Mahotas Tutorial

Overview
Curriculum
Instructor

Here we will learn:
Mahotas – Introduction
Mahotas – Computer Vision
Mahotas – History
Mahotas – Features
Mahotas – Installation
Mahotas – Handling Images
Mahotas – Loading an Image
Mahotas – Loading Image as Grey
Mahotas – Displaying an Image
Mahotas – Displaying Shape of an Image
Mahotas – Saving an Image
Mahotas – Centre of Mass of an Image
Mahotas – Convolution of Image
Mahotas – Creating RGB Image
Mahotas – Euler Number of an Image
Mahotas – Fraction of Zeros in an Image
Mahotas – Getting Image Moments
Mahotas – Local Maxima in an Image
Mahotas – Image Ellipse Axes
Mahotas – Image Stretch RGB
Mahotas – Color-Space Conversion
Mahotas – RGB to Gray Conversion
Mahotas – RGB to LAB Conversion
Mahotas – RGB to Sepia
Mahotas – RGB to XYZ Conversion
Mahotas – XYZ to LAB Conversion
Mahotas – XYZ to RGB Conversion
Mahotas – Increase Gamma Correction
Mahotas – Stretching Gamma Correction
Mahotas – Filtering Regions
Mahotas – Border Pixels
Mahotas – Morphological Operators
Mahotas – Finding Image Mean
Mahotas – Cropping an Image
Mahotas – Eccentricity of an Image
Mahotas – Overlaying Image
Mahotas – Roundness of Image
Mahotas – Resizing an Image
Mahotas – Histogram of Image
Mahotas – Dilating an Image
Mahotas – Eroding Image
Mahotas – Watershed
Mahotas – Opening Process on Image
Mahotas – Closing Process on Image
Mahotas – Closing Holes in an Image
Mahotas – Conditional Dilating Image
Mahotas – Conditional Eroding Image
Mahotas – Conditional Watershed of Image
Mahotas – Local Minima in Image
Mahotas – Regional Maxima of Image
Mahotas – Regional Maxima of Image
Mahotas – Image Thresholding
Mahotas – Setting Threshold
Mahotas – Soft Threshold
Mahotas – Bernsen Local Thresholding
Mahotas – Wavelet Transforms
Mahotas – Making Image Wavelet Center
Mahotas – Distance Transform
Mahotas – Polygon Utilities
Mahotas – Local Binary Patterns
Mahotas – Threshold Adjacency Statistics
Mahotas – Haralic Features
Mahotas – Weight of Labeled Region
Mahotas – Zernike Features
Mahotas – Zernike Moments
Mahotas – Rank Filter
Mahotas – 2D Laplacian Filter
Mahotas – Majority Filter
Mahotas – Mean Filter
Mahotas – Median Filter
Mahotas – Gaussian Filtering
Mahotas – Hit & Miss Transform
Mahotas – Labeled Max Array
Mahotas – Mean Value of Image
Mahotas – SURF Dense Points
Mahotas – SURF Integral
Mahotas – Haar Transform
Mahotas – Highlighting Image Maxima
Mahotas – Computing Linear Binary Patterns
Mahotas – Getting Border of Labels
Mahotas – Reversing Haar Transform
Mahotas – Riddler-Calvard Method
Mahotas – Sizes of Labelled Region
Mahotas – Template Matching
Mahotas – Speeded-Up Robust Features
Mahotas – Removing Bordered Labelled
Mahotas – Daubechies Wavelet
Mahotas – Sobel Edge Detection

Curriculum

91 Sections
391 Lessons
10 Weeks

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Mahotas - Otsu's Method
3
- 1.1
  overview
- 1.2
  Otsu’s Method in Mahotas
- 1.3
  Ignoring the Zero Valued Pixels
Mahotas - Loading Image as Grey
4
- 2.1
  overview
- 2.2
  Loading Grayscale Images
- 2.3
  Loading Different Image Formats as Grayscale
- 2.4
  Using the Color Mode ‘L’
Mahotas – Introduction
5
- 3.1
  overview
- 3.2
  Overview of Mahotas Library
- 3.3
  The Power of Mahotas
- 3.4
  Machine Learning with Mahotas
- 3.5
  Use Cases and Real-World Applications
Mahotas - Computer Vision
5
- 4.1
  overview
- 4.2
  Understanding Computer Vision
