def load_image_volume(image_directory, h5, extension='tif'):
filenames = sorted(glob.glob(os.path.join(image_directory, '*.' + extension)))
size = imread.imread(filenames[0]).shape
images = h5.require_dataset('images', shape=((len(filenames),) + size), dtype=np.uint8)
for idx, f in enumerate(filenames):
images[idx, ...] = imread.imread(f)
return images
def load_images(image_dir):
filenames = sorted(glob.glob(os.path.join(image_dir, '*.tif')))
for f in filenames:
imread.imread(f)
ims = [imread.imread(f) for f in filenames]
print("read images", len(ims))
return ims
def append_images(orig):
def scale(out, sz):
subprocess.check_call(["convert", orig,
"-scale", sz,
out])
subprocess.check_call(["convert", out,
"-background", "none",
"-gravity", "center",
"-extent", sz,
out])
def append(f1, f2):
subprocess.check_call(["convert",
f1, f2,
"-append", f1])
tmpdir = tempfile.mkdtemp()
out_16 = os.path.join(tmpdir, "16.png")
out_32 = os.path.join(tmpdir, "32.png")
scale(out_16, "16x16")
scale(out_32, "32x32")
append("images/flags16.png", out_16)
append("images/flags32.png", out_32)
# sanity checks
sh16 = imread.imread("images/flags16.png").shape
assert sh16[0] % 16 == 0
assert sh16[1] == 16
sh32 = imread.imread("images/flags32.png").shape
assert sh32[0] % 32 == 0
assert sh32[1] == 32
return sh16[0] - 16, sh32[0] - 32
def append_images(orig):
def scale(out, sz):
subprocess.check_call(["convert", orig, "-scale", sz, out])
subprocess.check_call([
"convert", out, "-background", "none", "-gravity", "center",
"-extent", sz, out
])
def append(f1, f2):
subprocess.check_call(["convert", f1, f2, "-append", f1])
tmpdir = tempfile.mkdtemp()
out_16 = os.path.join(tmpdir, "16.png")
out_32 = os.path.join(tmpdir, "32.png")
scale(out_16, "16x16")
scale(out_32, "32x32")
append("images/flags16.png", out_16)
append("images/flags32.png", out_32)
# sanity checks
sh16 = imread.imread("images/flags16.png").shape
assert sh16[0] % 16 == 0
assert sh16[1] == 16
sh32 = imread.imread("images/flags32.png").shape
assert sh32[0] % 32 == 0
assert sh32[1] == 32
return sh16[0] - 16, sh32[0] - 32
def test_horizontal_predictor():
im = imread(file_path('arange512_16bit.png'))
im2 = im.copy()
imsave(_filename, im, opts={'tiff:horizontal-predictor': True})
assert np.all(im == im2)
im3 = imread(_filename)
assert np.all(im == im3)
def test_horizontal_predictor():
im = imread(file_path('arange512_16bit.png'))
im2 = im.copy()
imsave(_filename, im, opts={'tiff:horizontal-predictor': True})
assert np.all(im == im2)
im3 = imread(_filename)
assert np.all(im == im3)
def parse_segments_from_outlines(outline_path, dataset):
if dataset == 'BBBC006':
import imread
masks = imread.imread(outline_path)
rles = []
for idx in range(1, masks.max() + 1):
rles.append(mask_util.encode(np.asarray(masks == idx, dtype=np.uint8, order='F')))
return rles
if dataset == 'BBBC020':
out_dir, prefix = outline_path.rsplit('/', 1)
files = os.listdir(out_dir)
masks = []
for f_name in files:
if prefix in f_name:
m = imageio.imread(os.path.join(out_dir, f_name))
m[m > 0] = 1
m = scipy.ndimage.zoom(m, (0.4, 0.4), order=1)
masks.append(m)
rles = []
for m in masks:
rles.append(mask_util.encode(np.asarray(m, dtype=np.uint8, order='F')))
return rles
if dataset == '2009_ISBI_2DNuclei':
import imread
outlines = imread.imread(outline_path)
else:
outlines = imageio.imread(outline_path)
if dataset == 'cluster_nuclei' or dataset == '2009_ISBI_2DNuclei':
outlines[outlines != [255, 0, 0]] = 0
imgray = cv2.cvtColor(outlines, cv2.COLOR_RGB2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
elif dataset == 'BBBC007':
thresh = np.asarray(outlines, np.uint8)
elif dataset == 'BBBC018':
thresh = outlines
thresh[0, :] = 1
thresh[:, 0] = 1
thresh[:, -1] = 1
thresh[-1, :] = 1
im, contours, hierarchy = cv2.findContours(thresh,
cv2.RETR_CCOMP,
cv2.CHAIN_APPROX_SIMPLE)
seg = [[float(x) for x in c.flatten()] for c in contours]
seg = [cont for cont in seg if len(cont) > 4] # filter all polygons that are boxes
