def pre_change(self):
##### Comment/Uncomment below to set directory by user and not date #####
todays_dir = make_daily_dir(IMAGE_DIR)
if pre_check_dewar():
while math.isclose(int(epics.PV('XF:17IDB-ES:AMX{Dew:1-Ax:R}Mtr.RBV').get()), int(epics.PV('XF:17IDB-ES:AMX{Dew:1-Ax:R}Mtr.VAL').get()), abs_tol=2) is False:
print('Dewar is at: {} \nRotating to: {}'.format(int(epics.PV(
'XF:17IDB-ES:AMX{Dew:1-Ax:R}Mtr.RBV').get()), int(epics.PV('XF:17IDB-ES:AMX{Dew:1-Ax:R}Mtr.VAL').get())))
time.sleep(2)
# current_position = int(epics.PV('XF:17IDB-ES:AMX{Dew:1-Ax:R}Mtr.RBV').get())
# position_goal = int(epics.PV('XF:17IDB-ES:AMX{Dew:1-Ax:R}Mtr.VAL').get())
# if not math.isclose(current_position,position_goal, abs_tol = 2):
# print('Restarted for New Rotation')
# break
inner_dir = make_inner_dir(todays_dir)
img = take_image(todays_dir, inner_dir)
try:
p1 = multiprocessing.Process(
target=crop_image(img, inner_dir, ROOT_DIR))
p1.start()
except Exception as e:
print('Prediction Failed: {}'.format(e))
# A fifteen second wait to allow conditions to change
time.sleep(0.5)
self.post_change()
def detect(self, lidar):
birdseye = ld.lidar_to_birdseye(lidar, ld.slice_config())
mc = self.squeezedet.mc
im = ci.crop_image(birdseye,
(mc.IMAGE_WIDTH + 1, mc.IMAGE_HEIGHT + 1, 3),
(mc.IMAGE_WIDTH, mc.IMAGE_HEIGHT, 3))
found_car = None
found_ped = None
boxes, probs, classes = self.squeezedet.detect(im)
# Assume decreasing order of probability
for box, prob, class_idx in zip(boxes, probs, classes):
global_box = ld.birdseye_to_global(box, ld.slice_config())
car_box = rd.get_birdseye_box(lidar,
(global_box[0], global_box[1]))
predicted_yaw = self.rotation_detector.detect_rotation(car_box)
sd.correct_global(global_box, class_idx)
pose = np.array(
[global_box[0], global_box[1], -0.9, predicted_yaw])
if found_car is None and class_idx == sd.CAR_CLASS:
found_car = pose
if found_ped is None and class_idx == sd.PED_CLASS:
found_ped = pose
if found_car is not None and found_ped is not None:
break
return (found_car, found_ped)
def get_birdseye_box(lidar, obs_position):
slice_config = ld.slice_config()
slice_config.SIDE_RANGE = (-2.5, 2.5)
slice_config.FWD_RANGE = (-2.5, 2.5)
birdseye = ld.lidar_to_birdseye(lidar,
slice_config,
return_points=False,
center=(obs_position[0], obs_position[1]))
return ci.crop_image(birdseye, (51, 51, 3), INPUT_SHAPE)
def generate_lidar_birdseye(bag_file, mc):
im = None
generator = ns.generate_numpystream(bag_file, tracklet_file = None)
for numpydata in generator:
lidar = numpydata.lidar
if lidar is not None:
birdseye = ld.lidar_to_birdseye(lidar, ld.slice_config())
im = ci.crop_image(birdseye,
(mc.IMAGE_WIDTH + 1, mc.IMAGE_HEIGHT + 1, 3),
(mc.IMAGE_WIDTH, mc.IMAGE_HEIGHT, 3))
