def test_distance_of_0_0_from_flat_line_yintercept_is_zero(self):
x = LineModel(0, 1, -3)
test_point = Point(0, 0)
distance_actual = x.compute_distance(test_point)
distance_expected = 3
self.assertEqual(distance_actual, distance_expected)
pass
def test_distance_of_origin_from_slope_45degrees_yintercept_is_zero(self):
x = LineModel(-1, 1, 0)
test_origin = Point(0, 0)
distance_actual = x.compute_distance(test_origin)
distance_expected = 0
self.assertEqual(distance_actual, distance_expected)
pass
def test_distance_of_1_0_from_slope_45degrees_yintercept_is_zero(self):
x = LineModel(-1, 1, 0)
test_point = Point(1, 0)
distance_actual = x.compute_distance(test_point)
distance_expected = 1 / math.sqrt(2)
self.assertEqual(distance_actual, distance_expected)
pass
def test_x_y_intercept_45_degrees_1_0(self):
line = LineModel(-1, 1, 1)
xintercept_actual = line.xintercept()
xintercept_expected = 1
yintercept_actual = line.yintercept()
yintercept_expected = -1
self.assertEqual(xintercept_actual, xintercept_expected)
self.assertEqual(yintercept_actual, yintercept_expected)
def test_x_y_intercept_90_degrees_5_inf(self):
line = LineModel(1, 0, -5)
xintercept_actual = line.xintercept()
xintercept_expected = 5
yintercept_actual = line.yintercept()
yintercept_expected = math.inf
self.assertEqual(xintercept_actual, xintercept_expected)
self.assertEqual(yintercept_actual, yintercept_expected)
def test_x_y_intercept_0_degrees_inf_5(self):
line = LineModel(0, 1, -5)
xintercept_actual = line.xintercept()
xintercept_expected = math.inf
yintercept_actual = line.yintercept()
yintercept_expected = 5
self.assertEqual(xintercept_actual, xintercept_expected)
self.assertEqual(yintercept_actual, yintercept_expected)
def test_generate_points_from_line_90_degrees_passing_through_5_0(self):
line = LineModel(1, 0, -5)
x1 = 20
x2 = 40
y1 = 10
y2 = 30
new_points = LineModel.generate_points_from_line(line, x1, y1, x2, y2)
for p in new_points:
print("Testing point=%s" % (p))
self.assertTrue(p.Y >= y1)
self.assertTrue(p.Y <= y2)
def test_generate_points_from_line_45_degrees_passing_through_0_0(self):
line = LineModel(-1, 1, 0)
x1 = 20
x2 = 40
y1 = 10
y2 = 30
new_points = LineModel.generate_points_from_line(line, x1, y1, x2, y2)
for p in new_points:
print("Testing point=%s" % (p))
self.assertTrue(p.X >= x1)
self.assertTrue(p.X <= x2)
def compute_average_distance(self, model: LineModel,
points: list) -> float:
lst_distances = list()
for p in points:
distance = model.compute_distance(p)
lst_distances.append(distance)
mean = statistics.mean(lst_distances)
return mean
def create_model(self, points: list) -> LineModel:
mean_x = 0
mean_y = 0
for p in points:
mean_x += p.X
mean_y += p.Y
mean_x = mean_x / len(points)
mean_y = mean_y / len(points)
slope_numerator = 0
slope_denominator = 0
slope = 0
#use the formula for least squares
for p in points:
slope_numerator += (p.X - mean_x) * (p.Y - mean_y)
slope_denominator += (p.X - mean_x) * (p.X - mean_x)
if (math.fabs(slope_denominator) < 0.001):
#perpendicular line
x_intercept = mean_x
#equation (1)x + (0)y + (-xintercept) + 1
vertical_line_a = 1
vertical_line_b = 0
vertical_line_c = -x_intercept
model = LineModel(vertical_line_a, vertical_line_b,
vertical_line_c)
return model
slope = slope_numerator / slope_denominator
y_intercept = mean_y - (slope * mean_x)
line_a = slope
line_b = -1
line_c = y_intercept
# standard form of line equation
# ------------------------------
# y=mx+c
# mx - y + c=0
# ax + by + c=0
# slope= -a/b
# yint= -c/b
#
model = LineModel(line_a, line_b, line_c)
return model
def test_add_points_to_line(self):
model = LineModel.create_line_from_2points(0, 0, 100, 100)
test_point1 = Point(1, 0)
test_point2 = Point(2, 0)
model.points.append(test_point1)
model.points.append(test_point2)
self.assertTrue(test_point1 in model.points)
self.assertTrue(test_point2 in model.points)
