def test_appd_map_resolution_invariance(self):
perturbed_K = self.test_image_K * 0.98
perturbed_D = self.test_image_D * 1.02
perturbed_calibration = Calibration(K=perturbed_K,
D=perturbed_D,
width=self.test_image.shape[1],
height=self.test_image.shape[0])
appd_full, dm_full = perturbed_calibration.appd(
self.calibration,
height=self.test_image.shape[0],
width=self.test_image.shape[1],
interpolation=cv2.INTER_LANCZOS4,
min_cropped_size=None,
return_diff_map=True)
appd_half, dm_half = perturbed_calibration.appd(
self.calibration,
height=int(self.test_image.shape[0]),
width=int(self.test_image.shape[1]),
interpolation=cv2.INTER_LANCZOS4,
min_cropped_size=None,
return_diff_map=True,
map_width=int(self.test_image.shape[1] / 2),
map_height=int(self.test_image.shape[0] / 2))
self.assertAlmostEqual(appd_full, appd_half, places=3)
self.assertTrue(
np.allclose(dm_full,
cv2.resize(dm_half,
(dm_full.shape[1], dm_full.shape[0]),
interpolation=cv2.INTER_LANCZOS4),
rtol=1e-1,
atol=1e-1))
def test_appd(self):
perturbed_K = self.test_image_K * 0.98
perturbed_D = self.test_image_D * 1.01
perturbed_calibration = Calibration(K=perturbed_K,
D=perturbed_D,
width=self.test_image.shape[1],
height=self.test_image.shape[0])
appd1, dm1 = self.calibration.appd(perturbed_calibration,
height=self.test_image.shape[0],
width=self.test_image.shape[1],
interpolation=cv2.INTER_LANCZOS4,
min_cropped_size=None,
return_diff_map=True)
appd2, dm2 = perturbed_calibration.appd(
self.calibration,
height=self.test_image.shape[0],
width=self.test_image.shape[1],
interpolation=cv2.INTER_LANCZOS4,
min_cropped_size=None,
return_diff_map=True)
self.assertTrue(np.isclose(appd1, appd2, rtol=1e-2, atol=1e-2))
self.assertTrue(np.allclose(dm1, dm2, rtol=1e-2, atol=1e-2))
self.assertEqual(dm1.shape,
(self.test_image.shape[0], self.test_image.shape[1]))
cv2.imwrite(path_here("test_appd_diff_map.png"), dm1)
def test_region_of_validity_check(self):
perturbed_K = self.test_image_K * 2
perturbed_D = self.test_image_D * 9
perturbed_calibration = Calibration(K=perturbed_K,
D=perturbed_D,
width=self.test_image.shape[1],
height=self.test_image.shape[0])
with self.assertRaises(RuntimeError):
_, _ = perturbed_calibration.appd(self.calibration,
height=self.test_image.shape[0],
width=self.test_image.shape[1],
interpolation=cv2.INTER_LANCZOS4,
min_cropped_size=None,
return_diff_map=True)
def setUp(self):
# Load an image an image and its calibration parameters
self.test_image = cv2.imread(path_here('test_image_before.png'))
self.test_image_D = np.array([
-0.2967039649743125, 0.06795775093662262, 0.0008927768064001824,
-0.001327854648098482, 0.0
])
self.test_image_K = np.array([
336.7755634193813, 0.0, 333.3575643300718, 0.0, 336.02729840829176,
212.77376312080065, 0.0, 0.0, 1.0
]).reshape((3, 3))
self.width = self.test_image.shape[1]
self.height = self.test_image.shape[0]
self.ranges = {
'fx': (0.95 * 336.7755634193813, 1.05 * 336.7755634193813),
'fy': (0.95 * 336.02729840829176, 1.05 * 336.02729840829176),
'cx': (0.95 * 333.3575643300718, 1.05 * 333.3575643300718),
'cy': (0.95 * 212.77376312080065, 1.05 * 212.77376312080065),
'k1': (1.05 * -0.2967039649743125, 0.95 * -0.2967039649743125),
'k2': (0.95 * 0.06795775093662262, 1.05 * 0.06795775093662262),
'p1': (0.95 * 0.0008927768064001824, 1.05 * 0.0008927768064001824),
'p2': (1.05 * -0.001327854648098482, 0.95 * -0.001327854648098482),
