def test_challenge2018(self):
dat = H.ImgReader(BallDatasetHandler.TRAIN.CHALLENGE_2018,
batch_size=8)
dat_filtered = H.ImgReader(BallDatasetHandler.TRAIN.CHALLENGE_2018,
batch_size=8,
label_content="ball",
filter_labels=True)
# 35327 number of ball annotations
# 35952 number of images total
self.assertEqual(dat.get_dataset_size(), 35952)
self.assertEqual(dat_filtered.get_dataset_size(), 35327)
def test_heatmap_labels2D_rectangular_dummy_data(self):
# set up dummy data
heatmap = np.zeros(shape=(3, 10, 10, 1), dtype=np.float32)
heatmap[0, 1:4, 1:4, :] = 1.0 # 0: top left
heatmap[1, 5:10, 7:10, :] = 1.0 # 1: bottom right
heatmap[2, 1:4, 1:4, :] = 1.0 # 2: both
heatmap[2, 5:10, 7:10, :] = 1.0
# set up dummy labels
bbx1 = H.LabelBoundingBox2D(x1=1, y1=1, x2=3, y2=3)
bbx2 = H.LabelBoundingBox2D(x1=7, y1=5, x2=9, y2=9)
labels = [[bbx1], [bbx2], [bbx1, bbx2]]
ret = H.heatmap.labels2D_rectangular(heatmap, labels)
self.assertTrue(np.array_equal(heatmap, ret))
def test_set01_multiple_goalpost(self):
dat = H.LblReader(
[os.path.join(os.environ["ROBO_AI_DATA"], "bitbots-set00-01")],
img_dim=(800, 600),
label_content="goalpost")
imgs = ["jan16_seq__000.531.png"]
paths = [
os.path.join(os.environ["ROBO_AI_DATA"], "bitbots-set00-01",
imgs[0])
]
labels = dat.get_labels_for_batch(paths)
# image 'jan16_seq__000.531.png' contains two goalposts - check for two labels
self.assertEqual(len(labels[0]), 2)
# extract label objects
# left goal post: x1=26 and y1=0
if abs(labels[0][0].x1 - 26) <= 2 and abs(labels[0][0].y1 - 0) <= 2:
# if x1 and y1 of labels[0][0] matched, then labels[0][0] is the left goalpost
goalpost1 = labels[0][0]
goalpost2 = labels[0][1]
else:
# if it did not match, then just assume right goalpost is labels[0][0]
goalpost2 = labels[0][0]
goalpost1 = labels[0][1]
# check left goalpost information
self.assertAlmostEqual(goalpost1.x1, 26, delta=2)
self.assertAlmostEqual(goalpost1.y1, 0, delta=2)
self.assertAlmostEqual(goalpost1.x2, 110, delta=2)
self.assertAlmostEqual(goalpost1.y2, 150, delta=2)
self.assertAlmostEqual(goalpost1.center_x, 68, delta=2)
self.assertAlmostEqual(goalpost1.center_y, 75, delta=2)
self.assertAlmostEqual(goalpost1.width, 84, delta=2)
self.assertAlmostEqual(goalpost1.height, 150, delta=2)
# check right goalpost information
self.assertAlmostEqual(goalpost2.x1, 731, delta=2)
self.assertAlmostEqual(goalpost2.y1, 0, delta=2)
self.assertAlmostEqual(goalpost2.x2, 799, delta=2)
self.assertAlmostEqual(goalpost2.y2, 123, delta=2)
self.assertAlmostEqual(goalpost2.center_x, 765, delta=2)
self.assertAlmostEqual(goalpost2.center_y, 61.5, delta=2)
self.assertAlmostEqual(goalpost2.width, 68, delta=2)
self.assertAlmostEqual(goalpost2.height, 123, delta=2)
def test_mock_multiple_goalpost(self):
dat = H.LblReader(TestLabelReader.pathlist,
img_dim=(640, 480),
label_content="goalpost")
imgs = ["fake.png"]
paths = [str(TestLabelReader.mock_dataset_dir / img) for img in imgs]
labels = dat.get_labels_for_batch(paths)
goalposts = labels[0]
self.assertEqual(len(goalposts), 2,
"Should contain two goalpost labels")
expected_goalposts = [{
'x1': 398,
'y1': 0,
'x2': 458,
'y2': 225,
'center_x': 428,
'center_y': 112,
'width': 60,
'height': 225
}, {
'x1': 197,
'y1': 14,
'x2': 252,
'y2': 236,
'center_x': 224,
'center_y': 125,
'width': 55,
'height': 222
}]
eps = 1e-2
for expected_goalpost in expected_goalposts:
matching_goalposts = [
gp for gp in goalposts
if abs(gp.x1 - expected_goalpost['x1']) < eps
]
