def load_scenes(filename):
zipped_scenes = []
print 'Working on: ' + filename
img = data.imread('scenes/' + filename, as_grey=True)
tmp = img
tmp = filter.canny(tmp, sigma=2.0)
tmp = ndimage.binary_fill_holes(tmp)
#tmp = morphology.dilation(tmp, morphology.disk(2))
tmp = morphology.remove_small_objects(tmp, 2000)
contours = measure.find_contours(tmp, 0.8)
ymin, xmin = contours[0].min(axis=0)
ymax, xmax = contours[0].max(axis=0)
if xmax - xmin > ymax - ymin:
xdest = 1000
ydest = 670
else:
xdest = 670
ydest = 1000
src = np.array(((0, 0), (0, ydest), (xdest, ydest), (xdest, 0)))
dst = np.array(((xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin)))
tform3 = tf.ProjectiveTransform()
tform3.estimate(src, dst)
warped = tf.warp(img, tform3, output_shape=(ydest, xdest))
tmp = filter.canny(warped, sigma=2.0)
tmp = morphology.dilation(tmp, morphology.disk(2))
descriptor_extractor.detect_and_extract(tmp)
obj_key = descriptor_extractor.keypoints
scen_desc = descriptor_extractor.descriptors
zipped_scenes.append([warped, scen_desc, obj_key, filename])
return zipped_scenes
def canny(data, sigma=1, sliceId=2):
edges = np.zeros(data.shape, dtype=np.bool)
if sliceId == 2:
for idx in range(data.shape[2]):
edges[:, :, idx] = skifil.canny(data[:, :, idx], sigma=sigma)
elif sliceId == 0:
for idx in range(data.shape[0]):
edges[idx, :, :] = skifil.canny(data[idx, :, :], sigma=sigma)
return edges
def _canny_edge_fired(self):
self.im = self.orig
r,g,b = np.rollaxis(self.im,axis=-1)
edge_r = canny(tv_denoise(r, weight=1))
edge_g = canny(tv_denoise(g, weight=1))
edge_b = canny(tv_denoise(b, weight=1))
edges = edge_r + edge_g + edge_b
self.im = np.dstack((edges,edges,edges))
self.im[self.im > 0.] = 1.
try:
self.axes.imshow(self.im)
self.figure.canvas.draw()
except:
pass
def canny(parameters):
"""Canny edge extraction filter.
This wraps `skimage.filter.canny`. The `low_threshold`, `high_threshold`
and `mask` options are not supported.
The wrapped function returns a boolean array with pixel values True or
False. Since it is not very convenient to pass such an array to other
functions, the return value is cast to uint8, thus containing 0 or 1
values.
..warning::
During testing there have been some issues with the results. Check the
corresponding test function for details.
:param parameters['data'][0]: input image
:type parameters['data'][0]: numpy.array
:param parameters['sigma']: standard deviation of the gaussian filter,
defaults to 1.0
:type parameters['sigma']: float
:return: numpy.array, with dtype('uint8') containing 0 or 1 values
"""
img = parameters['data'][0]
sigma = parameters.get('sigma', 1.0)
result = filter.canny(img, sigma=sigma)
return result.astype('uint8')
def getRegions():
"""Geocode address and retreive image centered
around lat/long"""
address = request.args.get('address')
results = Geocoder.geocode(address)
lat, lng = results[0].coordinates
zip_code = results[0].postal_code
map_url = 'https://maps.googleapis.com/maps/api/staticmap?center={0},{1}&size=640x640&zoom=19&sensor=false&maptype=roadmap&&style=visibility:simplified|gamma:0.1'
request_url = map_url.format(lat, lng)
req = urllib.urlopen(request_url)
img = io.imread(req.geturl(), flatten=True)
labels, numobjects = ndimage.label(img)
image = filter.canny(img, sigma=3)
thresh = threshold_otsu(image)
bw = closing(image > thresh, square(3))
# remove artifacts connected to image border
cleared = bw.copy()
clear_border(cleared)
# label image regions
label_image = label(cleared)
borders = np.logical_xor(bw, cleared)
label_image[borders] = -1
image_label_overlay = label2rgb(label_image, image=image)
fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6, 6))
ax.imshow(image_label_overlay)
def find_edges(self, sigma=None):
if sigma is not None:
self.sigma = sigma
print 'Identifying edges...'
self.edges = filter.canny(self.data, sigma=self.sigma)
return self.edges
def detect_edges(image_array):
""" Detect edges in a given image
Takes a numpy.array representing an image,
apply filters and edge detection and return a numpy.array
Parameters
----------
image_array : ndarray (2D)
Image data to be processed. Detect edges on this 2D array representing the image
Returns
-------
edges : ndarray (2D)
Edges of an image.
