def analyze_lowexp(self):
try:
summed = np.add(np.add(self.img[:, :, 0], self.img[:, :, 1]),
self.img[:, :, 2])
smoothed = filters.gaussian(summed, 5)
mask = cutoff(smoothed, self.specs)
edges = filters.sobel(mask)
# Detect two radii
hough_radii = np.arange(CAPILLARY_DIAM_LOW_BOUND,
CAPILLARY_DIAM_UP_BOUND, 2)
hough_res = trans.hough_circle(edges, hough_radii)
print('\tcalculating hough transform...')
# Select the most prominent 1 circles
accums, cx, cy, radii = trans.hough_circle_peaks(hough_res,
hough_radii,
total_num_peaks=1)
self.cap_x = cx[0]
self.cap_y = cy[0]
self.cap_r = radii[0]
# Detect two radii
hough_radii = np.arange(self.specs[1], self.specs[2], 2)
hough_res = trans.hough_circle(edges, hough_radii)
# Select the most prominent 5 circles
accums, cx, cy, radii = trans.hough_circle_peaks(hough_res,
hough_radii,
total_num_peaks=1)
print("\tbore - accums, cx, cy, radii " +
str((accums, cx, cy, radii)))
self.bore_x = cx[0]
self.bore_y = cy[0]
self.bore_r = radii[0]
self.axes.imshow(self.img)
self.axes.add_patch(
Circle((self.bore_x, self.bore_y),
self.bore_r,
color='r',
fill=False))
self.axes.add_patch(
Circle((self.cap_x, self.cap_y),
self.cap_r,
color='r',
fill=False))
except:
raise ValueError("Failed during lowexp analysis of img: " +
str(self.name))
raise
'''
---------------------------- additional methods -----------------------
'''
"""
def analyze_otsu(self):
try:
# convert to grey
grey = color.rgb2gray(self.img)
#smooth to remove noise, high noise makes hough transform take significantly more time
grey_smooth = filters.gaussian(grey, 3)
# threshold via otsu histogram binning
otsu = grey_smooth > filters.threshold_otsu(grey_smooth)
# calculate edges via sobel method
edges = filters.sobel(otsu)
print('\tcalculating hough transform...')
# Detect two radii
hough_radii = np.arange(CAPILLARY_DIAM_LOW_BOUND,
CAPILLARY_DIAM_UP_BOUND, 2)
hough_res = trans.hough_circle(edges, hough_radii)
# Select the most prominent circle
accums, cx, cy, radii = trans.hough_circle_peaks(hough_res,
hough_radii,
total_num_peaks=1)
self.cap_x = cx[0]
self.cap_y = cy[0]
self.cap_r = radii[0]
# Detect two radii
hough_radii = np.arange(self.specs[1], self.specs[2], 2)
hough_res = trans.hough_circle(edges, hough_radii)
# Select the most prominent circle
accums, cx, cy, radii = trans.hough_circle_peaks(hough_res,
hough_radii,
total_num_peaks=1)
print("\taccums, cx, cy, radii " + str((accums, cx, cy, radii)))
self.bore_x = cx[0]
self.bore_y = cy[0]
self.bore_r = radii[0]
self.axes.imshow(self.img)
self.axes.set_title(str(self.name))
self.axes.add_patch(
Circle((self.bore_x, self.bore_y),
self.bore_r,
color='r',
fill=False))
self.axes.add_patch(
Circle((self.cap_x, self.cap_y),
self.cap_r,
color='r',
fill=False))
except:
raise ValueError("Failed during Otsu analysis of img: " +
str(self.name))
raise
def get_circle_locations(image, name, outputs, specs):
try:
summed = np.add(image[:,:,0], image[:,:,1])
summed = np.add(summed, image[:,:,2])
smoothed = filters.gaussian(summed, 5)
mask = cutoff(smoothed, specs)
# plt.imshow(mask)
# plt.show()
edges = filters.sobel(mask)
# Detect two radii
hough_radii = np.arange(CAPILLARY_DIAM_LOW_BOUND, CAPILLARY_DIAM_UP_BOUND, 2)
hough_res = trans.hough_circle(edges, hough_radii)
# Select the most prominent 1 circles
accums, cx, cy, radii = trans.hough_circle_peaks(hough_res, hough_radii,
total_num_peaks=1)
cap_x = cx[0]
cap_y = cy[0]
cap_r = radii[0]
# Detect two radii
hough_radii = np.arange(specs[1], specs[2], 2)
hough_res = trans.hough_circle(edges, hough_radii)
# Select the most prominent 5 circles
accums, cx, cy, radii = trans.hough_circle_peaks(hough_res, hough_radii,
total_num_peaks=1)
print("accums, cx, cy, radii " + str( (accums, cx, cy, radii) ))
bore_x = cx[0]
bore_y = cy[0]
bore_r = radii[0]
fig1, ax1 = plt.subplots(ncols=1, nrows=1, figsize=(10, 4))
ax1.imshow(image)
ax1.add_patch(Circle((bore_x,bore_y),bore_r, color='r', fill=False))
ax1.add_patch(Circle((cap_x,cap_y),cap_r, color='r', fill=False))
fig1.savefig(outputs+'\\'+name[:-4]+'-circled.png')
plt.close('all')
return (cap_x, cap_y, cap_r), (bore_x,bore_y,bore_r)
except:
print('failed at ' + str(name))
print('expected outputs ' + str(outputs))
print()
raise
def find_circles(edge_img, min_radius, max_radius):
hough_radii = np.arange(min_radius, max_radius, 2)
hough_res = hough_circle(edge_img, hough_radii)
accums, cx, cy, radii = hough_circle_peaks(hough_res,
hough_radii,
total_num_peaks=1)
return list(zip(cy, cx, radii))
def mid_phantom(image_ACR, imarray):
# Detect edges in image
edges = filters.canny(imarray,
sigma=3,
low_threshold=200,
high_threshold=1000)
hough_radii = np.array([190 / 2 / image_ACR.PixelSpacing[1]])
print type(edges)
print type(hough_radii)
hough_res = hough_circle(edges, hough_radii)
# Detect contours and middle of phantom
centers = []
radii = []
for radius, h in zip(hough_radii, hough_res):
peaks = peak_local_max(h, num_peaks=1)
centers.extend(peaks)
radii.extend([radius, radius])
center_x, center_y = centers[0]
radius = radii[1] # Niet nodig?
radius = np.int32(radius) # Niet nodig?
cy, cx = circle_perimeter(center_y, center_x, radius) # Niet nodig?
