def test_tags():
f = Image([1, 2, 3], foo='bar', sigma='delta')
g = Image([3, 2, 1], sun='moon')
h = Image([1, 1, 1])
assert_equal(f.tags['foo'], 'bar')
assert_array_equal((g + 2).tags['sun'], 'moon')
assert_equal(h.tags, {})
def test_repr_png_roundtrip():
# Use RGB-like shape since some backends convert grayscale to RGB
original_array = 255 * np.ones((5, 5, 3), dtype=np.uint8)
image = Image(original_array)
array = imread(BytesIO(image._repr_png_()))
# Force output to ubyte range for plugin compatibility.
# For example, Matplotlib will return floats even if the image is uint8.
assert_array_equal(img_as_ubyte(array), original_array)
def scaled_image(self, scale=1):
"""
Returns a nearest-neighbor upscaled scaled version of the image.
Parameters
----------
scale : int
Upscaling using nearest neighbor, e.g. a scale of 5 will make each
pixel a 5x5 rectangle in the output.
Returns
-------
scaled_image : skimage.io.Image, (height, width, 3)
Returns a scaled up RGB image. If you do not have scikit-image, it
will be returned as a regular Numpy array. The benefit of wrapping
it in `Image`, is so that it will be automatically displayed in
IPython notebook, without having to issue any drawing calls.
"""
if scale == 1:
return self._data
else:
from skimage.transform import resize
data = resize(self._data, tuple([self._data.shape[i] * scale
for i in range(2)]), order=0)
return Image(data)
def image(self):
"""
Returns the image as a skimage.io.Image class.
"""
return Image(self._data)
def image(self):
"""
Returns the image as a skimage.io.Image class.
"""
Image = _load_image_class()
return Image(self._data)
from pylab import *
#from skimage.io import Image
from skimage.morphology import square
import skimage as si
import numpy as np
from numpy import array
import matplotlib.pyplot as pl
def rgb2gray(rgb):
return np.dot(rgb[...,:3], [0.299, 0.587, 0.144])
# #canny + sobel
if __name__ == '__main__':
picture_table=['samolot17.jpg','samolot05.jpg','samolot10.jpg','samolot11.jpg','samolot12.jpg','samolot01.jpg']
wyn_table = ['wynik.jpg','wynik1.jpg','wynik2.jpg','wynik3.jpg','wynik4.jpg','wynik5.jpg']
new_im = Image.new('RGB', (600,600))
i=0
j=0
for index,image in enumerate(picture_table):
im = Image.open(image)
img = img_as_float(data.imread(image))
tmp = rgb2gray(img)
global_thresh = threshold_otsu(tmp)
binary_global = tmp > global_thresh
# block_size = 5
# binary_adaptive = threshold_adaptive(tmp, block_size, offset=10)
sob = filters.sobel(binary_global)**0.9
#final= skimage.feature.canny(sob, sigma=1)
#
meanshape = ((meanshape-mean)+[cropsize[0]/2,cropsize[1]/2])
imshape = (cropsize[0], cropsize[1], 3)
avim = np.zeros(imshape)
imlen = len(data_pca.keys())
# for each imaged
count = 0
for filename, values in data_pca.iteritems():
# warp to meanshape
im = imread( os.path.join(data_folder, "cropped/", filename) )
tform = PiecewiseAffineTransform()
tform.estimate(meanshape, values+[cropsize[0]/2,cropsize[1]/2])
# store in array
outim = warp(im, tform, output_shape=cropsize)
#imsave("./averageface/test.bmp", outim)
avim += skimage.util.img_as_float(outim)
count += 1
print str(count)
avim /= imlen
avim *= 255
avim = avim.astype(np.uint8)
imsave("./average.bmp", Image(avim))
if cleanUp:
import shutil
if os.path.exists(os.path.join(config.data_folder, "cropped")):
shutil.rmtree(os.path.join(config.data_folder, "cropped"))