"""
#Transform image into grayscale
img = rgb2gray(image_array)
#Remove some noise from the image
img = denoise_tv_chambolle(img, weight=0.55)
#Apply canny
edges = filter.canny(img, sigma=3.2)
#Clear the borders
clear_border(edges, 15)
#Dilate edges to make them more visible and connected
edges = binary_dilation(edges, selem=diamond(3))
return edges
def getRegions():
"""Geocode address and retreive image centered
around lat/long"""
address = request.args.get('address')
results = Geocoder.geocode(address)
lat, lng = results[0].coordinates
zip_code = results[0].postal_code
map_url = 'https://maps.googleapis.com/maps/api/staticmap?center={0},{1}&size=640x640&zoom=19&sensor=false&maptype=roadmap&&style=visibility:simplified|gamma:0.1'
request_url = map_url.format(lat, lng)
req = urllib.urlopen(request_url)
img = io.imread(req.geturl(),flatten=True)
labels, numobjects = ndimage.label(img)
image = filter.canny(img, sigma=3)
thresh = threshold_otsu(image)
bw = closing(image > thresh, square(3))
# remove artifacts connected to image border
cleared = bw.copy()
clear_border(cleared)
# label image regions
label_image = label(cleared)
borders = np.logical_xor(bw, cleared)
label_image[borders] = -1
image_label_overlay = label2rgb(label_image, image=image)
fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6, 6))
ax.imshow(image_label_overlay)
def encode(self): data_array = np.array(self.image)#data).reshape((28,28)) edges = filt.canny(data_array, sigma=3) def linear_mapping(data): # using principal components analysis pca = decomposition.PCA(n_components=784) pca.fit(data) mapping = pca.transform(data) return mapping # encoded = linear_mapping(edges) encoded = np.array(edges).reshape(784) # encoded = [] # for d in self.data: # if (d > 45): # encoded.append(1) # else: # encoded.append(0) return encoded
def CanNuc(datatype, maxrange, outputfile, outputfiletype):
h = open(outputfile, outputfiletype)
TC = 0
for i in range(0, maxrange):
A = datatype[i][0]
T = mahotas.thresholding.otsu(A)
C = A.copy()
if T < 1:
C[ C <= T ] = 0
C[ C > T ] = 1
else:
C[ C < T ] = 0
C[ C >= T ] = 1
filled = scipy.ndimage.morphology.binary_fill_holes(C)
filled = filled.astype(np.uint8)
edges1 = filter.canny(filled, sigma=1)
edges1 = edges1.astype(np.uint8)
edges1 = np.where(edges1 == 1)
TC += len(edges1[0])
XY1 = np.vstack((edges1[0], edges1[1], [i*5]*len(edges1[0])))
for p in range(0, len(XY1[0])):
for yel in range(0, len(XY1)):
h.write(str(XY1[yel][p]) + '\t')
h.write('\n')
h.write(str(TC) + '\n')
h.write('.' + '\n')
h.close()
def getConfidenceImage(dist, segcpimg_crop, clr=None):
num_points = dist.shape[0]
numpy.asarray(dist, dtype=numpy.float)
m, n, _ = segcpimg_crop.shape
bbox = numpy.array([1, 1, n, m])
clrstr = numpy.array([[0, 1, 0], [0, 0, 1], [1, 0, 0], [1, 0, 1],
[0, 1, 1], [1, 1, 0], [0, 0, 0]])
if clr != None:
clrstr = numpy.tile(clr, (9, 1))
uintimg = numpy.asarray(segcpimg_crop, dtype=numpy.uint8)
grayimg = color.rgb2gray(uintimg)
edges = filter.canny(grayimg)
background = numpy.asarray(~edges, dtype=numpy.float)
bg = background[:, :, numpy.newaxis]
pdf_img = 0.2 * numpy.tile(bg, (1, 1, 3)) + 0.8 * numpy.ones(
(bbox[3], bbox[2], 3))
# Normalise the distributions for visualisation
for c in numpy.arange(num_points - 1, -1, -1):
d = dist[c, :, :] / numpy.amax(numpy.amax(dist[c, :, :]))
dd = d[:, :, numpy.newaxis]
alpha = numpy.tile(dd, (1, 1, 3))
t = clrstr[c, :, numpy.newaxis, numpy.newaxis]
single_joint_pdf = numpy.tile(t, (1, dist.shape[1], dist.shape[2]))
single_joint_pdf = numpy.transpose(single_joint_pdf, (1, 2, 0))
pdf_img = alpha * single_joint_pdf + (1 - alpha) * pdf_img
return pdf_img
def update_image(self, event=None):
self.imgview.image = canny(self.original_image,
sigma=self.sigma,
low_threshold=self.low,
high_threshold=self.high)
self.imgview.redraw()
def getVoidBorder(self):
"""Create boolean array where border points are True and all others
False.
Input:
- none
Example:
>>> import pycoresis as pcs
>>> fid = r'C:\YOUR\FILE\HERE.txt'
>>> crs = pcs.corescan(fid)
>>> crs.getVoidBorder()
Number of border points : 2449
Number of border points : 3245
array([[ True, True, True, ..., True, True, True],
[ True, False, False, ..., False, False, True],
[ True, False, False, ..., False, False, True],
...,
[ True, False, False, ..., False, False, True],
[ True, False, False, ..., False, False, True],
[ True, True, True, ..., True, True, True]], dtype=bool)
"""
self.voidedges = filter.canny(self.data)
point_num = np.where(self.voidedges==True)
self.pointnum = np.size(point_num[0])
print "Number of border points :", self.pointnum
return self.voidedges
def label_particles_edge(im, sigma=2, closing_size=0, **extra_args):
""" Segment image using Canny edge-finding filter.
parameters
----------
im : image in which to find particles
sigma : size of the Canny filter
closing_size : size of the closing filter
returns
-------
labels : an image array of uniquely labeled segments
"""
from skimage.morphology import square, binary_closing, skeletonize
if skimage_version < StrictVersion('0.11'):
from skimage.filter import canny
else:
from skimage.filters import canny
edges = canny(im, sigma=sigma)
if closing_size > 0:
edges = binary_closing(edges, square(closing_size))
edges = skeletonize(edges)
labels = sklabel(edges)
print "found {} segments".format(labels.max())
# in ma.array mask, False is True, and vice versa
labels = np.ma.array(labels, mask=edges == 0)
return labels
def parse(filename):
image = io.imread(filename, as_grey = True)
if image.shape[0] != 768:
print "WARN: Resizing image to old iPad Size. TODO> Move forward to retina images!"
