def templateMatchingDemo(console):
root_path = os.path.dirname(os.path.abspath(__file__))
file_path = root_path
if console:
file_path += "/../../assets/examples/images/square.png"
else:
file_path += "/../../assets/examples/images/man.jpg"
img_color = af.load_image(file_path, True)
# Convert the image from RGB to gray-scale
img = af.color_space(img_color, af.CSPACE.GRAY, af.CSPACE.RGB)
iDims = img.dims()
print("Input image dimensions: ", iDims)
# Extract a patch from the input image
patch_size = 100
tmp_img = img[100:100 + patch_size, 100:100 + patch_size]
result = af.match_template(img, tmp_img) # Default disparity metric is
# Sum of Absolute differences (SAD)
# Currently supported metrics are
# AF_SAD, AF_ZSAD, AF_LSAD, AF_SSD,
# AF_ZSSD, AF_LSSD
disp_img = img / 255.0
disp_tmp = tmp_img / 255.0
disp_res = normalize(result)
minval, minloc = af.imin(disp_res)
print("Location(linear index) of minimum disparity value = {}".format(
minloc))
if not console:
marked_res = af.tile(disp_img, 1, 1, 3)
marked_res = draw_rectangle(marked_res, minloc%iDims[0], minloc/iDims[0],\
patch_size, patch_size)
print(
"Note: Based on the disparity metric option provided to matchTemplate function"
)
print(
"either minimum or maximum disparity location is the starting corner"
)
print(
"of our best matching patch to template image in the search image")
wnd = af.Window(512, 512, "Template Matching Demo")
while not wnd.close():
wnd.set_colormap(af.COLORMAP.DEFAULT)
wnd.grid(2, 2)
wnd[0, 0].image(disp_img, "Search Image")
wnd[0, 1].image(disp_tmp, "Template Patch")
wnd[1, 0].image(marked_res, "Best Match")
wnd.set_colormap(af.COLORMAP.HEAT)
wnd[1, 1].image(disp_res, "Disparity Values")
wnd.show()
def simple_image(verbose = False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = 10 * af.randu(6, 6)
a3 = 10 * af.randu(5,5,3)
dx,dy = af.gradient(a)
display_func(dx)
display_func(dy)
display_func(af.resize(a, scale=0.5))
display_func(af.resize(a, odim0=8, odim1=8))
t = af.randu(3,2)
display_func(af.transform(a, t))
display_func(af.rotate(a, 3.14))
display_func(af.translate(a, 1, 1))
display_func(af.scale(a, 1.2, 1.2, 7, 7))
display_func(af.skew(a, 0.02, 0.02))
h = af.histogram(a, 3)
display_func(h)
display_func(af.hist_equal(a, h))
display_func(af.dilate(a))
display_func(af.erode(a))
display_func(af.dilate3(a3))
display_func(af.erode3(a3))
display_func(af.bilateral(a, 1, 2))
display_func(af.mean_shift(a, 1, 2, 3))
display_func(af.medfilt(a))
display_func(af.minfilt(a))
display_func(af.maxfilt(a))
display_func(af.regions(af.round(a) > 3))
dx,dy = af.sobel_derivatives(a)
display_func(dx)
display_func(dy)
display_func(af.sobel_filter(a))
ac = af.gray2rgb(a)
display_func(ac)
display_func(af.rgb2gray(ac))
ah = af.rgb2hsv(ac)
display_func(ah)
display_func(af.hsv2rgb(ah))
display_func(af.color_space(a, af.CSPACE.RGB, af.CSPACE.GRAY))
def simple_image(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = 10 * af.randu(6, 6)
a3 = 10 * af.randu(5, 5, 3)
dx, dy = af.gradient(a)
display_func(dx)
display_func(dy)
display_func(af.resize(a, scale=0.5))
display_func(af.resize(a, odim0=8, odim1=8))
t = af.randu(3, 2)
display_func(af.transform(a, t))
display_func(af.rotate(a, 3.14))
display_func(af.translate(a, 1, 1))
display_func(af.scale(a, 1.2, 1.2, 7, 7))
display_func(af.skew(a, 0.02, 0.02))
h = af.histogram(a, 3)
display_func(h)
