Gradient and Edge Detection

def gradient_image_filter(input, use_image_spacing=True):
    """
    Compute gradient at each pixel using finite differences.
    
    Parameters:
    - input: itk.Image - Input scalar image
    - use_image_spacing: bool - Account for image spacing in gradient computation
    
    Returns:
    itk.Image - Gradient image with CovariantVector pixels
    """

class GradientImageFilter:
    """Computes the gradient of an image using finite differences."""
    
    def SetUseImageSpacing(self, use_spacing):
        """Enable/disable using image spacing."""

def gradient_magnitude_image_filter(input, use_image_spacing=True):
    """
    Compute gradient magnitude at each pixel.
    
    Parameters:
    - input: itk.Image - Input scalar image
    - use_image_spacing: bool - Account for image spacing
    
    Returns:
    itk.Image - Gradient magnitude image
    """

class GradientMagnitudeImageFilter:
    """Computes the magnitude of the image gradient."""
    
    def SetUseImageSpacing(self, use_spacing):
        """Enable/disable using image spacing."""

def gradient_magnitude_recursive_gaussian_image_filter(input, sigma=1.0, normalize_across_scale=False):
    """
    Compute gradient magnitude with Gaussian smoothing.
    
    Parameters:
    - input: itk.Image - Input image
    - sigma: float - Gaussian sigma for smoothing
    - normalize_across_scale: bool - Normalize derivatives across scale
    
    Returns:
    itk.Image - Smoothed gradient magnitude
    """

class GradientMagnitudeRecursiveGaussianImageFilter:
    """Computes gradient magnitude with recursive Gaussian smoothing."""
    
    def SetSigma(self, sigma):
        """Set the Gaussian sigma."""
    
    def SetNormalizeAcrossScale(self, normalize):
        """Enable scale normalization."""

def gradient_recursive_gaussian_image_filter(input, sigma=1.0):
    """
    Compute gradient vector with Gaussian smoothing.
    
    Parameters:
    - input: itk.Image - Input image
    - sigma: float - Gaussian sigma
    
    Returns:
    itk.Image - Gradient vector image
    """

def canny_edge_detection_image_filter(input, variance=1.0, upper_threshold=0.0, lower_threshold=0.0, maximum_error=0.01):
    """
    Canny edge detector with hysteresis thresholding.
    
    Parameters:
    - input: itk.Image - Input image
    - variance: float - Variance of the Gaussian smoothing
    - upper_threshold: float - Upper threshold for hysteresis
    - lower_threshold: float - Lower threshold for hysteresis
    - maximum_error: float - Maximum error in Gaussian approximation
    
    Returns:
    itk.Image - Binary edge map
    """

class CannyEdgeDetectionImageFilter:
    """Multi-stage Canny edge detector."""
    
    def SetVariance(self, variance):
        """Set Gaussian variance for smoothing."""
    
    def SetUpperThreshold(self, threshold):
        """Set upper threshold for strong edges."""
    
    def SetLowerThreshold(self, threshold):
        """Set lower threshold for weak edges."""
    
    def SetMaximumError(self, error):
        """Set maximum Gaussian approximation error."""

def sobel_edge_detection_image_filter(input):
    """
    Sobel edge detection operator.
    
    Parameters:
    - input: itk.Image - Input image
    
    Returns:
    itk.Image - Sobel edge magnitude
    """

class SobelEdgeDetectionImageFilter:
    """Sobel operator for edge detection."""

def laplacian_image_filter(input, use_image_spacing=True):
    """
    Compute Laplacian (second derivative).
    
    Parameters:
    - input: itk.Image - Input image
    - use_image_spacing: bool - Account for image spacing
    
    Returns:
    itk.Image - Laplacian image
    """

class LaplacianImageFilter:
    """Computes the Laplacian of an image."""
    
    def SetUseImageSpacing(self, use_spacing):
        """Enable/disable using image spacing."""

def zero_crossing_image_filter(input, foreground_value=1, background_value=0):
    """
    Detect zero crossings in an image (useful after Laplacian).
    
    Parameters:
    - input: itk.Image - Input image (typically Laplacian)
    - foreground_value: pixel_type - Value for detected edges
    - background_value: pixel_type - Value for non-edges
    
    Returns:
    itk.Image - Binary edge map
    """

def hessian_recursive_gaussian_image_filter(input, sigma=1.0):
    """
    Compute Hessian matrix (second derivatives) with Gaussian smoothing.
    
    Parameters:
    - input: itk.Image - Input image
    - sigma: float - Gaussian sigma
    
    Returns:
    itk.Image - Hessian matrix image (SymmetricSecondRankTensor pixels)
    """

class HessianRecursiveGaussianImageFilter:
    """Computes the Hessian matrix using recursive Gaussian filters."""
    
    def SetSigma(self, sigma):
        """Set the Gaussian sigma."""
    
    def SetNormalizeAcrossScale(self, normalize):
        """Enable scale normalization."""

import itk

image = itk.imread('input.png', itk.F)

# Compute gradient vectors
gradient = itk.gradient_image_filter(image)

# Compute gradient magnitude
magnitude = itk.gradient_magnitude_image_filter(image)
itk.imwrite(magnitude, 'gradient_magnitude.png')

# With Gaussian smoothing
smoothed_magnitude = itk.gradient_magnitude_recursive_gaussian_image_filter(
    image, sigma=2.0
)
itk.imwrite(smoothed_magnitude, 'smooth_gradient.png')

import itk

image = itk.imread('input.png', itk.F)

# Canny edge detection
edges = itk.canny_edge_detection_image_filter(
    image,
    variance=2.0,          # Gaussian smoothing
    upper_threshold=0.1,   # Strong edges
    lower_threshold=0.05   # Weak edges
)

itk.imwrite(edges, 'canny_edges.png')

import itk

image = itk.imread('input.png', itk.F)

# Sobel edges
sobel_edges = itk.sobel_edge_detection_image_filter(image)
itk.imwrite(sobel_edges, 'sobel_edges.png')

import itk

image = itk.imread('input.png', itk.F)

# Smooth first
smoothed = itk.discrete_gaussian_image_filter(image, variance=2.0)

# Compute Laplacian
laplacian = itk.laplacian_image_filter(smoothed)

# Detect zero crossings
edges = itk.zero_crossing_image_filter(laplacian)
itk.imwrite(edges, 'zero_crossing_edges.png')