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- # -*- coding: utf-8 -*-
- # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
- # File:
-
- import numpy as np
- import unittest
- import cv2
- import torch
-
- from detectron2.data import MetadataCatalog
- from detectron2.structures import BoxMode, Instances, RotatedBoxes
- from detectron2.utils.visualizer import Visualizer
-
-
- class TestVisualizer(unittest.TestCase):
- def _random_data(self):
- H, W = 100, 100
- N = 10
- img = np.random.rand(H, W, 3) * 255
- boxxy = np.random.rand(N, 2) * (H // 2)
- boxes = np.concatenate((boxxy, boxxy + H // 2), axis=1)
-
- def _rand_poly():
- return np.random.rand(3, 2).flatten() * H
-
- polygons = [[_rand_poly() for _ in range(np.random.randint(1, 5))] for _ in range(N)]
-
- mask = np.zeros_like(img[:, :, 0], dtype=np.bool)
- mask[:10, 10:20] = 1
-
- labels = [str(i) for i in range(N)]
- return img, boxes, labels, polygons, [mask] * N
-
- @property
- def metadata(self):
- return MetadataCatalog.get("coco_2017_train")
-
- def test_draw_dataset_dict(self):
- img = np.random.rand(512, 512, 3) * 255
- dic = {
- "annotations": [
- {
- "bbox": [
- 368.9946492271106,
- 330.891438763377,
- 13.148537455410235,
- 13.644708680142685,
- ],
- "bbox_mode": BoxMode.XYWH_ABS,
- "category_id": 0,
- "iscrowd": 1,
- "segmentation": {
- "counts": "_jh52m?2N2N2N2O100O10O001N1O2MceP2",
- "size": [512, 512],
- },
- }
- ],
- "height": 512,
- "image_id": 1,
- "width": 512,
- }
- v = Visualizer(img, self.metadata)
- v.draw_dataset_dict(dic)
-
- def test_overlay_instances(self):
- img, boxes, labels, polygons, masks = self._random_data()
-
- v = Visualizer(img, self.metadata)
- output = v.overlay_instances(masks=polygons, boxes=boxes, labels=labels).get_image()
- self.assertEqual(output.shape, img.shape)
-
- # Test 2x scaling
- v = Visualizer(img, self.metadata, scale=2.0)
- output = v.overlay_instances(masks=polygons, boxes=boxes, labels=labels).get_image()
- self.assertEqual(output.shape[0], img.shape[0] * 2)
-
- # Test overlay masks
- v = Visualizer(img, self.metadata)
- output = v.overlay_instances(masks=masks, boxes=boxes, labels=labels).get_image()
- self.assertEqual(output.shape, img.shape)
-
- def test_overlay_instances_no_boxes(self):
- img, boxes, labels, polygons, _ = self._random_data()
- v = Visualizer(img, self.metadata)
- v.overlay_instances(masks=polygons, boxes=None, labels=labels).get_image()
-
- def test_draw_instance_predictions(self):
- img, boxes, _, _, masks = self._random_data()
- num_inst = len(boxes)
- inst = Instances((img.shape[0], img.shape[1]))
- inst.pred_classes = torch.randint(0, 80, size=(num_inst,))
- inst.scores = torch.rand(num_inst)
- inst.pred_boxes = torch.from_numpy(boxes)
- inst.pred_masks = torch.from_numpy(np.asarray(masks))
-
- v = Visualizer(img, self.metadata)
- v.draw_instance_predictions(inst)
-
- def test_draw_empty_mask_predictions(self):
- img, boxes, _, _, masks = self._random_data()
- num_inst = len(boxes)
- inst = Instances((img.shape[0], img.shape[1]))
- inst.pred_classes = torch.randint(0, 80, size=(num_inst,))
- inst.scores = torch.rand(num_inst)
- inst.pred_boxes = torch.from_numpy(boxes)
- inst.pred_masks = torch.from_numpy(np.zeros_like(np.asarray(masks)))
-
- v = Visualizer(img, self.metadata)
- v.draw_instance_predictions(inst)
-
- def test_correct_output_shape(self):
- img = np.random.rand(928, 928, 3) * 255
- v = Visualizer(img, self.metadata)
- out = v.output.get_image()
- self.assertEqual(out.shape, img.shape)
-
- def test_overlay_rotated_instances(self):
- H, W = 100, 150
- img = np.random.rand(H, W, 3) * 255
- num_boxes = 50
- boxes_5d = torch.zeros(num_boxes, 5)
- boxes_5d[:, 0] = torch.FloatTensor(num_boxes).uniform_(-0.1 * W, 1.1 * W)
- boxes_5d[:, 1] = torch.FloatTensor(num_boxes).uniform_(-0.1 * H, 1.1 * H)
- boxes_5d[:, 2] = torch.FloatTensor(num_boxes).uniform_(0, max(W, H))
- boxes_5d[:, 3] = torch.FloatTensor(num_boxes).uniform_(0, max(W, H))
- boxes_5d[:, 4] = torch.FloatTensor(num_boxes).uniform_(-1800, 1800)
- rotated_boxes = RotatedBoxes(boxes_5d)
- labels = [str(i) for i in range(num_boxes)]
-
- v = Visualizer(img, self.metadata)
- output = v.overlay_instances(boxes=rotated_boxes, labels=labels).get_image()
- self.assertEqual(output.shape, img.shape)
-
- def test_draw_no_metadata(self):
- img, boxes, _, _, masks = self._random_data()
- num_inst = len(boxes)
- inst = Instances((img.shape[0], img.shape[1]))
- inst.pred_classes = torch.randint(0, 80, size=(num_inst,))
- inst.scores = torch.rand(num_inst)
- inst.pred_boxes = torch.from_numpy(boxes)
- inst.pred_masks = torch.from_numpy(np.asarray(masks))
-
- v = Visualizer(img, MetadataCatalog.get("asdfasdf"))
- v.draw_instance_predictions(inst)
-
- def test_draw_binary_mask(self):
- img, boxes, _, _, masks = self._random_data()
- img[:, :, 0] = 0 # remove red color
- mask = masks[0]
- mask_with_hole = np.zeros_like(mask).astype("uint8")
- mask_with_hole = cv2.rectangle(mask_with_hole, (10, 10), (50, 50), 1, 5)
-
- for m in [mask, mask_with_hole]:
- v = Visualizer(img)
- o = v.draw_binary_mask(m, color="red", text="test")
- o = o.get_image().astype("float32")
- # red color is drawn on the image
- self.assertTrue(o[:, :, 0].sum() > 0)
-
-
- if __name__ == "__main__":
- unittest.main()
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