
- Scikit Image – Introduction
- Scikit Image - Image Processing
- Scikit Image - Numpy Images
- Scikit Image - Image datatypes
- Scikit Image - Using Plugins
- Scikit Image - Image Handlings
- Scikit Image - Reading Images
- Scikit Image - Writing Images
- Scikit Image - Displaying Images
- Scikit Image - Image Collections
- Scikit Image - Image Stack
- Scikit Image - Multi Image
- Scikit Image - Data Visualization
- Scikit Image - Using Matplotlib
- Scikit Image - Using Ploty
- Scikit Image - Using Mayavi
- Scikit Image - Using Napari
- Scikit Image - Color Manipulation
- Scikit Image - Alpha Channel
- Scikit Image - Conversion b/w Color & Gray Values
- Scikit Image - Conversion b/w RGB & HSV
- Scikit Image - Conversion to CIE-LAB Color Space
- Scikit Image - Conversion from CIE-LAB Color Space
- Scikit Image - Conversion to luv Color Space
- Scikit Image - Conversion from luv Color Space
- Scikit Image - Image Inversion
- Scikit Image - Painting Images with Labels
- Scikit Image - Contrast & Exposure
- Scikit Image - Contrast
- Scikit Image - Contrast enhancement
- Scikit Image - Exposure
- Scikit Image - Histogram Matching
- Scikit Image - Histogram Equalization
- Scikit Image - Local Histogram Equalization
- Scikit Image - Tinting gray-scale images
- Scikit Image - Image Transformation
- Scikit Image - Scaling an image
- Scikit Image - Rotating an Image
- Scikit Image - Warping an Image
- Scikit Image - Affine Transform
- Scikit Image - Piecewise Affine Transform
- Scikit Image - ProjectiveTransform
- Scikit Image - EuclideanTransform
- Scikit Image - Radon Transform
- Scikit Image - Line Hough Transform
- Scikit Image - Probabilistic Hough Transform
- Scikit Image - Circular Hough Transforms
- Scikit Image - Elliptical Hough Transforms
- Scikit Image - Polynomial Transform
- Scikit Image - Image Pyramids
- Scikit Image - Pyramid Gaussian Transform
- Scikit Image - Pyramid Laplacian Transform
- Scikit Image - Swirl Transform
- Scikit Image - Morphological Operations
- Scikit Image - Erosion
- Scikit Image - Dilation
- Scikit Image - Black & White Tophat Morphologies
- Scikit Image - Convex Hull
- Scikit Image - Generating footprints
- Scikit Image - Isotopic Dilation & Erosion
- Scikit Image - Isotopic Closing & Opening of an Image
- Scikit Image - Skelitonizing an Image
- Scikit Image - Morphological Thinning
- Scikit Image - Masking an image
- Scikit Image - Area Closing & Opening of an Image
- Scikit Image - Diameter Closing & Opening of an Image
- Scikit Image - Morphological reconstruction of an Image
- Scikit Image - Finding local Maxima
- Scikit Image - Finding local Minima
- Scikit Image - Removing Small Holes from an Image
- Scikit Image - Removing Small Objects from an Image
- Scikit Image - Filters
- Scikit Image - Image Filters
- Scikit Image - Median Filter
- Scikit Image - Mean Filters
- Scikit Image - Morphological gray-level Filters
- Scikit Image - Gabor Filter
- Scikit Image - Gaussian Filter
- Scikit Image - Butterworth Filter
- Scikit Image - Frangi Filter
- Scikit Image - Hessian Filter
- Scikit Image - Meijering Neuriteness Filter
- Scikit Image - Sato Filter
- Scikit Image - Sobel Filter
- Scikit Image - Farid Filter
- Scikit Image - Scharr Filter
- Scikit Image - Unsharp Mask Filter
- Scikit Image - Roberts Cross Operator
- Scikit Image - Lapalace Operator
- Scikit Image - Window Functions With Images
- Scikit Image - Thresholding
- Scikit Image - Applying Threshold
- Scikit Image - Otsu Thresholding
- Scikit Image - Local thresholding
- Scikit Image - Hysteresis Thresholding
- Scikit Image - Li thresholding
- Scikit Image - Multi-Otsu Thresholding
- Scikit Image - Niblack and Sauvola Thresholding
- Scikit Image - Restoring Images
- Scikit Image - Rolling-ball Algorithm
- Scikit Image - Denoising an Image
- Scikit Image - Wavelet Denoising
- Scikit Image - Non-local means denoising for preserving textures
- Scikit Image - Calibrating Denoisers Using J-Invariance
- Scikit Image - Total Variation Denoising
- Scikit Image - Shift-invariant wavelet denoising
- Scikit Image - Image Deconvolution
- Scikit Image - Richardson-Lucy Deconvolution
- Scikit Image - Recover the original from a wrapped phase image
- Scikit Image - Image Inpainting
- Scikit Image - Registering Images
- Scikit Image - Image Registration
- Scikit Image - Masked Normalized Cross-Correlation
- Scikit Image - Registration using optical flow
- Scikit Image - Assemble images with simple image stitching
- Scikit Image - Registration using Polar and Log-Polar
- Scikit Image - Feature Detection
- Scikit Image - Dense DAISY Feature Description
- Scikit Image - Histogram of Oriented Gradients
- Scikit Image - Template Matching
- Scikit Image - CENSURE Feature Detector
- Scikit Image - BRIEF Binary Descriptor
- Scikit Image - SIFT Feature Detector and Descriptor Extractor
- Scikit Image - GLCM Texture Features
- Scikit Image - Shape Index
- Scikit Image - Sliding Window Histogram
- Scikit Image - Finding Contour
- Scikit Image - Texture Classification Using Local Binary Pattern
- Scikit Image - Texture Classification Using Multi-Block Local Binary Pattern
- Scikit Image - Active Contour Model
- Scikit Image - Canny Edge Detection
- Scikit Image - Marching Cubes
- Scikit Image - Foerstner Corner Detection
- Scikit Image - Harris Corner Detection
- Scikit Image - Extracting FAST Corners
