def _loadToArray(self, imagePath):
"""
Creates input array. Applies scale factor to each image.
"""
try:
image = PIL.Image.open(imagePath)
except IOError as e:
#print("Trying to open by converting to png")
png = os.path.splitext(imagePath)[0] + '.png'
wand.image.Image(filename=imagePath).convert('PNG').save(filename=png)
image = PIL.Image.open(png)
#resize
scaleFactor = np.divide(self.imageSize, image.size)
newSize = tuple(round(x * s) for x, s in zip(image.size, scaleFactor))
image.thumbnail(newSize)
#greyscale
image = image.convert('L')
# neurolab seems to expect 1d input, so rescale the images in the
# input array as linear (the network does't know about shape anyway)
imageArray = np.array(image)
newSize = mul(*imageArray.shape)
return imageArray.reshape(newSize)
def thumbnail_images(images):
"""
:param images: list of PIL.Image objects
"""
sizes = [image.size for image in images]
for image in images:
if image.size != min(sizes):
image.thumbnail(min(sizes))
yield image
def _loadToArray(imagePath):
try:
image = PIL.Image.open(imagePath)
except IOError as e:
print("Trying to open by converting to png")
png = os.path.splitext(imagePath)[0] + '.png'
wand.image.Image(filename=imagePath).convert('PNG').save(filename=png)
image = PIL.Image.open(png)
#resize
newSize = tuple(x * scaleFactor for x in image.size)
image.thumbnail(newSize)
#greyscale
image = image.convert('F')
return np.array(image)