if not seg:
return []
rles = mask_util.frPyObjects(seg, outlines.shape[0], outlines.shape[1])
rles = dedupe_contours(rles, dataset)
return rles
def load_label_volume(label_directory, num_labels, h5):
filenames = [os.path.join(label_directory, 'labels_{:06d}.png'.format(idx + 1)) for idx in range(num_labels)]
size = imread.imread(filenames[0]).shape
labels = h5.require_dataset('labels', shape=((len(filenames),) + size), dtype=np.int32)
for idx, f in enumerate(filenames):
if os.path.exists(f):
labels[idx, ...] = imread.imread(f)
# use skimage to label individual objects
relabel = skimage.measure.label(labels[...], background=0)
labels[...] = relabel
return labels
def test_quality():
def pixel_diff(a):
return np.mean(np.abs(a.astype(float) - data))
data = np.arange(256*256*3)
data %= 51
data = data.reshape((256,256,3))
data = data.astype(np.uint8)
imsave('imread_def.jpg', data)
imsave('imread_def91.jpg', data, opts={'jpeg:quality': 91} )
readback = imread('imread_def.jpg')
readback91 = imread('imread_def91.jpg')
assert pixel_diff(readback91) < pixel_diff(readback)
def test_quality():
def pixel_diff(a):
return np.mean(np.abs(a.astype(float) - data))
data = np.arange(256 * 256 * 3)
data %= 51
data = data.reshape((256, 256, 3))
data = data.astype(np.uint8)
imsave("imread_def.jpg", data)
imsave("imread_def91.jpg", data, opts={"jpeg:quality": 91})
readback = imread("imread_def.jpg")
readback91 = imread("imread_def91.jpg")
assert pixel_diff(readback91) < pixel_diff(readback)
def _read_png_matrix(cls, name):
print "Reading LUT image: %s" % name
return imread.imread(
AppDirectoriesFinder().storages.get(
'django_instakit').path(join(
'django_instakit', 'lut',
"%s.png" % name)))
def test_read_back_colour_16bit():
im = np.random.random((16, 8, 3)) * 65535.0
im = im.astype(np.uint16)
imsave(_filename, im)
im2 = imread(_filename)
assert im.shape == im2.shape
assert np.all(im == im2)
def readxcf(xcf_filename, formatstr, _flags):
'''
im = readxcf(xcf_filename, formatstr)
Returns a numpy array with the (flattened) XCF file.
Depends on `xcf2png` being available.
Parameters
----------
xcf_filename : str
Filename
formatstr : str
format. Must be 'xcf'
Returns
-------
im : ndarray
'''
from os import system, unlink
from tempfile import NamedTemporaryFile
if formatstr != 'xcf':
raise ValueError('imread.imread.readxcf: Format string must be \'xcf\'')
N = NamedTemporaryFile(suffix='.png')
output = system('xcf2png %s >%s' % (xcf_filename,N.name))
if output:
raise OSError('imread.readxcf: xcf format is only supported through the xcf2png utility, which imread could not run')
return imread.imread(N.name, return_metadata=True)
def readxcf(xcf_filename, formatstr):
'''
im = readxcf(xcf_filename, formatstr)
Returns a numpy array with the (flattened) XCF file.
Depends on `xcf2png` being available.
Parameters
----------
xcf_filename : str
Filename
formatstr : str
format. Must be 'xcf'
Returns
-------
im : ndarray
'''
from os import system, unlink
from tempfile import NamedTemporaryFile
if formatstr != 'xcf':
raise ValueError(
'imread.imread.readxcf: Format string must be \'xcf\'')
N = NamedTemporaryFile(suffix='.png')
output = system('xcf2png %s >%s' % (xcf_filename, N.name))
if output:
raise OSError(
'imread.readxcf: xcf format is only supported through the xcf2png utility, which imread could not run'
)
return imread.imread(N.name)
def test_indexed():
im = imread(file_path('py-installer-indexed.bmp'))
assert np.any(im)
assert im.shape == (352, 162, 3)
assert np.any(im[:, :, 0])
assert np.any(im[:, :, 1])
assert np.any(im[:, :, 2])
def test_read_back_colour():
im = np.arange(256).astype(np.uint8).reshape((32, -1))
im = np.dstack([im, im * 0, 255 - im])
imsave(_filename, im)
im2 = imread(_filename)
assert im.shape == im2.shape
assert np.all(im == im2)
def readxcf(xcf_filename, formatstr, _flags):
'''
im = readxcf(xcf_filename, formatstr)
Returns a numpy array with the (flattened) XCF file.
