Mercurial Hosting > traffic-intelligence
view scripts/classify-objects.py @ 684:6d2ece4aed7c dev
bug forgot to svae objects
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Fri, 05 Jun 2015 13:26:51 +0200 |
parents | f3a0b652b590 |
children | 94b291a5f933 |
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#! /usr/bin/env python import cvutils, moving, ml, storage import numpy as np import sys, argparse #from cv2 import SVM_RBF, SVM_C_SVC import cv2 from scipy.stats import norm, lognorm # TODO add mode detection live parser = argparse.ArgumentParser(description='The program processes indicators for all pairs of road users in the scene') parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file', required = True) parser.add_argument('--plot-speed-distributions', dest = 'plotSpeedDistribution', help = 'simply plots the distributions used for each user type', action = 'store_true') parser.add_argument('--max-speed-distribution-plot', dest = 'maxSpeedDistributionPlot', help = 'if plotting the user distributions, the maximum speed to display', type = float, default = 50.) #parser.add_argument('-u', dest = 'undistort', help = 'undistort the video (because features have been extracted that way)', action = 'store_true') #parser.add_argument('-f', dest = 'firstFrameNum', help = 'number of first frame number to display', type = int) #parser.add_argument('--last-frame', dest = 'lastFrameNum', help = 'number of last frame number to save (for image saving, no display is made)', type = int) # parser.add_argument('--min-speed-equiprobable', dest = 'minSpeedEquiprobable', help = 'speed value below which all classes are equiprobable (distributions give odd values there) (km/h)', type = float, default = 3.33) # parser.add_argument('--speed-aggregation', dest = 'speedAggregationMethod', help = 'method to aggregate road user speed', type = str, choices = ['median', 'mean', 'quantile'], default = 'median') # parser.add_argument('--speed-aggregation-quantile', dest = 'speedAggregationQuantile', help = 'quantile for the speed aggregation, if quantile is chosen', type = int, default = 50) args = parser.parse_args() params = storage.ProcessParameters(args.configFilename) params.convertToFrames(3.6) if params.homography is not None: invHomography = np.linalg.inv(params.homography) if params.speedAggregationMethod == 'median': speedAggregationFunc = np.median elif params.speedAggregationMethod == 'mean': speedAggregationFunc = np.mean elif params.speedAggregationMethod == 'quantile': speedAggregationFunc = lambda speeds: np.percentile(speeds, args.speedAggregationQuantile) else: print('Unknown speed aggregation method: {}. Exiting'.format(params.speedAggregationMethod)) sys.exit() pedBikeCarSVM = ml.SVM() pedBikeCarSVM.load(params.pedBikeCarSVMFilename) bikeCarSVM = ml.SVM() bikeCarSVM.load(params.bikeCarSVMFilename) # log logistic for ped and bik otherwise ((pedBeta/pedAlfa)*((sMean/pedAlfa)**(pedBeta-1)))/((1+(sMean/pedAlfa)**pedBeta)**2.) speedProbabilities = {'car': lambda s: norm(params.meanVehicleSpeed, params.stdVehicleSpeed).pdf(s), 'pedestrian': lambda s: norm(params.meanPedestrianSpeed, params.stdPedestrianSpeed).pdf(s), 'bicycle': lambda s: lognorm(params.scaleCyclistSpeed, loc = 0., scale = np.exp(params.locationCyclistSpeed)).pdf(s)} # lognorm shape, loc, scale if args.plotSpeedDistribution: import matplotlib.pyplot as plt plt.figure() for k in speedProbabilities: plt.plot(np.arange(0.1, args.maxSpeedDistributionPlot, 0.1), [speedProbabilities[k](s/3.6/25) for s in np.arange(0.1, args.maxSpeedDistributionPlot, 0.1)], label = k) plt.xlabel('Speed (km/h)') plt.ylabel('Probability') plt.legend() plt.title('Probability Density Function') plt.show() sys.exit() objects = storage.loadTrajectoriesFromSqlite(params.databaseFilename, 'object') features = storage.loadTrajectoriesFromSqlite(params.databaseFilename, 'feature') intervals = [] for obj in objects: obj.setFeatures(features) intervals.append(obj.getTimeInterval()) timeInterval = moving.unionIntervals(intervals) capture = cv2.VideoCapture(params.videoFilename) width = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH)) height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)) pastObjects = [] if params.undistort: # setup undistortion [map1, map2] = computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients) if capture.isOpened(): ret = True frameNum = timeInterval.first capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, frameNum) lastFrameNum = timeInterval.last while ret and frameNum <= lastFrameNum: ret, img = capture.read() if ret: if frameNum%50 == 0: print('frame number: {}'.format(frameNum)) currentObjects = [] for obj in objects: if obj.getLastInstant() < frameNum: obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = params.minSpeedEquiprobable, speedProbabilities = speedProbabilities) pastObjects.append(obj) else: currentObjects.append(obj) objects = currentObjects if params.undistort: img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR) for obj in objects: if obj.existsAtInstant(frameNum): if obj.getFirstInstant() == frameNum: obj.initClassifyUserTypeHoGSVM(speedAggregationFunc, pedBikeCarSVM, bikeCarSVM, params.maxPedestrianSpeed, params.maxCyclistSpeed, params.nFramesIgnoreAtEnds) obj.classifyUserTypeHoGSVMAtInstant(img, frameNum, invHomography, width, height, 0.2, 0.2, 800) # px, py, pixelThreshold frameNum += 1 for obj in objects: obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = params.minSpeedEquiprobable, speedProbabilities = speedProbabilities) pastObjects.append(obj) print('Saving user types') storage.setRoadUserTypes(params.databaseFilename, pastObjects)