print image.shape
image = transform.resize(image, (768, 1024))
print image.shape
ff = filter.canny(image)
# io.imshow(ff)
pi = 0
for i in xrange(len(ff)):
cnt = np.count_nonzero(ff[i])
if cnt > 800:
diff = i - pi
if diff > 50 and diff < 60:
slit = image[i-52:i]
yield slit
# data = slit.flatten()
# yield data
# io.imshow(slit)
if __name__ == "__main__":
print i, cnt, i-pi
pi = i
def label_particles_edge(im, sigma=2, closing_size=0, **extra_args):
""" Segment image using Canny edge-finding filter.
parameters
----------
im : image in which to find particles
sigma : size of the Canny filter
closing_size : size of the closing filter
returns
-------
labels : an image array of uniquely labeled segments
"""
from skimage.morphology import square, binary_closing, skeletonize
if skimage_version < StrictVersion('0.11'):
from skimage.filter import canny
else:
from skimage.filters import canny
edges = canny(im, sigma=sigma)
if closing_size > 0:
edges = binary_closing(edges, square(closing_size))
edges = skeletonize(edges)
labels = sklabel(edges)
print "found {} segments".format(labels.max())
# in ma.array mask, False is True, and vice versa
labels = np.ma.array(labels, mask=edges == 0)
return labels
def _get_canny_image(self):
ci = canny(
self.original_image, sigma=self.canny_sigma,
low_threshold=self.canny_low_threshold,
high_threshold=self.canny_high_threshold,
)
return ci
def filter(data, filtType, par):
if filtType == "sobel":
filt_data = sobel(data)
elif filtType == "roberts":
filt_data = roberts(data)
elif filtType == "canny":
filt_data = canny(data)
elif filtType == "lowpass_avg":
from scipy import ndimage
p = int(par)
kernel = np.ones((p, p), np.float32) / (p * p)
filt_data = ndimage.convolve(data, kernel)
elif filtType == "lowpass_gaussian":
s = float(par)
filt_data = gaussian_filter(data, sigma=s)
elif filtType == "highpass_gaussian":
s = float(par)
lp_data = gaussian_filter(data, sigma=s)
filt_data = data - lp_data
elif filtType == "highpass_avg":
from scipy import ndimage
p = int(par)
kernel = np.ones((p, p), np.float32) / (p * p)
lp_data = ndimage.convolve(data, kernel)
filt_data = data - lp_data
#elif filtType == "gradient":
return filt_data
def segment(self, src):
ndsrc = src.ndarray / 255.
edges = canny(ndsrc,
# low_threshold=0.001,
# high_threshold=0.1,
# low_threshold=self.canny_low_threshold,
# high_threshold=self.canny_high_threshold,
sigma=self.canny_sigma)
filled = ndimage.binary_fill_holes(edges)
filled = invert(filled) * 255
# label_objects, _ = ndimage.label(filled)
# sizes = bincount(label_objects.ravel())
#
# mask_sizes = sizes > 1
# mask_sizes[0] = 0
# cleaned = mask_sizes[label_objects]
# cleaned = asarray(cleaned, 'uint8')
# cleaned = closing(cleaned, square(5))
# self._locate_helper(invert(cleaned), **kw)
nsrc = asarray(filled, 'uint8')
return nsrc
def get_edge(name, sig = 8):
im = ndimage.imread(name, True)
edge = filter.canny(im, sigma = sig)
modded = (255.0 / edge.max() * (edge - edge.min())).astype(np.uint8)
edged = Image.fromarray(modded)
edged.save("photos/edge.png")
return edged
def find_iris(image, pupil, **kwargs):
buffer = 20
# run canny
image = filter.canny(image, sigma=1, low_threshold=10, high_threshold=50)
cx, cy, radius = pupil
segments = get_segments(400, step=0.01)
# get ray directions
directions = zip(map(cos, segments[0]), map(sin, segments[0]))
shape = image.shape
points = []
for d in directions:
start = (cx + (radius + buffer) * d[0], cy + (radius + buffer)*d[1])
ray = Ray(image, start, d)
point = ray.fire()
if point != None:
points.append(point)
for p in points:
x, y = circle_perimeter(int(p[0]), int(p[1]), 3)
x = x[x < rgb.shape[0]]
y = y[y < rgb.shape[1]]
rgb[x,y] = (220, 40, 40)
e = Ellipse().fit_with_center(None, points)
return image, points, e
def main():
plt.figure(figsize=(25, 24))
planes = ['samolot00.jpg', 'samolot01.jpg', 'samolot03.jpg', 'samolot04.jpg', 'samolot05.jpg','samolot07.jpg',
'samolot08.jpg', 'samolot09.jpg', 'samolot10.jpg', 'samolot11.jpg', 'samolot12.jpg', 'samolot13.jpg',
'samolot14.jpg', 'samolot15.jpg', 'samolot16.jpg', 'samolot17.jpg', 'samolot18.jpg', 'samolot20.jpg']
i = 1
for file in planes:
img = data.imread(file, as_grey=True)
img2 = data.imread(file)
ax = plt.subplot(6, 3, i)
ax.axis('off')
img **= 0.4
img = filter.canny(img, sigma=3.0)
img = morphology.dilation(img, morphology.disk(4))
img = ndimage.binary_fill_holes(img)
img = morphology.remove_small_objects(img, 1000)
contours = measure.find_contours(img, 0.8)
ax.imshow(img2, aspect='auto')
for n, contour in enumerate(contours):
ax.plot(contour[:, 1], contour[:, 0], linewidth=1.5)
center = (sum(contour[:, 1])/len(contour[:, 1]), sum(contour[:, 0])/len(contour[:, 0]))
ax.scatter(center[0], center[1], color='white')
i += 1
plt.savefig('zad2.pdf')
def watershed_segmentation(image_gray, markers_trace, markers_background):
'''
Segments the image into regions with one of two types (i.e. foreground
and background) using a watershed algorithm.