self.assertEqual(len(matching_goalposts), 1)
goalposts.remove(matching_goalposts[0])
compare_goalpost = {
attr: getattr(matching_goalposts[0], attr)
for attr in expected_goalpost.keys()
}
self.assertDictEqual(expected_goalpost, compare_goalpost)
def test_mock_goalpost(self):
dat = H.LblReader(TestLabelReader.pathlist,
img_dim=(800, 600),
label_content="goalpost")
imgs = ["img_fr_nov15_000.000.png"]
paths = [str(TestLabelReader.mock_dataset_dir / img) for img in imgs]
labels = dat.get_labels_for_batch(paths)
self.assertEqual(len(labels[0]), 1)
goalpost = labels[0][0]
self.assertAlmostEqual(goalpost.x1, 688)
self.assertAlmostEqual(goalpost.y1, 0)
self.assertAlmostEqual(goalpost.x2, 772)
self.assertAlmostEqual(goalpost.y2, 203)
# calculated in LabelBoundingBox2D - not from label file
self.assertAlmostEqual(goalpost.center_x, 730)
# calculated in LabelBoundingBox2D - not from label file
self.assertAlmostEqual(goalpost.center_y, 101)
self.assertAlmostEqual(goalpost.width, 84)
self.assertAlmostEqual(goalpost.height, 203)
import os
import MLHelper as H
from MLHelper.datasets.bitbots import BallDatasetHandler
from matplotlib import pyplot as plt
from matplotlib import patches
for img_dim in [(800, 600), (200, 150)]:
dat = H.ImgLblReader(BallDatasetHandler.TRAIN.NAGOYA,
batch_size=4,
queue_size=4,
img_dim=img_dim,
label_content="ball",
filter_labels=True)
iterations = 5
for i in range(iterations):
# retrieve data
batch = dat.get_next_batch()
imgs = batch.get_data()
labels = batch.get_labels()
plt.title(f"img_dim {img_dim} iteration {i+1}/{iterations}")
for p, j in zip(range(1, 5), range(4)):
# active subplot p
ax = plt.subplot(2, 2, p)
plt.imshow(imgs[j], interpolation="None")
# labels
for label in labels[j]:
rect = patches.Rectangle((label.x1, label.y1),
import os
import MLHelper as H
from MLHelper.datasets.bitbots import BallDatasetHandler
from matplotlib import pyplot as plt
from matplotlib import patches
for img_dim in [(800, 600), (200, 150)]:
dat = H.ImgLblReader([os.path.join(os.environ["ROBO_AI_DATA"], "5")],
batch_size=4,
queue_size=4,
img_dim=img_dim,
label_content="goalpost",
filter_labels=True)
iterations = 5
for i in range(iterations):
# retrieve data
batch = dat.get_next_batch()
imgs = batch.get_data()
labels = batch.get_labels()
plt.title(f"img_dim {img_dim} iteration {i+1}/{iterations}")
for p, j in zip(range(1, 5), range(4)):
# active subplot p
ax = plt.subplot(2, 2, p)
plt.imshow(imgs[j], interpolation="None")
# labels
for label in labels[j]:
rect = patches.Rectangle((label.x1, label.y1),
def setUpClass(cls):
TestLabelReader.data = H.LblReader(TestLabelReader.pathlist,
img_dim=(800, 600),
label_content="ball")
def test_montreal_and_size(self):
dat = H.ImgReader(BallDatasetHandler.TRAIN.MONTREAL, batch_size=8)
self.assertEqual(dat.get_dataset_size(), 16866)
def test_nagoya_and_size(self):
dat = H.ImgReader(BallDatasetHandler.TRAIN.NAGOYA, batch_size=8)
self.assertEqual(dat.get_dataset_size(), 8243)
def test_leipzig_ball_label(self):
dat = H.ImgReader(BallDatasetHandler.TRAIN.LEIPZIG,
batch_size=8,
label_content="ball",
filter_labels=True)
self.assertEqual(dat.get_dataset_size(), 14684)
def test_mock_ball_label(self):
dat = H.ImgReader(TestImageReader.pathlist,
batch_size=5,
label_content="ball",
filter_labels=True)
self.assertEqual(dat.get_dataset_size(), 11)
def setUpClass(cls):
TestImageReader.data = H.ImgReader(
TestImageReader.pathlist,
batch_size=TestImageReader.batch_size,
label_content="ball")