display_func(af.hist_equal(a, h))
display_func(af.dilate(a))
display_func(af.erode(a))
display_func(af.dilate3(a3))
display_func(af.erode3(a3))
display_func(af.bilateral(a, 1, 2))
display_func(af.mean_shift(a, 1, 2, 3))
display_func(af.medfilt(a))
display_func(af.minfilt(a))
display_func(af.maxfilt(a))
display_func(af.regions(af.round(a) > 3))
dx, dy = af.sobel_derivatives(a)
display_func(dx)
display_func(dy)
display_func(af.sobel_filter(a))
ac = af.gray2rgb(a)
display_func(ac)
display_func(af.rgb2gray(ac))
ah = af.rgb2hsv(ac)
display_func(ah)
display_func(af.hsv2rgb(ah))
display_func(af.color_space(a, af.CSPACE.RGB, af.CSPACE.GRAY))
def susan_demo(console):
root_path = os.path.dirname(os.path.abspath(__file__))
file_path = root_path
if console:
file_path += "/../../assets/examples/images/square.png"
else:
file_path += "/../../assets/examples/images/man.jpg"
img_color = af.load_image(file_path, True)
img = af.color_space(img_color, af.CSPACE.GRAY, af.CSPACE.RGB)
img_color /= 255.0
features = af.susan(img)
xs = features.get_xpos().to_list()
ys = features.get_ypos().to_list()
draw_len = 3
num_features = features.num_features().value
for f in range(num_features):
print(f)
x = xs[f]
y = ys[f]
# TODO fix coord order to x,y after upstream fix
img_color = draw_corners(img_color, y, x, draw_len)
print("Features found: {}".format(num_features))
if not console:
# Previews color image with green crosshairs
wnd = af.Window(512, 512, "SUSAN Feature Detector")
while not wnd.close():
wnd.image(img_color)
else:
print(xs)
print(ys)
af.display(af.hist_equal(a, h)) af.display(af.dilate(a)) af.display(af.erode(a)) af.display(af.dilate3(a3)) af.display(af.erode3(a3)) af.display(af.bilateral(a, 1, 2)) af.display(af.mean_shift(a, 1, 2, 3)) af.display(af.medfilt(a)) af.display(af.minfilt(a)) af.display(af.maxfilt(a)) af.display(af.regions(af.round(a) > 3)) dx,dy = af.sobel_derivatives(a) af.display(dx) af.display(dy) af.display(af.sobel_filter(a)) ac = af.gray2rgb(a) af.display(ac) af.display(af.rgb2gray(ac)) ah = af.rgb2hsv(ac) af.display(ah) af.display(af.hsv2rgb(ah)) af.display(af.color_space(a, af.AF_RGB, af.AF_GRAY))
def simple_image(verbose = False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = 10 * af.randu(6, 6)
a3 = 10 * af.randu(5,5,3)
dx,dy = af.gradient(a)
display_func(dx)
display_func(dy)
display_func(af.resize(a, scale=0.5))
display_func(af.resize(a, odim0=8, odim1=8))
t = af.randu(3,2)
display_func(af.transform(a, t))
display_func(af.rotate(a, 3.14))
display_func(af.translate(a, 1, 1))
display_func(af.scale(a, 1.2, 1.2, 7, 7))
display_func(af.skew(a, 0.02, 0.02))
h = af.histogram(a, 3)
display_func(h)
display_func(af.hist_equal(a, h))
display_func(af.dilate(a))
display_func(af.erode(a))
display_func(af.dilate3(a3))
display_func(af.erode3(a3))
display_func(af.bilateral(a, 1, 2))
display_func(af.mean_shift(a, 1, 2, 3))
display_func(af.medfilt(a))
display_func(af.minfilt(a))
display_func(af.maxfilt(a))
display_func(af.regions(af.round(a) > 3))
dx,dy = af.sobel_derivatives(a)
display_func(dx)
display_func(dy)
display_func(af.sobel_filter(a))
display_func(af.gaussian_kernel(3, 3))
display_func(af.gaussian_kernel(3, 3, 1, 1))
ac = af.gray2rgb(a)
display_func(ac)
display_func(af.rgb2gray(ac))
ah = af.rgb2hsv(ac)
display_func(ah)
display_func(af.hsv2rgb(ah))
display_func(af.color_space(a, af.CSPACE.RGB, af.CSPACE.GRAY))
