example-x-ray-image-processing/xray.py at main · robocorp/example-x-ray-image-processing · GitHub

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# Inspired by the original example at
# https://numpy.org/numpy-tutorials/content/tutorial-x-ray-image-processing.html

import imageio
import numpy as np
import matplotlib.pyplot as plt
from scipy import ndimage

def display_xray(image_path):
    xray_image = imageio.imread(image_path)
    plt.imshow(xray_image, cmap="gray")
    plt.axis("off")
    plt.show()

def display_canny_filter(image_path):
    xray_image = imageio.imread(image_path)
    fourier_gaussian = ndimage.fourier_gaussian(xray_image, sigma=0.05)
    x_prewitt = ndimage.prewitt(fourier_gaussian, axis=0)
    y_prewitt = ndimage.prewitt(fourier_gaussian, axis=1)
    xray_image_canny = np.hypot(x_prewitt, y_prewitt)
    xray_image_canny *= 255.0 / np.max(xray_image_canny)
    print("The data type - ", xray_image_canny.dtype)
    fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(12, 4))
    axes[0].set_title("Original")
    axes[0].imshow(xray_image, cmap="gray")
    axes[1].set_title("Canny (edges) - prism")
    axes[1].imshow(xray_image_canny, cmap="prism")
    axes[2].set_title("Canny (edges) - nipy_spectral")
    axes[2].imshow(xray_image_canny, cmap="nipy_spectral")
    axes[3].set_title("Canny (edges) - terrain")
    axes[3].imshow(xray_image_canny, cmap="terrain")
    for i in axes:
        i.axis("off")
    plt.show()

def display_gaussian_gradient(image_path):
    xray_image = imageio.imread(image_path)
    x_ray_image_gaussian_gradient = ndimage.gaussian_gradient_magnitude(xray_image, sigma=2)
    fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 4))
    axes[0].set_title("Original")
    axes[0].imshow(xray_image, cmap="gray")
    axes[1].set_title("Gaussian gradient (edges)")
    axes[1].imshow(x_ray_image_gaussian_gradient, cmap="gray")
    for i in axes:
        i.axis("off")
    plt.show()

def display_laplacian_gaussian(image_path):
    xray_image = imageio.imread(image_path)
    xray_image_laplace_gaussian = ndimage.gaussian_laplace(xray_image, sigma=1)
    fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 4))
    axes[0].set_title("Original")
    axes[0].imshow(xray_image, cmap="gray")
    axes[1].set_title("Laplacian-Gaussian (edges)")
    axes[1].imshow(xray_image_laplace_gaussian, cmap="gray")
    for i in axes:
        i.axis("off")
    plt.show()

def display_sobel_feldman(image_path):
    xray_image = imageio.imread(image_path)
    x_sobel = ndimage.sobel(xray_image, axis=0)
    y_sobel = ndimage.sobel(xray_image, axis=1)
    xray_image_sobel = np.hypot(x_sobel, y_sobel)
    xray_image_sobel *= 255.0 / np.max(xray_image_sobel)
    print("The data type - before: ", xray_image_sobel.dtype)
    xray_image_sobel = xray_image_sobel.astype("float32")
    print("The data type - after: ", xray_image_sobel.dtype)
    fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(10, 4))
    axes[0].set_title("Original")
    axes[0].imshow(xray_image, cmap="gray")
    axes[1].set_title("Sobel (edges) - grayscale")
    axes[1].imshow(xray_image_sobel, cmap="gray")
    axes[2].set_title("Sobel (edges) - CMRmap")
    axes[2].imshow(xray_image_sobel, cmap="CMRmap")
    for i in axes:
        i.axis("off")
    plt.show()