def test_get_polygons_from_binary_img1():
"""
"""
"""
label map with four quadrants.
| Sky | Road |
----------------
| Person | Horse |
"""
H = 6
W = 10
img_rgb = np.ones((H, W, 3), dtype=np.uint8)
label_map = np.zeros((H, W), dtype=np.uint8)
label_map[:H // 2, :W // 2] = 0
label_map[:H // 2, W // 2:] = 1
label_map[H // 2:, :W // 2] = 2
label_map[H // 2:, W // 2:] = 3
id_to_class_name_map = {0: 'sky', 1: 'road', 2: 'person', 3: 'horse'}
class_idx = 0
binary_img = class_idx == label_map
polygons, has_holes = get_polygons_from_binary_img(binary_img)
assert len(polygons) == 1
assert has_holes == 0
gt_poly = np.array([[0, 0], [0, 1], [0, 2], [1, 2], [2, 2], [3, 2], [4, 2],
[4, 1], [4, 0], [3, 0], [2, 0], [1, 0]],
dtype=np.int32)
assert np.allclose(polygons[0], gt_poly)
class_idx = 1
binary_img = class_idx == label_map
polygons, has_holes = get_polygons_from_binary_img(binary_img)
assert len(polygons) == 1
assert has_holes == 0
gt_poly = np.array([[5, 0], [5, 1], [5, 2], [6, 2], [7, 2], [8, 2], [9, 2],
[9, 1], [9, 0], [8, 0], [7, 0], [6, 0]],
dtype=np.int32)
assert np.allclose(polygons[0], gt_poly)
class_idx = 2
binary_img = class_idx == label_map
polygons, has_holes = get_polygons_from_binary_img(binary_img)
assert len(polygons) == 1
assert has_holes == 0
gt_poly = np.array([[0, 3], [0, 4], [0, 5], [1, 5], [2, 5], [3, 5], [4, 5],
[4, 4], [4, 3], [3, 3], [2, 3], [1, 3]],
dtype=np.int32)
assert np.allclose(polygons[0], gt_poly)
class_idx = 3
binary_img = class_idx == label_map
polygons, has_holes = get_polygons_from_binary_img(binary_img)
assert len(polygons) == 1
assert has_holes == 0
gt_poly = np.array([[5, 3], [5, 4], [5, 5], [6, 5], [7, 5], [8, 5], [9, 5],
[9, 4], [9, 3], [8, 3], [7, 3], [6, 3]],
dtype=np.int32)
assert np.allclose(polygons[0], gt_poly)
def test_get_polygons_from_binary_img4():
"""
Test degenerate case (empty mask)
"""
rgb_img_fpath = f'{TEST_DATA_ROOT}/ADE20K_test_data/ADEChallengeData2016'
rgb_img_fpath += '/images/training/ADE_train_00000001.jpg'
label_fpath = f'{TEST_DATA_ROOT}/ADE20K_test_data/ADEChallengeData2016'
label_fpath += '/annotations/training/ADE_train_00000001.png'
label_img = imageio.imread(label_fpath)
binary_img = np.zeros((480, 640), dtype=np.uint8)
polygons, has_holes = get_polygons_from_binary_img(binary_img)
assert len(polygons) == 0
assert has_holes == False
def test_get_polygons_from_binary_img3():
""" """
rgb_img_fpath = f'{TEST_DATA_ROOT}/ADE20K_test_data/ADEChallengeData2016'
rgb_img_fpath += '/images/training/ADE_train_00000001.jpg'
label_fpath = f'{TEST_DATA_ROOT}/ADE20K_test_data/ADEChallengeData2016'
label_fpath += '/annotations/training/ADE_train_00000001.png'
label_img = imageio.imread(label_fpath)
# fountain class
class_idx = 105
binary_img = label_img == class_idx
polygons, has_holes = get_polygons_from_binary_img(binary_img)
assert len(polygons) == 1
assert np.sum(polygons) == 315790
assert np.median(polygons[0][:, 0]) == 342.0
assert np.median(polygons[0][:, 1]) == 430.0
def test_get_polygons_from_binary_img2():
"""
Create label map with two embedded circles. Each circle
represents class 1 (the "person" class).