Depends on `xcf2png` being available.
Parameters
----------
xcf_filename : str
Filename
formatstr : str
format. Must be 'xcf'
Returns
-------
im : ndarray
'''
from os import system
from tempfile import NamedTemporaryFile
from distutils.spawn import find_executable as which
if formatstr != 'xcf':
raise ValueError('imread.imread.readxcf: Format string must be \'xcf\'')
if not which('xcf2png'):
raise OSError('imread.readxcf: xcf format is only supported through the xcf2png utility, which imread could not find')
N = NamedTemporaryFile(suffix='.png')
output = system('xcf2png %s >%s' % (xcf_filename,N.name))
if output:
raise OSError('imread.readxcf: xcf format is only supported through the xcf2png utility, which failed to run')
return imread.imread(N.name, return_metadata=True)
def test_indexed():
im = imread('imread/tests/data/py-installer-indexed.bmp')
assert np.any(im)
assert im.shape == (352, 162, 3)
assert np.any(im[:,:,0])
assert np.any(im[:,:,1])
assert np.any(im[:,:,2])
def test_jpeg():
f = np.arange(64 * 16).reshape((64, 16))
f %= 16
f = f.astype(np.uint8)
imsave(_filename, f, "jpeg")
g = imread(_filename).squeeze()
assert np.mean(np.abs(f.astype(float) - g)) < 1.0
def test_read_back_colour():
im = np.arange(256).astype(np.uint8).reshape((32,-1))
im = np.dstack([im, im*0, 255-im])
imsave(_filename, im)
im2 = imread(_filename)
assert im.shape == im2.shape
assert np.all(im == im2)
def changeindexcombo5(self):
"""méthode exécutée en cas de changement d'affichage du combo5
"""
#self.combo5.clear()#surtout pas!!
#quel le parent?
#recup l'item actif de combo4
print "combo5 cur Index", self.combo5.currentIndex()
#identifier le node correspondant4
## suppossons que ce soit==self.combo1.currentIndex()
##parent est un etree.Element, son nom devrait être l'item courant de combo1
#parent=self.node5[self.combo5.currentIndex()]
#demander ses enfants,
##demander le nom des enfants
# childrenlist=self.GetChildrenName(parent)
# print "from changeindex combo5",childrenlist
# self.combo5.addItems(QtCore.QStringList(childrenlist))
pathto=os.path.join(str(self.combo1.currentText()),\
str(self.combo2.currentText()),\
str(self.combo3.currentText()),\
str(self.combo4.currentText()),\
str(self.combo5.currentText()))
self.lbl.setText(str(pathto))
workdir = "/home/simon/MFISH/"
ndimage = imread.imread(workdir + str(pathto))
convert = qnd.array2qimage(ndimage[::2, ::2], normalize=True)
qpix = QtGui.QPixmap(convert)
image = QtGui.QLabel(self)
image.setPixmap(qpix)
#posit = QtGui.QGridLayout()
self.posit.addWidget(image, 2, 0)
self.setLayout(self.posit)
self.setWindowTitle('Cascade Menus')
self.show()
def test_indexed():
im = imread('imread/tests/data/py-installer-indexed.bmp')
assert np.any(im)
assert im.shape == (352, 162, 3)
assert np.any(im[:, :, 0])
assert np.any(im[:, :, 1])
assert np.any(im[:, :, 2])
def main(pth):
from pylire.compatibility.utils import timecheck
from pylire.process.channels import RGB
from imread import imread
(R, G, B) = RGB(imread(pth))
'''@timecheck
def timetest_scalar(R, G, B):
