def __init__(self, port):
#Build Socket
self.connections = []
self.connections_limit = 2
self.shock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self.shock.bind(("0.0.0.0", port))
self.shock.setblocking(True)
self.shock.listen(10)
self.connections.append(self.shock)
#Start Stream
self.dev = rs.pipeline()
config = rs.config()
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
self.dev.start(config)
self.signature_dict = {0x00: self.scan_all}
#Start modules
self.post_processing = PostProcessing()
self.yolo = Yolo(None)
self.yolo.load_model()
while True:
try:
self.spin_socket()
except KeyboardInterrupt:
self.dev.close()
self.shock.close()
def __init__(self):
rospy.init_node('Object_Detection')
self.target = ""
self.yolo = Yolo()
self.bridge = CvBridge()
self.depth_finder = Depth_Finder()
self.TIMEOUT = 15
self.coord_pub_map = rospy.Publisher('/cv/detected_obj/coords/map',
PointStamped,
queue_size=10,
latch=True)
self.coord_pub_odom = rospy.Publisher('/cv/detected_obj/coords/odom',
PointStamped,
queue_size=10,
latch=True)
self.coord_pub_json = rospy.Publisher('/cv/detected_obj/coords/json',
String,
queue_size=10,
latch=True)
self.img_pub = rospy.Publisher('/yolo/img',
Image,
queue_size=1,
latch=True)
img_subscriber = rospy.Subscriber(
'/hsrb/head_rgbd_sensor/rgb/image_color', Image, self.img_callback)
info_subscriber = rospy.Subscriber(
'/hsrb/head_rgbd_sensor/rgb/camera_info', CameraInfo,
self.info_callback)
def __init__(self):
self.__batch_size = cfg.BATCH_SIZE
self.__image_size = cfg.IMAGE_SIZE
self.__cell_size = cfg.CELL_SIZE
self.__box_per_cell = cfg.BOX_PRE_CELL
self.__num_class = len(cfg.CLASSES)
self.__learn_rate_base = cfg.LEARN_RATE_BASE
self.__max_periods = cfg.MAX_PERIODS
self.__model_dir = cfg.MODEL_DIR
self.__model_file = cfg.MODEL_FILE
self.__log_dir = cfg.LOG_DIR
self.__moving_ave_decay = cfg.MOVING_AVE_DECAY
self.__save_iter = cfg.SAVE_ITER
self.__train_data = PascalVoc('train')
self.__test_data = PascalVoc('test')
with tf.name_scope('input'):
self.__samples = tf.placeholder(dtype=tf.float32,
name='samples',
shape=(self.__batch_size,
self.__image_size,
self.__image_size, 3))
self.__labels = tf.placeholder(
dtype=tf.float32,
name='labels',
shape=(self.__batch_size, self.__cell_size, self.__cell_size,
self.__box_per_cell, 5 + self.__num_class))
self.__is_training = tf.placeholder(dtype=tf.bool,
name='is_training')
self.__yolo = Yolo()
self.__yolo_output = self.__yolo.build_network(self.__samples,
self.__is_training)
self.__yolo_loss = self.__yolo.loss(self.__yolo_output, self.__labels)
with tf.name_scope('learn'):
self.__learn_rate = tf.Variable(self.__learn_rate_base,
trainable=False,
name='learn_rate_base')
moving_ave = tf.train.ExponentialMovingAverage(
self.__moving_ave_decay).apply(tf.trainable_variables())
optimize = tf.train.AdamOptimizer(self.__learn_rate).minimize(
self.__yolo_loss)
with tf.control_dependencies([optimize]):
with tf.control_dependencies([moving_ave]):
self.__train_op = tf.no_op()
with tf.name_scope('load'):
self.__load = tf.train.Saver(tf.trainable_variables())
self.__save = tf.train.Saver(tf.global_variables(), max_to_keep=50)
with tf.name_scope('summary'):
tf.summary.scalar('batch_loss', self.__yolo_loss)
self.__summary_op = tf.summary.merge_all()
self.__summary_writer = tf.summary.FileWriter(self.__log_dir)
self.__summary_writer.add_graph(tf.get_default_graph())
self.__sess = tf.Session()
def run_lpr():
logging.info(f'Debug mode {app.debug}')
if request.method == 'POST':
file = request.files['file']
img_bytes = file.read()
file.close()
if(img_bytes is not None):
nparr = np.fromstring(img_bytes, np.uint8)
inputImage = cv.imdecode(nparr, cv.IMREAD_COLOR)
# TODO: state management: avoid loading net for every request
yolo = Yolo(img_width=1056, img_height=576,
debug=DEBUG, confidence_threshold=0.6, non_max_supress_theshold=0.4,
classes_filename='../config/classes.names',
model_architecture_filename="../config/yolov3_license_plates.cfg",
