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 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)
def load_image(caffe_net, image):
tmp = af.load_image(image, is_color=True)
af_image = af.resize(tmp, odim0=227, odim1=227)
caffe_image = af_image.__array__()
py_caffe = '/home/aatish/caffe/python/'
t = caffe.io.Transformer({'data': caffe_net.blobs['data'].data.shape})
t.set_mean(
'data',
np.load(py_caffe +
'caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1))
t.set_transpose('data', (2, 0, 1))
t.set_channel_swap('data', (2, 1, 0))
t.set_raw_scale('data', 255.0)
caffe_image = t.preprocess('data',
af_image.__array__()).reshape(1, 3, 227, 227)
caffe_net.blobs['data'].reshape(1, 3, 227, 227)
caffe_net.blobs['data'].data[...] = caffe_image
af_image = af.np_to_af_array(caffe_image)
af_image = af.reorder(af_image, 2, 3, 1, 0)
return af_image
# The complete license agreement can be obtained at:
# http://arrayfire.com/licenses/BSD-3-Clause
########################################################
import arrayfire as af
import sys
import os
if __name__ == "__main__":
if (len(sys.argv) == 1):
raise RuntimeError("Expected to the image as the first argument")
if not os.path.isfile(sys.argv[1]):
raise RuntimeError("File %s not found" % sys.argv[1])
if (len(sys.argv) > 2):
af.set_device(int(sys.argv[2]))
af.info()
hist_win = af.Window(512, 512, "3D Plot example using ArrayFire")
img_win = af.Window(480, 640, "Input Image")
img = (af.load_image(sys.argv[1])).(af.Dtype.u8)
hist = af.histogram(img, 256, 0, 255)
while (not hist_win.close()) and (not img_win.close()):
hist_win.hist(hist, 0, 255)
img_win.image(img)
# The complete license agreement can be obtained at:
# http://arrayfire.com/licenses/BSD-3-Clause
########################################################
import arrayfire as af
import sys
import os
if __name__ == "__main__":
if (len(sys.argv) == 1):
raise RuntimeError("Expected to the image as the first argument")
if not os.path.isfile(sys.argv[1]):
raise RuntimeError("File %s not found" % sys.argv[1])
if (len(sys.argv) > 2):
af.set_device(int(sys.argv[2]))
af.info()
hist_win = af.Window(512, 512, "3D Plot example using ArrayFire")
img_win = af.Window(480, 640, "Input Image")
img = af.load_image(sys.argv[1]).as_type(af.Dtype.u8)
hist = af.histogram(img, 256, 0, 255)
while (not hist_win.close()) and (not img_win.close()):
hist_win.hist(hist, 0, 255)
img_win.image(img)
# The complete license agreement can be obtained at:
# http://arrayfire.com/licenses/BSD-3-Clause
########################################################
import arrayfire as af
import sys
import os
if __name__ == "__main__":
if (len(sys.argv) == 1):
raise RuntimeError("Expected to the image as the first argument")
if not os.path.isfile(sys.argv[1]):
raise RuntimeError("File %s not found" % sys.argv[1])
if (len(sys.argv) > 2):
af.set_device(int(sys.argv[2]))
af.info()
hist_win = af.Window(512, 512, "3D Plot example using ArrayFire")
img_win = af.Window(480, 640, "Input Image")
img = af.load_image(sys.argv[1]).as_type(af.Dtype.u8)
hist = af.histogram(img, 256, 0, 255)
while (not hist_win.close()) and (not img_win.close()):
hist_win.hist(hist, 0, 255)
img_win.image(img)
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)