42 lines
1.8 KiB
Cython
42 lines
1.8 KiB
Cython
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# Import necessary packages
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import cython
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import numpy as np
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cimport numpy as np
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# Declare types for function arguments and variables
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cpdef np.ndarray[np.float64_t, ndim=2] convolve2d(np.ndarray[np.float64_t, ndim=2] image,
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np.ndarray[np.float64_t, ndim=2] kernel):
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cdef int image_height, image_width, kernel_height, kernel_width, pad_height, pad_width, row, col
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cdef np.ndarray[np.float64_t, ndim=2] padded_image, convolved_image, patch, product
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# Get the dimensions of the input image and kernel
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# Get the dimensions of the input image and kernel
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image_height, image_width = int(image.shape[0]), int(image.shape[1])
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kernel_height, kernel_width = int(kernel.shape[0]), int(kernel.shape[1])
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# Compute the padding needed to handle boundary effects
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pad_height = (kernel_height - 1) // 2
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pad_width = (kernel_width - 1) // 2
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padded_image = np.pad(image, ((pad_height, pad_height), (pad_width, pad_width)), mode='constant')
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# Initialize the output image
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convolved_image = np.zeros((image_height, image_width), dtype=np.float64)
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# Loop over each pixel in the output image and compute the convolved value
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for row in range(image_height):
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for col in range(image_width):
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# Extract the patch centered at the current pixel
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patch = padded_image[row : row + kernel_height, col : col + kernel_width]
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# Compute the element-wise product of the patch and the flipped kernel
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product = patch * np.flip(kernel, axis=(0, 1))
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# Compute the sum of the element-wise products
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convolved_value = np.sum(product)
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# Store the convolved value in the output image
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convolved_image[row, col] = convolved_value
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return convolved_image
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