機子上面要先安裝好scikit-image、numpy、imutils、opencv這幾個包。

# import the necessary packages
from skimage.exposure import rescale_intensity
import numpy as np
import argparse
import cv2

def convolve(image, K):
	# grab the spatial dimensions of the image and kernel
	(iH, iW) = image.shape[:2]
	(kH, kW) = K.shape[:2]
	
	# allocate memory for the output image, taking care to "pad"
	# the borders of the input image so the spatial size (i.e.,
	# width and height) are not reduced
	pad = (kW - 1) // 2
	image = cv2.copyMakeBorder(image, pad, pad, pad, pad,
		cv2.BORDER_REPLICATE)
	output = np.zeros((iH, iW), dtype="float")

	# loop over the input image, "sliding" the kernel across
	# each (x, y)-coordinate from left-to-right and top-to-bottom
	for y in np.arange(pad, iH + pad):
		for x in np.arange(pad, iW + pad):
			# extract the ROI of the image by extracting the
			# *center* region of the current (x, y)-coordinates
			# dimensions
			roi = image[y - pad:y + pad + 1, x - pad:x + pad + 1]
			
			# perform the actual convolution by taking the
			# element-wise multiplication between the ROI and
			# the kernel, then summing the matrix
			k = (roi * K).sum()
			
			# store the convolved value in the output (x, y)-
			# coordinate of the output image
			output[y - pad, x - pad] = k

	# rescale the output image to be in the range [0, 255]
	output = rescale_intensity(output, in_range=(0, 255))
	output = (output * 255).astype("uint8")
	
	# return the output image
	return output

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
#ap.add_argument("-i", "--image", required=True,
ap.add_argument("-i", "--image", default='keras_api_result01.jpg',
	help="path to the input image")
args = vars(ap.parse_args())

# construct average blurring kernels used to smooth an image
smallBlur = np.ones((7, 7), dtype="float") * (1.0 / (7 * 7))
largeBlur = np.ones((21, 21), dtype="float") * (1.0 / (21 * 21))

# construct a sharpening filter
sharpen = np.array((
	[0, -1, 0],
	[-1, 5, -1],
	[0, -1, 0]), dtype="int")

# construct the Laplacian kernel used to detect edge-like
# regions of an image
laplacian = np.array((
	[0, 1, 0],
	[1, -4, 1],
	[0, 1, 0]), dtype="int")

# construct the Sobel x-axis kernel
sobelX = np.array((
	[-1, 0, 1],
	[-2, 0, 2],
	[-1, 0, 1]), dtype="int")
	
# construct the Sobel y-axis kernel
sobelY = np.array((
	[-1, -2, -1],
	[0, 0, 0],
	[1, 2, 1]), dtype="int")

# construct an emboss kernel
emboss = np.array((
	[-2, -1, 0],
	[-1, 1, 1],
	[0, 1, 2]), dtype="int")

# construct the kernel bank, a list of kernels we're going to apply
# using both our custom 'convole' function and OpenCV's 'filter2D'
# function
kernelBank = (
	("small_blur", smallBlur),
	("large_blur", largeBlur),
	("sharpen", sharpen),
	("laplacian", laplacian),
	("sobel_x", sobelX),
	("sobel_y", sobelY),
	("emboss", emboss))

# load the input image and convert it to grayscale
image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# loop over the kernels
for (kernelName, K) in kernelBank:
	# apply the kernel to the grayscale image using both our custom
	# 'convolve' function and OpenCV's 'filter2D' function
	print("[INFO] applying {} kernel".format(kernelName))
	convolveOutput = convolve(gray, K)
	opencvOutput = cv2.filter2D(gray, -1, K)
	
	# show the output images
	cv2.imshow("Original", gray)
	cv2.imshow("{} - convole".format(kernelName), convolveOutput)
	cv2.imshow("{} - opencv".format(kernelName), opencvOutput)
	cv2.waitKey(0)
	cv2.destroyAllWindows()
 

special thanks to pyimagesearch.com，for providing such good example

實現效果和OpenCV提供的函式差不多。

使用卷積的話，不同的kernel有不同的功能。

以上程式碼來自Deep Learning for Computer Vision with Python Starter Bundle第十一章，相關內容也請開啟該電子書進行查閱。