histogram_keys = opponent_histogram_scalar_keys(R, G, B)
hh = histogram_normalize(
histogram_count(
histogram_keys))
print "\tvectorized scalar func:"
print oh_str(h)
print ""
print "\tbinary hash:"
print oh_bithash_str(h)
print "\t"
return hh
timetest_scalar(R, G, B)
'''
@timecheck
def timetest_vector(R, G, B):
histogram_key_vec = opponent_histogram_key_vector_ORIG(R, G, B)
hh = histogram_normalize(
histogram_count(
histogram_key_vec))
print "\tpure-python vector func:"
print oh_str(hh)
print ""
print "\tbinary hash:"
print oh_bithash_str(hh)
print "\t"
return hh
timetest_vector(R, G, B)
@timecheck
def timetest_vector(R, G, B):
histogram_key_vec_inline = opponent_histogram_key_vector(R, G, B)
hh = histogram_normalize(
histogram_count(
histogram_key_vec_inline))
print "\tnative (inlined and cythonized) vector func:"
print oh_str(hh)
print ""
print "\tbinary hash:"
print oh_bithash_str(hh)
print "\t"
return hh
timetest_vector(R, G, B)
def changeindexcombo4(self):
"""méthode exécutée en cas de changement d'affichage du combo4
"""
self.combo5.clear()
print "combo4 cur Index", self.combo4.currentIndex()
self.node4 = self.node3[self.combo3.currentIndex()].getchildren()
parent = self.node4[self.combo4.currentIndex()]
#demander ses enfants,
##demander le nom des enfants
childrenlist = self.GetChildrenName(parent)
self.combo5.addItems(QtCore.QStringList(childrenlist))
pathto=os.path.join(str(self.combo1.currentText()),\
str(self.combo2.currentText()),\
str(self.combo3.currentText()),\
str(self.combo4.currentText()),\
str(self.combo5.currentText()))
self.lbl.setText(str(pathto))
workdir = "/home/simon/MFISH/"
ndimage = imread.imread(workdir + str(pathto))
convert = qnd.array2qimage(ndimage[::2, ::2], normalize=True)
qpix = QtGui.QPixmap(convert)
image = QtGui.QLabel(self)
image.setPixmap(qpix)
#posit = QtGui.QGridLayout()
self.posit.addWidget(image, 2, 0)
self.setLayout(self.posit)
self.setWindowTitle('Cascade Menus')
self.show()
def load_letter(folder, min_num_images):
image_files = os.listdir(folder)
dataset = np.ndarray(shape=(len(image_files), image_size, image_size))
print(folder)
num_images = 0
for image in image_files:
image_file = os.path.join(folder, image)
try:
image_data = (imread.imread(image_file, as_grey=True).astype(float)
- pixel_depth / 2) / pixel_depth
if image_data.shape != (image_size, image_size):
raise Exception("Unexpected Image Shape %s" % image_data.shape)
dataset[num_images, :, :] = image_data
num_images += 1
except (IOError, RuntimeError) as e:
print("Could not read file :", image_file, " : ", e,
"Skipping this file.")
dataset = dataset[0:num_images, :, :]
if num_images < min_num_images:
raise Exception("Count of images less than expected: %d < %d" %
(num_images, min_num_images))
print("Full Dataset Tensor: ", dataset.shape)
print("Mean : ", np.mean(dataset))
print("Standard Deviation: ", np.std(dataset))
return dataset
def changeindexcombo5(self):
"""méthode exécutée en cas de changement d'affichage du combo5
"""
#self.combo5.clear()#surtout pas!!
#quel le parent?
