# 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)