Mercurial Hosting > traffic-intelligence

annotate scripts/extract-appearance-images.py @ 906:a57e6fbcd8e3

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author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Fri, 23 Jun 2017 00:03:17 -0400
parents 0e017178f7ab
children cd038493f8c6
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
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1 #! /usr/bin/env python
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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 imsave, imshow, figure
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7
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8 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('--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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19 parser.add_argument('--prefix', dest = 'imagePrefix', help = 'image prefix', default = 'img')
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20 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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21 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 #parser.add_argument('-d', dest = 'directoryName', help = 'parent directory name for the directories containing the samples for the different road users', required = True)
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25
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26 args = parser.parse_args()
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27 params, videoFilename, databaseFilename, invHomography, intrinsicCameraMatrix, distortionCoefficients, undistortedImageMultiplication, undistort, firstFrameNum = storage.processVideoArguments(args)
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28 classifierParams = storage.ClassifierParameters(params.classifierFilename)
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29
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30 classificationAnnotations = read_csv(args.classificationAnnotationFilename, index_col=0, delimiter = args.classificationAnnotationFilenameDelimiter, names = ["object_num", "road_user_type"])
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31 annotatedObjectNumbers = classificationAnnotations.index.tolist()
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32
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33 # objects has the objects for which we want to extract labeled images
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34 if args.extractAllObjectImages:
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35 objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', args.nObjects, withFeatures = True)
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36 else:
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37 if len(annotatedObjectNumbers) > args.nObjects:
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38 classificationAnnotations = classificationAnnotations[:args.nObjects]
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39 annotatedObjectNumbers = classificationAnnotations.index.tolist()
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40 objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', annotatedObjectNumbers, withFeatures = True)
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41 for obj in objects:
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42 if obj.getNum() in annotatedObjectNumbers:
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43 obj.setUserType(classificationAnnotations.loc[obj.getNum(), 'road_user_type'])
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44 timeInterval = moving.TimeInterval.unionIntervals([obj.getTimeInterval() for obj in objects])
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45
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46 for userType in classificationAnnotations['road_user_type'].unique():
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47 if not os.path.exists(args.directoryName+os.sep+moving.userTypeNames[userType]):
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48 os.mkdir(args.directoryName+os.sep+moving.userTypeNames[userType])
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49
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50 capture = cv2.VideoCapture(videoFilename)
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51 width = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH))
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52 height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT))
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53
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54 if undistort: # setup undistortion
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55 [map1, map2] = cvutils.computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients)
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56 height, width = map1.shape
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57
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58 if capture.isOpened():
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59 ret = True
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60 frameNum = timeInterval.first
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61 capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, frameNum)
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62 lastFrameNum = timeInterval.last
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63 while ret and frameNum <= timeInterval.last:
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64 ret, img = capture.read()
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65 if ret:
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66 if frameNum%50 == 0:
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67 print('frame number: {}'.format(frameNum))
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68 if undistort: # undistort only if necessary
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69 img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR)
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70 for obj in objects:
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71 if obj.existsAtInstant(frameNum):
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72 if (10+frameNum-obj.getFirstInstant())%args.nFramesStep == 0:
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73 # todo find next non zero image if none
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74 # todo get several images if different features (measure of similarity)
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75 croppedImg = cvutils.imageBox(img, obj, frameNum, invHomography, width, height, classifierParams.percentIncreaseCrop, classifierParams.percentIncreaseCrop, classifierParams.minNPixels)
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76 if croppedImg is not None:
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77 imsave(args.directoryName+os.sep+moving.userTypeNames[obj.getUserType()]+os.sep+args.imagePrefix+'-{}-{}.png'.format(obj.getNum(), frameNum), croppedImg)
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78 elif obj.getLastInstant() == frameNum:
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79 objects.remove(obj)
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80 frameNum += 1
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81
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82 # todo speed info: distributions AND min speed equiprobable
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83