class ContoursExtractionTest(TestBase):
def setUp(self):
super().setUp()
self.processor = EdgeDetectionProcessor(debug_enabled=True)
def _process(self, case_name, img):
self.dump_debug_img(Path(case_name) / '01_input.png', img)
edges = self.processor.process(img)
self.dump_debug_img(Path(case_name) / '02_gaussed_grayscale.png', np.uint8(self.processor._debug_last_values['gaussed_grayscale']))
self.dump_debug_img(Path(case_name) / '03_intencity_dx.png', np.uint8(127 + self.processor._debug_last_values['intensity_dx']))
self.dump_debug_img(Path(case_name) / '04_intencity_dy.png', np.uint8(127 + self.processor._debug_last_values['intensity_dy']))
self.dump_debug_img(Path(case_name) / '05_intencity_dnorm.png', np.uint8(self.processor._debug_last_values['intensity_dnorm']))
self.dump_debug_img(Path(case_name) / '06_is_extremum.png', np.uint8(self.processor._debug_last_values['is_extremum']) * 255)
self.dump_debug_img(Path(case_name) / '07_weak_edges.png', np.uint8(self.processor._debug_last_values['weak_edges']) * 255)
self.dump_debug_img(Path(case_name) / '08_strong_edges.png', np.uint8(self.processor._debug_last_values['strong_edges']) * 255)
self.dump_debug_img(Path(case_name) / '09_edges.png', edges * 255)
contours = extract_contours(edges)
pointsNumber = 0
h, w = img.shape[:2]
extracted_edges = np.zeros((h, w, 3), np.uint8)
np.random.seed(239)
for contour in contours:
colour = np.random.randint(60, 255, (3,))
draw_pixels(extracted_edges, contour, colour)
pointsNumber += len(contour)
self.assertTrue(np.all(edges == draw_pixels(edges.copy(), contour, 1))) # assertion, that all pixels from contours are from edges
self.dump_debug_img(Path(case_name) / '10_extracted_edges.png', extracted_edges)
self.assertEqual(pointsNumber, len(edges.nonzero()[0]))
return contours
def lena_test(self):
img = np.array(support.load_image(resources_dir_path / 'data' / 'lena.png'))
edges = self._process('lena', img)
def ellipse_test(self):
w, h = 300, 150
ellipse = shapes.Ellipse(w // 2, h // 2, w // 3, h // 3, 20)
img_contour = np.zeros((h, w), np.uint8)
ellipse.draw(img_contour, 255)
self.dump_debug_img(Path('ellipse') / '00_ellipse.png', img_contour)
img = np.zeros((h, w), np.uint8)
ellipse.draw(img, 255, fill=True)
edges = self._process('ellipse', img)
class EdgeDetectionProcessorTest(TestBase):
def setUp(self):
super().setUp()
self.processor = EdgeDetectionProcessor(debug_enabled=True)
def _process(self, case_name, img):
self.dump_debug_img(Path(case_name) / '1_input.png', img)
edges = self.processor.process(img)
self.dump_debug_img(Path(case_name) / '2_gaussed_grayscale.png', np.uint8(self.processor._debug_last_values['gaussed_grayscale']))
self.dump_debug_img(Path(case_name) / '3_intencity_dx.png', np.uint8(127 + self.processor._debug_last_values['intensity_dx']))
self.dump_debug_img(Path(case_name) / '4_intencity_dy.png', np.uint8(127 + self.processor._debug_last_values['intensity_dy']))
self.dump_debug_img(Path(case_name) / '5_intencity_dnorm.png', np.uint8(self.processor._debug_last_values['intensity_dnorm']))
self.dump_debug_img(Path(case_name) / '6_is_extremum.png', np.uint8(self.processor._debug_last_values['is_extremum']) * 255)
self.dump_debug_img(Path(case_name) / '7_weak_edges.png', np.uint8(self.processor._debug_last_values['weak_edges']) * 255)
self.dump_debug_img(Path(case_name) / '8_strong_edges.png', np.uint8(self.processor._debug_last_values['strong_edges']) * 255)
