RE-OWOD/tests/test_visualizer.py

# -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved

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, 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_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)

    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_border(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)


if __name__ == "__main__":
    unittest.main()