import math import random import warnings from itertools import cycle from typing import List, Optional, Tuple, Callable from PIL import Image as pil_image, ImageDraw as pil_img_draw, ImageFont from more_itertools.recipes import grouper from taming.data.conditional_builder.utils import COLOR_PALETTE, WHITE, GRAY_75, BLACK, FULL_CROP, filter_annotations, \ additional_parameters_string, horizontally_flip_bbox, pad_list, get_circle_size, get_plot_font_size, \ absolute_bbox, rescale_annotations from taming.data.helper_types import BoundingBox, Annotation from taming.data.image_transforms import convert_pil_to_tensor from torch import LongTensor, Tensor class ObjectsCenterPointsConditionalBuilder: def __init__(self, no_object_classes: int, no_max_objects: int, no_tokens: int, encode_crop: bool, use_group_parameter: bool, use_additional_parameters: bool): self.no_object_classes = no_object_classes self.no_max_objects = no_max_objects self.no_tokens = no_tokens self.encode_crop = encode_crop self.no_sections = int(math.sqrt(self.no_tokens)) self.use_group_parameter = use_group_parameter self.use_additional_parameters = use_additional_parameters @property def none(self) -> int: return self.no_tokens - 1 @property def object_descriptor_length(self) -> int: return 2 @property def embedding_dim(self) -> int: extra_length = 2 if self.encode_crop else 0 return self.no_max_objects * self.object_descriptor_length + extra_length def tokenize_coordinates(self, x: float, y: float) -> int: """ Express 2d coordinates with one number. Example: assume self.no_tokens = 16, then no_sections = 4: 0 0 0 0 0 0 # 0 0 0 0 0 0 0 0 x Then the # position corresponds to token 6, the x position to token 15. @param x: float in [0, 1] @param y: float in [0, 1] @return: discrete tokenized coordinate """ x_discrete = int(round(x * (self.no_sections - 1))) y_discrete = int(round(y * (self.no_sections - 1))) return y_discrete * self.no_sections + x_discrete def coordinates_from_token(self, token: int) -> (float, float): x = token % self.no_sections y = token // self.no_sections return x / (self.no_sections - 1), y / (self.no_sections - 1) def bbox_from_token_pair(self, token1: int, token2: int) -> BoundingBox: x0, y0 = self.coordinates_from_token(token1) x1, y1 = self.coordinates_from_token(token2) return x0, y0, x1 - x0, y1 - y0 def token_pair_from_bbox(self, bbox: BoundingBox) -> Tuple[int, int]: return self.tokenize_coordinates(bbox[0], bbox[1]), \ self.tokenize_coordinates(bbox[0] + bbox[2], bbox[1] + bbox[3]) def inverse_build(self, conditional: LongTensor) \ -> Tuple[List[Tuple[int, Tuple[float, float]]], 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] table_of_content = grouper(conditional_list, self.object_descriptor_length) assert conditional.shape[0] == self.embedding_dim return [ (object_tuple[0], self.coordinates_from_token(object_tuple[1])) for object_tuple in table_of_content if object_tuple[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) circle_size = get_circle_size(figure_size) 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, (x, y)), color in zip(description, cycle(COLOR_PALETTE)): x_abs, y_abs = x * width, y * height ann = self.representation_to_annotation(representation) label = label_for_category_no(ann.category_no) + ' ' + additional_parameters_string(ann) ellipse_bbox = [x_abs - circle_size, y_abs - circle_size, x_abs + circle_size, y_abs + circle_size] draw.ellipse(ellipse_bbox, fill=color, width=0) draw.text((x_abs, y_abs), label, anchor='md', 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. def object_representation(self, annotation: Annotation) -> int: modifier = 0 if self.use_group_parameter: modifier |= 1 * (annotation.is_group_of is True) if self.use_additional_parameters: modifier |= 2 * (annotation.is_occluded is True) modifier |= 4 * (annotation.is_depiction is True) modifier |= 8 * (annotation.is_inside is True) return annotation.category_no + self.no_object_classes * modifier def representation_to_annotation(self, representation: int) -> Annotation: category_no = representation % self.no_object_classes modifier = representation // self.no_object_classes # noinspection PyTypeChecker return Annotation( area=None, image_id=None, bbox=None, category_id=None, id=None, source=None, confidence=None, category_no=category_no, is_group_of=bool((modifier & 1) * self.use_group_parameter), is_occluded=bool((modifier & 2) * self.use_additional_parameters), is_depiction=bool((modifier & 4) * self.use_additional_parameters), is_inside=bool((modifier & 8) * self.use_additional_parameters) ) def _crop_encoder(self, crop_coordinates: BoundingBox) -> List[int]: return list(self.token_pair_from_bbox(crop_coordinates)) def _make_object_descriptors(self, annotations: List[Annotation]) -> List[Tuple[int, ...]]: object_tuples = [ (self.object_representation(a), self.tokenize_coordinates(a.bbox[0] + a.bbox[2] / 2, a.bbox[1] + a.bbox[3] / 2)) for a in annotations ] empty_tuple = (self.none, self.none) object_tuples = pad_list(object_tuples, empty_tuple, self.no_max_objects) return object_tuples def build(self, annotations: List, crop_coordinates: Optional[BoundingBox] = None, horizontal_flip: bool = False) \ -> LongTensor: if len(annotations) == 0: warnings.warn('Did not receive any annotations.') if len(annotations) > self.no_max_objects: warnings.warn('Received more annotations than allowed.') annotations = annotations[:self.no_max_objects] if not crop_coordinates: crop_coordinates = FULL_CROP random.shuffle(annotations) annotations = filter_annotations(annotations, crop_coordinates) if self.encode_crop: annotations = rescale_annotations(annotations, FULL_CROP, horizontal_flip) if horizontal_flip: crop_coordinates = horizontally_flip_bbox(crop_coordinates) extra = self._crop_encoder(crop_coordinates) else: annotations = rescale_annotations(annotations, crop_coordinates, horizontal_flip) extra = [] object_tuples = self._make_object_descriptors(annotations) flattened = [token for tuple_ in object_tuples for token in tuple_] + extra assert len(flattened) == self.embedding_dim assert all(0 <= value < self.no_tokens for value in flattened) return LongTensor(flattened)