- 4.3
  Foundations of Computer Vision
- 4.4
  Applications of Computer Vision
- 4.5
  Challenges in Computer Vision
Mahotas - History
8
- 5.1
  overview
- 5.2
  Origins and Early Development
- 5.3
  Release and Growth
- 5.4
  Continuous Development and Expansion
- 5.5
  Integration with Scientific Python Ecosystem
- 5.6
  Adoption and Impact
- 5.7
  Community and Support
- 5.8
  Continuous Innovation and Future Prospects
Mahotas - Features
Mahotas is a popular image processing library. It has numerous functions on image processing and analysis. Some of its functionalities are given below −
9
- 6.1
  Feature Detection
- 6.2
  Image Filtering
- 6.3
  Image Segmentation
- 6.4
  Image Measurements
- 6.5
  Image Input Output
- 6.6
  Connected Component Analysis
- 6.7
  Mathematical Morphology
- 6.8
  Morphological Operations
- 6.9
  Image Classification
Mahotas - Installation
4
- 7.1
  overview
- 7.2
  Using Pip
- 7.3
  Using Conda
- 7.4
  From Source
Mahotas - Handling Images
6
- 8.1
  overview
- 8.2
  Image Handling with Mahotas
- 8.3
  Reading Images
- 8.4
  Displaying an Image
- 8.5
  Writing Images
- 8.6
  Image Processing with Mahotas
Mahotas - Loading an Image
7
- 9.1
  overview
- 9.2
  Loading an Image in Mahotas
- 9.3
  Loading Different Image Formats
- 9.4
  Loading Color Images
- 9.5
  Loading Different Image Formats
- 9.6
  Loading Color Images
- 9.7
  Loading Color and Grayscale image
Mahotas - Displaying an Image
4
- 10.1
  overview
- 10.2
  Displaying an Image in Mahotas
- 10.3
  Displaying Multiple Images
- 10.4
  Customizing Image Display
Mahotas - Displaying Shape of an Image
2
- 11.1
  overview
- 11.2
  Displaying the Shape of an Image in Mahotas
Mahotas - Saving an Image
2
- 12.1
  overview
- 12.2
  Saving an Image in Mahotas
Mahotas - Centre of Mass of an Image
4
- 13.1
  overview
- 13.2
  Center of Mass of an Image in Mahotas
- 13.3
  Center of Mass Using Numpy Functions
- 13.4
  Center of Mass of a Specific Region
Mahotas - Convolution of Image
6
- 14.1
  overview
- 14.2
  Convolution of an Image in Mahotas
- 14.3
  Convolution with a Gaussian Kernel
- 14.4
  Convolution with Padding
- 14.5
  Convolution with a Box Filter for Blurring
- 14.6
  Convolution with a Sobel Filter for Edge Detection
Mahotas - Creating RGB Image
4
- 15.1
  overview
- 15.2
  Creating RGB Images in Mahotas
- 15.3
  Creating an RGB Image from Color Intensities
- 15.4
  Creating an RGB Image from a Single Color
Mahotas - Euler Number of an Image
5
- 16.1
  Overview
- 16.2
  Euler Number of an Image in Mahotas
- 16.3
  Euler Number with Different Connectivity
- 16.4
  Euler Number Calculation with Labelled Image
- 16.5
  Euler Number Calculation with Binary Image
Mahotas - Fraction of Zeros in an Image
4
- 17.1
  overview
- 17.2
  Fraction of Zeros in an Image in Mahotas
- 17.3
  Using the count_nonzero() Function
- 17.4
  Using Numpy
Mahotas - Getting Image Moments
5
- 18.1
  overview
- 18.2
  Getting Image moments in Mahotas
- 18.3
  Getting Image Moments by Specifying Center of Mass
- 18.4
  Getting Image moments by Disabling Float Conversion
- 18.5
  Getting Higher Order Image Moments
Mahotas - Labeled Image Functions
4
- 19.1
  overview
- 19.2
  Functions for Labeling Images
- 19.3
  The label() Function
- 19.4
  The labeled.label() Function
Mahotas - Labeling Images
3
- 20.1
  overview
- 20.2
  Labeling Images in Mahotas
- 20.3
