import pyopencv as cv
import numpy as np
import matplotlib.pyplot as plt
# 读入图片并缩小为1/2
img0 = cv.imread("lena.jpg")
size = img0.size()
w, h = size.width, size.height
img1 = cv.Mat()
cv.resize(img0, img1, cv.Size(w // 2, h // 2))
# 各种卷积核
kernels = [("低通滤波器", np.array([[1, 1, 1], [1, 2, 1], [1, 1, 1]]) * 0.1),
("高通滤波器", np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])),
("边缘检测", np.array([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]]))]
index = 0
for name, kernel in kernels:
plt.subplot(131 + index)
# 将卷积核转换为Mat对象
kmat = cv.asMat(kernel.astype(np.float), force_single_channel=True)
img2 = cv.Mat()
cv.filter2D(img1, img2, -1, kmat)
# 由于matplotlib的颜色顺序和OpenCV的顺序相反
plt.imshow(img2[:, :, ::-1])
plt.title(name)
index += 1
plt.gca().set_axis_off()
plt.subplots_adjust(0.02, 0, 0.98, 1, 0.02, 0)
plt.show()
def redraw(self):
img2 = cv.Mat()
kernel = cv.asMat(self.kernel * self.scale, force_single_channel=True)
cv.filter2D(self.img, img2, -1, kernel)
cv.imshow("Filter Demo", img2)
# -*- coding: utf-8 -*-
import pyopencv as cv
import numpy as np
import time
img = cv.asMat(np.random.rand(1000, 1000))
row = cv.getGaussianKernel(7, -1)
col = cv.getGaussianKernel(5, -1)
kernel = cv.asMat(np.dot(col[:], row[:].T), force_single_channel=True)
img2 = cv.Mat()
img3 = cv.Mat()
start = time.clock()
cv.filter2D(img, img2, -1, kernel)
print(("filter2D:", time.clock() - start))
start = time.clock()
cv.sepFilter2D(img, img3, -1, row, col)
print(("sepFilter3D:", time.clock() - start))
print(("error=", np.max(np.abs(img2[:] - img3[:]))))
def redraw(self):
img2 = cv.Mat()
kernel = cv.asMat(self.kernel*self.scale, force_single_channel=True)
cv.filter2D(self.img, img2, -1, kernel)
cv.imshow("Filter Demo", img2)
# -*- coding: utf-8 -*- import pyopencv as cv import numpy as np import time img = cv.asMat(np.random.rand(1000,1000)) row = cv.getGaussianKernel(7, -1) col = cv.getGaussianKernel(5, -1) kernel = cv.asMat(np.dot(col[:], row[:].T), force_single_channel=True) img2 = cv.Mat() img3 = cv.Mat() start = time.clock() cv.filter2D(img, img2, -1, kernel) print "filter2D:", time.clock() - start start = time.clock() cv.sepFilter2D(img, img3, -1, row, col) print "sepFilter3D:", time.clock() - start print "error=", np.max(np.abs(img2[:] - img3[:]))
# 读入图片并缩小为1/2
img0 = cv.imread("lena.jpg")
size = img0.size()
w, h = size.width, size.height
img1 = cv.Mat()
cv.resize(img0, img1, cv.Size(w//2, h//2))
# 各种卷积核
kernels = [
(u"低通滤波器",np.array([[1,1,1],[1,2,1],[1,1,1]])*0.1),
(u"高通滤波器",np.array([[0,-1,0],[-1,5,-1],[0,-1,0]])),
(u"边缘检测",np.array([[-1,-1,-1],[-1,8,-1],[-1,-1,-1]]))
]
index = 0
for name, kernel in kernels:
plt.subplot(131+index)
# 将卷积核转换为Mat对象
kmat = cv.asMat(kernel.astype(np.float), force_single_channel=True)
img2 = cv.Mat()
cv.filter2D(img1, img2, -1, kmat)
# 由于matplotlib的颜色顺序和OpenCV的顺序相反
plt.imshow(img2[:,:,::-1])
plt.title(name)
index += 1
plt.gca().set_axis_off()
plt.subplots_adjust(0.02, 0, 0.98, 1, 0.02, 0)
plt.show()
