def sumImages(self):
unification = Unification(self.firstDecoder.name,
self.secondDecoder.name, 'L')
firstImage, secondImage = unification.grayUnification()
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), numpy.uint8)
for h in range(height):
for w in range(width):
pom = (firstImage[h, w] - (firstImage[h, w] * scaleFactor)) + (
secondImage[h, w] - (secondImage[h, w] * scaleFactor))
result[h, w] = numpy.ceil(pom)
ImageHelper.Save(result, self.imageType, 'sum-gray-images', False,
self.firstDecoder, self.secondDecoder)
result = Commons.Normalization(firstImage, result)
ImageHelper.Save(result, self.imageType, 'sum-gray-images', True,
self.firstDecoder, self.secondDecoder)
def divideImages(self):
print('Divide gray image {} with image {}'.format(
self.firstDecoder.name, self.secondDecoder.name))
unification = Unification(self.firstDecoder.name,
self.secondDecoder.name, 'L')
firstImage, secondImage = unification.grayUnification()
width, height = firstImage.shape[0], firstImage.shape[1]
maxValue = float(numpy.iinfo(firstImage.dtype).max)
maxSum = float(
numpy.amax(
numpy.add(firstImage.astype(numpy.uint32),
secondImage.astype(numpy.uint32))))
scaleFactor = maxValue / maxSum
result = numpy.ones((height, width), numpy.uint8)
for h in range(height):
for w in range(width):
pom = (int(firstImage[h, w]) +
int(secondImage[h, w])) * scaleFactor
result[h, w] = math.ceil(pom)
ImageHelper.Save(result, self.imageType, 'divide-gray-images', False,
self.firstDecoder, self.secondDecoder)
result = Commons.Normalization(firstImage, result)
ImageHelper.Save(result, self.imageType, 'divide-gray-images', True,
self.firstDecoder, self.secondDecoder)
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 multiplyImages(self):
print('Multiply gray image {} with image {}'.format(
self.firstDecoder.name, self.secondDecoder.name))
unification = Unification(self.firstDecoder.name,
self.secondDecoder.name, 'L')
firstImage, secondImage = unification.grayUnification()
width, height = firstImage.shape[0], firstImage.shape[1]
maxValue = float(numpy.iinfo(firstImage.dtype).max)
result = numpy.ones((height, width), numpy.uint8)
for h in range(height):
for w in range(width):
pom = int(firstImage[h, w]) * int(secondImage[h, w]) / maxValue
result[h, w] = pom
ImageHelper.Save(result, self.imageType, 'multiply-gray-images', False,
self.firstDecoder, self.secondDecoder)
result = Commons.Normalization(firstImage, result)
ImageHelper.Save(result, self.imageType, 'multiply-gray-images', True,
self.firstDecoder, self.secondDecoder)
def blendImages(self, ratio):
print('Blending gray image {} with image {} and ratio {}'.format(
self.firstDecoder.name, self.secondDecoder.name, ratio))
if ratio < 0 or ratio > 1.0:
raise ValueError('ratio is wrong')
unification = Unification(self.firstDecoder.name,
self.secondDecoder.name, 'L')
firstImage = unification.scaleUpGray(self.firstDecoder)
secondImage = unification.scaleUpGray(self.secondDecoder)
width, height = firstImage.shape[0], firstImage.shape[1]
result = numpy.ones((height, width), numpy.uint8)
for h in range(height):
for w in range(width):
pom = ratio * firstImage[h, w] + (1 - ratio) * secondImage[h,
w]
result[h, w] = pom
ImageHelper.Save(result, self.imageType, 'blend-gray-images', False,
self.firstDecoder, None, ratio)
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 unificationModule():
unification = Unification('Resources/kobieta.png', 'Resources/zawody.png',
"L")
unification.geometricGray()
unification.rasterGray()