Mercurial Hosting > traffic-intelligence
view scripts/classify-objects.py @ 1280:2abeccdbb985 default tip
removing print debug
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
---|---|
date | Wed, 03 Jul 2024 15:13:15 -0400 |
parents | 2397de73770d |
children |
line wrap: on
line source
#! /usr/bin/env python3 import sys, argparse import numpy as np import cv2 from scipy.stats import norm, lognorm from pathlib import Path try: from ultralytics import YOLO ultralyticsAvailable = True except ImportError: print('Ultralytics library could not be loaded') # TODO change to logging module ultralyticsAvailable = False from trafficintelligence import cvutils, moving, ml, storage, utils # TODO add mode detection live, add choice of kernel and svm type (to be saved in future classifier format) parser = argparse.ArgumentParser(description='The program processes indicators for all pairs of road users in the scene', epilog='The integer ids for the categories are stored in the moving module:\n{}'.format(moving.userType2Num)) 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('-n', dest = 'nObjects', help = 'number of objects to classify', type = int, default = None) 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 (km/h)', type = float, default = 50.) parser.add_argument('--verbose', dest = 'verbose', help = 'verbose information', 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) classifierParams.convertToFrames(params.videoFrameRate, 3.6) # conversion from km/h to m/frame speedAggregationFunc = utils.aggregationFunction(classifierParams.speedAggregationMethod, classifierParams.speedAggregationCentile) if speedAggregationFunc is None: sys.exit() if ultralyticsAvailable and Path(classifierParams.dlFilename).is_file(): # use Yolo pedBikeCarSVM = None bikeCarSVM = None yolo = YOLO(classifierParams.dlFilename, task='detect') useYolo = True print('Using Yolov8 model '+classifierParams.dlFilename) else: useYolo = False pedBikeCarSVM = ml.SVM_load(classifierParams.pedBikeCarSVMFilename) bikeCarSVM = ml.SVM_load(classifierParams.bikeCarSVMFilename) # log logistic for ped and bik otherwise ((pedBeta/pedAlfa)*((sMean/pedAlfa)**(pedBeta-1)))/((1+(sMean/pedAlfa)**pedBeta)**2.) carNorm = norm(classifierParams.meanVehicleSpeed, classifierParams.stdVehicleSpeed) pedNorm = norm(classifierParams.meanPedestrianSpeed, classifierParams.stdPedestrianSpeed) # numpy lognorm shape, loc, scale: shape for numpy is scale (std of the normal) and scale for numpy is exp(location) (loc=mean of the normal) cycLogNorm = lognorm(classifierParams.scaleCyclistSpeed, loc = 0., scale = np.exp(classifierParams.locationCyclistSpeed)) speedProbabilities = {moving.userType2Num['car']: lambda s: carNorm.pdf(s), moving.userType2Num['pedestrian']: lambda s: pedNorm.pdf(s), moving.userType2Num['cyclist']: lambda s: cycLogNorm.pdf(s)} if useYolo: # consider other user types for i in [3, 5, 6, 7]: speedProbabilities[i] = speedProbabilities[moving.userType2Num['car']] if args.plotSpeedDistribution: import matplotlib.pyplot as plt plt.figure() speeds = np.arange(0.1, args.maxSpeedDistributionPlot, 0.1) for k in speedProbabilities: plt.plot(speeds, [speedProbabilities[k](s/(3.6*params.videoFrameRate)) for s in speeds], label = k) # the distribution parameters are in video intrinsic units, unit of distance per frame maxProb = -1. for k in speedProbabilities: maxProb = max(maxProb, np.max([speedProbabilities[k](s/(3.6*params.videoFrameRate)) for s in speeds])) plt.plot([classifierParams.minSpeedEquiprobable*3.6*params.videoFrameRate]*2, [0., maxProb], 'k-') plt.text(classifierParams.minSpeedEquiprobable*3.6*params.videoFrameRate, maxProb, 'threshold for equiprobable class') plt.xlabel('Speed (km/h)') plt.ylabel('Probability density function') plt.legend() #plt.title('Probability Density Function') plt.show() sys.exit() objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object', args.nObjects, withFeatures = True) timeInterval = moving.TimeInterval.unionIntervals([obj.getTimeInterval() for obj in objects]) capture = cv2.VideoCapture(videoFilename) width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)) #if undistort: # setup undistortion # [map1, map2] = cvutils.computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients) # height, width = map1.shape # newImgSize = (int(round(width*undistortedImageMultiplication)), int(round(height*undistortedImageMultiplication))) # newCameraMatrix = cv2.getDefaultNewCameraMatrix(intrinsicCameraMatrix, newImgSize, True) #else: # newCameraMatrix = None pastObjects = [] currentObjects = [] if capture.isOpened(): ret = True frameNum = timeInterval.first capture.set(cv2.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)) #if undistort: # img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR) if useYolo: results = yolo.predict(img, conf = classifierParams.confidence, classes=list(moving.cocoTypeNames.keys()), verbose=False) for obj in objects[:]: if obj.getFirstInstant() <= frameNum: # if images are skipped obj.initClassifyUserTypeHoGSVM(speedAggregationFunc, pedBikeCarSVM, bikeCarSVM, classifierParams.maxPedestrianSpeed, classifierParams.maxCyclistSpeed, classifierParams.nFramesIgnoreAtEnds, invHomography, intrinsicCameraMatrix, distortionCoefficients) currentObjects.append(obj) objects.remove(obj) for obj in currentObjects[:]: if obj.getLastInstant() <= frameNum: obj.classifyUserTypeHoGSVM(classifierParams.minSpeedEquiprobable, speedProbabilities, classifierParams.maxPercentUnknown) pastObjects.append(obj) currentObjects.remove(obj) else: if useYolo: # if one feature falls in bike, it's a bike # could one count all hits in various objects, or one takes majority at the instant? #print('yolo', len(results[0].boxes)) obj.classifyUserTypeYoloAtInstant(frameNum, results[0].boxes) else: obj.classifyUserTypeHoGSVMAtInstant(img, frameNum, width, height, classifierParams.percentIncreaseCrop, classifierParams.percentIncreaseCrop, classifierParams.minNPixels, classifierParams.hogRescaleSize, classifierParams.hogNOrientations, classifierParams.hogNPixelsPerCell, classifierParams.hogNCellsPerBlock, classifierParams.hogBlockNorm) if args.verbose: print('obj {}@{}: {}'.format(obj.getNum(), frameNum, moving.userTypeNames[obj.userTypes[frameNum]])) frameNum += 1 for obj in currentObjects: obj.classifyUserTypeHoGSVM(classifierParams.minSpeedEquiprobable, speedProbabilities, classifierParams.maxPercentUnknown) pastObjects.append(obj) print('Saving user types') storage.setRoadUserTypes(databaseFilename, pastObjects)