def test_save_residual_plot(self):
ver = sys.version[:3]
check = True
if ver != "2.7":
file_path = "data/plot1.png"
list_data = np.ones((64, 2), dtype=np.float32)
list_data[:, 0] = 0.5 * np.random.rand(64)
list_data[:, 1] = np.arange(64)
losa.save_residual_plot(file_path, list_data, 64, 64, dpi=100)
check = os.path.isfile(file_path)
self.assertTrue(check)
dist_ver,
ratio=0.1,
num_dot_miss=2,
accepted_ratio=0.8)
# Remove residual dots
list_hor_lines = prep.remove_residual_dots_hor(list_hor_lines, slope_hor, 2.0)
list_ver_lines = prep.remove_residual_dots_ver(list_ver_lines, slope_ver, 2.0)
# Save output for checking
io.save_plot_image(output_base + "/horizontal_lines.png", list_hor_lines,
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))
# Optional: remove residual dots
list_hor_lines = prep.remove_residual_dots_hor(list_hor_lines, slope_hor, 2.0)
list_ver_lines = prep.remove_residual_dots_ver(list_ver_lines, slope_ver, 2.0)
print("5-> Correct perspective effect !!!!")
# Optional: correct perspective effect.
list_hor_lines, list_ver_lines = proc.regenerate_grid_points_parabola(
list_hor_lines, list_ver_lines, perspective=True)
io.save_plot_image(output_base + "/hor_lines.png", list_hor_lines, height,
width)
io.save_plot_image(output_base + "/ver_lines.png", list_ver_lines, height,
width)
# Check if the distortion is significant.
list_hor_data = post.calc_residual_hor(list_hor_lines, 0.0, 0.0)
io.save_residual_plot(output_base + "/residual_horizontal_points_before.png",
list_hor_data, height, width)
list_ver_data = post.calc_residual_ver(list_ver_lines, 0.0, 0.0)
io.save_residual_plot(output_base + "/residual_vertical_points_before.png",
list_ver_data, height, width)
print("6-> Calculate the centre of distortion !!!!")
(xcenter, ycenter) = proc.find_cod_coarse(list_hor_lines, list_ver_lines)
print(" X-center: {0}, Y-center: {1}".format(xcenter, ycenter))
print("7-> Calculate radial distortion coefficients !!!!")
list_fact = proc.calc_coef_backward(list_hor_lines, list_ver_lines, xcenter,
ycenter, num_coef)
# Output
print("8-> Apply correction to image !!!!")
corrected_mat = post.unwarp_image_backward(mat0, xcenter, ycenter, list_fact)
hor_slope,
residual=2.0)
list_ver_lines = prep.remove_residual_dots_ver(list_ver_lines,
ver_slope,
residual=2.0)
io.save_plot_image(output_base + "/horizontal_dots_refined.png",
list_hor_lines, height, width)
io.save_plot_image(output_base + "/vertical_dots_refined.png", list_ver_lines,
height, width)
time_stop = timeit.default_timer()
print("Group dots into horizontal lines and vertical lines in {}"
" second!".format(time_stop - time_start))
# Optional step: check if the distortion is significant.
list_hor_data = post.calc_residual_hor(list_hor_lines, 0.0, 0.0)
io.save_residual_plot(output_base + "/residual_horizontal_dots_before.png",
list_hor_data, height, width)
list_ver_data = post.calc_residual_ver(list_ver_lines, 0.0, 0.0)
io.save_residual_plot(output_base + "/residual_vertical_dots_before.png",
list_ver_data, height, width)
check1 = post.check_distortion(list_hor_data)
check2 = post.check_distortion(list_ver_data)
if (not check1) and (not check2):
print("!!! Distortion is not significant !!!")
# Optional: correct perspective effect. Only available from Discorpy 1.4
if perspective is True:
try:
list_hor_lines, list_ver_lines = proc.regenerate_grid_points_parabola(
list_hor_lines, list_ver_lines, perspective=True)
except AttributeError:
raise ValueError(