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| # -*- coding: utf-8 -*- | |
| # Copyright (c) Facebook, Inc. and its affiliates. | |
| import numpy as np | |
| import os | |
| import tempfile | |
| import unittest | |
| import cv2 | |
| import torch | |
| from detectron2.data import MetadataCatalog | |
| from detectron2.structures import BoxMode, Instances, RotatedBoxes | |
| from detectron2.utils.visualizer import ColorMode, 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=bool) | |
| mask[:40, 10:20] = 1 | |
| labels = [str(i) for i in range(N)] | |
| return img, boxes, labels, polygons, [mask] * N | |
| 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) | |
| v.draw_dataset_dict(dic) | |
| v = Visualizer(img, self.metadata) | |
| v.draw_dataset_dict(dic) | |
| def test_draw_rotated_dataset_dict(self): | |
| img = np.random.rand(512, 512, 3) * 255 | |
| dic = { | |
| "annotations": [ | |
| { | |
| "bbox": [ | |
| 368.9946492271106, | |
| 330.891438763377, | |
| 13.148537455410235, | |
| 13.644708680142685, | |
| 45.0, | |
| ], | |
| "bbox_mode": BoxMode.XYWHA_ABS, | |
| "category_id": 0, | |
| "iscrowd": 1, | |
| } | |
| ], | |
| "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) | |
| v.draw_instance_predictions(inst) | |
| v = Visualizer(img, self.metadata) | |
| v.draw_instance_predictions(inst) | |
| def test_BWmode_nomask(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) | |
| v = Visualizer(img, self.metadata, instance_mode=ColorMode.IMAGE_BW) | |
| v.draw_instance_predictions(inst) | |
| # check that output is grayscale | |
| inst = inst[:0] | |
| v = Visualizer(img, self.metadata, instance_mode=ColorMode.IMAGE_BW) | |
| output = v.draw_instance_predictions(inst).get_image() | |
| self.assertTrue(np.allclose(output[:, :, 0], output[:, :, 1])) | |
| self.assertTrue(np.allclose(output[:, :, 0], output[:, :, 2])) | |
| 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]: | |
| for save in [True, False]: | |
| v = Visualizer(img) | |
| o = v.draw_binary_mask(m, color="red", text="test") | |
| if save: | |
| with tempfile.TemporaryDirectory(prefix="detectron2_viz") as d: | |
| path = os.path.join(d, "output.png") | |
| o.save(path) | |
| o = cv2.imread(path)[:, :, ::-1] | |
| else: | |
| o = o.get_image().astype("float32") | |
| # red color is drawn on the image | |
| self.assertTrue(o[:, :, 0].sum() > 0) | |
| def test_draw_soft_mask(self): | |
| img = np.random.rand(100, 100, 3) * 255 | |
| img[:, :, 0] = 0 # remove red color | |
| mask = np.zeros((100, 100), dtype=np.float32) | |
| mask[30:50, 40:50] = 1.0 | |
| cv2.GaussianBlur(mask, (21, 21), 10) | |
| v = Visualizer(img) | |
| o = v.draw_soft_mask(mask, color="red", text="test") | |
| o = o.get_image().astype("float32") | |
| # red color is drawn on the image | |
| self.assertTrue(o[:, :, 0].sum() > 0) | |
| # test draw empty mask | |
| v = Visualizer(img) | |
| o = v.draw_soft_mask(np.zeros((100, 100), dtype=np.float32), color="red", text="test") | |
| o = o.get_image().astype("float32") | |
| def test_border_mask_with_holes(self): | |
| H, W = 200, 200 | |
| img = np.zeros((H, W, 3)) | |
| img[:, :, 0] = 255.0 | |
| v = Visualizer(img, scale=3) | |
| mask = np.zeros((H, W)) | |
| mask[:, 100:150] = 1 | |
| # create a hole, to trigger imshow | |
| mask = cv2.rectangle(mask, (110, 110), (130, 130), 0, thickness=-1) | |
| output = v.draw_binary_mask(mask, color="blue") | |
| output = output.get_image()[:, :, ::-1] | |
| first_row = {tuple(x.tolist()) for x in output[0]} | |
| last_row = {tuple(x.tolist()) for x in output[-1]} | |
| # Check quantization / off-by-1 error: the first and last row must have two colors | |
| self.assertEqual(len(last_row), 2) | |
| self.assertEqual(len(first_row), 2) | |
| self.assertIn((0, 0, 255), last_row) | |
| self.assertIn((0, 0, 255), first_row) | |
| def test_border_polygons(self): | |
| H, W = 200, 200 | |
| img = np.zeros((H, W, 3)) | |
| img[:, :, 0] = 255.0 | |
| v = Visualizer(img, scale=3) | |
| mask = np.zeros((H, W)) | |
| mask[:, 100:150] = 1 | |
| output = v.draw_binary_mask(mask, color="blue") | |
| output = output.get_image()[:, :, ::-1] | |
| first_row = {tuple(x.tolist()) for x in output[0]} | |
| last_row = {tuple(x.tolist()) for x in output[-1]} | |
| # Check quantization / off-by-1 error: | |
| # the first and last row must have >=2 colors, because the polygon | |
| # touches both rows | |
| self.assertGreaterEqual(len(last_row), 2) | |
| self.assertGreaterEqual(len(first_row), 2) | |
| self.assertIn((0, 0, 255), last_row) | |
| self.assertIn((0, 0, 255), first_row) | |
| if __name__ == "__main__": | |
| unittest.main() | |