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 .utils import COLOR_PALETTE, WHITE, GRAY_75, BLACK, additional_parameters_string, pad_list, get_circle_size, \ get_plot_font_size, absolute_bbox from ..helper_types import BoundingBox, Annotation, Image from torch import LongTensor, Tensor from torchvision.transforms import PILToTensor pil_to_tensor = PILToTensor() def convert_pil_to_tensor(image: Image) -> Tensor: with warnings.catch_warnings(): # to filter PyTorch UserWarning as described here: https://github.com/pytorch/vision/issues/2194 warnings.simplefilter("ignore") return pil_to_tensor(image) class ObjectsCenterPointsConditionalBuilder: def __init__(self, no_object_classes: int, no_max_objects: int, no_tokens: int, num_beams: int): self.no_object_classes = no_object_classes self.no_max_objects = no_max_objects self.no_tokens = no_tokens # self.no_sections = int(math.sqrt(self.no_tokens)) self.no_sections = (self.no_tokens // num_beams, num_beams) # (width, height) @property def none(self) -> int: return self.no_tokens - 1 @property def object_descriptor_length(self) -> int: return 2 @property def empty_tuple(self) -> Tuple: return (self.none,) * self.object_descriptor_length @property def embedding_dim(self) -> int: return self.no_max_objects * self.object_descriptor_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[0] - 1))) y_discrete = int(round(y * (self.no_sections[1] - 1))) return y_discrete * self.no_sections[0] + x_discrete def coordinates_from_token(self, token: int) -> (float, float): x = token % self.no_sections[0] y = token // self.no_sections[0] return x / (self.no_sections[0] - 1), y / (self.no_sections[1] - 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) # x2, y2 = self.coordinates_from_token(token3) # x3, y3 = self.coordinates_from_token(token4) return x0, y0, x1, y1 def token_pair_from_bbox(self, bbox: BoundingBox) -> Tuple: # return self.tokenize_coordinates(bbox[0], bbox[1]), self.tokenize_coordinates(bbox[2], bbox[3]), self.tokenize_coordinates(bbox[4], bbox[5]), self.tokenize_coordinates(bbox[6], bbox[7]) return self.tokenize_coordinates(bbox[0], bbox[1]), self.tokenize_coordinates(bbox[4], bbox[5]) def inverse_build(self, conditional: LongTensor) \ -> Tuple[List[Tuple[int, Tuple[float, float]]], Optional[BoundingBox]]: conditional_list = conditional.tolist() 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 ], None 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)) font = ImageFont.load_default() 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_id) + ' ' + 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: return annotation.category_id def representation_to_annotation(self, representation: int) -> Annotation: category_id = representation % self.no_object_classes # noinspection PyTypeChecker return Annotation( bbox=None, category_id=category_id, ) def _make_object_descriptors(self, annotations: List[Annotation]) -> List[Tuple[int, ...]]: object_tuples = [ (self.object_representation(a), self.tokenize_coordinates(a.center[0], a.center[1])) 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[Annotation]) \ -> LongTensor: if len(annotations) == 0: warnings.warn('Did not receive any annotations.') random.shuffle(annotations) if len(annotations) > self.no_max_objects: warnings.warn('Received more annotations than allowed.') annotations = annotations[:self.no_max_objects] object_tuples = self._make_object_descriptors(annotations) flattened = [token for tuple_ in object_tuples for token in tuple_] assert len(flattened) == self.embedding_dim assert all(0 <= value < self.no_tokens for value in flattened) return LongTensor(flattened)