imgcv.filters.linear

Functions

check_image(image)

Check if the input is a numpy array

apply_convolution(img, kernels, seperate[, take_mean])

Apply convolution operation to the image.

box_filter(img, filter_size)

Apply box filter to the image. This is a simple averaging filter.

laplacian_filter(img[, diagonal, return_edges])

Apply Laplacian filter to the image.

robert_cross_filter(img)

Apply Robert Cross filter to the image. This uses 2x2 kernels. Extending this to 3x3 kernel will give us Sobel filter.

sobel_filter(img)

Apply Sobel filter to the image. This uses 3x3 kernels. This is extension of Robert Cross filter. Center of kerenel is weighted more than the corners. This gives us more accurate edge detection.

Module Contents

imgcv.filters.linear.check_image(image)

Check if the input is a numpy array

Parameters:

image (np.ndarray) – Input image to be transformed

Raises:

  • TypeError – If the input is not a numpy array

  • ValueError – If the input is not a grayscale or color image

imgcv.filters.linear.apply_convolution(img, kernels, seperate, take_mean=False)

Apply convolution operation to the image.

Parameters:

  • img (np.ndarray) – Input Image array

  • kernels (List[np.ndarray, np.ndarray]) – List of kernels to apply.

  • seperate (bool) – If True, apply the kernels seperately in x and y direction respectively.

Raises:

  • ValueError – If kernels is not a list of length 1 or 2.

  • ValueError – If seperate is not a boolean value.

  • ValueError – If both kernels are not of same shape.

Returns:

Image array after applying convolution.

Return type:

np.ndarray

imgcv.filters.linear.box_filter(img, filter_size)

Apply box filter to the image. This is a simple averaging filter.

Parameters:

  • img (np.ndarray) – Input Image array

  • filter_size (Tuple[int, int]) – Size of the filter.

Raises:

ValueError – If filter_size is not a tuple.

Returns:

Filtered image array.

Return type:

np.ndarray

imgcv.filters.linear.laplacian_filter(img, diagonal=False, return_edges=True)

Apply Laplacian filter to the image.

Parameters:

  • img (np.ndarray) – Input Image array

  • diagonal (bool, optional) – If True, apply diagonal laplacian filter. Defaults to False.

  • return_edges (bool, optional) – If True, return image with edges detected else return the sharpened image. Defaults to True.

Raises:

ValueError – If diagonal is not a boolean value.

Returns:

Image with edges detected

Return type:

np.ndarray

imgcv.filters.linear.robert_cross_filter(img)

Apply Robert Cross filter to the image. This uses 2x2 kernels. Extending this to 3x3 kernel will give us Sobel filter.

Parameters:

img (np.ndarray) – Input Image array

Returns:

Image with edges detected

Return type:

np.ndarray

imgcv.filters.linear.sobel_filter(img)

Apply Sobel filter to the image. This uses 3x3 kernels. This is extension of Robert Cross filter. Center of kerenel is weighted more than the corners. This gives us more accurate edge detection.

Parameters:

img (np.ndarray) – Input Image array

Returns:

Image with edges detected

Return type:

np.ndarray