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syurkevipradeep
syurkevi
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pradeep
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adds computer vision example
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‎examples/computer_vision/fast.py‎

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#!/usr/bin/python
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#######################################################
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# Copyright (c) 2018, ArrayFire
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# All rights reserved.
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#
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# This file is distributed under 3-clause BSD license.
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# The complete license agreement can be obtained at:
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# http://arrayfire.com/licenses/BSD-3-Clause
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########################################################
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from time import time
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import arrayfire as af
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import sys
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def draw_corners(img, x, y, draw_len):
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# Draw vertical line of (draw_len * 2 + 1) pixels centered on the corner
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# Set only the first channel to 1 (green lines)
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xmin = max(0, x - draw_len)
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xmax = min(img.dims()[1], x + draw_len)
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img[y, xmin : xmax, 0] = 0.0
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img[y, xmin : xmax, 1] = 1.0
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img[y, xmin : xmax, 2] = 0.0
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# Draw vertical line of (draw_len * 2 + 1) pixels centered on the corner
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# Set only the first channel to 1 (green lines)
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ymin = max(0, y - draw_len)
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ymax = min(img.dims()[0], y + draw_len)
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img[ymin : ymax, x, 0] = 0.0
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img[ymin : ymax, x, 1] = 1.0
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img[ymin : ymax, x, 2] = 0.0
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return img
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def fast_demo(console):
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if console:
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img_color = af.load_image("../../assets/examples/images/square.png", True);
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else:
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img_color = af.load_image("../../assets/examples/images/man.jpg", True);
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img_color /= 255.0
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img = af.color_space(img_color, af.CSPACE.GRAY, af.CSPACE.RGB)
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features = af.fast(img)
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xs = features.get_xpos()
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ys = features.get_ypos()
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draw_len = 3;
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num_features = features.num_features().value
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for f in range(num_features):
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print(f)
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x = xs[f]
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y = ys[f]
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img_color = draw_corners(img_color, x, y, draw_len)
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print("Features found: {}".format(num_features))
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if not console:
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# Previews color image with green crosshairs
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wnd = af.Window(512, 512, "FAST Feature Detector")
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while not wnd.close():
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wnd.image(img_color)
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else:
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print(xs);
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print(ys);
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if __name__ == "__main__":
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if (len(sys.argv) > 1):
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af.set_device(int(sys.argv[1]))
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console = (sys.argv[2] == '-') if len(sys.argv) > 2 else False
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af.info()
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print("** ArrayFire FAST Feature Detector Demo **\n")
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fast_demo(console)
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‎examples/computer_vision/harris.py‎

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#!/usr/bin/python
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#######################################################
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# Copyright (c) 2018, ArrayFire
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# All rights reserved.
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#
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# This file is distributed under 3-clause BSD license.
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# The complete license agreement can be obtained at:
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# http://arrayfire.com/licenses/BSD-3-Clause
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########################################################
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from time import time
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import arrayfire as af
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import sys
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def draw_corners(img, x, y, draw_len):
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# Draw vertical line of (draw_len * 2 + 1) pixels centered on the corner
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# Set only the first channel to 1 (green lines)
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xmin = max(0, x - draw_len)
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xmax = min(img.dims()[1], x + draw_len)
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img[y, xmin : xmax, 0] = 0.0
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img[y, xmin : xmax, 1] = 1.0
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img[y, xmin : xmax, 2] = 0.0
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# Draw vertical line of (draw_len * 2 + 1) pixels centered on the corner
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# Set only the first channel to 1 (green lines)
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ymin = max(0, y - draw_len)
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ymax = min(img.dims()[0], y + draw_len)
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img[ymin : ymax, x, 0] = 0.0
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img[ymin : ymax, x, 1] = 1.0
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img[ymin : ymax, x, 2] = 0.0
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return img
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def harris_demo(console):
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if console:
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img_color = af.load_image("../../assets/examples/images/square.png", True);
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else:
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img_color = af.load_image("../../assets/examples/images/man.jpg", True);
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img_color /= 255.0
