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(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 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 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 == "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 == "lowpass_gaussian":
filt_data = gaussian(data, sigma=float(par))
elif filtType == "highpass_gaussian":
lp_data = gaussian(data, sigma=float(par))
filt_data = data - lp_data
#elif filtType == "gradient":
return filt_data
def main():
plt.figure(facecolor='black')
plt.gray()
for i in range(6):
img = io.imread(images[i], as_grey=True)
img = filters.canny(img)
img = morphology.erosion(img, morphology.square(1))
plt.subplot(2,3,i)
plt.axis('off')
io.imshow(img)
plt.show()
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 eoh(image):
data = { 0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0 }
canny_image = canny(image, low_threshold=40)
row, col = canny_image.shape
sobel_v_image = sobel_v(image)
sobel_h_image = sobel_h(image)
for r in xrange(row):
for c in xrange(col):
if not canny_image[r][c]:
continue
interval = which_interval(sobel_v_image[r][c], sobel_h_image[r][c])
if data.has_key(interval):
data[interval] += 1
return data
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 run_process(img_name, cf): if type(img_name) is str: img = load_image(img_name) else: img = img_name height = img.shape[0]/cf width = img.shape[1]/cf r_img = sk.transform.resize(img, (img.shape[0]/cf, img.shape[1]/3, cf)) g_img = greyout(r_img) f_img = gaussian_filter(g_img, ST_DEV) t_img = thresholder(f_img) return height, width, t_img edges = canny(t_img, 3) skio.imshow(r_img) skio.show() skio.imshow(f_img) skio.show() skio.imshow(t_img) skio.show() skio.imshow(edges) skio.show()
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":
p = int(par)
kernel = np.ones((p, p), np.float32) / (p * p)
filt_data = ndimage.convolve(data, kernel)
elif filtType == "highpass_avg":
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 == "lowpass_gaussian":
filt_data = gaussian(data, sigma=float(par))
elif filtType == "highpass_gaussian":
lp_data = gaussian(data, sigma=float(par))
filt_data = data - lp_data
#elif filtType == "gradient":
return filt_data
def test_canny_import():
data = moon()
with expected_warnings(['skimage.feature.canny']):
from skimage.filters import canny
canny(data)
def seeds(args):
"""
%prog seeds [pngfile|jpgfile]
Extract seed metrics from [pngfile|jpgfile]. Use --rows and --cols to crop image.
"""
p = OptionParser(seeds.__doc__)
p.set_outfile()
opts, args, iopts = add_seeds_options(p, args)
if len(args) != 1:
sys.exit(not p.print_help())
pngfile, = args
pf = opts.prefix or op.basename(pngfile).rsplit(".", 1)[0]
sigma, kernel = opts.sigma, opts.kernel
rows, cols = opts.rows, opts.cols
labelrows, labelcols = opts.labelrows, opts.labelcols
ff = opts.filter
calib = opts.calibrate
outdir = opts.outdir
if outdir != '.':
mkdir(outdir)
if calib:
calib = json.load(must_open(calib))
pixel_cm_ratio, tr = calib["PixelCMratio"], calib["RGBtransform"]
tr = np.array(tr)
resizefile, mainfile, labelfile, exif = \
convert_image(pngfile, pf, outdir=outdir,
rotate=opts.rotate,
rows=rows, cols=cols,
labelrows=labelrows, labelcols=labelcols)
oimg = load_image(resizefile)
img = load_image(mainfile)
fig, (ax1, ax2, ax3, ax4) = plt.subplots(ncols=4, nrows=1,
figsize=(iopts.w, iopts.h))
# Edge detection
img_gray = rgb2gray(img)
logging.debug("Running {0} edge detection ...".format(ff))
if ff == "canny":
edges = canny(img_gray, sigma=opts.sigma)
elif ff == "roberts":
edges = roberts(img_gray)
elif ff == "sobel":
edges = sobel(img_gray)
edges = clear_border(edges, buffer_size=opts.border)
selem = disk(kernel)
closed = closing(edges, selem) if kernel else edges
filled = binary_fill_holes(closed)
# Watershed algorithm
if opts.watershed:
distance = distance_transform_edt(filled)
local_maxi = peak_local_max(distance, threshold_rel=.05, indices=False)
coordinates = peak_local_max(distance, threshold_rel=.05)
markers, nmarkers = label(local_maxi, return_num=True)
logging.debug("Identified {0} watershed markers".format(nmarkers))
labels = watershed(closed, markers, mask=filled)
else:
labels = label(filled)
# Object size filtering
w, h = img_gray.shape
canvas_size = w * h
min_size = int(round(canvas_size * opts.minsize / 100))
max_size = int(round(canvas_size * opts.maxsize / 100))
logging.debug("Find objects with pixels between {0} ({1}%) and {2} ({3}%)"\
.format(min_size, opts.minsize, max_size, opts.maxsize))
# Plotting
ax1.set_title('Original picture')
ax1.imshow(oimg)
params = "{0}, $\sigma$={1}, $k$={2}".format(ff, sigma, kernel)
if opts.watershed:
params += ", watershed"
ax2.set_title('Edge detection\n({0})'.format(params))
closed = gray2rgb(closed)
ax2_img = labels
if opts.edges:
ax2_img = closed
elif opts.watershed:
ax2.plot(coordinates[:, 1], coordinates[:, 0], 'g.')