'k3': (0.0, 0.0)
}
# Initialize a reference calibration object
self.reference = Calibration(K=self.test_image_K,
D=self.test_image_D,
width=self.test_image.shape[1],
height=self.test_image.shape[0])
def setUp(self):
# Load an image an image and its calibration parameters
self.test_image = cv2.imread(path_here('test_image_before.png'))
self.test_image_D = np.array([
-0.2967039649743125, 0.06795775093662262, 0.0008927768064001824,
-0.001327854648098482, 0.0
])
self.test_image_K = np.array([
336.7755634193813, 0.0, 333.3575643300718, 0.0, 336.02729840829176,
212.77376312080065, 0.0, 0.0, 1.0
]).reshape((3, 3))
# Initialize a calibration object
self.calibration = Calibration(K=self.test_image_K,
D=self.test_image_D,
width=self.test_image.shape[1],
height=self.test_image.shape[0])
class TestCalibrationMethods(unittest.TestCase):
def setUp(self):
# Load an image an image and its calibration parameters
self.test_image = cv2.imread(path_here('test_image_before.png'))
self.test_image_D = np.array([
-0.2967039649743125, 0.06795775093662262, 0.0008927768064001824,
-0.001327854648098482, 0.0
])
self.test_image_K = np.array([
336.7755634193813, 0.0, 333.3575643300718, 0.0, 336.02729840829176,
212.77376312080065, 0.0, 0.0, 1.0
]).reshape((3, 3))
# Initialize a calibration object
self.calibration = Calibration(K=self.test_image_K,
D=self.test_image_D,
width=self.test_image.shape[1],
height=self.test_image.shape[0])
def test_print_image_dimensions(self):
print("Test image dimensions: %s" % str(self.test_image.shape))
self.assertTrue(True)
def test_check_ar(self):
self.assertAlmostEqual(self.calibration.aspect_ratio, 640.0 / 480)
def test_scaling_of_K(self):
self.assertTrue(
np.allclose(self.test_image_K, self.calibration.get_K(height=480)))
def test_storing_of_D(self):
self.assertTrue(
np.allclose(self.test_image_D, self.calibration.get_D()))
def test_check_undistort_maps_original_size(self):
width = self.test_image.shape[1]
height = self.test_image.shape[0]
undistort_x, undistort_y = self.calibration.undistortion_maps(
width=width, height=height)
self.assertEqual(undistort_x.shape, self.test_image.shape[:2])
def test_check_undistort_maps_reduced_size(self):
width = self.test_image.shape[1]
height = self.test_image.shape[0]
undistort_x, undistort_y = self.calibration.undistortion_maps(
width=width,
height=height,
map_width=int(width / 2),
map_height=int(height / 2))
self.assertEqual(
undistort_x.shape,
(self.test_image.shape[0] / 2, self.test_image.shape[1] / 2))
def test_check_undistortion_maps_preserving_original_size(self):
width = self.test_image.shape[1]
height = self.test_image.shape[0]
undistort_x, undistort_y = self.calibration.undistortion_maps_preserving(
width=width, height=height)
self.assertEqual(undistort_x.shape, self.test_image.shape[:2])
def test_check_undistortion_maps_preserving_reduced_size(self):
width = self.test_image.shape[1]
height = self.test_image.shape[0]
undistort_x, undistort_y = self.calibration.undistortion_maps_preserving(
width=width,
height=height,
map_width=int(width / 2),
map_height=int(height / 2))
self.assertEqual(
undistort_x.shape,
(self.test_image.shape[0] / 2, self.test_image.shape[1] / 2))
def test_rectified_standard_size(self):
# Apply our rectification
rect_ours = self.calibration.rectify(self.test_image, mode='standard')
self.assertEqual(rect_ours.shape, self.test_image.shape)