Parameters
-----------
image_gray : 2-D numpy array
A grayscale image.
markers_trace : 2-D Boolean numpy array
An array with the same shape as image_gray, where the seeds of
the trace regions are True.
markers_background : 2-D Boolean numpy array
An array with the same shape as image_gray, where the seeds of
the background regions are True.
Returns
---------
image_bin : 2-D Boolean numpy array
A 2-D array with the same shape as the input image. Foreground pixels
are True, and background pixels are False.
'''
bin_markers = np.zeros_like(image_gray, dtype=int)
bin_markers = np.where(markers_trace, 2, 0)
bin_markers = np.where(markers_background, 1, bin_markers)
#image_sobel = sobel(gaussian_filter(image_gray, 1))
image_sobel = sobel(image_gray)
edges = np.maximum(canny(image_gray).astype(float), image_sobel)
image_bin = watershed(edges, bin_markers)
image_bin = image_bin == 2
return image_bin
def auto_canny(array, average=None, gaussian_sigma=1, strongness=2.5):
if average is None:
average = array.size**0.5 / array.size
array -= array.min()
array /= array.max()
def canny_average(hard_threshold):
soft_threshold = hard_threshold / strongness
edges = canny(array, gaussian_sigma, hard_threshold, soft_threshold)
return edges.mean()
hard_threshold = 0.4
epsilon = 0.0001
bottom, top = 0., 1.
for iteration in xrange(20):
current_average = canny_average(hard_threshold)
print(hard_threshold, current_average)
if abs(current_average - average) < epsilon:
break
elif current_average < average:
top = hard_threshold
hard_threshold = (bottom + top) / 2
else:
bottom = hard_threshold
hard_threshold = (bottom + top) / 2
else:
print("Agotados los intentos")
soft_threshold = hard_threshold / strongness
return canny(array, gaussian_sigma, hard_threshold, soft_threshold)
def findPlantsCanny(stackVar, stackSum, showImages=True):
edges = canny(stackVar)
fill_stack = ndimage.binary_fill_holes(edges)
label_objects, nb_labels = ndimage.label(fill_stack)
sizes = np.bincount(label_objects.ravel())
mask_sizes = sizes > 25
for label in range(len(mask_sizes)):
'''
Get rid of lines in addition to the straight size threshold.
'''
pts = np.where(label_objects == label)
xRange = (max(pts[0]) - min(pts[0]))
yRange = (max(pts[1]) - min(pts[1]))
areaCovered = float(len(pts[0])) / (xRange*yRange)
if (areaCovered < .33) or (xRange < 3) or (yRange < 3):
mask_sizes[label] = False
mask_sizes[0] = 0
plants_cleaned = mask_sizes[label_objects]
labeled_plants, numPlants = ndimage.label(plants_cleaned)
center = findCenters(labeled_plants, stackSum)
if showImages:
fig, axs = plt.subplots(1,3, figsize=(14,4), sharey=True)
axs[0].imshow(stackVar)
axs[1].imshow(stackVar, cmap=plt.cm.jet, interpolation='nearest') #@UndefinedVariable
axs[1].contour(plants_cleaned, [0.5], linewidths=1.2, colors='y')
axs[2].imshow(labeled_plants, cmap=plt.cm.spectral, interpolation='nearest') #@UndefinedVariable
axs[2].scatter(np.array(center.tolist())[:,1], np.array(center.tolist())[:,0],
color='grey')
for ax in axs: ax.axis('off')
fig.subplots_adjust(wspace=.01)
return labeled_plants, center
def auto_canny(array, average=None, gaussian_sigma=1, strongness=2.5):
if average is None:
average = array.size ** 0.5 / array.size
array -= array.min()
array /= array.max()
def canny_average(hard_threshold):
soft_threshold = hard_threshold / strongness
edges = canny(array, gaussian_sigma, hard_threshold, soft_threshold)
return edges.mean()
hard_threshold = 0.4
epsilon = 0.0001
bottom, top = 0., 1.
for iteration in xrange(20):
current_average = canny_average(hard_threshold)
print(hard_threshold, current_average)
if abs(current_average - average) < epsilon:
break
elif current_average < average:
top = hard_threshold
hard_threshold = (bottom + top) / 2
else:
bottom = hard_threshold
hard_threshold = (bottom + top) / 2
else:
print("Agotados los intentos")
soft_threshold = hard_threshold / strongness
return canny(array, gaussian_sigma, hard_threshold, soft_threshold)
def get_gravatar_array(email, sz=100, edge=(5, 20, 2, 2), mask=((0, 10), (80, 90)), shrink=0.0):
g = hashlib.md5(email.lower()).hexdigest() + ".jpg"
fname = "/tmp/" + g
url = "http://www.gravatar.com/avatar/" + g
os.system("curl -s -o %s %s" % (fname, url))
g = scipy.misc.imread(fname, flatten=True)[5:75, 5:75]
g = 255 - scipy.misc.imresize(g, (sz, sz))
if edge:
(sigma, hi, low, sigma2) = edge
g = 255 * canny(g, sigma, hi, low)
g = (ndimage.gaussian_filter(g, sigma=sigma2) > 25) * 255
if mask:
((r1, c1), (r2, c2)) = mask
g[r2:, :] = 0
g[:r1, :] = 0
g[:, :c1] = 0
g[:, c2:] = 0
if shrink > 0.0:
(nx, ny) = g.shape
px = int(nx * shrink / 2)
py = int(ny * shrink / 2)
g = concatenate((zeros((py, nx), dtype=g.dtype), g, zeros((py, nx), dtype=g.dtype)), axis=0)
g = concatenate((zeros((ny + 2 * py, px), dtype=g.dtype), g, zeros((ny + 2 * py, px), dtype=g.dtype)), axis=1)
g = scipy.misc.imresize(g, (sz, sz))
g = g.flatten()
return g
def find_edges(self,sigma=None):
if sigma is not None:
self.sigma = sigma
print 'Identifying edges...'
