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