self.assertTrue(len(model.points) == 2)
def test_projection_of_points_on_line_with_slope_45degrees_through_origin(
self):
line = LineModel(-1, 1, 0)
test_point1 = Point(4, 0)
expected_projected1 = Point(2, 2)
test_point2 = Point(0, 4)
expected_projected2 = Point(2, 2)
lst_inputs = [test_point1, test_point2]
lst_expected = [expected_projected1, expected_projected2]
lst_actual = LineModel.compute_projection_of_points(line, lst_inputs)
for index in range(0, len(lst_inputs)):
actual = lst_actual[index]
expected = lst_expected[index]
self.assertAlmostEqual(actual.X, expected.X, 0.1)
self.assertAlmostEqual(actual.Y, expected.Y, 0.1)
def compute_mean_squared_distance(self, model: LineModel,
points: list) -> float:
lst_distances = list()
for p in points:
distance = model.compute_distance(p)
lst_distances.append(distance**2)
sum_of_squared_distances = sum(lst_distances)
sqroot = sum_of_squared_distances**0.5
mean = sqroot / len(lst_distances)
return mean
def test_create_line_from_start_and_end_vertical_line(self):
start_x = 1
start_y = 5
end_x = 1
end_y = 25
model = LineModel.create_line_from_2points(start_x, start_y, end_x,
end_y)
expected_B = 0
expected_x_intercept = start_x
self.assertAlmostEqual(model.B, 0)
self.assertAlmostEqual(model.C / model.A, -expected_x_intercept)
def get_inliers_from_model(self, model: LineModel,
points_old_inliers: list) -> list:
lst_inliers = list()
for p in self.points:
if ((p in points_old_inliers) == True):
continue
distance_from_model: float = model.compute_distance(p)
if (distance_from_model > self.threshold_error):
continue
lst_inliers.append(p)
return lst_inliers
def test_generate_points_at_regular_intervals_stline(self):
start_x = 0
start_y = 1
end_x = 10
end_y = 11
model = LineModel.create_line_from_2points(start_x, start_y, end_x,
end_y)
slope = -model.A / model.B
yintercept = -model.C / model.B
gap = 5
new_points = LineModel.generate_points_at_regular_intervals_stline(
start_x, start_y, end_x, end_y, gap)
count_of_new_points = len(new_points)
self.assertTrue(count_of_new_points > 0)
self.assertTrue(count_of_new_points < 4)
for new_point in new_points:
new_x = new_point.X
new_y = new_point.Y
expected_y = slope * new_x + yintercept
self.assertAlmostEqual(new_y, expected_y)
def test_create_line_from_start_and_end_horizontal_line(self):
start_x = 0
start_y = 1
end_x = 1
end_y = 2
model = LineModel.create_line_from_2points(start_x, start_y, end_x,
end_y)
#equation ax+by+c=0
#slope=-a/b
#yinter=-c/b
expected_slope = 1
expected_y_intercept = 1
self.assertAlmostEqual(model.A / model.B, -expected_slope)
self.assertAlmostEqual(model.C / model.B, -expected_y_intercept)
def test_run_with_very_simple_image(self):
#
#get a list of points
#
folder_script=os.path.dirname(__file__)
filename_input="Line_50x30.png"
file_noisy_line=os.path.join(folder_script,"./data/",filename_input)
np_image=skimage.io.imread(file_noisy_line,as_gray=True)
lst_points=Util.create_points_from_numpyimage(np_image)
#
#initialize RansalHelper
#
helper1=RansacLineHelper()
helper1.add_points(lst_points)
helper1.max_iterations=1000
#10000 did not work
helper1.min_points_for_model=2
helper1.threshold_error=3 #10
helper1.threshold_inlier_count=3
result_model=helper1.run()
print("RANSAC-complete")
print("Found model %s , polar=%s" % (result_model,result_model.display_polar()))
#
#Superimpose the new line over the image
#
folder_results=os.path.join(folder_script,"../out/")
count_of_files=len(os.listdir(folder_results))
filename_results=("Line_50x30.%d.png" % (count_of_files) )
file_result=os.path.join(folder_results,filename_results)
x_lower=0
x_upper=np_image.shape[1]-1
y_lower=0
y_upper=np_image.shape[0]-1
new_points=LineModel.generate_points_from_line(result_model,x_lower,y_lower,x_upper,y_upper)
np_superimposed=Util.superimpose_points_on_image(np_image,new_points,100,255,100)
skimage.io.imsave(file_result,np_superimposed)
#
#Asserts!