ax2.imshow(ax2_img, cmap=iopts.cmap)
ax3.set_title('Object detection')
ax3.imshow(img)
filename = op.basename(pngfile)
if labelfile:
accession = extract_label(labelfile)
else:
accession = pf
# Calculate region properties
rp = regionprops(labels)
rp = [x for x in rp if min_size <= x.area <= max_size]
nb_labels = len(rp)
logging.debug("A total of {0} objects identified.".format(nb_labels))
objects = []
for i, props in enumerate(rp):
i += 1
if i > opts.count:
break
y0, x0 = props.centroid
orientation = props.orientation
major, minor = props.major_axis_length, props.minor_axis_length
major_dx = cos(orientation) * major / 2
major_dy = sin(orientation) * major / 2
minor_dx = sin(orientation) * minor / 2
minor_dy = cos(orientation) * minor / 2
ax2.plot((x0 - major_dx, x0 + major_dx),
(y0 + major_dy, y0 - major_dy), 'r-')
ax2.plot((x0 - minor_dx, x0 + minor_dx),
(y0 - minor_dy, y0 + minor_dy), 'r-')
npixels = int(props.area)
# Sample the center of the blob for color
d = min(int(round(minor / 2 * .35)) + 1, 50)
x0d, y0d = int(round(x0)), int(round(y0))
square = img[(y0d - d):(y0d + d), (x0d - d):(x0d + d)]
pixels = []
for row in square:
pixels.extend(row)
logging.debug("Seed #{0}: {1} pixels ({2} sampled) - {3:.2f}%".\
format(i, npixels, len(pixels), 100. * npixels / canvas_size))
rgb = pixel_stats(pixels)
objects.append(Seed(filename, accession, i, rgb, props, exif))
minr, minc, maxr, maxc = props.bbox
rect = Rectangle((minc, minr), maxc - minc, maxr - minr,
fill=False, ec='w', lw=1)
ax3.add_patch(rect)
mc, mr = (minc + maxc) / 2, (minr + maxr) / 2
ax3.text(mc, mr, "{0}".format(i), color='w',
ha="center", va="center", size=6)
for ax in (ax2, ax3):
ax.set_xlim(0, h)
ax.set_ylim(w, 0)
# Output identified seed stats
ax4.text(.1, .92, "File: {0}".format(latex(filename)), color='g')
ax4.text(.1, .86, "Label: {0}".format(latex(accession)), color='m')
yy = .8
fw = must_open(opts.outfile, "w")
if not opts.noheader:
print(Seed.header(calibrate=calib), file=fw)
for o in objects:
if calib:
o.calibrate(pixel_cm_ratio, tr)
print(o, file=fw)
i = o.seedno
if i > 7:
continue
ax4.text(.01, yy, str(i), va="center", bbox=dict(fc='none', ec='k'))
ax4.text(.1, yy, o.pixeltag, va="center")
yy -= .04
ax4.add_patch(Rectangle((.1, yy - .025), .12, .05, lw=0,
fc=rgb_to_hex(o.rgb)))
ax4.text(.27, yy, o.hashtag, va="center")
yy -= .06
ax4.text(.1 , yy, "(A total of {0} objects displayed)".format(nb_labels),
color="darkslategrey")
normalize_axes(ax4)
for ax in (ax1, ax2, ax3):
xticklabels = [int(x) for x in ax.get_xticks()]
yticklabels = [int(x) for x in ax.get_yticks()]
ax.set_xticklabels(xticklabels, family='Helvetica', size=8)
ax.set_yticklabels(yticklabels, family='Helvetica', size=8)
image_name = op.join(outdir, pf + "." + iopts.format)
savefig(image_name, dpi=iopts.dpi, iopts=iopts)
return objects
coordinates = peak_local_max(image, min_distance=20)
ax3.imshow(image, cmap=plt.cm.gray)
ax3.autoscale(False)
ax3.plot(coordinates[:, 1],
coordinates[:, 0], c='r.')
