def ht_road():
list_of_images = [
os.path.join(image_folder, i) for i in os.listdir(image_folder)
]
for i in list_of_images:
print(i)
img = cv2.imread(i)
print(img)
img = np.array(img, dtype=np.uint8)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, 50, 150, apertureSize=3)
new_img = hough.hough_transform(img, edges)
new_path = os.path.join(hough_folder, 'h' + os.path.basename(i))
cv2.imwrite(new_path, new_img)
# print d.shape
I = d['I'].reshape(1024, 1024)
I = I.T[:, ::-1] # first time, then freq, both ascending
print I.shape
t = 2.219968e3 / 1024 * np.arange(1024) # ms
f = np.genfromtxt(indir + 'freq.txt', dtype='f4', delimiter=',')
print f.shape, f[0], f[-1]
f = f[::-1] # make ascending
f *= 1.0e-3 # GHz
A, dl, dh, t0l, t0h = hough.hough_transform(I,
t,
f,
dl,
dh,
nd=ND,
nt0=NC,
threshold=threshold,
comm=mpiutil._comm)
# A, dl, dh, t0l, t0h = hough.hough_transform(I, t, f, dl, dh, threshold=3.0, comm=mpiutil._comm)
if mpiutil.rank0:
print A.shape
# save data
if not os.path.exists(outdir):
os.makedirs(outdir)
with h5py.File(outdir + 'A.hdf5', 'w') as f:
f.create_dataset('A', data=A)
f.attrs['axes'] = '(offset, DM)'
I[ti, fi] += np.random.normal(loc=mu_s, scale=sigma_s)
# # plot I
# plt.figure()
# plt.imshow(I.T, origin='lower', aspect='auto', extent=(tl, th, f[0], f[-1]), cmap='gray')
# plt.xlabel('Time [ms]')
# plt.ylabel('Frequency [GHz]')
# # plt.colorbar()
# plt.savefig(outdir+'I_%.1f.png' % sigma_n)
# plt.close()
A, dl, dh, Cl, Ch = hough.hough_transform(I,
time,
freq,
dl,
dh,
nd=ND,
nt0=NC,
threshold=threshold,
comm=mpiutil._comm)
dD = (Dh - Dl) / ND
dC = (Ch - Cl) / NC
# peak finding
p = np.argmax(A)
Cp = p / ND
Dp = p % ND
print 'peak at: (DM, offset) = (%g, %g), with value %g' % (
Dl + Dp * dD, Cl + Cp * dC, A.flatten()[p])
# compute peak-to-median ratio (PMR)
from simulator.evaluator import Evaluator
""" setup evaluator """
evaluator = Evaluator("hough", detected_walls="N/A")
""" generate points """
points = generate_points(evaluator.sigma)
""" non-generated points """
# sys.path.append('../..')
# from simulator.points_dataset_real2 import points
# from outlierfilter import outlierfilterlib
# points = outlierfilterlib.filter_outliers(points)
""" hough transform """
rhos, thetas, hgrid = hough.hough_transform(points, n_theta=460, n_rho=360)
# print " hough transform complete"
""" get lines from hough transform (matlab) """
MATLAB_HOUGH_FILE = "matlab_io/input_hough.mat"
MATLAB_PEAKS_FILE = "matlab_io/output_peaks.mat"
# check if matlab can be skipped
skip_matlab = False
if os.path.isfile(MATLAB_HOUGH_FILE):
data = loadmat(MATLAB_HOUGH_FILE)
if data["H"].shape == hgrid.T.shape and (data["H"] == hgrid.T).all():
skip_matlab = True
# if not skip_matlab:
if True:
# create input file
savemat(MATLAB_HOUGH_FILE, {"H": hgrid.T})
sys.path.append('..')
from simulator.plotter import *
from simulator.points_generator import *
import hough
from simulator.evaluator import Evaluator
""" setup evaluator """
evaluator = Evaluator("hough", detected_walls="N/A")
""" generate points """
points = generate_points(evaluator.sigma)
""" non-generated points """
#sys.path.append('../..')
#from simulator.points_dataset_real2 import points
#from outlierfilter import outlierfilterlib
#points = outlierfilterlib.filter_outliers(points)
""" hough transform """
rhos, thetas, hgrid = hough.hough_transform(points, n_theta=460, n_rho=360)
#print " hough transform complete"
""" get lines from hough transform (matlab) """
MATLAB_HOUGH_FILE = 'matlab_io/input_hough.mat'
MATLAB_PEAKS_FILE = 'matlab_io/output_peaks.mat'
# check if matlab can be skipped
skip_matlab = False
if os.path.isfile(MATLAB_HOUGH_FILE):
data = loadmat(MATLAB_HOUGH_FILE)
if data['H'].shape == hgrid.T.shape and (data['H'] == hgrid.T).all():
skip_matlab = True
#if not skip_matlab:
if True:
# create input file
savemat(MATLAB_HOUGH_FILE, {'H': hgrid.T})
# run script