid}.png')
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cv2.imwrite(imgpath, img)
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for fid, bid, img in SubimgsGetout_1:
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imgpath = os.path.join(subimgpairs, f'getout_1_{fid}_{bid}.png')
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cv2.imwrite(imgpath, img)
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for fid, bid, img in SubimgsInput_0:
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imgpath = os.path.join(subimgpairs, f'input_0_{fid}_{bid}.png')
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cv2.imwrite(imgpath, img)
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for fid, bid, img in SubimgsInput_1:
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imgpath = os.path.join(subimgpairs, f'input_1_{fid}_{bid}.png')
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cv2.imwrite(imgpath, img)
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if 'SubimgsError_0' in vars() and 'SubimgsError_1' in vars():
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for fid, bid, img in SubimgsError_0:
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imgpath = os.path.join(subimgpairs, f'errMatch_0_{fid}_{bid}.png')
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cv2.imwrite(imgpath, img)
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for fid, bid, img in SubimgsError_1:
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imgpath = os.path.join(subimgpairs, f'errMatch_1_{fid}_{bid}.png')
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cv2.imwrite(imgpath, img)
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def one2n_deleted(all_list):
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corrpairs, errpairs, correct_similarity, err_similarity = [], [], [], []
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for s_list in all_list:
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seqdir = s_list['SeqDir'].strip()
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delete = s_list['Deleted'].strip()
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barcodes = [s.strip() for s in s_list['barcode']]
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similarity_comp, similarity_front = [], []
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for simil in s_list['similarity']:
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ss = [float(s.strip()) for s in simil.split(',')]
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similarity_comp.append(ss[0])
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if len(ss)==3:
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similarity_front.append(ss[2])
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if len(similarity_front):
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similarity = [s for s in similarity_front]
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else:
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similarity = [s for s in similarity_comp]
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index = similarity.index(max(similarity))
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matched_barcode = barcodes[index]
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if matched_barcode == delete:
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corrpairs.append((seqdir, delete))
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correct_similarity.append(max(similarity))
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else:
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errpairs.append((seqdir, delete, matched_barcode))
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err_similarity.append(max(similarity))
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return corrpairs, errpairs, correct_similarity, err_similarity
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def one2n_return(all_list):
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corrpairs, corr_similarity, errpairs, err_similarity = [], [], [], []
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for s_list in all_list:
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seqdir = s_list['SeqDir'].strip()
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delete = s_list['Deleted'].strip()
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barcodes = [s.strip() for s in s_list['barcode']]
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events = [s.strip() for s in s_list['event']]
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types = [s.strip() for s in s_list['type']]
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## =================== 读入相似度值
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similarity_comp, similarity_front = [], []
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for simil in s_list['similarity']:
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ss = [float(s.strip()) for s in simil.split(',')]
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similarity_comp.append(ss[0])
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if len(ss)==3:
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similarity_front.append(ss[2])
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if len(similarity_front):
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similarity = [s for s in similarity_front]
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else:
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similarity = [s for s in similarity_comp]
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index = similarity.index(max(similarity))
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matched_barcode = barcodes[index]
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if matched_barcode == delete:
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corrpairs.append((seqdir, events[index]))
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corr_similarity.append(max(similarity))
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else:
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idx = [i for i, name in enumerate(events) if name.split('_')[-1] == delete]
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idxmax, simimax = -1, -1
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# idxmax, simimax = k, similarity[k] for k in idx if similarity[k] > simimax
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for k in idx:
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if similarity[k] > simimax:
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idxmax = k
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simimax = similarity[k]
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if idxmax>-1:
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input_event = events[idxmax]
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else:
