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Gradient Visualization

Compute and visualize image gradients in X, Y, and combined XY directions.

Visualizers

ImgVisFeat provides two gradient visualizers:

ColorGradientVisualizer

For color (RGB) images.

import cv2
import imvf

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

# Create visualizer
visualizer = imvf.ColorGradientVisualizer()

# Apply visualization
result = visualizer(image)

# Display results
cv2.imshow("Gradient X", result.gradient_x)
cv2.imshow("Gradient Y", result.gradient_y)
cv2.imshow("Gradient XY", result.gradient_xy)
cv2.waitKey(0)

GrayGradientVisualizer

For grayscale images.

import cv2
import imvf

# Load and convert to grayscale
image = cv2.imread("path/to/image.jpg")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Create visualizer
visualizer = imvf.GrayGradientVisualizer()

# Apply visualization
result = visualizer(gray)

# Display results
cv2.imshow("Gradient X", result.gradient_x)
cv2.imshow("Gradient Y", result.gradient_y)
cv2.imshow("Gradient XY", result.gradient_xy)
cv2.waitKey(0)

Result Structure

The GradientResult dataclass contains:

  • gradient_x: Gradient in X direction (horizontal)
  • gradient_y: Gradient in Y direction (vertical)
  • gradient_xy: Combined gradient magnitude

Use Cases

  • Edge detection
  • Feature extraction for object recognition
  • Image analysis and preprocessing
  • Understanding directional changes in images

See Also