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
import numpy as np
import discorpy.losa.loadersaver as io
import discorpy.prep.preprocessing as prep
import discorpy.proc.processing as proc
import discorpy.post.postprocessing as post
# Initial parameters
file_path = "C:/data/dot_pattern_01.jpg"
output_base = "./output_demo_01/"
num_coef = 5 # Number of polynomial coefficients
mat0 = io.load_image(file_path) # Load image
(height, width) = mat0.shape
# Segment dots
mat1 = prep.binarization(mat0)
# Calculate the median dot size and distance between them.
(dot_size, dot_dist) = prep.calc_size_distance(mat1)
# Remove non-dot objects
mat1 = prep.select_dots_based_size(mat1, dot_size)
# Remove non-elliptical objects
mat1 = prep.select_dots_based_ratio(mat1)
io.save_image(output_base + "/segmented_dots.jpg", mat1)
# Calculate the slopes of horizontal lines and vertical lines.
hor_slope = prep.calc_hor_slope(mat1)
ver_slope = prep.calc_ver_slope(mat1)
print("Horizontal slope: {0}. Vertical slope: {1}".format(hor_slope, ver_slope))
# Group points to horizontal lines
list_hor_lines = prep.group_dots_hor_lines(mat1, hor_slope, dot_dist)
# Group points to vertical lines
list_ver_lines = prep.group_dots_ver_lines(mat1, ver_slope, dot_dist)
# Optional: remove horizontal outliners
mat0 = io.load_image(file_path)
(height, width) = mat0.shape
# Optional step: correct non-uniform background for global thresholding method.
# Background is generated by applying a strong low-pass filter to the image.
# Then, the image is normalized with the background.
if norm is True:
mat1 = prep.normalization_fft(mat0, sigma=5, pad=30)
else:
mat1 = np.copy(mat0)
# Binarization using Otsu's method if thres = None
# Cropped image (30% of the size around the middle) is used for calculating
# the threshold. This is to avoid a case which a dot-pattern doesn't
# cover the whole field of view of a camera.
mat1 = prep.binarization(mat1, ratio=0.3, thres=None)
check = prep.check_num_dots(mat1)
if check:
raise ValueError(
"Number of objects detected is not enough !!! Parameters of"
" the binarization method need to be adjusted!!!")
io.save_image(output_base + "/binarized_image.tif", mat1)
# Calculate the median dot size and the median distance of two nearest dots
# using the middle part of the image (30%). This is based on an assumption
# that there's no distortion around the middle part of the image.
(dot_size, dot_dist) = prep.calc_size_distance(mat1, ratio=0.3)
print("Median size of dots: {0}\nMedian distance between two dots: {1}".format(
dot_size, dot_dist))
# Select dots with size in the range of [dot_size - dot_size*ratio; dot_size +
def test_check_num_dots(self):
bck = 0.5 * np.random.rand(self.hei, self.wid)
mat_bin = prep.binarization(self.mat_dots + bck, denoise=False)
check = prep.check_num_dots(mat_bin)
self.assertTrue(not check)
def test_binarization(self):
bck = 0.5 * np.random.rand(self.hei, self.wid)
mat_bin = prep.binarization(self.mat_dots + bck, denoise=False)
num_dots2 = ndi.label(mat_bin)[-1]
self.assertTrue(self.num_dots == num_dots2)
import discorpy.prep.preprocessing as prep
import discorpy.proc.processing as proc
import discorpy.post.postprocessing as post
# Initial parameters
file_path = "C:/data/dot_pattern_06.jpg"
output_base = "./for_demo_05/"
num_coef = 4 # Number of polynomial coefficients
mat0 = io.load_image(file_path) # Load image
(height, width) = mat0.shape
# Normalize background
mat1 = prep.normalization_fft(mat0, sigma=20)
# Segment dots
threshold = prep.calculate_threshold(mat1, bgr="bright", snr=1.5)
mat1 = prep.binarization(mat1, thres=threshold)
io.save_image(output_base + "/segmented_dots.jpg", mat1)
# Calculate the median dot size and distance between them.
(dot_size, dot_dist) = prep.calc_size_distance(mat1)
# Calculate the slopes of horizontal lines and vertical lines.
hor_slope = prep.calc_hor_slope(mat1)
ver_slope = prep.calc_ver_slope(mat1)
print("Horizontal slope: {0}. Vertical slope: {1}".format(
hor_slope, ver_slope))
# Group dots into lines.
list_hor_lines0 = prep.group_dots_hor_lines(mat1,
hor_slope,
dot_dist,
ratio=0.3,
import discorpy.post.postprocessing as post
# Initial parameters
file_path = "C:/data/dot_pattern_04.jpg"
output_base = "./output_demo_03/"
num_coef = 5 # Number of polynomial coefficients
mat0 = io.load_image(file_path) # Load image
(height, width) = mat0.shape
# Correct non-uniform background.
mat1 = prep.normalization_fft(mat0, sigma=20)
io.save_image(output_base + "/image_normed.tif", mat1)
# Segment dots.
threshold = prep.calculate_threshold(mat1, bgr="bright", snr=3.0)
mat1 = prep.binarization(mat1, ratio=0.5, thres=threshold)
io.save_image(output_base + "/image_binarized.tif", mat1)
# Calculate the median dot size and distance between them.
(dot_size, dot_dist) = prep.calc_size_distance(mat1)
# Remove non-dot objects
mat1 = prep.select_dots_based_size(mat1, dot_size, ratio=0.8)
# Remove non-elliptical objects
mat1 = prep.select_dots_based_ratio(mat1, ratio=0.8)
io.save_image(output_base + "/image_cleaned.tif", mat1)
# Calculate the slopes of horizontal lines and vertical lines.
hor_slope = prep.calc_hor_slope(mat1)
ver_slope = prep.calc_ver_slope(mat1)
print("Horizontal slope: {0}. Vertical slope: {1}".format(hor_slope, ver_slope))
