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All-in-One Visualizer

The Visualizer class provides a convenient way to apply all visualization methods at once.

Usage

import imvf

# Create visualizer instance
visualizer = imvf.Visualizer()

# Visualize all features
visualizer.visualize("path/to/image.jpg")

What It Does

The Visualizer class:

  1. Loads the image from the specified path
  2. Applies all available visualizers: - Color Channel Visualization - Gradient Visualization (both color and grayscale) - HoG Features - LBP Features - Keypoint Detection (SIFT, AKAZE, ORB) - Power Spectrum
  3. Displays all results in OpenCV windows
  4. Saves all results to a directory named after the image

Output Directory

Results are saved to a directory structure:

path/to/image/
├── color_channels/
│   ├── blue.png
│   ├── green.png
│   └── red.png
├── gradients/
│   ├── gradient_x.png
│   ├── gradient_y.png
│   └── gradient_xy.png
├── hog/
│   └── hog.png
├── lbp/
│   └── lbp.png
├── keypoints/
│   ├── sift.png
│   ├── akaze.png
│   └── orb.png
└── power_spectrum/
    └── power_spectrum.png

Advanced Usage

Using with Pre-loaded Images

import cv2
import imvf

# Load image
image = cv2.imread("path/to/image.jpg")

# Create visualizer
visualizer = imvf.Visualizer()

# Process the loaded image
# (Note: visualize() expects a path, use individual visualizers for pre-loaded images)

Accessing Individual Visualizers

The Visualizer class internally uses individual visualizers. For more control, use them directly:

import cv2
import imvf

image = cv2.imread("path/to/image.jpg")

# Use individual visualizers
color_viz = imvf.ColorChannelVisualizer()
hog_viz = imvf.HoGVisualizer()

color_result = color_viz(image)
hog_result = hog_viz(image)

See Also