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 strip_image(image: PIL.Image.Image) -> PIL.Image.Image:
"""Remove white and black edges"""
for x in range(image.width):
for y in range(image.height):
r, g, b, _ = image.getpixel((x, y))
if r > 247 and g > 247 and b > 247:
image.putpixel((x, y), (0, 0, 0, 0))
image = image.crop(image.getbbox())
return image
