def annotate(flags, net, framework):
log = get_logger()
io = SharedFlagIO(flags, subprogram=True)
flags = io.read_flags() if io.read_flags() is not None else flags
for video in flags.video:
frame_count = 0
capture = cv2.VideoCapture(video)
total_frames = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
annotation_file = f'{os.path.splitext(video)[0]}_annotations.csv'
if os.path.exists(annotation_file):
log.info("Overwriting existing annotations")
os.remove(annotation_file)
log.info(f'Annotating {video}')
with open(annotation_file, mode='a') as file:
file_writer = csv.writer(file,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
while capture.isOpened():
frame_count += 1
if frame_count % 10 == 0:
flags.progress = round((100 * frame_count / total_frames),
0)
io.io_flags()
ret, frame = capture.read()
if ret:
frame = np.asarray(frame)
h, w, _ = frame.shape
im = framework.resize_input(frame)
this_inp = np.expand_dims(im, 0)
boxes = framework.findboxes(
np.concatenate(net(this_inp), 0))
pred = [
framework.process_box(b, h, w, flags.threshold)
for b in boxes
]
pred = filter(None, pred)
time_elapsed = capture.get(cv2.CAP_PROP_POS_MSEC) / 1000
[
file_writer.writerow([time_elapsed, *result])
for result in pred
]
else:
break
if flags.kill:
capture.release()
exit(1)
capture.release()
class TestBackend(TestCase):
@classmethod
def setUpClass(cls) -> None:
open('tests/resources/checkpoint', 'w').close()
with ZipFile('tests/resources/BCCD.v1-resize-416x416.voc.zip',
'r') as f:
f.extractall('tests/resources/BCCD')
time.sleep(5)
def setUp(self):
self.io = SharedFlagIO(subprogram=False)
self.flags = self.io.flags
def testBackendWrapperYoloV2(self):
self.flags.model = 'tests/resources/yolov2-lite-3c.cfg'
self.flags.dataset = 'tests/resources/BCCD/train'
self.flags.labels = 'tests/resources/BCCD.classes'
self.flags.annotation = 'tests/resources/BCCD/train'
self.flags.backup = 'tests/resources/ckpt'
self.flags.project_name = '_test'
self.flags.trainer = 'adam'
self.flags.lr = 0.00001
self.flags.max_lr = 0.0001
self.flags.step_size_coefficient = 10
self.flags.load = 0
self.flags.batch = 4
self.flags.epoch = 1
self.flags.train = True
self.io.io_flags()
proc = Popen([sys.executable, BACKEND_ENTRYPOINT],
stdout=PIPE,
shell=False)
proc.communicate()
self.assertEqual(proc.returncode, 0)
self.flags.load = 1
self.io.io_flags()
proc = Popen([sys.executable, BACKEND_ENTRYPOINT],
stdout=PIPE,
shell=False)
proc.communicate()
self.flags.train = False
self.flags.imgdir = 'tests/resources/BCCD/test'
self.io.io_flags()
proc = Popen([sys.executable, BACKEND_ENTRYPOINT],
stdout=PIPE,
shell=False)
proc.communicate()
self.flags.video = ['tests/resources/test.mp4']
self.io.io_flags()
proc = Popen([sys.executable, BACKEND_ENTRYPOINT],
stdout=PIPE,
shell=False)
proc.communicate()
self.assertEqual(proc.returncode, 0)
def testBackendGradientExplosion(self):
self.flags.model = 'tests/resources/yolov2-lite-3c.cfg'
self.flags.dataset = 'tests/resources/BCCD/train'
self.flags.labels = 'tests/resources/BCCD.classes'
self.flags.annotation = 'tests/resources/BCCD/train'
self.flags.backup = 'tests/resources/ckpt'
self.flags.project_name = '_test'
self.flags.trainer = 'adam'
self.flags.lr = 100000000.0
self.flags.max_lr = 100000000.0
self.flags.load = 0
self.flags.batch = 4
self.flags.epoch = 1
self.flags.train = True
self.io.io_flags()
proc = Popen([sys.executable, BACKEND_ENTRYPOINT],
stdout=PIPE,
shell=False)
proc.communicate()
self.assertNotEqual(proc.returncode, 0)
def tearDown(self) -> None:
self.io.cleanup_flags()
@classmethod
def tearDownClass(cls):
for f in os.listdir('tests/resources/ckpt'):
if f.endswith(
('.data-00000-of-00001', '.index', '.meta', '.profile')):
f = os.path.join('tests/resources/ckpt', f)
os.remove(f)
os.remove('tests/resources/ckpt/checkpoint')
rmtree('tests/resources/BCCD')
try:
rmtree('data/summaries/_test')
except FileNotFoundError:
pass
import os
import sys
sys.path.append(os.getcwd())
from beagles.io.flags import SharedFlagIO
from beagles.backend.net import NetBuilder, train, predict, annotate
if __name__ == '__main__':
io = SharedFlagIO(subprogram=True)
flags = io.read_flags()
flags.started = True
net_builder = NetBuilder(flags=flags)
net, framework, manager = net_builder()
flags = io.read_flags()
if flags.train:
train(net_builder.annotation_data, net_builder.class_weights, flags,
net, framework, manager)
elif flags.video:
annotate(flags, net, framework)
else:
predict(flags, net, framework)
flags = io.read_flags()
flags.progress = 100.0
flags.done = True
io.io_flags()
exit(0)