Mercurial Hosting > traffic-intelligence
diff python/moving.py @ 812:21f10332c72b
moved the classification parameters from tracking.cfg to a new classifier.cfg and made all classification parameters apparent
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Fri, 10 Jun 2016 17:07:36 -0400 |
parents | 3aa6102ccc12 |
children | 14e4ad7c7420 |
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--- a/python/moving.py Fri Jun 10 15:44:08 2016 -0400 +++ b/python/moving.py Fri Jun 10 17:07:36 2016 -0400 @@ -1542,17 +1542,20 @@ self.userTypes = {} - def classifyUserTypeHoGSVMAtInstant(self, img, instant, homography, width, height, px = 0.2, py = 0.2, minNPixels = 800): - '''Extract the image box around the object and - applies the SVM model on it''' + def classifyUserTypeHoGSVMAtInstant(self, img, instant, homography, width, height, px, py, minNPixels, rescaleSize, orientations, pixelsPerCell, cellsPerBlock): + '''Extracts the image box around the object + (of square size max(width, height) of the box around the features, + with an added px or py for width and height (around the box)) + computes HOG on this cropped image (with parameters rescaleSize, orientations, pixelsPerCell, cellsPerBlock) + and applies the SVM model on it''' croppedImg, yCropMin, yCropMax, xCropMin, xCropMax = cvutils.imageBox(img, self, instant, homography, width, height, px, py, minNPixels) if croppedImg is not None and len(croppedImg) > 0: - hog = cvutils.HOG(croppedImg)#HOG(image, rescaleSize = (64, 64), orientations=9, pixelsPerCell=(8, 8), cellsPerBlock=(2, 2), visualize=False, normalize=False) + hog = cvutils.HOG(croppedImg, rescaleSize, orientations, pixelsPerCell, cellsPerBlock, visualize=False, normalize=False) self.userTypes[instant] = int(self.appearanceClassifier.predict(hog)) else: self.userTypes[instant] = userType2Num['unknown'] - def classifyUserTypeHoGSVM(self, pedBikeCarSVM = None, width = 0, height = 0, homography = None, images = None, bikeCarSVM = None, pedBikeSpeedTreshold = float('Inf'), bikeCarSpeedThreshold = float('Inf'), minSpeedEquiprobable = -1, speedProbabilities = None, aggregationFunc = median, nInstantsIgnoredAtEnds = 0, px = 0.2, py = 0.2, minNPixels = 800): + def classifyUserTypeHoGSVM(self, pedBikeCarSVM = None, width = 0, height = 0, homography = None, images = None, bikeCarSVM = None, pedBikeSpeedTreshold = float('Inf'), bikeCarSpeedThreshold = float('Inf'), minSpeedEquiprobable = -1, speedProbabilities = None, aggregationFunc = median, nInstantsIgnoredAtEnds = 0, px = 0.2, py = 0.2, minNPixels = 800, rescaleSize = (64, 64), orientations = 9, pixelsPerCell = (8,8), cellsPerBlock = (2,2)): '''Agregates SVM detections in each image and returns probability (proportion of instants with classification in each category) @@ -1567,7 +1570,7 @@ if len(self.userTypes) != self.length() and images is not None: # if classification has not been done previously for t in self.getTimeInterval(): if t not in self.userTypes: - self.classifyUserTypeHoGSVMAtInstant(images[t], t, homography, width, height, px, py, minNPixels) + self.classifyUserTypeHoGSVMAtInstant(images[t], t, homography, width, height, px, py, minNPixels, rescaleSize, orientations, pixelsPerCell, cellsPerBlock) # compute P(Speed|Class) if speedProbabilities is None or self.aggregatedSpeed < minSpeedEquiprobable: # equiprobable information from speed userTypeProbabilities = {userType2Num['car']: 1., userType2Num['pedestrian']: 1., userType2Num['bicycle']: 1.}