Mercurial Hosting > traffic-intelligence
view scripts/extract-appearance-images.py @ 1269:ca70a79688ae
adding a speed threshold to avoid computing TTC at very low speeds
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Wed, 05 Jun 2024 10:12:43 -0400 |
parents | 2397de73770d |
children |
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#! /usr/bin/env python3 import numpy as np, cv2 import argparse, os from pandas import read_csv from matplotlib.pyplot import imshow, figure from trafficintelligence import cvutils, moving, ml, storage parser = argparse.ArgumentParser(description='The program extracts labeled image patches to train the HoG-SVM classifier, and optionnally speed information') parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file', required = True) parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file (overrides the configuration file)') parser.add_argument('-i', dest = 'videoFilename', help = 'name of the video file (overrides the configuration file)') parser.add_argument('--gt', dest = 'classificationAnnotationFilename', help = 'name of the file containing the correct classes (user types)', required = True) parser.add_argument('--delimiter', dest = 'classificationAnnotationFilenameDelimiter', help = 'delimiter for the fields in the correct classification file', default= ' ') parser.add_argument('-s', dest = 'nFramesStep', help = 'number of frames between each saved patch', default = 50, type = int) parser.add_argument('-n', dest = 'nObjects', help = 'number of objects to use to extract patches from', type = int, default = None) 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') parser.add_argument('-o', dest = 'overlap', help = 'maximum intersection over union of the features nFramesStep apart to save image', type = float, default = 0.2) 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') parser.add_argument('--prefix', dest = 'imagePrefix', help = 'image prefix', default = 'img') parser.add_argument('--ouput', dest = 'directoryName', help = 'parent directory name for the directories containing the samples for the different road users', default = '.') 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') args = parser.parse_args() params, videoFilename, databaseFilename, homography, invHomography, intrinsicCameraMatrix, distortionCoefficients, undistortedImageMultiplication, undistort, firstFrameNum = storage.processVideoArguments(args) classifierParams = storage.ClassifierParameters(params.classifierFilename) classificationAnnotations = read_csv(args.classificationAnnotationFilename, index_col=0, delimiter = args.classificationAnnotationFilenameDelimiter, names = ["object_num", "road_user_type"]) annotatedObjectNumbers = classificationAnnotations.index.tolist() # objects has the objects for which we want to extract labeled images if args.extractAllObjectImages: objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', args.nObjects, withFeatures = True) else: if len(annotatedObjectNumbers) > args.nObjects: classificationAnnotations = classificationAnnotations[:args.nObjects] annotatedObjectNumbers = classificationAnnotations.index.tolist() objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', annotatedObjectNumbers, withFeatures = True) for obj in objects: if obj.getNum() in annotatedObjectNumbers: obj.setUserType(classificationAnnotations.loc[obj.getNum(), 'road_user_type']) timeInterval = moving.TimeInterval.unionIntervals([obj.getTimeInterval() for obj in objects]) if args.startFrame0: timeInterval.first = 0 for userType in classificationAnnotations['road_user_type'].unique(): if not os.path.exists(args.directoryName+os.sep+moving.userTypeNames[userType]): os.mkdir(args.directoryName+os.sep+moving.userTypeNames[userType]) capture = cv2.VideoCapture(videoFilename) width = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH)) height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)) if undistort: # setup undistortion [map1, map2] = cvutils.computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients) height, width = map1.shape if capture.isOpened(): ret = True frameNum = timeInterval.first if not args.startFrame0: capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, frameNum) lastFrameNum = timeInterval.last while ret and frameNum <= timeInterval.last: ret, img = capture.read() distorted = True if ret: if frameNum%50 == 0: print('frame number: {}'.format(frameNum)) for obj in objects[:]: if obj.existsAtInstant(frameNum): if (10+frameNum-obj.getFirstInstant())%args.nFramesStep == 0: currentImageFeatures = set([f.num for f in obj.getFeatures() if f.existsAtInstant(frameNum)]) if not hasattr(obj, 'lastImageFeatures') or len(currentImageFeatures.intersection(obj.lastImageFeatures))/len(currentImageFeatures.union(obj.lastImageFeatures)) < args.overlap: obj.lastImageFeatures = currentImageFeatures if undistort and distorted: # undistort only if necessary img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR) distorted = False croppedImg = cvutils.imageBox(img, obj, frameNum, invHomography, width, height, classifierParams.percentIncreaseCrop, classifierParams.percentIncreaseCrop, classifierParams.minNPixels) if croppedImg is not None: cv2.imwrite(args.directoryName+os.sep+moving.userTypeNames[obj.getUserType()]+os.sep+args.imagePrefix+'-{}-{}.png'.format(obj.getNum(), frameNum), croppedImg) elif obj.getLastInstant() == frameNum: objects.remove(obj) frameNum += 1 # todo speed info: distributions AND min speed equiprobable