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from itertools import cycle
from typing import List, Tuple, Callable, Optional
from PIL import Image as pil_image, ImageDraw as pil_img_draw, ImageFont
from more_itertools.recipes import grouper
from taming.data.image_transforms import convert_pil_to_tensor
from torch import LongTensor, Tensor
from taming.data.helper_types import BoundingBox, Annotation
from taming.data.conditional_builder.objects_center_points import ObjectsCenterPointsConditionalBuilder
from taming.data.conditional_builder.utils import COLOR_PALETTE, WHITE, GRAY_75, BLACK, additional_parameters_string, \
pad_list, get_plot_font_size, absolute_bbox
class ObjectsBoundingBoxConditionalBuilder(ObjectsCenterPointsConditionalBuilder):
@property
def object_descriptor_length(self) -> int:
return 3
def _make_object_descriptors(self, annotations: List[Annotation]) -> List[Tuple[int, ...]]:
object_triples = [
(self.object_representation(ann), *self.token_pair_from_bbox(ann.bbox))
for ann in annotations
]
empty_triple = (self.none, self.none, self.none)
object_triples = pad_list(object_triples, empty_triple, self.no_max_objects)
return object_triples
def inverse_build(self, conditional: LongTensor) -> Tuple[List[Tuple[int, BoundingBox]], Optional[BoundingBox]]:
conditional_list = conditional.tolist()
crop_coordinates = None
if self.encode_crop:
crop_coordinates = self.bbox_from_token_pair(conditional_list[-2], conditional_list[-1])
conditional_list = conditional_list[:-2]
object_triples = grouper(conditional_list, 3)
assert conditional.shape[0] == self.embedding_dim
return [
(object_triple[0], self.bbox_from_token_pair(object_triple[1], object_triple[2]))
for object_triple in object_triples if object_triple[0] != self.none
], crop_coordinates
def plot(self, conditional: LongTensor, label_for_category_no: Callable[[int], str], figure_size: Tuple[int, int],
line_width: int = 3, font_size: Optional[int] = None) -> Tensor:
plot = pil_image.new('RGB', figure_size, WHITE)
draw = pil_img_draw.Draw(plot)
font = ImageFont.truetype(
"/usr/share/fonts/truetype/lato/Lato-Regular.ttf",
size=get_plot_font_size(font_size, figure_size)
)
width, height = plot.size
description, crop_coordinates = self.inverse_build(conditional)
for (representation, bbox), color in zip(description, cycle(COLOR_PALETTE)):
annotation = self.representation_to_annotation(representation)
class_label = label_for_category_no(annotation.category_no) + ' ' + additional_parameters_string(annotation)
bbox = absolute_bbox(bbox, width, height)
draw.rectangle(bbox, outline=color, width=line_width)
draw.text((bbox[0] + line_width, bbox[1] + line_width), class_label, anchor='la', fill=BLACK, font=font)
if crop_coordinates is not None:
draw.rectangle(absolute_bbox(crop_coordinates, width, height), outline=GRAY_75, width=line_width)
return convert_pil_to_tensor(plot) / 127.5 - 1.
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