def view(self):
"""Create a matplotlib animated view of the contents.
"""
cv = CollectionViewer(self.images)
cv.show()
return cv
def __init__(self, search_pattern):
bottom_px = 60
self.bottom_px = bottom_px
plugin = Plugin(image_filter=self.image_filter, dock="right")
self.setup_names = ['Bottom']
self.show_orig = CheckBox('show_orig', value=False, alignment='left')
plugin += self.show_orig
plugin += ComboBox('setup', self.setup_names)
plugin += Slider('bottom_px',
0,
500,
value=self.bottom_px,
value_type='int')
plugin += Button("Print", callback=self.on_print_click)
fnames = [path for path in glob.iglob(search_pattern, recursive=True)]
images, gray_images = read_images(fnames)
self.viewer = CollectionViewer(images)
self.viewer += plugin
def weights_viewer(path):
w = []
for i in range(1000):
filename = "model.ckpt.{:04d}.hdf5".format(i)
m.load_weights(path + filename)
k, b = m.get_weights()
i = np.concatenate((k, np.ones((2,100)), b[None,]))
w.append(i)
CollectionViewer(w).show()
def __init__(self, search_pattern):
blur_kernel_size = 1
cal_fnames = [
path for path in glob.iglob('camera_cal/*.jpg', recursive=True)
]
cal_images, cal_gray_images = read_images(cal_fnames)
self.objpoints, self.imgpoints, _ = get_chessboard_corners(
cal_images, cal_gray_images)
self.blur_kernel_size = blur_kernel_size
self.top_left_x = top_left_x
self.top_left_y = top_left_y
self.top_right_x = top_right_x
self.top_right_y = top_right_y
self.bottom_right_x = bottom_right_x
self.bottom_right_y = bottom_right_y
self.bottom_left_x = bottom_left_x
self.bottom_left_y = bottom_left_y
plugin = Plugin(image_filter=self.image_filter, dock="right")
self.setup_names = [
'ROI poly', "ROI Transformation", 'ROI Transformed',
"Final Transformation poly", 'Final Transformation',
'Final Transformed'
]
self.show_orig = CheckBox('show_orig', value=False, alignment='left')
plugin += self.show_orig
plugin += ComboBox('setup', self.setup_names)
plugin += Slider('blur_kernel_size',
0,
31,
value=self.blur_kernel_size,
value_type='int')
plugin += Slider('top_left_x', 0, 1, value=self.top_left_x)
plugin += Slider('top_left_y', 0, 1, value=self.top_left_y)
plugin += Slider('top_right_x', 0, 1, value=self.top_right_x)
plugin += Slider('top_right_y', 0, 1, value=self.top_right_y)
plugin += Slider('bottom_right_x', 0, 1, value=self.bottom_right_x)
plugin += Slider('bottom_right_y', 0, 1, value=self.bottom_right_y)
plugin += Slider('bottom_left_x', 0, 1, value=self.bottom_left_x)
plugin += Slider('bottom_left_y', 0, 1, value=self.bottom_left_y)
plugin += Button("Print", callback=self.on_print_click)
fnames = [path for path in glob.iglob(search_pattern, recursive=True)]
images, gray_images = read_images(fnames)
self.viewer = CollectionViewer(images)
self.viewer += plugin
def test_collection_viewer():
img = data.astronaut()
img_collection = tuple(pyramid_gaussian(img, multichannel=True))
view = CollectionViewer(img_collection)
make_key_event(48)
view.update_index('', 2),
assert_equal(view.image, img_collection[2])
view.keyPressEvent(make_key_event(53))
assert_equal(view.image, img_collection[5])
view._format_coord(10, 10)
def testrun():
h_tasks = hierarchical_load('../train/')
print(len(h_tasks))
nsamples = process_the_study(h_tasks[10])
print('We have {} samples'.format(len(nsamples)))
to_show = []
for t in nsamples:
to_show.append(t.imgs[0, :, :])
to_show.append(t.imgs[1, :, :])
to_show.append(t.imgs[10, :, :])
to_show.append(t.imgs[20, :, :])
CollectionViewer(to_show).show()
"""
=====================
CollectionViewer demo
=====================
Demo of CollectionViewer for viewing collections of images. This demo uses
the different layers of the gaussian pyramid as image collection.