model_weights_filename="../config/yolov3_license_plates_last.weights",
output_directory='../debug/')
roi_imgs = yolo.detect(inputImage)
ocr = OCR(model_filename="../config/emnist_net_custom.pt", num_classes=36, use_cuda=False, debug=DEBUG)
index = 0
for roi_img in roi_imgs:
logging.info(f'\n\nProcessing ROI {index}')
box = [yolo.bounding_boxes[index][0], yolo.bounding_boxes[index][1], yolo.bounding_boxes[index][2], yolo.bounding_boxes[index][3]]
predict(yolo.img, roi_img, box, str(index), (0,255,0), ocr)
index += 1
# API response: the highest confidence one
logging.info(f'\n\n---Processing the Highest Confidence ROI---\n')
bounding_box = None
emnist_net_preds = None
tesseract_preds = None
if(yolo.highest_object_confidence > 0 and yolo.roi_img is not None):
bounding_box = {
'x': yolo.box_x,
'y': yolo.box_y,
'w': yolo.box_w,
'h': yolo.box_h
}
_, emnist_net_preds, tesseract_preds = predict(yolo.img, yolo.roi_img, [yolo.box_x, yolo.box_y, yolo.box_w, yolo.box_h], "", (255,255,0), ocr)
if(DEBUG):
cv.imwrite("../debug/result.jpg", yolo.img.astype(np.uint8))
data = {
'bounding_box': bounding_box,
'confidence': yolo.highest_object_confidence,
'classId': str(yolo.classId_highest_object),
'emnist_net_preds': emnist_net_preds,
'tesseract_preds': tesseract_preds
}
response = jsonify(data)
response.status_code = 200
return response
def setup(sender=None, **kwargs):
global yolo
yolo = Yolo()
global face_filter
face_filter = FaceFilterClass()
#warm up
yolo.crop_persons("test3.png")
def reset_model():
yolo = Yolo()
random_image = tf.convert_to_tensor(np.random.random(
(1, image_size, image_size, 3)),
dtype=np.float32)
_ = yolo(random_image)
yolo.save_weights("./weights/yolo")
def __init__(self):
self.net = NetworkManager(9999)
self.yolo = Yolo()
self.yolo.load_model()
self.net.addCallback(0x00, self.yolo_callback)
while True:
try:
self.net.spinSocket()
except KeyboardInterrupt:
self.net.close()
def show_prediction(data_index):
with open("../data/data_detect_local_evaluate_100.json") as json_file:
data = json.load(json_file)
if (data_index not in data):
print("Index {} out of range".format(data_index))
return
yolo = Yolo()
yolo.load_weights("./weights/yolo")
img_path = "../pictures/pictures_detect_local_evaluate_100/{}.png".format(
data_index)
img = get_img(img_path)
preds = yolo(img)
boxes = get_boxes(preds)
show(data_index, img_path, boxes)
def __init__(self, updater):
self.sd = SpoilerDetector()
self.ms = MythicSpoiler()
self.yolo = Yolo(config.model, config.classes, config.conf)
self.reddit = Reddit(subreddit="magicTCG")
self.scryfall_futur_cards_id = []
self.reddit_futur_cards_subm_id = []
self.mythicspoiler_futur_cards_url = []
self.limit_days = 45
# List of Spoiler Objects
limit_date = datetime.today() - timedelta(days=self.limit_days)
self.spoiled = Session.query(Spoiler).filter(
Spoiler.found_at > limit_date).all()
# Job queues:
updater.job_queue.run_repeating(self.general_crawl,
interval=60,
first=10)
class YoloServer(object):
def __init__(self):
self.net = NetworkManager(9999)
self.yolo = Yolo()
self.yolo.load_model()
self.net.addCallback(0x00, self.yolo_callback)
while True:
try:
self.net.spinSocket()
except KeyboardInterrupt:
self.net.close()
def yolo_callback(self, arg):
byte_img = bytes(arg['img'], 'ascii')
img_decoded = base64.b64decode(byte_img)
img = numpy.frombuffer(img_decoded, dtype=numpy.uint8)
cv_img = cv2.imdecode(img, flags=1)
names = self.yolo.run(numpy.asarray(cv_img), False)
return (0x00, {'names': names})
def main():
cfgfilepath, datafilepath, weightfilepath, host, port, uploaddir, pub_img_flag = importargs(
)
yolo = Yolo(cfgfilepath, weightfilepath, datafilepath)
server = DarknetServer('yolo_server', uploaddir, ['jpg', 'png'],
pub_img_flag, yolo)
server.setup_converter()
print("server run")
server.run(host=host, port=port)
def compute_detections(directory, ground_truth_filename, output):
'''
Computes the total number of detections
in the data set for each image in the dataset
and compares it to its ground truth.
