def testAllNetworks():
fileHandle = open(RESULT_FILE, 'w', 0)
appendHtmlHeader(fileHandle)
path = 'signs/'
images = [path+f for f in listdir(path)]
print images
netHebb = HopfieldNetwork(SIDE_OF_ARRAY*SIDE_OF_ARRAY)
netPseudoInv = HopfieldNetwork(SIDE_OF_ARRAY*SIDE_OF_ARRAY)
netDelta = HopfieldNetwork(SIDE_OF_ARRAY*SIDE_OF_ARRAY)
for image in images:
print "Learning image " + image
imageName = image
imgProc = ImgPreprocessor(image,SIDE_OF_ARRAY,127)
image = imgProc.getImageForHopfield().copy()
cv2.imshow('test',imgProc.getImage())
ImageSaver(imageName.replace('signs','results'), imgProc.getImage(),SIDE_OF_ARRAY)
netHebb.trainHebb(image)
netDelta.trainDelta(image)
netPseudoInv.appendTrainingVectorForPseudoInversion(image)
netPseudoInv.trainPseudoInversion()
print "Learning done"
#noise processing
for image in images:
tmpImage = image
for noiseFactor in [0.05, 0.10, 0.15]:
image = tmpImage
fileHandle.write('<tr>\n')
start = time.time()
print "Noise factor: " + str(int(noiseFactor*100)) + "%"
print "Processing image " + image
imageName = image
fileHandle.write('<td>\n')
fileHandle.write(imageName.replace('signs/','').replace('.png','') +' '+ '<img src=' + imageName.replace('signs','results') + '>')
fileHandle.write('\n')
fileHandle.write('</td>\n')
fileHandle.write('<td>\n')
fileHandle.write('Szum: ' + str(int(noiseFactor*100)) +'%')
fileHandle.write('\n')
fileHandle.write('</td>\n')
imgProc = ImgPreprocessor(image,SIDE_OF_ARRAY,127)
image = imgProc.getImageForHopfield().copy()
image = ImageNoise(image, noiseFactor).get()
ImageSaver(imageName.replace('signs/','results/rec_noise_' + str(int(noiseFactor*100)) + '_'), image,SIDE_OF_ARRAY)
fileHandle.write('<td>\n')
fileHandle.write('<img src=' + imageName.replace('signs/','results/rec_noise_' + str(int(noiseFactor*100)) + '_') + '>')
fileHandle.write('\n')
fileHandle.write('</td>\n')
netPseudoInv.initNeurons(image)
netDelta.initNeurons(image)
netHebb.initNeurons(image)
netHebb.updateUntilSatisfied()
ImageSaver(imageName.replace('signs/','results/hebb_noise_' + str(int(noiseFactor*100)) + '_'), netHebb.getNeuronsMatrix(),SIDE_OF_ARRAY)
netDelta.updateUntilSatisfied()
ImageSaver(imageName.replace('signs/','results/delta_noise_' + str(int(noiseFactor*100)) + '_'), netDelta.getNeuronsMatrix(),SIDE_OF_ARRAY)
netPseudoInv.updateUntilSatisfied()
ImageSaver(imageName.replace('signs/','results/pinv_noise_' + str(int(noiseFactor*100)) + '_'), netPseudoInv.getNeuronsMatrix(),SIDE_OF_ARRAY)
end = time.time()
fileHandle.write('<td>\n')
fileHandle.write('<img src=' + imageName.replace('signs/','results/hebb_noise_' + str(int(noiseFactor*100)) + '_') + '>')
fileHandle.write('\n')
fileHandle.write('</td>\n')
fileHandle.write('<td>\n')
fileHandle.write('<img src=' + imageName.replace('signs/','results/delta_noise_' + str(int(noiseFactor*100)) + '_') + '>')
fileHandle.write('\n')
fileHandle.write('</td>\n')
fileHandle.write('<td>\n')
fileHandle.write('<img src=' + imageName.replace('signs/','results/pinv_noise_' + str(int(noiseFactor*100)) + '_') + '>')
fileHandle.write('\n')
fileHandle.write('</td>\n')
print "Time elapsed " + str(int(end-start)) + " [s]"
