本文整理汇总了Python中mmdet.core.evaluation.bbox_overlaps.bbox_overlaps方法的典型用法代码示例。如果您正苦于以下问题：Python bbox_overlaps.bbox_overlaps方法的具体用法？Python bbox_overlaps.bbox_overlaps怎么用？Python bbox_overlaps.bbox_overlaps使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类mmdet.core.evaluation.bbox_overlaps的用法示例。
在下文中一共展示了bbox_overlaps.bbox_overlaps方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_min_iou_random_crop
# 需要导入模块: from mmdet.core.evaluation import bbox_overlaps [as 别名]
# 或者: from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps [as 别名]
def test_min_iou_random_crop():
def create_random_bboxes(num_bboxes, img_w, img_h):
bboxes_left_top = np.random.uniform(0, 0.5, size=(num_bboxes, 2))
bboxes_right_bottom = np.random.uniform(0.5, 1, size=(num_bboxes, 2))
bboxes = np.concatenate((bboxes_left_top, bboxes_right_bottom), 1)
bboxes = (bboxes * np.array([img_w, img_h, img_w, img_h])).astype(
np.int)
return bboxes
results = dict()
img = mmcv.imread(
osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
results['img'] = img
results['img_shape'] = img.shape
results['ori_shape'] = img.shape
results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore']
# Set initial values for default meta_keys
results['pad_shape'] = img.shape
results['scale_factor'] = 1.0
h, w, _ = img.shape
gt_bboxes = create_random_bboxes(1, w, h)
gt_bboxes_ignore = create_random_bboxes(1, w, h)
results['gt_bboxes'] = gt_bboxes
results['gt_bboxes_ignore'] = gt_bboxes_ignore
transform = dict(type='MinIoURandomCrop')
crop_module = build_from_cfg(transform, PIPELINES)
# Test for img_fields
results_test = copy.deepcopy(results)
results_test['img1'] = results_test['img']
results_test['img_fields'] = ['img', 'img1']
with pytest.raises(AssertionError):
crop_module(results_test)
results = crop_module(results)
patch = np.array([0, 0, results['img_shape'][1], results['img_shape'][0]])
ious = bbox_overlaps(patch.reshape(-1, 4),
results['gt_bboxes']).reshape(-1)
ious_ignore = bbox_overlaps(
patch.reshape(-1, 4), results['gt_bboxes_ignore']).reshape(-1)
mode = crop_module.mode
if mode == 1:
assert np.equal(results['gt_bboxes'], gt_bboxes).all()
assert np.equal(results['gt_bboxes_ignore'], gt_bboxes_ignore).all()
else:
assert (ious >= mode).all()
assert (ious_ignore >= mode).all() 示例2: __call__
# 需要导入模块: from mmdet.core.evaluation import bbox_overlaps [as 别名]
# 或者: from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps [as 别名]
def __call__(self, img, boxes, labels):
h, w, c = img.shape
while True:
mode = random.choice(self.sample_mode)
if mode == 1:
return img, boxes, labels
min_iou = mode
for i in range(50):
new_w = random.uniform(self.min_crop_size * w, w)
new_h = random.uniform(self.min_crop_size * h, h)
# h / w in [0.5, 2]
if new_h / new_w < 0.5 or new_h / new_w > 2:
continue
left = random.uniform(w - new_w)
top = random.uniform(h - new_h)
patch = np.array((int(left), int(top), int(left + new_w),
int(top + new_h)))
overlaps = bbox_overlaps(
patch.reshape(-1, 4), boxes.reshape(-1, 4)).reshape(-1)
if overlaps.min() < min_iou:
continue
# center of boxes should inside the crop img
center = (boxes[:, :2] + boxes[:, 2:]) / 2
mask = (center[:, 0] > patch[0]) * (
center[:, 1] > patch[1]) * (center[:, 0] < patch[2]) * (
center[:, 1] < patch[3])
if not mask.any():
continue
boxes = boxes[mask]
labels = labels[mask]
# adjust boxes
img = img[patch[1]:patch[3], patch[0]:patch[2]]
boxes[:, 2:] = boxes[:, 2:].clip(max=patch[2:])
boxes[:, :2] = boxes[:, :2].clip(min=patch[:2])
boxes -= np.tile(patch[:2], 2)
return img, boxes, labels 示例3: __call__
# 需要导入模块: from mmdet.core.evaluation import bbox_overlaps [as 别名]
# 或者: from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps [as 别名]
def __call__(self, img, boxes, labels):
h, w, c = img.shape
while True:
mode = random.choice(self.sample_mode)
if mode == 1:
return img, boxes, labels
min_iou = mode
for i in range(50):