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img = af.color_space(img_color, af.CSPACE.GRAY, af.CSPACE.RGB)
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ix, iy = af.gradient(img)
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ixx = ix * ix
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ixy = ix * iy
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iyy = iy * iy
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# Compute a Gaussian kernel with standard deviation of 1.0 and length of 5 pixels
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# These values can be changed to use a smaller or larger window
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gauss_filt = af.gaussian_kernel(5, 5, 1.0, 1.0)
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# Filter second order derivatives
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ixx = af.convolve(ixx, gauss_filt)
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ixy = af.convolve(ixy, gauss_filt)
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iyy = af.convolve(iyy, gauss_filt)
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# Calculate trace
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itr = ixx + iyy
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# Calculate determinant
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idet = ixx * iyy - ixy * ixy
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# Calculate Harris response
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response = idet - 0.04 * (itr * itr)
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# Get maximum response for each 3x3 neighborhood
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mask = af.constant(1, 3, 3)
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max_resp = af.dilate(response, mask)
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# Discard responses that are not greater than threshold
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corners = response > 1e5
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corners = corners * response
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# Discard responses that are not equal to maximum neighborhood response,
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# scale them to original value
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corners = (corners == max_resp) * corners
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# Copy device array to python list on host
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corners_list = corners.to_list()
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draw_len = 3
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good_corners = 0
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for x in range(img_color.dims()[1]):
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for y in range(img_color.dims()[0]):
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if corners_list[x][y] > 1e5:
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img_color = draw_corners(img_color, x, y, draw_len)
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good_corners += 1
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print("Corners found: {}".format(good_corners))
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if not console:
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# Previews color image with green crosshairs
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wnd = af.Window(512, 512, "FAST Feature Detector")
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while not wnd.close():
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wnd.image(img_color)
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else:
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idx = af.where(corners)
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corners_x = idx / corners.dims()[0]
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corners_y = idx % corners.dims()[0]
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print(corners_x)
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print(corners_y)
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if __name__ == "__main__":
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if (len(sys.argv) > 1):
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af.set_device(int(sys.argv[1]))
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console = (sys.argv[2] == '-') if len(sys.argv) > 2 else False
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af.info()
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print("** ArrayFire Harris Corner Detector Demo **\n")
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harris_demo(console)
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‎examples/computer_vision/matching.py‎

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#!/usr/bin/python
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#######################################################
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# Copyright (c) 2018, ArrayFire
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# All rights reserved.
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#
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# This file is distributed under 3-clause BSD license.
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# The complete license agreement can be obtained at:
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# http://arrayfire.com/licenses/BSD-3-Clause
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########################################################
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from time import time
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import arrayfire as af
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import sys
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def normalize(a):
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max_ = float(af.max(a))
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min_ = float(af.min(a))
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return (a - min_) / (max_ - min_)
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def draw_rectangle(img, x, y, wx, wy):
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print("\nMatching patch origin = ({}, {})\n".format(x, y))
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# top edge
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img[y, x : x + wx, 0] = 0.0
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img[y, x : x + wx, 1] = 0.0
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img[y, x : x + wx, 2] = 1.0
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# bottom edge
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img[y + wy, x : x + wx, 0] = 0.0
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img[y + wy, x : x + wx, 1] = 0.0
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img[y + wy, x : x + wx, 2] = 1.0
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# left edge
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img[y : y + wy, x, 0] = 0.0
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img[y : y + wy, x, 1] = 0.0
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img[y : y + wy, x, 2] = 1.0
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# left edge
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img[y : y + wy, x + wx, 0] = 0.0
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img[y : y + wy, x + wx, 1] = 0.0
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img[y : y + wy, x + wx, 2] = 1.0
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return img
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def templateMatchingDemo(console):
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if console:
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img_color = af.load_image("../../assets/examples/images/square.png", True);
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else:
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img_color = af.load_image("../../assets/examples/images/man.jpg", True);
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# Convert the image from RGB to gray-scale
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img = af.color_space(img_color, af.CSPACE.GRAY, af.CSPACE.RGB)
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iDims = img.dims()
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print("Input image dimensions: ", iDims)
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# Extract a patch from the input image
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patch_size = 100
