459 lines
15 KiB
Python
459 lines
15 KiB
Python
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 utils.plotting import Annotator, colors
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import sys
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sys.path.append(r"D:\DetectTracking")
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from tracking.utils.read_data import extract_data, read_deletedBarcode_file, read_tracking_output
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from tracking.utils.plotting import draw_tracking_boxes
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VideoFormat = ['.mp4', '.avi']
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def video2imgs(videopath, savepath):
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k = 0
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have = False
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for filename in os.listdir(videopath):
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file, ext = os.path.splitext(filename)
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if ext not in VideoFormat:
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continue
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basename = os.path.basename(videopath)
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imgbase = basename + '_' + file
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imgdir = os.path.join(savepath, imgbase)
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if not os.path.exists(imgdir):
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os.mkdir(imgdir)
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video = os.path.join(videopath, filename)
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cap = cv2.VideoCapture(video)
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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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imgp = os.path.join(imgdir, file+f"_{i}.png")
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i += 1
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cv2.imwrite(imgp, frame)
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cap.release()
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print(filename + f" haved resolved")
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k+=1
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if k==1000:
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break
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def showHist(err, correct):
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err = np.array(err)
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correct = np.array(correct)
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fig, axs = plt.subplots(2, 1)
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axs[0].hist(err, bins=50, edgecolor='black')
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axs[0].set_xlim([0, 1])
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axs[0].set_title('err')
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axs[1].hist(correct, bins=50, edgecolor='black')
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axs[1].set_xlim([0, 1])
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axs[1].set_title('correct')
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# plt.show()
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return plt
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def show_recall_prec(recall, prec, ths):
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# x = np.linspace(start=-0, stop=1, num=11, endpoint=True).tolist()
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fig = plt.figure(figsize=(10, 6))
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plt.plot(ths, recall, color='red', label='recall')
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plt.plot(ths, prec, color='blue', label='PrecisePos')
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plt.legend()
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plt.xlabel(f'threshold')
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# plt.ylabel('Similarity')
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plt.grid(True, linestyle='--', alpha=0.5)
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# plt.savefig('accuracy_recall_grid.png')
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# plt.show()
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# plt.close()
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return plt
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def compute_recall_precision(err_similarity, correct_similarity):
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ths = np.linspace(0, 1, 51)
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recall, prec = [], []
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for th in ths:
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TP = len([num for num in correct_similarity if num >= th])
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FP = len([num for num in err_similarity if num >= th])
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if (TP+FP) == 0:
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prec.append(1)
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recall.append(0)
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else:
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prec.append(TP / (TP + FP))
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recall.append(TP / (len(err_similarity) + len(correct_similarity)))
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return recall, prec, ths
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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(pair, basepath, savepath):
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'''
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basepath: 原始测试数据文件夹的路径
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savepath: 保存的目标文件夹
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'''
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getoutpath, inputpath, errorpath = get_contrast_paths(pair, basepath)
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if len(inputpath)==0:
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return
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'''==== 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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'''==== 读取放入、取出事件对应的 tracking 输出：boxes, feats ===='''
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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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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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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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savedir = pair[0] + pair[1]
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if len(errorpath):
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savedir = savedir + '_' + errorpath.split('_')[-1]
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foldname = os.path.join(savepath, 'imgpairs', savedir)
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if not os.path.exists(foldname):
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os.makedirs(foldname)
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for i, img in enumerate(ImgsInput_0):
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imgpath = os.path.join(foldname, f'input_0_{i}.png')
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cv2.imwrite(imgpath, img)
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for i, img in enumerate(ImgsInput_1):
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imgpath = os.path.join(foldname, f'input_1_{i}.png')
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cv2.imwrite(imgpath, img)
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for i, img in enumerate(ImgsGetout_0):
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imgpath = os.path.join(foldname, f'getout_0_{i}.png')
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cv2.imwrite(imgpath, img)
