class TestLogin():
def setup_class(self):
self.login = PageLogin(getdriver())
def teardown_class(self):
self.login.driver.quit()
# 定义test方法,来输入,姓名,密码.点击登录
@pytest.mark.parametrize('username,password,result',demo.read())
def test_login(self,username,password,result):
self.login.page_input_username(username)
self.login.page_input_password(password)
self.login.page_click_login_btn()
print(result)
# if __name__ == '__main__':
# pytest.main()
# coding: utf-8
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from skimage import io, util
from skimage import data
from scipy.ndimage import convolve
from scipy.signal import medfilt
from demo import read, show
I = read('imgs/Fig0219.tif')
show(I, 'raw_image', 'gray')
# get an average filter
r = 10
f = np.full((r, r), 1 / r**2)
I1 = convolve(I, f)
show(I1, 'after applying average filter', 'gray')
def matlab_style_gauss2D(shape=(3, 3), sigma=0.5):
"""
2D gaussian mask - should give the same result as MATLAB's
fspecial('gaussian',[shape],[sigma])
"""
m, n = [(ss - 1.) / 2. for ss in shape]
y, x = np.ogrid[-m:m + 1, -n:n + 1]
h = np.exp(-(x * x + y * y) / (2. * sigma * sigma))
h[h < np.finfo(h.dtype).eps * h.max()] = 0
import numpy as np
from demo import read, show
from skimage import transform
img = read("../matlab_example/imgs/0401.tif")
#tf_rotate = transform.SimilarityTransform(rotation=np.deg2rad(30))
#img = transform.warp(img, tf_rotate)
#tf_shift = transform.SimilarityTransform(translation=[60, 60])
#img = transform.warp(img, tf_shift)
show(img, cmap='gray')
fft2 = np.fft.fft2(img)
show(np.abs(fft2), cmap='gray')
shift2center = np.fft.fftshift(fft2)
show(np.abs(fft2), cmap='gray')
log_fft2 = np.log(1 + np.abs(fft2))
show(np.abs(log_fft2), cmap='gray')
log_shift2center = np.log(1 + np.abs(shift2center))
show(np.abs(log_shift2center), cmap='gray')
# coding: utf-8
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from skimage import io, util
from skimage import data
from scipy.ndimage import convolve, laplace, sobel
from scipy.signal import medfilt
from demo import read, show
I = read('imgs/lenna.tif')
show(I, 'raw_image', 'gray')
# apply laplace filter
I1 = laplace(I)
show(I1, 'after applying laplace filter', 'gray')
# apply a gussian filter
I2 = sobel(I)
show(I2, 'after applying sobel filter', 'gray')
import numpy as np
from demo import read, show
raw_img = read("../matlab_example/imgs/0402.tif")
img = np.pad(raw_img, ((0, raw_img.shape[0]), (0, raw_img.shape[1])),
'constant')
show(img, cmap='gray')
fft2 = np.fft.fft2(img)
shift2center = np.fft.fftshift(fft2)
show(np.log(1 + np.abs(shift2center)), cmap='gray')
def dftuv(m, n):
u = np.array(list(range(0, m)))
v = np.array(list(range(0, n)))
u[m // 2:] -= m
v[n // 2:] -= n
u, v = np.meshgrid(u, v)
return u**2 + v**2
sigma = 0.05 * img.shape[0]
d = dftuv(img.shape[0], img.shape[1])
h = np.exp(-d / (2 * (sigma**2)))
res = np.fft.fft2(raw_img, (img.shape[0], img.shape[1])) * h
res = np.fft.ifft2(res)
show(np.abs(res), cmap='gray')
import demo demo.read()
# coding: utf-8
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from skimage import io
from skimage import data
from scipy.ndimage import convolve
from demo import read, show
I = read('imgs/Fig0216.tif')
show(I, 'raw_image')
# get a filter
f = np.ones((31, 31))
# nearest is same as replicate in matlab
I1 = convolve(I, f, mode='nearest')
I2 = convolve(I, f, mode='mirror')
I3 = convolve(I, f, mode='constant', cval=0)
show(I1, 'using nearest padding')
show(I2, 'using mirror padding')
show(I3, 'using constant 0 padding')