https://github.com/robocorp/example-x-ray-image-processing
X-ray Image Viewer (NumPy, imageio, Matplotlib, SciPy)
https://github.com/robocorp/example-x-ray-image-processing
data-science numpy python robotframework xray-images
Last synced: 5 months ago
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X-ray Image Viewer (NumPy, imageio, Matplotlib, SciPy)
- Host: GitHub
- URL: https://github.com/robocorp/example-x-ray-image-processing
- Owner: robocorp
- License: apache-2.0
- Created: 2022-04-08T13:34:45.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-05-30T10:17:38.000Z (over 2 years ago)
- Last Synced: 2025-05-13T00:47:09.203Z (5 months ago)
- Topics: data-science, numpy, python, robotframework, xray-images
- Language: Python
- Homepage:
- Size: 4.71 MB
- Stars: 3
- Watchers: 17
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# X-ray Image Viewer (NumPy, imageio, Matplotlib, SciPy)
[Robocorp](https://robocorp.com/) is great for automation. It can also be used in data science!
The most tedious part of Python data science projects has to be setting up the Python environment and installing all the required dependencies. Not to mention keeping all that up-to-date. Or making the same setup work on another machine than yours!
Let Robocorp handle the Python environment for you. Focus on data science - the stuff that matters.
This example application reads and processes X-ray images with [NumPy](https://numpy.org/), [imageio](https://imageio.readthedocs.io/), [Matplotlib](https://matplotlib.org/), and [SciPy](https://scipy.org/).
This application was inspired by the original [NumPy tutorial - X-ray image processing](https://numpy.org/numpy-tutorials/content/tutorial-x-ray-image-processing.html).
> This project uses a deprecated library [RPA.Dialogs](https://robocorp.com/docs/libraries/rpa-framework/rpa-dialogs) that has been replaced by [RPA.Assistant](https://robocorp.com/docs/libraries/rpa-framework/rpa-assistant). You can find an example detailing the new library [here](https://robocorp.com/portal/robot/robocorp/template-assistant).
## Dependencies
`conda.yaml`:
```yaml
channels:
- conda-forge
dependencies:
- python=3.10.4
- imageio=2.16.1
- matplotlib=3.5.1
- scipy=1.8.0
- pip=20.1
- pip:
- rpaframework-dialogs==2.0.0
```
## The X-ray image operation dialog
```robot
*** Settings ***
Documentation View X-ray images.
... Inspired by the original example at
... https://numpy.org/numpy-tutorials/content/tutorial-x-ray-image-processing.html
Library RPA.Dialogs
Library xray.py
*** Variables ***
${OP_CANNY_FILTER}= Canny filter
${OP_GAUSSIAN_GRADIENT}= Gaussian Gradient (edges)
${OP_LAPLACIAN_GAUSSIAN}= Laplacian Gaussian (edges)
${OP_SOBEL_FELDMAN}= Sobel-Feldman operator
${OP_VIEW}= View
*** Tasks ***
View X-ray image
${image_path} ${operation}= Select X-ray images and operation
IF "${operation}" == "${OP_VIEW}"
Display Xray ${image_path}
ELSE IF "${operation}" == "${OP_CANNY_FILTER}"
Display Canny Filter ${image_path}
ELSE IF "${operation}" == "${OP_GAUSSIAN_GRADIENT}"
Display Gaussian Gradient ${image_path}
ELSE IF "${operation}" == "${OP_LAPLACIAN_GAUSSIAN}"
Display Laplacian Gaussian ${image_path}
ELSE IF "${operation}" == "${OP_SOBEL_FELDMAN}"
Display Sobel Feldman ${image_path}
END
*** Keywords ***
Select X-ray images and operation
Add file input
... name=xray_image
... source=%{ROBOT_ROOT}${/}x-ray-images
Add operation options
${result}= Run dialog title=X-ray Image Viewer
RETURN ${result.xray_image}[0] ${result.operation}
Add operation options
${options}=
... Create List
... ${OP_VIEW}
... ${OP_CANNY_FILTER}
... ${OP_GAUSSIAN_GRADIENT}
... ${OP_LAPLACIAN_GAUSSIAN}
... ${OP_SOBEL_FELDMAN}
Add Radio Buttons
... name=operation
... options=${options}
... default=${OP_VIEW}
```
## The X-ray processing library
```py
# 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()
```