a = af.randu(6,6)
b = af.unwrap(a, 2, 2, 2, 2)
c = af.wrap(b, 6, 6, 2, 2, 2, 2)
display_func(a)
display_func(b)
display_func(c)
display_func(af.sat(a))
a = af.randu(10,10,3)
display_func(af.rgb2ycbcr(a))
display_func(af.ycbcr2rgb(a))
a = af.randu(10, 10)
b = af.canny(a, low_threshold = 0.2, high_threshold = 0.8)
display_func(af.anisotropic_diffusion(a, 0.125, 1.0, 64, af.FLUX.QUADRATIC, af.DIFFUSION.GRAD))
def simple_image(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = 10 * af.randu(6, 6)
a3 = 10 * af.randu(5, 5, 3)
dx, dy = af.gradient(a)
display_func(dx)
display_func(dy)
display_func(af.resize(a, scale=0.5))
display_func(af.resize(a, odim0=8, odim1=8))
t = af.randu(3, 2)
display_func(af.transform(a, t))
display_func(af.rotate(a, 3.14))
display_func(af.translate(a, 1, 1))
display_func(af.scale(a, 1.2, 1.2, 7, 7))
display_func(af.skew(a, 0.02, 0.02))
h = af.histogram(a, 3)
display_func(h)
display_func(af.hist_equal(a, h))
display_func(af.dilate(a))
display_func(af.erode(a))
display_func(af.dilate3(a3))
display_func(af.erode3(a3))
display_func(af.bilateral(a, 1, 2))
display_func(af.mean_shift(a, 1, 2, 3))
display_func(af.medfilt(a))
display_func(af.minfilt(a))
display_func(af.maxfilt(a))
display_func(af.regions(af.round(a) > 3))
dx, dy = af.sobel_derivatives(a)
display_func(dx)
display_func(dy)
display_func(af.sobel_filter(a))
display_func(af.gaussian_kernel(3, 3))
display_func(af.gaussian_kernel(3, 3, 1, 1))
ac = af.gray2rgb(a)
display_func(ac)
display_func(af.rgb2gray(ac))
ah = af.rgb2hsv(ac)
display_func(ah)
display_func(af.hsv2rgb(ah))
display_func(af.color_space(a, af.CSPACE.RGB, af.CSPACE.GRAY))
a = af.randu(6, 6)
b = af.unwrap(a, 2, 2, 2, 2)
c = af.wrap(b, 6, 6, 2, 2, 2, 2)
display_func(a)
display_func(b)
display_func(c)
display_func(af.sat(a))
a = af.randu(10, 10, 3)
display_func(af.rgb2ycbcr(a))
display_func(af.ycbcr2rgb(a))
a = af.randu(10, 10)
b = af.canny(a, low_threshold=0.2, high_threshold=0.8)
display_func(
af.anisotropic_diffusion(a, 0.125, 1.0, 64, af.FLUX.QUADRATIC,
af.DIFFUSION.GRAD))
af.display(af.hist_equal(a, h)) af.display(af.dilate(a)) af.display(af.erode(a)) af.display(af.dilate3(a3)) af.display(af.erode3(a3)) af.display(af.bilateral(a, 1, 2)) af.display(af.mean_shift(a, 1, 2, 3)) af.display(af.medfilt(a)) af.display(af.minfilt(a)) af.display(af.maxfilt(a)) af.display(af.regions(af.round(a) > 3)) dx, dy = af.sobel_derivatives(a) af.display(dx) af.display(dy) af.display(af.sobel_filter(a)) ac = af.gray2rgb(a) af.display(ac) af.display(af.rgb2gray(ac)) ah = af.rgb2hsv(ac) af.display(ah) af.display(af.hsv2rgb(ah)) af.display(af.color_space(a, af.AF_RGB, af.AF_GRAY))
def harris_demo(console):
root_path = os.path.dirname(os.path.abspath(__file__))
file_path = root_path
if console:
file_path += "/../../assets/examples/images/square.png"
else:
file_path += "/../../assets/examples/images/man.jpg"
img_color = af.load_image(file_path, True)
img = af.color_space(img_color, af.CSPACE.GRAY, af.CSPACE.RGB)
img_color /= 255.0
ix, iy = af.gradient(img)
ixx = ix * ix
ixy = ix * iy
iyy = iy * iy
# Compute a Gaussian kernel with standard deviation of 1.0 and length of 5 pixels
# These values can be changed to use a smaller or larger window
gauss_filt = af.gaussian_kernel(5, 5, 1.0, 1.0)
# Filter second order derivatives
ixx = af.convolve(ixx, gauss_filt)
