def test_find_cod_fine(self):
x_cod, y_cod = proc.find_cod_coarse(self.list_hor_dlines,
self.list_ver_dlines)
x_cod, y_cod = proc.find_cod_fine(self.list_hor_dlines,
self.list_ver_dlines, x_cod, y_cod,
self.dot_dist)
self.assertTrue(isinstance(x_cod, float) and isinstance(y_cod, float))
def test_calc_coef_fordward(self):
x_cod, y_cod = proc.find_cod_coarse(self.list_hor_dlines,
self.list_ver_dlines)
list_fact = proc.calc_coef_forward(self.list_hor_dlines,
self.list_ver_dlines, x_cod, y_cod,
2)
error1 = np.abs((list_fact[0] - self.list_fact[0]) / self.list_fact[0])
error2 = np.abs((list_fact[1] + self.list_fact[1]) / self.list_fact[1])
self.assertTrue(error1 < 0.1 and error2 < 0.2)
def calc_distor_coef(mat, num_coef, perspective=False):
# Pre-processing
mat1 = prep.binarization(mat)
(dot_size, dot_dist) = prep.calc_size_distance(mat1)
mat1 = prep.select_dots_based_size(mat1, dot_size)
mat1 = prep.select_dots_based_ratio(mat1)
hor_slope = prep.calc_hor_slope(mat1)
ver_slope = prep.calc_ver_slope(mat1)
list_hor_lines = prep.group_dots_hor_lines(mat1, hor_slope, dot_dist)
list_ver_lines = prep.group_dots_ver_lines(mat1, ver_slope, dot_dist)
list_hor_lines = prep.remove_residual_dots_hor(list_hor_lines, hor_slope)
list_ver_lines = prep.remove_residual_dots_ver(list_ver_lines, ver_slope)
if perspective is True:
try:
list_hor_lines, list_ver_lines = proc.regenerate_grid_points_parabola(
list_hor_lines, list_ver_lines, perspective=perspective)
except AttributeError:
raise ValueError("Perspective correction only available "
"from Discorpy 1.4!!!")
# Processing
(xcenter, ycenter) = proc.find_cod_coarse(list_hor_lines, list_ver_lines)
list_fact = proc.calc_coef_backward(list_hor_lines, list_ver_lines,
xcenter, ycenter, num_coef)
return xcenter, ycenter, list_fact
height, width)
io.save_plot_image(output_base + "/vertical_lines.png", list_ver_lines, height,
width)
list_hor_data = post.calc_residual_hor(list_hor_lines, 0.0, 0.0)
list_ver_data = post.calc_residual_ver(list_ver_lines, 0.0, 0.0)
io.save_residual_plot(output_base + "/hor_residual_before_correction.png",
list_hor_data, height, width)
io.save_residual_plot(output_base + "/ver_residual_before_correction.png",
list_ver_data, height, width)
# Regenerate grid points after correcting the perspective effect.
list_hor_lines, list_ver_lines = proc.regenerate_grid_points_parabola(
list_hor_lines, list_ver_lines, perspective=True)
# Calculate parameters of the radial correction model
(xcenter, ycenter) = proc.find_cod_coarse(list_hor_lines, list_ver_lines)
list_fact = proc.calc_coef_backward(list_hor_lines, list_ver_lines, xcenter,
ycenter, num_coef)
io.save_metadata_txt(output_base + "/coefficients_radial_distortion.txt",
xcenter, ycenter, list_fact)
print("X-center: {0}. Y-center: {1}".format(xcenter, ycenter))
print("Coefficients: {0}".format(list_fact))
# Check the correction results:
# Apply correction to the lines of points
list_uhor_lines = post.unwarp_line_backward(list_hor_lines, xcenter, ycenter,
list_fact)
list_uver_lines = post.unwarp_line_backward(list_ver_lines, xcenter, ycenter,
list_fact)
# Calculate the residual of the unwarpped points.
list_hor_data = post.calc_residual_hor(list_uhor_lines, xcenter, ycenter)
def test_find_cod_coarse(self):
x_cod, y_cod = proc.find_cod_coarse(self.list_hor_dlines,
self.list_ver_dlines)
self.assertTrue((np.abs(x_cod - self.x0) < self.dot_dist)
and (np.abs(y_cod - self.y0) < self.dot_dist))
