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))
#!/usr/bin/python ####################################################### # Copyright (c) 2015, ArrayFire # All rights reserved. # # This file is distributed under 3-clause BSD license. # The complete license agreement can be obtained at: # http://arrayfire.com/licenses/BSD-3-Clause ######################################################## import arrayfire as af a = 10 * af.randu(6, 6) a3 = 10 * af.randu(5,5,3) dx,dy = af.gradient(a) af.display(dx) af.display(dy) af.display(af.resize(a, scale=0.5)) af.display(af.resize(a, odim0=8, odim1=8)) t = af.randu(3,2) af.display(af.transform(a, t)) af.display(af.rotate(a, 3.14)) af.display(af.translate(a, 1, 1)) af.display(af.scale(a, 1.2, 1.2, 7, 7)) af.display(af.skew(a, 0.02, 0.02)) h = af.histogram(a, 3) af.display(h) af.display(af.hist_equal(a, h))
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))
#!/usr/bin/python ####################################################### # Copyright (c) 2015, ArrayFire # All rights reserved. # # This file is distributed under 3-clause BSD license. # The complete license agreement can be obtained at: # http://arrayfire.com/licenses/BSD-3-Clause ######################################################## import arrayfire as af a = 10 * af.randu(6, 6) a3 = 10 * af.randu(5, 5, 3) dx, dy = af.gradient(a) af.display(dx) af.display(dy) af.display(af.resize(a, scale=0.5)) af.display(af.resize(a, odim0=8, odim1=8)) t = af.randu(3, 2) af.display(af.transform(a, t)) af.display(af.rotate(a, 3.14)) af.display(af.translate(a, 1, 1)) af.display(af.scale(a, 1.2, 1.2, 7, 7)) af.display(af.skew(a, 0.02, 0.02)) h = af.histogram(a, 3) af.display(h) af.display(af.hist_equal(a, h))
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))