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tmp_img = img[100 : 100+patch_size, 100 : 100+patch_size]
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result = af.match_template(img, tmp_img) # Default disparity metric is
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# Sum of Absolute differences (SAD)
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# Currently supported metrics are
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# AF_SAD, AF_ZSAD, AF_LSAD, AF_SSD,
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# AF_ZSSD, AF_LSSD
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disp_img = img / 255.0
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disp_tmp = tmp_img / 255.0
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disp_res = normalize(result)
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minval, minloc = af.imin(disp_res)
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print("Location(linear index) of minimum disparity value = {}".format(minloc))
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if not console:
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marked_res = af.tile(disp_img, 1, 1, 3)
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marked_res = draw_rectangle(marked_res, minloc%iDims[0], minloc/iDims[0],\
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patch_size, patch_size)
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print("Note: Based on the disparity metric option provided to matchTemplate function")
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print("either minimum or maximum disparity location is the starting corner")
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print("of our best matching patch to template image in the search image")
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wnd = af.Window(512, 512, "Template Matching Demo")
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while not wnd.close():
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wnd.set_colormap(af.COLORMAP.DEFAULT)
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wnd.grid(2, 2)
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wnd[0, 0].image(disp_img, "Search Image" )
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wnd[0, 1].image(disp_tmp, "Template Patch" )
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wnd[1, 0].image(marked_res, "Best Match" )
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wnd.set_colormap(af.COLORMAP.HEAT)
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wnd[1, 1].image(disp_res, "Disparity Values")
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wnd.show()
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if __name__ == "__main__":
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if (len(sys.argv) > 1):
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af.set_device(int(sys.argv[1]))
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console = (sys.argv[2] == '-') if len(sys.argv) > 2 else False
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af.info()
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print("** ArrayFire template matching Demo **\n")
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templateMatchingDemo(console)
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‎examples/computer_vision/susan.py‎

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#!/usr/bin/python
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#######################################################
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# Copyright (c) 2018, ArrayFire
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# All rights reserved.
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#
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# This file is distributed under 3-clause BSD license.
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# The complete license agreement can be obtained at:
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# http://arrayfire.com/licenses/BSD-3-Clause
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########################################################
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from time import time
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import arrayfire as af
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import sys
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def draw_corners(img, x, y, draw_len):
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# Draw vertical line of (draw_len * 2 + 1) pixels centered on the corner
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# Set only the first channel to 1 (green lines)
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xmin = max(0, x - draw_len)
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xmax = min(img.dims()[1], x + draw_len)
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img[y, xmin : xmax, 0] = 0.0
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img[y, xmin : xmax, 1] = 1.0
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img[y, xmin : xmax, 2] = 0.0
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# Draw vertical line of (draw_len * 2 + 1) pixels centered on the corner
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# Set only the first channel to 1 (green lines)
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ymin = max(0, y - draw_len)
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ymax = min(img.dims()[0], y + draw_len)
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img[ymin : ymax, x, 0] = 0.0
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img[ymin : ymax, x, 1] = 1.0
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img[ymin : ymax, x, 2] = 0.0
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return img
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def susan_demo(console):
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if console:
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img_color = af.load_image("../../assets/examples/images/square.png", True);
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else:
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img_color = af.load_image("../../assets/examples/images/man.jpg", True);
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img_color /= 255.0
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img = af.color_space(img_color, af.CSPACE.GRAY, af.CSPACE.RGB)
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features = af.susan(img)
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xs = features.get_xpos()
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ys = features.get_ypos()
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draw_len = 3;
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num_features = features.num_features().value
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for f in range(num_features):
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print(f)
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x = xs[f]
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y = ys[f]
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img_color = draw_corners(img_color, x, y, draw_len)
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print("Features found: {}".format(num_features))
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if not console:
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# Previews color image with green crosshairs
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wnd = af.Window(512, 512, "SUSAN Feature Detector")
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while not wnd.close():
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wnd.image(img_color)
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else:
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print(xs);
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print(ys);
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if __name__ == "__main__":
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if (len(sys.argv) > 1):
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af.set_device(int(sys.argv[1]))
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console = (sys.argv[2] == '-') if len(sys.argv) > 2 else False
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af.info()
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print("** ArrayFire SUSAN Feature Detector Demo **\n")
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susan_demo(console)
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