  Using Custom Structuring Element
Mahotas - Local Maxima in an Image
4
- 21.1
  overview
- 21.2
  Local Maxima in an Image in Mahotas
- 21.3
  Using the regmax() Function
- 21.4
  Regional Maxima of a Colored Image
Mahotas - Image Ellipse Axes
4
- 22.1
  overview
- 22.2
  Image Ellipse Axes in Mahotas
- 22.3
  Fitting an Ellipse to a Set of Points
- 22.4
  Fitting an Ellipse to a Grayscale Image Using ROI
Mahotas - Image Stretch RGB
3
- 23.1
  overview
- 23.2
  Image Stretch RGB in Mahotas
- 23.3
  Stretching Image with Percentile Value
Mahotas - Color-Space Conversion
5
- 24.1
  overview
- 24.2
  RGB Color Space
- 24.3
  The rgb2grey() Function
- 24.4
  The rgb2grey() Function
- 24.5
  The rgb2sepia() Function
Mahotas - RGB to Gray Conversion
4
- 25.1
  overview
- 25.2
  RGB to Gray Conversion in Mahotas
- 25.3
  Using Average of RGB Channels
- 25.4
  Using Luminosity
Mahotas - RGB to LAB Conversion
3
- 26.1
  overview
- 26.2
  RGB to LAB Conversion in Mahotas
- 26.3
  RGB to LAB Conversion of a Random Image
Mahotas - RGB to Sepia
3
- 27.1
  overview
- 27.2
  RGB to Sepia Conversion in Mahotas
- 27.3
  Using Transformation Factor
Mahotas - RGB to XYZ Conversion
3
- 28.1
  overview
- 28.2
  RGB to XYZ Conversion in Mahotas
- 28.3
  Using Conversion Matrix
Mahotas - XYZ to LAB Conversion
3
- 29.1
  overview
- 29.2
  XYZ to LAB Conversion in Mahotas
- 29.3
  XYZ to LAB Conversion of a Random Image
Mahotas - XYZ to RGB Conversion
3
- 30.1
  overview
- 30.2
  XYZ to RGB Conversion in Mahotas
- 30.3
  Using Transformation Matrix
Mahotas - Increase Gamma Correction
5
- 31.1
  overview
- 31.2
  Increasing Gamma Correction in Mahotas
- 31.3
  Using Interactive Gamma Correction Slider
- 31.4
  Using Interactive Gamma Correction Slider
- 31.5
  Using Batch Gamma correction
Mahotas - Stretching Gamma Correction
4
- 32.1
  overview
- 32.2
  Stretching Gamma Correction in Mahotas
- 32.3
  Stretching Gamma Correction of RGB Image
- 32.4
  Interactive Stretching Gamma Correction Slider
Mahotas - Filtering Regions
4
- 33.1
  overview
- 33.2
  Filtering Regions in Mahotas
- 33.3
  Filtering on Regions of Specific Size.
- 33.4
  Filtering on Border Regions and Regions of Specific Size
Mahotas - Border Pixels
3
- 34.1
  overview
- 34.2
  Border Pixels in Mahotas
- 34.3
  Using Custom Structuring Element
Mahotas - Morphological Operators
10
- 35.1
  overview
- 35.2
  Mahotas and Morphological Operators
- 35.3
  Dilation
- 35.4
  Erosion
- 35.5
  Opening
- 35.6
  Closing
- 35.7
  Conditional Erosion
- 35.8
  Conditional Dilation
- 35.9
  Regional Maxima
- 35.10
  Regional Minima
Mahotas - Finding Image Mean
4
- 36.1
  overview
- 36.2
  Finding Image Mean in Mahotas
- 36.3
  Image Mean of each Channel Individually
- 36.4
  Finding the Mean of an ROI in an Image
Mahotas - Cropping an Image
4
- 37.1
  overview
- 37.2
  Cropping an Image in Mahotas
- 37.3
  Cropping a Square Region
- 37.4
  Cropping a Circular Region
Mahotas - Eccentricity of an Image
4
- 38.1
  overview
- 38.2
  Eccentricity of an Image in Mahotas
- 38.3
  Calculating Eccentricity using Binary Image
- 38.4
  Calculating Eccentricity using the Skeletonization
Mahotas - Overlaying Image
5
- 39.1
  overview
- 39.2
  Overlaying Image in Mahotas
- 39.3
  The mahotas.overlay() function
- 39.4
  Overlaying a Transparent Image on another Image
- 39.5
  Overlaying an Image with a specified Transparency Level.
