def test_minsize():
# single-channel:
img = data.coins()[20:168, 0:128]
for min_size in np.arange(10, 100, 10):
segments = felzenszwalb(img, min_size=min_size, sigma=3)
counts = np.bincount(segments.ravel())
# actually want to test greater or equal.
assert_greater(counts.min() + 1, min_size)
# multi-channel:
coffee = data.coffee()[::4, ::4]
for min_size in np.arange(10, 100, 10):
segments = felzenszwalb(coffee, min_size=min_size, sigma=3)
counts = np.bincount(segments.ravel())
# actually want to test greater or equal.
assert_greater(counts.min() + 1, min_size)
def fun_compare_colorsegmentation_and_display(image_data, number_segments=250, compactness_factor=10):
"""
The function is a copy of what does this link http://scikit-image.org/docs/dev/auto_examples/plot_segmentations.html
"""
segments_fz = felzenszwalb(image_data, scale=100, sigma=0.5, min_size=50)
segments_slic = slic(image_data, n_segments=number_segments, compactness=compactness_factor, sigma=1)
segments_quick = quickshift(image_data, kernel_size=3, max_dist=6, ratio=0.5)
print ("Felzenszwalb's number of segments: %d" % len(np.unique(segments_fz)))
print ("Slic number of segments: %d" % len(np.unique(segments_slic)))
print ("Quickshift number of segments: %d" % len(np.unique(segments_quick)))
fig, ax = plt.subplots(1, 3, sharex=True, sharey=True, subplot_kw={"adjustable": "box-forced"})
fig.set_size_inches(8, 3, forward=True)
fig.subplots_adjust(0.05, 0.05, 0.95, 0.95, 0.05, 0.05)
ax[0].imshow(mark_boundaries(image_data, segments_fz, color=(1, 0, 0)))
ax[0].set_title("Felzenszwalbs's method")
ax[1].imshow(mark_boundaries(image_data, segments_slic, color=(1, 0, 0)))
ax[1].set_title("SLIC")
ax[2].imshow(mark_boundaries(image_data, segments_quick, color=(1, 0, 0)))
ax[2].set_title("Quickshift")
for a in ax:
a.set_xticks(())
a.set_yticks(())
plt.show()
def SegmentationFelz_run_2d(rod):
img = img_as_float(
RodriguesToUnambiguousColor(rod["x"], rod["y"], rod["z"], maxRange=None, centerR=None).astype("uint8")
)
segments_slic = felzenszwalb(img, scale=100, sigma=0.0, min_size=10)
print("Slic number of segments: %d" % len(np.unique(segments_slic)))
return segments_slic
def selectiveSearch(image):
segments = felzenszwalb(image, scale=kFelzenszwalbScale)
numRegions = segments.max()
rectangles = []
for regionTag in range(numRegions):
selectedRegion = segments == regionTag
regionPixelIndices = np.transpose(np.nonzero(selectedRegion))
rectangle = aabb(regionPixelIndices)
rectangles.append(rectangle)
# Implement similarities, neighbourhood merging.
# Felzenszwalb's segmentation is ridiculously good already.
def debug():
marked = np.zeros(image.shape, dtype=np.uint8)
for rectangle in rectangles:
rr, cc = rectangle.pixels(marked.shape)
randcolor = randint(0, 255), randint(0, 255), randint(0, 255)
marked[rr, cc] = randcolor
print(image.shape, segments.shape, marked.shape)
io.imshow_collection([image, segments, marked])
io.show()
# debug()
return rectangles
def mask_felz(image, config):
#constants for felzenszwalb segmentation function
scale = config[':felzenszwalb'][':scale']
sigma = config[':felzenszwalb'][':sigma']
min_size = config[':felzenszwalb'][':min_size']
segments = felzenszwalb(image, scale, sigma, min_size)
return segments
def test_minsize():
# single-channel:
img = data.coins()[20:168,0:128]
for min_size in np.arange(10, 100, 10):
segments = felzenszwalb(img, min_size=min_size, sigma=3)
counts = np.bincount(segments.ravel())
# actually want to test greater or equal.
assert_greater(counts.min() + 1, min_size)
# multi-channel:
coffee = data.coffee()[::4, ::4]
for min_size in np.arange(10, 100, 10):
segments = felzenszwalb(coffee, min_size=min_size, sigma=3)
counts = np.bincount(segments.ravel())
# actually want to test greater or equal.
# the construction doesn't guarantee min_size is respected
# after intersecting the sementations for the colors
assert_greater(np.mean(counts) + 1, min_size)
def doSegment(param, img):
if param[0] == 'slic':
segments_res = slic(img, n_segments=int(param[1]), compactness=int(param[2]), sigma=int(param[3]), convert2lab=True)
elif param[0] == 'pff':
segments_res = felzenszwalb(img, scale=int(param[1]), sigma=float(param[2]), min_size=int(param[3]))
elif param[0] == 'quick':
segments_res = quickshift(img, kernel_size=int(param[1]), max_dist=int(param[2]), ratio=float(param[3]), convert2lab=True)
return segments_res
def test_merging():
# test region merging in the post-processing step
img = np.array([[0, 0.3], [0.7, 1]])
# With scale=0, only the post-processing is performed.
seg = felzenszwalb(img, scale=0, sigma=0, min_size=2)
# we expect 2 segments:
assert_equal(len(np.unique(seg)), 2)
assert_array_equal(seg[0, :], 0)
assert_array_equal(seg[1, :], 1)
def __init__(self, fname):
self.fname = fname
self.image_cache = {}
print 'loading image'
self.raw_img = io.imread(fname)
self._segment_ids = None
img_float = img_as_float(self.raw_img)
print 'segmenting image'
self.segments = felzenszwalb(img_float, scale=300, sigma=0.5, min_size=100)
def extract(self, image):
"""
Performs segmentation and returns a set of nd.array
"""
# Init the list of segmentations
segmentations = []
for param in self.params_:
sc, sg, m = param
segm_mask = segmentation.felzenszwalb(image, sc, sg, m)
segmentations.append(segm_mask)
return segmentations
def test_color():
# very weak tests.
img = np.zeros((20, 21, 3))
img[:10, :10, 0] = 1
img[10:, :10, 1] = 1
img[10:, 10:, 2] = 1
seg = felzenszwalb(img, sigma=0)
# we expect 4 segments:
assert_equal(len(np.unique(seg)), 4)
assert_array_equal(seg[:10, :10], 0)
assert_array_equal(seg[10:, :10], 2)
assert_array_equal(seg[:10, 10:], 1)
assert_array_equal(seg[10:, 10:], 3)
def test_grey():
# very weak tests.
img = np.zeros((20, 21))
img[:10, 10:] = 0.2
img[10:, :10] = 0.4
img[10:, 10:] = 0.6
seg = felzenszwalb(img, sigma=0)
# we expect 4 segments:
assert_equal(len(np.unique(seg)), 4)
# that mostly respect the 4 regions:
for i in range(4):
hist = np.histogram(img[seg == i], bins=[0, 0.1, 0.3, 0.5, 1])[0]
assert_greater(hist[i], 40)
def __init__(self, fname):
self.fname = fname
print 'loading image'
img = Image.open(fname)
img.thumbnail((1280,1024), Image.ANTIALIAS)
width, height = img.size
square_size = width * height
min_size = square_size / 300
self.raw_img = np.array(enhance(img).convert('RGB'))
self._segment_ids = None
img_float = img_as_float(self.raw_img)
print 'segmenting image'
self.segments = felzenszwalb(img_float, scale=300, sigma=0.25, min_size=min_size)
def clustering(inPixNP, width, height, scale=50, sigma=4.5, min_size=10):
segmentsNP = felzenszwalb(inPixNP, scale, sigma, min_size)
# felzenszwalb(image, scale=1, sigma=0.8, min_size=20)
# image : (width, height, 3) or (width, height) ndarray - Input image.
# scale : float - Free parameter. Higher means larger clusters.
# sigma : float - Width of Gaussian kernel used in preprocessing.
# min_size : int - Minimum component size. Enforced using postprocessing.
# create a data structure with regionIDs as keys and lists of their pixel coordinates as values:
maxSegmentID = np.max(segmentsNP)
segmentsByIDlist = [[] for i in range(maxSegmentID + 1)]
for y in range(0,height):
for x in range(0,width):
regionID = segmentsNP[y,x]
segmentsByIDlist[regionID].append((x, y))
return segmentsNP, segmentsByIDlist
def demo(image_name,color_space_list=None,ks=None,sim_feats_list=None,net='vgg16', cpu_mode=True):
''' Object Recognition Demo : Selective Search + RCNN
parameters
----------
image_name : filename of image stored in 'Data/img'
color_space_list : list of colorspaces to be used. Refer color_utils for list of possible colorspaces.
Default : [ 'HSV', 'LAB']
ks : list felzenszwalb scale/threshold and minimum segment size.