self.edges = filter.canny(self.data,sigma=self.sigma)
return self.edges
def encode(self):
data_array = np.array(self.image) #data).reshape((28,28))
edges = filt.canny(data_array, sigma=3)
def linear_mapping(data): # using principal components analysis
pca = decomposition.PCA(n_components=784)
pca.fit(data)
mapping = pca.transform(data)
return mapping
# encoded = linear_mapping(edges)
encoded = np.array(edges).reshape(784)
# encoded = []
# for d in self.data:
# if (d > 45):
# encoded.append(1)
# else:
# encoded.append(0)
return encoded
def path2tab(path, nbI, threshold):
tab = []
maxAddr = 0
# Ouverture dossier
dirs = os.listdir(path)
# Pour chaque fichier
for file in dirs:
# Si le fichier est une image .jpg
if file[-4:] == '.jpg':
# Chargement image sous-forme tableau
image = io.imread(path + file, as_grey=True)
edges = canny(image)
address = 0
# Pour chaque ligne de l'image
for ligne in edges:
# Pour chaque pixel de la ligne
for pix in ligne:
# Si le pixel est blanc (c'est un contour)
if pix == True:
# Ajout du pixel au tableau pour déclencher un influx
# Par le neurone correspondant
tab.append((address, nbI))
if address > maxAddr:
maxAddr = address
# Incrémentation adresse
address += 1
# Incrémentation nombre d'images traitées
nbI += 1
print maxAddr
return tab, nbI
def label_particles_edge(im, sigma=2, closing_size=0, **extra_args):
""" label_particles_edge(image, sigma=3, closing_size=3)
Returns the labels for an image.
Segments using Canny edge-finding filter.
keyword arguments:
image -- The image in which to find particles
sigma -- The size of the Canny filter
closing_size -- The size of the closing filter
"""
from skimage.morphology import square, binary_closing, skeletonize
if skversion < version('0.11'):
from skimage.filter import canny
else:
from skimage.filters import canny
edges = canny(im, sigma=sigma)
if closing_size > 0:
edges = binary_closing(edges, square(closing_size))
edges = skeletonize(edges)
labels = sklabel(edges)
print "found {} segments".format(labels.max())
labels = np.ma.array(
labels,
mask=edges == 0) # in ma.array mask, False is True, and vice versa
return labels
def _get_canny_image(self):
ci = canny(
self.original_image,
sigma=self.canny_sigma,
low_threshold=self.canny_low_threshold,
high_threshold=self.canny_high_threshold,
)
return ci
def mapEdge(req):
"""Convert img to bytearray and do edge detection
on centered building"""
img = io.imread(req.geturl(),flatten=True)
labels, numobjects = ndimage.label(img)
edges = filter.canny(img, sigma=3)
return(edges)
def mapEdge(req):
"""Convert img to bytearray and do edge detection
on centered building"""
img = io.imread(req.geturl(), flatten=True)
labels, numobjects = ndimage.label(img)
edges = filter.canny(img, sigma=3)
return (edges)
def image_filter(self, image, **kwargs):
canny_keys = ('sigma', 'low_threshold', 'high_threshold')
canny_kwargs = dict([(k, kwargs.pop(k)) for k in canny_keys])
hough_kwargs = kwargs
edges = canny(image, **canny_kwargs)
lines = probabilistic_hough(edges, **hough_kwargs)
self._lines = lines
return edges
def makeEdges(self):
image = self.image
edges = canny(image / 255.)
ax = self.axs[0, 4]
ax.imshow(edges, cmap=plt.cm.gray, interpolation='nearest')
ax.axis('off')
ax.set_title('Canny detector')
self.edges = edges #fig.canvas.show()
def getArea(address):
"""Geocode address and retreive image centered
around lat/long"""
address = address
results = Geocoder.geocode(address)
lat, lng = results[0].coordinates
zip_code = results[0].postal_code
map_url = 'https://maps.googleapis.com/maps/api/staticmap?center={0},{1}&size=640x640&zoom=19&sensor=false&maptype=roadmap&&style=visibility:simplified|gamma:0.1'
request_url = map_url.format(lat, lng)
req = urllib.urlopen(request_url)
img = io.imread(req.geturl(),flatten=True)
labels, numobjects = ndimage.label(img)
image = filter.canny(img, sigma=3)
thresh = threshold_otsu(image)
bw = closing(image > thresh, square(3))
# remove artifacts connected to image border
cleared = bw.copy()
clear_border(cleared)
# label image regions
label_image = label(cleared)
borders = np.logical_xor(bw, cleared)
label_image[borders] = -1
image_label_overlay = label2rgb(label_image, image=image)
fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6, 6))
ax.imshow(image_label_overlay)
dist = []
rp = regionprops(label_image)
rp = [x for x in rp if 100 < x.area <= 900]
for region in rp:
# skip small images
#if region.area < 100:
# continue
dist.append(sqrt( ( 320-region.centroid[0] )**2 + ( 320-region.centroid[1] )**2 ))
# draw rectangle around segmented coins
#minr, minc, maxr, maxc = region.bbox
#rect = mpatches.Rectangle((minc, minr), maxc - minc, maxr - minr,
# fill=False, edgecolor='red', linewidth=2)
#ax.add_patch(rect)
roof_index = dist.index(min(dist))
minr, minc, maxr, maxc = rp[roof_index].bbox
rect = mpatches.Rectangle((minc, minr), maxc - minc, maxr - minr,
fill=False, edgecolor='red', linewidth=2)
ax.add_patch(rect)
img = StringIO()
fig.savefig(img)
img.seek(0)
session['roof_area'] = rp[roof_index].area
roof_area = (rp[roof_index].area)*12
return(roof_area)
def find_lines(sourcefile=SOURCEFILE, plot=PLOT, shrink=2, threshold=128,
scale_first=True,
**transformparams):
"""
This function borrows from
http://scikits-image.org/docs/0.3/auto_examples/plot_hough_transform.html
"""
# Line finding, using the Probabilistic Hough Transform
params = {}
params.update(TRANSFORMPARAMS)
params.update(transformparams)
print params
image_orig = imread(sourcefile)
# scale up
if scale_first:
image_orig = scale_up(image_orig)
# thin lines
if shrink > 0:
# note that this returns a one-bit image
shrunk_image = shrink_lines(image_orig,threshold=threshold,
iterations=shrink)
else:
# in this case shrunk_image may still be grayscale
shrunk_image = image_orig
# switch the y axis
image = shrunk_image[::-1,:]
#edge detection
edges = canny(image, 2, 1, 25)
#actual transform
lines = probabilistic_hough(edges, **params)
if PLOT:
plt.figure(figsize=(12, 4))
plt.subplot(131)
plt.imshow(image, cmap=plt.cm.gray)
plt.title('Input image')
plt.subplot(132)
plt.imshow(edges, cmap=plt.cm.gray)
plt.title('Sobel edges')
plt.subplot(133)
plt.imshow(edges * 0)
for line in lines:
p0, p1 = line
plt.plot((p0[0], p1[0]), (p0[1], p1[1]))
plt.title('Lines found with PHT')
plt.axis('image')
plt.show()
if scale_first:
lines = np.array(lines)/2
return lines
def process_image(request, image):
"""Creates an edge image and calculates the values needed for the score calculation if necessary. This function is called as soon as an image is requested.