#
x_intercept=result_model.xintercept()
y_intercept=result_model.yintercept()
self.assertTrue ( x_intercept > 30,"X intercept below threshold")
self.assertTrue ( x_intercept < 50,"X intercept above threshold")
self.assertTrue ( y_intercept > 30,"Y intercept above threshold")
self.assertTrue ( y_intercept < 45,"Y intercept below threshold")
self.assertTrue(len(result_model.points),5)
for pt in result_model.points:
distance_from_line=result_model.compute_distance(pt)
self.assertTrue(distance_from_line <= helper1.threshold_error)
def run_ransac(filename):
folder_script = os.path.dirname(__file__)
#Images which did not generate good results:
# NoisyImage_3.png
# NoisyLine-Gaussian-sp-0.80.111.png
file_noisy_line = os.path.join(folder_script, "./input/", filename)
np_image = skimage.io.imread(file_noisy_line, as_gray=True)
#
#Iterate over all cells of the NUMPY array and convert to array of Point classes
#
lst_all_points = Util.create_points_from_numpyimage(np_image)
#
#begin RANSAC
#
ransac_maxiterations = 12000
#12000
#6000
#12000 worked well
ransac_minpoints = 5
#5 worked well
#2 gave very bad results
#20 worked well
ransac_threshold = 5
#25 worked well for 'NoisyLine-Gaussian-sp-0.80.104.png' 15 and 5 did not
#Nothing worked well for 'NoisyLine-Gaussian-sp-0.80.111.png" , tried increasing to 35
#3 for first set when points were much closer
#5 produced too much deviation
ransac_mininliers = 10
helper = RansacLineHelper()
helper.max_iterations = ransac_maxiterations
helper.min_points_for_model = ransac_minpoints
helper.threshold_error = ransac_threshold
helper.threshold_inlier_count = ransac_mininliers
helper.add_points(lst_all_points)
model = helper.run()
#Display the model , you could render over the original picture
print("-------------------------------------")
print("RANSAC-complete")
print("Found model %s , polar=%s" % (model, model.display_polar()))
#
#Generate an output image with the model line
#
filename_noextension = no_extension = os.path.splitext(filename)[0]
now = datetime.datetime.now()
filename_result = ("%s-%s.result.png") % (
filename_noextension, now.strftime("%Y-%m-%d-%H-%M-%S"))
file_result = os.path.join(folder_script, "./out/", filename_result)
#Load input image into array
np_image_result = skimage.io.imread(file_noisy_line, as_gray=True)
new_points = LineModel.generate_points_from_line(
model, 0, 0, np_image_result.shape[1] - 1,
np_image_result.shape[0] - 1)
np_superimposed = Util.superimpose_points_on_image(np_image_result,
new_points, 100, 255,
100)
#Save new image
skimage.io.imsave(file_result, np_superimposed)
def test_display_polar(self):
line = LineModel(-1, 1, 1)
s = line.display_polar()
print(s)
def test_construction(self):
x = LineModel(100, 200, 300)
self.assertEqual(x.A, 100)
self.assertEqual(x.B, 200)
self.assertEqual(x.C, 300)
pass