return center_x, center_y, radii
def _detect_nodes(edges, node_radius: Tuple[int, int], num_nodes: int, n):
"""
"""
from skimage.transform import hough_circle, hough_circle_peaks
from skimage import color
from skimage.draw import circle_perimeter
nodes = []
hough_radii = np.arange(node_radius[0], node_radius[1], 1)
hough_res = hough_circle(edges, hough_radii)
accums, cx, cy, radii = hough_circle_peaks(hough_res,
hough_radii,
total_num_peaks=num_nodes)
# fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(10, 10))
# image = color.gray2rgb(img.copy())
i = 0
for center_y, center_x, radius in zip(cy, cx, radii):
# circy, circx = circle_perimeter(center_y, center_x, radius,
# shape=image.shape)
# image[circy, circx] = (255, 20, 20)
n.add_node(str(i),
x=center_x,
y=center_y,
r=radius,
coordinates=(center_x, -center_y))
i += 1
def find_circle(noisy_img, model):
# Fill in this function
noisy_img = np.expand_dims(np.expand_dims(noisy_img, axis=0), axis=-1)
img = model.predict(noisy_img / np.amax(noisy_img)).reshape(IMAGE_SIZE, IMAGE_SIZE)
edges = canny(img, low_threshold=0.5, high_threshold=1.0)
try_radii = np.arange(10, MAX_RAD, 1)
hough_res = hough_circle(edges, try_radii)
_, cy, cx, radii = hough_circle_peaks(hough_res, try_radii, total_num_peaks=1)
if cx.size == 0:
cx = 0
else:
cx = cx[-1]
if cy.size == 0:
cy = 0
else:
cy = cy[-1]
if radii.size == 0:
radii = 0
else:
radii = radii[-1]
return (cx, cy, radii)
def GetCircles(I):
Y = cv2.cvtColor(I, cv2.COLOR_BGR2GRAY)
r_min = 50
radius_list = np.arange(r_min, int(round(2.1 * r_min)))
num_circles_per_scale = 50
scale = 1.0
pyramid = []
circle_list = []
while min(Y.shape[:2]) > r_min:
M = canny(Y, sigma=2, low_threshold=10, high_threshold=20)
# plt.imshow(M, cmap='gray')
# plt.waitforbuttonpress()
H = hough_circle(M, radius_list[radius_list < min(Y.shape[:2]) / 2])
r_idx, y, x = np.unravel_index(
H.reshape(-1).argsort()[::-1][:min(num_circles_per_scale, H.size)],
H.shape)
r = radius_list[r_idx]
pyramid += [Y]
circle_list += [
np.array([scale * x, scale * y, scale * r, H[r_idx, y, x] / r]).T
]
Y = cv2.resize(Y, (0, 0), fx=0.5, fy=0.5)
scale *= 2.0
circle_list = np.concatenate(circle_list)
return circle_list
def analyze_ra_rotation(rotate_fn):
""" Get RA axis center of rotation XY coordinates
Args:
rotate_fn (str): FITS file of RA rotation
Returns:
tuple(int): A tuple of integers corresponding to the XY pixel position
of the center of rotation around RA axis
"""
d0 = fits.getdata(rotate_fn) # - 2048
# Get center
position = (d0.shape[1] // 2, d0.shape[0] // 2)
size = (1500, 1500)
d1 = Cutout2D(d0, position, size)
d1.data = d1.data / d1.data.max()
# Get edges for rotation
rotate_edges = canny(d1.data, sigma=1.0)
rotate_hough_radii = np.arange(100, 500, 50)
rotate_hough_res = hough_circle(rotate_edges, rotate_hough_radii)
rotate_accums, rotate_cx, rotate_cy, rotate_radii = \
hough_circle_peaks(rotate_hough_res, rotate_hough_radii, total_num_peaks=1)
return d1.to_original_position((rotate_cx[-1], rotate_cy[-1]))
def processImage(imgNumber = -1):
if imgNumber > -1:
greyimage = rgb2gray(io.imread("data/{img}.JPG".format(img=imgNumber)))
greyimage = greyimage > .5
else:
greyimage = data.coins()
image = img_as_ubyte(greyimage)
edges = sobel(image)
edges = mp.dilation(canny(image, sigma=3, low_threshold=30, high_threshold=60))
drawPlot(edges)
# Detect two radii
hough_radii = np.arange(100, 250, 20)
hough_res = hough_circle(edges, hough_radii)
# Select the most prominent 5 circles
accums, cx, cy, radii = hough_circle_peaks(hough_res, hough_radii,
total_num_peaks=20)
# Draw them
image = color.gray2rgb(image)
for center_y, center_x, radius in zip(cy, cx, radii):
circy, circx = circle_perimeter(center_y, center_x, radius)
image[circy, circx] = (220, 20, 20)
drawPlot(image)
def get_best_circles(mask, resize_factor=8):
'''
Get the best the best fit to a circle using the hough transform.
'''
#%%
#resize image to increase speed. I don't want
#%%
min_size = min(mask.shape)
resize_factor = min_size / max(128, min_size / resize_factor)
dsize = tuple(int(x / resize_factor) for x in mask.shape[::-1])
mask_s = cv2.dilate(mask, disk(resize_factor / 2))
mask_s = cv2.resize(mask_s, dsize)
#%%
r_min = min(mask_s.shape)
r_max = max(mask_s.shape)
#use the first peak of the circle hough transform to initialize the food shape
hough_radii = np.arange(r_min / 4, r_max / 2, 2)
hough_res = hough_circle(mask_s, hough_radii)
accums, cx, cy, radii = hough_circle_peaks(hough_res,
hough_radii,
total_num_peaks=9)
#%%
cx, cy, radii = [
np.round(x * resize_factor).astype(np.int) for x in (cx, cy, radii)
]
#%%
return list(zip(accums, cx, cy, radii))
def analyze_ra_rotation(rotate_fn):
""" Get RA axis center of rotation XY coordinates
Args:
rotate_fn (str): FITS file of RA rotation
Returns:
tuple(int): A tuple of integers corresponding to the XY pixel position
of the center of rotation around RA axis
"""
d0 = fits.getdata(rotate_fn) # - 2048
# Get center
position = (d0.shape[1] // 2, d0.shape[0] // 2)
size = (1500, 1500)
d1 = Cutout2D(d0, position, size)
d1.data = d1.data / d1.data.max()
# Get edges for rotation
rotate_edges = canny(d1.data, sigma=1.0)
rotate_hough_radii = np.arange(100, 500, 50)
rotate_hough_res = hough_circle(rotate_edges, rotate_hough_radii)
rotate_accums, rotate_cx, rotate_cy, rotate_radii = \
hough_circle_peaks(rotate_hough_res, rotate_hough_radii, total_num_peaks=1)
return d1.to_original_position((rotate_cx[-1], rotate_cy[-1]))
def test_hough_circle():
# Prepare picture
img = np.zeros((120, 100), dtype=int)
radius = 20
x_0, y_0 = (99, 50)
y, x = circle_perimeter(y_0, x_0, radius)
img[x, y] = 1
out1 = tf.hough_circle(img, radius)
out2 = tf.hough_circle(img, [radius])
assert_equal(out1, out2)
out = tf.hough_circle(img, np.array([radius], dtype=np.intp))
assert_equal(out, out1)
x, y = np.where(out[0] == out[0].max())
assert_equal(x[0], x_0)
assert_equal(y[0], y_0)
def process(self, im):
(width, height, _) = im.image.shape
img_adapted = im.prep(self.transform)
if width > self.max_resized or height > self.max_resized:
scale_height = self.max_resized / height
scale_width = self.max_resized / width
scale = min(scale_height, scale_width)
img_adapted = resize(img_adapted, (int(width * scale), int(height * scale)))
edges = canny(img_adapted, sigma=self.sigma)
# Detect two radii
# Calculate image diameter
shape = im.image.shape
diam = math.sqrt(shape[0] ** 2 + shape[1] ** 2)
radii = np.arange(diam / 3, diam * 0.8, 2)
hough_res = hough_circle(edges, radii)
accums = []
for radius, h in zip(radii, hough_res):
# For each radius, extract two circles
peaks = peak_local_max(h, num_peaks=1, min_distance=1)
if len(peaks) > 0:
accums.extend(h[peaks[:, 0], peaks[:, 1]])
if len(accums) == 0: # TODO: fix, should not happen
return [0]
idx = np.argmax(accums)
return [accums[idx]]
def test_hough_circle_peaks_normalize():
x_0, y_0, rad_0 = (50, 50, 20)
img = np.zeros((120, 100), dtype=int)
y, x = circle_perimeter(y_0, x_0, rad_0)
img[x, y] = 1
x_1, y_1, rad_1 = (60, 60, 30)
y, x = circle_perimeter(y_1, x_1, rad_1)
img[x, y] = 1
radii = [rad_0, rad_1]
hspaces = transform.hough_circle(img, radii)
out = transform.hough_circle_peaks(hspaces,
radii,
min_xdistance=15,
min_ydistance=15,
threshold=None,
num_peaks=np.inf,
total_num_peaks=np.inf,
normalize=False)
