def test_GetRandomPoints(self):
helper1=RansacLineHelper()
lst=list()
lst.append(Point(0,0))
lst.append(Point(1,1))
lst.append(Point(2,2))
lst.append(Point(3,3))
print("displaying orignal points")
print("------------")
for p in lst:
print(p)
print("------------")
helper1.add_points(lst)
print("displaying points after adding to collection")
print("------------")
for p in helper1.points:
print(p)
print("------------")
rnd_pts=helper1.select_random_points(2)
self.assertEquals(len(rnd_pts), 2)
for rnd_pt in rnd_pts:
is_member=(rnd_pt in lst)
print("ID of random pt=%d" % rnd_pt.ID)
self.assertEqual(is_member,True)
def test_run_with_100x100_image(self):
#
#get a list of points
#
folder_script=os.path.dirname(__file__)
filename_input="Line_100x100.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=20
helper1.min_points_for_model=2
helper1.threshold_error=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=("%s.Run.%d.png" % (filename_input,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
#
#Superimpose a line over the inliers only
#
new_points=Util.generate_plottable_points_from_projection_of_points(result_model,result_model.points)
np_superimposed=Util.superimpose_points_on_image(np_image,new_points,100,255,100)
skimage.io.imsave(file_result,np_superimposed)
#11 inlier points in total which give us the good model
self.assertEqual(len(result_model.points),11)
#
#No of detected inliers must be more than or equal to threshold
#
self.assertTrue(len(result_model.points) >= helper1.threshold_inlier_count,"Number of inliers should be >= threshold")
#
#There should be no-duplicates in the RANSAC inlier points
#
set_ids=set(map(lambda x: x.ID, result_model.points))
list_ids=list(map(lambda x: x.ID, result_model.points))
self.assertEqual(len(set_ids),len(list_ids),"Inliers should be unique")
#
#All the RANSAC linlier points must be within the threshold distance from the RANSAC line
#
for inlier_pt in result_model.points:
distance=result_model.compute_distance(inlier_pt)
self.assertTrue(distance < helper1.threshold_error,"Distance of inlier from RANSAC line must be less than threshold")
def test_AddPoints(self):
helper=RansacLineHelper()
lst=list()
lst.append(Point(0,0))
lst.append(Point(1,1))
lst.append(Point(2,2))
lst.append(Point(3,3))
helper.add_points(lst)
expected_count=len(lst)
actual_count=len(helper.points)
self.assertEqual(expected_count,actual_count)
pass
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 find_line_using_ransac(self, points_in_patch: List[Point],
np_patchregion: np.ndarray):
count_of_points_in_patch = len(points_in_patch)
#create thresholds for RANSAC
ransac_threshold_distance = (
self._cropdimension) * 1 / 10 #should be adaptive
ransac_mininliers = 0.5 * count_of_points_in_patch #automatically try out various inliers
helper = RansacLineHelper()
helper.max_iterations = (count_of_points_in_patch) * (
count_of_points_in_patch - 1) / 2 * 5 #mpy by 5
helper.min_points_for_model = 2
helper.threshold_error = ransac_threshold_distance
helper.threshold_inlier_count = ransac_mininliers
helper.add_points(points_in_patch)
model = helper.run()
return model
pass
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)