#recup l'item actif de combo4
print "combo5 cur Index",self.combo5.currentIndex()
#identifier le node correspondant4
## suppossons que ce soit==self.combo1.currentIndex()
##parent est un etree.Element, son nom devrait être l'item courant de combo1
#parent=self.node5[self.combo5.currentIndex()]
#demander ses enfants,
##demander le nom des enfants
# childrenlist=self.GetChildrenName(parent)
# print "from changeindex combo5",childrenlist
# self.combo5.addItems(QtCore.QStringList(childrenlist))
pathto=os.path.join(str(self.combo1.currentText()),\
str(self.combo2.currentText()),\
str(self.combo3.currentText()),\
str(self.combo4.currentText()),\
str(self.combo5.currentText()))
self.lbl.setText(str(pathto))
workdir="/home/simon/MFISH/"
ndimage=imread.imread(workdir+str(pathto))
convert=qnd.array2qimage(ndimage[::2,::2],normalize=True)
qpix = QtGui.QPixmap(convert)
image = QtGui.QLabel(self)
image.setPixmap(qpix)
#posit = QtGui.QGridLayout()
self.posit.addWidget(image, 2, 0)
self.setLayout(self.posit)
self.setWindowTitle('Cascade Menus')
self.show()
def changeindexcombo4(self):
"""méthode exécutée en cas de changement d'affichage du combo4
"""
self.combo5.clear()
print "combo4 cur Index",self.combo4.currentIndex()
self.node4=self.node3[self.combo3.currentIndex()].getchildren()
parent=self.node4[self.combo4.currentIndex()]
#demander ses enfants,
##demander le nom des enfants
childrenlist=self.GetChildrenName(parent)
self.combo5.addItems(QtCore.QStringList(childrenlist))
pathto=os.path.join(str(self.combo1.currentText()),\
str(self.combo2.currentText()),\
str(self.combo3.currentText()),\
str(self.combo4.currentText()),\
str(self.combo5.currentText()))
self.lbl.setText(str(pathto))
workdir="/home/simon/MFISH/"
ndimage=imread.imread(workdir+str(pathto))
convert=qnd.array2qimage(ndimage[::2,::2],normalize=True)
qpix = QtGui.QPixmap(convert)
image = QtGui.QLabel(self)
image.setPixmap(qpix)
#posit = QtGui.QGridLayout()
self.posit.addWidget(image, 2, 0)
self.setLayout(self.posit)
self.setWindowTitle('Cascade Menus')
self.show()
def test_jpeg():
f = np.arange(64*16).reshape((64,16))
f %= 16
f = f.astype(np.uint8)
imsave(_filename, f, 'jpeg')
g = imread(_filename).squeeze()
assert np.mean(np.abs(f.astype(float)-g)) < 1.
def _hela_load_function(path):
from imread import imread
import numpy as np
im = imread(path)
if im.dtype == np.bool_:
return im.astype(np.uint8)
return im
def loadimgs(path, n=0):
X = []
y = []
cat_dict = {}
lang_dict = {}
curr_y = n
for alphabet in os.listdir(path):
print("loading alphabet: " + alphabet)
lang_dict[alphabet] = [curr_y, None]
alphabet_path = os.path.join(path, alphabet)
for letter in os.listdir(alphabet_path):
cat_dict[curr_y] = (alphabet, letter)
category_images = []
letter_path = os.path.join(alphabet_path, letter)
for filename in os.listdir(letter_path):
image_path = os.path.join(letter_path, filename)
image = imread(image_path)
category_images.append(image)
y.append(curr_y)
try:
X.append(np.stack(category_images))
except ValueError as e:
print(e)
print("error - category_images: ", category_images)
lang_dict[alphabet][1] = curr_y
curr_y += 1
y = np.vstack(y)
X = np.stack(X)
return X, y, lang_dict
def make_neume_chart(times, pitches, basename):
# background_file = 'images/neumy A.bmp'
# background_file = 'images/neumy text 3.bmp'
# background_file = 'images/neumy neumy neumy.bmp'
# background_file = 'images/neumy uni.bmp'
background_file = 'images/big uni.bmp'
manuscript = imread.imread(background_file)
# small version: works
# my_dpi = 30
# plt.figure(figsize=(577/my_dpi,216/my_dpi), dpi=my_dpi, frameon=False)
# plt.imshow(manuscript)
# note_start = 100
# note_end = 520
# pitch_offset = 220
# pitch_scale = 2.5
# # plt.plot([note_start, note_start],[100,150],'r')
# # plt.plot([note_end, note_end],[100,150],'r')
my_dpi = 72
plt.figure(figsize=(2401/my_dpi,901/my_dpi), dpi=my_dpi, frameon=False)
plt.imshow(manuscript)
note_start = 350
note_end = 2100
pitch_offset = 220
