
- 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 - Dense DAISY Feature Description
The Dense DAISY feature description is a local image descriptor that relies on gradient orientation histograms, similar to the SIFT descriptor. However, it is designed in such a way that it enables rapid and dense extraction of features. This characteristic makes it particularly valuable for tasks such as generating bag-of-features image representations.
The scikit image library provides a dedicated function within its feature module for extracting Dense DAISY feature descriptors from grayscale images.
Using the skimage.feature.daisy() function
The skimage.feature.daisy() function is used to extract DAISY feature descriptors from a grayscale image. DAISY is a feature descriptor similar to SIFT (Scale-Invariant Feature Transform) but is designed for fast dense extraction, making it suitable for bag-of-features image representations.
Syntax
Following is the syntax of this function −
skimage.feature.daisy(image, step=4, radius=15, rings=3, histograms=8, orientations=8, normalization='l1', sigmas=None, ring_radii=None, visualize=False)
Parameters
Here are the details of the parameters −
image: Input grayscale image (M, N) for which DAISY feature descriptors are extracted.
step (int, optional): It determines the distance between descriptor sampling points.
radius (int, optional): it specifies the radius (in pixels) of the outermost ring.
rings (int, optional): Number of rings. It determines how many concentric rings are created around each sampling point.
histograms (int, optional): Number of histograms sampled per ring.
orientations (int, optional): Number of orientations (bins) per histogram.
-
normalization (optional): Specifies how to normalize the descriptors. It can be one of the following options −
'l1': L1-normalization of each descriptor.
'l2': L2-normalization of each descriptor.
'daisy': L2-normalization of individual histograms.
'off': Disable normalization.
sigmas (1D array of float, optional): Standard deviation of spatial Gaussian smoothing for the center histogram and each ring of histograms. This parameter controls the amount of spatial smoothing applied to the histograms. Specifying sigmas overrides the following parameter: rings = len(sigmas) - 1
-
ring_radii (1D array of int, optional): Radius (in pixels) for each ring. Specifying ring_radii overrides the following two parameters.
rings = len(ring_radii) radius = ring_radii[-1]
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If both sigmas and ring_radii parameters are given, they must satisfy the following predicate since no radius is needed for the center histogram.
len(ring_radii) == len(sigmas) + 1
visualize (bool, optional): If set to True, it generates a visualization of the DAISY descriptors.
The function returns a grid of DAISY descriptors for the given image as a NumPy array with dimensions (P, Q, R), where −
- P = ceil((M - radius * 2) / step)
- Q = ceil((N - radius * 2) / step)
- R = (rings * histograms + 1) * orientations
Additionally, if visualize is set to True, it also returns a visualization of the DAISY descriptors as a NumPy array with dimensions (M, N, 3).
Example
Here is an example that applies the DAISY descriptors with default parameters, and displays the shape of the descriptors array along with count of the total number of DAISY descriptors extracted.
import numpy as np from skimage import io, feature # Load an example grayscale image image = io.imread('Images/image5.jpg', as_gray=True) # apply the DAISY descriptors with default parameters descriptors = feature.daisy(image) # Display the results print("Shape of descriptors:", descriptors.shape) print("Total DAISY descriptors extracted:", descriptors.shape[0] * descriptors.shape[1])
Output
Shape of descriptors: (82, 94, 200) Total DAISY descriptors extracted: 7708
Example
The following example demonstrates how to extract DAISY features from a grayscale image.
from skimage.feature import daisy from skimage import io import matplotlib.pyplot as plt # Load a sample grayscale image image = io.imread('Images/image5.jpg', as_gray=True) # Extract DAISY features with visualization descs, descs_img = daisy(image, step=180, radius=58, rings=2, histograms=6, orientations=8, visualize=True) # Create subplots for input and output visualization fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5)) # Display the original input image ax1.imshow(image, cmap='gray') ax1.set_title("Input Image") ax1.axis("off") # Display the DAISY descriptors visualization ax2.imshow(descs_img) ax2.set_title(f"{descs.shape[0] * descs.shape[1]} DAISY descriptors extracted") ax2.axis("off") plt.tight_layout() plt.show()
Output