Default : [50, 100]
'''
blob_array = []
priority = []
img = plt.imread('Data/img/' + image_name + '.jpg')
seg_dir = 'Data/segments/'
if color_space_list is None: color_space_list = ['HSV','LAB']
if ks is None: ks = [50,100]
if sim_feats_list is None: sim_feats_list = [[ sf.color_hist_sim(), sf.texture_hist_sim(), sf.size_sim(img.shape), sf.fill_sim(img.shape) ],[ sf.texture_hist_sim(), sf.size_sim(img.shape), sf.fill_sim(img.shape) ]]
cc = convert_colorspace(img,color_space_list)
seg_filename = [seg_dir + 'HSV/50/' + image_name +'.mat',seg_dir + 'HSV/100/' + image_name +'.mat', seg_dir + 'LAB/50/' + image_name +'.mat',seg_dir + 'LAB/100/' + image_name +'.mat']
for i in range(len(color_space_list)):
for j in range(len(ks)):
for k in range(len(sim_feats_list)):
_img = cc[i]
_file = "%s%s/%d/%s.mat"%(seg_dir,color_space_list[i].upper(),ks[j],image_name)
if not os.path.exists(_file):
segment_mask = felzenszwalb(_img,scale=ks[j],sigma=0.8,min_size=ks[j])
_temp_dict = dict()
_temp_dict['blobIndIm'] = segment_mask + 1
scipy.io.savemat(_file,_temp_dict)
_blob_array = ssearch._ssearch(_img,ssearch.load_segment_mask(_file),sim_feats = sim_feats_list[k])
blob_array.append(_blob_array)
priority.append( np.arange(len(_blob_array),0,-1).clip(0,(len(_blob_array)+1)/2))
bboxes = ssearch.remove_duplicate(blob_array,priority)
bbox_dict = {}
bbox_dict['boxes'] = np.vstack([np.asarray(bboxes)[:,2],np.asarray(bboxes)[:,1],np.asarray(bboxes)[:,4],np.asarray(bboxes)[:,3]]).T
print('\nComputed %d proposals'%(len(bboxes)))
scipy.io.savemat('Data/Boxes/' + image_name + '.mat',bbox_dict)
rcnn.rcnn_demo(image_name,net=net, cpu_mode=cpu_mode)
def FELZENSZWALB(Input_Image, scale, sigma, min_size):
'''
Description: Computes Felsenszwalbs efficient graph based image segmentation.
source: skimage, openCv python
parameters: Input_Image : ndarray
Input image
min-size : int
Minimum component size. Enforced using postprocessing.
scale: float
The parameter scale sets an observation level. Higher scale means less and larger segments.
sigma : float
Width of Gaussian smoothing kernel for preprocessing. Zero means no smoothing.
return: Segment_mask : ndarray
Integer mask indicating segment labels.
'''
#default values, set in case of 0 as input
if scale == 0:
scale = 5
if sigma == 0:
sigma = 0.5
if min_size == 0:
min_size = 30
#print Input
img = cv2.imread(Input_Image)
#print img
#print img.shape
segments_fz = felzenszwalb(img, scale, sigma, min_size)
print segments_fz.shape
#print ('segments_fz datatype',segments_fz.dtype )
print("Felzenszwalb's number of segments: %d" % len(np.unique(segments_fz)))
print ('segments_fz datatype',segments_fz.dtype )
return segments_fz
def doSegment(im):
timestr = time.strftime("%Y%m%d-%H%M%S")
global count
img = img_as_float(im)
# img = img_as_float(imgO[::2, ::2])
segments_fz = felzenszwalb(img, scale=2.0, sigma=1.0, min_size=0)
# segments_slic = slic(img, n_segments=250, compactness=10, sigma=1)
# segments_quick = quickshift(img, kernel_size=5, max_dist=10, ratio=0.0)
if count % 2 == 0:
bounds = mark_boundaries(img, segments_fz, color=(0.0, 0.0, 0.0), mode="inner")
else:
bounds = mark_boundaries(img, segments_fz, color=(1, 1, 1), mode="inner")
labeled = label2rgb(
segments_fz,
img,
alpha=1.0,
bg_color=(0, 0, 0),
bg_label=0,
colors=[
(0, 0, 0),
(1, 1, 1),
(0.25, 0.25, 0.25),
(0.5, 0.5, 0.5),
(0.75, 0.75, 0.75),
(0.1, 0.1, 0.1),
(0.05, 0.05, 0.05),
(0.2, 0.2, 0.2),
(0.15, 0.15, 0.15),
],
)
bb = Image.fromarray(np.uint8(labeled * 255))
bb.show()
bb.save("output/white" + timestr + ".png")
# bb = bb.resize((500, 500), Image.NEAREST)
count = count + 1
def image2seg(folder, filename, algo='felzenszwalb'):
image = cv2.imread(os.path.join(tgt_dir, folder, filename))
image = cv2.resize(image, (640, 480))
if algo == 'felzenszwalb':
segment = felzenszwalb(image, scale=100, sigma=0.5,
min_size=50).astype(np.int16)
elif algo == 'slic':
segment = slic(image, n_segments=500, sigma=0.5,
compactness=1).astype(np.int16)
else:
raise NotImplementedError("Segmentation algorithm not implemented.")
os.makedirs(os.path.join(output_dir, folder), exist_ok=True)
filename_wo_extn = os.path.splitext(os.path.basename(filename))[0]
np.savez(os.path.join(output_dir, folder,
"seg_{}.npz".format(filename_wo_extn)),
segment_0=segment)
return
def felzenszwalb(request):
image, response = process(request, color = True)
if len(image.shape) == 2:
image = cv2.cvtColor(image,cv2.COLOR_GRAY2RGB)
scale = int(request.GET.get('felzenszwalb-scale'))
sigma = float(request.GET.get('felzenszwalb-sigma'))
min_size = int(request.GET.get('felzenszwalb-min'))
segments = segmentation.felzenszwalb(image, scale = scale, sigma = sigma, min_size = min_size)
image = segmentation.mark_boundaries(image, segments)
io.imsave(response['filename'], image)
return JsonResponse(response)
def test_skimage(selenium):
import numpy as np
from skimage import color, data
from skimage.util import view_as_blocks
# get astronaut from skimage.data in grayscale
l = color.rgb2gray(data.astronaut())
assert l.size == 262144
assert l.shape == (512, 512)
# size of blocks
block_shape = (4, 4)
# see astronaut as a matrix of blocks (of shape block_shape)
view = view_as_blocks(l, block_shape)
assert view.shape == (128, 128, 4, 4)
from skimage.filters import threshold_otsu
to = threshold_otsu(l)
assert to.hex() == "0x1.8e00000000000p-2"
from skimage.color import rgb2gray
from skimage.data import astronaut
from skimage.filters import sobel
from skimage.segmentation import felzenszwalb, quickshift, slic, watershed
from skimage.util import img_as_float
img = img_as_float(astronaut()[::2, ::2])
segments_fz = felzenszwalb(img, scale=100, sigma=0.5, min_size=50)
segments_slic = slic(img, n_segments=250, compactness=10, sigma=1)
segments_quick = quickshift(img, kernel_size=3, max_dist=6, ratio=0.5)
gradient = sobel(rgb2gray(img))
segments_watershed = watershed(gradient, markers=250, compactness=0.001)
assert len(np.unique(segments_fz)) == 194
assert len(np.unique(segments_slic)) == 196
assert len(np.unique(segments_quick)) == 695
assert len(np.unique(segments_watershed)) == 256
def makeSegmentation(self):
segments_fz = felzenszwalb(self.img,
scale=1000,
sigma=0.5,
min_size=250)
#segments_slic = slic(self.img, n_segments=4, compactness=10, sigma=1)
segments_quick = quickshift(self.img,
kernel_size=3,
max_dist=100,
ratio=0.5)
gradient = sobel(rgb2gray(self.img))
segments_watershed = watershed(gradient, markers=10, compactness=0.001)
print("Felzenszwalb number of segments: {}".format(
len(np.unique(segments_fz))))
#print('SLIC number of segments: {}'.format(len(np.unique(segments_slic))))
print('Quickshift number of segments: {}'.format(
len(np.unique(segments_quick))))
fig, ax = plt.subplots(2,
2,
figsize=(10, 10),
sharex=True,
sharey=True)
ax[0, 0].imshow(mark_boundaries(self.img, segments_fz))
ax[0, 0].set_title("Felzenszwalbs's method")
#ax[0, 1].imshow(mark_boundaries(self.img, segments_slic))
ax[0, 1].set_title('SLIC')
ax[1, 0].imshow(mark_boundaries(self.img, segments_quick))
ax[1, 0].set_title('Quickshift')
ax[1, 1].imshow(mark_boundaries(self.img, segments_watershed))
ax[1, 1].set_title('Compact watershed')
for a in ax.ravel():
a.set_axis_off()
plt.tight_layout()
plt.show()
def cluster_image(self, img):
edge_img = skeletonize(
block_reduce(img[:, :,
2], (self.reduce_factor, self.reduce_factor),
func=np.amax) > 0.1).astype(np.float64)
adjacency = img_to_graph(edge_img)
eigenvectors = spectral_embedding(adjacency,
n_components=self.n_components)
eigenvectors = eigenvectors / np.amax(eigenvectors)
eigenvectors = np.reshape(
eigenvectors,
(edge_img.shape[0], edge_img.shape[1], self.n_components))
eigenvectors = ndimage.median_filter(eigenvectors,
size=self.median_smooth)
print('cluestering..')