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for i, img in enumerate(ImgsGetout_1):
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imgpath = os.path.join(foldname, f'getout_1_{i}.png')
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cv2.imwrite(imgpath, img)
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for i, img in enumerate(ImgsError_0):
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imgpath = os.path.join(foldname, f'errMatch_0_{i}.png')
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cv2.imwrite(imgpath, img)
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for i, img in enumerate(ImgsError_1):
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imgpath = os.path.join(foldname, f'errMatch_1_{i}.png')
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cv2.imwrite(imgpath, img)
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def performance_evaluate(all_list, isshow=False):
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corrpairs, correct_barcode_list, correct_similarity, errpairs, err_barcode_list, 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 = [float(s.strip()) for s in s_list['similarity']]
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if delete in barcodes[:1]:
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corrpairs.append((seqdir, delete))
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correct_barcode_list.append(delete)
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correct_similarity.append(similarity[0])
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else:
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errpairs.append((seqdir, delete, barcodes[0]))
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err_barcode_list.append(delete)
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err_similarity.append(similarity[0])
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'''3. 计算比对性能 '''
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if isshow:
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recall, prec, ths = compute_recall_precision(err_similarity, correct_similarity)
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show_recall_prec(recall, prec, ths)
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showHist(err_similarity, correct_similarity)
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return errpairs, corrpairs, err_similarity, correct_similarity
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def contrast_analysis(del_barcode_file, basepath, savepath, saveimgs=False):
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'''
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del_barcode_file: 测试数据文件，利用该文件进行算法性能分析
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'''
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'''1. 读取 deletedBarcode 文件 '''
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all_list = read_deletedBarcode_file(del_barcode_file)
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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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errpairs, corrpairs, _, _ = performance_evaluate(all_list)
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'''3. 获取 (取出，删除, 错误匹配) 对应路径，保存相应轨迹图像'''
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relative_paths = []
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for errpair in errpairs:
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GetoutPath, InputPath, ErrorPath = get_contrast_paths(errpair, basepath)
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relative_paths.append((GetoutPath, InputPath, ErrorPath))
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if saveimgs:
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save_tracking_imgpairs(errpair, basepath, savepath)
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return relative_paths
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def contrast_loop(fpath):
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savepath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\illustration'
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# savepath = r'D:\contrast\dataset\1_to_n\illustration'
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if not os.path.exists(savepath):
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os.mkdir(savepath)
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if os.path.isfile(fpath):
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fpath, filename = os.path.split(fpath)
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BarLists, blists = {}, []
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for filename in os.listdir(fpath):
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file = os.path.splitext(filename)[0][15:]
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filepath = os.path.join(fpath, filename)
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blist = read_deletedBarcode_file(filepath)
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BarLists.update({file: blist})
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blists.extend(blist)
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BarLists.update({file: blist})
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BarLists.update({"Total": blists})
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for file, blist in BarLists.items():
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errpairs, corrpairs, err_similarity, correct_similarity = performance_evaluate(blist)
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recall, prec, ths = compute_recall_precision(err_similarity, correct_similarity)
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plt1 = show_recall_prec(recall, prec, ths)
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# plt1.show()
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plt1.xlabel(f'threshold, Num: {len(blist)}')
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plt1.savefig(os.path.join(savepath, file+'_pr.png'))
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# plt1.close()
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# plt2 = showHist(err_similarity, correct_similarity)
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# plt2.show()
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# plt2.savefig(os.path.join(savepath, file+'_hist.png'))
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# plt.close()
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def main():
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fpath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\other'
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contrast_loop(fpath)
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def main1():
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del_barcode_file = 'D:/contrast/dataset/compairsonResult/deletedBarcode_20240709_pm.txt'
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basepath = r'D:\contrast\dataset\1_to_n\709'
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savepath = r'D:\contrast\dataset\result'
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try:
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relative_path = contrast_analysis(del_barcode_file, basepath, savepath)
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except Exception as e:
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print(f'Error Type: {e}')
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def resolve_vidoes():
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videopath = r"\\192.168.1.28\share\测试_202406\0719\719_1\20240719-103533_"
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savepath = r"D:\contrast\result"
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video2imgs(videopath, savepath)
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if __name__ == '__main__':
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main()
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# main1()
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# resolve_vidoes()
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