ax3.set_title('Peak local maxima', fontsize=24)
ax3.axis('off')
# Detect edges.
from skimage import filters
edges = filters.canny(image, sigma=3,
low_threshold=10,
high_threshold=80)
ax4.imshow(edges, cmap=plt.cm.gray)
ax4.set_title('Edges', fontsize=24)
ax4.axis('off')
# Label image regions.
from skimage.measure import regionprops
import matplotlib.patches as mpatches
from skimage.morphology import label
label_image = label(edges)
ax5.imshow(image, cmap=plt.cm.gray)
def seeds(args):
"""
%prog seeds [pngfile|jpgfile]
Extract seed metrics from [pngfile|jpgfile]. Use --rows and --cols to crop image.
"""
p = OptionParser(seeds.__doc__)
p.set_outfile()
opts, args, iopts = add_seeds_options(p, args)
if len(args) != 1:
sys.exit(not p.print_help())
pngfile, = args
pf = opts.prefix or op.basename(pngfile).rsplit(".", 1)[0]
sigma, kernel = opts.sigma, opts.kernel
rows, cols = opts.rows, opts.cols
labelrows, labelcols = opts.labelrows, opts.labelcols
ff = opts.filter
calib = opts.calibrate
outdir = opts.outdir
if outdir != '.':
mkdir(outdir)
if calib:
calib = json.load(must_open(calib))
pixel_cm_ratio, tr = calib["PixelCMratio"], calib["RGBtransform"]
tr = np.array(tr)
resizefile, mainfile, labelfile, exif = \
convert_image(pngfile, pf, outdir=outdir,
rotate=opts.rotate,
rows=rows, cols=cols,
labelrows=labelrows, labelcols=labelcols)
oimg = load_image(resizefile)
img = load_image(mainfile)
fig, (ax1, ax2, ax3, ax4) = plt.subplots(ncols=4, nrows=1,
figsize=(iopts.w, iopts.h))
# Edge detection
img_gray = rgb2gray(img)
logging.debug("Running {0} edge detection ...".format(ff))
if ff == "canny":
edges = canny(img_gray, sigma=opts.sigma)
elif ff == "roberts":
edges = roberts(img_gray)
elif ff == "sobel":
edges = sobel(img_gray)
edges = clear_border(edges, buffer_size=opts.border)
selem = disk(kernel)
closed = closing(edges, selem) if kernel else edges
filled = binary_fill_holes(closed)
# Watershed algorithm
if opts.watershed:
distance = distance_transform_edt(filled)
local_maxi = peak_local_max(distance, threshold_rel=.05, indices=False)
coordinates = peak_local_max(distance, threshold_rel=.05)
markers, nmarkers = label(local_maxi, return_num=True)
logging.debug("Identified {0} watershed markers".format(nmarkers))
labels = watershed(closed, markers, mask=filled)
else:
labels = label(filled)
# Object size filtering
w, h = img_gray.shape
canvas_size = w * h
min_size = int(round(canvas_size * opts.minsize / 100))
max_size = int(round(canvas_size * opts.maxsize / 100))
logging.debug("Find objects with pixels between {0} ({1}%) and {2} ({3}%)"\
.format(min_size, opts.minsize, max_size, opts.maxsize))
# Plotting
ax1.set_title('Original picture')
ax1.imshow(oimg)
params = "{0}, $\sigma$={1}, $k$={2}".format(ff, sigma, kernel)
if opts.watershed:
params += ", watershed"
ax2.set_title('Edge detection\n({0})'.format(params))
closed = gray2rgb(closed)
ax2_img = labels
if opts.edges:
ax2_img = closed
elif opts.watershed:
ax2.plot(coordinates[:, 1], coordinates[:, 0], 'g.')
