# Copyright (c) OpenMMLab. All rights reserved. import math from typing import List, Union import mmcv import numpy as np import torch from mmdet.structures.bbox import HorizontalBoxes from mmdet.visualization import DetLocalVisualizer from mmdet.visualization.palette import _get_adaptive_scales, get_palette from mmengine.structures import InstanceData from torch import Tensor from mmyolo.registry import VISUALIZERS @VISUALIZERS.register_module() class YOLOAssignerVisualizer(DetLocalVisualizer): """MMYOLO Detection Assigner Visualizer. This class is provided to the `assigner_visualization.py` script. Args: name (str): Name of the instance. Defaults to 'visualizer'. """ def __init__(self, name: str = 'visualizer', *args, **kwargs): super().__init__(name=name, *args, **kwargs) # need priors_size from config self.priors_size = None def draw_grid(self, stride: int = 8, line_styles: Union[str, List[str]] = ':', colors: Union[str, tuple, List[str], List[tuple]] = (180, 180, 180), line_widths: Union[Union[int, float], List[Union[int, float]]] = 1): """Draw grids on image. Args: stride (int): Downsample factor of feature map. line_styles (Union[str, List[str]]): The linestyle of lines. ``line_styles`` can have the same length with texts or just single value. If ``line_styles`` is single value, all the lines will have the same linestyle. Reference to https://matplotlib.org/stable/api/collections_api.html?highlight=collection#matplotlib.collections.AsteriskPolygonCollection.set_linestyle for more details. Defaults to ':'. colors (Union[str, tuple, List[str], List[tuple]]): The colors of lines. ``colors`` can have the same length with lines or just single value. If ``colors`` is single value, all the lines will have the same colors. Reference to https://matplotlib.org/stable/gallery/color/named_colors.html for more details. Defaults to (180, 180, 180). line_widths (Union[Union[int, float], List[Union[int, float]]]): The linewidth of lines. ``line_widths`` can have the same length with lines or just single value. If ``line_widths`` is single value, all the lines will have the same linewidth. Defaults to 1. """ assert self._image is not None, 'Please set image using `set_image`' # draw vertical lines x_datas_vertical = ((np.arange(self.width // stride - 1) + 1) * stride).reshape((-1, 1)).repeat( 2, axis=1) y_datas_vertical = np.array([[0, self.height - 1]]).repeat( self.width // stride - 1, axis=0) self.draw_lines( x_datas_vertical, y_datas_vertical, colors=colors, line_styles=line_styles, line_widths=line_widths) # draw horizontal lines x_datas_horizontal = np.array([[0, self.width - 1]]).repeat( self.height // stride - 1, axis=0) y_datas_horizontal = ((np.arange(self.height // stride - 1) + 1) * stride).reshape((-1, 1)).repeat( 2, axis=1) self.draw_lines( x_datas_horizontal, y_datas_horizontal, colors=colors, line_styles=line_styles, line_widths=line_widths) def draw_instances_assign(self, instances: InstanceData, retained_gt_inds: Tensor, not_show_label: bool = False): """Draw instances of GT. Args: instances (:obj:`InstanceData`): gt_instance. It usually includes ``bboxes`` and ``labels`` attributes. retained_gt_inds (Tensor): The gt indexes assigned as the positive sample in the current prior. not_show_label (bool): Whether to show gt labels on images. """ assert self.dataset_meta is not None classes = self.dataset_meta['classes'] palette = self.dataset_meta['palette'] if len(retained_gt_inds) == 0: return self.get_image() draw_gt_inds = torch.from_numpy( np.array( list(set(retained_gt_inds.cpu().numpy())), dtype=np.int64)) bboxes = instances.bboxes[draw_gt_inds] labels = instances.labels[draw_gt_inds] if not isinstance(bboxes, Tensor): bboxes = bboxes.tensor edge_colors = [palette[i] for i in labels] max_label = int(max(labels) if len(labels) > 0 else 0) text_palette = get_palette(self.text_color, max_label + 1) text_colors = [text_palette[label] for label in labels] self.draw_bboxes( bboxes, edge_colors=edge_colors, alpha=self.alpha, line_widths=self.line_width) if not not_show_label: positions = bboxes[:, :2] + self.line_width areas = (bboxes[:, 3] - bboxes[:, 1]) * ( bboxes[:, 2] - bboxes[:, 0]) scales = _get_adaptive_scales(areas) for i, (pos, label) in enumerate(zip(positions, labels)): label_text = classes[ label] if classes is not None else f'class {label}' self.draw_texts( label_text, pos, colors=text_colors[i], font_sizes=int(13 * scales[i]), bboxes=[{ 'facecolor': 'black', 'alpha': 0.8, 'pad': 0.7, 'edgecolor': 'none' }]) def draw_positive_assign(self, grid_x_inds: Tensor, grid_y_inds: Tensor, class_inds: Tensor, stride: int, bboxes: Union[Tensor, HorizontalBoxes], retained_gt_inds: Tensor, offset: float = 0.5): """ Args: grid_x_inds (Tensor): The X-axis indexes of the positive sample in current prior. grid_y_inds (Tensor): The Y-axis indexes of