Args:
directory: Path to images directory
output: Output file name
'''
detections = []
total = 0
yolo = Yolo('yolov3.cfg', 'yolov3.weights', 'yolov3.txt')
count = 1
files = os.listdir(directory)
for filename in files:
_, indices, _ = yolo.extract_objects(directory + '/' + filename)
num_objects = len(indices)
detections.append([filename.split('.')[0], num_objects])
total += num_objects
print('Number of objects detected in {}: {}'.format(
filename, num_objects))
if count % 10 == 0:
print("\nProcessed {} out of {}".format(count, len(files)))
count += 1
detections.append(['Total Objects Detected', total])
predictions = pd.DataFrame(detections)
ground_truth = parse_ground_truth(ground_truth_filename)
result = pd.merge(predictions,
ground_truth,
how='outer',
left_on=0,
right_on=0)
result['1_x'] = result['1_x'].fillna(0).astype(int)
result.to_csv(output, index=False, header=False)
def __init__(self, args):
if (args['method'] == 'HOG'):
self.method = Hog(args['confidence'])
elif (args['method'] == 'CAFFE'):
self.method = Caffe(args['confidence'])
elif (args['method'] == 'YOLO'):
self.method = Yolo(args['confidence'])
else:
print(
'Fail, unknown algorithm. Try with -m HOG, -m CAFFE, -m YOLO')
rospy.signal_shutdown('Quit')
# Initialize the bridge to transform the image detect by topic's ROS
self.bridge = CvBridge()
# initialize our centroid tracker and frame dimensions
self.ct = CentroidTracker()
# Initialize the subscriber and publisher
self.image_sub = rospy.Subscriber("/ardrone/image_raw", Image,
self.callback)
self.image_pub = rospy.Publisher("image_processed",
Image,
queue_size=10)
self.controller = DroneController(args)
self.droneStatus = DroneStatus()
self.previous_position = None
self.actual_position = None
self.detection_status = DETECTION_STATUS['SearchingPerson']
self.last_action = {
'roll': 0,
'pitch': 0,
'yaw_velocity': 0,
'z_velocity': 0
}
self.shutdown = 500
def __init__(self):
rospy.init_node('Semantic_Labelling')
self.yolo = Yolo()
self.bridge = CvBridge()
self.depth_finder = Depth_Finder()
self.img_pub = rospy.Publisher('/semantic_labels/img',
Image,
queue_size=10)
self.nav_pub = rospy.Publisher('/azm_nav/semantic_label_additions',
String,
queue_size=10)
info_subscriber = rospy.Subscriber(
'/hsrb/head_rgbd_sensor/rgb/camera_info',
CameraInfo,
self.info_callback,
queue_size=None)
img_subscriber = rospy.Subscriber(
'/hsrb/head_rgbd_sensor/rgb/image_color',
Image,
self.img_callback,
queue_size=1,
buff_size=52428800)
def __init__(self, weights_file):
self.verbose = True
# detection params
self.S = 7 # cell size
self.B = 2 # boxes_per_cell
self.classes = [
"recoon", "other", "other", "other", "other", "other", "other",
"other", "other", "other", "other", "other", "other", "other",
"other", "other", "other", "other", "other", "other"
]
self.weights_file = weights_file
self.C = len(self.classes) # number of classes
# offset for box center (top left point of each cell)
self.x_offset = np.transpose(
np.reshape(np.array([np.arange(self.S)] * self.S * self.B),
[self.B, self.S, self.S]), [1, 2, 0])
#将第0维放置第三维
self.y_offset = np.transpose(self.x_offset, [1, 0, 2])
self.threshold = 0.2 # confidence scores threshold
self.iou_threshold = 0.5
self.model_path = 'model'
self.sess = tf.Session()
self.net = Yolo()
self.predicts = self.net.logits
self.variable_to_restore = tf.global_variables()
self.saver = tf.train.Saver(self.variable_to_restore, max_to_keep=None)
self.total_loss = 0.0
self.total_TP = 0
self.total_p = 0
self.ckpt_file = os.path.join(self.model_path, 'yolo')
gpuConfig = tf.ConfigProto(device_count={'gpu': 0})
#gpuConfig.gpu_options.allow_growth = True
#gpu_options = tf.GPUOptions()
#config = tf.ConfigProto(gpu_options=gpuConfig)
