本文整理匯總了Python中utils.boxes.xywh_to_xyxy方法的典型用法代碼示例。如果您正苦於以下問題:Python boxes.xywh_to_xyxy方法的具體用法?Python boxes.xywh_to_xyxy怎麽用?Python boxes.xywh_to_xyxy使用的例子?那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類utils.boxes
的用法示例。
在下文中一共展示了boxes.xywh_to_xyxy方法的3個代碼示例,這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚,您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: _add_gt_annotations
# 需要導入模塊: from utils import boxes [as 別名]
# 或者: from utils.boxes import xywh_to_xyxy [as 別名]
def _add_gt_annotations(self, entry):
"""Add ground truth annotation metadata to an roidb entry."""
ann_ids = self.COCO.getAnnIds(imgIds=entry['id'], iscrowd=None)
objs = self.COCO.loadAnns(ann_ids)
# Sanitize bboxes -- some are invalid
valid_objs = []
valid_segms = []
width = entry['width']
height = entry['height']
for obj in objs:
# crowd regions are RLE encoded and stored as dicts
if obj['area'] < cfg.TRAIN.GT_MIN_AREA:
continue
if 'ignore' in obj and obj['ignore'] == 1:
continue
# Convert form (x1, y1, w, h) to (x1, y1, x2, y2)
x1, y1, x2, y2 = box_utils.xywh_to_xyxy(obj['bbox'])
x1, y1, x2, y2 = box_utils.clip_xyxy_to_image(
x1, y1, x2, y2, height, width
)
# Require non-zero seg area and more than 1x1 box size
if obj['area'] > 0 and x2 > x1 and y2 > y1:
obj['clean_bbox'] = [x1, y1, x2, y2]
valid_objs.append(obj)
num_valid_objs = len(valid_objs)
gt_classes = np.zeros((num_valid_objs), dtype=entry['gt_classes'].dtype)
for ix, obj in enumerate(valid_objs):
cls = self.json_category_id_to_contiguous_id[obj['category_id']]
gt_classes[ix] = cls
for cls in gt_classes:
entry['gt_classes'][0, cls] = 1
示例2: test_bbox_dataset_to_prediction_roundtrip
# 需要導入模塊: from utils import boxes [as 別名]
# 或者: from utils.boxes import xywh_to_xyxy [as 別名]
def test_bbox_dataset_to_prediction_roundtrip(self):
"""Simulate the process of reading a ground-truth box from a dataset,
make predictions from proposals, convert the predictions back to the
dataset format, and then use the COCO API to compute IoU overlap between
the gt box and the predictions. These should have IoU of 1.
"""
weights = (5, 5, 10, 10)
# 1/ "read" a box from a dataset in the default (x1, y1, w, h) format
gt_xywh_box = [10, 20, 100, 150]
# 2/ convert it to our internal (x1, y1, x2, y2) format
gt_xyxy_box = box_utils.xywh_to_xyxy(gt_xywh_box)
# 3/ consider nearby proposal boxes
prop_xyxy_boxes = random_boxes(gt_xyxy_box, 10, 10)
# 4/ compute proposal-to-gt transformation deltas
deltas = box_utils.bbox_transform_inv(
prop_xyxy_boxes, np.array([gt_xyxy_box]), weights=weights
)
# 5/ use deltas to transform proposals to xyxy predicted box
pred_xyxy_boxes = box_utils.bbox_transform(
prop_xyxy_boxes, deltas, weights=weights
)
# 6/ convert xyxy predicted box to xywh predicted box
pred_xywh_boxes = box_utils.xyxy_to_xywh(pred_xyxy_boxes)
# 7/ use COCO API to compute IoU
not_crowd = [int(False)] * pred_xywh_boxes.shape[0]
ious = COCOmask.iou(pred_xywh_boxes, np.array([gt_xywh_box]), not_crowd)
np.testing.assert_array_almost_equal(ious, np.ones(ious.shape))
示例3: save_im_masks
# 需要導入模塊: from utils import boxes [as 別名]
# 或者: from utils.boxes import xywh_to_xyxy [as 別名]
def save_im_masks(im, M, id, dir):
from utils.boxes import xywh_to_xyxy
import os
try:
os.mkdir(os.path.join('vis', dir))
except:
pass
M[M > 0] = 1
aug_rles = mask_util.encode(np.asarray(M, order='F'))
boxes = xywh_to_xyxy(np.asarray(mask_util.toBbox(aug_rles)))
boxes = np.append(boxes, np.ones((len(boxes), 2)), 1)
from utils.vis import vis_one_image
vis_one_image(im, str(id), os.path.join('vis', dir), boxes, segms=aug_rles, keypoints=None, thresh=0.9,box_alpha=0.8, show_class=False,)