def test_normalize_bboxes():
bboxes = [[15, 25, 100, 200], [15, 25, 100, 200, 99]]
normalized_bboxes_1 = normalize_bboxes(bboxes, 200, 400)
normalized_bboxes_2 = [
normalize_bbox(bboxes[0], 200, 400),
normalize_bbox(bboxes[1], 200, 400)
]
assert normalized_bboxes_1 == normalized_bboxes_2
def __call__(self, image, targets=None):
# some bugs
if self.p == -2: # abs_test
targets = np.zeros((1, 5))
targets[0] = image.shape[0]
targets[0] = image.shape[1]
image = preproc_for_test(image, self.resize)
return torch.from_numpy(image), targets
boxes = targets[:, :-1].copy()
labels = targets[:, -1].copy()
if len(boxes) == 0:
targets = np.zeros((1, 5))
image = preproc_for_test(
image, self.resize
) # some ground truth in coco do not have bounding box! weird!
return torch.from_numpy(image), targets
if self.p == -1: # eval
height, width, _ = image.shape
boxes = np.array(normalize_bboxes(boxes, height, width))
labels = np.expand_dims(labels, 1)
targets = np.hstack((boxes, labels))
image = preproc_for_test(image, self.resize)
return torch.from_numpy(image), targets
image_o = image.copy()
targets_o = targets.copy()
if self.transforms:
h, w, _ = image_o.shape
targets_o[:, 0] = np.clip(targets_o[:, 0], 0, w)
targets_o[:, 1] = np.clip(targets_o[:, 1], 0, h)
targets_o[:, 2] = np.clip(targets_o[:, 2], 0, w)
targets_o[:, 3] = np.clip(targets_o[:, 3], 0, h)
targets_o = targets_o[targets_o[:, 0] < targets_o[:, 2], :]
targets_o = targets_o[targets_o[:, 1] < targets_o[:, 3], :]
ann = {
'image': image_o,
'bboxes': targets_o,
}
augs = self.transforms(**ann)
image_o = augs["image"]
targets_o = np.array(augs["bboxes"])
if len(targets_o) > 0:
image, targets = image_o, targets_o
image, targets = preproc_for_train(image, targets, self.resize)
return torch.from_numpy(image), targets
def preproc_for_train(image, targets, resize):
height, width, _ = image.shape
image = cv2.resize(image, (resize[0], resize[1]))
image = image.transpose(2, 0, 1) / 255.0
image = image.astype(np.float32)
boxes = targets[:, :-1]
labels = targets[:, -1]
boxes = np.array(normalize_bboxes(boxes, height, width))
b_w = boxes[:, 2] - boxes[:, 0]
b_h = boxes[:, 3] - boxes[:, 1]
mask_b = np.minimum(b_w, b_h) > 0.001
labels = np.expand_dims(labels[mask_b], 1)
targets = np.hstack((boxes[mask_b], labels))
return image, targets