self.sess = tf.Session(config=gpuConfig)
self.sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(self.model_path) #获取checkpoints对象
if ckpt and ckpt.model_checkpoint_path: ##判断ckpt是否为空，若不为空，才进行模型的加载，否则从头开始训练
print('Restoring weights from: ' + ckpt.model_checkpoint_path)
self.saver.restore(self.sess,
ckpt.model_checkpoint_path) #恢复保存的神经网络结构，实现断点续训
elif self.weights_file is not None:
print('Restoring weights from: ' + self.weights_file)
self.saver.restore(self.sess, self.weights_file)
def main():
tf.reset_default_graph()
parser = argparse.ArgumentParser()
parser.add_argument('--weights', default="YOLO_small.ckpt", type=str)
parser.add_argument('--data_dir', default="data", type=str)
parser.add_argument('--threshold', default=0.2, type=float)
parser.add_argument('--iou_threshold', default=0.5, type=float)
parser.add_argument('--gpu', default='0', type=str)
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = "0"
yolo = Yolo()
pascal = pascal_voc('test')
solver = Solver(yolo, pascal)
print('Start testing ...')
solver.train()
print('Done testing.')
def __init__(self):
self.__image_size = cfg.IMAGE_SIZE
self.__cell_size = cfg.CELL_SIZE
self.__down_sample_size = 1.0 * self.__image_size / self.__cell_size
self.__box_per_cell = cfg.BOX_PRE_CELL
self.__classes = cfg.CLASSES
self.__num_class = len(cfg.CLASSES)
self.__anchor = cfg.ANCHOR
self.__prob_threshold = cfg.PROB_THRESHOLD
self.__nms_threshold = cfg.NMS_THRESHOLD
self.__yolo_input = tf.placeholder(shape=(1, self.__image_size,
self.__image_size, 3),
dtype=tf.float32,
name='input')
self.__is_training = tf.placeholder(dtype=tf.bool, name='is_training')
self.__yolo_output = Yolo().build_network(self.__yolo_input,
self.__is_training)
self.__sess = tf.Session()
self.__saver = tf.train.Saver(tf.global_variables())
self.__saver.restore(self.__sess,
os.path.join(cfg.MODEL_DIR, cfg.MODEL_FILE))
def train():
yolo = Yolo()
yolo.load_weights("./weights/yolo")
opt = Adam(learning_rate=5e-5)
with open("../data/data_detect_local_train.json") as json_file:
data = json.load(json_file)
data_index = 0
while str(data_index) in data:
img = get_img("../pictures/pictures_detect_local_train/{}.png".format(
data_index))
true_labels, true_boxes, true_preds = get_localization_data(
data[str(data_index)])
def get_loss():
preds = yolo(img)
return calculate_loss(preds, true_labels, true_boxes, true_preds)
opt.minimize(get_loss, [yolo.trainable_weights])
if (data_index % 100 == 99):
yolo.save_weights("./weights/yolo")
data_index += 1
if __name__ == '__main__':
cfgfile = "cfg/yolov3.cfg"
weightsfile = "yolov3.weights"
num_classes = 80
args = arg_parse()
confidence = float(args.confidence)
nms_thesh = float(args.nms_thresh)
#bbox_attrs = 5 + num_classes
inp_dim = int(args.reso)
assert inp_dim % 32 == 0
assert inp_dim > 32
yolo = Yolo(confidence, nms_thesh, args.reso, cfgfile, weightsfile, num_classes)
# if a video path was not supplied, grab the reference to the web cam
if not args.video:
cap = cv2.VideoCapture(0)
# otherwise, grab a reference to the video file
else:
cap = cv2.VideoCapture(args.video)
time.sleep(1.0)
fps = cap.get(cv2.CAP_PROP_FPS)
assert fps > 0
train_dset = VOCDetection(root=data_root, split='train')
train_dloader = DataLoader(train_dset,
batch_size=batch_size,
shuffle=True,
drop_last=True,
num_workers=8)
test_dset = VOCDetection(root=data_root, split='test')
test_dloader = DataLoader(test_dset,
batch_size=batch_size,
shuffle=False,
drop_last=False,
num_workers=8)
model = Yolo(grid_size, num_boxes, num_classes)
model = model.to(device)
pretrained_weights = torch.load(pretrained_backbone_path)
model.load_state_dict(pretrained_weights)
print('loaded pretrained weight')