:param request: The request object containing the user request.
:type request: :class:`django.http.HttpRequest`.
:param image: The image to be processed.
:type image: :class:`models.Image`.
"""
# detect edges
if not image.edge_image:
greyscale_image = io.imread(os.path.join(settings.MEDIA_ROOT, image.image.name), as_grey=True)
# resize image
height = len(greyscale_image)
width = len(greyscale_image[0])
factor = 768.0 / height
greyscale_image = transform.resize(greyscale_image, [height * factor, width * factor])
# detect edges
edges = filter.canny(greyscale_image, sigma=image.canny_sigma, low_threshold=image.canny_low_threshold, high_threshold=image.canny_high_threshold)
# save edge image
temp_filename = '/tmp/' + request.session.session_key + '.png'
io.imsave(temp_filename, ~edges * 1.)
image.edge_image.save(slugify(os.path.splitext(os.path.basename(image.image.name))[0]) + '.png', File(open(temp_filename)))
os.remove(temp_filename)
if not image.dilated_edge_image:
edge_image = io.imread(os.path.join(settings.MEDIA_ROOT, image.edge_image.name), as_grey=True)
edge_image = edge_image.astype(np.float64)
if edge_image.max() > 1.:
edge_image /= 255.
# map values greater .5 as edge
edge_image = (1. - edge_image) / .5
# save dilated edge image
temp_filename = '/tmp/' + request.session.session_key + '.png'
io.imsave(temp_filename, ~ndimage.binary_dilation(edge_image, iterations=2) * 1.)
image.dilated_edge_image.save(slugify(os.path.splitext(os.path.basename(image.image.name))[0]) + '.png', File(open(temp_filename)))
os.remove(temp_filename)
# save maximum distance (needed for score calculation)
if not image.max_distance:
ones = np.ones(image.edge_image.height * image.edge_image.width).reshape((image.edge_image.height, image.edge_image.width))
dilated_edge_image = io.imread(os.path.join(settings.MEDIA_ROOT, image.dilated_edge_image.name), as_grey=True)
dilated_edge_image = dilated_edge_image.astype(np.float64)
if dilated_edge_image.max() > 1.:
dilated_edge_image /= 255.
image.max_distance = np.sum(np.absolute(ones - dilated_edge_image))
image.save()
def profiling(self):
image = resize(self.image, (257, 257))
edges = ff.canny(image)
edges = skeletonize(edges)
edges = edges.astype(int)
edges = edges[1:256, 1:256]
lt = self.feature_extraction_profiling(edges, 2, 5)
profiling = lt.ravel()
return np.append(profiling, [float(self.ratio) / 10])
def getEdgeMatrix(img, sigpercent=.01, axis=0):
'''Use the canny filter to produce a boolean matrix with the same dimensions
as the image where the value True indicates an edge and False indicates
no edge. sig is the tuning parameter for the canny filter: a higher sig
means fewer edges detected (smoother image) '''
# If the image is not already grayscale, scale it down
if np.ndim(img) == 3:
img = img.mean(axis=2)
edgeMat = filter.canny(img, sigma=sigpercent*np.shape(img)[axis])
return edgeMat
def profiling(self):
image = resize(self.image, (257, 257))
edges = ff.canny(image)
edges = skeletonize(edges)
edges = edges.astype(int)
edges = edges[1:256, 1:256]
lt = self.feature_extraction_profiling(edges, 2, 5)
profiling = lt.ravel()
return np.append(profiling, [float(self.ratio)/10])
def edge_change_ratio(frame1, frame2, sigma=3, low_threshold=20,
high_threshold=80, distance=24, edge_width=10,
float_accuracy=3):
"""
Calculate Edge Change Ratio for the given 2 frames (n-1, n)
:param frame1: Frame N-1
:param frame2: Frame N
:param sigma: Edge detection level
:param low_threshold: Dark threshold
:param high_threshold: Bright threshold
:param distance: Dialtion Distance
:param edge_width: Distance of Edges Measured
:param float_accuracy: Floating point precision
:return: Float
"""
frame1_grey = desaturate(frame1)
frame2_grey = desaturate(frame2)
frame1_edge = canny(frame1_grey, sigma, low_threshold, high_threshold)
frame2_edge = canny(frame2_grey, sigma, low_threshold, high_threshold)
frame1_inv_edge = invert(frame1_edge).astype('uint8') * 255
frame2_inv_edge = invert(frame2_edge).astype('uint8') * 255
frame1_contours = measure.find_contours(frame1_inv_edge, edge_width)
frame2_contours = measure.find_contours(frame2_inv_edge, edge_width)
frame1_dialate = dilation(frame1_edge, square(distance))
frame2_dialate = dilation(frame2_edge, square(distance))
frame1_comp = frame1_inv_edge + frame2_dialate
