greenErrors = np.zeros((height, width))
for i in range(height):
for j in range(width):
yellowErros[i] = yellowGMM.getLogLikelihoodError(image[i,j])
orangeErros[i] = orangeGMM.getLogLikelihoodError(image[i,j])
greenErros[i] = greenGMM.getLogLikelihoodError(image[i,j])
'''
yellowMask = np.zeros((height, width)).astype('uint8')
orangeMask = np.zeros((height, width)).astype('uint8')
greenMask = np.zeros((height, width)).astype('uint8')
for i in range(height):
for j in range(width):
yellowError = yellowGMM.getLogLikelihoodError(image[i, j])
orangeError = orangeGMM.getLogLikelihoodError(image[i, j])
greenError = greenGMM.getLogLikelihoodError(image[i, j])
if (yellowError > 12 and orangeError > 15 and greenError > 12):
continue
elif (yellowError == min(yellowError, orangeError, greenError)):
yellowMask[i, j] = 255
elif (orangeError == min(yellowError, orangeError, greenError)):
orangeMask[i, j] = 255
elif (greenError == min(yellowError, orangeError, greenError)):
greenMask[i, j] = 255
yellowMask = cv2.erode(yellowMask, None, iterations=1)
yellowMask = cv2.dilate(yellowMask, None, iterations=2)
