def test_stitcher_aquaduct(self):
stitcher = Stitcher(nfeatures=250)
result = stitcher.stitch(["s1.jpg", "s2.jpg"])
cv.imwrite("result.jpg", result)
max_image_shape_derivation = 3
np.testing.assert_allclose(result.shape[:2], (700, 1811),
atol=max_image_shape_derivation)
def test_stitcher_boat_aquaduct_subset(self):
settings = {"final_megapix": 1, "crop": True}
stitcher = Stitcher(**settings)
result = stitcher.stitch([
"boat5.jpg", "s1.jpg", "s2.jpg", "boat2.jpg", "boat3.jpg",
"boat4.jpg", "boat1.jpg", "boat6.jpg"
])
cv.imwrite("subset_low_res.jpg", result)
max_image_shape_derivation = 100
np.testing.assert_allclose(result.shape[:2], (705, 3374),
atol=max_image_shape_derivation)
def test_timelapse(self):
stitcher = Stitcher(timelapse='as_is')
_ = stitcher.stitch(["s1.jpg", "s2.jpg"])
frame1 = cv.imread("fixed_s1.jpg")
max_image_shape_derivation = 3
np.testing.assert_allclose(frame1.shape[:2],
(700, 1811),
atol=max_image_shape_derivation)
left = cv.cvtColor(frame1[:, :1300, ], cv.COLOR_BGR2GRAY)
right = cv.cvtColor(frame1[:, 1300:, ], cv.COLOR_BGR2GRAY)
self.assertGreater(cv.countNonZero(left), 800000)
self.assertEqual(cv.countNonZero(right), 0)
def test_stitcher_boat2(self):
settings = {"warper_type": "compressedPlaneA2B1",
"finder": "dp_colorgrad",
"compensator": "channel_blocks",
"conf_thresh": 0.3}
stitcher = Stitcher(**settings)
result = stitcher.stitch(["boat5.jpg", "boat2.jpg",
"boat3.jpg", "boat4.jpg",
"boat1.jpg", "boat6.jpg"])
cv.imwrite("boat_plane.jpg", result)
max_image_shape_derivation = 600
np.testing.assert_allclose(result.shape[:2],
(7400, 12340),
atol=max_image_shape_derivation)
def test_stitcher_boat1(self):
settings = {"warper_type": "fisheye",
"wave_correct_kind": "no",
"finder": "dp_colorgrad",
"compensator": "no",
"conf_thresh": 0.3}
stitcher = Stitcher(**settings)
result = stitcher.stitch(["boat5.jpg", "boat2.jpg",
"boat3.jpg", "boat4.jpg",
"boat1.jpg", "boat6.jpg"])
cv.imwrite("boat_fisheye.jpg", result)
max_image_shape_derivation = 600
np.testing.assert_allclose(result.shape[:2],
(14488, 7556),
atol=max_image_shape_derivation)
def test_stitcher_budapest(self):
settings = {"matcher_type": "affine",
"estimator": "affine",
"adjuster": "affine",
"warper_type": "affine",
"wave_correct_kind": "no",
"confidence_threshold": 0.3}
stitcher = Stitcher(**settings)
result = stitcher.stitch(["budapest1.jpg", "budapest2.jpg",
"budapest3.jpg", "budapest4.jpg",
"budapest5.jpg", "budapest6.jpg"])
cv.imwrite("budapest.jpg", result)
max_image_shape_derivation = 50
np.testing.assert_allclose(result.shape[:2],
(1155, 2310),
atol=max_image_shape_derivation)
def test_performance(self):
print("Run new Stitcher class:")
start = time.time()
tracemalloc.start()
stitcher = Stitcher(final_megapix=3)
stitcher.stitch([
"boat5.jpg", "boat2.jpg", "boat3.jpg", "boat4.jpg", "boat1.jpg",
"boat6.jpg"
])
_, peak_memory = tracemalloc.get_traced_memory()
tracemalloc.stop()
end = time.time()
time_needed = end - start
print(f"Peak was {peak_memory / 10**6} MB")
print(f"Time was {time_needed} s")
print("Run original stitching_detailed.py:")
start = time.time()
tracemalloc.start()
main()
_, peak_memory_detailed = tracemalloc.get_traced_memory()
tracemalloc.stop()
end = time.time()
time_needed_detailed = end - start
print(f"Peak was {peak_memory_detailed / 10**6} MB")
print(f"Time was {time_needed_detailed} s")
self.assertLessEqual(peak_memory / 10**6, peak_memory_detailed / 10**6)
uncertainty_based_on_run = 0.25
self.assertLessEqual(time_needed - uncertainty_based_on_run,
time_needed_detailed)
def test_exposure_compensation(self):
img = cv.imread("s1.jpg")
img = increase_brightness(img, value=25)
cv.imwrite("s1_bright.jpg", img)
stitcher = Stitcher(compensator="no", blender_type="no")
result = stitcher.stitch(["s1_bright.jpg", "s2.jpg"])
cv.imwrite("without_exposure_comp.jpg", result)
stitcher = Stitcher(blender_type="no")
result = stitcher.stitch(["s1_bright.jpg", "s2.jpg"])
cv.imwrite("with_exposure_comp.jpg", result)
parser.add_argument('--output',
action='store',
default='result.jpg',
help="The default is 'result.jpg'",
type=str,
dest='output')
__doc__ += '\n' + parser.format_help()
if __name__ == '__main__':
print(__doc__)
args = parser.parse_args()
args_dict = vars(args)
# Extract In- and Output
img_names = args_dict.pop("img_names")
img_names = [cv.samples.findFile(img_name) for img_name in img_names]
output = args_dict.pop("output")
stitcher = Stitcher(**args_dict)
panorama = stitcher.stitch(img_names)
cv.imwrite(output, panorama)
zoom_x = 600.0 / panorama.shape[1]
preview = cv.resize(panorama, dsize=None, fx=zoom_x, fy=zoom_x)
cv.imshow(output, preview)
cv.waitKey()
cv.destroyAllWindows()