def fn5():
bboxes_xywh = np.zeros_like(bboxes_xyxy)
bboxes_xywh[:, 2] = bboxes_xyxy[:, 2] - bboxes_xyxy[:, 0] + 1.
bboxes_xywh[:, 3] = bboxes_xyxy[:, 3] - bboxes_xyxy[:, 1] + 1.
bboxes_xywh[:, 0] = bboxes_xyxy[:, 0] + bboxes_xywh[:, 2] / 2.
bboxes_xywh[:, 1] = bboxes_xyxy[:, 1] + bboxes_xywh[:, 3] / 2.
h, w = im.shape[:2]
bboxes_xywh_normed = bboxes_xywh.astype("float32")
bboxes_xywh_normed[...,
0::2] = bboxes_xywh_normed[..., 0::2] / float(w)
bboxes_xywh_normed[...,
1::2] = bboxes_xywh_normed[..., 1::2] / float(h)
currentAxis = plt.gca()
ret_im, ret_bboxes = random_expand(
im,
bboxes_xywh_normed,
thresh=1,
xy_ratio_same=False,
# fill=[np.mean(im[..., i]) for i in range(im.shape[-1])],
fill=[100, 100, 100],
xywh_do_normalize=True,
xywh_is_normalize=True)
oh, ow = ret_im.shape[:2]
ret_bboxes_decode = ret_bboxes.astype(np.float)
ret_bboxes_decode[:, 0::2] = ret_bboxes_decode[:, 0::2] * ow
ret_bboxes_decode[:, 1::2] = ret_bboxes_decode[:, 1::2] * oh
plt.imshow(ret_im.astype("uint8"))
for bbox in ret_bboxes_decode:
draw_rectangle(currentAxis, bbox, edgecolor="b")
def fn1():
bboxes_xywh = np.zeros_like(bboxes_xyxy)
bboxes_xywh[:, 2] = bboxes_xyxy[:, 2] - bboxes_xyxy[:, 0] + 1.
bboxes_xywh[:, 3] = bboxes_xyxy[:, 3] - bboxes_xyxy[:, 1] + 1.
bboxes_xywh[:, 0] = bboxes_xyxy[:, 0] + bboxes_xywh[:, 2] / 2.
bboxes_xywh[:, 1] = bboxes_xyxy[:, 1] + bboxes_xywh[:, 3] / 2.
currentAxis = plt.gca()
ret_im, ret_bboxes = random_expand(im,
bboxes_xywh,
thresh=1,
xy_ratio_same=False,
xywh_do_normalize=False,
xywh_is_normalize=False)
plt.imshow(ret_im.astype("uint8"))
for bbox in ret_bboxes:
draw_rectangle(currentAxis, bbox, edgecolor="b")
def fn3():
bboxes_xywh = np.zeros_like(bboxes_xyxy)
bboxes_xywh[:, 2] = bboxes_xyxy[:, 2] - bboxes_xyxy[:, 0] + 1.
bboxes_xywh[:, 3] = bboxes_xyxy[:, 3] - bboxes_xyxy[:, 1] + 1.
bboxes_xywh[:, 0] = bboxes_xyxy[:, 0] + bboxes_xywh[:, 2] / 2.
bboxes_xywh[:, 1] = bboxes_xyxy[:, 1] + bboxes_xywh[:, 3] / 2.
currentAxis = plt.gca()
ret_im, ret_bboxes = random_expand(im,
bboxes_xywh,
thresh=1,
xy_ratio_same=False,
xywh_do_normalize=True,
xywh_is_normalize=False)
oh, ow = ret_im.shape[:2]
ret_bboxes_decode = ret_bboxes.astype(np.float)
ret_bboxes_decode[:, 0::2] = ret_bboxes_decode[:, 0::2] * ow
ret_bboxes_decode[:, 1::2] = ret_bboxes_decode[:, 1::2] * oh
plt.imshow(ret_im.astype("uint8"))
for bbox in ret_bboxes_decode:
draw_rectangle(currentAxis, bbox, edgecolor="b")
def fn2():
bboxes_xywh = np.zeros_like(bboxes_xyxy)
bboxes_xywh[:, 2] = bboxes_xyxy[:, 2] - bboxes_xyxy[:, 0] + 1.
bboxes_xywh[:, 3] = bboxes_xyxy[:, 3] - bboxes_xyxy[:, 1] + 1.
bboxes_xywh[:, 0] = bboxes_xyxy[:, 0] + bboxes_xywh[:, 2] / 2.
bboxes_xywh[:, 1] = bboxes_xyxy[:, 1] + bboxes_xywh[:, 3] / 2.
h, w = im.shape[:2]
bboxes_xywh_normed = bboxes_xywh.astype("float32")
bboxes_xywh_normed[...,
0::2] = bboxes_xywh_normed[..., 0::2] / float(w)
bboxes_xywh_normed[...,
1::2] = bboxes_xywh_normed[..., 1::2] / float(h)
currentAxis = plt.gca()
ret_im, ret_bboxes = random_expand(im,
bboxes_xywh_normed,
thresh=1,
xy_ratio_same=False,
xywh_do_normalize=False,
xywh_is_normalize=True)
plt.imshow(ret_im.astype("uint8"))
for bbox in ret_bboxes:
draw_rectangle(currentAxis, bbox, edgecolor="b")