labels = felzenszwalb(eigenvectors,
scale=self.scale,
sigma=self.sigma,
min_size=self.min_size)
labels = cv2.resize(labels, (img.shape[1], img.shape[0]),
interpolation=cv2.INTER_NEAREST)
labels[np.argmax(img, axis=2) == 0] = 0
labels[np.argmax(img, axis=2) == 1] += 1
labels_post = np.zeros(
(labels.shape[0], labels.shape[1], np.amax(labels) + 1))
for i in range(1, np.amax(labels) + 1):
labels_post[:, :, i] = labels == i
labels_post = ndimage.morphology.binary_erosion(labels_post,
structure=np.ones(
(self.smooth,
self.smooth, 1)))
labels_post = ndimage.morphology.binary_dilation(labels_post,
structure=np.ones(
(self.smooth,
self.smooth, 1)))
return np.argmax(labels_post, axis=2)
def doSegment(imgPath, outImagePath):
im = load_image(imgPath)
count = 0
timestr = time.strftime("%Y%m%d-%H%M%S")
global count
img = img_as_float(im)
#img = img_as_float(imgO[::2, ::2])
segments_fz = felzenszwalb(img, scale=2.0, sigma=1.0, min_size=0)
#segments_slic = slic(img, n_segments=250, compactness=10, sigma=1)
#segments_quick = quickshift(img, kernel_size=5, max_dist=10, ratio=0.0)
if (count % 2 == 0):
bounds = mark_boundaries(img,
segments_fz,
color=(0.0, 0.0, 0.0),
mode="inner")
else:
bounds = mark_boundaries(img,
segments_fz,
color=(1, 1, 1),
mode="inner")
labeled = label2rgb(segments_fz,
img,
alpha=1.0,
bg_color=(0, 0, 0),
bg_label=0,
colors=[(0, 0, 0), (1, 1, 1), (0.25, 0.25, 0.25),
(0.5, 0.5, 0.5), (0.75, 0.75, 0.75),
(0.1, 0.1, 0.1), (0.05, 0.05, 0.05),
(0.2, 0.2, 0.2), (0.15, 0.15, 0.15)])
bb = Image.fromarray(np.uint8(labeled * 255))
# bb.show()
bb.save(outImagePath)
#bb = bb.resize((500, 500), Image.NEAREST)
count = count + 1
def segm_felzenszwalb(input_image,
scale=800,
sigma=10,
min_size=5,
multichannel=True):
"""Función para generar la segmentación mediante la técnica
de Felzenszwalb.
Args:
input_image ([Numpy Array]): Imagen de entrada sobre la que obtener
la segmentación
scale (int, optional): A mayor, más grandes son los grupos.
Defaults to 800.
sigma (int, optional): Desviación estándar del kernel Gaussiano.
Defaults to 10.
min_size (int, optional): Tamano mínimo de los componentes.
Defaults to 5.
multichannel (bool, optional): Si el último valor del shape
se interpreta como múltiples canales.
Defaults to True.
Returns:
Tuple (output image, labels, number classes): Tupla con la imagen
segmentada, las etiquetas y el número
total de segmentos encontrados.
"""
segments_felz = felzenszwalb(np.uint8(input_image),
scale=scale,
sigma=sigma,
min_size=min_size,
multichannel=multichannel)
output_image = mark_boundaries(input_image, segments_felz)
labeled_fz = color.label2rgb(segments_felz,
input_image,
kind='avg',
bg_label=0)
return (output_image, labeled_fz, len(np.unique(segments_felz)))
def segment(img):
img = pil2cv(img)
h, w = img.shape[0:2]
img = cv2.bilateralFilter(img, 9, 100, 100)
scale = int(h * w / 1000)
segments = felzenszwalb(img, scale=scale, sigma=0.5, min_size=150)
out_image = np.zeros((h, w, 3))
num_segments = len(np.unique(segments))
for s in tqdm(range(num_segments)):
label_map = segments == s
label_map3 = np.dstack([label_map] * 3)
masked_img = np.multiply(label_map3, img)
#avg_color = np.sum(np.sum(masked_img, axis=0), axis=0) / np.count_nonzero(label_map) # maybe median is better
nonzeros = [masked_img[:, :, c].reshape((h * w)) for c in range(3)]
median_color = [
np.median(np.take(nonzeros[c], nonzeros[c].nonzero()))
for c in range(3)
]
smooth_segment = (label_map3 * median_color).astype('uint8')
out_image += smooth_segment
out_image = Image.fromarray(out_image.astype('uint8'))
return out_image
def pre_trans(self, img_arr, img_file_path):
"""
return pre-filter feature extraction
For color extraction use color_kmeans()
local_equalize
local_threshold
Segmentation algorithms: felzenszwalb, slic, quickshift
"""
# initialize pre_trans vector size
pre_trans = np.zeros(
5 * (img_arr.shape[0] * img_arr.shape[1] * img_arr.shape[2]))
# All extractions using raw colored image array:
color_kmeans = Color_Clustering(img_file_path, 10, img_arr.shape[:2])
dom_colors = np.ravel(color_kmeans.main())
local_eq_raw = self.local_equalize(img_arr)
local_threshold_raw = self.local_thresh(img_arr)
# Segmentation: felzenszwalb
img = img_as_float(img_arr)
segments_fz = np.ravel(
felzenszwalb(img, scale=100, sigma=0.5, min_size=50))
prior_length = dom_colors.shape[0] + local_eq_raw.shape[0]
+local_threshold_raw.shape[0] + segments_fz.shape[0]
# apply feature detection algorithms to grayscaled image
img_arr_grey = color.rgb2gray(img_arr)
feat_det_img_arr = self.feat_detect(img_arr_grey)
stand_feat_det_img_arr = self.stand_vector_size(feat_det_img_arr)
pre_trans_prior = np.concatenate(
(dom_colors, local_eq_raw, local_threshold_raw, segments_fz),
axis=0)
pre_trans[:prior_length] = pre_trans_prior
pre_trans[prior_length:prior_length +
stand_feat_det_img_arr.shape[0]] = stand_feat_det_img_arr
return pre_trans, feat_det_img_arr.shape
def get_felzenszwalb(slices: np.array) -> np.array:
slices = slices.cpu().detach().numpy()
mid_slice = slices.shape[0] // 2
segments = felzenszwalb(slices[mid_slice, :, :],
scale=150,
sigma=0.7,
min_size=50)
#segments = chan_vese(slices[mid_slice,:,:], mu=0.1)
selected_pixels = np.array([
[5 / 16, 5 / 16], [5 / 16, 11 / 16], [11 / 16, 5 / 16],
[11 / 16, 11 / 16]
]) @ np.array([[256, 0], [0, 256]]) # don't hard code image resolution
selected_pixels = selected_pixels.astype('int32')
selected_segments = [segments[tuple(sp)] for sp in selected_pixels]
pre_mask = [segments == ss for ss in selected_segments]
mask = np.logical_or.reduce(pre_mask)
bg_segment = int(np.floor(np.mean(segments[0])))
mid_col = segments.shape[1] // 2
start = timeit.default_timer()
max_gap = smart_strip_edges(segments, bg_segment, 1)
max_h_gap = smart_strip_edges(segments, bg_segment, 0)
stop = timeit.default_timer()
print("Step: {} - Runtime: {}".format(3, stop - start))
return segments[max_gap[0]:max_gap[1],
max_h_gap[0]:max_h_gap[1]], mask[max_gap[0]:max_gap[1],
max_h_gap[0]:max_h_gap[1]]
def pre_trans(self, img_arr, img_file_path):
"""
return pre-filter feature extraction
For color extraction use color_kmeans()
local_equalize
local_threshold
Segmentation algorithms: felzenszwalb, slic, quickshift
"""
# initialize pre_trans vector size
pre_trans = np.zeros(18000)
# All extractions using raw colored image array:
color_kmeans = Color_Clustering(img_file_path, 10, img_arr.shape[:2])
dom_colors = np.ravel(color_kmeans.main())
local_eq_raw = self.local_equalize(img_arr)
local_threshold_raw = self.local_thresh(img_arr)
# Segmentation: felzenszwalb
img = img_as_float(img_arr)
segments_fz = np.ravel(
felzenszwalb(
img,
scale=100,
sigma=0.5,
min_size=50))
prior_length = dom_colors.shape[0] + local_eq_raw.shape[0]
+ local_threshold_raw.shape[0] + segments_fz.shape[0]
pre_trans_prior = np.concatenate(
(dom_colors,
local_eq_raw,
local_threshold_raw,
segments_fz),
axis=0)
pre_trans[:prior_length] = pre_trans_prior
return pre_trans
def radioBtn_refresh(self):
self.slider_pack()
if (self.segment_type_choice.get() == 1):
numSeg = self.slider1.get()
comp = self.slider2.get()
sigma = float(self.slider3.get())
self.segments = slic(self.image,
n_segments=numSeg,
compactness=comp,
sigma=sigma,
slic_zero=True)
if (self.segment_type_choice.get() == 2):
scale = self.slider1.get()
min_size = self.slider2.get()
sigma = self.slider3.get()
self.segments = felzenszwalb(self.image,
scale=scale,
sigma=sigma,
min_size=min_size)
if (self.segment_type_choice.get() == 3):
kernel_size = self.slider1.get()
max_dist = self.slider2.get()
ratio = self.slider3.get()
self.segments = quickshift(self.image,
kernel_size=3,
max_dist=6,
ratio=0.5)
imageOUT = cv2.bitwise_or(self.image, self.mask)
#imageOUT = cv2.addWeighted(self.image,0.3,self.mask,0.7,0)
#imageOUT=np.where(self.mask==(0,0,0),self.image,self.mask)
imageOUT = toimage(mark_boundaries(imageOUT, self.segments))
imageOUT = ImageTk.PhotoImage(imageOUT)
self.panelA.create_image(0, 0, image=imageOUT, anchor=NW)
self.panelA.image = imageOUT
def segment_image(image, color_space_list = ['HSV','LAB'],
ks = [50,100]):
blob_array =[]
priority = []
seg_masks = []
converted_images = convert_colorspace(image,color_space_list)
sim_feats_list = [ sf.color_hist_sim(), sf.texture_hist_sim(),
sf.size_sim(image.shape), sf.fill_sim(image.shape) ]
for img in converted_images:
for j in ks:
print("segmenting",j)
segmented_mask = felzenszwalb(img,j,sigma = 0.8,
min_size = j)
print("blobbing",j)
blobs = ssearch._ssearch(img,segmented_mask,sim_feats =
sim_feats_list)
blob_array.append(blobs)
priority.append(
np.arange(len(blobs),0,-1).clip(0,(len(blobs)+1)/2))
seg_masks.append(segmented_mask)
blob_array = ssearch.remove_duplicate(blob_array)
return blob_array
def ncut(img=None, thresh=0.001, num_cuts=10, sp_met='slic'):
