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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 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)
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