机子上面要先安装好scikit-image、numpy、imutils、opencv这几个包。
# 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
实现效果和OpenCV提供的函数差不多。
使用卷积的话,不同的kernel有不同的功能。
以上代码来自Deep Learning for Computer Vision with Python Starter Bundle第十一章,相关内容也请打开该电子书进行查阅。