Mercurial Hosting > traffic-intelligence

comparison python/cvutils.py @ 365:22ddb8f85495

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
added function to create HOG training set
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Fri, 12 Jul 2013 11:28:47 -0400
parents 5f75d6c23ed5
children 387cc0142211
comparison
equal deleted inserted replaced
364:a50a69e04c2a 365:22ddb8f85495
2 '''Image/Video utilities''' 2 '''Image/Video utilities'''
3 3
4 import Image, ImageDraw # PIL 4 import Image, ImageDraw # PIL
5 try: 5 try:
6 import cv2 6 import cv2
7 opencvExists = True 7 opencvAvailable = True
8 except ImportError: 8 except ImportError:
9 print('OpenCV library could not be loaded') 9 print('OpenCV library could not be loaded')
10 opencvExists = False 10 opencvAvailable = False
11 try:
12 import skimage
13 skimageAvailable = True
14 except ImportError:
15 print('Scikit-image library could not be loaded')
16 skimageAvailable = False
17
11 from sys import stdout 18 from sys import stdout
12 19
13 import utils 20 import utils
14 21
15 #import aggdraw # agg on top of PIL (antialiased drawing) 22 #import aggdraw # agg on top of PIL (antialiased drawing)
68 for i in xrange(cvmat.rows): 75 for i in xrange(cvmat.rows):
69 for j in xrange(cvmat.cols): 76 for j in xrange(cvmat.cols):
70 a[i,j] = cvmat[i,j] 77 a[i,j] = cvmat[i,j]
71 return a 78 return a
72 79
73 if opencvExists: 80 if opencvAvailable:
74 def computeHomography(srcPoints, dstPoints, method=0, ransacReprojThreshold=0.0): 81 def computeHomography(srcPoints, dstPoints, method=0, ransacReprojThreshold=0.0):
75 '''Returns the homography matrix mapping from srcPoints to dstPoints (dimension Nx2)''' 82 '''Returns the homography matrix mapping from srcPoints to dstPoints (dimension Nx2)'''
76 H, mask = cv2.findHomography(srcPoints, dstPoints, method, ransacReprojThreshold) 83 H, mask = cv2.findHomography(srcPoints, dstPoints, method, ransacReprojThreshold)
77 return H 84 return H
78 85
207 from numpy.linalg.linalg import inv 214 from numpy.linalg.linalg import inv
208 invH = inv(homography) 215 invH = inv(homography)
209 invH /= invH[2,2] 216 invH /= invH[2,2]
210 return invH 217 return invH
211 218
212 if opencvExists: 219 if opencvAvailable:
213 def computeTranslation(img1, img2, img1Points, maxTranslation2, minNMatches, windowSize = (5,5), level = 5, criteria = (cv2.TERM_CRITERIA_EPS, 0, 0.01)): 220 def computeTranslation(img1, img2, img1Points, maxTranslation2, minNMatches, windowSize = (5,5), level = 5, criteria = (cv2.TERM_CRITERIA_EPS, 0, 0.01)):
214 '''Computes the translation of img2 with respect to img1 221 '''Computes the translation of img2 with respect to img1
215 (loaded using OpenCV as numpy arrays) 222 (loaded using OpenCV as numpy arrays)
216 img1Points are used to compute the translation 223 img1Points are used to compute the translation
217 224
233 if len(delta) >= minNMatches: 240 if len(delta) >= minNMatches:
234 return median(delta, axis=0) 241 return median(delta, axis=0)
235 else: 242 else:
236 print(dp) 243 print(dp)
237 return None 244 return None
245
246 if skimageAvailable:
247 def createHOGTrainingSet(imageDirectory, classLabel, rescaleSize = (64, 64), orientations=9, pixelsPerCell=(8, 8), cellsPerBlock=(3, 3), visualize=False, normalize=False):
248 from os import listdir
249 from matplotlib.pyplot import imread, imshow, figure, subplot
250 from skimage.feature import hog
251 from skimage import color, transform
252 from numpy import array, float32
253
254 inputData = []
255 for filename in listdir(imageDirectory):
256 img = imread(imageDirectory+filename)
257 bwImg = color.rgb2gray(img)
258 inputImg = transform.resize(bwImg, rescaleSize)
259 features = hog(inputImg, orientations, pixelsPerCell, cellsPerBlock, visualize, normalize)
260 if visualize:
261 hogViz = features[1]
262 features = features[0]
263 figure()
264 subplot(1,2,1)
265 imshow(img)
266 subplot(1,2,2)
267 imshow(hogViz)
268 inputData.append(features)
269
270 nImages = len(inputData)
271 return array(inputData, dtype = float32), array([classLabel]*nImages, dtype = float32)
272
273