Mercurial Hosting > traffic-intelligence
comparison 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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810:082a5c2685f4 | 812:21f10332c72b |
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1540 return userType2Num['car'] | 1540 return userType2Num['car'] |
1541 self.appearanceClassifier = CarClassifier() | 1541 self.appearanceClassifier = CarClassifier() |
1542 | 1542 |
1543 self.userTypes = {} | 1543 self.userTypes = {} |
1544 | 1544 |
1545 def classifyUserTypeHoGSVMAtInstant(self, img, instant, homography, width, height, px = 0.2, py = 0.2, minNPixels = 800): | 1545 def classifyUserTypeHoGSVMAtInstant(self, img, instant, homography, width, height, px, py, minNPixels, rescaleSize, orientations, pixelsPerCell, cellsPerBlock): |
1546 '''Extract the image box around the object and | 1546 '''Extracts the image box around the object |
1547 applies the SVM model on it''' | 1547 (of square size max(width, height) of the box around the features, |
1548 with an added px or py for width and height (around the box)) | |
1549 computes HOG on this cropped image (with parameters rescaleSize, orientations, pixelsPerCell, cellsPerBlock) | |
1550 and applies the SVM model on it''' | |
1548 croppedImg, yCropMin, yCropMax, xCropMin, xCropMax = cvutils.imageBox(img, self, instant, homography, width, height, px, py, minNPixels) | 1551 croppedImg, yCropMin, yCropMax, xCropMin, xCropMax = cvutils.imageBox(img, self, instant, homography, width, height, px, py, minNPixels) |
1549 if croppedImg is not None and len(croppedImg) > 0: | 1552 if croppedImg is not None and len(croppedImg) > 0: |
1550 hog = cvutils.HOG(croppedImg)#HOG(image, rescaleSize = (64, 64), orientations=9, pixelsPerCell=(8, 8), cellsPerBlock=(2, 2), visualize=False, normalize=False) | 1553 hog = cvutils.HOG(croppedImg, rescaleSize, orientations, pixelsPerCell, cellsPerBlock, visualize=False, normalize=False) |
1551 self.userTypes[instant] = int(self.appearanceClassifier.predict(hog)) | 1554 self.userTypes[instant] = int(self.appearanceClassifier.predict(hog)) |
1552 else: | 1555 else: |
1553 self.userTypes[instant] = userType2Num['unknown'] | 1556 self.userTypes[instant] = userType2Num['unknown'] |
1554 | 1557 |
1555 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): | 1558 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)): |
1556 '''Agregates SVM detections in each image and returns probability | 1559 '''Agregates SVM detections in each image and returns probability |
1557 (proportion of instants with classification in each category) | 1560 (proportion of instants with classification in each category) |
1558 | 1561 |
1559 images is a dictionary of images indexed by instant | 1562 images is a dictionary of images indexed by instant |
1560 With default parameters, the general (ped-bike-car) classifier will be used | 1563 With default parameters, the general (ped-bike-car) classifier will be used |
1565 self.initClassifyUserTypeHoGSVM(aggregationFunc, pedBikeCarSVM, bikeCarSVM, pedBikeSpeedTreshold, bikeCarSpeedThreshold, nInstantsIgnoredAtEnds) | 1568 self.initClassifyUserTypeHoGSVM(aggregationFunc, pedBikeCarSVM, bikeCarSVM, pedBikeSpeedTreshold, bikeCarSpeedThreshold, nInstantsIgnoredAtEnds) |
1566 | 1569 |
1567 if len(self.userTypes) != self.length() and images is not None: # if classification has not been done previously | 1570 if len(self.userTypes) != self.length() and images is not None: # if classification has not been done previously |
1568 for t in self.getTimeInterval(): | 1571 for t in self.getTimeInterval(): |
1569 if t not in self.userTypes: | 1572 if t not in self.userTypes: |
1570 self.classifyUserTypeHoGSVMAtInstant(images[t], t, homography, width, height, px, py, minNPixels) | 1573 self.classifyUserTypeHoGSVMAtInstant(images[t], t, homography, width, height, px, py, minNPixels, rescaleSize, orientations, pixelsPerCell, cellsPerBlock) |
1571 # compute P(Speed|Class) | 1574 # compute P(Speed|Class) |
1572 if speedProbabilities is None or self.aggregatedSpeed < minSpeedEquiprobable: # equiprobable information from speed | 1575 if speedProbabilities is None or self.aggregatedSpeed < minSpeedEquiprobable: # equiprobable information from speed |
1573 userTypeProbabilities = {userType2Num['car']: 1., userType2Num['pedestrian']: 1., userType2Num['bicycle']: 1.} | 1576 userTypeProbabilities = {userType2Num['car']: 1., userType2Num['pedestrian']: 1., userType2Num['bicycle']: 1.} |
1574 else: | 1577 else: |
1575 userTypeProbabilities = {userType2Num[userTypename]: speedProbabilities[userTypename](self.aggregatedSpeed) for userTypename in speedProbabilities} | 1578 userTypeProbabilities = {userType2Num[userTypename]: speedProbabilities[userTypename](self.aggregatedSpeed) for userTypename in speedProbabilities} |