def add_shapes(
background: PIL.Image.Image,
shape_img: PIL.Image.Image,
shape_params,
) -> Tuple[List[Tuple[int, int, int, int, int]], PIL.Image.Image]:
"""Paste shapes onto background and return bboxes"""
shape_bboxes: List[Tuple[int, int, int, int, int]] = []
for i, shape_param in enumerate(shape_params):
x = shape_param[-2]
y = shape_param[-1]
x1, y1, x2, y2 = shape_img.getbbox()
bg_at_shape = background.crop((x1 + x, y1 + y, x2 + x, y2 + y))
bg_at_shape.paste(shape_img, (0, 0), shape_img)
background.paste(bg_at_shape, (x, y))
# Slightly expand the bounding box in order to simulate variability with
# the detection boxes. Always make the crop larger than needed because training
# augmentations will only be able to crop down.
dx = random.randint(0, int(0.1 * (x2 - x1)))
dy = random.randint(0, int(0.1 * (y2 - y1)))
x1 -= dx
x2 += dx
y1 -= dy
y2 += dy
background = background.crop((x1 + x, y1 + y, x2 + x, y2 + y))
background = background.filter(ImageFilter.SMOOTH_MORE)
return background.convert("RGB")
def add_shapes(
background: PIL.Image.Image,
shape_imgs: PIL.Image.Image,
shape_params,
blur_radius: int,
) -> Tuple[List[Tuple[int, int, int, int, int]], PIL.Image.Image]:
"""Paste shapes onto background and return bboxes"""
shape_bboxes: List[Tuple[int, int, int, int, int]] = []
for i, shape_param in enumerate(shape_params):
x = shape_param[-2]
y = shape_param[-1]
shape_img = shape_imgs[i]
shape_img = shape_img.filter(ImageFilter.GaussianBlur(1))
x1, y1, x2, y2 = shape_img.getbbox()
bg_at_shape = background.crop((x1 + x, y1 + y, x2 + x, y2 + y))
bg_at_shape.paste(shape_img, (0, 0), shape_img)
bg_at_shape = bg_at_shape.filter(ImageFilter.SMOOTH_MORE)
background.paste(bg_at_shape, (x, y))
im_w, im_h = background.size
x /= im_w
y /= im_h
w = (x2 - x1) / im_w
h = (y2 - y1) / im_h
shape_bboxes.append((CLASSES.index(shape_param[0]), x, y, w, h))
"""
shape_bboxes.append(
(
CLASSES.index(shape_param[2]),
x + (0.1 * w),
y + (0.1 * h),
0.8 * w,
0.8 * h,
)
)
"""
return shape_bboxes, background.convert("RGB")