# Two perfect circles are close but the second one is bigger.
# Therefore, it is picked due to its high peak.
assert_equal(out[1], np.array([y_1]))
assert_equal(out[2], np.array([x_1]))
assert_equal(out[3], np.array([rad_1]))
def find_fingers_circles(edges, image, impy=None):
hough_radii = np.arange(20, 60, 2)
hough_res = hough_circle(edges, cv2.HOUGH_GRADIENT, 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=5)
centers.extend(peaks - hough_radii.max())
accums.extend(h[peaks[:, 0], peaks[:, 1]])
radii.extend([radius, radius])
fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6, 6))
# Draw the most prominent 5 circles
image = color.gray2rgb(edges)
for idx in np.argsort(accums)[::-1][:5]:
center_x, center_y = centers[idx]
radius = radii[idx]
cx, cy = circle_perimeter(center_y, center_x, radius)
image[cy, cx] = (250, 0, 0)
ax.imshow(image, cmap=plt.cm.gray)
plt.show()
def blackout_outside(self, img, sigma=3):
img_g = skic.rgb2gray(img)
edges = skif.canny(img_g, sigma=sigma)
hough_radii = np.arange(180, 210, 2)
hough_res = skit.hough_circle(edges, hough_radii)
centers = []
accums = []
radii = []
for radius, h in zip(hough_radii, hough_res):
# For each radius, extract two circles
num_peaks = 1
peaks = skif.peak_local_max(h, min_distance=40, num_peaks=num_peaks)
if peaks != []:
centers.extend(peaks)
accums.extend(h[peaks[:, 0], peaks[:, 1]])
radii.extend([radius] * num_peaks)
# print radius, np.max(h.ravel()), len(peaks)
if accums == [] and sigma==3:
return self.blackout_outside(img, sigma=3)
# Draw the most prominent 5 circles
image = (img.copy() / 4.0).astype(np.uint8)
cx, cy = skid.circle(*self.average_hough_detections(hough_radii, hough_res))
image[cy, cx] = img[cy, cx]
return image
def electrode_detection(input_path, output_path):
# approach one, using cv2
img = cv.imread(input_path, 0)
# Load picture and detect edges
edges = canny(img, sigma=2, low_threshold=35, high_threshold=40)
#plot the graph to get a better feeling
"""
fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(10, 10))
ax.imshow(edges, cmap=plt.cm.gray)
plt.show()
"""
# Detect circles with size of hough_radii
hough_radii = np.arange(5, 40, 1)
hough_res = hough_circle(edges, hough_radii)
# Select the most prominent 30 circles
accums, cx, cy, radii = hough_circle_peaks(hough_res,
hough_radii,
total_num_peaks=30)
#this returns the list of points that are on the perimeters, and change the value to white
#for better visualization
fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(10, 10))
for center_y, center_x, radius in zip(cy, cx, radii):
circy, circx = circle_perimeter(center_y,
center_x,
radius,
shape=img.shape)
img[circy, circx] = 255
ax.imshow(img, cmap=plt.cm.gray)
plt.show()
#np.save(output_path, list(zip(cy,cx)))
np.savetxt(output_path, list(zip(cy, cx)), fmt='%s')
return list(zip(cy, cx))
def hough_fun(img): """fit circles to segmented image based on plausible range of radii""" # based on screen shot, I'm guessing about 25px radius for now hough_radii = np.arange(28,45) hough_res = hough_circle(img, hough_radii) blank = img.copy() blank[::] = 0 """ accum, cx, cy, rad = hough_circle_peaks(hough_res, hough_radii) for i, ac in enumerate(np.argsort(accum)[::-1][:10]): center_x = cx[i] center_y = cy[i] radius = rad[i] cx, cy = draw.circle_perimeter(center_y, center_x, radius) blank[cy, cx] = 255 return blank """ # if can't import hough_circle_peaks, try to replicate: centers = [] accums = [] radii = [] for radius, h in zip(hough_radii, hough_res): # For each radius, extract, say, 3 circles peaks = peak_local_max(h, num_peaks=2) centers.extend(peaks - hough_radii.max()) accums.extend(h[peaks[:, 0], peaks[:, 1]]) radii.extend([radius, radius]) for idx in np.argsort(accums)[::-1][:25]: center_x, center_y = centers[idx] radius = radii[idx] cx, cy = draw.circle_perimeter(center_y, center_x, radius) blank[cy, cx] = 255 return blank
def detect_Hough(data):
image = data.copy()
edges = canny(image, sigma=10, low_threshold=60, high_threshold=90)
# Detect circles between 80% and 100% of image semi-diagonal
lx, ly = data.shape
sizex, sizey = lx/2., ly/2.
max_r = numpy.sqrt(sizex**2 + sizey**2)
hough_radii = numpy.linspace(0.5*max_r, 0.9 * max_r, 20)
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
num_peaks = 2
peaks = peak_local_max(h, num_peaks=num_peaks)
centers.extend(peaks)
accums.extend(h[peaks[:, 0], peaks[:, 1]])
radii.extend([radius] * num_peaks)
# Use the most prominent circle
idx = numpy.argsort(accums)[::-1][:1]
center_x, center_y = centers[idx]
radius = radii[idx]
return center_x, center_y, radius
def circular_hough_transform(r1, r2, step, edge, peak_num):
hough_radii = np.arange(r1, r2, step)
hough_res = hough_circle(edge, hough_radii)
accums, cx, cy, radii = hough_circle_peaks(hough_res,
hough_radii,
total_num_peaks=peak_num)
return accums, cx, cy, radii
def _get_hough_circles(self):
hough_radii = np.arange(self.radius_low, self.radius_high,
self.step)[::-1]
hough_res = hough_circle(self.edges, hough_radii)
centers = []
accums = []
radii = [] # For each radius, extract num_peaks circles
num_peaks = 3
for radius, h in zip(hough_radii, hough_res):
peaks = peak_local_max(h, num_peaks=num_peaks)
centers.extend(peaks)
print 'circle centers = ', peaks
accums.extend(h[peaks[:, 0], peaks[:, 1]])
radii.extend([radius] * num_peaks)
im = mpimg.imread(self.image_path)
# crop image
im = im[100:1000, 200:1100]
for idx in np.arange(len(centers)):
center_x, center_y = centers[idx]
radius = radii[idx]
cx, cy = circle_perimeter(center_y, center_x, radius)
mask = (cx < im.shape[0]) * (cy < im.shape[1])
im[cy[mask], cx[mask]] = (220., 20., 20.)