ax2.imshow(ax2_img, cmap=iopts.cmap)
ax3.set_title('Object detection')
ax3.imshow(img)
filename = op.basename(pngfile)
if labelfile:
accession = extract_label(labelfile)
else:
accession = pf
# Calculate region properties
rp = regionprops(labels)
rp = [x for x in rp if min_size <= x.area <= max_size]
nb_labels = len(rp)
logging.debug("A total of {0} objects identified.".format(nb_labels))
objects = []
for i, props in enumerate(rp):
i += 1
if i > opts.count:
break
y0, x0 = props.centroid
orientation = props.orientation
major, minor = props.major_axis_length, props.minor_axis_length
major_dx = cos(orientation) * major / 2
major_dy = sin(orientation) * major / 2
minor_dx = sin(orientation) * minor / 2
minor_dy = cos(orientation) * minor / 2
ax2.plot((x0 - major_dx, x0 + major_dx),
(y0 + major_dy, y0 - major_dy), 'r-')
ax2.plot((x0 - minor_dx, x0 + minor_dx),
(y0 - minor_dy, y0 + minor_dy), 'r-')
npixels = int(props.area)
# Sample the center of the blob for color
d = min(int(round(minor / 2 * .35)) + 1, 50)
x0d, y0d = int(round(x0)), int(round(y0))
square = img[(y0d - d):(y0d + d), (x0d - d):(x0d + d)]
pixels = []
for row in square:
pixels.extend(row)
logging.debug("Seed #{0}: {1} pixels ({2} sampled) - {3:.2f}%".\
format(i, npixels, len(pixels), 100. * npixels / canvas_size))
rgb = pixel_stats(pixels)
objects.append(Seed(filename, accession, i, rgb, props, exif))
minr, minc, maxr, maxc = props.bbox
rect = Rectangle((minc, minr), maxc - minc, maxr - minr,
fill=False, ec='w', lw=1)
ax3.add_patch(rect)
mc, mr = (minc + maxc) / 2, (minr + maxr) / 2
ax3.text(mc, mr, "{0}".format(i), color='w',
ha="center", va="center", size=6)
for ax in (ax2, ax3):
ax.set_xlim(0, h)
ax.set_ylim(w, 0)
# Output identified seed stats
ax4.text(.1, .92, "File: {0}".format(latex(filename)), color='g')
ax4.text(.1, .86, "Label: {0}".format(latex(accession)), color='m')
yy = .8
fw = must_open(opts.outfile, "w")
if not opts.noheader:
print >> fw, Seed.header(calibrate=calib)
for o in objects:
if calib:
o.calibrate(pixel_cm_ratio, tr)
print >> fw, o
i = o.seedno
if i > 7:
continue
ax4.text(.01, yy, str(i), va="center", bbox=dict(fc='none', ec='k'))
ax4.text(.1, yy, o.pixeltag, va="center")
yy -= .04
ax4.add_patch(Rectangle((.1, yy - .025), .12, .05, lw=0,
fc=rgb_to_hex(o.rgb)))
ax4.text(.27, yy, o.hashtag, va="center")
yy -= .06
ax4.text(.1 , yy, "(A total of {0} objects displayed)".format(nb_labels),
color="darkslategrey")
normalize_axes(ax4)
for ax in (ax1, ax2, ax3):
xticklabels = [int(x) for x in ax.get_xticks()]
yticklabels = [int(x) for x in ax.get_yticks()]
ax.set_xticklabels(xticklabels, family='Helvetica', size=8)
ax.set_yticklabels(yticklabels, family='Helvetica', size=8)
image_name = op.join(outdir, pf + "." + iopts.format)
savefig(image_name, dpi=iopts.dpi, iopts=iopts)
return objects
from skimage import data,filters,io,color
# img=color.rgb2gray(data.coffee())
# 若要读取自己的图片，可以采用以下代码：
# 其中，path是图片所在的目录，尽量是完整的目录，灰度化处理同样，采用
img=io.imread(".\img\yu1.png",1)
