def __init__(self):
#读入图像
img = cv.imread("lena_full.jpg")
img2 = cv.Mat()
cv.cvtColor(img, img2, cv.CV_BGR2GRAY)
img = cv.Mat()
cv.resize(img2, img, cv.Size(N, N))
self.fimg = fft.fft2(img[:]) # 图像的频域信号
mag_img = np.log10(np.abs(self.fimg))
# 创建计算用图像
filtered_img = np.zeros((N, N), dtype=np.float)
self.mask = np.zeros((N, N), dtype=np.float)
self.mask_img = cv.asMat(self.mask) # 在self.mask上绘制多边形用的图像
# 创建数据源
self.data = ArrayPlotData(mag_img=fft.fftshift(mag_img),
filtered_img=filtered_img,
mask_img=self.mask)
# 创建三个图像绘制框以及容器
meg_plot, img = self.make_image_plot("mag_img")
mask_plot, _ = self.make_image_plot("mask_img")
filtered_plot, _ = self.make_image_plot("filtered_img")
self.plot = HPlotContainer(meg_plot, mask_plot, filtered_plot)
# 创建套索工具
lasso_selection = LassoSelection(component=img)
lasso_overlay = LassoOverlay(lasso_selection=lasso_selection,
component=img,
selection_alpha=0.3)
img.tools.append(lasso_selection)
img.overlays.append(lasso_overlay)
self.lasso_selection = lasso_selection
# 监听套索工具的事件、开启时钟事件
lasso_selection.on_trait_change(self.lasso_updated,
"disjoint_selections")
self.timer = Timer(50, self.on_timer)
def __init__(self):
#读入图像
img = cv.imread("lena_full.jpg")
img2 = cv.Mat()
cv.cvtColor(img, img2, cv.CV_BGR2GRAY)
img = cv.Mat()
cv.resize(img2, img, cv.Size(N, N))
self.fimg = fft.fft2(img[:]) # 图像的频域信号
mag_img = np.log10(np.abs(self.fimg))
# 创建计算用图像
filtered_img = np.zeros((N, N), dtype=np.float)
self.mask = np.zeros((N, N), dtype=np.float)
self.mask_img = cv.asMat(self.mask) # 在self.mask上绘制多边形用的图像
# 创建数据源
self.data = ArrayPlotData(
mag_img = fft.fftshift(mag_img),
filtered_img = filtered_img,
mask_img = self.mask
)
# 创建三个图像绘制框以及容器
meg_plot, img = self.make_image_plot("mag_img")
mask_plot, _ = self.make_image_plot("mask_img")
filtered_plot, _ = self.make_image_plot("filtered_img")
self.plot = HPlotContainer(meg_plot, mask_plot, filtered_plot)
# 创建套索工具
lasso_selection = LassoSelection(component=img)
lasso_overlay = LassoOverlay(lasso_selection = lasso_selection, component=img, selection_alpha=0.3)
img.tools.append(lasso_selection)
img.overlays.append(lasso_overlay)
self.lasso_selection = lasso_selection
# 监听套索工具的事件、开启时钟事件
lasso_selection.on_trait_change(self.lasso_updated, "disjoint_selections")
self.timer = Timer(50, self.on_timer)
# -*- coding: utf-8 -*-
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()
# -*- coding: utf-8 -*-
import numpy as np
from numpy import fft
import pyopencv as cv
import matplotlib.pyplot as plt
N = 256
img = cv.imread("lena_full.jpg")
img2 = cv.Mat()
cv.cvtColor(img, img2, cv.CV_BGR2GRAY)
img = cv.Mat()
cv.resize(img2, img, cv.Size(N, N))
fimg = fft.fft2(img[:])
mag_img = np.log10(np.abs(fimg))
shift_mag_img = fft.fftshift(mag_img)
rects = [(80,125,85,130),(90,90,95,95),
(150, 10, 250, 250), (110, 110, 146, 146)]
filtered_results = []
for i, (x0, y0, x1, y1) in enumerate(rects):
mask = np.zeros((N, N), dtype=np.bool)
mask[x0:x1+1, y0:y1+1] = True
mask[N-x1:N-x0+1, N-y1:N-y0+1] = True
mask = fft.fftshift(mask)
fimg2 = fimg * mask
filtered_img = fft.ifft2(fimg2).real
def resize(src, w, h, dst=None):
if dst is None:
dst = src.clone()
cv.resize(src, dst, cv.Size(w, h))
return dst
# -*- coding: utf-8 -*-
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 = [
(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()
# -*- coding: utf-8 -*-
import numpy as np
from numpy import fft
import pyopencv as cv
import matplotlib.pyplot as plt
N = 256
img = cv.imread("lena_full.jpg")
img2 = cv.Mat()
cv.cvtColor(img, img2, cv.CV_BGR2GRAY)
img = cv.Mat()
cv.resize(img2, img, cv.Size(N, N))
fimg = fft.fft2(img[:])
mag_img = np.log10(np.abs(fimg))
shift_mag_img = fft.fftshift(mag_img)
rects = [(80, 125, 85, 130), (90, 90, 95, 95), (150, 10, 250, 250),
(110, 110, 146, 146)]
filtered_results = []
for i, (x0, y0, x1, y1) in enumerate(rects):
mask = np.zeros((N, N), dtype=np.bool)
mask[x0:x1 + 1, y0:y1 + 1] = True
mask[N - x1:N - x0 + 1, N - y1:N - y0 + 1] = True
mask = fft.fftshift(mask)
fimg2 = fimg * mask
filtered_img = fft.ifft2(fimg2).real
filtered_results.append(filtered_img)
### 绘图部分 ###