return im
def test_circles2():
data = np.memmap("E:\\guts_tracking\\data\\fish202_aligned_masked_8bit_150x200x440.raw", dtype='uint8', shape=(440,200,150)).copy()
i = 157
hough_radii = np.arange(10, 100, 10)
edges = feature.canny(data[i], sigma=3.0, low_threshold=0.4, high_threshold=0.8)
hough_res = hough_circle(edges, hough_radii)
centers = []
accums = []
radii = []
for radius, h in zip(hough_radii, hough_res):
peaks = feature.peak_local_max(h)
centers.extend(peaks)
accums.extend(h[peaks[:, 0], peaks[:, 1]])
radii.extend([radius] * len(peaks))
image = ski.color.gray2rgb(data[i])
for idx in np.argsort(accums)[::-1][:5]:
center_x, center_y = centers[idx]
radius = radii[idx]
cx, cy = circle_perimeter(center_y, center_x, radius)
if max(cx) < 150 and max(cy) < 200:
image[cy, cx] = (220, 20, 20)
plt.imshow(image, cmap='gray')
plt.show()
def animate(i):
print 'Frame %d' % i
plt.title('Frame %d' % i)
image = data[i]
hough_radii = np.arange(10, 100, 10)
edges = feature.canny(data[i], sigma=3.0, low_threshold=0.4, high_threshold=0.8)
hough_res = hough_circle(edges, hough_radii)
centers = []
accums = []
radii = []
for radius, h in zip(hough_radii, hough_res):
peaks = feature.peak_local_max(h)
centers.extend(peaks)
accums.extend(h[peaks[:, 0], peaks[:, 1]])
radii.extend([radius] * len(peaks))
image = ski.color.gray2rgb(data[i])
for idx in np.argsort(accums)[::-1][:5]:
center_x, center_y = centers[idx]
radius = radii[idx]
cx, cy = circle_perimeter(center_y, center_x, radius)
if max(cx) < 150 and max(cy) < 200:
image[cy, cx] = (220, 20, 20)
im.set_data(image)
return im,
def test_hough_circle_peaks_total_peak():
img = np.zeros((120, 100), dtype=int)
x_0, y_0, rad_0 = (99, 50, 20)
y, x = circle_perimeter(y_0, x_0, rad_0)
img[x, y] = 1
x_1, y_1, rad_1 = (49, 60, 30)
y, x = circle_perimeter(y_1, x_1, rad_1)
img[x, y] = 1
radii = [rad_0, rad_1]
hspaces = transform.hough_circle(img, radii)
out = transform.hough_circle_peaks(hspaces,
radii,
min_xdistance=1,
min_ydistance=1,
threshold=None,
num_peaks=np.inf,
total_num_peaks=1)
assert_equal(out[1][0], np.array([
y_1,
]))
assert_equal(out[2][0], np.array([
x_1,
]))
assert_equal(out[3][0], np.array([
rad_1,
]))
def process(self, im):
(width, height, _) = im.image.shape
img_adapted = im.prep(self.transform)
if width > self.max_resized or height > self.max_resized:
scale_height = self.max_resized / height
scale_width = self.max_resized / width
scale = min(scale_height, scale_width)
img_adapted = resize(img_adapted,
(int(width * scale), int(height * scale)))
edges = canny(img_adapted, sigma=self.sigma)
# Detect two radii
# Calculate image diameter
shape = im.image.shape
diam = math.sqrt(shape[0]**2 + shape[1]**2)
radii = np.arange(diam / 3, diam * 0.8, 2)
hough_res = hough_circle(edges, radii)
accums = []
for radius, h in zip(radii, hough_res):
# For each radius, extract two circles
peaks = peak_local_max(h, num_peaks=1, min_distance=1)
if len(peaks) > 0:
accums.extend(h[peaks[:, 0], peaks[:, 1]])
if len(accums) == 0: # TODO: fix, should not happen
return [0]
idx = np.argmax(accums)
return [accums[idx]]
def reshape_image(raw_patient):
#generate pixels
# approach one, using cv2
img = cv2.imread(raw_patient, 0)
# Load picture and detect edges
edges = canny(img, sigma=3, low_threshold=10, high_threshold=50)
# Detect two radii
hough_radii = np.arange(200, 600, 1)
hough_res = hough_circle(edges, hough_radii)
# Select the most prominent 1 circles
accums, cx, cy, radii = hough_circle_peaks(hough_res,
hough_radii,
total_num_peaks=1)
mask_internal = np.zeros((2 * radii[0], 2 * radii[0]), dtype=np.uint8)
y_min = cy[0] - radii[0]
x_min = cx[0] - radii[0]
#filtering the ones only in the pixels
for y in range(0, (2 * radii[0])):
for x in range(0, (2 * radii[0])):
mask_internal[y, x] = img[y + y_min, x + x_min]
# change the internal white pixels to black
for y in range(0, (2 * radii[0])):
for x in range(0, (2 * radii[0])):
if mask_internal[y, x] >= white_pixel_min and mask_internal[
y, x] <= white_pixel_max:
mask_internal[y, x] = black_pixel
#np.save('Data/Modified_fluoro2/patient_'+str(i)+'.npy', mask_internal)
return (mask_internal, (y_min, x_min), radii[0])
def test_hough_circle_peaks_min_distance():
x_0, y_0, rad_0 = (50, 50, 20)
img = np.zeros((120, 100), dtype=int)
y, x = circle_perimeter(y_0, x_0, rad_0)
img[x, y] = 1
x_1, y_1, rad_1 = (60, 60, 30)
y, x = circle_perimeter(y_1, x_1, rad_1)
# Add noise and create an imperfect circle to lower the peak in Hough space
y[::2] += 1
x[::2] += 1
img[x, y] = 1
x_2, y_2, rad_2 = (70, 70, 20)
y, x = circle_perimeter(y_2, x_2, rad_2)
# Add noise and create an imperfect circle to lower the peak in Hough space
y[::2] += 1
x[::2] += 1
img[x, y] = 1
radii = [rad_0, rad_1, rad_2]
hspaces = transform.hough_circle(img, radii)
out = transform.hough_circle_peaks(hspaces, radii, min_xdistance=15,
min_ydistance=15, threshold=None,
num_peaks=np.inf,
total_num_peaks=np.inf,
normalize=True)
# The second circle is too close to the first one
# and has a weaker peak in Hough space due to imperfectness.
# Therefore it got removed.
assert_equal(out[1], np.array([y_0, y_2]))
assert_equal(out[2], np.array([x_0, x_2]))
assert_equal(out[3], np.array([rad_0, rad_2]))
def find_plate(img, radii_range):
"""
Identifies the location of the plate
"""
# Read image, and convert to floating point
img = img_as_bool(imread(img, mode="F", flatten=True))
# Detect edges
edges = canny(img, sigma=2)
# Find circles
hough_radii = np.arange(radii_range[0], radii_range[1], 2)
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
num_peaks = 1
peaks = peak_local_max(h, num_peaks=num_peaks)
centers.extend(peaks)
accums.extend(h[peaks[:, 0], peaks[:, 1]])
radii.extend([radius] * num_peaks)
center = np.mean(centers, axis=0)
radius = (sum(radii) * 1.0) / len(radii)
return center, radius
def find_inner_circle_parameters(plane_array, rmin=200, rmax=250):
"""Given a single planar image (e.g. a section from CT data), find the
locations of the inner of two circles in the image."""
xdim, ydim = plane_array.shape
edges = find_edges_sobel(plane_array)
hough_radii = np.arange(rmin, rmax, 3)
hough_res = hough_circle(edges, hough_radii)
# Find the two clearest circles
c1, c2 = find_n_best_hough_circles(hough_radii, hough_res, 2)
# Work out which is the inner circle
r1 = c1[2]
r2 = c2[2]
if r1 > r2:
inner_circle_radius = r2
cx, cy, r = c2
else:
inner_circle_radius = r1
cx, cy, r = c1
return cx, cy, r
def find_circles(binary_patch):
# Get size of the patch
min_image_size = min(binary_patch.shape[0], binary_patch.shape[1])
max_image_size = max(binary_patch.shape[0], binary_patch.shape[1])
# Determine min and max radius based on patch size
min_radius = max(10, int(min_image_size / 4))
max_radius = int(max_image_size / 2)
hough_radii = np.arange(min_radius, max_radius)
# Apply Hough circle transform
hough_res = hough_circle(binary_patch, hough_radii)
# Set parameters for hough_circle_peaks
min_distance = int(max_image_size / 3)
threshold = 0.6
num_peaks = np.inf
total_num_peaks = 5
normalize = True
# Filter result of Hough circle transform based on parameters
_, cx, cy, radii = hough_circle_peaks(hough_res, hough_radii, min_distance,
min_distance, threshold, num_peaks,
total_num_peaks, normalize)
detected_circles = zip(cx, cy, radii)
# Return found circles
return detected_circles
def hugh_circle_detection(image): # Load picture and detect edges edges = canny(image, sigma=3, low_threshold=10, high_threshold=50) fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(5, 2)) # Detect two radii hough_radii = np.arange(15, 30, 2) 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 num_peaks = 2 peaks = peak_local_max(h, num_peaks=num_peaks) centers.extend(peaks) accums.extend(h[peaks[:, 0], peaks[:, 1]]) radii.extend([radius] * num_peaks) # Draw the most prominent 5 circles image = color.gray2rgb(image) for idx in np.argsort(accums)[::-1][:5]: center_x, center_y = centers[idx] radius = radii[idx] cx, cy = circle_perimeter(center_y, center_x, radius) image[cy, cx] = (220, 20, 20) ax.imshow(image, cmap=plt.cm.gray) plt.show()
def find_plate2(path):
# Load picture and detect edges
image_rgb = io.imread(path)
image = color.rgb2gray(image_rgb)
# image = resize(image, (100, 100))
image = rescale(image, 0.1)
edges = canny(image, sigma=2, low_threshold=0.40, high_threshold=0.80)
plt.imshow(edges)
plt.show()
# Detect two radii
hough_radii = np.arange(20, 60, 2)
hough_res = hough_circle(edges, hough_radii)
# Select the most prominent 5 circles
accums, cx, cy, radii = hough_circle_peaks(hough_res,
hough_radii,
total_num_peaks=3)
# Draw them
fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(10, 4))
image = color.gray2rgb(image)
for center_y, center_x, radius in zip(cy, cx, radii):
circy, circx = circle_perimeter(center_y, center_x, radius)
try:
image[circy, circx] = (220, 20, 20)
except IndexError as e:
print(e)
ax.imshow(image, cmap=plt.cm.gray)
plt.show()
def analyze(FileName):
img = io.imread(FileName)
xc, yc, t = img.data.shape
grayscale = rgb2gray(
img
) # To use the later image analyis, the initial image must be converted to grayscale
image = img_as_ubyte(
grayscale[0:xc, 0:yc]
