def testImage(self, image, scale=1, subwindow=None):
imgProcessor = ImageProcessor(image, scale=scale)
imgProcessor.getIntegralChannels(6)
if subwindow != None:
nsx, nsy, nw, nh = utils.getDetectionWindow(
subwindow, imgProcessor.getWidth(), imgProcessor.getHeight(),
scale)
else:
nsx, nsy, nw, nh = 0, 0, imgProcessor.getWidth(
), imgProcessor.getHeight()
windows = []
for startx in xrange(nsx, nsx + nw - 64, 16):
for starty in xrange(nsy, nsy + nh - 128, 16):
windows.append(
(startx, starty,
imgProcessor.getFeature(startx, starty,
self.featureCoords)))
probs = self.classifier.predict_proba([w[2] for w in windows])
classes = self.classifier.predict([w[2] for w in windows])
results = []
for i in xrange(0, len(windows)):
results.append((windows[i][0], windows[i][1], probs[i]))
return results, classes
def getImgFeatures(self, imgPath, noSamples=1, test=False):
imgProc = ImageProcessor(
path.realpath(path.join(self.dirPath, imgPath)))
imgProc.getIntegralChannels(6)
features = []
for i in xrange(0, noSamples):
if test:
startx = random.randrange(3, imgProc.getWidth() - 3 - 64, 16)
starty = random.randrange(3, imgProc.getHeight() - 3 - 128, 16)
else:
startx = random.randrange(3, imgProc.getWidth() - 3 - 64, 16)
starty = random.randrange(3, imgProc.getHeight() - 3 - 128, 16)
features.append(
imgProc.getFeature(startx, starty, self.featureCoords))
return features
def getImgFeature(self, imgPath, test=False):
imgProc = ImageProcessor(
path.realpath(path.join(self.dirPath, imgPath)))
imgProc.getIntegralChannels(6)
if test:
return imgProc.getFeature(3, 3, self.featureCoords)
return imgProc.getFeature(16, 16, self.featureCoords)
def getFeature(self, img):
imgProcessor = ImageProcessor(img)
imgProcessor.getIntegralChannels(6)
# needed for centering the 64x128 image
startx = int((imgProcessor.getWidth() - 64) / 2)
starty = int((imgProcessor.getHeight() - 128) / 2)
feature = imgProcessor.getFeature(startx, starty, self.featureCoords)
return feature
def getWindowsAndDescriptors(self, image, scale, subwindow=None):
imgProcessor = ImageProcessor(image, scale=scale)
imgProcessor.getIntegralChannels(6)
if subwindow != None:
nsx, nsy, nw, nh = utils.getDetectionWindow(
subwindow, imgProcessor.getWidth(), imgProcessor.getHeight(),
scale)
else:
nsx, nsy, nw, nh = 0, 0, imgProcessor.getWidth(
), imgProcessor.getHeight()
windows = []
for startx in xrange(nsx, nsx + nw - 64, 16):
for starty in xrange(nsy, nsy + nh - 128, 16):
feature = imgProcessor.getFeature(startx, starty,
self.featureCoords)
windows.append([(startx, starty, 64, 128), feature])
return windows
def getFeature(self, img):
imgProcessor = ImageProcessor(img)
imgProcessor.getIntegralChannels(6)
# needed for centering the 64x128 image
startx = int((imgProcessor.getWidth() - 64) / 2)
starty = int((imgProcessor.getHeight() - 128) / 2)
feature = imgProcessor.getFeature(startx, starty, self.featureCoords)
return feature
def getWindowsAndDescriptors(self, image, scale, subwindow=None):
imgProcessor = ImageProcessor(image, scale=scale)
imgProcessor.getIntegralChannels(6)
if subwindow != None:
nsx, nsy, nw, nh = utils.getDetectionWindow(subwindow, imgProcessor.getWidth(), imgProcessor.getHeight(), scale)
else:
nsx, nsy, nw, nh = 0, 0, imgProcessor.getWidth(), imgProcessor.getHeight()
windows = []
for startx in xrange(nsx, nsx + nw - 64, 16):
for starty in xrange(nsy, nsy + nh - 128, 16):
feature = imgProcessor.getFeature(startx, starty, self.featureCoords)
windows.append([(startx, starty, 64, 128), feature])
return windows
def testImage(self, image, scale=1, subwindow=None):
imgProcessor = ImageProcessor(image, scale=scale)
imgProcessor.getIntegralChannels(6)
if subwindow != None:
nsx, nsy, nw, nh = utils.getDetectionWindow(subwindow, imgProcessor.getWidth(), imgProcessor.getHeight(), scale)
else:
nsx, nsy, nw, nh = 0, 0, imgProcessor.getWidth(), imgProcessor.getHeight()
windows = []
for startx in xrange(nsx, nsx + nw - 64, 16):
for starty in xrange(nsy, nsy + nh - 128, 16):
windows.append((startx, starty, imgProcessor.getFeature(startx, starty, self.featureCoords)))
probs = self.classifier.predict_proba([w[2] for w in windows])
classes = self.classifier.predict([w[2] for w in windows])
results = []
for i in xrange(0, len(windows)):
results.append((windows[i][0], windows[i][1], probs[i]))
return results, classes