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 test_large_circle_50X50_no_noise_1(self):
folder_script=os.path.dirname(__file__)
filename_input="NoisyCircle_x_-10_y_-14.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=BullockCircleFitting(lst_points)
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=2
self.assertAlmostEquals(result.R, 48.0, delta=delta);
self.assertAlmostEquals(result.X, -10.0, delta=delta);
self.assertAlmostEquals(result.Y, -14.0, 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_generate_points_from_circle_model_center_at_0_0_radius_1(self):
center_x = 0
center_y = 0
radius = 1.0
model = CircleModel(center_x, center_y, radius)
points = CircleModel.generate_points_from_circle(model)
pt: Point
for pt in points:
distance_from_center = math.sqrt((pt.X - center_x)**2 +
(pt.Y - center_y)**2)
self.assertAlmostEqual(distance_from_center, radius, 2)
def run(filename, threshold, inlier, sampling_fraction=0.25, matplot=False):
print("Going to process file:%s" % (filename))
folder_script = os.path.dirname(__file__)
file_noisy_circle = os.path.join(folder_script, "./input/", filename)
try:
np_image = skimage.io.imread(file_noisy_circle, 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
#
helper = RansacCircleHelper()
helper.threshold_error = threshold
helper.threshold_inlier_count = inlier
helper.add_points(lst_all_points)
helper.sampling_fraction = sampling_fraction
best_model = helper.run()
print("RANSAC-complete")
if (best_model == None):
print(
"ERROR! Could not find a suitable model. Try altering ransac-threshold and min inliner count"
)
return
#
#Generate an output image with the model circle overlayed on top of original image
#
now = datetime.datetime.now()
filename_result = ("%s-%s.png" %
(filename, 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_circle, as_gray=True)
new_points = CircleModel.generate_points_from_circle(best_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("------------------------------------------------------------")
if (matplot == True):
plot_new_points_over_existing_points(
lst_all_points, new_points, "Outcome of RANSAC algorithm",
"Original points", "RANSAC")
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
def superimpose_circle_over_original_image(self, original_image_file,
circle):
np_image = skimage.io.imread(original_image_file, as_gray=True)
folder_script = os.path.dirname(__file__)
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(circle,
distance=2)
np_superimposed = Util.superimpose_points_on_image(
np_image, new_points, 255, 255, 0)
skimage.io.imsave(file_result, np_superimposed)
def test_generate_points_from_circle_model_center_at_0_0_radius_10(self):
center_x = 0
center_y = 0
radius = 100.0
distance = 5
model = CircleModel(center_x, center_y, radius)
points = CircleModel.generate_points_from_circle(model,
distance=distance)
pt: Point
for pt in points:
distance_from_center = math.sqrt((pt.X - center_x)**2 +
(pt.Y - center_y)**2)
self.assertAlmostEqual(distance_from_center, radius, 2)
#compare distance
for index in range(1, len(points)):
point1 = points[index]
point2 = points[index - 1]
actual_distance = Point.euclidean_distance(point1, point2)
self.assertAlmostEqual(distance, actual_distance, delta=2)
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