def test_safe_bbox():
print('test single list')
bbox = [1, 2, 3, 4]
good_bbox = safe_bbox(bbox)
print(good_bbox)
print(good_bbox.shape)
assert CHECK_EQ_NUMPY(good_bbox, np.array(bbox).reshape((1, 4)))
print('test list of list of 4 elements')
bbox = [[1, 2, 3, 4], [5, 6, 7, 8]]
good_bbox = safe_bbox(bbox)
print(good_bbox)
print(good_bbox.shape)
assert CHECK_EQ_NUMPY(good_bbox, np.array(bbox).reshape((2, 4)))
print('test (4, ) numpy array')
bbox = np.array([1, 2, 3, 4])
good_bbox = safe_bbox(bbox)
print(bbox.shape)
print(good_bbox.shape)
assert CHECK_EQ_NUMPY(good_bbox, np.array(bbox).reshape((1, 4)))
print('test (N, 4) numpy array')
bbox = np.random.rand(10, 4)
good_bbox = safe_bbox(bbox)
print(bbox.shape)
print(good_bbox.shape)
assert CHECK_EQ_NUMPY(good_bbox, bbox)
######################################## test failure cases
bbox = [1, 2, 4]
try:
good_bbox = safe_bbox(bbox)
sys.exit('\nwrong! never should be here\n\n')
except AssertionError:
print('the length of list should be 4')
bbox = [[1, 2, 4], [5, 7, 8]]
try:
good_bbox = safe_bbox(bbox)
sys.exit('\nwrong! never should be here\n\n')
except AssertionError:
print('the length of list should be 4')
bbox = np.array([1, 2, 4]).reshape(3, )
try:
good_bbox = safe_bbox(bbox)
sys.exit('\nwrong! never should be here\n\n')
except AssertionError:
print('the numpy array should be columns of 4')
bbox = np.array([[1, 2, 4], [5, 7, 8]])
try:
good_bbox = safe_bbox(bbox)
sys.exit('\nwrong! never should be here\n\n')
except AssertionError:
print('the numpy array should be columns of 4')
print('\n\nDONE! SUCCESSFUL!!\n')
def clip_bboxes_TLBR(bboxes_in, im_width, im_height, warning=True, debug=True):
'''
this function clips bboxes inside the image boundary, the coordinates in the clipped bbox are half-included [x, y)
parameters:
bboxes_in: TLBR format, a list of 4 elements, a listoflist of 4 elements: e.g., [[1,2,3,4], [5,6,7,8]],
a numpy array with shape or (N, 4) or (4, )
im_width/im_height: scalar
outputs:
clipped_bboxes: TLBR format, numpy array with shape of (N, 4)
'''
np_bboxes = safe_bbox(bboxes_in, warning=warning, debug=debug)
if debug:
assert isinteger(im_width) and isinteger(
im_height), 'the image width and height are not correct'
assert bboxcheck_TLBR(np_bboxes, warning=warning,
debug=debug), 'the input bboxes are not good'
clipped_bboxes = np.zeros_like(np_bboxes)
clipped_bboxes[:, 0] = np.maximum(np.minimum(np_bboxes[:, 0], im_width),
0) # x1 >= 0 & x1 <= width, included
clipped_bboxes[:, 1] = np.maximum(np.minimum(np_bboxes[:, 1], im_height),
0) # y1 >= 0 & y1 <= height, included
clipped_bboxes[:, 2] = np.maximum(np.minimum(np_bboxes[:, 2], im_width),
0) # x2 >= 0 & x2 <= width, not included
clipped_bboxes[:,
3] = np.maximum(np.minimum(np_bboxes[:, 3], im_height),
0) # y2 >= 0 & y2 <= height, not included
return clipped_bboxes
def bbox2center(bboxes_in, 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
'''
np_bboxes = safe_bbox(bboxes_in, debug=debug)
if debug:
assert bboxcheck_TLBR(
np_bboxes), 'the input bounding box should be TLBR format'
num_bbox = np_bboxes.shape[0]
center = np.zeros((num_bbox, 2), dtype='float32')
center[:, 0] = (np_bboxes[:, 0] + np_bboxes[:, 2]) / 2.
center[:, 1] = (np_bboxes[:, 1] + np_bboxes[:, 3]) / 2.
# if vis:
# fig = plt.figure()
# plt.scatter(np_bboxes[0, 0], -np_bboxes[0, 1], color='b') # -1 is to convert the coordinate from image to cartesian
# plt.scatter(np_bboxes[0, 2], -np_bboxes[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 clip_bboxes_TLWH(bboxes_in, im_width, im_height, warning=True, debug=True):
'''
this function clips bboxes inside the image boundary
parameters:
bboxes_in: TLWH format, a list of 4 elements, a listoflist of 4 elements: e.g., [[1,2,3,4], [5,6,7,8]],
a numpy array with shape or (N, 4) or (4, )
im_width/im_height: scalar
outputs:
clipped_bboxes_TLWH: TLWH format, numpy array with shape of (N, 4)
'''
np_bboxes = safe_bbox(bboxes_in, warning=warning, debug=debug)
if debug:
assert isinteger(im_width) and isinteger(
im_height), 'the image width and height are not correct'
assert bboxcheck_TLWH(np_bboxes, warning=warning,
debug=debug), 'the input bboxes are not good'
bboxes_TLBR = bbox_TLWH2TLBR(np_bboxes, debug=debug)
clipped_bboxes_TLBR = clip_bboxes_TLBR(bboxes_TLBR,
im_width,
im_height,
warning=warning,
debug=debug)
clipped_bboxes_TLWH = bbox_TLBR2TLWH(clipped_bboxes_TLBR,
warning=warning,
debug=debug)
return clipped_bboxes_TLWH
def pts_conversion_back_bbox(pts_array, bboxes_in, debug=True):
'''
convert pts in the cropped image to the pts in the original image
parameters:
bboxes_in: 1 X 4 numpy array, TLBR or TLWH format
pts_array: 2(3) x N numpy array, N should >= 1
'''
np_bboxes = safe_bbox(bboxes_in, debug=debug)
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 isbbox(np_bboxes), 'the input bounding box is not correct'
pts_out = pts_array.copy()
pts_out[0, :] = pts_array[0, :] + np_bboxes[0, 0]
pts_out[1, :] = pts_array[1, :] + np_bboxes[0, 1]
return pts_out
def bbox_TLWH2TLBR(bboxes_in, debug=True): ''' transform the input bounding box with TLWH format to TLBR format parameters: bboxes_in: TLWH format, a list of 4 elements, a listoflist of 4 elements: e.g., [[1,2,3,4], [5,6,7,8]], a numpy array with shape or (N, 4) or (4, ) outputs: bbox_TLBR: N X 4 numpy array, TLBR format ''' np_bboxes = safe_bbox(bboxes_in, debug=debug) if debug: assert bboxcheck_TLWH(np_bboxes, debug=debug), 'the input bounding box should be TLBR format' bbox_TLBR = np.zeros_like(np_bboxes) bbox_TLBR[:, 0] = np_bboxes[:, 0] bbox_TLBR[:, 1] = np_bboxes[:, 1] bbox_TLBR[:, 2] = np_bboxes[:, 2] + np_bboxes[:, 0] bbox_TLBR[:, 3] = np_bboxes[:, 3] + np_bboxes[:, 1] return bbox_TLBR