ixy = af.convolve(ixy, gauss_filt)
iyy = af.convolve(iyy, gauss_filt)
# Calculate trace
itr = ixx + iyy
# Calculate determinant
idet = ixx * iyy - ixy * ixy
# Calculate Harris response
response = idet - 0.04 * (itr * itr)
# Get maximum response for each 3x3 neighborhood
mask = af.constant(1, 3, 3)
max_resp = af.dilate(response, mask)
# Discard responses that are not greater than threshold
corners = response > 1e5
corners = corners * response
# Discard responses that are not equal to maximum neighborhood response,
# scale them to original value
corners = (corners == max_resp) * corners
# Copy device array to python list on host
corners_list = corners.to_list()
draw_len = 3
good_corners = 0
for x in range(img_color.dims()[1]):
for y in range(img_color.dims()[0]):
if corners_list[x][y] > 1e5:
img_color = draw_corners(img_color, x, y, draw_len)
good_corners += 1
print("Corners found: {}".format(good_corners))
if not console:
# Previews color image with green crosshairs
wnd = af.Window(512, 512, "Harris Feature Detector")
while not wnd.close():
wnd.image(img_color)
else:
idx = af.where(corners)
corners_x = idx / float(corners.dims()[0])
corners_y = idx % float(corners.dims()[0])
print(corners_x)
print(corners_y)
def simple_image(verbose=False):
display_func = _util.display_func(verbose)
a = 10 * af.randu(6, 6)
a3 = 10 * af.randu(5, 5, 3)
dx, dy = af.gradient(a)
display_func(dx)
display_func(dy)
display_func(af.resize(a, scale=0.5))
display_func(af.resize(a, odim0=8, odim1=8))
t = af.randu(3, 2)
display_func(af.transform(a, t))
display_func(af.rotate(a, 3.14))
display_func(af.translate(a, 1, 1))
display_func(af.scale(a, 1.2, 1.2, 7, 7))
display_func(af.skew(a, 0.02, 0.02))
h = af.histogram(a, 3)
display_func(h)
display_func(af.hist_equal(a, h))
display_func(af.dilate(a))
display_func(af.erode(a))
display_func(af.dilate3(a3))
display_func(af.erode3(a3))
display_func(af.bilateral(a, 1, 2))
display_func(af.mean_shift(a, 1, 2, 3))
display_func(af.medfilt(a))
display_func(af.minfilt(a))
display_func(af.maxfilt(a))
display_func(af.regions(af.round(a) > 3))
display_func(
af.confidenceCC(af.randu(10,
10), (af.randu(2) * 9).as_type(af.Dtype.u32),
(af.randu(2) * 9).as_type(af.Dtype.u32), 3, 3, 10,
0.1))
dx, dy = af.sobel_derivatives(a)
display_func(dx)
display_func(dy)
display_func(af.sobel_filter(a))
display_func(af.gaussian_kernel(3, 3))
display_func(af.gaussian_kernel(3, 3, 1, 1))
ac = af.gray2rgb(a)
display_func(ac)
display_func(af.rgb2gray(ac))
ah = af.rgb2hsv(ac)
display_func(ah)
display_func(af.hsv2rgb(ah))
display_func(af.color_space(a, af.CSPACE.RGB, af.CSPACE.GRAY))
a = af.randu(6, 6)
b = af.unwrap(a, 2, 2, 2, 2)
c = af.wrap(b, 6, 6, 2, 2, 2, 2)
display_func(a)
display_func(b)
display_func(c)
display_func(af.sat(a))
a = af.randu(10, 10, 3)
display_func(af.rgb2ycbcr(a))
display_func(af.ycbcr2rgb(a))
a = af.randu(10, 10)
b = af.canny(a, low_threshold=0.2, high_threshold=0.8)
display_func(
af.anisotropic_diffusion(a, 0.125, 1.0, 64, af.FLUX.QUADRATIC,
af.DIFFUSION.GRAD))
a = af.randu(10, 10)
psf = af.gaussian_kernel(3, 3)
cimg = af.convolve(a, psf)
display_func(
af.iterativeDeconv(cimg, psf, 100, 0.5, af.ITERATIVE_DECONV.LANDWEBER))
display_func(
af.iterativeDeconv(cimg, psf, 100, 0.5,
af.ITERATIVE_DECONV.RICHARDSONLUCY))
display_func(af.inverseDeconv(cimg, psf, 1.0, af.INVERSE_DECONV.TIKHONOV))