fileHandle.write('</tr>\n')
#lines processing
for image in images:
tmpImage = image
for linesEvery in [16,8]:
image = tmpImage
fileHandle.write('<tr>\n')
start = time.time()
print "Lines every: " + str(int(linesEvery))
print "Processing image " + image
imageName = image
fileHandle.write('<td>\n')
fileHandle.write(imageName.replace('signs/','').replace('.png','') +' '+ '<img src=' + imageName.replace('signs','results') + '>')
fileHandle.write('\n')
fileHandle.write('</td>\n')
fileHandle.write('<td>\n')
fileHandle.write('Linie co: ' + str(int(linesEvery)))
fileHandle.write('\n')
fileHandle.write('</td>\n')
imgProc = ImgPreprocessor(image,SIDE_OF_ARRAY,127)
image = imgProc.getImageForHopfield().copy()
image = ImageLines(image, linesEvery).get()
ImageSaver(imageName.replace('signs/','results/rec_lines_' + str(linesEvery) + '_'), image,SIDE_OF_ARRAY)
fileHandle.write('<td>\n')
fileHandle.write('<img src=' + imageName.replace('signs/','results/rec_lines_' + str(linesEvery) + '_') + '>')
fileHandle.write('\n')
fileHandle.write('</td>\n')
netPseudoInv.initNeurons(image)
netDelta.initNeurons(image)
netHebb.initNeurons(image)
netHebb.updateUntilSatisfied()
ImageSaver(imageName.replace('signs/','results/hebb_lines_' + str(linesEvery) + '_'), netHebb.getNeuronsMatrix(),SIDE_OF_ARRAY)
netDelta.updateUntilSatisfied()
ImageSaver(imageName.replace('signs/','results/delta_lines_' + str(linesEvery) + '_'), netDelta.getNeuronsMatrix(),SIDE_OF_ARRAY)
netPseudoInv.updateUntilSatisfied()
ImageSaver(imageName.replace('signs/','results/pinv_lines_' + str(linesEvery) + '_'), netPseudoInv.getNeuronsMatrix(),SIDE_OF_ARRAY)
end = time.time()
fileHandle.write('<td>\n')
fileHandle.write('<img src=' + imageName.replace('signs/','results/hebb_lines_' + str(linesEvery) + '_') + '>')
fileHandle.write('\n')
fileHandle.write('</td>\n')
fileHandle.write('<td>\n')
fileHandle.write('<img src=' + imageName.replace('signs/','results/delta_lines_' + str(linesEvery) + '_') + '>')
fileHandle.write('\n')
fileHandle.write('</td>\n')
fileHandle.write('<td>\n')
fileHandle.write('<img src=' + imageName.replace('signs/','results/pinv_lines_' + str(linesEvery) + '_') + '>')
fileHandle.write('\n')
fileHandle.write('</td>\n')
print "Time elapsed " + str(int(end-start)) + " [s]"
fileHandle.write('</tr>\n')
appendHtmlFooter(fileHandle)
fileHandle.close()
def main(self):
SIDE_OF_ARRAY = 32
F = npyscreen.Form(name = "Road signs recognition via Hopfield Network",)
F.add(npyscreen.TitleFixedText, name = "Using dimensions: " + str(SIDE_OF_ARRAY) + "x" + str(SIDE_OF_ARRAY),)
netHebb = HopfieldNetwork(SIDE_OF_ARRAY*SIDE_OF_ARRAY)
netPseudoInv = HopfieldNetwork(SIDE_OF_ARRAY*SIDE_OF_ARRAY)
netDelta = HopfieldNetwork(SIDE_OF_ARRAY*SIDE_OF_ARRAY)
F.add(npyscreen.TitleFixedText, name = "Network instances initialized",)
F.display()
signsList, imgToRecogn = self.loadParameters(sys.argv)
F.add(npyscreen.TitleFixedText, name = "Image to recognize: " + imgToRecogn,)
images = list()
for image in signsList:
imageName = image
imgProc = ImgPreprocessor(image,SIDE_OF_ARRAY,127)
image = imgProc.getImageForHopfield().copy()
#print image