Mahotas - Roundness of Image
5
- 40.1
  overview
- 40.2
  Roundness of Image in Mahotas
- 40.3
  The mahotas.features.roundness() function
- 40.4
  Blob Roundness in Binary Image
- 40.5
  Using zernike_moment
Mahotas - Resizing an Image
5
- 41.1
  overview
- 41.2
  Resizing an Image in Mahotas
- 41.3
  The imresiz() function
- 41.4
  Using Bilinear Interpolation
- 41.5
  Using Quadratic Interpolation
Mahotas - Histogram of Image
5
- 42.1
  overview
- 42.2
  Histogram of Image in Mahotas
- 42.3
  The mahotas.fullhistogram() function
- 42.4
  Grayscale Image Histogram
- 42.5
  Blue Channel RGB Image Histogram
Mahotas - Dilating an Image
5
- 43.1
  overview
- 43.2
  Dilating an Image in Mahotas
- 43.3
  The mahotas.dilate() function
- 43.4
  Dilation with Varying Structuring Element Sizes
- 43.5
  Dilation with a Circular-shaped Kernel
Mahotas - Eroding Image
5
- 44.1
  overview
- 44.2
  Eroding an Image in Mahotas
- 44.3
  The mahotas.erode() function
- 44.4
  Erosion with Varying Structuring Element Sizes
- 44.5
  Erosion with a Circular-shaped Kernel
Mahotas - Watershed
3
- 45.1
  overview
- 45.2
  Working of Watershed Algorithm
- 45.3
  Watershed in Mahotas
Mahotas - Opening Process on Image
5
- 46.1
  overview
- 46.2
  Opening Process on Image in Mahotas
- 46.3
  The mahotas.open() function
- 46.4
  Using Random Binary Image
- 46.5
  Using a Grayscale Image
Mahotas - Closing Process on Image
5
- 47.1
  overview
- 47.2
  Closing Process on Image in Mahotas
- 47.3
  The mahotas.close() function
- 47.4
  Using Random Binary Image
- 47.5
  Using a Grayscale Image
Mahotas - Closing Holes in an Image
5
- 48.1
  overview
- 48.2
  Closing Holes in an Image in Mahotas
- 48.3
  The mahotas.close_holes() function
- 48.4
  Using Random Binary Image
- 48.5
  Closing Holes with Multiple Iterations
Mahotas - Conditional Dilating Image
7
- 49.1
  overview
- 49.2
  Conditional Dilating Image in Mahotas
- 49.3
  The mahotas.cdilate() function
- 49.4
  The mahotas.cdilate() function
- 49.5
  Using Structuring Element
- 49.6
  Using Structuring Element
- 49.7
  Using a Custom Condition Function
Mahotas - Conditional Eroding Image
5
- 50.1
  overview
- 50.2
  Conditional Eroding Image in Mahotas
- 50.3
  The mahotas.cerode() function
- 50.4
  Using Structuring Element
- 50.5
  Using a Custom Condition Function
Mahotas - Conditional Watershed of Image
4
- 51.1
  overview
- 51.2
  Watershed in Mahotas
- 51.3
  The mahotas.cwatershed() function
- 51.4
  Conditional Watershed with Custom Structuring Element
Mahotas - Local Minima in Image
5
- 52.1
  overview
- 52.2
  Local Minima in Image in Mahotas
- 52.3
  The mahotas.locmin() function
- 52.4
  Using Custom Structuring Element
- 52.5
  Using a Binary Image
Mahotas - Regional Maxima of Image
5
- 53.1
  overview
- 53.2
  Regional Maxima of Image in Mahotas
- 53.3
  The mahotas.regmax() function
- 53.4
  Using Custom Structuring Element
- 53.5
  Using a Specific Region of an Image
Mahotas - Regional Maxima of Image
5
- 54.1
  overview
- 54.2
  Regional Minima of Image in Mahotas
- 54.3
  The mahotas.regmin() function
- 54.4
  Using Custom Structuring Element
