def _stitch_two(img1: Image, img2: Image, cell_size: int, overlap: int,
direction: str) -> Image:
if direction == "hor":
img1 = segment_module_part(
img1,
0,
0,
img1.get_meta("cols") - (overlap // 2),
img1.get_meta("rows"),
cell_size,
)
img2 = segment_module_part(
img2,
overlap // 2,
0,
img2.get_meta("cols") - (overlap // 2),
img2.get_meta("rows"),
cell_size,
)
return img1.from_self(
data=np.concatenate([img1.data, img2.data], axis=1),
meta=dict(cols=img1.get_meta("cols") + img2.get_meta("cols") -
overlap),
)
else:
img1 = segment_module_part(
img1,
0,
0,
img1.get_meta("cols"),
img1.get_meta("rows") - (overlap // 2),
cell_size,
)
img2 = segment_module_part(
img2,
0,
overlap // 2,
img2.get_meta("cols"),
img2.get_meta("rows") - (overlap // 2),
cell_size,
)
return img1.from_self(
data=np.concatenate([img1.data, img2.data], axis=0),
meta=dict(rows=img1.get_meta("rows") + img2.get_meta("rows") -
overlap),
)
def test_segment_padding_transform():
img = detection.locate_module_and_cells(data.datasets.poly10x6(1)[0])
part = detection.segment_module_part(img, 0, 0, 3, 2, padding=0.5, size=20)
x1 = part.get_meta("transform")(np.array([[0.0, 1.0]])).flatten()
x2 = part.get_meta("transform")(np.array([[2.0, 3.0]])).flatten()
assert_equal(x1[0], 10)
assert_equal(x1[1], 30)
assert_equal(x2[0], 50)
assert_equal(x2[1], 70)
def test_segment_module_part():
mod = data.datasets.poly10x6(1)[0]
mod = detection.locate_module_and_cells(mod)
part = detection.segment_module_part(mod, 1, 2, 2, 3)
assert_equal(part.shape[1], 2 / 3 * part.shape[0], 0.1)
assert part.first_col == 1
assert part.first_row == 2
assert part.cols == 2
assert part.rows == 3
def test_locate_partial_module():
img = detection.locate_module_and_cells(data.datasets.poly10x6(1)[0])
part = detection.segment_module_part(img, 0, 0, 3, 2, padding=0.5)
part_det = detection.locate_module_and_cells(part, False)
x1_true = [part_det.shape[1] * 1 / 8, part_det.shape[0] * 1 / 6]
x2_true = [part_det.shape[1] * 7 / 8, part_det.shape[0] * 5 / 6]
x1 = part_det.get_meta("transform")(np.array([[0.0, 0.0]])).flatten()
x2 = part_det.get_meta("transform")(np.array([[3.0, 2.0]])).flatten()
eps = 0.05 * part_det.shape[1]
assert_equal(x1[0], x1_true[0], eps)
assert_equal(x1[1], x1_true[1], eps)
assert_equal(x2[0], x2_true[0], eps)
assert_equal(x2[1], x2_true[1], eps)
def test_segment_padding():
img = detection.locate_module_and_cells(data.datasets.poly10x6(1)[0])
part = detection.segment_module_part(img, 0, 0, 3, 2, padding=0.5)
assert_equal(part.shape[1] / part.shape[0], 8 / 6)
def test_segment_size():
img = detection.locate_module_and_cells(data.datasets.poly10x6(1)[0])
part = detection.segment_module_part(img, 1, 3, 2, 1, size=20)
assert_equal(part.shape[1], 2 * 20)
assert_equal(part.shape[0], 1 * 20)