F.add(npyscreen.TitleFixedText, name = "Image to learn: " + imageName,)
F.display()
images.append(image)
netHebb.trainHebb(image)
netDelta.trainDelta(image)
netPseudoInv.appendTrainingVectorForPseudoInversion(image)
netPseudoInv.trainPseudoInversion()
F.add(npyscreen.TitleFixedText, name = "Networks teached by above images",)
F.display()
imgProc = ImgPreprocessor(imgToRecogn,SIDE_OF_ARRAY,127)
imageRec = imgProc.getImageForHopfield()
netHebb.initNeurons(imageRec)
netDelta.initNeurons(imageRec)
netPseudoInv.initNeurons(imageRec)
hebbProgressWidget = F.add(npyscreen.TitleSlider, out_of=100, name = "Hebb")
F.add(npyscreen.TitleFixedText, name = " ",)
deltaProgressWidget = F.add(npyscreen.TitleSlider, out_of=100, name = "Delta")
F.add(npyscreen.TitleFixedText, name = " ",)
pInvProgressWidget = F.add(npyscreen.TitleSlider, out_of=100, name = "PInv")
for i in range(50):
netHebb.update(100)
hebbProgressWidget.value = 2 + i*2
F.display()
for i in range(50):
netDelta.update(100)
deltaProgressWidget.value = 2 + i*2
F.display()
for i in range(50):
netPseudoInv.update(100)
pInvProgressWidget.value = 2 + i*2
F.display()
networkResultHebb = netHebb.getNeuronsMatrix().copy()
networkResultDelta = netDelta.getNeuronsMatrix().copy()
networkResultPseudoInv = netPseudoInv.getNeuronsMatrix().copy()
#netHebb.saveNetworkConfigToFile('networkConfig.txt');
"""
#print "================================"
matchFound = False
for idx, image in enumerate(images):
if np.array_equiv(networkResultHebb, image):
#print "Found matching image as sample " + str(idx)
matchFound = True
break
elif np.array_equiv(self.invertMatrix(networkResultHebb), image):
#print "Found inverse matching image as sample" + str(idx)
matchFound = True
break
if(not matchFound):
pass
#print "Unfortunately no match!"
#print "================================"
"""
subplotIndex = 201
subplotIndex += 10 * (len(images)+1)
for idx, image in enumerate(images):
plt.subplot(subplotIndex+idx)
image2d = np.reshape(image, (SIDE_OF_ARRAY,SIDE_OF_ARRAY))
plt.imshow(image2d, cmap='gray', interpolation = 'nearest')
plt.xticks([]), plt.yticks([]), plt.title("Sample %d" % idx)
newLineSubplotIndex = subplotIndex + len(images)
plt.subplot(newLineSubplotIndex)
image2d = np.reshape(imageRec, (SIDE_OF_ARRAY,SIDE_OF_ARRAY))
plt.imshow(image2d, cmap='gray', interpolation = 'nearest')
plt.xticks([]), plt.yticks([]), plt.title("To recognize")
plt.subplot(newLineSubplotIndex+1)
image2dResult = np.reshape(networkResultHebb , (SIDE_OF_ARRAY,SIDE_OF_ARRAY))
plt.imshow(image2dResult, cmap='gray', interpolation = 'nearest')
plt.xticks([]), plt.yticks([]), plt.title("Returned by net (HEBB)")
plt.subplot(newLineSubplotIndex+2)
image2dResult = np.reshape(networkResultDelta, (SIDE_OF_ARRAY,SIDE_OF_ARRAY))
plt.imshow(image2dResult, cmap='gray', interpolation = 'nearest')
plt.xticks([]), plt.yticks([]), plt.title("Returned by net (DELTA)")
plt.subplot(newLineSubplotIndex+3)
image2dResult = np.reshape(networkResultPseudoInv, (SIDE_OF_ARRAY,SIDE_OF_ARRAY))
plt.imshow(image2dResult, cmap='gray', interpolation = 'nearest')
plt.xticks([]), plt.yticks([]), plt.title("Returned by net (PINVERSE)")
plt.show()