- 54.5
  Using a Specific Region of an Image
Mahotas - Image Thresholding
6
- 55.1
  overview
- 55.2
  Bernsen Thresholding
- 55.3
  Generalized Bernsen Thresholding
- 55.4
  Otsu Thresholding
- 55.5
  Riddler-Calvard Thresholding
- 55.6
  Soft Thresholding
Mahotas - Setting Threshold
4
- 56.1
  overview
- 56.2
  Setting Threshold in Mahotas
- 56.3
  Set an Inverse Threshold
- 56.4
  Set a Custom Threshold value
Mahotas - Soft Threshold
4
- 57.1
  overview
- 57.2
  Soft Threshold in Mahotas
- 57.3
  Soft Threshold using the Mean value
- 57.4
  Soft Threshold using the Percentile value
Mahotas - Bernsen Local Thresholding
3
- 58.1
  overview
- 58.2
  Bernsen Local Thresholding in Mahotas
- 58.3
  Bernsen Local Thresholding using Mean
Mahotas - Wavelet Transforms
5
- 59.1
  overview
- 59.2
  Daubechies Transformation
- 59.3
  Inverse Daubechies Transformation
- 59.4
  Haar Transformation
- 59.5
  Inverse Haar
Mahotas - Making Image Wavelet Center
4
- 60.1
  overview
- 60.2
  Making Image Wavelet Center in Mahotas
- 60.3
  Centering using a Border
- 60.4
  Centering by applying Padding
Mahotas - Distance Transform
4
- 61.1
  overview
- 61.2
  Distance Transform in Mahotas
- 61.3
  Using Labeled Image
- 61.4
  Using Euclidean Distance
Mahotas - Polygon Utilities
6
- 62.1
  overview
- 62.2
  Polygon Utilities Functions in Mahotas
- 62.3
  The convexhull() function
- 62.4
  The fill_convexhull() function
- 62.5
  The fill_polygon() function
- 62.6
  The line() function
Mahotas - Local Binary Patterns
2
- 63.1
  overview
- 63.2
  Linear Binary Patterns
Mahotas - Threshold Adjacency Statistics
3
- 64.1
  overview
- 64.2
  Threshold Adjacency Statistics in Mahotas
- 64.3
  Using Mean Threshold Value
Mahotas - Haralic Features
4
- 65.1
  overview
- 65.2
  Haralic Features in Mahotas.
- 65.3
  Haralick Features with Ignore Zeros.
- 65.4
  Computing Haralick Features with 14th Feature
Mahotas - Weight of Labeled Region
4
- 66.1
  overview
- 66.2
  Weight of Labeled Region in Mahotas
- 66.3
  Weight of Labeled Regions with a Minimum Length
- 66.4
  Weight of Labeled Regions with Custom Labels
Mahotas - Zernike Features
4
- 67.1
  overview
- 67.2
  Zernike Features in Mahotas
- 67.3
  Using Custom Center of Mass
- 67.4
  Calculating Zernike Features of Multiple Images
Mahotas - Zernike Moments
4
- 68.1
  overview
- 68.2
  Zernike Moments in Mahotas
- 68.3
  Using Custom Center of Mass
- 68.4
  Using a Specific Order
Mahotas - Rank Filter
4
- 69.1
  overview
- 69.2
  Rank Filter in Mahotas
- 69.3
  Rank Filter on an RGB Image
- 69.4
  Using ‘Wrap’ Mode
Mahotas - 2D Laplacian Filter
4
- 70.1
  overview
- 70.2
  2D Laplacian Filter in Mahotas
- 70.3
  Scaling the Laplacian Response
- 70.4
  Using Randomly Generated Array
Mahotas - Majority Filter
4
- 71.1
  overview
- 71.2
  Majority Filter in Mahotas
- 71.3
  By Specifying Window Size
- 71.4
  By Storing Result in an Output Array
Mahotas - Mean Filter
4
- 72.1
  overview
- 72.2
  Mean Filter in Mahotas
- 72.3
  Mean Filter with Reflect Mode
- 72.4
  By Storing Result in an Output Array
Mahotas - Median Filter
4
- 73.1
  overview
