Mercurial Hosting > traffic-intelligence

comparison scripts/extract-appearance-images.py @ 904:8f60ecfc2f06

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work in progress, almost ready
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Thu, 22 Jun 2017 18:08:46 -0400
parents c69a8defe5c3
children 0e017178f7ab
comparison
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903:81ee5aaf213d 904:8f60ecfc2f06
1 #! /usr/bin/env python 1 #! /usr/bin/env python
2 2
3 import numpy as np 3 import numpy as np, cv2
4 import argparse 4 import argparse, os
5 from cv2 import SVM_RBF, SVM_C_SVC 5 from pandas import read_csv
6 #from cv2.ml import SVM_RBF, SVM_C_SVC, ROW_SAMPLE # row_sample for layout in cv2.ml.SVM_load 6 from matplotlib.pyplot import imsave
7 7
8 import cvutils, moving, ml, storage 8 import cvutils, moving, ml, storage
9 9
10 parser = argparse.ArgumentParser(description='The program extracts labeled image patches to train the HoG-SVM classifier, and optionnally speed information') 10 parser = argparse.ArgumentParser(description='The program extracts labeled image patches to train the HoG-SVM classifier, and optionnally speed information')
11 parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file', required = True) 11 parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file', required = True)
12 parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file (overrides the configuration file)') 12 parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file (overrides the configuration file)')
13 parser.add_argument('-i', dest = 'videoFilename', help = 'name of the video file (overrides the configuration file)') 13 parser.add_argument('-i', dest = 'videoFilename', help = 'name of the video file (overrides the configuration file)')
14 parser.add_argument('--gt', dest = 'classificationAnnotationFilename', help = 'name of the file containing the correct classes (user types)', required = True) 14 parser.add_argument('--gt', dest = 'classificationAnnotationFilename', help = 'name of the file containing the correct classes (user types)', required = True)
15 parser.add_argument('--delimiter', dest = 'classificationAnnotationFilenameDelimiter', help = 'delimiter for the fields in the correct classification file', default= ' ')
15 parser.add_argument('-s', dest = 'nFramesStep', help = 'number of frames between each saved patch', default = 50, type = int) 16 parser.add_argument('-s', dest = 'nFramesStep', help = 'number of frames between each saved patch', default = 50, type = int)
16 parser.add_argument('-n', dest = 'nObjects', help = 'number of objects to use to extract patches from', type = int, default = None) 17 parser.add_argument('-n', dest = 'nObjects', help = 'number of objects to use to extract patches from', type = int, default = None)
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')
19 parser.add_argument('--prefix', dest = 'imagePrefix', help = 'image prefix', default = 'img')
20 parser.add_argument('--ouput', dest = 'directoryName', help = 'parent directory name for the directories containing the samples for the different road users', default = '.')
17 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') 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')
18 22
19 23
20 #parser.add_argument('-d', dest = 'directoryName', help = 'parent directory name for the directories containing the samples for the different road users', required = True) 24 #parser.add_argument('-d', dest = 'directoryName', help = 'parent directory name for the directories containing the samples for the different road users', required = True)
21 25
22 args = parser.parse_args() 26 args = parser.parse_args()
23 params, videoFilename, databaseFilename, invHomography, intrinsicCameraMatrix, distortionCoefficients, undistortedImageMultiplication, undistort, firstFrameNum = storage.processVideoArguments(args) 27 params, videoFilename, databaseFilename, invHomography, intrinsicCameraMatrix, distortionCoefficients, undistortedImageMultiplication, undistort, firstFrameNum = storage.processVideoArguments(args)
24 classifierParams = storage.ClassifierParameters(params.classifierFilename) 28 classifierParams = storage.ClassifierParameters(params.classifierFilename)
25 29
26 objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', args.nObjects, withFeatures = True) 30 classificationAnnotations = read_csv(args.classificationAnnotationFilename, index_col=0, delimiter = args.classificationAnnotationFilenameDelimiter, names = ["object_num", "road_user_type"])
31 annotatedObjectNumbers = classificationAnnotations.index.tolist()
32
33 # objects has the objects for which we want to extract labeled images
34 if args.extractAllObjectImages:
35 objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', args.nObjects, withFeatures = True)
36 else:
37 if len(annotatedObjectNumbers) > args.nObjects:
38 classificationAnnotations = classificationAnnotations[:args.nObjects]
39 annotatedObjectNumbers = classificationAnnotations.index.tolist()
40 objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', annotatedObjectNumbers, withFeatures = True)
41 for obj in objects:
42 if obj.getNum() in annotatedObjectNumbers:
43 obj.setUserType(classificationAnnotations.loc[obj.getNum(), 'road_user_type'])
27 timeInterval = moving.TimeInterval.unionIntervals([obj.getTimeInterval() for obj in objects]) 44 timeInterval = moving.TimeInterval.unionIntervals([obj.getTimeInterval() for obj in objects])
45
46 for userType in classificationAnnotations['road_user_type'].unique():
47 if not os.path.exists(args.directoryName+os.sep+moving.userTypeNames[userType]):
48 os.mkdir(args.directoryName+os.sep+moving.userTypeNames[userType])
28 49
29 capture = cv2.VideoCapture(videoFilename) 50 capture = cv2.VideoCapture(videoFilename)
30 width = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH)) 51 width = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH))
31 height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)) 52 height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT))
32 53
33 if undistort: # setup undistortion 54 if undistort: # setup undistortion
34 [map1, map2] = cvutils.computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients) 55 [map1, map2] = cvutils.computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients)
56
57 print(timeInterval)
35 if capture.isOpened(): 58 if capture.isOpened():
36 ret = True 59 ret = True
37 frameNum = timeInterval.first 60 frameNum = timeInterval.first
38 capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, frameNum) 61 capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, frameNum)
39 lastFrameNum = timeInterval.last 62 lastFrameNum = timeInterval.last
40 while ret and frameNum <= lastFrameNum: 63 while ret and frameNum <= timeInterval.last:
41 ret, img = capture.read() 64 ret, img = capture.read()
42 if ret: 65 if ret:
43 if frameNum%50 == 0: 66 if frameNum%50 == 0:
44 print('frame number: {}'.format(frameNum)) 67 print('frame number: {}'.format(frameNum))
45 if undistort: 68 if undistort: # undistort only if necessary
46 img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR) 69 img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR)
47 70 for obj in objects:
48 71 if obj.existsAtInstant(frameNum):
72 if (obj.getFirstInstant()-frameNum)%args.nFramesStep == 0: # todo find next non zero image if none
73 croppedImg = cvutils.imageBox(img, obj, frameNum, invHomography, width, height, classifierParams.percentIncreaseCrop, classifierParams.percentIncreaseCrop, classifierParams.minNPixels)
74 if croppedImg is not None:
75 imsave(args.directoryName+os.sep+moving.userTypeNames[obj.getUserType()]+args.imagePrefix+'-{}-{}.png'.format(obj.getNum(), frameNum), croppedImg)
76 elif obj.getLastInstant() == frameNum:
77 objects.remove(obj)
49 frameNum += 1 78 frameNum += 1
50
51
52 79
53 # todo speed info: distributions AND min speed equiprobable 80 # todo speed info: distributions AND min speed equiprobable
54 81
55 # provide csv delimiter for the classification file as arg
56