"""
H = 15
W = 30
img_rgb = np.ones((H, W, 3), dtype=np.uint8)
label_map = np.zeros((H, W), dtype=np.uint8)
label_map[7, 7] = 1
label_map[7, 22] = 1
# only 2 pixels will have value 1
mask_diff = np.ones_like(label_map).astype(np.uint8) - label_map
# Calculates the distance to the closest zero pixel for each pixel of the source image.
distance_mask = cv2.distanceTransform(mask_diff,
distanceType=cv2.DIST_L2,
maskSize=cv2.DIST_MASK_PRECISE)
distance_mask = distance_mask.astype(np.float32)
label_map = (distance_mask <= 5).astype(np.uint8)
id_to_class_name_map = {0: 'road', 1: 'person'}
binary_img = label_map == 1
polygons, has_holes = get_polygons_from_binary_img(binary_img)
assert len(polygons) == 2
assert has_holes == 0
# right side circle
gt_poly1 = np.array(
[[7, 2], [6, 3], [5, 3], [4, 3], [3, 4], [3, 5], [3, 6], [2, 7],
[3, 8], [3, 9], [3, 10], [4, 11], [5, 11], [6, 11], [7, 12], [8, 11],
[9, 11], [10, 11], [11, 10], [11, 9], [11, 8], [12, 7], [11, 6],
[11, 5], [11, 4], [10, 3], [9, 3], [8, 3]],
dtype=np.int32)
# left side circle
gt_poly2 = np.array(
[[22, 2], [21, 3], [20, 3], [19, 3], [18, 4], [18, 5], [18, 6],
[17, 7], [18, 8], [18, 9], [18, 10], [19, 11], [20, 11], [21, 11],
[22, 12], [23, 11], [24, 11], [25, 11], [26, 10], [26, 9], [26, 8],
[27, 7], [26, 6], [26, 5], [26, 4], [25, 3], [24, 3], [23, 3]],
dtype=np.int32)
assert np.allclose(polygons[1], gt_poly1)
assert np.allclose(polygons[0], gt_poly2)
# Will get exterior rectangle, and then bounding areas on the two holes
binary_img = label_map == 0
polygons, has_holes = get_polygons_from_binary_img(binary_img)
assert len(polygons) == 3
assert has_holes == 1
gt_poly_outer = np.array(
[[0, 0], [0, 1], [0, 2], [0, 3], [0, 4], [0, 5], [0, 6], [0, 7],
[0, 8], [0, 9], [0, 10], [0, 11], [0, 12], [0, 13], [0, 14], [1, 14],
[2, 14], [3, 14], [4, 14], [5, 14], [6, 14], [7, 14], [8, 14],
[9, 14], [10, 14], [11, 14], [12, 14], [13, 14], [14, 14], [15, 14],
[16, 14], [17, 14], [18, 14], [19, 14], [20, 14], [21, 14], [22, 14],
[23, 14], [24, 14], [25, 14], [26, 14], [27, 14], [28, 14], [29, 14],
[29, 13], [29, 12], [29, 11], [29, 10], [29, 9], [29, 8], [29, 7],
[29, 6], [29, 5], [29, 4], [29, 3], [29, 2], [29, 1], [29, 0],
[28, 0], [27, 0], [26, 0], [25, 0], [24, 0], [23, 0], [22, 0],
[21, 0], [20, 0], [19, 0], [18, 0], [17, 0], [16, 0], [15, 0],
[14, 0], [13, 0], [12, 0], [11, 0], [10, 0], [9, 0], [8, 0], [7, 0],
[6, 0], [5, 0], [4, 0], [3, 0], [2, 0], [1, 0]],
dtype=np.int32)
assert np.allclose(gt_poly_outer, polygons[0])