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annotate scripts/extract-appearance-images.py @ 1240:bb14f919d1cb

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cleaned use of centile (np only) and added info in classify-objects
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Mon, 05 Feb 2024 14:14:14 -0500
parents cc5cb04b04b0
children 2397de73770d
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933670761a57 updated code to python 3 (tests pass and scripts run, but non-executed parts of code are probably still not correct)
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1 #! /usr/bin/env python3
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2
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3 import numpy as np, cv2
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4 import argparse, os
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5 from pandas import read_csv
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6 from matplotlib.pyplot import imshow, figure
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cc5cb04b04b0 major update using the trafficintelligence package name and install through pip
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8 from trafficintelligence import cvutils, moving, ml, storage
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9
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10 parser = argparse.ArgumentParser(description='The program extracts labeled image patches to train the HoG-SVM classifier, and optionnally speed information')
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11 parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file', required = True)
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12 parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file (overrides the configuration file)')
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13 parser.add_argument('-i', dest = 'videoFilename', help = 'name of the video file (overrides the configuration file)')
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14 parser.add_argument('--gt', dest = 'classificationAnnotationFilename', help = 'name of the file containing the correct classes (user types)', required = True)
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15 parser.add_argument('--delimiter', dest = 'classificationAnnotationFilenameDelimiter', help = 'delimiter for the fields in the correct classification file', default= ' ')
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16 parser.add_argument('-s', dest = 'nFramesStep', help = 'number of frames between each saved patch', default = 50, type = int)
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17 parser.add_argument('-n', dest = 'nObjects', help = 'number of objects to use to extract patches from', type = int, default = None)
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18 parser.add_argument('--start-frame0', dest = 'startFrame0', help = 'starts with first frame for videos with index problem where frames cannot be reached', action = 'store_true')
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19 parser.add_argument('-o', dest = 'overlap', help = 'maximum intersection over union of the features nFramesStep apart to save image', type = float, default = 0.2)
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20 parser.add_argument('--extract-all', dest = 'extractAllObjectImages', help = 'extracts the images for all objects, well classified or not (otherwise, extracts only for the misclassified)', action = 'store_true')
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21 parser.add_argument('--prefix', dest = 'imagePrefix', help = 'image prefix', default = 'img')
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22 parser.add_argument('--ouput', dest = 'directoryName', help = 'parent directory name for the directories containing the samples for the different road users', default = '.')
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23 parser.add_argument('--compute-speed-distributions', dest = 'computeSpeedDistribution', help = 'computes the distribution of the road users of each type and fits parameters to each', action = 'store_true')
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24
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25 args = parser.parse_args()
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26 params, videoFilename, databaseFilename, invHomography, intrinsicCameraMatrix, distortionCoefficients, undistortedImageMultiplication, undistort, firstFrameNum = storage.processVideoArguments(args)
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27 classifierParams = storage.ClassifierParameters(params.classifierFilename)
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28
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29 classificationAnnotations = read_csv(args.classificationAnnotationFilename, index_col=0, delimiter = args.classificationAnnotationFilenameDelimiter, names = ["object_num", "road_user_type"])
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30 annotatedObjectNumbers = classificationAnnotations.index.tolist()
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31
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32 # objects has the objects for which we want to extract labeled images
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33 if args.extractAllObjectImages:
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34 objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', args.nObjects, withFeatures = True)
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35 else:
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36 if len(annotatedObjectNumbers) > args.nObjects:
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37 classificationAnnotations = classificationAnnotations[:args.nObjects]
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38 annotatedObjectNumbers = classificationAnnotations.index.tolist()
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39 objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', annotatedObjectNumbers, withFeatures = True)
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40 for obj in objects:
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41 if obj.getNum() in annotatedObjectNumbers:
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42 obj.setUserType(classificationAnnotations.loc[obj.getNum(), 'road_user_type'])
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43 timeInterval = moving.TimeInterval.unionIntervals([obj.getTimeInterval() for obj in objects])
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44 if args.startFrame0:
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45 timeInterval.first = 0
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46
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47 for userType in classificationAnnotations['road_user_type'].unique():
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48 if not os.path.exists(args.directoryName+os.sep+moving.userTypeNames[userType]):
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49 os.mkdir(args.directoryName+os.sep+moving.userTypeNames[userType])
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50
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51 capture = cv2.VideoCapture(videoFilename)
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52 width = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH))
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53 height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT))
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54
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55 if undistort: # setup undistortion
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56 [map1, map2] = cvutils.computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients)
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57 height, width = map1.shape
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58
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59 if capture.isOpened():
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60 ret = True
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61 frameNum = timeInterval.first
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62 if not args.startFrame0:
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63 capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, frameNum)
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64 lastFrameNum = timeInterval.last
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65 while ret and frameNum <= timeInterval.last:
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66 ret, img = capture.read()
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67 distorted = True
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68 if ret:
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69 if frameNum%50 == 0:
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70 print('frame number: {}'.format(frameNum))
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71 for obj in objects[:]:
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72 if obj.existsAtInstant(frameNum):
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73 if (10+frameNum-obj.getFirstInstant())%args.nFramesStep == 0:
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74 currentImageFeatures = set([f.num for f in obj.getFeatures() if f.existsAtInstant(frameNum)])
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75 if not hasattr(obj, 'lastImageFeatures') or len(currentImageFeatures.intersection(obj.lastImageFeatures))/len(currentImageFeatures.union(obj.lastImageFeatures)) < args.overlap:
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76 obj.lastImageFeatures = currentImageFeatures
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77 if undistort and distorted: # undistort only if necessary
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78 img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR)
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79 distorted = False
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80 croppedImg = cvutils.imageBox(img, obj, frameNum, invHomography, width, height, classifierParams.percentIncreaseCrop, classifierParams.percentIncreaseCrop, classifierParams.minNPixels)
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81 if croppedImg is not None:
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82 cv2.imwrite(args.directoryName+os.sep+moving.userTypeNames[obj.getUserType()]+os.sep+args.imagePrefix+'-{}-{}.png'.format(obj.getNum(), frameNum), croppedImg)
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83 elif obj.getLastInstant() == frameNum:
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84 objects.remove(obj)
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85 frameNum += 1
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86
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87 # todo speed info: distributions AND min speed equiprobable
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88