# -*- 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=np.bool) mask[:40, 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) 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()