# labels1 = segmentation.felzenszwalb(m_img, scale=50, sigma=0.5, min_size=100)
if sp_met == 'slic':
labels1 = segmentation.slic(img, compactness=30, n_segments=400)
if sp_met == 'fl':
labels1 = segmentation.felzenszwalb(img,
scale=100,
sigma=0.5,
min_size=50)
# if sp_met == 'qs':
# labels1 = quickshift(img, kernel_size=3, max_dist=6, ratio=0.5)
out1 = color.label2rgb(labels1, img, kind='avg')
g = graph.rag_mean_color(img, labels1, mode='similarity')
labels2 = graph.cut_normalized(labels1,
g,
thresh=thresh,
num_cuts=num_cuts)
out2 = color.label2rgb(labels2, img, kind='avg')
return labels2
def task3(self):
img1 = img_as_float64(mimg.imread("fish.bmp"))
imgray = rgb2gray(img1)
# Method 1
# img2=sgm.watershed(imgray, markers=300, watershed_line=True)
# Method 2
# img2 = sgm.slic(imgray, n_segments=30, compactness=0.3, sigma=20)
# Method 3
# img2 = sgm.quickshift(img1, kernel_size=5, max_dist=15, ratio=0.05)
# Method 4
img2 = sgm.felzenszwalb(imgray, scale=200, sigma=1, min_size=100)
img3 = sgm.mark_boundaries(img1, img2)
fg, (ax1, ax2, ax3) = plt.subplots(1, 3)
self.show(ax1, img1, "original")
self.show(ax2, img2, "markers")
self.show(ax3, img3, "with segmentation")
plt.show()
def extract_colored_map(self, image_with_boundaries, contours_image):
segments = felzenszwalb(image_with_boundaries,
scale=self.segs,
min_size=self.min_size)
for i in range(contours_image.shape[0]):
for j in range(contours_image.shape[1]):
if contours_image[i, j] == 0:
segments[i, j] = 255
occurence_map = self.get_occurence_map(segments)
keys = list(occurence_map.keys())
for key in keys:
if occurence_map[key] > self.threshold:
del occurence_map[key]
for i in range(segments.shape[0]):
for j in range(segments.shape[1]):
if segments[i, j] in occurence_map.keys():
segments[i, j] = 255
return segments
def applyfelzenszwab(source_img, mask):
fine_mask = mask.copy()
input_img = source_img.copy()
cv2.imshow("inputs", np.hstack((input_img, fine_mask)))
cv2.waitKey(0)
image_felzenszwalb = seg.felzenszwalb(mask)
image_felzenszwalb_colored = color.label2rgb(image_felzenszwalb, mask, kind='avg')
print(image_felzenszwalb.shape)
print(image_felzenszwalb[:100])
image_show(image_felzenszwalb)
image_show(image_felzenszwalb_colored)
#plt.imshow(image_felzenszwalb)
plt.show()
#cv2.imshow("adsf", image_felzenszwalb); #, cmap='gray'
#cv2.waitKey(0)
return fine_mask
def segmentation(path):
ds = gdal.Open(path, GA_ReadOnly )
img = np.array(ds.GetRasterBand(1).ReadAsArray())
segments_fz = felzenszwalb(img, scale=100, sigma=0.5, min_size=50)
#segments_slic = slic(img, n_segments=250, compactness=10, sigma=1)
#segments_quick = quickshift(img, kernel_size=3, max_dist=6, ratio=0.5)
print("Felzenszwalb's number of segments: %d" % len(np.unique(segments_fz)))
fig, ax = plt.subplots(1, 3)
fig.set_size_inches(8, 3, forward=True)
fig.subplots_adjust(0.05, 0.05, 0.95, 0.95, 0.05, 0.05)
ax[0].imshow(mark_boundaries(img, segments_fz))
ax[0].set_title("Felzenszwalbs's method")
# ax[1].imshow(mark_boundaries(img, segments_slic))
# ax[1].set_title("SLIC")
# ax[2].imshow(mark_boundaries(img, segments_quick))
# ax[2].set_title("Quickshift")
for a in ax:
a.set_xticks(())
a.set_yticks(())
plt.show()
def felzenszwalb_skimage(input_band_list, scale, sigma, min_size):
'''Felzenszwalb segmentation from Skimage library
:param input_band_list: list of 2darrays (list of numpy arrays)
:param scale: defines the observation level, higher scale means less and larger segments (float)
:param sigma: idth of Gaussian smoothing kernel for preprocessing, zero means no smoothing (float)
:param min_size: minimum size, minimum component size. Enforced using postprocessing. (integer)
:returns: 2darray with the result of the segmentation (numpy array)
Author: Daniele De Vecchi - Mostapha Harb
Last modified: 22/03/2014
Reference: http://scikit-image.org/docs/dev/api/skimage.segmentation.html#skimage.segmentation.felzenszwalb
'''
#TODO: Also here seems to me that only RGB is used and each band is segmented separately and than merged.
#TODO: Would be more powerful if full spectral content would be used and one segmentation is run on the n-D feature space.
#default values, set in case of 0 as input
if scale == 0:
scale = 2
if sigma == 0:
sigma = 0.1
if min_size == 0:
min_size = 2
if len(input_band_list) == 4:
img = np.dstack((input_band_list[0], input_band_list[1],
input_band_list[2], input_band_list[3]))
if len(input_band_list) == 3:
img = np.dstack(
(input_band_list[0], input_band_list[1], input_band_list[2]))
if len(input_band_list) == 1:
img = input_band_list[0]
segments_fz = felzenszwalb(img, scale, sigma, min_size)
return segments_fz
def press_wasdqe_to_adjust_parameter_of_felz(img):
paras = [64, 0.5, 128] # pieces
# paras = [1, 0.8, 20] # default
act_dict = {0: (0, 1.0)}
act_dict.update(
zip(
[119, 97, 113],
[(i, 1.2) for i in range(len(paras))],
)) # _KeyBoard: W A Q
act_dict.update(
zip(
[115, 100, 101],
[(i, 0.8) for i in range(len(paras))],
)) # KeyBoard: S D E
key = 0
while True:
if key != -1:
i, multi = act_dict[key]
paras[i] *= multi
print(key, paras)
seg_map = segmentation.felzenszwalb(img,
scale=int(paras[0]),
sigma=paras[1],
min_size=int(paras[2]))
show = mark_boundaries(img, seg_map)
cv2.imshow('', show)
wait_time = 1
else:
wait_time = 100
key = cv2.waitKey(wait_time)
break
cv2.imwrite('tiger_felz.jpg', show * 255)
def test_FH_time(scale, final_ls):
"""
scale : list
final_ls : list
This function takes as input data the list of indexes allowing access to your
ground truth and your images. It also takes the scale parameter as a list.
This allows to calculate the average time taken by the algorithm of Felznwalb and Huttenlocher
to segment according to the parameter z.
"""
results_dic = {}
for i, z in enumerate(scale):
print("z = {}".format(z))
results = np.zeros((1, len(final_ls)))
for idx, path in enumerate(final_ls):
image = cv2.imread('Data/train/{}.jpg'.format(path))
GT = np.load("Data/ground_truth/{}.npy".format(path))
t0 = time.time()
segments = felzenszwalb(img_as_float(image), scale=z)
tend = time.time() - t0
results[0][idx] = tend
if (idx % 20) == 0:
print('Image : {}, Time : {}'.format(idx, results[0][idx]))
results_dic[z] = results
print('-' * 10)
result_2 = np.zeros((len(scale), 1))
for idx, z in enumerate(scale):
result_2[idx, 0] = results_dic[z][0].mean()
df = pd.DataFrame(result_2, index=scale, columns=["Time"])
return df
def createSuperPixelMain(params):
"""Calculates super pixel segmentation
Arguments:
params {tuple} -- parameters for the function
"""
super_pixel_method, img_file, i, out_dir = params
img = cv2.imread(img_file[i], cv2.IMREAD_COLOR)
if super_pixel_method == "Felzenszwalb":
labels = felzenszwalb(img, scale=100, sigma=0.5, min_size=50)
elif super_pixel_method == "Quickshift":
labels = quickshift(img, kernel_size=3, max_dist=6, ratio=0.5)
elif super_pixel_method == "Slic":
labels = slic(img, n_segments=250, compactness=10, sigma=1)
elif super_pixel_method == "Watershed":
gradient = sobel(rgb2gray(img))
labels = watershed(gradient, markers=250, compactness=0.001)
np.save(
os.path.join(out_dir, "{0}_{1}.npy".format(i, super_pixel_method)),
labels.astype(np.uint8),
)
def extract_superpixel(filename, index):
CROP = 16
scales = [1]
markers = [400]
filename = os.path.join(data_path, filename)
image = cv2.imread(filename)
# undistort
image = undistort(image)
image = image[CROP:-CROP, CROP:-CROP, :]
segments = []
Adjs = []
Adjs_color = []
for s, m in zip(scales, markers):
image = cv2.resize(image, (384 // s, 288 // s))
image = img_as_float(image)
gradient = sobel(rgb2gray(image))
# segment = watershed(gradient, markers=m, compactness=0.001)
segment = felzenszwalb(image, scale=100, sigma=0.5, min_size=50)
segments.append(segment)
return segments[0].astype(np.int16)
def felzenszwalb_skimage(input_band_list, scale, sigma, min_size):
"""Felzenszwalb segmentation from Skimage library
:param input_band_list: list of 2darrays (list of numpy arrays)
:param scale: defines the observation level, higher scale means less and larger segments (float)
:param sigma: idth of Gaussian smoothing kernel for preprocessing, zero means no smoothing (float)
:param min_size: minimum size, minimum component size. Enforced using postprocessing. (integer)
:returns: 2darray with the result of the segmentation (numpy array)
Author: Daniele De Vecchi - Mostapha Harb
Last modified: 22/03/2014
Reference: http://scikit-image.org/docs/dev/api/skimage.segmentation.html#skimage.segmentation.felzenszwalb
"""
# TODO: Also here seems to me that only RGB is used and each band is segmented separately and than merged.
# TODO: Would be more powerful if full spectral content would be used and one segmentation is run on the n-D feature space.
