def get_centered_bbox(pts_array, width, height, debug=True):
'''
given a set of points, return a set of bbox which are centered at the points
parameters:
pts_array: 2 x num_pts
return:
bbox: N x 4 (TLBR format)
'''
if debug:
assert is2dpts(pts_array) or is2dptsarray(pts_array) or is2dptsarray_occlusion(pts_array), 'the input points should have shape: 2 or 3 x num_pts vs %d x %s' % (pts_array.shape[0], pts_array.shape[1])
if is2dpts(pts_array):
xmin = pts_array[0] - np.ceil(width/2.0) + 1
ymin = pts_array[1] - np.ceil(height/2.0) + 1
else:
xmin = pts_array[0, :] - np.ceil(width/2.0) + 1
ymin = pts_array[1, :] - np.ceil(height/2.0) + 1
xmax = xmin + width - 1;
ymax = ymin + height - 1;
bbox = np.vstack((xmin, ymin, xmax, ymax))
return np.transpose(bbox)
def pts2bbox(pts, debug=True, vis=False):
'''
convert a set of 2d points to a bounding box
parameter:
pts: 2 x N numpy array, N should >= 2
return:
bbox: 1 x 4 numpy array, TLBR format
'''
if debug:
assert is2dptsarray(pts) or is2dptsarray_occlusion(pts), 'the input points should have shape: 2 or 3 x num_pts vs %d x %s' % (pts.shape[0], pts.shape[1])
assert pts.shape[1] >= 2, 'number of points should be larger or equal than 2'
bbox = np.zeros((1, 4), dtype='float32')
bbox[0, 0] = np.min(pts[0, :]) # x coordinate of left top point
bbox[0, 1] = np.min(pts[1, :]) # y coordinate of left top point
bbox[0, 2] = np.max(pts[0, :]) # x coordinate of bottom right point
bbox[0, 3] = np.max(pts[1, :]) # y coordinate of bottom right point
if vis:
fig = plt.figure()
pts = imagecoor2cartesian(pts)
plt.scatter(pts[0, :], pts[1, :], color='r')
plt.scatter(bbox[0, 0], -bbox[0, 1], color='b') # -1 is to convert the coordinate from image to cartesian
plt.scatter(bbox[0, 2], -bbox[0, 3], color='b')
plt.show()
plt.close(fig)
return bbox
def append(self,
pts_root,
pts_forward,
pts_backward=None,
pts_anno=None,
image_prev_path=None,
image_next_path=None,
index=None):
if index is None:
index = self.length
key = self.convert_index2key(index)
if key in self.key_list:
assert False, 'the data with index %d already exists' % index
else:
self.key_list.append(key)
self.index_dict[key] = index
if self.debug:
assert is2dptsarray_occlusion(pts_root) or is2dptsarray(
pts_root
), 'the shape of input point array %s in the root frame is not correct' % print_np_shape(
pts_root)
assert is2dptsarray_occlusion(pts_forward) or is2dptsarray(
pts_forward
), 'the shape of input point array %s in the forward frame is not correct' % print_np_shape(
pts_forward)
assert is2dptsarray_occlusion(pts_backward) or is2dptsarray(
pts_backward
) or pts_backward is None, 'the shape of input point array %s in the backward frame is not correct' % print_np_shape(
pts_backward)
assert is2dptsarray_occlusion(pts_anno) or is2dptsarray(
pts_anno
) or pts_anno is None, 'the shape of input point array %s from the annotations is not correct' % print_np_shape(
pts_anno)
assert (isstring(image_prev_path) or image_prev_path is None) and (
isstring(image_next_path) or
image_next_path is None), 'the input image path is not correct'
self.pts_root[key] = pts_root
self.pts_forward[key] = pts_forward
self.pts_backward[key] = pts_backward
self.pts_anno[key] = pts_anno
self.image_prev_path[key] = image_prev_path
self.image_next_path[key] = image_next_path
self.length += 1
def pts_conversion_back_bbox(pts_array, bbox, debug=True):
'''
convert pts in the cropped image to the pts in the original image
parameters:
bbox: 1 X 4 numpy array, TLBR or TLWH format
pts_array: 2(3) x N numpy array, N should >= 1
'''
if debug:
assert is2dptsarray(pts_array) or is2dptsarray_occlusion(pts_array), 'the input points should have shape: 2 or 3 x num_pts vs %d x %s' % (pts_array.shape[0], pts_array.shape[1])
assert bboxcheck(bbox), 'the input bounding box is not correct'
pts_array[0, :] = pts_array[0, :] + bbox[0, 0]
pts_array[1, :] = pts_array[1, :] + bbox[0, 1]
return pts_array