new_w = random.uniform(self.min_crop_size * w, w)
new_h = random.uniform(self.min_crop_size * h, h)
# h / w in [0.5, 2]
if new_h / new_w < 0.5 or new_h / new_w > 2:
continue
left = random.uniform(w - new_w)
top = random.uniform(h - new_h)
patch = np.array(
(int(left), int(top), int(left + new_w), int(top + new_h)))
overlaps = bbox_overlaps(
patch.reshape(-1, 4), boxes.reshape(-1, 4)).reshape(-1)
if overlaps.min() < min_iou:
continue
# center of boxes should inside the crop img
center = (boxes[:, :2] + boxes[:, 2:]) / 2
mask = (center[:, 0] > patch[0]) * (
center[:, 1] > patch[1]) * (center[:, 0] < patch[2]) * (
center[:, 1] < patch[3])
if not mask.any():
continue
boxes = boxes[mask]
labels = labels[mask]
# adjust boxes
img = img[patch[1]:patch[3], patch[0]:patch[2]]
boxes[:, 2:] = boxes[:, 2:].clip(max=patch[2:])
boxes[:, :2] = boxes[:, :2].clip(min=patch[:2])
boxes -= np.tile(patch[:2], 2)
return img, boxes, labels 示例4: __call__
# 需要导入模块: from mmdet.core.evaluation import bbox_overlaps [as 别名]
# 或者: from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps [as 别名]
def __call__(self, results):
img, boxes, labels = [
results[k] for k in ('img', 'gt_bboxes', 'gt_labels')
]
h, w, c = img.shape
while True:
mode = random.choice(self.sample_mode)
if mode == 1:
return results
min_iou = mode
for i in range(50):
new_w = random.uniform(self.min_crop_size * w, w)
new_h = random.uniform(self.min_crop_size * h, h)
# h / w in [0.5, 2]
if new_h / new_w < 0.5 or new_h / new_w > 2:
continue
left = random.uniform(w - new_w)
top = random.uniform(h - new_h)
patch = np.array(
(int(left), int(top), int(left + new_w), int(top + new_h)))
overlaps = bbox_overlaps(
patch.reshape(-1, 4), boxes.reshape(-1, 4)).reshape(-1)
if overlaps.min() < min_iou:
continue
# center of boxes should inside the crop img
center = (boxes[:, :2] + boxes[:, 2:]) / 2
mask = (center[:, 0] > patch[0]) * (
center[:, 1] > patch[1]) * (center[:, 0] < patch[2]) * (
center[:, 1] < patch[3])
if not mask.any():
continue
boxes = boxes[mask]
labels = labels[mask]
# adjust boxes
img = img[patch[1]:patch[3], patch[0]:patch[2]]
boxes[:, 2:] = boxes[:, 2:].clip(max=patch[2:])
boxes[:, :2] = boxes[:, :2].clip(min=patch[:2])
boxes -= np.tile(patch[:2], 2)
results['img'] = img
results['gt_bboxes'] = boxes
results['gt_labels'] = labels
if 'gt_masks' in results:
valid_masks = [
results['gt_masks'][i] for i in range(len(mask))
if mask[i]
]
results['gt_masks'] = [
gt_mask[patch[1]:patch[3], patch[0]:patch[2]]
for gt_mask in valid_masks
]
return results 示例5: __call__
# 需要导入模块: from mmdet.core.evaluation import bbox_overlaps [as 别名]
# 或者: from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps [as 别名]
def __call__(self, results):
img, boxes, labels = [
results[k] for k in ('img', 'gt_bboxes', 'gt_labels')
]
h, w, c = img.shape
while True:
mode = random.choice(self.sample_mode)
if mode == 1:
return results
min_iou = mode
for i in range(50):
new_w = random.uniform(self.min_crop_size * w, w)
new_h = random.uniform(self.min_crop_size * h, h)
# h / w in [0.5, 2]
if new_h / new_w < 0.5 or new_h / new_w > 2:
continue
left = random.uniform(w - new_w)
top = random.uniform(h - new_h)
patch = np.array(
(int(left), int(top), int(left + new_w), int(top + new_h)))
overlaps = bbox_overlaps(
patch.reshape(-1, 4), boxes.reshape(-1, 4)).reshape(-1)
if overlaps.min() < min_iou:
continue
# center of boxes should inside the crop img
center = (boxes[:, :2] + boxes[:, 2:]) / 2
mask = (center[:, 0] > patch[0]) * (
center[:, 1] > patch[1]) * (center[:, 0] < patch[2]) * (
center[:, 1] < patch[3])
if not mask.any():
continue
boxes = boxes[mask]
labels = labels[mask]
# adjust boxes
img = img[patch[1]:patch[3], patch[0]:patch[2]]
boxes[:, 2:] = boxes[:, 2:].clip(max=patch[2:])
boxes[:, :2] = boxes[:, :2].clip(min=patch[:2])
boxes -= np.tile(patch[:2], 2)
results['img'] = img
results['gt_bboxes'] = boxes
results['gt_labels'] = labels
return results