) # converts the image to a specific file format that can later be analysed
# the array after 'grayscale' holds the size of the image.
edges = canny(image,
sigma=3,
low_threshold=max_radius * .1,
high_threshold=max_radius *
.2) # canny identifies any edges on the shape
# sigma is required standard deviation, low and high threshold are the bounds for linking edges
# It is recommended that you use 10% of the max possible cirlce size for the low_threshold and twice that for the high threshold
h_radii = np.arange(
min_radius, max_radius, 1
) # The first two are used to identify the high and low end of radii to search for
# This will need to be adjusted as different files are uploaded.
hough_res = hough_circle(
edges, h_radii
) # function that calculates a 3D array where a very similar circle can live.
# It takes in the number of pixels that will be used to create the
print("Analysis almost complete...")
accums, x, y, radius = hough_circle_peaks(
hough_res, h_radii, total_num_peaks=number_of_circles)
# the hough_cirlce_peaks function takes the imaginary 'hough circle', the anticipated radii, and the anticiapted number of circles
# it uses this to find the Peak values in 'hough space', the positions of the circles and the radii of the circles
print("radius is:", radius)
area = 3.1415926535897984626 * radius * radius
return area
print("area is: ", area)
# Prints the radii of the identified circles
fig, ax = alt.subplots(ncols=1, nrows=1, figsize=(10, 4))
image = color.gray2rgb(image)
for center_y, center_x, radius in zip(
y, x, radius
): # This for loop draws the circles that were generated earlier
circy, circx = circle_perimeter(center_y,
center_x,
radius,
shape=image.shape)
image[circy, circx] = (220, 20, 20)
ax.imshow(image) # This loads the edited image into the environment
alt.show() # this takes the uploaded image and displays it for the user
def generate_circles(im, circle_edges, greens, piece_size, CIRCLE_THRESHOLD,
CIRCLE_GREEN_THRESHOLD):
if im.shape[0] == im.shape[1] == 1:
return []
# Find circles (othello pieces)
print("Circle detection... ", end="", flush=True)
radii_lim = np.arange(piece_size * 0.8, piece_size * 1.2)
hough_res = hough_circle(circle_edges, radii_lim)
prob, cx, cy, radii = hough_circle_peaks(hough_res,
radii_lim,
min_xdistance=int(piece_size),
min_ydistance=int(piece_size),
threshold=CIRCLE_THRESHOLD)
circles = list(zip(prob, cx, cy, radii))
circles_not_intersecting = []
for i, circle in enumerate(circles):
prob, x, y, rad = circle
intersecting = False
for (_, x_, y_, rad_) in circles[:i]:
dist_sqrd = (x - x_)**2 + (y - y_)**2
if dist_sqrd < ((rad + rad_) / 2)**2:
intersecting = True
break
if not intersecting:
circles_not_intersecting.append(circle)
circles_with_colors = []
for prob, x, y, rad in circles_not_intersecting:
rad = int(rad)
brightness_in_rad = []
green_in_rad = []
for x_ in range(-rad, rad):
for y_ in range(-rad, rad):
if (x_**2 + y_**2) < (rad - 3)**2:
if 0 <= y + y_ < im.shape[0] and \
0 <= x + x_ < im.shape[1]:
brightness_in_rad.append(im[y + y_, x + x_].sum() / 3)
green_in_rad.append(greens[y + y_, x + x_])
brightness_in_rad = np.array(brightness_in_rad)
avg_green = sum(green_in_rad) / (len(green_in_rad) + 1)
blacks = (brightness_in_rad < 0.6).sum()
whites = (brightness_in_rad > 0.6).sum()
if avg_green < CIRCLE_GREEN_THRESHOLD:
circles_with_colors.append((prob, x, y, rad, blacks > whites / 2))
circles = circles_with_colors
print("Done")
return circles
def add_auto_masks_area(areaFile,addMasks=False):
from skimage.morphology import binary_dilation, binary_erosion, disk
from skimage import exposure
from skimage.transform import hough_circle
from skimage.morphology import convex_hull_image
from skimage.feature import canny
from skimage.draw import circle_perimeter
import h5py
MASKs = np.zeros(areaFile.attrs['ROI_patches'].shape)
if 'ROI_masks' in (areaFile.attrs.iterkeys()):
print 'Masks have already been created'
awns = raw_input('Would you like to redo them from scratch: (answer y/n): ')
else:
awns = 'y'
if awns=='y':
MASKs = np.zeros(areaFile.attrs['ROI_patches'].shape)
for i in range(areaFile.attrs['ROI_patches'].shape[2]):
patch = areaFile.attrs['ROI_patches'][:,:,i]
tt0 = exposure.equalize_hist(patch)
tt = 255*tt0/np.max(tt0)
thresh = 1*tt>0.3*255
thresh2 = 1*tt<0.1*255
tt[thresh] = 255
tt[thresh2] = 0
edges = canny(tt, sigma=2, low_threshold=20, high_threshold=30)
try_radii = np.arange(3,5)
res = hough_circle(edges, try_radii)
ridx, r, c = np.unravel_index(np.argmax(res), res.shape)
r, c, try_radii[ridx]
image = np.zeros([20,20,4])
cx, cy = circle_perimeter(c, r, try_radii[ridx]+2)
try:
image[cy, cx] = (220, 80, 20, 1)
original_image = np.copy(image[:,:,3])
chull = convex_hull_image(original_image)
image[chull,:] = (255, 0, 0, .4)
except:
pass
MASKs[:,:,i] = (1*image[:,:,-1]>0)
if addMasks:
areaFile.attrs['ROI_masks'] = MASKs
else:
pass
return np.array(MASKs)
def find_the_ball(nome):
pasta = 'processing/img_recortadas/'
im = io.imread(pasta + nome)
im2 = im.copy()
noisy_image = img_as_ubyte(im2[:,:,0])
noise = np.random.random(noisy_image.shape)
noisy_image[noise > 0.99] = 255
noisy_image[noise < 0.01] = 0
im[:,:,0] = median(noisy_image, disk(1))
noisy_image = img_as_ubyte(im2[:,:,1])
noise = np.random.random(noisy_image.shape)
noisy_image[noise > 0.99] = 255
noisy_image[noise < 0.01] = 0
im[:,:,1] = median(noisy_image, disk(1))
noisy_image = img_as_ubyte(im2[:,:,2])
noise = np.random.random(noisy_image.shape)
noisy_image[noise > 0.99] = 255
noisy_image[noise < 0.01] = 0
im[:,:,2] = median(noisy_image, disk(1))
# Load picture and detect edges
image = img_as_ubyte(im[:,:,0])
edges = canny(image, sigma=3, low_threshold=30, high_threshold=85)
# Detect two radii
hough_radii = np.arange(3, 15, 1)
hough_res = hough_circle(edges, hough_radii)
# Select the most prominent 3 circles
accums, cx, cy, radii = hough_circle_peaks(hough_res, hough_radii,
total_num_peaks=3)
#mean = np.mean(im[cy[:],cx[:],], axis=1)
# Draw them
#_id = ball(mean, accums)
#if(_id != -1):
#accums, center_x, center_y, radius = accums[_id], cx[_id], cy[_id], radii[_id]
for center_y, center_x, radius, ac in zip(cy, cx, radii, accums):
#accums, center_x, center_y, radius = accums[0], cx[0], cy[0], radii[0]
image = color.gray2rgb(image)
circy, circx = circle(center_y, center_x, radius,
shape=image.shape)
#image[circy, circx] = 255
image = im
if(image[circy, circx, :].mean() > 160 and image[circy, circx, :].mean() < 180 and ac > 0.55 and np.std(image[circy, circx, :]) > 20 and np.std(image[circy, circx, :]) < 45 ):
print(nome +' - accums: ' +str(round(ac,2)) + ' - mean: '+ str(round(image[circy, circx, :].mean(),2)))
image[circy, circx] = 255
io.imsave('img_ball/'+nome, (image).astype('uint8'))
#plt.imshow(image, cmap=plt.cm.gray)
#plt.show()
circy, circx = circle_perimeter(center_y, center_x, radius,
shape=image.shape)
return center_y, center_x
return -1,-1
def get_edges(np_image):
# Get the coordinates of the circle edges
X = []
Y = []
Z = []
circles = [] # to get the outlines of the circles
C = [] # to get the centres of the circles, in relation to the different areas
R = [] # to get radii
coords = np.column_stack(np.nonzero(np_image))
X = np.array(coords[:, 0])
Y = np.array(coords[:, 1])
# Fit a circle and compare measured circle area with
# area from the amount of pixels to remove trash
XC, YC, RAD, RESID = leastsq_circle(X, Y)
# Hough radius estimate
hough_radii = np.arange(RAD - RAD / 2., RAD + RAD / 2.)
