Tumour-Segmentation/Preprocess.py at master · MachineLearning-IIUI/Tumour-Segmentation · GitHub

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# Import the required libraries.
import cv2
import numpy as np
from skimage.transform import resize
from nilearn.image import smooth_img, resample_to_img


# Pre-process class.
class Process(object):
    # Expand the dimensions of the image.
    def expandDims(self, image):
        expDim = np.expand_dims(image, axis=-1)
        print("Original Image Shape: ", image.shape)
        print("New Image Shape: ", expDim.shape)
        return expDim

    # Resample an image.
    def resampleImage(self, image, target):
        resampledImage = resize(
                image,
                (image.shape[0],
                 target, target),
                mode='constant',
                anti_aliasing=True
        )
        print("Original Image Shape: ", image.shape)
        print("New Image Shape: ", resampledImage.shape)
        return resampledImage

    # Function to either upsample or downsample a list of images
    def resampleImages(self, images, target):
        sampledImages = []
        for i in range(len(images)):
            print("Original Shape: ", images[i].shape)
            sample = resize(
                    images[i],
                    (images[i].shape[0],
                     target, target),
                    mode='constant',
                    anti_aliasing=True
            )
            sampledImages.append(sample)
            print("New Image Shape: ", sampledImages[i].shape)
            print("\n")
        return sampledImages

    # Smooth a list of images.
    def smoothImage(self, image, threshold):
        smoothedImage = smooth_img(image, threshold)
        return smoothedImage

    # Smooth a list of images.
    def smoothImages(self, images, threshold):
        smoothedImages = []
        for i in range(len(images)):
            smoothedImage = smooth_img(images[i], threshold)
            smoothedImages.append(smoothedImage)
            print("Image Smoothened")
        return smoothedImages

    # Resample the mask to the size of the MRI image.
    def resampleToImage(self, mask, image):
        resampledImage = resample_to_img(mask, image)
        return resampledImage

    # Resample the masks to the MRI images.
    def resampleToImages(self, images, masks):
        resampledImages = []
        for i in range(len(images)):
            resampledImage = resample_to_img(masks, images)
            resampledImages.append(resampledImage)
            print("Mask Resampled")
        return resampledImages

    # Extract each slice from an MRI image
    def sliceExtractor(
            self,
            imageData,
            numSlices,
            OUTDIR=' ',
            Image=False,
            Mask=False,
            coronal=False,
            sagittal=False,
            transversal=False):
        for i in range(numSlices):
            # If extracting an image use this.
            if Image:
                # Coronal
                if coronal:
                    slices = imageData[:, i, :]
                    flipVertical = cv2.flip(slices, 0)
                    cv2.imwrite(
                            OUTDIR +
                            "Coronal_" +
                            str(i) +
                            ".png",
                            flipVertical
                    )
                # Sagittal
                elif sagittal:
                    slices = imageData[:, :, i]
                    flipVertical = cv2.flip(slices, 0)
                    cv2.imwrite(
                            OUTDIR +
                            "Sagittal_" +
                            str(i) +
                            ".png",
                            flipVertical
                    )
                # Transversal
                elif transversal:
                    slices = imageData[i, :, :]
                    flipVertical = cv2.flip(slices, 0)
                    cv2.imwrite(
                            OUTDIR +
                            "Transversal_" +
                            str(i) +
                            ".png",
                            flipVertical
                    )
                else:
                    print("Extraction failed")
                    break

            # If extracting a mask use this.
            if Mask:
                # Coronal
                if coronal:
                    slices = imageData[:, i, :]
                    flipVertical = cv2.flip(slices, 0)
                    formatted = (flipVertical * 255).astype('uint8')
                    cv2.imwrite(
                            OUTDIR +
                            "Coronal_" +
                            str(i) +
                            ".png",
                            formatted
                    )
                # Sagittal
                elif sagittal:
                    slices = imageData[:, :, i]
                    flipVertical = cv2.flip(slices, 0)
                    formatted = (flipVertical * 255).astype('uint8')
                    cv2.imwrite(
                            OUTDIR +
                            "Sagittal_" +
                            str(i) +
                            ".png",
                            formatted
                    )
                # Transversal
                elif transversal:
                    slices = imageData[i, :, :]
                    flipVertical = cv2.flip(slices, 0)
                    formatted = (flipVertical * 255).astype('uint8')
                    cv2.imwrite(
                            OUTDIR +
                            "Transversal_" +
                            str(i) +
                            ".png",
                            formatted
                    )
                else:
                    print("Extraction failed")
                    break
        print("Extraction complete")