import os
from unittest import TestCase
import cv2
import numpy as np
import torch
from mmengine.structures import InstanceData, PixelData
from mmdet.evaluation import INSTANCE_OFFSET
from mmdet.structures import DetDataSample
from mmdet.visualization import DetLocalVisualizer, TrackLocalVisualizer
def _rand_bboxes(num_boxes, h, w):
cx, cy, bw, bh = torch.rand(num_boxes, 4).T
tl_x = ((cx * w) - (w * bw / 2)).clamp(0, w)
tl_y = ((cy * h) - (h * bh / 2)).clamp(0, h)
br_x = ((cx * w) + (w * bw / 2)).clamp(0, w)
br_y = ((cy * h) + (h * bh / 2)).clamp(0, h)
bboxes = torch.stack([tl_x, tl_y, br_x, br_y], dim=0).T
return bboxes
def _create_panoptic_data(num_boxes, h, w):
sem_seg = np.zeros((h, w), dtype=np.int64) + 2
bboxes = _rand_bboxes(num_boxes, h, w).int()
labels = torch.randint(2, (num_boxes, ))
for i in range(num_boxes):
x, y, w, h = bboxes[i]
sem_seg[y:y + h, x:x + w] = (i + 1) * INSTANCE_OFFSET + labels[i]
return sem_seg[None]
class TestDetLocalVisualizer(TestCase):
def test_add_datasample(self):
h = 12
w = 10
num_class = 3
num_bboxes = 5
out_file = 'out_file.jpg'
image = np.random.randint(0, 256, size=(h, w, 3)).astype('uint8')
# test gt_instances
gt_instances = InstanceData()
gt_instances.bboxes = _rand_bboxes(num_bboxes, h, w)
gt_instances.labels = torch.randint(0, num_class, (num_bboxes, ))
det_data_sample = DetDataSample()
det_data_sample.gt_instances = gt_instances
det_local_visualizer = DetLocalVisualizer()
det_local_visualizer.add_datasample(
'image', image, det_data_sample, draw_pred=False)
# test out_file
det_local_visualizer.add_datasample(
'image',
image,
det_data_sample,
draw_pred=False,
out_file=out_file)
assert os.path.exists(out_file)
drawn_img = cv2.imread(out_file)
assert drawn_img.shape == (h, w, 3)
os.remove(out_file)
# test gt_instances and pred_instances
pred_instances = InstanceData()
pred_instances.bboxes = _rand_bboxes(num_bboxes, h, w)
pred_instances.labels = torch.randint(0, num_class, (num_bboxes, ))
pred_instances.scores = torch.rand((num_bboxes, ))
det_data_sample.pred_instances = pred_instances
det_local_visualizer.add_datasample(
'image', image, det_data_sample, out_file=out_file)
self._assert_image_and_shape(out_file, (h, w * 2, 3))
det_local_visualizer.add_datasample(
'image', image, det_data_sample, draw_gt=False, out_file=out_file)
self._assert_image_and_shape(out_file, (h, w, 3))
det_local_visualizer.add_datasample(
'image',
image,
det_data_sample,
draw_pred=False,
out_file=out_file)
self._assert_image_and_shape(out_file, (h, w, 3))
# test gt_panoptic_seg and pred_panoptic_seg
det_local_visualizer.dataset_meta = dict(classes=('1', '2'))
gt_sem_seg = _create_panoptic_data(num_bboxes, h, w)
panoptic_seg = PixelData(sem_seg=gt_sem_seg)
det_data_sample = DetDataSample()
det_data_sample.gt_panoptic_seg = panoptic_seg
pred_sem_seg = _create_panoptic_data(num_bboxes, h, w)
panoptic_seg = PixelData(sem_seg=pred_sem_seg)
det_data_sample.pred_panoptic_seg = panoptic_seg
det_local_visualizer.add_datasample(
'image', image, det_data_sample, out_file=out_file)
self._assert_image_and_shape(out_file, (h, w * 2, 3))
# class information must be provided
det_local_visualizer.dataset_meta = {}
with self.assertRaises(AssertionError):
det_local_visualizer.add_datasample(
'image', image, det_data_sample, out_file=out_file)
def _assert_image_and_shape(self, out_file, out_shape):
assert os.path.exists(out_file)
drawn_img = cv2.imread(out_file)
assert drawn_img.shape == out_shape
os.remove(out_file)
class TestTrackLocalVisualizer(TestCase):
@staticmethod
def _get_gt_instances():
bboxes = np.array([[912, 484, 1009, 593], [1338, 418, 1505, 797]])
masks = np.zeros((2, 1080, 1920), dtype=np.bool_)
for i, bbox in enumerate(bboxes):
masks[i, bbox[1]:bbox[3], bbox[0]:bbox[2]] = True
instances_data = dict(
bboxes=torch.tensor(bboxes),
masks=masks,
instances_id=torch.tensor([1, 2]),
labels=torch.tensor([0, 1]))
instances = InstanceData(**instances_data)
return instances
@staticmethod
def _get_pred_instances():
instances_data = dict(
bboxes=torch.tensor([[900, 500, 1000, 600], [1300, 400, 1500,
800]]),
instances_id=torch.tensor([1, 2]),
labels=torch.tensor([0, 1]),
scores=torch.tensor([0.955, 0.876]))
instances = InstanceData(**instances_data)
return instances
@staticmethod
def _assert_image_and_shape(out_file, out_shape):
assert os.path.exists(out_file)
drawn_img = cv2.imread(out_file)
assert drawn_img.shape == out_shape
os.remove(out_file)
def test_add_datasample(self):
out_file = 'out_file.jpg'
h, w = 1080, 1920
image = np.random.randint(0, 256, size=(h, w, 3)).astype('uint8')
gt_instances = self._get_gt_instances()
pred_instances = self._get_pred_instances()
image_data_sample = DetDataSample()
image_data_sample.gt_instances = gt_instances
image_data_sample.pred_track_instances = pred_instances
track_local_visualizer = TrackLocalVisualizer(alpha=0.2)
track_local_visualizer.dataset_meta = dict(
classes=['pedestrian', 'vehicle'])
# test gt_instances
track_local_visualizer.add_datasample('image', image,
image_data_sample, None)
# test out_file
track_local_visualizer.add_datasample(
'image', image, image_data_sample, None, out_file=out_file)
self._assert_image_and_shape(out_file, (h, w, 3))
# test gt_instances and pred_instances
track_local_visualizer.add_datasample(
'image', image, image_data_sample, out_file=out_file)
self._assert_image_and_shape(out_file, (h, 2 * w, 3))
track_local_visualizer.add_datasample(
'image',
image,
image_data_sample,
draw_gt=False,
out_file=out_file)
self._assert_image_and_shape(out_file, (h, w, 3))
track_local_visualizer.add_datasample(
'image',
image,
image_data_sample,
draw_pred=False,
out_file=out_file)
self._assert_image_and_shape(out_file, (h, w, 3))