def prepareData(images_mask, images_raw):
inputArray = []
expectedArray = []
for file in os.listdir(images_raw):
testImage = Image.open(images_raw + file)
testMask = Image.open(images_mask + file.split(".")[0] + ".jpg")
for augmented in augmentImage(testImage,
"",
applyGrayscale=False,
printGeneratedLists=False):
image = augmented[1]
image = padImage(image, (IMG_WIDTH, IMG_HEIGHT))
inputValue = img_to_array(image)
inputValue /= 255
inputArray.append(inputValue)
for augmentedMask in augmentImage(testMask,
"",
applyGrayscale=False,
isMask=True,
printGeneratedLists=False):
image = padImage(augmentedMask[1], (IMG_WIDTH, IMG_HEIGHT))
expectedOutput = img_to_array(image)
expectedOutput /= 255
expectedArray.append(expectedOutput)
inputArray = numpy.array(inputArray)
expectedArray = numpy.array(expectedArray)
return expectedArray, inputArray
def predict(imagePath):
imageList = os.listdir(imagePath)
for imageName in imageList:
img_path = imagePath + imageName
x = image.img_to_array(padImage(image.load_img(img_path), (256, 256)))
x /= 255
x = np.expand_dims(x, axis=0)
predict = model.predict(x)
predict[0] *= 255
array_to_img(predict[0]).show()