def viz_coco_example(img, imginfo, annotations, classnames):
height, width = imginfo['height'], imginfo['width']
assert (img.shape[0] == height)
assert (img.shape[1] == width)
masks = SegmentationMask([ann['segmentation'] for ann in annotations],
size=(width, height),
mode='poly')
bboxes = []
classids = []
for ann in annotations:
x, y, width, height = ann['bbox']
x1, y1, x2, y2 = x, y, x + width, y + height
bboxes.append([y1, x1, y2, x2])
classids.append(ann['category_id'] -
1) # category_ids are 1-indexed in COCO datasets
classids = np.array(list(map(int, classids)))
bboxes = np.stack(bboxes, axis=0)
masks = masks.get_mask_tensor().numpy()
if masks.ndim == 2:
masks = masks[np.newaxis, :, :]
masks = np.transpose(masks, (1, 2, 0))
display_instances(img,
boxes=bboxes,
masks=masks,
class_ids=classids,
class_names=classnames)
class TestSegmentationMask(unittest.TestCase):
def __init__(self, method_name='runTest'):
super(TestSegmentationMask, self).__init__(method_name)
poly = [[
[
423.0, 306.5, 406.5, 277.0, 400.0, 271.5, 389.5, 277.0, 387.5,
292.0, 384.5, 295.0, 374.5, 220.0, 378.5, 210.0, 391.0, 200.5,
404.0, 199.5, 414.0, 203.5, 425.5, 221.0, 438.5, 297.0, 423.0,
306.5
],
[100, 100, 200, 100, 200, 200, 100, 200],
]]
# poly[0][0] = [round(x) for x in poly[0][0]]
width = 640
height = 480
size = width, height
self.P = SegmentationMask(poly, size, 'poly')
self.M = SegmentationMask(poly, size, 'poly').convert('mask')
def L1(self, A, B):
diff = A.get_mask_tensor() - B.get_mask_tensor()
diff = torch.sum(torch.abs(diff.float())).item()
return diff
def test_convert(self):
M_hat = self.M.convert('poly').convert('mask')
P_hat = self.P.convert('mask').convert('poly')
diff_mask = self.L1(self.M, M_hat)
diff_poly = self.L1(self.P, P_hat)
self.assertTrue(diff_mask == diff_poly)
print(diff_mask, diff_poly)
# print(self.P.instances.polygons[0].polygons)
# print(M_hat.instances)
# print(P_hat.instances)
# print(P_hat.instances.polygons[0].polygons)
self.assertTrue(diff_mask <= 8169.)
self.assertTrue(diff_poly <= 8169.)
def test_crop(self):
box = [400, 250, 500, 300] # xyxy
diff = self.L1(self.M.crop(box), self.P.crop(box))
print(diff)
self.assertTrue(diff <= 1.)
def test_resize(self):
new_size = 50, 25
M_hat = self.M.resize(new_size)
P_hat = self.P.resize(new_size)
diff = M_hat.get_mask_tensor() - P_hat.get_mask_tensor()
print(diff.shape, diff.min(), diff.max())
print(diff.sum())
diff = self.L1(M_hat, P_hat)
print(M_hat.size)
print(P_hat.size)
print(self.M.get_mask_tensor().sum())
print(self.P.get_mask_tensor().sum())
print(M_hat.get_mask_tensor().sum())
print(P_hat.get_mask_tensor().sum())
print(P_hat.instances.polygons[0].polygons)
torch.set_printoptions(profile="full")
print(M_hat.instances.masks)
print(P_hat.get_mask_tensor())
torch.set_printoptions(profile="default")
self.assertTrue(self.M.size == self.P.size)
self.assertTrue(M_hat.size == P_hat.size)
self.assertTrue(self.M.size != M_hat.size)
print(diff)
self.assertTrue(diff <= 255.)
def test_transpose(self):
FLIP_LEFT_RIGHT = 0
FLIP_TOP_BOTTOM = 1
diff_hor = self.L1(self.M.transpose(FLIP_LEFT_RIGHT),
self.P.transpose(FLIP_LEFT_RIGHT))
diff_ver = self.L1(self.M.transpose(FLIP_TOP_BOTTOM),
self.P.transpose(FLIP_TOP_BOTTOM))
print(diff_hor, diff_ver)
self.assertTrue(diff_hor <= 53250.)
self.assertTrue(diff_ver <= 42494.)