pitch_scale = 8
# plt.plot([note_start, note_start],[100,500],'r')
# plt.plot([note_end, note_end],[100,500],'r')
pitch_time_range = times[-1] - times[0]
manuscript_time_range = note_end - note_start
times_scaled = (times - times[0]) * (manuscript_time_range/pitch_time_range) + note_start
# E (lowest line is pitch value 26)
# small works
# pitches_scaled = 136 - (np.array(pitches)-26) * pitch_scale
pitches_scaled = 565 - (np.array(pitches)-26) * pitch_scale
# small works
# plt.plot(times_scaled, pitches_scaled, 'ks', markersize=12)
plt.plot(times_scaled, pitches_scaled, 'ks', markersize=24)
# plt.plot([100, 200],[136,136],'r')
# plt.plot([100, 200],[127,127],'r')
# plt.show()
plt.axis([0,2400,900,0])
plt.axis('off')
plt.tight_layout()
# plt.savefig('images/generated/' + basename.replace('csv','png'), dpi=my_dpi * 10)
plt.savefig('images/generated/' + basename.replace('csv','png'), dpi=my_dpi)
def test_read_back_16():
np.random.seed(21)
simple = np.random.random_sample((128,128))
simple *= 8192
simple = simple.astype(np.uint16)
imsave(_filename, simple)
back = imread(_filename)
assert np.all(simple == back)
def test_read_back_with_metadata():
simple = np.arange(16*16).reshape((16,16))
simple = simple.astype(np.uint8)
meta = b'123qwe'
imsave(_filename, simple, metadata=meta)
back,meta_read = imread(_filename, return_metadata=True)
assert np.all(simple == back)
assert meta == meta_read
def test_read_back_16():
np.random.seed(21)
simple = np.random.random_sample((128, 128))
simple *= 8192
simple = simple.astype(np.uint16)
imsave(_filename, simple)
back = imread(_filename)
assert np.all(simple == back)
def _img():
if IMG_CACHED[0] is None:
i = imread.imread("images/gelface.jpg") / 255.0
i = i[::4, ::4]
i[0] = 0.0 # ensure we get all black
i[1] = 1.0 # ensure we get all white
IMG_CACHED[0] = np.ascontiguousarray(i)
return IMG_CACHED[0]
def test_read_back_with_metadata():
simple = np.arange(16 * 16).reshape((16, 16))
simple = simple.astype(np.uint8)
meta = b'123qwe'
imsave(_filename, simple, metadata=meta)
back, meta_read = imread(_filename, return_metadata=True)
assert np.all(simple == back)
assert meta == meta_read
def nda_from_path(image_file):
try:
from imread import imread
except ImportError:
from PIL import Image
import numpy
return numpy.array(Image.open(image_file))
else:
return imread(os.path.abspath(image_file))
def test_imsave_multi():
im = imread(file_path('arange512_16bit.png'))
im2 = im[::4, ::4]
ims = [im, im2]
imsave_multi(_filename, ims)
ims2 = imread_multi(_filename)
assert len(ims) == len(ims2)
for a, b in zip(ims, ims2):
assert np.all(a == b)
def nda_from_path(image_file):
try:
from imread import imread
except ImportError:
from PIL import Image
import numpy
return numpy.array(Image.open(image_file))
else:
return imread(os.path.abspath(image_file))
def test_non_carray():
np.random.seed(87)
simple = np.random.random_sample((128, 128, 3))
simple *= 255
simple = simple.astype(np.uint8)
simple = simple[32:64, 32::2]
imsave(_filename, simple, 'png')
back = imread(_filename)
assert np.all(simple == back)
def test_imsave_multi():
im = imread(file_path('arange512_16bit.png'))
im2 = im[::4, ::4]
ims = [im, im2]
imsave_multi(_filename, ims)
ims2 = imread_multi(_filename)
assert len(ims) == len(ims2)
for a,b in zip(ims, ims2):
assert np.all(a == b)
def test_non_carray():
np.random.seed(87)
simple = np.random.random_sample((128,128,3))
simple *= 255
simple = simple.astype(np.uint8)
simple = simple[32:64,32::2]
imsave(_filename, simple, 'png')
back = imread(_filename)
assert np.all(simple == back)
def compare_with_blob(filename, formatstr):
from os import path
filename = path.join(
path.dirname(__file__),
'data',
filename)
fromfile= imread.imread(filename)
fromblob = imread_from_blob(open(filename, 'rb').read(), formatstr)
assert np.all(fromblob == fromfile)
def readxcf(xcffilename):
'''
img = readxcf(xcf_filename)
Returns a numpy array with the (flattened) XCF file.
