def test_large_circle_50X50_no_noise_2(self):
folder_script = os.path.dirname(__file__)
filename_input = "NoisyCircle_x_6_y_-30_r_118.162.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)
helper = GradientDescentCircleFitting(None,
lst_points,
learningrate=0.4,
iterations=5000)
result: CircleModel = helper.FindBestFittingCircle()
#
#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 = ("%s.%d.png" % (__name__, count_of_files))
file_result = os.path.join(folder_results, filename_results)
new_points = CircleModel.generate_points_from_circle(result)
np_superimposed = Util.superimpose_points_on_image(
np_image, new_points, 100, 255, 100)
skimage.io.imsave(file_result, np_superimposed)
delta = 10
self.assertAlmostEquals(result.R, +118.0, delta=delta)
self.assertAlmostEquals(result.X, +06.0, delta=delta)
self.assertAlmostEquals(result.Y, -30.0, delta=delta)
pass
def run_image2matplot(filename):
folder_script = os.path.dirname(__file__)
absolute_path = os.path.join(folder_script, "./input/", filename)
try:
np_image = skimage.io.imread(absolute_path, as_gray=True)
lst_all_points = Util.create_points_from_numpyimage(np_image)
plot_new_points_over_existing_points(lst_all_points, [], "Input data",
"Original points", "")
lrate = 0.3
iterations = 5000
helper = GradientDescentCircleFitting(None,
points=lst_all_points,
learningrate=lrate,
iterations=iterations)
start_time = time.time()
model: CircleModel = helper.FindBestFittingCircle()
new_points = CircleModel.generate_points_from_circle(model)
plot_new_points_over_existing_points(
lst_all_points, new_points, "Gradient descent circle fitting",
"Original points", "Gradient descent")
except Exception as e:
tb = traceback.format_exc()
print("Error:%s while doing RANSAC on the file: %s , stack=%s" %
(str(e), filename, str(tb)))
print("------------------------------------------------------------")
pass
pass
def test_3points_around_origin_unit_radius(self):
p1 = Point(+1, 0)
p2 = Point(+0, 1)
p3 = Point(-1, 0)
expected_list = list()
expected_list.append(p1)
expected_list.append(p2)
expected_list.append(p3)
helper = GradientDescentCircleFitting(None, expected_list)
result: CircleModel = helper.FindBestFittingCircle()
delta = 0.01
self.assertAlmostEquals(result.R, 1.0, delta=delta)
self.assertAlmostEquals(result.X, 0.0, delta=delta)
self.assertAlmostEquals(result.Y, 0.0, delta=delta)
def test_5points_around_3_3_and_radius_5(self):
#Plotted this circle using desmos.com
#\left(5\cdot\cos\left(t\right)+3,5\cdot\sin\left(t\right)+3\right)
p1 = Point(7, 0)
p2 = Point(8, 3)
p3 = Point(6, 7)
p4 = Point(-2, 3)
p5 = Point(0, -1)
expected_list = list()
expected_list.append(p1)
expected_list.append(p2)
expected_list.append(p3)
expected_list.append(p4)
expected_list.append(p5)
helper = GradientDescentCircleFitting(None, expected_list)
result: CircleModel = helper.FindBestFittingCircle()
delta = 0.01
self.assertAlmostEquals(result.R, 5.0, delta=delta)
self.assertAlmostEquals(result.X, 3.0, delta=delta)
self.assertAlmostEquals(result.Y, 3.0, delta=delta)
def run_image2image(filename):
print("Going to fit circle in the file:%s" % (filename))
folder_script = os.path.dirname(__file__)
absolute_path = os.path.join(folder_script, "./input/", filename)
try:
np_image = skimage.io.imread(absolute_path, as_gray=True)
lst_all_points = Util.create_points_from_numpyimage(np_image)
lrate = 0.3
iterations = 5000
helper = GradientDescentCircleFitting(None,
points=lst_all_points,
learningrate=lrate,
iterations=iterations)
start_time = time.time()
model: CircleModel = helper.FindBestFittingCircle()
print("--- %s seconds for gradient descent algo ---" %
(time.time() - start_time))
#
#Generate an output image with the model circle overlayed on top of original image
#
now = datetime.datetime.now()
filename_result = ("gradient-descent-%s.png" % (filename))
file_result = os.path.join(folder_script, "./out/", filename_result)
#Load input image into array
np_image_result = skimage.io.imread(absolute_path, as_gray=True)
new_points = CircleModel.generate_points_from_circle(model)
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)
print("Results saved to file:%s" % (file_result))
print("------------------------------------------------------------")
except Exception as e:
tb = traceback.format_exc()
print("Error:%s while doing RANSAC on the file: %s , stack=%s" %
(str(e), filename, str(tb)))
print("------------------------------------------------------------")
pass
pass
def test_Constructor_With_Default_Learning_Rate(self):
p1 = Point(1, 1)
p2 = Point(2, 2)
p3 = Point(3, 3)
expected_list = list()
expected_list.append(p1)
expected_list.append(p2)
expected_list.append(p3)
expected_lrate = 0.05
helper = GradientDescentCircleFitting(None, points=expected_list)
self.assertEqual(expected_lrate, helper._learningrate)
self.assertEqual(len(expected_list), len(helper._points))
self.assertTrue(p1 in helper._points)
self.assertTrue(p2 in helper._points)
def test_When_GD_Is_Invoked_With_less_than_3_points_Exception_MustBe_Thrown(
self):
p1 = Point(+1, 0)
p2 = Point(+0, 1)
expected_list = list()
expected_list.append(p1)
expected_list.append(p2)
try:
helper = GradientDescentCircleFitting(None, expected_list)
self.fail("Exception was expected")
except Exception as e:
#Ok
message = str(e)
self.assertEquals(message, "Need 3 or more points")
pass