You can scroll through images with the slider, or you can interact with the
viewer using your keyboard:
left/right arrows
Previous/next image in collection.
number keys, 0--9
0% to 90% of collection. For example, "5" goes to the image in the
middle (i.e. 50%) of the collection.
home/end keys
First/last image in collection.
"""
import numpy as np
from skimage import data
from skimage.viewer import CollectionViewer
from skimage.transform import pyramid_gaussian
img = data.lena()
img_collection = tuple(pyramid_gaussian(img))
view = CollectionViewer(img_collection)
view.show()
def __init__(self, search_pattern):
self.plugin = Plugin(image_filter=self.image_filter, dock="right")
self.setup_names = ['Yellow', 'White', 'Yellow / White']
self.color_spaces = ['LAB', 'HSV', 'HLS', 'LUV']
self.show_orig = CheckBox('show_orig', value=False, alignment='left')
self.plugin += self.show_orig
self.setup = ComboBox('setup', self.setup_names)
self.color_space = ComboBox('color_space', self.color_spaces)
self.sensity_range = Slider('sensity_range',
10,
100,
value=sensity_range,
value_type='int')
self.lower_yellow_1 = Slider('lower_yellow_1',
0,
255,
value=lower_yellow_1,
value_type='int')
self.lower_yellow_2 = Slider('lower_yellow_2',
0,
255,
value=lower_yellow_2,
value_type='int')
self.lower_yellow_3 = Slider('lower_yellow_3',
0,
255,
value=lower_yellow_3,
value_type='int')
self.upper_yellow_1 = Slider('upper_yellow_1',
0,
255,
value=upper_yellow_1,
value_type='int')
self.upper_yellow_2 = Slider('upper_yellow_2',
0,
255,
value=upper_yellow_2,
value_type='int')
self.upper_yellow_3 = Slider('upper_yellow_3',
0,
255,
value=upper_yellow_3,
value_type='int')
self.lower_white_1 = Slider('lower_white_1',
0,
255,
value=lower_white_1,
value_type='int')
self.lower_white_2 = Slider('lower_white_2',
0,
255,
value=lower_white_2,
value_type='int')
self.lower_white_3 = Slider('lower_white_3',
0,
255,
value=lower_white_3,
value_type='int')
self.upper_white_1 = Slider('upper_white_1',
0,
255,
value=upper_white_1,
value_type='int')
self.upper_white_2 = Slider('upper_white_2',
0,
255,
value=upper_white_2,
value_type='int')
self.upper_white_3 = Slider('upper_white_3',
0,
255,
value=upper_white_3,
value_type='int')
self.plugin += self.setup
self.plugin += self.color_space
# self.plugin += self.sensity_range
self.plugin += self.lower_yellow_1
self.plugin += self.lower_yellow_2
self.plugin += self.lower_yellow_3
self.plugin += self.upper_yellow_1
self.plugin += self.upper_yellow_2
self.plugin += self.upper_yellow_3
self.plugin += self.lower_white_1
self.plugin += self.lower_white_2
self.plugin += self.lower_white_3
self.plugin += self.upper_white_1
self.plugin += self.upper_white_2
self.plugin += self.upper_white_3
fnames = [path for path in glob.iglob(search_pattern, recursive=True)]
images, gray_images = read_images(fnames)
self.viewer = CollectionViewer(images)
self.viewer.connect_event('button_press_event', self.on_filter_color)
self.viewer.connect_event('key_press_event', self.on_press)
self.viewer += self.plugin
def show(self):
"""Show the stack of images in a skimage CollectionViewer window"""
#stackims=[self[i] for i in range(len(self))]
cv=CollectionViewer(self)
cv.show()
return cv
def __init__(self, search_pattern):
plugin = Plugin(image_filter=self.image_filter, dock="right")
self.setup_names = [
'Sobel Thresh X', 'Sobel Thresh Y', 'Sobel Thresh X / Y',
'Magnitude Thresh', 'Gradient Direction',
'Magnitude / Gradient Direction Thresh', "2 & 2", "2 | 2"
]
self.show_orig = CheckBox('show_orig', value=False, alignment='left')
plugin += self.show_orig
plugin += ComboBox('setup', self.setup_names)
plugin += Slider('grad_ksize',
0,
31,
value=grad_ksize,
value_type='int')
