def img_array_resize(img,sz):
""" Resize an image array using PIL.
input: img can be array(must has two dimensions) or pil image.
output: resized array
"""
t = Image.fromarray(uint8(img))
return array(t.resize(sz))
def filter_homemade(im):
h,w = im.shape[0],im.shape[1]
# cvt to gray level
img = pl.zeros((h,w))
for i in xrange(h):
for j in xrange(w):
img[i,j] = int(pl.mean(im[i,j]))
# low pass filter
Ker = 1./16 * pl.array([[1,2,1],[2,4,2],[1,2,1]])
imf = pl.uint8(img.copy())
for i in xrange(1,h-1):
for j in xrange(1,w-1):
imf[i,j] = int(pl.sum(Ker * img[i-1:i+2,j-1:j+2]))
return imf
from PIL import Image
from pylab import imshow, show, axis, array, figure
from pylab import gray, subplot, uint8
# load the image file
img = Image.open('../../data/cv_data/empire.jpg')
img = array(img)
print img.min(), img.max()
img = img * 1000
print img.min(), img.max()
print img.shape, img.dtype
img = uint8(img)
print img.min(), img.max()
print img.shape, img.dtype
img_new = Image.fromarray(img) #the input array must be type of 'uinit8'
imshow(img_new)
show()
print area_mode
#print img_bin,stats.mode(img_bin,axis=None)
#print img_bin,np.max(img_bin)
# do gaussian blur to the bin img
#img_bin = filters.gaussian_filter(img_bin,0.26935)
#print img_bin,stats.mode(img_bin,axis=None)
#print img_bin,np.max(img_bin)
# binary again
#img_bin = filters.maximum_filter(img_bin,7)
#img_bin = filter.threshold_adaptive(img_bin,7)
#img_bin[img_bin>0]=255
Image.fromarray(uint8(img_bin)).save('feature_points.png')
figure(); gray(); # don't use colors
# show the two pics on 1*2 frame
#subplot(1,3,1)
imshow(img_gray)
#subplot(1,3,2)
figure(); gray(); # don't use colors
imshow(img_bin)
figure(); gray(); # don't use colors
imshow(img_bin_words)
#subplot(1,3,3)
#imshow(labeled_array)
#ob = labeled_array[obj_list[100]]
figure(); gray(); # don't use colors
from PIL import Image
from pylab import imshow, show, axis, array, zeros, uint8, imsave
from pylab import jet
# load the image file
img = Image.open('../../data/cv_data/empire.jpg')
img = array(img)
#invert image
im2 = 255 - img
im3 = zeros(im2.shape)
im3[15:15 + 10, 15:15 + 10] = 200
#
im4 = uint8(im3)
#Image.fromarray(im4).save('test.png') # im4 must be uinit8 type
#http://docs.scipy.org/doc/scipy/reference/misc.html#scipy.misc.comb
imsave('test.png', im4)
print im4[15:15 + 10, 15:15 + 10]
imshow(im4)
show()
from PIL import Image
from skimage.morphology import disk
from skimage.color import rgb2hed
from skimage.measure import label, regionprops
print __doc__
glom_rgb = Image.open('s3.jpg')
glom_gl = glom_rgb.convert('L') #Gray Level
glom_hed = rgb2hed(glom_rgb) #hed
glom_h = glom_hed[:, :, 0] #hematoxylim
glom_h = ia.ianormalize(glom_h)
selem = disk(10) #elemento estruturante
glom_h = np.array(glom_h)
glom_h = 255 - uint8(glom_h)
#Segmentation
glom_by_reconsTopHat = morph.closerecth(glom_h,selem) #reconstrução morfológicas de fechamento
global_thresh = threshold_otsu(glom_by_reconsTopHat) #Otsu
glom_bin = glom_by_reconsTopHat > global_thresh + global_thresh*0.1
glom_bin = img_as_ubyte(glom_bin)
selem = disk(3)
glom_seg = morph.open(glom_bin, selem)
glom_seg = morph.close(glom_seg, selem) #Fechamento final
#Mostra as etapas
fig, axes = plt.subplots(2, 3, figsize=(14, 10))
fig.suptitle('Preprocessing, segmentation')
ax1, ax2, ax3, ax4, ax5, ax6 = axes.ravel()
def imresize(im, sz):
""" Resize an image array using PIL. """
pil_im = Image.fromarray(uint8(im))
return array(pil_im.resize(sz))
from PIL import Image
from pylab import imshow,show,axis,array,zeros,uint8,imsave
from pylab import jet
# load the image file
img = Image.open('../../data/cv_data/empire.jpg')
img=array(img)
#invert image
im2 = 255 - img
im3 = zeros(im2.shape)
im3[15:15+10,15:15+10]=200
#
im4 = uint8(im3)
#Image.fromarray(im4).save('test.png') # im4 must be uinit8 type
#http://docs.scipy.org/doc/scipy/reference/misc.html#scipy.misc.comb
imsave('test.png', im4)
print im4[15:15+10,15:15+10]
imshow(im4)
show()
print area_mode
#print img_bin,stats.mode(img_bin,axis=None)
#print img_bin,np.max(img_bin)
# do gaussian blur to the bin img
#img_bin = filters.gaussian_filter(img_bin,0.26935)
#print img_bin,stats.mode(img_bin,axis=None)
#print img_bin,np.max(img_bin)
# binary again
#img_bin = filters.maximum_filter(img_bin,7)
#img_bin = filter.threshold_adaptive(img_bin,7)
#img_bin[img_bin>0]=255
Image.fromarray(uint8(img_bin)).save('feature_points.png')
figure()
gray()
# don't use colors
# show the two pics on 1*2 frame
#subplot(1,3,1)
imshow(img_gray)
#subplot(1,3,2)
figure()
gray()
# don't use colors
imshow(img_bin)
figure()
gray()
def savetofile(self,filename): deflatted = np.array(self.data) deflatted.shape = self.shape img = Image.fromarray(uint8(deflatted)) img.save(filename)
import pylab as pl
from scipy.ndimage import filters
from PIL import Image
im = pl.array(Image.open("/home/aaryen/Desktop/empire.jpg"))
im2 = pl.zeros(im.shape)
for i in range(3):
im2[:, :, i] = filters.gaussian_filter(im[:, :, i], 1)
im2 = pl.uint8(im2)
im_unsharp = im2 - im
im_sharp = im - im_unsharp
pl.figure()
pl.title("Original")
pl.imshow(im)
pl.figure()
pl.title("Gaussian Blur")
pl.imshow(im2)
pl.figure()
pl.title("Unsharp")
pl.imshow(im_unsharp)
pl.figure()
pl.title("Sharp")
pl.imshow(im_sharp)
pl.show()
def imresize(im, sz):
'''使用 PIL 对象重新定义图像数组的大小'''
from PIL import Image
pil_im = Image.fromarray(uint8(im))
return array(pil_im.resize(sz))