def apply_rotation_tight(bbox_in, angle_in_degree, im_shape, debug=True):
'''
return 4 points clockwise
'''
if debug:
assert isnparray(bbox_in) and bbox_in.size == 4, 'box is not correct'
bbox_in = np.reshape(bbox_in, (4, ))
bbox_tight = bbox_rotation_inv(bbox_in, angle_in_degree, im_shape, debug=debug) # get top left and bottom right coordinate of the rotated bbox in the image coordinate
# print('bbox after inverse the rotation')
# print(bbox_tight)
pts_total = np.zeros((4, 2), dtype=np.int)
pts_tl = np.array([bbox_tight[0], bbox_tight[1]])
pts_br = np.array([bbox_tight[2], bbox_tight[3]])
line1 = get_line(imagecoor2cartesian(pts_tl, debug=debug), angle_in_degree + 90.00, debug=debug)
line2 = get_line(imagecoor2cartesian(pts_br, debug=debug), angle_in_degree, debug=debug)
pts_bl = cartesian2imagecoor(get_intersection(line1, line2, debug=debug), debug=debug)
pts_tr = cartesian2imagecoor(get_intersection(get_line(imagecoor2cartesian(pts_tl, debug=debug), angle_in_degree, debug=debug), get_line(imagecoor2cartesian(pts_br, debug=debug), angle_in_degree + 90.00, debug=debug), debug=debug), debug=debug)
# print np.reshape(pts_tl, (1, 2)).shape
# print pts_total[0, :].shape
pts_total[0, :] = np.reshape(pts_tl, (1, 2))
pts_total[1, :] = np.reshape(pts_tr, (1, 2))
pts_total[2, :] = np.reshape(pts_br, (1, 2))
pts_total[3, :] = np.reshape(pts_bl, (1, 2))
return pts_total
def bbox_rotatedtight2rotatedloose(bbox_in, angle_in_degree, debug=True):
'''
transfer the rotated bbox with tight version to loose version, both contains only two points (top left and bottom right)
only a single box is feeded into
'''
if debug:
assert isnparray(bbox_in) and bbox_in.size == 4, 'box is not correct'
pts_tl = np.array([bbox_in[0], bbox_in[1]])
pts_br = np.array([bbox_in[2], bbox_in[3]])
line1 = get_line(imagecoor2cartesian(pts_tl), angle_in_degree + 90.00)
line2 = get_line(imagecoor2cartesian(pts_br), angle_in_degree)
pts_bl = cartesian2imagecoor(get_intersection(line1, line2))
pts_tr = cartesian2imagecoor(get_intersection(get_line(imagecoor2cartesian(pts_tl), angle_in_degree), get_line(imagecoor2cartesian(pts_br), angle_in_degree + 90.00)))
# assert_almost_equal(np.dot(pts_bl - pts_br, pts_bl - pts_tl), 0, err_msg='The intersection points are wrong')
# assert_almost_equal(np.dot(pts_tr - pts_br, pts_tr - pts_tl), 0, err_msg='The intersection points are wrong')
pts_tl_final = np.zeros((2), dtype=np.float32)
pts_br_final = np.zeros((2), dtype=np.float32)
pts_tl_final[0] = min({pts_tl[0], pts_br[0], pts_bl[0], pts_tr[0]})
pts_tl_final[1] = min({pts_tl[1], pts_br[1], pts_bl[1], pts_tr[1]})
pts_br_final[0] = max({pts_tl[0], pts_br[0], pts_bl[0], pts_tr[0]})
pts_br_final[1] = max({pts_tl[1], pts_br[1], pts_bl[1], pts_tr[1]})
# print(pts_tl_final)
# print(pts_br_final)
test = np.hstack((pts_tl_final, pts_br_final))
return test
def bbox2center(bbox, debug=True, vis=False):
'''
convert a bounding box to a point, which is the center of this bounding box
parameter:
bbox: N x 4 numpy array, TLBR format
return:
center: 2 x N numpy array, x and y correspond to first and second row respectively
'''
if debug:
assert bboxcheck_TLBR(bbox), 'the input bounding box should be TLBR format'
num_bbox = bbox.shape[0]
center = np.zeros((num_bbox, 2), dtype='float32')
center[:, 0] = (bbox[:, 0] + bbox[:, 2]) / 2.
center[:, 1] = (bbox[:, 1] + bbox[:, 3]) / 2.
if vis:
fig = plt.figure()
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')
center_show = imagecoor2cartesian(center)
plt.scatter(center_show[0], center_show[1], color='r')
plt.show()
plt.close(fig)
return np.transpose(center)
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