def test_boundary_iou():
points = [0, 0, 0, 1, 1, 1, 1, 0]
points1 = [10, 20, 30, 40, 50, 60, 70, 80]
assert utils.boundary_iou(points, points1) == 0
# test overlapping boundaries
assert utils.boundary_iou(points, points) == 1
def test_boundary_iou():
points = [0, 0, 0, 1, 1, 1, 1, 0]
points1 = [10, 20, 30, 40, 50, 60, 70, 80]
points2 = [0, 0, 0, 0, 0, 0, 0, 0] # Invalid polygon
points3 = [0, 0, 0, 1, 1, 0, 1, 1] # Self-intersected polygon
assert utils.boundary_iou(points, points1) == 0
# test overlapping boundaries
assert utils.boundary_iou(points, points) == 1
# test invalid boundaries
assert utils.boundary_iou(points2, points2) == 0
assert utils.boundary_iou(points3, points3, zero_division=1) == 1
assert utils.boundary_iou(points2, points3) == 0
def test_ocr_mask_rcnn(cfg_file):
model = _get_detector_cfg(cfg_file)
model['pretrained'] = None
from mmocr.models import build_detector
detector = build_detector(model)
input_shape = (1, 3, 224, 224)
mm_inputs = _demo_mm_inputs(0, input_shape)
imgs = mm_inputs.pop('imgs')
img_metas = mm_inputs.pop('img_metas')
gt_labels = mm_inputs.pop('gt_labels')
gt_masks = mm_inputs.pop('gt_masks')
# Test forward train
gt_bboxes = mm_inputs['gt_bboxes']
losses = detector.forward(
imgs,
img_metas,
gt_bboxes=gt_bboxes,
gt_labels=gt_labels,
gt_masks=gt_masks)
assert isinstance(losses, dict)
# Test forward test
with torch.no_grad():
img_list = [g[None, :] for g in imgs]
batch_results = []
for one_img, one_meta in zip(img_list, img_metas):
result = detector.forward([one_img], [[one_meta]],
return_loss=False)
batch_results.append(result)
# Test get_boundary
results = ([[[1]]], [[
np.array([[1, 1, 0, 0, 0], [1, 1, 0, 0, 0], [0, 0, 0, 0, 0],
[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]])
]])
boundaries = detector.get_boundary(results)
assert utils.boundary_iou(boundaries['boundary_result'][0][:-1],
[1, 1, 0, 1, 0, 0, 1, 0]) == 1
# Test show_result
results = {'boundary_result': [[0, 0, 1, 0, 1, 1, 0, 1, 0.9]]}
img = np.random.rand(5, 5)
detector.show_result(img, results)
def poly_nms(polygons, threshold):
assert isinstance(polygons, list)
polygons = np.array(sorted(polygons, key=lambda x: x[-1]))
keep_poly = []
index = [i for i in range(polygons.shape[0])]
while len(index) > 0:
keep_poly.append(polygons[index[-1]].tolist())
A = polygons[index[-1]][:-1]
index = np.delete(index, -1)
iou_list = np.zeros((len(index), ))
for i in range(len(index)):
B = polygons[index[i]][:-1]
iou_list[i] = boundary_iou(A, B)
remove_index = np.where(iou_list > threshold)
index = np.delete(index, remove_index)
return keep_poly