# Apply Hough transform
hough_res = hough_circle(np_image, hough_radii)
centers = []
accums = []
radii = []
img = np.zeros_like(np_image)
# For each radius, extract one circle
for radius, h in zip(hough_radii, hough_res):
peaks = peak_local_max(h, num_peaks=2)
centers.extend(peaks)
accums.extend(h[peaks[:, 0], peaks[:, 1]])
radii.extend([radius, radius])
for idx in np.argsort(accums)[::-1][:1]:
center_x, center_y = centers[idx]
C.append((center_x, center_y))
radius = radii[idx]
R.append(radius)
cx, cy = circle_perimeter(int(round(center_x, 0)),
int(round(center_y, 0)),
int(round(radius, 0)))
circles.append((cx, cy))
if C:
for cent in C:
xc, yc = cent
np_image[int(xc), int(yc)] = 255
np_image[int(xc)-5:int(xc)+5, int(yc)-5:int(yc)+5] = 255
if circles:
for per in circles:
e1, e2 = per
np_image[e1, e2] = 255
return [C, R, circles, []], np_image
def find_well_border(image, segmethod='bimodal', detmethod='region'):
"""
finds the border of the well to motivate future cropping around spots
hough_radii are potential radii of the well in pixels
this should be motivated based on magnification
edge filter
fit hough circle
find the peak of the SINGULAR hough circle
:param image: np.ndarray
raw image, not inverted
:param segmethod: str
'otsu' or 'hough'
:return: center x, center y, radius of the one hough circle
"""
well_mask = thresh_and_binarize(image, method=segmethod, invert=False)
# Now remove small objects.
str_elem_size = 10
str_elem = disk(str_elem_size)
well_mask = binary_opening(well_mask, str_elem)
# well_mask = binary_fill_holes(well_mask)
if detmethod == 'region':
labels = measure.label(well_mask)
props = measure.regionprops(labels)
# let's assume ONE circle for now (take only props[0])
props = select_props(props,
attribute="area",
condition="greater_than",
condition_value=10**5)
props = select_props(props,
attribute="eccentricity",
condition="less_than",
condition_value=0.5)
well_mask[labels != props[0].label] = 0
cy, cx = props[
0].centroid # notice that the coordinate order is different from hough.
radii = int((props[0].minor_axis_length + props[0].major_axis_length) /
4 / np.sqrt(2))
# Otsu threshold fails occasionally and leads to asymmetric region. Averaging both axes makes the segmentation robust.
# If above files, try bounding box.
elif detmethod == 'hough':
hough_radii = [300, 400, 500, 600]
well_mask = thresh_and_binarize(image, method='bimodal')
edges = canny(well_mask, sigma=3)
hough_res = hough_circle(edges, hough_radii)
aaccums, cx, cy, radii = hough_circle_peaks(hough_res,
hough_radii,
total_num_peaks=1)
cx, cy = cx[0], cy[0]
radii = radii[0]
else:
cx, cy, radii = None, None, None
return [cy, cx], radii, well_mask
def _detect_spots_hough_circle(image, radius): edges = canny(image) imshow(edges) show() hough_radii = np.arange(radius/2, radius*2, 10) hough_circles = hough_circle(edges, hough_radii) print(hough_circles)
def count_dishes(out, sink):
edges = canny(
sink,
sigma=2,
#low_threshold=10,
high_threshold=0.3
)
hough_radii = np.arange(25, 70, 1)
hough_res = hough_circle(edges, hough_radii)
centers = []
accums = []
radii = []
for radius, h in zip(hough_radii, hough_res):
num_peaks = 2
peaks = peak_local_max(h, num_peaks=num_peaks)
centers.extend(peaks)
accums.extend(h[peaks[:, 0], peaks[:, 1]])
radii.extend([radius] * num_peaks)
sink = color.gray2rgb(sink)
hits = {}
for idx in np.argsort(accums)[::-1][:25]:
center_x, center_y = centers[idx]
radius = radii[idx]
if is_sink_hole(center_x, center_y, radius):
continue
for d in hits.keys():
dx, dy, dr = d
dt = distance(center_x, dx, center_y, dy)
if dt <= 40 and abs(dr - radius) < 50:
hits[d] += 1
break
else:
hits[(center_x, center_y, radius)] = 1
dishes = [k for k,v in hits.iteritems()]
for dish in dishes:
center_x, center_y, radius = dish
cx, cy = circle_perimeter(center_y, center_x, radius)
try:
sink[cy, cx] = (220, 250, 20)
except IndexError:
continue
draw_res(out, sink, edges)
return len(dishes)
def __tutorial_hough_circle_detection_skiimage(img_path, min_dim=40, max_dim=60):
"""
This algorithm is crap, the one using opencv is much much better
:param img_path:
:param min_dim:
:param max_dim:
:return:
"""
# load picture and detect edges
img = cv2.imread(img_path)
img = cv2.cvtColor(img, cv2.COLOR_BGRA2GRAY)
img_edges = canny(img, sigma=3, low_threshold=10, high_threshold=50)
centers = []
accums = []
radii = []
# detect all radii within the range
min_radius = int(min_dim / 2)
max_radius = int(max_dim / 2)
hough_radii = np.arange(start=min_radius, stop=max_radius, step=1)
hough_res = hough_circle(img_edges, hough_radii)
for radius, h in zip(hough_radii, hough_res):
# for each radius, extract 2 circles
num_peaks = 2
peaks = peak_local_max(h, num_peaks=num_peaks)
centers.extend(peaks)
accums.extend(h[peaks[:, 0], peaks[:, 1]])
radii.extend([radius] * num_peaks)
img_width = img.shape[1]
img_height = img.shape[0]
# get the sorted accumulated values
# accums_sorted = np.asarray(accums)
# accums_sorted = accums_sorted[idx_sorted]
# don't consider circles with accum value less than the threshold
accum_threshold = 0.3
idx_sorted = np.argsort(accums)[::-1]
# draw the most prominent n circles, i.e those
# with the highest n peaks
img_color = color.gray2rgb(img)
for idx in idx_sorted:
if accums[idx] < accum_threshold:
continue
center_y, center_x = centers[idx]
radius = radii[idx]
cx, cy = circle_perimeter(center_x, center_y, radius)
cx[cx > img_width - 1] = img_width - 1
cy[cy > img_height - 1] = img_height - 1
img_color[cy, cx] = (220, 20, 20)
# save the result
skimage.io.imsave("D://_Dataset//GTSDB//Test_Regions//_img2_.png", img_color)
def punchhole_removal(im):
import numpy as np
from PIL import Image
from skimage import io
from skimage.color import rgba2rgb, rgb2gray
from skimage.transform import hough_circle, hough_circle_peaks
from skimage.feature import canny
from skimage.draw import circle
from skimage.util import img_as_ubyte
''' check for punch holes and remove '''
max_peaks = 24 #maximum number of peaks to be found. changed from 99 to 24 for reducing the unnecessary punch holes being filled.