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input_event = ''
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errpairs.append((seqdir, input_event, events[index]))
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err_similarity.append(max(similarity))
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return corrpairs, errpairs, corr_similarity, err_similarity
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def test_rpath_deleted():
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'''deletedBarcode.txt 格式的 1:n 数据结果文件, returnGoods.txt格式数据文件不需要调用该函数'''
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del_bfile = r'\\192.168.1.28\share\测试_202406\709\deletedBarcode.txt'
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basepath = r'\\192.168.1.28\share\测试_202406\709'
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savepath = r'D:\DetectTracking\contrast\result'
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saveimgs = True
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relative_paths = []
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'''1. 读取 deletedBarcode 文件 '''
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all_list = read_deletedBarcode_file(del_bfile)
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'''2. 算法性能评估，并输出 (取出，删除, 错误匹配) 对 '''
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corrpairs, errpairs, _, _ = one2n_deleted(all_list)
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'''3. 构造事件组合(取出，放入并删除, 错误匹配) 对应路径 '''
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for errpair in errpairs:
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GetoutPath, InputPath, ErrorPath = get_contrast_paths(errpair, basepath)
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pairs = (GetoutPath, InputPath, ErrorPath)
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relative_paths.append(pairs)
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print(InputPath)
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'''3. 获取 (取出，放入并删除, 错误匹配) 对应路径，保存相应轨迹图像'''
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if saveimgs:
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save_tracking_imgpairs(pairs, savepath)
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def test_rpath_return():
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return_bfile = r'\\192.168.1.28\share\测试_202406\1101\images\returnGoods.txt'
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basepath = r'\\192.168.1.28\share\测试_202406\1101\images'
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savepath = r'D:\DetectTracking\contrast\result'
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all_list = read_returnGoods_file(return_bfile)
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corrpairs, errpairs, _, _ = one2n_return(all_list)
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for corrpair in corrpairs:
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GetoutPath = os.path.join(basepath, corrpair[0])
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InputPath = os.path.join(basepath, corrpair[1])
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pairs = (GetoutPath, InputPath)
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save_tracking_imgpairs(pairs, savepath)
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for errpair in errpairs:
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GetoutPath = os.path.join(basepath, errpair[0])
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InputPath = os.path.join(basepath, errpair[1])
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ErrorPath = os.path.join(basepath, errpair[2])
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pairs = (GetoutPath, InputPath, ErrorPath)
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save_tracking_imgpairs(pairs, savepath)
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|
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def test_one2n():
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'''
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||
1:n 性能测试
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兼容 2 种 txt 文件格式：returnGoods.txt, deletedBarcode.txt
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fpath: 文件路径、或文件夹，其中包含多个 txt 文件
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savepath: pr曲线保存路径
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'''
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# fpath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\other' # deletedBarcode.txt
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fpath = r'\\192.168.1.28\share\测试_202406\1108_展厅模型v800测试' # returnGoods.txt
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savepath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\illustration'
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|
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if os.path.isdir(fpath):
|
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filepaths = [os.path.join(fpath, f) for f in os.listdir(fpath)
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if f.find('.txt')>0
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||
and (f.find('deletedBarcode')>=0 or f.find('returnGoods')>=0)]
|
||
elif os.path.isfile(fpath):
|
||
filepaths = [fpath]
|
||
else:
|
||
return
|
||
|
||
if not os.path.exists(savepath):
|
||
os.mkdir(savepath)
|
||
|
||
BarLists, blists = {}, []
|
||
for pth in filepaths:
|
||
file = str(Path(pth).stem)
|
||
if file.find('deletedBarcode')>=0:
|
||
blist = read_deletedBarcode_file(pth)
|
||
if file.find('returnGoods')>=0:
|
||
blist = read_returnGoods_file(pth)
|
||
|
||
BarLists.update({file: blist})
|
||
blists.extend(blist)
|
||
|
||
if len(blists): BarLists.update({"Total": blists})
|
||
for file, blist in BarLists.items():
|
||
if all(b['filetype']=="deletedBarcode" for b in blist):
|
||
_, _, correct_similarity, err_similarity = one2n_deleted(blist)
|
||
if all(b['filetype']=="returnGoods" for b in blists):
|
||
_, _, correct_similarity, err_similarity = one2n_return(blist)
|
||
|
||
recall, prec, ths = compute_recall_precision(err_similarity, correct_similarity)
|
||
|
||
plt1 = show_recall_prec(recall, prec, ths)
|
||
# plt1.show()
|
||
plt1.xlabel(f'threshold, Num: {len(blist)}')
|
||
plt1.savefig(os.path.join(savepath, file+'_pr.png'))
|
||
# plt1.close()
|
||
|
||
plt2 = showHist(err_similarity, correct_similarity)
|
||
plt2.show()
|
||
plt2.savefig(os.path.join(savepath, file+'_hist.png'))
|
||
# plt.close()
|
||
|
||
|
||
|
||
if __name__ == '__main__':
|
||
# test_one2n()
|
||
test_rpath_return() # returnGoods.txt
|
||
# test_rpath_deleted() # deleteBarcode.txt
|
||
|
||
|
||
# try:
|
||
# test_rpath_return()
|
||
# test_rpath_deleted()
|
||
# except Exception as e:
|
||
# print(e)
|
||
|
||
|