| 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. |
|
|