# default values, set in case of 0 as input
if scale == 0:
scale = 2
if sigma == 0:
sigma = 0.1
if min_size == 0:
min_size = 2
if len(input_band_list) == 4:
img = np.dstack((input_band_list[0], input_band_list[1], input_band_list[2], input_band_list[3]))
if len(input_band_list) == 3:
img = np.dstack((input_band_list[0], input_band_list[1], input_band_list[2]))
if len(input_band_list) == 1:
img = input_band_list[0]
segments_fz = felzenszwalb(img, scale, sigma, min_size)
return segments_fz
def felzen_segment():
global segments, orig
img_b64 = request.values['data'].split("base64,")[1]
val = 600 - int(float(request.values['val']))
image_result = open('orig.png', 'wb')
image_result.write(base64.b64decode(img_b64))
img = io.imread('orig.png')
img = color.rgba2rgb(img)
segments = segmentation.felzenszwalb(img, min_size=val)
segmented_img = segmentation.mark_boundaries(img, segments)
back_img = np.zeros((segmented_img.shape[0], segmented_img.shape[1], 4))
back_img[:, :, 0] = img[:, :, 0] * 255
back_img[:, :, 1] = img[:, :, 1] * 255
back_img[:, :, 2] = img[:, :, 2] * 255
back_img[:, :, 3] = 255
orig = img
for file in os.listdir("./masks/"):
os.remove("./masks/" + file)
plt.imsave("masks/back.png", back_img.astype(np.uint8))
plt.imsave("segment.jpg", segmented_img)
np.save("segments", segments)
print('Image received: {}'.format(img.shape))
response = jsonify({'message': 'Happy Noises'})
return response
def test_3D():
grey_img = np.zeros((10, 10))
rgb_img = np.zeros((10, 10, 3))
three_d_img = np.zeros((10, 10, 10))
with assert_no_warnings():
felzenszwalb(grey_img, multichannel=True)
felzenszwalb(grey_img, multichannel=False)
felzenszwalb(rgb_img, multichannel=True)
with assert_warns(RuntimeWarning):
felzenszwalb(three_d_img, multichannel=True)
with testing.raises(ValueError):
felzenszwalb(rgb_img, multichannel=False)
felzenszwalb(three_d_img, multichannel=False)
def extractFeature(img):
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = imutils.resize(img, width=200)
# segment the image
labels = segmentation.felzenszwalb(img, scale=35.0)
# labels = segmentation.quickshift(img, ratio=1.0, kernel_size=4, max_dist=8)
# labels = segmentation.slic(img, compactness=15, n_segments=300)
# get the segmented image
seg_img = color.label2rgb(labels, img, kind='avg')
# Convert to hls space
seg_bgr_img = cv2.cvtColor(seg_img, cv2.COLOR_RGB2BGR)
hls = cv2.cvtColor(seg_bgr_img, cv2.COLOR_BGR2HLS)
hsv = cv2.cvtColor(seg_bgr_img, cv2.COLOR_BGR2HSV)
# get cluster information
cluster_dict = {}
max_light = 0
max_sat = 0
max_val = 0
min_light = 1000
min_sat = 1000
min_val = 1000
max_light_label = 0
max_sat_label = 0
max_val_label = 0
height, width, color_depth = img.shape
for y in range(0, height - 1):
for x in range(0, width - 1):
hls_pix = hls[y, x, :]
hsv_pix = hsv[y, x, :]
label = labels[y, x]
if label not in cluster_dict:
cluster_dict[label] = Cluster(hls_pix)
cluster_dict[label].add_pixel(x, y)
min_sat = min(min_sat, hls_pix[2])
if hls_pix[2] > max_sat:
max_sat = hls_pix[2]
max_sat_label = label
min_light = min(min_light, hls_pix[1])
if hls_pix[1] > max_light:
max_light = hls_pix[1]
max_light_label = label
min_val = min(min_val, hsv_pix[1])
if hsv_pix[1] > max_val:
max_val = hsv_pix[1]
max_val_label = label
# calculate cluster locations
for c in cluster_dict:
cluster_dict[c].finalize()
# calculate the actual features
# get location features
max_sat_locx_bin = NUM_LOC_BINS*[0]
max_sat_locy_bin = NUM_LOC_BINS*[0]
max_light_locx_bin = NUM_LOC_BINS*[0]
max_light_locy_bin = NUM_LOC_BINS*[0]
max_val_locx_bin = NUM_LOC_BINS*[0]
max_val_locy_bin = NUM_LOC_BINS*[0]
max_sat_locx_bin[util.getBinIndex(float(cluster_dict[max_sat_label].x) / width,
NUM_LOC_BINS, MAX_LOC_VAL)] = 1
max_sat_locy_bin[util.getBinIndex(float(cluster_dict[max_sat_label].y) / height,
NUM_LOC_BINS, MAX_LOC_VAL)] = 1
max_light_locx_bin[util.getBinIndex(float(cluster_dict[max_light_label].x) / width,
NUM_LOC_BINS, MAX_LOC_VAL)] = 1
max_light_locy_bin[util.getBinIndex(float(cluster_dict[max_light_label].y) / height,
NUM_LOC_BINS, MAX_LOC_VAL)] = 1
max_val_locx_bin[util.getBinIndex(float(cluster_dict[max_val_label].x) / width,
NUM_LOC_BINS, MAX_LOC_VAL)] = 1
max_val_locy_bin[util.getBinIndex(float(cluster_dict[max_val_label].y) / height,
NUM_LOC_BINS, MAX_LOC_VAL)] = 1
# get size features
total_pixels = height*width
sat_size = float(cluster_dict[max_sat_label].num_pixels) / total_pixels
light_size = float(cluster_dict[max_light_label].num_pixels) / total_pixels
val_size = float(cluster_dict[max_val_label].num_pixels) / total_pixels
# get contrast features
mini = 20 # from manual inspection
max_sat_diff = util.normalize(max_sat - min_sat, mini, util.MAX_SAT)
max_light_diff = util.normalize(max_light - min_light, mini, util.MAX_LIGHT)
max_val_diff = util.normalize(max_val - min_val, mini, util.MAX_VAL)
features = max_sat_locx_bin + max_sat_locy_bin + \
max_light_locx_bin + max_light_locy_bin + \
max_val_locx_bin + max_val_locy_bin + \
[sat_size, light_size, val_size] + \
[max_sat_diff, max_light_diff, max_val_diff]
assert len(features) == len(getFeatureName()), \
"length of segmentation features matches feature names"
# plot for debugging
if IS_DEBUG:
# plot_marker(cluster_dict[max_sat_label].x,
# cluster_dict[max_sat_label].y,
# (255, 0, 0), seg_bgr_img)
# plot_marker(cluster_dict[max_light_label].x,
# cluster_dict[max_light_label].y,
# (0, 255, 0), seg_bgr_img)
# plot_marker(cluster_dict[max_val_label].x,
# cluster_dict[max_val_label].y,
# (0, 0, 255), seg_bgr_img)
plt.figure()
cv2.imshow('img', seg_bgr_img)
cv2.waitKey(0)
return [features, seg_bgr_img]
return features
'''
from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
from skimage.data import lena
from skimage.segmentation import felzenszwalb, slic, quickshift
from skimage.segmentation import mark_boundaries
from skimage.util import img_as_float
'''
[::2,::2]表示每隔两个index娶一个sample，就是down-sampling
'''
img = img_as_float(lena()[::2, ::2])
segments_fz = felzenszwalb(img, scale=100, sigma=0.5, min_size=50)
print (segments_fz)
print (segments_fz.shape)
segments_slic = slic(img, n_segments=4, compactness=10, sigma=1)
np.savetxt('test.txt', segments_slic, fmt='%1i')
segments_quick = quickshift(img, kernel_size=3, max_dist=6, ratio=0.5)
print("Felzenszwalb's number of segments: %d" % len(np.unique(segments_fz)))
print("Slic number of segments: %d" % len(np.unique(segments_slic)))
print("Quickshift number of segments: %d" % len(np.unique(segments_quick)))
fig, ax = plt.subplots(1, 3)
fig.set_size_inches(8, 3, forward=True)
fig.subplots_adjust(0.05, 0.05, 0.95, 0.95, 0.05, 0.05)
ax[0].imshow(mark_boundaries(img, segments_fz))
'''
map_array=numpy.array(map_array)
finalLabel=numpy.array(finalLabel)
pickle.dump(g, file_g_final)
pickle.dump(finalLabel,file_final)
pickle.dump(map_array,file_map)
finalNum=len(numpy.unique(finalLabel))
print('finalNum')
print(finalNum)
plt.imshow(img)
plt.figure()
plt.imshow(mark_boundaries(img, segments_slic,color=(1,0,0)))
plt.imsave("fishMan_slic.jpg",mark_boundaries(img, segments_slic,color=(1,0,0)))
plt.figure()
plt.imshow(mark_boundaries(img,region_fh,color=(1,0,0)))
plt.imsave("fishMan_Hybrid.jpg",mark_boundaries(img,region_fh,color=(1,0,0)))
plt.figure()
plt.imshow(mark_boundaries(img,finalLabel,color=(1,0,0)))
plt.imsave("fishMan_final.jpg",mark_boundaries(img,finalLabel,color=(1,0,0)))
plt.show()
numpy.savetxt('finalLabel.txt', finalLabel, fmt='%1i')
fh = felzenszwalb(img, scale=100, sigma=0.5, min_size=50)
#savePath='/Users/yuan/BaiduYun/MyDocument/workspace/MyMatlab/superpixel_benchmark/result/fh/fh_200/62096..mat'
