def divideImages(self):
print('Divide color image {} with image {}'.format(
self.firstDecoder.name, self.secondDecoder.name))
unification = Unification(self.firstDecoder.name,
self.secondDecoder.name, self.imageType)
firstImage, secondImage = unification.colorUnification()
width, height = firstImage.shape[0], firstImage.shape[1]
maxValue = float(numpy.iinfo(firstImage.dtype).max)
sumR = float(
numpy.amax(
numpy.add(firstImage[:, :, 0].astype(numpy.uint32),
secondImage[:, :, 0].astype(numpy.uint32))))
sumG = float(
numpy.amax(
numpy.add(firstImage[:, :, 1].astype(numpy.uint32),
secondImage[:, :, 1].astype(numpy.uint32))))
sumB = float(
numpy.amax(
numpy.add(firstImage[:, :, 2].astype(numpy.uint32),
secondImage[:, :, 2].astype(numpy.uint32))))
scaleR = maxValue / sumR
scaleG = maxValue / sumG
scaleB = maxValue / sumB
result = numpy.ones((height, width, 3), numpy.uint8)
for h in range(height):
for w in range(width):
R = (int(firstImage[h, w, 0]) +
int(secondImage[h, w, 0])) * scaleR
G = (int(firstImage[h, w, 1]) +
int(secondImage[h, w, 1])) * scaleG
B = (int(firstImage[h, w, 2]) +
int(secondImage[h, w, 2])) * scaleB
result[h, w, 0] = numpy.ceil(R)
result[h, w, 1] = numpy.ceil(G)
result[h, w, 2] = numpy.ceil(B)
ImageHelper.Save(result, self.imageType, 'divide-color-images', False,
self.firstDecoder, self.secondDecoder)
result = Commons.Normalization(firstImage, result)
ImageHelper.Save(result, self.imageType, 'divide-color-images', True,
self.firstDecoder, self.secondDecoder)
def sumImages(self):
print('Sum color image {} with image {}'.format(
self.firstDecoder.name, self.secondDecoder.name))
unification = Unification(self.firstDecoder.name,
self.secondDecoder.name, self.imageType)
firstImage, secondImage = unification.colorUnification()
width, height = firstImage.shape[0], firstImage.shape[1]
maxSum = float(
numpy.amax(
numpy.add(firstImage.astype(numpy.uint32),
secondImage.astype(numpy.uint32))))
maxValue = float(numpy.iinfo(firstImage.dtype).max)
scaleFactor = (maxSum -
maxValue) / maxValue if maxSum > maxValue else 0
result = numpy.ones((height, width, 3), numpy.uint8)
for h in range(height):
for w in range(width):
R = (firstImage[h, w, 0] -
(firstImage[h, w, 0] * scaleFactor)) + (
secondImage[h, w, 0] -
(secondImage[h, w, 0] * scaleFactor))
G = (firstImage[h, w, 1] -
(firstImage[h, w, 1] * scaleFactor)) + (
secondImage[h, w, 1] -
(secondImage[h, w, 1] * scaleFactor))
B = (firstImage[h, w, 2] -
(firstImage[h, w, 2] * scaleFactor)) + (
secondImage[h, w, 2] -
(secondImage[h, w, 2] * scaleFactor))
result[h, w] = [numpy.ceil(R), numpy.ceil(G), numpy.ceil(B)]
ImageHelper.Save(result, self.imageType, 'sum-color-images', False,
self.firstDecoder, self.secondDecoder)
result = Commons.Normalization(firstImage, result)
ImageHelper.Save(result, self.imageType, 'sum-color-images', True,
self.firstDecoder, self.secondDecoder)
def multiplyImages(self):
print('Multiply color image {} with image {}'.format(
self.firstDecoder.name, self.secondDecoder.name))
unification = Unification(self.firstDecoder.name,
self.secondDecoder.name, self.imageType)
firstImage, secondImage = unification.colorUnification()
width, height = firstImage.shape[0], firstImage.shape[1]
maxValue = float(numpy.iinfo(firstImage.dtype).max)
result = numpy.ones((height, width, 3), numpy.uint8)
for h in range(height):
for w in range(width):
result[h, w, 0] = int(firstImage[h, w, 0]) * int(
secondImage[h, w, 0]) / maxValue
result[h, w, 1] = int(firstImage[h, w, 1]) * int(
secondImage[h, w, 1]) / maxValue
result[h, w, 2] = int(firstImage[h, w, 2]) * int(
secondImage[h, w, 2]) / maxValue
ImageHelper.Save(result, self.imageType, 'multiply-color-images',
False, self.firstDecoder, self.secondDecoder)
result = Commons.Normalization(firstImage, result)
ImageHelper.Save(result, self.imageType, 'multiply-color-images', True,
self.firstDecoder, self.secondDecoder)