img = np.array(im)# Load picture .
img_rgb = rgba2rgb(img)# convert to RGB
img_gray = rgb2gray(img_rgb)# convert to gray
image = img_as_ubyte(img_gray)
width, height = image.shape
x1 = punchhole_margin
x2 = (int)(width - punchhole_margin)
y1 = (int)(height - punchhole_margin)
y2 = punchhole_margin
edges = canny(image, 3, 10, 40) # perform canny to detect the edges
hough_radii = np.arange(31, 34, 1) #get the radius range with step as 1.
hough_res = hough_circle(edges, hough_radii) # detect the circles centres coordinates
# Select the most prominent circles based on the max_peaks
accums, cx, cy, radii = hough_circle_peaks(hough_res, hough_radii,total_num_peaks=max_peaks)
for center_y, center_x, radius in zip(cy, cx, radii):
#if the circles centres fall in the border regions,
#get the dominant color near the hole and fill the hole with a linear gradient of the dominant color
if(((0 < center_y < width) and (0 < center_x < y2)) or \
((0 < center_y < width) and (y1 < center_x < height)) or\
((0 < center_y < x1) and (0 < center_x < height)) or \
((x2 < center_y < width) and (0 < center_x < height))):
index=0
rr, cc= circle(center_y, center_x, radius+1, img.shape)
dominantpix = dominantcolor(center_x, center_y, radius, img)
dark_grad = [dominantpix[0], dominantpix[1],dominantpix[2]]
light_grad = [dominantpix[0]+1, dominantpix[1]+1, dominantpix[2]+1]
#white_grad = [255,255,255]
RGBA_list = lineargradient(dark_grad,light_grad,len(list(rr)))
for i , j in zip(list(rr), list(cc)):
pixlist = RGBA_list[index]
pixtuple = tuple(pixlist)
img[i,j]= (pixtuple[0], pixtuple[1], pixtuple[2], 255)
index += 1
finalimage=Image.fromarray(img)
return finalimage
def find_boundary(img, min_radius, max_radius):
edges = canny(img, sigma=0.)
radii = np.arange(min_radius, max_radius)
h = hough_circle(edges, radii)
h_max = np.max(h, axis=(1, 2))
k = np.min(np.argsort(h_max)[-2:])
radius = radii[k]
h = h[k, ...]
row, col = np.unravel_index(np.argmax(h), h.shape)
return row, col, radius
def find_circle(self, image, frame, dim, **kw):
dx, dy = None, None
pframe = self._preprocess(frame, blur=0)
edges = canny(pframe, sigma=3)
hough_radii = arange(dim * 0.9, dim * 1.1, 2)
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
num_peaks = 2
peaks = peak_local_max(h, num_peaks=num_peaks)
centers.extend(peaks)
accums.extend(h[peaks[:, 0], peaks[:, 1]])
radii.extend([radius] * num_peaks)
# for idx in argsort(accums)[::-1][:1]:
try:
idx = argsort(accums)[::-1][0]
except IndexError:
return dx, dy
center_y, center_x = centers[idx]
radius = radii[idx]
draw_circle_perimeter(frame, center_x, center_y, radius, (220, 20, 20))
# cx, cy = circle_perimeter(int(center_x), int(center_y), int(radius))
# draw perimeter
# try:
# frame[cy, cx] = (220, 20, 20)
# except IndexError:
# pass
# draw center
# cx, cy = circle(int(center_x), int(center_y), int(2))
# frame[cy, cx] = (220, 20, 20)
draw_circle(frame, center_x, center_y, 2, (220, 20, 20))
h, w = frame.shape[:2]
ox, oy = w / 2, h / 2
dx = center_x - ox
dy = center_y - oy
cx, cy = circle(int(ox), int(oy), int(2))
frame[cy, cx] = (20, 220, 20)
image.set_frame(frame)
return float(dx), -float(dy)
def find_circles(image, min_r=30, max_r=300, cutoff=0.5, step=4, blur_sigma=3):
rs = arange(min_r, max_r, step)
threshold_image = threshold(image)
hough_space = hough_circle(threshold(image), rs)
sigma_3d = (blur_sigma,
blur_sigma,
float(blur_sigma) / step)
blurred_hough_space = gaussian_filter(hough_space, sigma=sigma_3d)
local_maxima = peak_local_max(blurred_hough_space, exclude_border=False)
circles = column_stack((local_maxima, hough_space[tuple(local_maxima.T)]))
circles[:,0] = rs[list(circles[:,0])]
return circles[circles[:,3] > cutoff]
def test_hough_circle():
# Prepare picture
img = np.zeros((120, 100), dtype=int)
radius = 20
x_0, y_0 = (99, 50)
x, y = circle_perimeter(y_0, x_0, radius)
img[y, x] = 1
out = tf.hough_circle(img, np.array([radius]))
x, y = np.where(out[0] == out[0].max())
# Offset for x_0, y_0
assert_equal(x[0], x_0 + radius)
assert_equal(y[0], y_0 + radius)
def extract_hough_circle(img_rgb, img_gray, out_filepath):
# Canny
img = img_as_ubyte(img_gray)
edges = canny(img, sigma=3, low_threshold=10, high_threshold=50)
# fig, ax = plt.subplots(nrows=1, ncols=1)
# ax.imshow(edges, cmap=plt.cm.gray)
# ax.axis('off')
# ax.set_title('Canny Edges for Hough Circle', fontsize=18)
# plt.tight_layout()
# plt.savefig('canny_edges_for_hough_circle.png')
# Detect
min_radii = 15; max_radii = 30; step_radii = 1
plausible_radii = np.arange(min_radii, max_radii, step_radii)
hough_circles = hough_circle(edges, plausible_radii)
centers = []; accums = []; radii = []
for radius, h in zip(plausible_radii, hough_circles):
n_extracted_circle = 1 # ...for each radius
peaks = peak_local_max(h, num_peaks=n_extracted_circle)
centers.extend(peaks)
accums.extend(h[peaks[:, 0], peaks[:, 1]])
radii.extend([radius] * n_extracted_circle)
# Draw the most prominent circles
n_top_circle = 15
fig, ax = plt.subplots(ncols=1, nrows=1)
for idx in np.argsort(accums)[::-1][:n_top_circle]:
center_x, center_y = centers[idx]
center_color = (0, 225, 0)
img_rgb[center_x, center_y] = center_color
radius = radii[idx]
perim_color = (255, 0, 0)
perim_x_list, perim_y_list = circle_perimeter(center_y, center_x, radius)
# if all(i < img_rgb.shape[1] for i in perim_x_list) and all(i < img_rgb.shape[0] for i in perim_y_list):
# img_rgb[perim_y_list, perim_x_list] = perim_color
for perim_x, perim_y in zip(perim_x_list, perim_y_list):
if perim_x < img_rgb.shape[1] and perim_y < img_rgb.shape[0]:
img_rgb[perim_y, perim_x] = perim_color
ax.imshow(img_rgb, cmap=plt.cm.gray)
ax.axis('off')
ax.set_title('Hough Circle', fontsize=18)
plt.tight_layout()
plt.savefig(out_filepath)
plt.close(fig)
def test_hough_circle_extended():
# Prepare picture
# The circle center is outside the image
img = np.zeros((100, 100), dtype=int)
radius = 20
x_0, y_0 = (-5, 50)
y, x = circle_perimeter(y_0, x_0, radius)
img[x[np.where(x > 0)], y[np.where(x > 0)]] = 1
out = tf.hough_circle(img, np.array([radius], dtype=np.intp), full_output=True)
x, y = np.where(out[0] == out[0].max())
# Offset for x_0, y_0
assert_equal(x[0], x_0 + radius)
assert_equal(y[0], y_0 + radius)
def extract(image):
# used with default parameters, play around with params for other results
blob_dog_feature = blob_dog(image, min_sigma=1, max_sigma=50, sigma_ratio=1.6, threshold=2.0, overlap=.5)
# several sizes of radius, play around with params for other results
hough_radii = np.array([5, 10, 15, 20, 30, 50])
hough_circle_feature = hough_circle(image, hough_radii, normalize=True, full_output=False)
# prints a LOT... takes a while
# comment out the next line to print short version
np.set_printoptions(threshold='nan')
print([blob_dog_feature, hough_circle_feature])
return [blob_dog_feature, hough_circle_feature]
def findNodesHoughCircles(cannyEdges, hough_radii):
hough_res = hough_circle(cannyEdges, hough_radii)
centers = []
accums = []
radii = []
#print hough_res
for radius, h in zip(hough_radii, hough_res):
peaks = peak_local_max(h)
#print peaks
centers.extend(peaks)
accums.extend(h[peaks[:, 0], peaks[:, 1]])
radii.extend([radius] * len(peaks))
#Sort each list by accums
#We swap the centers, since they are ordered as y, x
tupleCenters = [(center[1], center[0]) for center in centers]
accums, radii, centers = zip(*sorted(zip(accums, radii, tupleCenters))[::-1])
disjointCenters = []
disjointRadii = []
disjointAccums = []
removed = [False for i in xrange(len(centers))]
#Loop through the circles in order of their weight
minRadiusDist = getMinNodeDist()
for i in xrange(len(centers)):
#print "Next index: ", i
if(not removed[i]):
#If our current circle does not overlap with previous circles, add it.