the positive sample in current prior. class_inds (Tensor): The classes indexes of the positive sample in current prior. stride (int): Downsample factor of feature map. bboxes (Union[Tensor, HorizontalBoxes]): Bounding boxes of GT. retained_gt_inds (Tensor): The gt indexes assigned as the positive sample in the current prior. offset (float): The offset of points, the value is normalized with corresponding stride. Defaults to 0.5. """ if not isinstance(bboxes, Tensor): # Convert HorizontalBoxes to Tensor bboxes = bboxes.tensor # The PALETTE in the dataset_meta is required assert self.dataset_meta is not None palette = self.dataset_meta['palette'] x = ((grid_x_inds + offset) * stride).long() y = ((grid_y_inds + offset) * stride).long() center = torch.stack((x, y), dim=-1) retained_bboxes = bboxes[retained_gt_inds] bbox_wh = retained_bboxes[:, 2:] - retained_bboxes[:, :2] bbox_area = bbox_wh[:, 0] * bbox_wh[:, 1] radius = _get_adaptive_scales(bbox_area) * 4 colors = [palette[i] for i in class_inds] self.draw_circles( center, radius, colors, line_widths=0, face_colors=colors, alpha=1.0) def draw_prior(self, grid_x_inds: Tensor, grid_y_inds: Tensor, class_inds: Tensor, stride: int, feat_ind: int, prior_ind: int, offset: float = 0.5): """Draw priors on image. Args: grid_x_inds (Tensor): The X-axis indexes of the positive sample in current prior. grid_y_inds (Tensor): The Y-axis indexes of the positive sample in current prior. class_inds (Tensor): The classes indexes of the positive sample in current prior. stride (int): Downsample factor of feature map. feat_ind (int): Index of featmap. prior_ind (int): Index of prior in current featmap. offset (float): The offset of points, the value is normalized with corresponding stride. Defaults to 0.5. """ palette = self.dataset_meta['palette'] center_x = ((grid_x_inds + offset) * stride) center_y = ((grid_y_inds + offset) * stride) xyxy = torch.stack((center_x, center_y, center_x, center_y), dim=1) device = xyxy.device if self.priors_size is not None: xyxy += self.priors_size[feat_ind][prior_ind].to(device) else: xyxy += torch.tensor( [[-stride / 2, -stride / 2, stride / 2, stride / 2]], device=device) colors = [palette[i] for i in class_inds] self.draw_bboxes( xyxy, edge_colors=colors, alpha=self.alpha, line_styles='--', line_widths=math.ceil(self.line_width * 0.3)) def draw_assign(self, image: np.ndarray, assign_results: List[List[dict]], gt_instances: InstanceData, show_prior: bool = False, not_show_label: bool = False) -> np.ndarray: """Draw assigning results. Args: image (np.ndarray): The image to draw. assign_results (list): The assigning results. gt_instances (:obj:`InstanceData`): Data structure for instance-level annotations or predictions. show_prior (bool): Whether to show prior on image. not_show_label (bool): Whether to show gt labels on images. Returns: np.ndarray: the drawn image which channel is RGB. """ img_show_list = [] for feat_ind, assign_results_feat in enumerate(assign_results): img_show_list_feat = [] for prior_ind, assign_results_prior in enumerate( assign_results_feat): self.set_image(image) h, w = image.shape[:2] # draw grid stride = assign_results_prior['stride'] self.draw_grid(stride) # draw prior on matched gt grid_x_inds = assign_results_prior['grid_x_inds'] grid_y_inds = assign_results_prior['grid_y_inds'] class_inds = assign_results_prior['class_inds'] prior_ind = assign_results_prior['prior_ind'] offset = assign_results_prior.get('offset', 0.5) if show_prior: self.draw_prior(grid_x_inds, grid_y_inds, class_inds, stride, feat_ind, prior_ind, offset) # draw matched gt retained_gt_inds = assign_results_prior['retained_gt_inds'] self.draw_instances_assign(gt_instances, retained_gt_inds, not_show_label) # draw positive self.draw_positive_assign(grid_x_inds, grid_y_inds, class_inds, stride, gt_instances.bboxes, retained_gt_inds, offset) # draw title if self.priors_size is not None: base_prior = self.priors_size[feat_ind][prior_ind] else: base_prior = [stride, stride, stride * 2, stride * 2] prior_size = (base_prior[2] - base_prior[0], base_prior[3] - base_prior[1]) pos = np.array((20, 20)) text = f'feat_ind: {feat_ind} ' \ f'prior_ind: {prior_ind} ' \ f'prior_size: ({prior_size[0]}, {prior_size[1]})' scales = _get_adaptive_scales(np.array([h * w / 16])) font_sizes = int(13 * scales) self.draw_texts( text, pos, colors=self.text_color, font_sizes=font_sizes, bboxes=[{ 'facecolor': 'black', 'alpha': 0.8, 'pad': 0.7, 'edgecolor': 'none' }]) img_show = self.get_image() img_show = mmcv.impad(img_show, padding=(5, 5, 5, 5)) img_show_list_feat.append(img_show) img_show_list.append(np.concatenate(img_show_list_feat, axis=1)) # Merge all images into one image # setting axis is to beautify the merged image axis = 0 if len(assign_results[0]) > 1 else 1 return np.concatenate(img_show_list, axis=axis)