plugin += Slider('grad_thresh_low',
0,
255,
value=grad_thresh_low,
value_type='int')
plugin += Slider('grad_thresh_high',
0,
255,
value=grad_thresh_high,
value_type='int')
plugin += Slider('mag_binary_ksize',
0,
31,
value=mag_binary_ksize,
value_type='int')
plugin += Slider('mag_binary_thresh_low',
0,
255,
value=mag_binary_thresh_low,
value_type='int')
plugin += Slider('mag_binary_thresh_high',
0,
255,
value=mag_binary_thresh_high,
value_type='int')
plugin += Slider('dir_binary_ksize',
0,
31,
value=dir_binary_ksize,
value_type='int')
plugin += Slider('dir_binary_thresh_low',
0,
np.pi,
value=dir_binary_thresh_low)
plugin += Slider('dir_binary_thresh_high',
0,
np.pi,
value=dir_binary_thresh_high)
fnames = [path for path in glob.iglob(search_pattern, recursive=True)]
images, gray_images = read_images(fnames)
self.viewer = CollectionViewer(images)
self.viewer += plugin
return util.img_as_ubyte(io.imread(f), force_copy=True)
slice_collection = io.ImageCollection(path.join(SLICE_DIR, SLICE_PATTERN),
load_func=load_uint8)
vol_img = slice_collection.concatenate()
img_otsu = otsu(vol_img)
save_samples('otsu_mask_initial', red_overlay(vol_img, img_otsu))
save_samples('otsu_initial', img_otsu)
# img_sobel = slice_by_slice(sobel_edges, vol_img, datatype=np.float)
# img_binary = img_sobel > EDGE_THRESHOLD
# save_samples('sobel', img_sobel)
# save_samples('sobel_binary', img_binary)
# save_samples('sobel_overlay', red_overlay(vol_img, img_binary))
img_binary = slice_by_slice(fill_interior, img_otsu)
save_samples('filled', red_overlay(vol_img, img_binary))
img_binary = slice_by_slice(erode_to_infill, img_binary)
save_samples('eroded', red_overlay(vol_img, img_binary))
img_binary[28:84, :, :] = 0
percentage = 100 * np.sum(img_otsu & img_binary) / np.sum(img_binary)
print('Infill percentage: {0} %'.format(percentage))
viewer = CollectionViewer(red_overlay(vol_img, img_binary))
viewer.show()
from skimage import morphology from scipy import ndimage as ndi from skimage.viewer import ImageViewer from skimage.viewer import CollectionViewer import numpy as np im3d = np.random.rand(100, 100, 100) viewer3d = CollectionViewer(im3d) viewer3d.show()
#!/usr/bin/env python
import numpy as np
import skimage.io as io
from skimage.viewer import CollectionViewer
import cremi.evaluation as evaluation
import cremi.io.CremiFile as CremiFile
if __name__ == "__main__":
img = [io.imread('example.png')]
for w in (0, 2, 4, 10):
target = np.copy(img[0])[...,np.newaxis]
evaluation.create_border_mask(img[0][...,np.newaxis],target,w,105,axis=2)
img.append(target[...,0])
v = CollectionViewer(img)
v.show()
cfIn = CremiFile('example.h5', 'r')
cfOut = CremiFile('output.h5', 'a')
evaluation.create_and_write_masked_neuron_ids(cfIn, cfOut, 3, 240, overwrite=True)
def show_image_collection(im3d):
viewer3d = CollectionViewer(im3d)
viewer3d.show()
import matplotlib.pyplot as plt
folder = r'..\tests\coretestdata\hysteresis_test'
editfolder = r'..\tests\coretestdata\hysteresis_test\edited_ims'
os.chdir(folder)
background = KerrArray('image0000_background.png')
background = background.convert_float()
print 'loading images...'
kl = KerrList('image000*unproccessed.png',
conserve_memory=False,
get_metadata=False)
list(kl) #make sure all images loaded
print 'images loaded'
#%%
print 'convert float'
kl.apply_all('convert_float') #convert pixels to decimal values
print 'correct_drift'
kl.apply_all('correct_drift', ref=background, upsample_factor=200)
print 'subtract'
for i, im in enumerate(kl):
kl[i] = 16 * (kl[i] - background) + 0.5
kl[i] = kl[i].clip_intensity()
#skimage.io.imsave(os.path.join(savefolder,kl.files[i]),kl[i])
print np.array(kl.slice_metadata(key='correct_drift', values_only=True))
cv = CollectionViewer(kl)
cv.show()
