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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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