self.dump_debug_img(Path(case_name) / '9_edges.png', edges * 255)
return edges
def lena_test(self):
img = np.array(support.load_image(resources_dir_path / 'data' / 'lena.png'))
edges = self._process('lena', img)
def ellipse_test(self):
w, h = 300, 150
ellipse = shapes.Ellipse(w // 2, h // 2, w // 3, h // 3, 20)
img_contour = np.zeros((h, w), np.uint8)
ellipse.draw(img_contour, 255)
self.dump_debug_img(Path('ellipse') / '0_ellipse.png', img_contour)
img = np.zeros((h, w), np.uint8)
ellipse.draw(img, 255, fill=True)
edges = self._process('ellipse', img)
edges_points = np.nonzero(edges)
distances = []
for y, x in zip(edges_points[0], edges_points[1]):
distances.append(y)
is_ok = False
for dy in [-1, 0, 1]:
for dx in [-1, 0, 1]:
if y > 0 and y < h - 1 and x > 0 and x < w - 1 and img_contour[y + dy, x + dx]:
is_ok = True
self.assertTrue(is_ok)
self.assertGreater(len(edges_points[0]), len(img_contour.nonzero()[0]) * 0.70)
self.assertLess(len(edges_points[0]), len(img_contour.nonzero()[0]) * 1.3)
def setUp(self):
super().setUp()
self.processor = EdgeDetectionProcessor(debug_enabled=True)
class ContoursExtractionTest(TestBase):
def setUp(self):
super().setUp()
self.processor = EdgeDetectionProcessor(debug_enabled=True)
def _process(self, case_name, img):
self.dump_debug_img(Path(case_name) / '01_input.png', img)
edges = self.processor.process(img)
self.dump_debug_img(
Path(case_name) / '02_gaussed_grayscale.png',
np.uint8(self.processor._debug_last_values['gaussed_grayscale']))
self.dump_debug_img(
Path(case_name) / '03_intencity_dx.png',
np.uint8(127 + self.processor._debug_last_values['intensity_dx']))
self.dump_debug_img(
Path(case_name) / '04_intencity_dy.png',
np.uint8(127 + self.processor._debug_last_values['intensity_dy']))
self.dump_debug_img(
Path(case_name) / '05_intencity_dnorm.png',
np.uint8(self.processor._debug_last_values['intensity_dnorm']))
self.dump_debug_img(
Path(case_name) / '06_is_extremum.png',
np.uint8(self.processor._debug_last_values['is_extremum']) * 255)
self.dump_debug_img(
Path(case_name) / '07_weak_edges.png',
np.uint8(self.processor._debug_last_values['weak_edges']) * 255)
self.dump_debug_img(
Path(case_name) / '08_strong_edges.png',
np.uint8(self.processor._debug_last_values['strong_edges']) * 255)
self.dump_debug_img(Path(case_name) / '09_edges.png', edges * 255)
contours = extract_contours(edges)
pointsNumber = 0
h, w = img.shape[:2]
extracted_edges = np.zeros((h, w, 3), np.uint8)
np.random.seed(239)
for contour in contours:
colour = np.random.randint(60, 255, (3, ))
draw_pixels(extracted_edges, contour, colour)
pointsNumber += len(contour)
self.assertTrue(
np.all(edges == draw_pixels(edges.copy(), contour, 1))
) # assertion, that all pixels from contours are from edges
self.dump_debug_img(
Path(case_name) / '10_extracted_edges.png', extracted_edges)
self.assertEqual(pointsNumber, len(edges.nonzero()[0]))
return contours
def lena_test(self):
img = np.array(
support.load_image(resources_dir_path / 'data' / 'lena.png'))
edges = self._process('lena', img)
def ellipse_test(self):
w, h = 300, 150
ellipse = shapes.Ellipse(w // 2, h // 2, w // 3, h // 3, 20)
img_contour = np.zeros((h, w), np.uint8)
ellipse.draw(img_contour, 255)
self.dump_debug_img(Path('ellipse') / '00_ellipse.png', img_contour)
img = np.zeros((h, w), np.uint8)
ellipse.draw(img, 255, fill=True)
edges = self._process('ellipse', img)