'''
from os import unlink
print 'readxcf(%s)' % xcffilename
N=tempfile.NamedTemporaryFile(suffix='.png')
system('xcf2png %s >%s' % (xcffilename,N.name))
return imread(N.name)
def test_random():
np.random.seed(23)
for i in range(8):
simple = np.random.random_sample((64,64,3))
simple *= 255
simple = simple.astype(np.uint8)
if i < 3:
simple[:,:,i] = 0
imsave(_filename, simple, 'png')
back = imread(_filename)
assert np.all(simple == back)
def test_random():
np.random.seed(23)
for i in range(8):
simple = np.random.random_sample((64, 64, 3))
simple *= 255
simple = simple.astype(np.uint8)
if i < 3:
simple[:, :, i] = 0
imsave(_filename, simple, 'png')
back = imread(_filename)
assert np.all(simple == back)
def test_image(model):
yes_no = True
while yes_no:
path = input("Please enter the path: ")
im = imread(path)
im = im.astype("float32")
im = im / 255
pr = model.predict_classes(im.reshape((28, 28, 1, 1)))
print("Result: ", pr)
yes_no = True if (input("Would you like to continue? [Y/n] ") == "y"
or "Y") else False
def open_hsv_image(path):
rgb = imread.imread(path)
hsv_image = color.rgb2hsv(rgb)
hsv_image *= 255
hsv_image = hsv_image.astype(np.uint8)
h = hsv_image[:, :, 0]
s = hsv_image[:, :, 1]
v = hsv_image[:, :, 2]
return HsvImage(h=h, s=s, v=v)
def __init__(self, json, dim, flatten=True, rot=''):
#self.base = json['base']
self.full = json['full']
self.tags = json['tags']
self.base = inferBase(self.full)
self.filePath = thumbBase(self.base, dim, rot)
if not os.path.exists(self.filePath):
raise Exception('file does not exist: %s' % self.filePath)
self.np_data = imread(self.filePath)
if flatten:
self.np_data = np.array(self.np_data).flatten()
self.np_label = np.array([tag2n(json, t) for t in TAGS])
def imread(fname, dtype=None):
"""Load an image from file.
Parameters
----------
fname : str
Name of input file
"""
im = _imread.imread(fname)
if dtype is not None:
im = convert(im, dtype)
return im
def imread(fname, dtype=None):
"""Load an image from file.
Parameters
----------
fname : str
Name of input file
"""
im = _imread.imread(fname)
if dtype is not None:
im = _convert(im, dtype)
return im
def main(pth):
from pylire.compatibility.utils import timecheck
from imread import imread
ndim = imread(pth)
#ndim_shape = shape_from_image(ndim)
@timecheck
def timetest_FDCT_python(ndim_shape):
FDCT(ndim_shape[0])
FDCT(ndim_shape[1])
FDCT(ndim_shape[2])
#print(ndim_shape)
return ndim_shape
@timecheck
def timetest_FDCT_cython(ndim_shape):
C_FDCT(ndim_shape[0])
C_FDCT(ndim_shape[1])
C_FDCT(ndim_shape[2])
#print(ndim_shape)
return ndim_shape
ns1 = shape_from_image(ndim)
out_py = timetest_FDCT_python(ns1)
ns2 = shape_from_image(ndim)
out_cy = timetest_FDCT_cython(ns2)
print("")
print("RESULTS EQUAL:")
print(numpy.equal(out_py, out_cy))
print("")
@timecheck
def timetest_color_layout_shape(ndim):
shape = shape_from_image(ndim)
print("image shape:")
print(ndim.shape)
print("")
# print("image 'shape':")
# print(shape)
# print("")
print("image 'shape' shape:")
print(shape.shape)
print("")
#timetest_color_layout_shape(ndim)
@timecheck
def timetest_color_layout(ndim):
(cY, cCb, cCr) = color_layout(ndim)
def old_main():
#imfuckingshowalready = lambda mx: Image.fromarray(mx).show()
old_identity = AppDirectoriesFinder().storages.get(
'django_instakit').path(join(
'django_instakit', 'lut', 'identity.png'))
im_old_identity = imread.imread(old_identity)
im_identity = numpy.zeros_like(im_old_identity)
for bx in xrange(0, 8):
for by in xrange(0, 8):
for r in xrange(0, 64):
for g in xrange(0, 64):
im_identity[
int(g + by * 64),
int(r + bx * 64)] = numpy.array((
int(r * 255.0 / 63.0 + 0.5),
int(g * 255.0 / 63.0 + 0.5),
int((bx + by * 8.0) * 255.0 / 63.0 + 0.5)),
dtype=numpy.uint8)
print "THE OLD: %s, %s, %s" % (
im_old_identity.size, im_old_identity.shape,
str(im_old_identity.dtype))
#print im_old_identity
print ""