color.rgb2gray(img)
edge_img=filters.canny(img)
io.imshow(edge_img)
io.show()
grad_x = np.diff(coffee_gray, axis=1)
io.imshow(grad_x)
#working with filters
#guassian filter
filtered1 = filters.gaussian(coffee, sigma=3)
io.imshow(filtered1)
filtered2 = filters.gaussian(coffee, sigma=[1, 10])
io.imshow(filtered2)
#sobel filter for edge detection
text = data.text()
io.imshow(text)
edges = filters.sobel(text)
io.imshow(edges)
#canny edge detector
edges1 = filters.canny(coffee_gray)
io.imshow(edges1)
edges2 = filters.canny(coffee_gray, sigma=2)
io.imshow(edges2)
edges3 = filters.canny(coffee_gray,
sigma=2,
low_threshold=0.2,
high_threshold=0.5)
io.imshow(edges3)
#feature detection
from skimage.feature import corner_harris, peak_local_max, corner_peaks
import matplotlib.pyplot as plt
from scipy.misc import imread
img = imread("Slides/Images/IMAG0537.jpg")
from skimage.color import rgb2grey
try:
# http://scikit-image.org/docs/dev/api/skimage.feature.html#skimage.feature.canny
from skimage.filters import canny
except ImportError:
# Must be an older version of skimage...
from skimage.filter import canny
img_g = rgb2grey(img)
# Canny only accepts greyscale images.
edges = canny(img_g, 2)
fig, (ax0, ax1) = plt.subplots(nrows=2)
ax0.imshow(img_g, cmap=plt.cm.Greys_r)
ax0.set_title("Original image")
ax0.axis("off")
ax1.imshow(edges, cmap=plt.cm.Greys_r)
ax1.set_title("Edges by Canny")
ax1.axis("off")
plt.show()
from skimage import measure, io, color, img_as_float, filters
import matplotlib.pyplot as plt
import numpy as np
img = img_as_float(io.imread('test_figs/ligo.jpg'))
#mask = color.deltaE_ciede2000(img, (1, 1, 1)) > 0.2
mask = filters.canny(color.rgb2gray(img))
labels = measure.label(mask)
regions = measure.regionprops(labels)
def bbox_area(bbox):
r0, c0, r1, c1 = bbox
return abs((r0 - r1) * (c0 - c1))
regions = [r for r in regions if r.convex_area / bbox_area(r.bbox) > 0.9]
regions = [r for r in regions if r.area > 200]
out = np.zeros_like(img)
for r in regions:
out[r.coords[:, 0], r.coords[:, 1]] = r.convex_area / bbox_area(r.bbox)
#plt.imshow((labels * 1123) % 87, cmap='spectral')
plt.imshow(out, cmap='gray')
plt.colorbar()
plt.show()
from skimage import measure, io, color, img_as_float, filters
import matplotlib.pyplot as plt
import numpy as np
img = img_as_float(io.imread('test_figs/ligo.jpg'))
#mask = color.deltaE_ciede2000(img, (1, 1, 1)) > 0.2
mask = filters.canny(color.rgb2gray(img))
labels = measure.label(mask)
regions = measure.regionprops(labels)
def bbox_area(bbox):
r0, c0, r1, c1 = bbox
return abs((r0 - r1) * (c0 - c1))
regions = [r for r in regions if r.convex_area / bbox_area(r.bbox) > 0.9]
regions = [r for r in regions if r.area > 200]
out = np.zeros_like(img)
for r in regions:
out[r.coords[:, 0], r.coords[:, 1]] = r.convex_area / bbox_area(r.bbox)
#plt.imshow((labels * 1123) % 87, cmap='spectral')
plt.imshow(out, cmap='gray')
plt.colorbar()
plt.show()