# Must yield item for each frame in generator.
yield im, lidar
#!/home/pi/miniconda3/envs/ml/bin/python
import os, sys
import json
import crop_images
if __name__ == "__main__":
out_dict = {}
out_dict['filename'] = sys.argv[1]
out_dict['event'] = int(sys.argv[2])
out_dict['frame_nr'] =int(sys.argv[3])
out_dict['width'] = int(sys.argv[4])
out_dict['height'] = int(sys.argv[5])
out_dict['center_x'] = int(sys.argv[6])
out_dict['center_y'] = int(sys.argv[7])
filename = out_dict['filename']
filename_txt = os.path.splitext(filename)[0]+'.json'
with open(filename_txt,'w') as outfile:
json.dump(out_dict,outfile)
os.system('sync')
crop_images.crop_image(filename_txt)
def generate_kitti(bag_tracklets, imagedir, labeldir, output_bbox,
slice_config):
if not os.path.exists(imagedir):
os.makedirs(imagedir)
if not os.path.exists(labeldir):
os.makedirs(labeldir)
print('bag_tracklets', bag_tracklets)
print('imagedir', imagedir)
print('labeldir', labeldir)
print('output_bbox', output_bbox)
print('slice_config', slice_config)
id = 0
expected_shape = get_expected_shape(slice_config)
new_shape = (expected_shape[0] - 1, expected_shape[1] - 1,
expected_shape[2])
assert_shape(new_shape)
print('expected_shape', expected_shape)
print('new_shape', new_shape)
stopwatch = util.stopwatch.Stopwatch()
stream = multibag.MultiBagStream(bag_tracklets,
numpystream.generate_numpystream)
stopwatch.stop()
print('Elapsed time: {}'.format(stopwatch.format_duration()))
stopwatch.start()
for numpydata in stream.generate(infinite=False):
lidar = numpydata.lidar
obs = numpydata.obs
if lidar is not None:
frame_idx, obs = obs
bbox = bbox_points(obs)
birdseye = ld.lidar_to_birdseye(lidar, slice_config)
birdseye_bbox = ld.lidar_to_birdseye(bbox,
slice_config,
return_points=True)
if birdseye_bbox.shape[0] == 2 and birdseye_bbox.shape[1] == 2:
if output_bbox:
bbox_tuple = ((birdseye_bbox[0][0], birdseye_bbox[0][1]),
(birdseye_bbox[1][0], birdseye_bbox[1][1]))
else:
bbox_tuple = None
crop = ci.crop_image(birdseye, expected_shape, new_shape)
image_file = os.path.join(imagedir, '{:06d}.png'.format(id))
imlib.save_np_image(crop, image_file, bbox_tuple)
label_path = os.path.join(labeldir, '{:06d}.txt'.format(id))
write_kitti_annotation(obs, birdseye_bbox, label_path)
id += 1
if id % 1000 == 0:
print('Working... Processed {} samples.'.format(id))
stopwatch.stop()
print('Elapsed time: {}'.format(
stopwatch.format_duration()))
stopwatch.start()
print('DONE. Processed {} samples.'.format(id))
stopwatch.stop()
print('Elapsed time: {}'.format(stopwatch.format_duration()))
sys.path.insert(0, root_dir)
from crop_images import crop_image
from predict_position import predict_image
user = getpass.getuser()
tmp_dir = os.getcwd() + '/' + user + '_puck_visualization_' + str(
random.randint(11111, 99999))
os.mkdir(tmp_dir)
shutil.copy(sys.argv[1], tmp_dir)
img = sys.argv[1]
tmp_fname = os.listdir(tmp_dir)[0]
fname = tmp_fname.split('.')[0]
fbase = fname + '_position'
os.remove(tmp_dir + '/' + tmp_fname)
print('Processing Image: {}'.format(fname))
print('Files in: {}'.format(tmp_dir))
crop_image(img, fbase, tmp_dir)
files = sorted([f for f in os.listdir(tmp_dir)])
position_number = 1
for my_file in files:
filepath = tmp_dir + '/' + my_file
print('Predicting Image: {}'.format(my_file))
predict_image(filepath)