# HG changeset patch
# User Nicolas Saunier <nicolas.saunier@polymtl.ca>
# Date 1496175018 14400
# Node ID ff92801e5c541425ddadcf527b745812c9d8b595
# Parent  f766fe0995f4f60f94f8f1225f9b8216d7019b7f
updated hog to scikit-image 0.13 (needed to add a block_norm attribute in classifier.cfg)

diff -r f766fe0995f4 -r ff92801e5c54 classifier.cfg
--- a/classifier.cfg	Fri May 05 00:07:33 2017 -0400
+++ b/classifier.cfg	Tue May 30 16:10:18 2017 -0400
@@ -14,6 +14,8 @@
 hog-npixels-cell = 8
 # number of cells per block for HoG computation
 hog-ncells-block = 2
+# block normalization method (L1, L1-sqrt, L2, L2-Hys)
+hog-block-norm = L2-Hys
 # method to aggregate road user speed
 speed-aggregation-method = median
 # number of frames to ignore at both ends of a series (noisy)
diff -r f766fe0995f4 -r ff92801e5c54 python/cvutils.py
--- a/python/cvutils.py	Fri May 05 00:07:33 2017 -0400
+++ b/python/cvutils.py	Tue May 30 16:10:18 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), visualize=False, normalize=False):
+    def HOG(image, rescaleSize = (64, 64), orientations=9, pixelsPerCell=(8,8), cellsPerBlock=(2,2), blockNorm='L1', visualize=False, normalize=False):
         bwImg = color.rgb2gray(image)
         inputImg = transform.resize(bwImg, rescaleSize)
-        features = hog(inputImg, orientations, pixelsPerCell, cellsPerBlock, visualize, normalize)
+        features = hog(inputImg, orientations, pixelsPerCell, cellsPerBlock, blockNorm, visualize, normalize)
         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), 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, normalize=False):
         inputData = []
         for filename in listdir(imageDirectory):
             img = imread(imageDirectory+filename)
-            features = HOG(img, rescaleSize, orientations, pixelsPerCell, cellsPerBlock, visualize, normalize)
+            features = HOG(img, rescaleSize, orientations, pixelsPerCell, cellsPerBlock, blockNorm, visualize, normalize)
             inputData.append(features)
 
         nImages = len(inputData)
diff -r f766fe0995f4 -r ff92801e5c54 python/moving.py
--- a/python/moving.py	Fri May 05 00:07:33 2017 -0400
+++ b/python/moving.py	Tue May 30 16:10:18 2017 -0400
@@ -1577,7 +1577,7 @@
         
         self.userTypes = {}
 
-    def classifyUserTypeHoGSVMAtInstant(self, img, instant, homography, width, height, px, py, minNPixels, rescaleSize, orientations, pixelsPerCell, cellsPerBlock):
+    def classifyUserTypeHoGSVMAtInstant(self, img, instant, homography, width, height, px, py, minNPixels, rescaleSize, orientations, pixelsPerCell, cellsPerBlock, blockNorm):
         '''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))
@@ -1585,7 +1585,7 @@
         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, rescaleSize, orientations, pixelsPerCell, cellsPerBlock, visualize=False, normalize=False)
+            hog = cvutils.HOG(croppedImg, rescaleSize, orientations, pixelsPerCell, cellsPerBlock, blockNorm, visualize=False, normalize=False)
             self.userTypes[instant] = int(self.appearanceClassifier.predict(hog))
         else:
             self.userTypes[instant] = userType2Num['unknown']
diff -r f766fe0995f4 -r ff92801e5c54 python/storage.py
--- a/python/storage.py	Fri May 05 00:07:33 2017 -0400
+++ b/python/storage.py	Tue May 30 16:10:18 2017 -0400
@@ -1252,6 +1252,7 @@
         self.hogNPixelsPerCell = (x, x)
         x = config.getint(self.sectionHeader, 'hog-ncells-block')
         self.hogNCellsPerBlock = (x, x)
+        self.hogBlockNorm = config.get(self.sectionHeader, 'hog-block-norm')
         
         self.speedAggregationMethod = config.get(self.sectionHeader, 'speed-aggregation-method')
         self.nFramesIgnoreAtEnds = config.getint(self.sectionHeader, 'nframes-ignore-at-ends')
diff -r f766fe0995f4 -r ff92801e5c54 scripts/classify-objects.py
--- a/scripts/classify-objects.py	Fri May 05 00:07:33 2017 -0400
+++ b/scripts/classify-objects.py	Tue May 30 16:10:18 2017 -0400
@@ -117,7 +117,7 @@
                         obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities)
                         pastObjects.append(obj)
                     else:
-                        obj.classifyUserTypeHoGSVMAtInstant(img, frameNum, invHomography, width, height, classifierParams.percentIncreaseCrop, classifierParams.percentIncreaseCrop, classifierParams.minNPixels, classifierParams.hogRescaleSize, classifierParams.hogNOrientations, classifierParams.hogNPixelsPerCell, classifierParams.hogNCellsPerBlock)
+                        obj.classifyUserTypeHoGSVMAtInstant(img, frameNum, invHomography, width, height, classifierParams.percentIncreaseCrop, classifierParams.percentIncreaseCrop, classifierParams.minNPixels, classifierParams.hogRescaleSize, classifierParams.hogNOrientations, classifierParams.hogNPixelsPerCell, classifierParams.hogNCellsPerBlock, classifierParams.hogBlockNorm)
                         currentObjects.append(obj)
                 else:
                     currentObjects.append(obj)