def test_rectified_reduced_size(self):
width = self.test_image.shape[1]
height = self.test_image.shape[0]
# Apply our rectification
rect_ours = self.calibration.rectify(self.test_image,
mode='standard',
result_width=int(width / 2),
result_height=int(height / 2))
self.assertEqual(
rect_ours.shape,
(self.test_image.shape[0] / 2, self.test_image.shape[1] / 2,
self.test_image.shape[2]))
def test_rectified_standard_with_ground_truth(self):
# Apply our rectification
rect_ours = self.calibration.rectify(self.test_image, mode='standard')
# Apply directly the OpenCV rectification
newCameraMatrix, validPixROI = cv2.getOptimalNewCameraMatrix(
self.test_image_K, self.test_image_D,
(self.test_image.shape[1], self.test_image.shape[0]), 1.0)
map1, map2 = cv2.initUndistortRectifyMap(
self.test_image_K, self.test_image_D, np.eye(3), newCameraMatrix,
(self.test_image.shape[1], self.test_image.shape[0]), cv2.CV_32FC1)
rect_direct = cv2.remap(self.test_image, map1, map2,
cv2.INTER_LANCZOS4)
cv2.imwrite(path_here("test_rectified_standard_with_ground_truth.png"),
np.hstack((rect_direct, rect_ours)))
self.assertTrue(np.allclose(rect_direct, rect_ours))
def test_rectified_preserving_with_ground_truth(self):
# Apply our rectification
rect_ours = self.calibration.rectify(self.test_image,
mode='preserving')
# Apply directly the OpenCV rectification
output_resolution = (self.test_image.shape[1],
self.test_image.shape[0])
newCameraMatrix, validPixROI = cv2.getOptimalNewCameraMatrix(
self.test_image_K, self.test_image_D,
(self.test_image.shape[1], self.test_image.shape[0]), 1.0)
map1, map2 = cv2.initUndistortRectifyMap(
self.test_image_K, self.test_image_D, np.eye(3), newCameraMatrix,
(self.test_image.shape[1], self.test_image.shape[0]), cv2.CV_32FC1)
rect_direct = cv2.remap(self.test_image, map1, map2,
cv2.INTER_LANCZOS4)
# Do the preserving on the direct image
target_aspect_ratio = float(rect_direct.shape[1]) / float(
rect_direct.shape[0])
validROI = {
'x': (validPixROI[0], validPixROI[0] + validPixROI[2]),
'y': (validPixROI[1], validPixROI[1] + validPixROI[3])
}
valid_aspect_ratio = float(validROI['x'][1] - validROI['x'][0]) / (
validROI['y'][1] - validROI['y'][0])
# If the image is taller than it should, cut its legs and head
if valid_aspect_ratio < target_aspect_ratio:
desired_number_of_rows = int(
round((validROI['x'][1] - validROI['x'][0]) /
target_aspect_ratio))
cut_top = int(((validROI['y'][1] - validROI['y'][0]) -
desired_number_of_rows) / 2)
cropped_ROI = {
'x':
validROI['x'],
'y': (validROI['y'][0] + cut_top,
validROI['y'][0] + cut_top + desired_number_of_rows)
}
else:
desired_number_of_cols = int(
round((validROI['y'][1] - validROI['y'][0]) *
target_aspect_ratio))
cut_left = int(((validROI['x'][1] - validROI['x'][0]) -
desired_number_of_cols) / 2)
cropped_ROI = {
'x': (validROI['x'][0] + cut_left,
validROI['x'][0] + cut_left + desired_number_of_cols),
'y':
validROI['y']
}
cropped_image = rect_direct[cropped_ROI['y'][0]:cropped_ROI['y'][1],
cropped_ROI['x'][0]:cropped_ROI['x'][1]]
resized_image = cv2.resize(cropped_image,
output_resolution,
interpolation=cv2.INTER_LANCZOS4)
# Calculate the cropped maps (need that for the px movement metric calculation)
map1 = copy.deepcopy(map1)
map2 = copy.deepcopy(map2)
map1 = map1[cropped_ROI['y'][0]:cropped_ROI['y'][1],
cropped_ROI['x'][0]:cropped_ROI['x'][1]]
map1 = cv2.resize(map1,
