Mercurial Hosting > traffic-intelligence
changeset 959:4f32d82ca390
corrected error due to change in Hog (scikit image)
| author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
|---|---|
| date | Thu, 24 Aug 2017 17:22:24 -0400 |
| parents | 747a5c68bd3c |
| children | 0c1d1eeef544 |
| files | python/cvutils.py python/moving.py python/tests/moving.txt |
| diffstat | 3 files changed, 22 insertions(+), 20 deletions(-) [+] |
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--- a/python/cvutils.py Fri Aug 18 18:00:11 2017 -0400 +++ b/python/cvutils.py Thu Aug 24 17:22:24 2017 -0400 @@ -605,10 +605,10 @@ from skimage.feature import hog from skimage import color, transform - def HOG(image, rescaleSize = (64, 64), orientations=9, pixelsPerCell=(8,8), cellsPerBlock=(2,2), blockNorm='L1', visualize=False, normalize=False): + def HOG(image, rescaleSize = (64, 64), orientations = 9, pixelsPerCell = (8,8), cellsPerBlock = (2,2), blockNorm = 'L1', visualize = False, transformSqrt = False): bwImg = color.rgb2gray(image) inputImg = transform.resize(bwImg, rescaleSize) - features = hog(inputImg, orientations, pixelsPerCell, cellsPerBlock, blockNorm, visualize, normalize, True) + features = hog(inputImg, orientations, pixelsPerCell, cellsPerBlock, blockNorm, visualize, transformSqrt, True) if visualize: from matplotlib.pyplot import imshow, figure, subplot hogViz = features[1] @@ -620,11 +620,11 @@ imshow(hogViz) return float32(features) - def createHOGTrainingSet(imageDirectory, classLabel, rescaleSize = (64,64), orientations=9, pixelsPerCell=(8,8), blockNorm='L1', cellsPerBlock=(2, 2), visualize=False, normalize=False): + def createHOGTrainingSet(imageDirectory, classLabel, rescaleSize = (64,64), orientations = 9, pixelsPerCell = (8,8), blockNorm = 'L1', cellsPerBlock = (2, 2), visualize = False, transformSqrt = False): inputData = [] for filename in listdir(imageDirectory): img = imread(imageDirectory+filename) - features = HOG(img, rescaleSize, orientations, pixelsPerCell, cellsPerBlock, blockNorm, visualize, normalize) + features = HOG(img, rescaleSize, orientations, pixelsPerCell, cellsPerBlock, blockNorm, visualize, transformSqrt) inputData.append(features) nImages = len(inputData)
--- a/python/moving.py Fri Aug 18 18:00:11 2017 -0400 +++ b/python/moving.py Thu Aug 24 17:22:24 2017 -0400 @@ -1108,6 +1108,11 @@ userType2Num = utils.inverseEnumeration(userTypeNames) +class CarClassifier: + def predict(self, hog): + return userType2Num['car'] +carClassifier = CarClassifier() + class MovingObject(STObject, VideoFilenameAddable): '''Class for moving objects: a spatio-temporal object with a trajectory and a geometry (constant volume over time) @@ -1615,10 +1620,7 @@ elif self.aggregatedSpeed < bikeCarSpeedThreshold: self.appearanceClassifier = bikeCarSVM else: - class CarClassifier: - def predict(self, hog): - return userType2Num['car'] - self.appearanceClassifier = CarClassifier() + self.appearanceClassifier = carClassifier # project feature positions if self.hasFeatures(): for f in self.getFeatures(): @@ -1634,7 +1636,7 @@ and applies the SVM model on it''' croppedImg = cvutils.imageBox(img, self, instant, width, height, px, py, minNPixels) if croppedImg is not None and len(croppedImg) > 0: - hog = cvutils.HOG(croppedImg, rescaleSize, orientations, pixelsPerCell, cellsPerBlock, blockNorm, visualize=False, normalize=False) + hog = cvutils.HOG(croppedImg, rescaleSize, orientations, pixelsPerCell, cellsPerBlock, blockNorm) self.userTypes[instant] = int(self.appearanceClassifier.predict(hog)) else: self.userTypes[instant] = userType2Num['unknown']
--- a/python/tests/moving.txt Fri Aug 18 18:00:11 2017 -0400 +++ b/python/tests/moving.txt Thu Aug 24 17:22:24 2017 -0400 @@ -203,13 +203,13 @@ >>> gt1 = BBMovingObject(1, TimeInterval(0,10), MovingObject.generate(1, Point(0.2,0.6), Point(1.,0.), TimeInterval(0,10)), MovingObject.generate(2, Point(-0.2,-0.4), Point(1.,0.), TimeInterval(0,10))) >>> gt1.computeCentroidTrajectory() >>> computeClearMOT([gt1], [], 0.2, 0, 10) -(None, 0.0, 11, 0, 0, 11) +(None, 0.0, 11, 0, 0, 11, None, None) >>> computeClearMOT([], [o1], 0.2, 0, 10) -(None, None, 0, 0, 11, 0) +(None, None, 0, 0, 11, 0, None, None) >>> computeClearMOT([gt1], [o1], 0.2, 0, 10) # doctest:+ELLIPSIS -(0.0999..., 1.0, 0, 0, 0, 11) +(0.0999..., 1.0, 0, 0, 0, 11, None, None) >>> computeClearMOT([gt1], [o1], 0.05, 0, 10) -(None, -1.0, 11, 0, 11, 11) +(None, -1.0, 11, 0, 11, 11, None, None) >>> o1 = MovingObject(1, TimeInterval(0,3), positions = Trajectory([range(4), [0.1, 0.1, 1.1, 1.1]])) >>> o2 = MovingObject(2, TimeInterval(0,3), positions = Trajectory([range(4), [0.9, 0.9, -0.1, -0.1]])) @@ -218,16 +218,16 @@ >>> gt2 = BBMovingObject(2, TimeInterval(0,3), MovingObject(positions = Trajectory([range(4), [1.]*4])), MovingObject(positions = Trajectory([range(4), [1.]*4]))) >>> gt2.computeCentroidTrajectory() >>> computeClearMOT([gt1, gt2], [o1, o2], 0.2, 0, 3) # doctest:+ELLIPSIS -(0.1..., 0.75, 0, 2, 0, 8) +(0.1..., 0.75, 0, 2, 0, 8, None, None) >>> computeClearMOT([gt2, gt1], [o2, o1], 0.2, 0, 3) # doctest:+ELLIPSIS -(0.1..., 0.75, 0, 2, 0, 8) +(0.1..., 0.75, 0, 2, 0, 8, None, None) >>> computeClearMOT([gt1], [o1, o2], 0.2, 0, 3) -(0.1, -0.25, 0, 1, 4, 4) +(0.1, -0.25, 0, 1, 4, 4, None, None) >>> computeClearMOT([gt1], [o2, o1], 0.2, 0, 3) # symmetry -(0.1, -0.25, 0, 1, 4, 4) +(0.1, -0.25, 0, 1, 4, 4, None, None) >>> computeClearMOT([gt1, gt2], [o1], 0.2, 0, 3) # doctest:+ELLIPSIS -(0.100..., 0.375, 4, 1, 0, 8) +(0.100..., 0.375, 4, 1, 0, 8, None, None) >>> computeClearMOT([gt2, gt1], [o1], 0.2, 0, 3) # doctest:+ELLIPSIS -(0.100..., 0.375, 4, 1, 0, 8) +(0.100..., 0.375, 4, 1, 0, 8, None, None) >>> computeClearMOT([gt1, gt2], [o1, o2], 0.08, 0, 3) -(None, -1.0, 8, 0, 8, 8) +(None, -1.0, 8, 0, 8, 8, None, None)