- Scikit Image - Shi-Tomasi Corner Detection
- Scikit Image - Haar Like Feature Detection
- Scikit Image - Haar Feature detection of coordinates
- Scikit Image - Hessian matrix
- Scikit Image - ORB feature Detection
- Scikit Image - Additional Concepts
- Scikit Image - Render text onto an image
- Scikit Image - Face detection using a cascade classifier
- Scikit Image - Face classification using Haar-like feature descriptor
- Scikit Image - Visual image comparison
- Scikit Image - Exploring Region Properties With Pandas
Scikit Image - Affine Transform
An affine transform is a type of geometric transformation, applied to an input image to move each pixel to a new position in the output image. The transformation involves shifting the image along the x- and y-axes by a specified amount in both horizontal (tx) and vertical (ty) directions. This transformation is used in image processing and computer vision to map points from one coordinate space to another. This is commonly used for tasks like image registration, alignment, geometric correction, and image warping.
The Scikit-image library in Python provides a class called AffineTransform() to perform this geometric transformation on images.
The class AffineTransform in skimage library
The class skimage.transform.AffineTransform is used to perform an affine transformation on images or coordinate data. It can perform translation, rotation, scaling, and shearing operations. The transformation is defined by a set of parameters or a homogeneous transformation matrix.
The AffineTransform class is inherited from the ProjectiveTransform class.
Syntax
Following is the syntax of this class −
class skimage.transform.AffineTransform(matrix=None, scale=None, rotation=None, shear=None, translation=None, *, dimensionality=2)
Parameters
- matrix: Optional. A (D+1, D+1) array-like representing the homogeneous transformation matrix. If this matrix is provided,then it cannot be used with scale, rotation, shear, or translation.
- scale: Optional. Scale factor(s) for the transformation. Can be a single scalar value or a tuple (sx, sy) representing scale factors in the x and y directions. Available only for 2D transformations.
- rotation: float, optional. Rotation angle in radians (clockwise). Available only for 2D transformations.
- shear: float or 2-tuple of float, optional. Shear angles around the x and y axes (clockwise rotation). Can be a single value (x shear) or a tuple (sx, sy). Available only for 2D transformations.
- translation: Optional. Translation parameters (tx, ty) for the transformation. Available only for 2D transformations.
- dimensionality: int, optional. The dimensionality of the transform. Not used if other parameters are provided.
Here is the attribute of the class −
- params: A (D+1, D+1) array representing the homogeneous transformation matrix.
The following are the methods of the class −
- estimate(src, dst): Estimate the transformation matrix based on a set of corresponding source and destination points.
- inverse: Get the inverse transformation of the affine transform.
Example
The following example demonstrates how to use the AffineTransform() class to perform the Affine transform on an image using the Homogeneous transformation matrix.
import numpy as np import matplotlib.pyplot as plt from skimage.transform import AffineTransform, warp from skimage import io # Load the input image image = io.imread('Images/butterfly.jpg') # Define source and destination points for the transformation src = np.array([[150, 150], [250, 100], [150, 200]]) dst = np.array([[200, 200], [300, 150], [150, 200]]) # Create an instance of the AffineTransform class tform = AffineTransform() # Estimate the transformation matrix based on source and destination points tform.estimate(src, dst) # Apply the transformation using the warp function warped = warp(image, inverse_map=tform.inverse) # Display the original and transformed images fig, axes = plt.subplots(1, 2, figsize=(10, 5)) axes[0].imshow(image) axes[0].set_title('Original Image') axes[0].axis('off') axes[1].imshow(warped) axes[1].set_title('Affine Transformed Image') axes[1].axis('off') plt.tight_layout() plt.show()
Output
On executing the above program, you will get the following output −

Example
The following example demonstrates how to use the AffineTransform() class to perform the Affine transform on an image using the rotation and translation parameters.
import numpy as np import matplotlib.pyplot as plt from skimage.transform import AffineTransform, warp from skimage import io # Load the input image image = io.imread('Images/Flower1.jpg') # Define rotation angle (in radians) and translation parameters angle = np.deg2rad(30) # 30 degrees translation = (50, 20) # 50 pixels in x, 20 pixels in y # Create an AffineTransform object tform = AffineTransform(rotation=angle, translation=translation) # Apply the transformation to the image transformed_image = warp(image, tform) # Plot the original and transformed images fig, axes = plt.subplots(1, 2, figsize=(10, 5)) axes[0].imshow(image, cmap='gray') axes[0].set_title('Original Image') axes[0].axis('off') axes[1].imshow(transformed_image, cmap='gray') axes[1].set_title('Transformed Image') axes[1].axis('off') plt.tight_layout() plt.show()
Output
On executing the above program, you will get the following output −