print "THE NEW: %s, %s, %s" % (
im_identity.size, im_identity.shape,
str(im_identity.dtype))
#print im_identity
print ""
print "THE END: %s" % bool(im_old_identity.shape == im_identity.shape)
#print im_old_identity == im_identity
#imfuckingshowalready(im_identity)
#imfuckingshowalready(im_old_identity)
pil_im_old_identity = Image.fromarray(im_old_identity)
pil_im_old_identity.save('/tmp/im_old_identity.jpg',
format="JPEG")
pil_im_identity = Image.fromarray(im_identity)
pil_im_identity.save('/tmp/im_identity.jpg',
format="JPEG")
def test_store_image(self):
#img = random.choice(os.listdir(settings.testimages))
print('', file=sys.stderr)
for img in os.listdir(settings.testimages):
print("Reading image:", img, file=sys.stderr)
nda = imread.imread(os.path.join(settings.testimages, img))
self.h5.save('%s.nda' % img, nda)
print("Repacking HDF5 storage...", file=sys.stderr)
self.h5._repack()
nda2 = self.h5.open('/%s.nda' % img)
self.assertEqual(nda.sum(), nda2.sum())
self.assertTrue(numpy.equal(nda, nda2).all())
def test_store_image(self):
#img = random.choice(os.listdir(settings.testimages))
print('', file=sys.stderr)
for img in os.listdir(settings.testimages):
print("Reading image:", img, file=sys.stderr)
nda = imread.imread(os.path.join(settings.testimages, img))
self.h5.save('%s.nda' % img, nda)
print("Repacking HDF5 storage...", file=sys.stderr)
self.h5._repack()
nda2 = self.h5.open('/%s.nda' % img)
self.assertEqual(nda.sum(), nda2.sum())
self.assertTrue(numpy.equal(nda, nda2).all())
def on_post(self, req, resp, **kwargs):
image = req.get_param('imagefile')
raw = image.file.read()
filename = image.filename
say('read file:', filename, 'len:', len(raw))
tmpfile = tempfile.mktemp()
say('writing to file %s' % tmpfile)
with open(tmpfile, 'wb') as tmp:
tmp.write(raw)
tmp.close()
data = np.array([imread(tmpfile)])
say('upload numpy shape: %s' % str(data.shape))
os.remove(tmpfile)
if self.model is None:
say('on_get, loading model')
self.model = loadModel()
say('on_get, loaded model')
# verify the shape of the data
# FIXME: this assumes unflattened data for 'mnist' model from ml-keras-plates.py
# If it ever tries to run on flattened data, the below will need to be smarter
if len(data.shape) != 4 or self.width != data.shape[
1] or self.height != data.shape[2] or data.shape[3] != 3:
logger.debug('data dimensions', len(data.shape), data.shape[1],
data.shape[2], data.shape[3])
msg = 'expect %dx%d image size, got shape %s' % (
self.width, self.height, str(data.shape))
logger.error('tag resource post:', msg)
raise falcon.HTTPError(falcon.HTTP_400, msg)
try:
#faulthandler.dump_traceback_later(4, repeat=True)
predicts = self.model.predict(data)
except:
print('EXCEPTION in predicts')
traceback.print_exc()
raise falcon.HTTPError(falcon.HTTP_500, 'internal error')
say('ran predictions: %s' % str(predicts))
colors = ['blue', 'gray', 'white']
say(', '.join(
['%s=%f' % (colors[i], predicts[0][i]) for i in range(0, 3)]))
body = {'tags': [n2c(predicts[0])]}
resp.body = json.dumps(body)
say('returning predictions %s' % str(resp.body))
def open_lab_image(path):
# The 1 tells openCV imread to get rgb channels
rgb = imread.imread(path)
lab_image = color.rgb2lab(rgb)
# NOTE the maximum values of l, a, b are 100, +/- 128, +/- 128
lab_image[:, :, 0] *= (2.55)
lab_image[:, :, 1] += 128.0
lab_image[:, :, 2] += 128.0
# Convert to 8bit so algorithms can work with it easily
lab_8bit = lab_image.astype(np.uint8)
return LabImage(
l=lab_8bit[:, :, 0],
a=lab_8bit[:, :, 1],
b=lab_8bit[:, :, 2]
)
def main(pth):
from pylire.compatibility.utils import timecheck
from pylire.process.channels import RGB
from imread import imread
im = imread(pth)
(R, G, B) = RGB(im)
@timecheck
def timetest_cythonized_HOG(ndim):
hogg = hog(ndim)
print("Cythonized HOG() function:")
print_array_info(hogg)
print("")
timetest_cythonized_HOG(im)
'''