output_resolution,
interpolation=cv2.INTER_LANCZOS4)
map2 = map2[cropped_ROI['y'][0]:cropped_ROI['y'][1],
cropped_ROI['x'][0]:cropped_ROI['x'][1]]
map2 = cv2.resize(map2,
output_resolution,
interpolation=cv2.INTER_LANCZOS4)
# Prepare the image output
rect_ours_gray = cv2.cvtColor(rect_ours, cv2.COLOR_BGR2GRAY)
resized_direct_gray = cv2.cvtColor(resized_image,
cv2.COLOR_BGR2GRAY,
dstCn=3)
overlayed = cv2.addWeighted(rect_ours_gray, 0.5, resized_direct_gray,
0.5, 0)
cv2.imwrite(
path_here("test_rectified_preserving_with_ground_truth.png"),
overlayed)
self.assertTrue(
np.allclose(rect_ours_gray, resized_direct_gray, rtol=15, atol=15))
def test_appd(self):
perturbed_K = self.test_image_K * 0.98
perturbed_D = self.test_image_D * 1.01
perturbed_calibration = Calibration(K=perturbed_K,
D=perturbed_D,
width=self.test_image.shape[1],
height=self.test_image.shape[0])
appd1, dm1 = self.calibration.appd(perturbed_calibration,
height=self.test_image.shape[0],
width=self.test_image.shape[1],
interpolation=cv2.INTER_LANCZOS4,
min_cropped_size=None,
return_diff_map=True)
appd2, dm2 = perturbed_calibration.appd(
self.calibration,
height=self.test_image.shape[0],
width=self.test_image.shape[1],
interpolation=cv2.INTER_LANCZOS4,
min_cropped_size=None,
return_diff_map=True)
self.assertTrue(np.isclose(appd1, appd2, rtol=1e-2, atol=1e-2))
self.assertTrue(np.allclose(dm1, dm2, rtol=1e-2, atol=1e-2))
self.assertEqual(dm1.shape,
(self.test_image.shape[0], self.test_image.shape[1]))
cv2.imwrite(path_here("test_appd_diff_map.png"), dm1)
def test_appd_self(self):
appd, dm = self.calibration.appd(self.calibration,
height=self.test_image.shape[0],
width=self.test_image.shape[1],
interpolation=cv2.INTER_LANCZOS4,
min_cropped_size=None,
return_diff_map=True)
self.assertTrue(np.isclose(appd, 0.0, rtol=1e-2, atol=1e-2))
self.assertTrue(
np.allclose(dm, np.zeros_like(dm), rtol=1e-2, atol=1e-2))
def test_region_of_validity_check(self):
perturbed_K = self.test_image_K * 2
perturbed_D = self.test_image_D * 9
perturbed_calibration = Calibration(K=perturbed_K,
D=perturbed_D,
width=self.test_image.shape[1],
height=self.test_image.shape[0])
with self.assertRaises(RuntimeError):
_, _ = perturbed_calibration.appd(self.calibration,
height=self.test_image.shape[0],
width=self.test_image.shape[1],
interpolation=cv2.INTER_LANCZOS4,
min_cropped_size=None,
return_diff_map=True)
def test_appd_map_resolution_invariance(self):
perturbed_K = self.test_image_K * 0.98
perturbed_D = self.test_image_D * 1.02
perturbed_calibration = Calibration(K=perturbed_K,
D=perturbed_D,
width=self.test_image.shape[1],
height=self.test_image.shape[0])
appd_full, dm_full = perturbed_calibration.appd(
self.calibration,
height=self.test_image.shape[0],
width=self.test_image.shape[1],
interpolation=cv2.INTER_LANCZOS4,
min_cropped_size=None,
return_diff_map=True)
appd_half, dm_half = perturbed_calibration.appd(
self.calibration,
height=int(self.test_image.shape[0]),
width=int(self.test_image.shape[1]),
interpolation=cv2.INTER_LANCZOS4,
min_cropped_size=None,
return_diff_map=True,
map_width=int(self.test_image.shape[1] / 2),
map_height=int(self.test_image.shape[0] / 2))
self.assertAlmostEqual(appd_full, appd_half, places=3)
self.assertTrue(
np.allclose(dm_full,
cv2.resize(dm_half,
(dm_full.shape[1], dm_full.shape[0]),
interpolation=cv2.INTER_LANCZOS4),
rtol=1e-1,
atol=1e-1))