frame2_comp = frame2_inv_edge + frame1_dialate
frame1_comp_contours = measure.find_contours(frame1_comp, edge_width)
frame2_comp_contours = measure.find_contours(frame2_comp, edge_width)
try:
return round(
max(float(len(frame1_comp_contours)) / float(len(frame1_contours)),
float(len(frame2_comp_contours)) / float(len(frame2_contours))),
float_accuracy) * 100
except ZeroDivisionError:
return 0
def distinct(planes): for i, img in enumerate(planes): #contours = measure.find_contours(planes[i], 0.8) planes[i] = filter.canny(planes[i], sigma=3) #planes[i] = filter.sobel(planes[i]) planes[i] = Image(morphology.dilation(planes[i], square(3))) #planes[i] = Image(morphology.erosion(planes[i], square(2))) message = "Distincted plane: " + str(i) print(message)
def processor(t_sigma, t_level, sigma, low_threshold, high_threshold):
t_sigma = sigmoid(t_sigma) * 20
t_level = sigmoid(t_level)
sigma = sigmoid(sigma) * 50
phase = p.unwrapped_phase
local_context = gaussian_filter(phase, t_sigma)
phase = (phase - local_context) / (1 - t_level)
phase /= phase.ptp()
phase -= phase.min()
cannied = canny(phase, sigma, low_threshold, high_threshold)
return np.vstack((phase, cannied))
def direction_skeleton(self):
edges = ff.canny(self.image)
edges = skeletonize(edges)
edges = edges.astype(int)
edges = edges[1:251, 1:251]
zones = self.zoning(edges)
for i in zones:
self.line_segmentation(i)
direction = self.direction_transfer(self.count_common(zones))
return np.append(direction, [float(self.ratio) / 10])
def find_center(saas_image, sigma=0.8, num_circles=5):
"""
Find the center of the image
:param saas_image: A SAAS image object.
:param sigma: The amount of gaussian blurring
:param num_circles: The number of circles to find.
Returns:
"""
edges = filter.canny(saas_image.roi_data, sigma=sigma)
hough_radii = np.arange(10, 70, 1)
hough_res = hough_circle(edges, hough_radii)
centers = []
accums = []
radii = []
for radius, h in zip(hough_radii, hough_res):
# For each radius, extract two circles
peaks = peak_local_max(h, num_peaks=2)
if peaks != []:
centers.extend(peaks)
accums.extend(h[peaks[:, 0], peaks[:, 1]])
radii.extend([radius, radius])
best_centers = []
best_radii = []
best_x = []
best_y = []
number_of_best_circles = num_circles
for idx in np.argsort(accums)[::-1][:number_of_best_circles]:
center_x, center_y = centers[idx]
best_x.append(center_x)
best_y.append(center_y)
best_centers.append(centers[idx])
best_radii.append(radii[idx])
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 6))
ax1.imshow(edges)
ax2.imshow(self.roi_data, cmap=cm.gray)
for center, radius in zip(best_centers, best_radii):
circle = plt.Circle((center[1], center[0]),
radius,
color='r',
fill=False)
ax2.add_patch(circle)
print("Calibrated Center X = %s +/- %s" %
(np.average(best_x), np.std(best_x)))
print("Calibrated Center Y = %s +/- %s" %
(np.average(best_y), np.std(best_y)))
return np.array([[np.average(best_x), np.std(best_x)],
[np.average(best_y), np.std(best_y)]])
def processDropboxImage(files):
job = get_current_job()
job.meta['handled_by'] = socket.gethostname()
job.meta['state'] = 'start'
job.save()
print 'Current job: %s' % (job.id)
#print job.meta
for file in files:
import uuid
url_to_grab = file['link']
image_path = '/vagrant/app/static/uploads/%s%s' % (
uuid.uuid4(), os.path.splitext(file['link'])[1])
urllib.urlretrieve(url_to_grab, image_path)
job.meta['state'] = 'download complete'
job.save()
#time.sleep(3)
im = ndimage.imread(image_path, True)
job.meta['state'] = 'image loaded complete'
job.save()
# time.sleep(3)
edges2 = filter.canny(im, sigma=3)
job.meta['state'] = 'filter complete'
job.save()
#time.sleep(3)
misc.imsave(image_path[:-4] + '-canny.jpg', edges2)
job.meta['state'] = 'image saved'
job.save()
#time.sleep(3)
return_data = {}
return_data['processed'] = image_path[:-4] + '-canny.jpg'
return_data['original'] = image_path
return_data['src'] = '/static/uploads/%s' % os.path.split(
return_data['original'])[1]
return_data['srcproc'] = '/static/uploads/%s' % os.path.split(
return_data['original'])[1][:-4] + '-canny.jpg'
#return_data['callback_id'] = callback_id
#print job.meta
return json.dumps(return_data)
def filter_function(img_grey, filt='canny'):
"""
Grayscales and apply edge detectors to image.
