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| import numpy as np
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| import unittest
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| import torch
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| from detectron2.data import MetadataCatalog
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| from detectron2.structures import BoxMode, Instances, RotatedBoxes
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| from detectron2.utils.visualizer import Visualizer
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| class TestVisualizer(unittest.TestCase):
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| def _random_data(self):
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| H, W = 100, 100
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| N = 10
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| img = np.random.rand(H, W, 3) * 255
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| boxxy = np.random.rand(N, 2) * (H // 2)
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| boxes = np.concatenate((boxxy, boxxy + H // 2), axis=1)
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| def _rand_poly():
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| return np.random.rand(3, 2).flatten() * H
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| polygons = [[_rand_poly() for _ in range(np.random.randint(1, 5))] for _ in range(N)]
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| mask = np.zeros_like(img[:, :, 0], dtype=np.bool)
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| mask[:10, 10:20] = 1
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| labels = [str(i) for i in range(N)]
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| return img, boxes, labels, polygons, [mask] * N
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| @property
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| def metadata(self):
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| return MetadataCatalog.get("coco_2017_train")
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| def test_draw_dataset_dict(self):
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| img = np.random.rand(512, 512, 3) * 255
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| dic = {
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| "annotations": [
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| {
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| "bbox": [
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| 368.9946492271106,
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| 330.891438763377,
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| 13.148537455410235,
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| 13.644708680142685,
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| ],
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| "bbox_mode": BoxMode.XYWH_ABS,
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| "category_id": 0,
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| "iscrowd": 1,
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| "segmentation": {
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| "counts": "_jh52m?2N2N2N2O100O10O001N1O2MceP2",
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| "size": [512, 512],
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| },
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| }
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| ],
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| "height": 512,
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| "image_id": 1,
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| "width": 512,
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| }
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| v = Visualizer(img, self.metadata)
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| v.draw_dataset_dict(dic)
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| def test_overlay_instances(self):
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| img, boxes, labels, polygons, masks = self._random_data()
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| v = Visualizer(img, self.metadata)
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| output = v.overlay_instances(masks=polygons, boxes=boxes, labels=labels).get_image()
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| self.assertEqual(output.shape, img.shape)
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| v = Visualizer(img, self.metadata, scale=2.0)
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| output = v.overlay_instances(masks=polygons, boxes=boxes, labels=labels).get_image()
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| self.assertEqual(output.shape[0], img.shape[0] * 2)
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| v = Visualizer(img, self.metadata)
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| output = v.overlay_instances(masks=masks, boxes=boxes, labels=labels).get_image()
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| self.assertEqual(output.shape, img.shape)
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| def test_overlay_instances_no_boxes(self):
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| img, boxes, labels, polygons, _ = self._random_data()
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| v = Visualizer(img, self.metadata)
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| v.overlay_instances(masks=polygons, boxes=None, labels=labels).get_image()
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| def test_draw_instance_predictions(self):
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| img, boxes, _, _, masks = self._random_data()
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| num_inst = len(boxes)
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| inst = Instances((img.shape[0], img.shape[1]))
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| inst.pred_classes = torch.randint(0, 80, size=(num_inst,))
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| inst.scores = torch.rand(num_inst)
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| inst.pred_boxes = torch.from_numpy(boxes)
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| inst.pred_masks = torch.from_numpy(np.asarray(masks))
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| v = Visualizer(img, self.metadata)
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| v.draw_instance_predictions(inst)
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| def test_draw_empty_mask_predictions(self):
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| img, boxes, _, _, masks = self._random_data()
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| num_inst = len(boxes)
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| inst = Instances((img.shape[0], img.shape[1]))
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| inst.pred_classes = torch.randint(0, 80, size=(num_inst,))
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| inst.scores = torch.rand(num_inst)
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| inst.pred_boxes = torch.from_numpy(boxes)
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| inst.pred_masks = torch.from_numpy(np.zeros_like(np.asarray(masks)))
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| v = Visualizer(img, self.metadata)
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| v.draw_instance_predictions(inst)
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| def test_correct_output_shape(self):
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| img = np.random.rand(928, 928, 3) * 255
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| v = Visualizer(img, self.metadata)
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| out = v.output.get_image()
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| self.assertEqual(out.shape, img.shape)
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| def test_overlay_rotated_instances(self):
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| H, W = 100, 150
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| img = np.random.rand(H, W, 3) * 255
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| num_boxes = 50
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| boxes_5d = torch.zeros(num_boxes, 5)
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| boxes_5d[:, 0] = torch.FloatTensor(num_boxes).uniform_(-0.1 * W, 1.1 * W)
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| boxes_5d[:, 1] = torch.FloatTensor(num_boxes).uniform_(-0.1 * H, 1.1 * H)
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| boxes_5d[:, 2] = torch.FloatTensor(num_boxes).uniform_(0, max(W, H))
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| boxes_5d[:, 3] = torch.FloatTensor(num_boxes).uniform_(0, max(W, H))
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| boxes_5d[:, 4] = torch.FloatTensor(num_boxes).uniform_(-1800, 1800)
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| rotated_boxes = RotatedBoxes(boxes_5d)
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| labels = [str(i) for i in range(num_boxes)]
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| v = Visualizer(img, self.metadata)
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| output = v.overlay_instances(boxes=rotated_boxes, labels=labels).get_image()
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| self.assertEqual(output.shape, img.shape)
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| def test_draw_no_metadata(self):
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| img, boxes, _, _, masks = self._random_data()
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| num_inst = len(boxes)
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| inst = Instances((img.shape[0], img.shape[1]))
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| inst.pred_classes = torch.randint(0, 80, size=(num_inst,))
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| inst.scores = torch.rand(num_inst)
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| inst.pred_boxes = torch.from_numpy(boxes)
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| inst.pred_masks = torch.from_numpy(np.asarray(masks))
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| v = Visualizer(img, MetadataCatalog.get("asdfasdf"))
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| v.draw_instance_predictions(inst)
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|