# Freeze the backbone network.
model.features.requires_grad_(False)
model_params = [v for v in model.parameters() if v.requires_grad is True]
optimizer = optim.SGD(model_params, lr=lr, momentum=0.9, weight_decay=5e-4)
compute_loss = Loss(grid_size, num_boxes, num_classes)
# Load the last checkpoint if exits.
ckpt_path = os.path.join(ckpt_dir, 'last.pth')
if os.path.exists(ckpt_path):
"textbox": "text_field",
"button": "button",
"combobox": "combo_box",
"tree": "tree",
"list": "list",
"scrollbar": "scroll_bar",
"menuitem": "menu_item",
"menu": "menu",
"togglebutton": "toggle_button",
"tabs": "tabs",
"slider": "slider",
"menuww": "menu",
}
yolo = Yolo()
real_folder = cfg.data_dir + "/../mac"
unproc_folder = real_folder + "/unproc"
files = [
os.path.join(dp, f)
for dp, dn, fn in os.walk(os.path.expanduser(unproc_folder +
"/images")) for f in fn
]
print(files)
label_files = [
(unproc_folder + "/labels/" + x[x.rfind("/") + 1:-4] + ".xml")
for x in files
]
from yolo import Yolo
import cv2
import time
src = "videos/sample.mp4"
yolofiles = {
"config": "yolo-coco/yolov3.cfg",
"weights": "yolo-coco/yolov3.weights",
"names": "yolo-coco/coco.names"
}
yolo = Yolo(yolofiles)
src_scale = 1
src_mirror = False
src_skip = 1
vid = cv2.VideoCapture(src)
t0 = time.time()
while True:
count = src_skip
while count>=1:
count -= 1
grabbed, image = vid.read()
parser.add_argument('--use_gpu',
type=bool,
default=False,
help='whether to use gpu')
args = parser.parse_args()
device = torch.device(
'cuda' if args.use_gpu and torch.cuda.is_available() else 'cpu')
FloatTensor = torch.cuda.FloatTensor if args.use_gpu and torch.cuda.is_available(
) else torch.FloatTensor
classes = utils.load_classes(
args.class_path) # Extracts class labels from file
# Set up model
model = Yolo().to(device)
if args.weights_path is not None:
# Load darknet weights
model.load_darknet_weights(args.weights_path)
model.eval() # Set in evaluation mode
# dataloader = DataLoader(
# ImageFolder(args.image_folder, img_size=args.img_size),
# batch_size=args.batch_size,
# shuffle=False,
# num_workers=args.n_cpu,
# )
if not os.path.exists(args.output_path):
from threading import Event
import torch
torch.no_grad()
torch.cuda.empty_cache()
# import our classes
import rocketvision as rv
from rocketvision.rate import Rate
from nada import Nada
from yolo import Yolo
nada = Nada()
yolo = Yolo(
img_size=256
) # default is 512 which yeilds about 3.8 fps (i7/940MX), 384 --> 5 fps, 256 --> 7 fps
context = zmq.Context()
socket = context.socket(zmq.PUB)
socket.connect('tcp://' + args['address'] + ':' + args['port'])
width = 1280
height = 720
displayWidth = 1280
displayHeight = 720
framerate = 30
flipmethod = 2
def gstreamer_pipeline(
#! /usr/bin/python3
import cv2
import sys
from detector import Detector
from yolo import Yolo
from timeit import default_timer as timer
# extract the yolo pipeline
yolo = Yolo(sys.argv[2])
vd = Detector(yolo)
if sys.argv[1] == '-i':
filename = 'images/test4.jpg'
image = cv2.imread(filename)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
result = vd.vehicleDetection(image)
result = cv2.cvtColor(result, cv2.COLOR_BGR2RGB)
cv2.imshow("result", result)
cv2.waitKey(0)
elif sys.argv[1] == '-v':
try:
skip = int(sys.argv[3])
except:
skip = 1
video_output = 'videos/out.mp4'
import logging
from yolo import Yolo
from ocr import OCR
app = Flask(__name__)