Returns the flattened filtered image array.
input: raw image 3d tensor
output: filtered image
filters: 'sobel', 'roberts', 'scharr'
default filter = 'canny'
"""
# grayscale filters:
if filt == 'sobel':
return sobel(img_grey)
elif filt == 'roberts':
return roberts(img_grey)
elif filt == 'canny':
return canny(img_grey)
elif filt == 'scharr':
return scharr(image_grey)
elif filt == ('canny', 'sobel'):
return canny(sobel(img_grey))
else:
raise Exception('No Such Filter!')
def init_pq(imgSize, radiance): boundary = np.zeros((imgSize, imgSize)) #calculate E_x and E_y to intialize E_x = np.array(radiance,copy = True); E_y = np.array(radiance,copy = True); E_x[:,1:-1] = 0.5*(E_x[:,2:] - E_x[:,:-2]) E_y[1:-1,:] = 0.5*(- E_y[2:,:] + E_y[:-2,:]) boundary = filter.canny(radiance,sigma = 1.0); p_init = np.array(E_x*boundary,copy =True) q_init = np.array(E_y*boundary,copy =True) return p_init,q_init,boundary
def init_fg(imgSize, radiance):
boundary = np.zeros((imgSize, imgSize))
#calculate E_x and E_y to intialize
E_x = np.array(radiance, copy=True)
E_y = np.array(radiance, copy=True)
E_x[:, 1:-1] = 0.5 * (E_x[:, 2:] - E_x[:, :-2])
E_y[1:-1, :] = 0.5 * (-E_y[2:, :] + E_y[:-2, :])
f, g = pq2fg(E_x, E_y)
boundary = filter.canny(radiance, sigma=3.0)
f_init = np.array(f * boundary, copy=True)
g_init = np.array(g * boundary, copy=True)
return f_init, g_init, boundary
def ContourLoss(self, img, label, IDMask):
contour = filter.canny(img)
selem = disk(5)
outlier = dilation(contour, selem)
outlier[np.where(np.absolute(IDMask) > 0)] = 0
contour = contour.astype(float)
contour *= IDMask
Similarity = np.ones_like(IDMask, dtype=np.float)
Similarity[np.where(outlier > 0)] = -0.5
if np.amax(IDMask) > 100000:
raise Exception("Wrong in IDMask!")
LocationsSrc = {
index: np.where(np.absolute(contour) == index + 1)
for index in range(np.amax(IDMask))
}
LocationsRef = {
index: np.where(IDMask == -(index + 1))
for index in range(np.amax(IDMask))
}
for index in range(np.amax(IDMask)):
SrcX = LocationsSrc[index][0]
SrcY = LocationsSrc[index][1]
RefX = LocationsRef[index][0]
RefY = LocationsRef[index][1]
ContourSimilarity = 0
if np.size(SrcX) > 0.9 * np.size(RefX) and np.size(
RefX) > 10 and np.size(SrcX) < 1.2 * np.size(RefX):
if np.size(np.unique(SrcX)) > np.size(np.unique(SrcY)):
VecSrc = np.polyfit(SrcX, SrcY, 3)[:3]
VecRef = np.polyfit(RefX, RefY, 3)[:3]
else:
VecSrc = np.polyfit(SrcY, SrcX, 3)[:3]
VecRef = np.polyfit(RefY, RefX, 3)[:3]
ContourSimilarity = np.absolute(np.inner(VecSrc, VecRef)) / (
LA.norm(VecSrc) + 1e-10) / (LA.norm(VecRef) + 1e-10)
Similarity[np.where(np.absolute(IDMask) == index +
1)] = ContourSimilarity
return Similarity
def build_r_table(image, origin):
'''
Build the R-table from the given shape image and a reference point
'''
edges = canny(image,
low_threshold=MIN_CANNY_THRESHOLD,
high_threshold=MAX_CANNY_THRESHOLD)
gradient = gradient_orientation(edges)
r_table = defaultdict(list)
for (i, j), value in np.ndenumerate(edges):
if value:
r_table[gradient[i, j]].append((origin[0] - i, origin[1] - j))
return r_table
def task_2_3():
I = imread('images/coins.png')
edges_I = canny(I)
radi = np.arange(3, 30)
hough1 = hough_circle(I, radi)
hough2 = hough_circle(edges_I, radi)
print hough1.shape
fig, ax = plt.subplots(1, 2)
ax[0].imshow(I, cmap='gray')
ax[1].imshow(hough2[0], cmap='gray')
ax[0].set_title('Original image')
ax[0].axis('off')
ax[1].set_title('Original Image segmented via Hough Circle')
ax[1].axis('off')
plt.show()
def accumulate_gradients(r_table, grayImage):
'''
Perform a General Hough Transform with the given image and R-table
'''
edges = canny(grayImage,
low_threshold=MIN_CANNY_THRESHOLD,
high_threshold=MAX_CANNY_THRESHOLD)
gradient = gradient_orientation(edges)
accumulator = np.zeros(grayImage.shape)
for (i, j), value in np.ndenumerate(edges):
if value:
for r in r_table[gradient[i, j]]:
accum_i, accum_j = i + r[0], j + r[1]
if accum_i < accumulator.shape[
0] and accum_j < accumulator.shape[1]:
accumulator[accum_i, accum_j] += 1
return accumulator
def test_01_02_circle_with_noise(self):
'''Test that the Canny filter finds the circle outlines
in a noisy image'''
np.random.seed(0)
i, j = np.mgrid[-200:200, -200:200].astype(float) / 200
c = np.abs(np.sqrt(i * i + j * j) - .5) < .02
cf = c.astype(float) * .5 + np.random.uniform(size=c.shape) * .5
result = F.canny(cf, 4, .1, .2, np.ones(c.shape, bool))
#
# erode and dilate the circle to get rings that should contain the
# outlines
#
cd = binary_dilation(c, iterations=4)
ce = binary_erosion(c, iterations=4)
cde = np.logical_and(cd, np.logical_not(ce))
self.assertTrue(np.all(cde[result]))
point_count = np.sum(result)
self.assertTrue(point_count > 1200)
self.assertTrue(point_count < 1600)