#sio.savemat(savePath,{'finalLabel':fh})
def segment_fz(fig, image):
segmented = felzenszwalb(image, scale=500, sigma=0.5, min_size=50)
fig_label_segments(fig, image, segmented, 'felzenszwalb')
def prep():
header = "acantharia_protist_big_center,acantharia_protist_halo,acantharia_protist,amphipods,appendicularian_fritillaridae,appendicularian_s_shape,appendicularian_slight_curve,appendicularian_straight,artifacts_edge,artifacts,chaetognath_non_sagitta,chaetognath_other,chaetognath_sagitta,chordate_type1,copepod_calanoid_eggs,copepod_calanoid_eucalanus,copepod_calanoid_flatheads,copepod_calanoid_frillyAntennae,copepod_calanoid_large_side_antennatucked,copepod_calanoid_large,copepod_calanoid_octomoms,copepod_calanoid_small_longantennae,copepod_calanoid,copepod_cyclopoid_copilia,copepod_cyclopoid_oithona_eggs,copepod_cyclopoid_oithona,copepod_other,crustacean_other,ctenophore_cestid,ctenophore_cydippid_no_tentacles,ctenophore_cydippid_tentacles,ctenophore_lobate,decapods,detritus_blob,detritus_filamentous,detritus_other,diatom_chain_string,diatom_chain_tube,echinoderm_larva_pluteus_brittlestar,echinoderm_larva_pluteus_early,echinoderm_larva_pluteus_typeC,echinoderm_larva_pluteus_urchin,echinoderm_larva_seastar_bipinnaria,echinoderm_larva_seastar_brachiolaria,echinoderm_seacucumber_auricularia_larva,echinopluteus,ephyra,euphausiids_young,euphausiids,fecal_pellet,fish_larvae_deep_body,fish_larvae_leptocephali,fish_larvae_medium_body,fish_larvae_myctophids,fish_larvae_thin_body,fish_larvae_very_thin_body,heteropod,hydromedusae_aglaura,hydromedusae_bell_and_tentacles,hydromedusae_h15,hydromedusae_haliscera_small_sideview,hydromedusae_haliscera,hydromedusae_liriope,hydromedusae_narco_dark,hydromedusae_narco_young,hydromedusae_narcomedusae,hydromedusae_other,hydromedusae_partial_dark,hydromedusae_shapeA_sideview_small,hydromedusae_shapeA,hydromedusae_shapeB,hydromedusae_sideview_big,hydromedusae_solmaris,hydromedusae_solmundella,hydromedusae_typeD_bell_and_tentacles,hydromedusae_typeD,hydromedusae_typeE,hydromedusae_typeF,invertebrate_larvae_other_A,invertebrate_larvae_other_B,jellies_tentacles,polychaete,protist_dark_center,protist_fuzzy_olive,protist_noctiluca,protist_other,protist_star,pteropod_butterfly,pteropod_theco_dev_seq,pteropod_triangle,radiolarian_chain,radiolarian_colony,shrimp_caridean,shrimp_sergestidae,shrimp_zoea,shrimp-like_other,siphonophore_calycophoran_abylidae,siphonophore_calycophoran_rocketship_adult,siphonophore_calycophoran_rocketship_young,siphonophore_calycophoran_sphaeronectes_stem,siphonophore_calycophoran_sphaeronectes_young,siphonophore_calycophoran_sphaeronectes,siphonophore_other_parts,siphonophore_partial,siphonophore_physonect_young,siphonophore_physonect,stomatopod,tornaria_acorn_worm_larvae,trichodesmium_bowtie,trichodesmium_multiple,trichodesmium_puff,trichodesmium_tuft,trochophore_larvae,tunicate_doliolid_nurse,tunicate_doliolid,tunicate_partial,tunicate_salp_chains,tunicate_salp,unknown_blobs_and_smudges,unknown_sticks,unknown_unclassified".split(',')
with open('namesClasses.dat','rb') as f:
namesClasses = cPickle.load(f)
labels = map(lambda s: s.split('\\')[-1], namesClasses)
for i in range(len(namesClasses)):
currentClass = namesClasses[i]
root_class.data_search(currentClass).id_list.append(i)
#get the total test images
#Full set
#fnames = glob.glob(os.path.join("competition_data", "test", "*.jpg"))
#Smaller set
fnames = glob.glob(os.path.join("smaller_comp", "test", "*.jpg"))
numberofTestImages = len(fnames)
X = np.zeros((numberofTestImages, num_features), dtype=float)
#Get filename separate from prefix path
images = map(lambda fileName: fileName.split('\\')[-1], fnames)
i = 0
# report progress for each 1% done
report = [int((j+1)*numberofTestImages/100.) for j in range(100)]
for fileName in fnames:
# Read in the images and create the features
image = imread(fileName, as_grey=True)
image = rotImage(image)
# Added from https://github.com/Newmu/Stroke-Prediction/blob/master/startPredictingGenCode.py
thresh = 0.9*255
# if image.min < 0.75*255:
# img = image < thresh
# else:
# img = image
# if img.sum() != 0:
# imgX,imgY = np.nonzero(img)
# imgW = imgX.max()-imgX.min()
# imgH = imgY.max()-imgY.min()
# if (imgW>1 and imgH>1):
# image = image[imgX.min():imgX.max(),imgY.min():imgY.max()]
# #----------------------------------
cvimage = cv2.imread(fileName)
features = getMinorMajorRatio(image)
#__Begin moved region
#From http://nbviewer.ipython.org/github/kqdtran/AY250-F13/blob/master/hw4/hw4.ipynb
pca = decomposition.PCA(n_components=25)
PCAFeatures = pca.fit_transform(image)
PCAevr = pca.explained_variance_ratio_
for evr in range(len(PCAevr)):
if np.isnan(PCAevr[evr]):
PCAevr[evr] = 0.0
#_____________________________________________________________
corners = getCorners(cvimage,features['orientation'])
horslice = image[:,maxPixel/2]
vertslice = image[maxPixel/2]
#correlation = signal.correlate(image,image)
#horcorrelation = signal.correlate(horslice,horslice)
#vertcorrelation = signal.correlate(vertslice,vertslice)
#crosscorrelation = signal.correlate(horslice,vertslice)
#correlation = correlation/correlation[correlation.shape[0]/2,correlation.shape[0]/2]
#horcorrelation = horcorrelation/horcorrelation[horcorrelation.shape[0]/2]
#vertcorrelation = vertcorrelation/vertcorrelation[vertcorrelation.shape[0]/2]
#crosscorrelation = crosscorrelation/crosscorrelation[horcorrelation.shape[0]/2]
hormean = np.mean(horslice)
horstd = np.std(horslice)
vertmean = np.mean(vertslice)
vertstd = np.std(vertslice)
horcount = vertcount = 0
for pix in horslice:
graycheck = False
if pix<thresh:
if not graycheck:
graycheck = True
horcount = horcount + 1
else:
graycheck = False
for pix in vertslice:
graycheck = False
if pix<thresh:
if not graycheck:
graycheck = True
vertcount = vertcount + 1
else:
graycheck = False
features['hsobel'] = np.nanmean(filter.hsobel(image))
features['vsobel'] = np.nanmean(filter.vsobel(image))
features['peaklocalmax'] = np.nanmean(peak_local_max(image))
features['felzen'] = np.nanmean(segmentation.felzenszwalb(image))
if np.isnan(features['peaklocalmax']):
features['peaklocalmax'] = 0.0
if np.isnan(features['felzen']):
features['felzen'] = 0.0
#hormirror = image[:maxPixel/2]-image[maxPixel-1:maxPixel/2:-1]
#vertmirror = image[:,:maxPixel/2]-image[:,maxPixel-1:maxPixel/2:-1]
image = resize(image, (maxPixel, maxPixel))
#From http://scikit-image.org/docs/dev/auto_examples/plot_local_binary_pattern.html
lbp = local_binary_pattern(image, n_points, radius, METHOD)
n_bins = lbp.max()+1
lbpcounts = np.histogram(lbp,n_bins,normed=True,range=(0, n_bins))[0]
#_____________________________________________________________
#__Moved region was here
#dist_trans = ndimage.distance_transform_edt(image[0])
# Store the rescaled image pixels and the axis ratio
#fd, hog_image = hog(image[0], orientations=8, pixels_per_cell=(2, 2),
# cells_per_block=(1, 1), visualise=True)
#X[i, 0:imageSize] = np.reshape(dist_trans, (1, imageSize))
#X[i, 0:imageSize] = np.reshape(hog_image, (1, imageSize))
# Store the rescaled image pixels and the axis ratio
X[i, 0:imageSize] = np.reshape(image, (1, imageSize))
#X[i, imageSize:imageSize+corr2dsize] = np.reshape(correlation, (1,corr2dsize))
#X[i, imageSize+corr2dsize:imageSize+corr2dsize+corrsize] = np.reshape(horcorrelation, (1,corrsize))
#X[i, imageSize+corr2dsize+corrsize:imageSize+corr2dsize+2*corrsize] = np.reshape(vertcorrelation, (1,corrsize))
#X[i, imageSize+corr2dsize+2*corrsize:imageSize+corr2dsize+3*corrsize] = np.reshape(crosscorrelation, (1,corrsize))
featcount = imageSize+3*corrsize+corr2dsize
for k,v in features.items():