- 73.2
  Median Filter in Mahotas
- 73.3
  Median Filter with Reflect Mode
- 73.4
  By Storing Result in an Output Array
Mahotas - Gaussian Filtering
4
- 74.1
  overview
- 74.2
  Gaussian Filtering in Mahotas
- 74.3
  Filtering with Different Order
- 74.4
  Filtering with ‘Mirror’ Mode
Mahotas - Hit & Miss Transform
4
- 75.1
  overview
- 75.2
  Hit & Miss transform in Mahotas
- 75.3
  By Detecting Edges
- 75.4
  By Detecting Diagonals
Mahotas - Labeled Max Array
3
- 76.1
  overview
- 76.2
  Labeled Max Array in Mahotas
- 76.3
  Labeled Max Arrays of a Random Boolean Image
Mahotas - Mean Value of Image
4
- 77.1
  overview
- 77.2
  Mean Value of Image in Mahotas
- 77.3
  Mean Value of each Channel
- 77.4
  Mean Value of Grayscale Image
Mahotas - SURF Dense Points
4
- 78.1
  overview
- 78.2
  SURF Dense Points in Mahotas
- 78.3
  By Adjusting the Scale
- 78.4
  By Including Interest Points
Mahotas - SURF Integral
4
- 79.1
  overview
- 79.2
  SURF Integral in Mahotas
- 79.3
  SURF Integral of a Random Image
- 79.4
  SURF Integral of a Threshold Image
Mahotas - Haar Transform
4
- 80.1
  overview
- 80.2
  Haar Transform in Mahotas
- 80.3
  Without Preserving Energy
- 80.4
  Inline Haar Transformation
Mahotas - Highlighting Image Maxima
3
- 81.1
  overview
- 81.2
  Highlighting Image Maxima in Mahotas
- 81.3
  Highlighting Maxima by using custom Structuring Element
Mahotas - Computing Linear Binary Patterns
4
- 82.1
  overview
- 82.2
  Computing Linear Binary Patterns in Mahotas
- 82.3
  Ignoring the Zero Valued Pixels
- 82.4
  LBP of a Specific Region
Mahotas - Getting Border of Labels
3
- 83.1
  overview
- 83.2
  Getting Border of labels in Mahotas
- 83.3
  Getting Borders by using a Custom Structuring Element
Mahotas - Reversing Haar Transform
4
- 84.1
  overview
- 84.2
  Reversing Haar Transform in Mahotas
- 84.3
  Without Preserving Energy
- 84.4
  Inline Reverse Haar Transformation
Mahotas - Riddler-Calvard Method
3
- 85.1
  overview
- 85.2
  Riddler-Calvard Method in Mahotas
- 85.3
  Ignoring the Zero Valued Pixels
Mahotas - Sizes of Labelled Region
4
- 86.1
  overview
- 86.2
  Sizes of Labeled Region in Mahotas
- 86.3
  Sizes in Grayscale Image
- 86.4
  Sizes in a Random Boolean Image
Mahotas - Template Matching
4
- 87.1
  overview
- 87.2
  Template Matching in Mahotas
- 87.3
  Matching by Wrapping Boundaries
- 87.4
  Matching by Ignoring Boundaries
Mahotas - Speeded-Up Robust Features
4
- 88.1
  overview
- 88.2
  SURF Surf
- 88.3
  SURF Dense
- 88.4
  SURF Integral
Mahotas - Removing Bordered Labelled
4
- 89.1
  overview
- 89.2
  Removing Bordered Labeled in Mahotas
- 89.3
  Removing Regions at a Specific Distance
- 89.4
  Removing Regions from a Specific part of an Image
Mahotas - Daubechies Wavelet
4
- 90.1
  overview
- 90.2
  Daubechies Wavelet in Mahotas
- 90.3
  Multiple Daubechies Wavelets
- 90.4
  Daubechies Wavelet on a Random Image
Mahotas - Sobel Edge Detection
4
- 91.1
  overview
- 91.2
  Sobel Edge Detection in Mahotas
- 91.3
  Without Thresholding output Image
- 91.4
  On a Threshold Image

Curriculum

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