#Then remove all later overlapping circles
center_y, center_x = centers[i]
disjointCenters.append(centers[i])
disjointRadii.append(radii[i])
disjointAccums.append(accums[i])
for j in xrange(i + 1, len(centers)):
newCenter_y, newCenter_x = centers[j]
newRadius = radii[i]
distSq = findDistSqPoints(center_x, center_y, newCenter_x, newCenter_y)
neededDist = radius + newRadius + minRadiusDist
if(distSq < neededDist*neededDist):
removed[j] = True
print "Disjoint centers: ", disjointCenters
print "Disjoint accums: ", disjointAccums
return disjointAccums, disjointCenters, disjointRadii
def fit_central_circle(image, radius_lower_bound=170, radius_upper_bound=190):
"""Find the centre and radius of the central circle in image. Analysis is
restrictied the given bounds for the radius of the circle."""
smoothed = smooth_gaussian(image.astype(np.float), sigma=5)
edges = find_edges_sobel(smoothed)
thresh = threshold_otsu(edges)
hmm = 170, 190
hough_radii = np.arange(140, 170, 2)
hough_res = hough_circle(thresh, hough_radii)
circles = find_n_best_hough_circles(hough_radii, hough_res, 1)
circle = circles[0]
return circle
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 find_circles(image, input_radii):
result = hough_circle(image, input_radii)
centers = []
accums = []
radii = []
for radius, h in zip(input_radii, result):
# For each radius, extract two circles
peaks = peak_local_max(h, num_peaks=2)
centers.extend(peaks)
accums.extend(h[peaks[:, 0], peaks[:, 1]])
radii.extend([radius, radius])
circles = []
for idx in np.argsort(accums)[::-1][:5]:
center_x, center_y = centers[idx]
radius = radii[idx]
circles.append((center_x, center_y, radius))
return circles
def test_hough_circle_peaks_total_peak():
img = np.zeros((120, 100), dtype=int)
x_0, y_0, rad_0 = (99, 50, 20)
y, x = circle_perimeter(y_0, x_0, rad_0)
img[x, y] = 1
x_1, y_1, rad_1 = (49, 60, 30)
y, x = circle_perimeter(y_1, x_1, rad_1)
img[x, y] = 1
radii = [rad_0, rad_1]
hspaces = tf.hough_circle(img, radii)
out = tf.hough_circle_peaks(hspaces, radii, min_xdistance=1, min_ydistance=1,
threshold=None, num_peaks=np.inf, total_num_peaks=1)
assert_equal(out[1][0], np.array([y_1,]))
assert_equal(out[2][0], np.array([x_1,]))
assert_equal(out[3][0], np.array([rad_1,]))
def centers_radii(cls, img):
centers = []
accums = []
radii = []
edges = canny(img, sigma=3.0)
# Detect two radii
hough_radii = np.arange(15, 30, 2)
hough_res = hough_circle(edges, hough_radii)
for radius, h in zip(hough_radii, hough_res):
# For each radius, extract two circles
num_peaks = 2
peaks = peak_local_max(h, num_peaks=num_peaks)
centers.extend(peaks)
accums.extend(h[peaks[:, 0], peaks[:, 1]])
radii.extend([radius] * num_peaks)
return centers, radii, edges
def getCircles(image, radius):
# use the circular Hough transform to find the circles
cimg = cv.cvtColor(img, cv.COLOR_GRAY2BGR)
cimg = cv.GaussianBlur(cimg, (5, 5), 0)
circles = []
# apply an edge filter first
canimg = filter.canny(img_as_ubyte(img), sigma=1, low_threshold=120,
high_threshold=126)
hough_radii = np.array([radius])
hough_res = hough_circle(canimg, hough_radii)
centers =[]; accums =[]; radii =[]
numcircles = 9
for r, h in zip(hough_radii, hough_res):
peaks = peak_local_max(h, num_peaks = numcircles, min_distance=50, threshold_rel=0.15)
centers.extend(peaks)
accums.extend(h[peaks[:, 0], peaks[:, 1]])
radii.extend ([r]* numcircles )
for i in np.argsort(accums)[::-1][:numcircles]:
circles.append([centers[i][1], centers[i][0], radii[i]])
return circles
def extract_hough(img_gray):
img = img_as_ubyte(img_gray)
edges = canny(img, sigma=5, low_threshold=10, high_threshold=50)
fig, ax = plt.subplots(nrows=1, ncols=1)
ax.imshow(edges, cmap=plt.cm.gray)
ax.axis('off')
ax.set_title('Canny filter', fontsize=20)
plt.savefig('canny_edges.png')
# Detect two radii
hough_radii = np.arange(15, 30, 2)
hough_res = hough_circle(edges, hough_radii)
print len(hough_res)
centers = []
accums = []
radii = []
for radius, h in zip(hough_radii, hough_res):
# For each radius, extract two circles
num_peaks = 2
peaks = peak_local_max(h, num_peaks=num_peaks)
centers.extend(peaks)
accums.extend(h[peaks[:, 0], peaks[:, 1]])
radii.extend([radius] * num_peaks)
# Draw the most prominent 15 circles
fig, ax = plt.subplots(ncols=1, nrows=1)
img_rgb = color.gray2rgb(img)
for idx in np.argsort(accums)[::-1][:15]:
center_x, center_y = centers[idx]
radius = radii[idx]
cx, cy = circle_perimeter(center_y, center_x, radius)
img_rgb[cy, cx] = (220, 20, 20)
ax.imshow(img_rgb, cmap=plt.cm.gray)
ax.axis('off')
ax.set_title('Hough Circle', fontsize=20)
plt.tight_layout()
plt.savefig('hough.png')