try:
X[i, featcount:featcount+len(v)] = v
featcount = featcount + len(v)
except TypeError, te:
X[i, featcount] = v
featcount = featcount + 1
X[i, featcount:featcount+lbpcounts.size] = lbpcounts
X[i, featcount+lbpcounts.size:featcount+lbpcounts.size+PCAsize] = PCAevr
X[i, featcount+lbpcounts.size+PCAsize] = np.mean(PCAFeatures)
i += 1
if i in report: print np.ceil(i *100.0 / numberofTestImages), "% done"
def felzenszwalb(image):
SCALE = 1000
SIGMA = 5
MIN_SIZE = 500
return segmentation.felzenszwalb(image, SCALE, SIGMA, MIN_SIZE)
"""
from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
from skimage.data import astronaut
from skimage.color import rgb2gray
from skimage.filters import sobel
from skimage.segmentation import felzenszwalb, slic, quickshift, watershed
from skimage.segmentation import mark_boundaries
from skimage.util import img_as_float
img = img_as_float(astronaut()[::2, ::2])
segments_fz = felzenszwalb(img, scale=100, sigma=0.5, min_size=50)
segments_slic = slic(img, n_segments=250, compactness=10, sigma=1)
segments_quick = quickshift(img, kernel_size=3, max_dist=6, ratio=0.5)
gradient = sobel(rgb2gray(img))
segments_watershed = watershed(gradient, markers=250, compactness=0.001)
print("Felzenszwalb number of segments: {}".format(len(
np.unique(segments_fz))))
print('SLIC number of segments: {}'.format(len(np.unique(segments_slic))))
print('Quickshift number of segments: {}'.format(len(
np.unique(segments_quick))))
fig, ax = plt.subplots(2, 2, figsize=(10, 10), sharex=True, sharey=True)
ax[0, 0].imshow(mark_boundaries(img, segments_fz))
ax[0, 0].set_title("Felzenszwalbs's method")
def main():
files = []
# Generate training data
i = 0
label = 0
# List of string of class names
namesClasses = list()
features = {}
features = features.copy()
print "Reading images"
# Navigate through the list of directories
for folder in directory_names:
#Get name of class directory separate from prefix path
currentClass = folder.split(os.sep)[-1]
namesClasses.append(currentClass)
root_class.data_search(currentClass).id_list.append(len(namesClasses)-1)
for fileNameDir in os.walk(folder):
for fileName in fileNameDir[2]:
# Only read in the images
if fileName[-4:] != ".jpg":
continue
# Read in the images and create the features
nameFileImage = "{0}{1}{2}".format(fileNameDir[0], os.sep, fileName)
image = []
image.append(imread(nameFileImage, as_grey=True))
features['original_size'] = image[0].size
#image[0] = equalize_hist(image[0])
image[0] = rotImage(image[0])
# Added from https://github.com/Newmu/Stroke-Prediction/blob/master/startPredictingGenCode.py
thresh = 0.9*255
# if image.min < 0.75*255:
# img = image < thresh
# else:
# img = image
# if img.sum() != 0:
# imgX,imgY = np.nonzero(img)
# imgW = imgX.max()-imgX.min()
# imgH = imgY.max()-imgY.min()
# if (imgW>1 and imgH>1):
# image = image[imgX.min():imgX.max(),imgY.min():imgY.max()]
#----------------------------------
cvimage = cv2.imread(nameFileImage)
#image[0] = gaussian_filter(image[0],sigma=2)
files.append(nameFileImage)
image.append(np.fliplr(image[0]))
image.append(np.flipud(image[0]))
image.append(np.fliplr(image[2]))
# image.append(np.rot90(image[0]))
# image.append(np.fliplr(image[4]))
# image.append(np.flipud(image[4]))
# image.append(np.fliplr(image[6]))
for j in range(len(image)):
features = getMinorMajorRatio(image[j])
#__Begin moved region
#From http://nbviewer.ipython.org/github/kqdtran/AY250-F13/blob/master/hw4/hw4.ipynb
pca = decomposition.PCA(n_components=25)
PCAFeatures = pca.fit_transform(image[0])
#_____________________________________________________________
corners = getCorners(cvimage,features['orientation'])
horslice = image[j][:,maxPixel/2]
vertslice = image[j][maxPixel/2]
#correlation = signal.correlate(image,image)
#horcorrelation = signal.correlate(horslice,horslice)
#vertcorrelation = signal.correlate(vertslice,vertslice)
#crosscorrelation = signal.correlate(horslice,vertslice)
#correlation = correlation/correlation[correlation.shape[0]/2,correlation.shape[0]/2]
#horcorrelation = horcorrelation/horcorrelation[horcorrelation.shape[0]/2]
#vertcorrelation = vertcorrelation/vertcorrelation[vertcorrelation.shape[0]/2]
#crosscorrelation = crosscorrelation/crosscorrelation[horcorrelation.shape[0]/2]
hormean = np.mean(horslice)
horstd = np.std(horslice)
vertmean = np.mean(vertslice)
vertstd = np.std(vertslice)
horcount = vertcount = 0
for pix in horslice:
graycheck = False
if pix<thresh:
if not graycheck:
graycheck = True
horcount = horcount + 1
else:
graycheck = False
for pix in vertslice:
graycheck = False
if pix<thresh:
if not graycheck:
graycheck = True
vertcount = vertcount + 1
else:
graycheck = False
features['hsobel'] = np.nanmean(filter.hsobel(image[j]))
features['vsobel'] = np.nanmean(filter.vsobel(image[j]))
features['peaklocalmax'] = np.nanmean(peak_local_max(image[j]))
features['felzen'] = np.nanmean(segmentation.felzenszwalb(image[j]))
if np.isnan(features['peaklocalmax']):
features['peaklocalmax'] = 0.0
if np.isnan(features['felzen']):
features['felzen'] = 0.0
#hormirror = image[j][:maxPixel/2]-image[j][maxPixel-1:maxPixel/2:-1]
#vertmirror = image[j][:,:maxPixel/2]-image[j][:,maxPixel-1:maxPixel/2:-1]
#__End moved region
image[j] = resize(image[j], (maxPixel, maxPixel))
#From http://scikit-image.org/docs/dev/auto_examples/plot_local_binary_pattern.html
lbp = local_binary_pattern(image[j], n_points, radius, METHOD)
n_bins = lbp.max()+1
lbpcounts = np.histogram(lbp,n_bins,normed=True,range=(0, n_bins))[0]
#_____________________________________________________________
#__Moved region was here
#dist_trans = ndimage.distance_transform_edt(image[0])
# Store the rescaled image pixels and the axis ratio
#fd, hog_image = hog(image[0], orientations=8, pixels_per_cell=(2, 2),
# cells_per_block=(1, 1), visualise=True)
#X[i*imagesperfile+j, 0:imageSize] = np.reshape(dist_trans, (1, imageSize))
#X[i*imagesperfile+j, 0:imageSize] = np.reshape(hog_image, (1, imageSize))
X[i*imagesperfile+j, 0:imageSize] = np.reshape(image[j], (1, imageSize))
#X[i*imagesperfile+j, imageSize:imageSize+corr2dsize] = np.reshape(correlation, (1,corr2dsize))
#X[i*imagesperfile+j, imageSize+corr2dsize:imageSize+corr2dsize+corrsize] = np.reshape(horcorrelation, (1,corrsize))
#X[i*imagesperfile+j, imageSize+corr2dsize+corrsize:imageSize+corr2dsize+2*corrsize] = np.reshape(vertcorrelation, (1,corrsize))
#X[i*imagesperfile+j, imageSize+corr2dsize+2*corrsize:imageSize+corr2dsize+3*corrsize] = np.reshape(crosscorrelation, (1,corrsize))
featcount = imageSize+3*corrsize+corr2dsize
for k,v in features.items():
try:
X[i*imagesperfile+j, featcount:featcount+len(v)] = v
featcount = featcount + len(v)
except TypeError, te:
X[i*imagesperfile+j, featcount] = v
featcount = featcount + 1
X[i*imagesperfile+j, featcount:featcount+lbpcounts.size] = lbpcounts
X[i*imagesperfile+j, featcount+lbpcounts.size:featcount+lbpcounts.size+PCAsize] = pca.explained_variance_ratio_
X[i*imagesperfile+j, featcount+lbpcounts.size+PCAsize] = np.mean(PCAFeatures)
# Store the classlabel
y[i*imagesperfile+j] = label
if i*imagesperfile+j in report: print np.ceil((i*imagesperfile+j) *100.0 / (num_rows)), "% done"
i += 1
label += 1
def draw_image(self):
float_image = img_as_float(self.image)
if self.method == 0:
if self.just_mask == 0:
self.segments = felzenszwalb(
float_image,
scale=self.felzenszwalb_scale,
sigma=self.felzenszwalb_sigma,
min_size=self.felzenszwalb_min_size)
elif self.method == 1:
if self.just_mask == 0:
self.segments = slic(float_image,
n_segments=self.slic_n_segments,
compactness=self.slic_compactness,
sigma=self.slic_sigma)
elif self.method == 2:
if self.just_mask == 0:
self.segments = quickshift(
float_image,
kernel_size=self.quickshift_kernel_size,
max_dist=self.quickshift_max_dist,
ratio=self.quickshift_ratio)
elif self.method == 3:
if self.just_mask == 0:
gradient = sobel(rgb2gray(float_image))
self.segments = watershed(
gradient,
markers=self.watershed_markers,
compactness=self.watershed_compactness)
elif self.method == 4:
self.segments = None
self.line = False
else:
self.segments = None
self.line = True
if self.segments is not None:
boundaries = mark_boundaries(self.image, self.segments)
cv_image = img_as_ubyte(boundaries)
if self.hide == 0:
if self.mask_on == 0:
result = cv2.addWeighted(cv_image, 0.5, self.mask, 0.5, 0)
else:
result = self.mask
else:
if self.mask_on == 0:
result = cv2.addWeighted(self.image, 0.5, self.mask, 0.5,
0)
else:
result = self.mask
else:
if self.mask_on == 0:
result = cv2.addWeighted(self.image, 0.5, self.mask, 0.5, 0)
else:
result = self.mask
height, width, channels = np.shape(result)
totalBytes = result.nbytes
bytesPerLine = int(totalBytes / height)
qimg = QtGui.QImage(result.data, result.shape[1], result.shape[0],
bytesPerLine, QtGui.QImage.Format_RGB888)
pixmap = QtGui.QPixmap.fromImage(qimg)
self.label.resize(width, height)
self.label.setPixmap(pixmap)
self.label.show()
self.candidate = []
self.delete_candidate = []
def felz_seg(img):
segments = felzenszwalb(img, scale=120.0, sigma=1.5, min_size=30)
return segments
def findIndividualSections(destination):
#input: - overview image of the sample glass containing sections
#output: - a matrix to be placed as a grid over the sample glass, indicating for each cell True or False for wheter there is section
#PARAMETERS TO BE ALTERED FOR DIFFERENT BATCHES:
size = 1200000
sizeGlass = 19.15 #mm
sideSection = 0.736 #mm #size of one side of the section
delta = 0.23 #ratio
limit = 0.5
ratioGlassToPic = 0.8 #assuming the diameter of the sample glass is at least this much of the short side of the picture
start = time.time()
warnings.filterwarnings("ignore")
filename = os.path.join(skimage.data_dir, destination)
im = io.imread(filename)
im = downsizeTo(im, size)
OG = im
pr = 0
p1, p2 = np.percentile(im, (pr, 100-pr))
imCircle = dab(im, 20)
s = 1.5
edgesCircle = canny(imCircle, sigma=s, low_threshold=0, high_threshold=1)
ratioGlassToPic = 0.8
circx, circy = findCircle(edgesCircle, ratioGlassToPic)
im = cropToCircle(im, circx, circy)
mm = im.shape[0]/sizeGlass #1mm in pixels
sideSectionPix= sideSection*mm
#FIRST METHOD
prEdges = 1.1
imEdges = SSI(im,prEdges)
s = 1.6
edges = canny(imEdges, sigma=s, low_threshold=0, high_threshold=1)
edges = cropToCircle(edges, circx, circy, crop = False, returnBool = True)
d = 0.02 #radio of blacke out circle
edges = blackoutCircle(edges, circx, circy, d)
deltaPix = delta* sideSectionPix
check = findHalfCircle(sideSectionPix, deltaPix)
viaEdges = selectFillEdges(edges, check, limit, deltaPix)
#SECOND METHOD
prFz = 7.6
imFz = SSI(im,prFz, returnRGB = True)
sc = sideSectionPix*20
s = 1.6
mins = int(sideSectionPix**2/5)
segments_fz = felzenszwalb(imFz, scale=sc, sigma=s, min_size=mins)
#viaFz = findSectionsFz(segments_fz)
#viaFz = cropToCircle(viaFz, circx, circy, crop = False, returnBool = True)
#COMBINE METHODS
sections = overlapIndividual(viaEdges, segments_fz)
#DISPLAY RESULTS
print("total ellapsed time = ", round(time.time()-start))
fig2, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, figsize=(40, 40),sharex=False, sharey=False)
ax1.set_title('Original picture')
ax1.imshow(OG)
ax2.set_title('result')
ax2.imshow(sections)
return sections
mask = np.isnan(C_BTemp)
C_BTemp[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), C_BTemp[~mask])
elif I_minute[C_i] == 30:
#slice out BT images for the current basin
C_BTemp = xr.open_dataset(IRDIR+ "\\" + BTemp_filename)['Tb'].values[1][DIM_BOUND[0]:DIM_BOUND[1]+1,DIM_BOUND[2]:DIM_BOUND[3]+1]
C_lat = xr.open_dataset(IRDIR+ "\\" + BTemp_filename)['lat'].values[DIM_BOUND[0]:DIM_BOUND[1]+1]
C_lon = xr.open_dataset(IRDIR+ "\\" + BTemp_filename)['lon'].values[DIM_BOUND[2]:DIM_BOUND[3]+1]
#interpolate NaN values in BT images
mask = np.isnan(C_BTemp)
C_BTemp[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), C_BTemp[~mask])
#%%
C_BTemp_nor = np.flipud(C_BTemp/max(C_BTemp.flatten()))
cm = plt.get_cmap('Greys')
C_BTemp_nor_cm = cm(C_BTemp_nor)
C_BTemp_nor_cm = np.uint8(C_BTemp_nor_cm * 255)
im = Image.fromarray(C_BTemp_nor_cm)
enhancer = ImageEnhance.Brightness(im)
im2 = enhancer.enhance(2.5)
im2_np = np.asarray(im2)
im2_np_3 = im2_np[:,:,0:3]
im2_np_3_fl = img_as_float(im2_np_3)
#%%
segments_slic = segmentation.slic(im2_np_3_fl, n_segments=400, compactness=10, sigma=1)
plt.imshow(segmentation.mark_boundaries(im2_np_3_fl, segments_slic))
#%%fig = plt.figure()
segments_fz = segmentation.felzenszwalb(im2_np_3_fl, scale=40, sigma=0.5, min_size=50)
plt.imshow(segmentation.mark_boundaries(im2_np_3_fl, segments_fz))
if not graycheck:
graycheck = True
horcount = horcount + 1
else:
graycheck = False
for pix in vertslice:
graycheck = False
if pix<thresh:
if not graycheck:
graycheck = True
vertcount = vertcount + 1
else:
graycheck = False
peaklocalmax = np.nanmean(peak_local_max(image))
felzen = np.nanmean(segmentation.felzenszwalb(image))
if np.isnan(peaklocalmax):
peaklocalmax = 0.0
if np.isnan(felzen):
felzen = 0.0
# Store the rescaled image pixels and the axis ratio
X[i, 0:imageSize] = np.reshape(image, (1, imageSize))
#Zak's note: I think I need to add new features into this array after axisratio
X[i, imageSize:imageSize+corr2dsize] = np.reshape(correlation, (1,corr2dsize))
X[i, imageSize+corr2dsize:imageSize+corr2dsize+corrsize] = np.reshape(horcorrelation, (1,corrsize))
X[i, imageSize+corr2dsize+corrsize:imageSize+corr2dsize+2*corrsize] = np.reshape(vertcorrelation, (1,corrsize))
X[i, imageSize+corr2dsize+2*corrsize:imageSize+corr2dsize+3*corrsize] = np.reshape(crosscorrelation, (1,corrsize))
X[i, imageSize+3*corrsize+corr2dsize] = axisratio
X[i, imageSize+3*corrsize+corr2dsize+1] = fillratio
X[i, imageSize+3*corrsize+corr2dsize+2] = eigenratio
X[i, imageSize+3*corrsize+corr2dsize+3] = solidity
from matplotlib.colors import LinearSegmentedColormap
import matplotlib.pyplot as plt
import pylab
import skimage as ski
import skimage.segmentation as sks
import cv2
import numpy as np
img = ski.img_as_float(plt.imread('239239_faces.jpg')[::2, ::2, :3])
#img = cv2.imread()
pylab.figure(figsize=(20, 10))
segments_fz = sks.felzenszwalb(img, scale=100, sigma=0.01, min_size=100)
borders = sks.find_boundaries(segments_fz)
unique_colors = np.unique(segments_fz.ravel())
segments_fz[borders] = -1
colors = [np.zeros(3)]
for color in unique_colors:
colors.append(np.mean(img[segments_fz == color], axis=0))
cm = LinearSegmentedColormap.from_list('pallete', colors, N=len(colors))
pylab.subplot(121), pylab.imshow(img), pylab.title('Original',
size=20), pylab.axis('off'),
pylab.subplot(122), pylab.imshow(segments_fz, cmap=cm),
pylab.title('Segmented with Felzenszwalbs\'s method',
size=20), pylab.axis('off'),
pylab.show()
def SegmentationFelzenszwalb(filename="/media/scratch/plasticity/lvp2d1024_s0_d.tar", time=None):
t, s = TarFile.LoadTarState(filename, time=time)
rod = s.CalculateRotationRodrigues()
img = img_as_float(
RodriguesToUnambiguousColor(rod["x"], rod["y"], rod["z"], maxRange=None, centerR=None).astype("uint8")
)
segments_slic = felzenszwalb(img, scale=100, sigma=0.0, min_size=10)
print("Slic number of segments: %d" % len(np.unique(segments_slic)))
fig = plt.figure()
plt.imshow(mark_boundaries(img, segments_slic, color=[0, 0, 0], outline_color=None))
plt.show()