DEBUG = True
# TODO: state management and how to handle multiple request on this?
yolo = Yolo(
img_width=1056,
img_height=576,
confidence_threshold=0.6,
non_max_supress_theshold=0.4,
classes_filename='../config/classes.names',
model_architecture_filename="../config/yolov3_license_plates.cfg",
model_weights_filename="../config/yolov3_license_plates_last.weights",
output_directory='../debug/',
output_image=True)
ocr = OCR(model_filename="../config/attention_ocr_model.pth",
use_cuda=False,
threshold=0.7)
@app.route('/')
def index():
return "Live and Running!"
from yolo import Yolo
import json
if __name__ == "__main__":
CONFIG_FILE = 'config.json'
with open(CONFIG_FILE) as f:
config = json.load(f)
yolo = Yolo(config)
yolo.start_training()
# Initiate the train and test generators with data Augumentation
# train_datagen = ImageDataGenerator(
# rescale=1. / 255,
# horizontal_flip=True,
# fill_mode="nearest",
# zoom_range=0.3,
# width_shift_range=0.3,
# height_shift_range=0.3,
# rotation_range=30)
#
# test_datagen = ImageDataGenerator(
# rescale=1. / 255,
# horizontal_flip=True,
# fill_mode="nearest",
# zoom_range=0.3,
# width_shift_range=0.3,
# height_shift_range=0.3,
# rotation_range=30)
#
# train_generator = train_datagen.flow_from_directory(
# train_data_dir,
imgs = (images/127.5)-1
return imgs
def convert_boxes(boxes):
return yolo.convert_net_to_bb(boxes, filter_top=False)[0]
if __name__ == '__main__':
# Collect events until released
with keyboard.Listener(on_release=on_release) as listener:
if len(sys.argv) > 1:
cfg.window_name = sys.argv[1]
event = 0
yolo = Yolo()
states = []
runtime = round(time.time())
output_dir = cfg.output_dir + "/" + str(runtime)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
if not os.path.exists(output_dir + "/images"):
os.makedirs(output_dir + "/images")
test_file = output_dir + "/test.txt"
class YoloTrain(object):
def __init__(self):
self.__batch_size = cfg.BATCH_SIZE
self.__image_size = cfg.IMAGE_SIZE
self.__cell_size = cfg.CELL_SIZE
self.__box_per_cell = cfg.BOX_PRE_CELL
self.__num_class = len(cfg.CLASSES)
self.__learn_rate_base = cfg.LEARN_RATE_BASE
self.__max_periods = cfg.MAX_PERIODS
self.__model_dir = cfg.MODEL_DIR
self.__model_file = cfg.MODEL_FILE
self.__log_dir = cfg.LOG_DIR
self.__moving_ave_decay = cfg.MOVING_AVE_DECAY
self.__save_iter = cfg.SAVE_ITER
self.__train_data = PascalVoc('train')
self.__test_data = PascalVoc('test')
with tf.name_scope('input'):
self.__samples = tf.placeholder(dtype=tf.float32,
name='samples',
shape=(self.__batch_size,
self.__image_size,
self.__image_size, 3))
self.__labels = tf.placeholder(
dtype=tf.float32,
name='labels',
shape=(self.__batch_size, self.__cell_size, self.__cell_size,
self.__box_per_cell, 5 + self.__num_class))
self.__is_training = tf.placeholder(dtype=tf.bool,
name='is_training')
self.__yolo = Yolo()
self.__yolo_output = self.__yolo.build_network(self.__samples,
self.__is_training)
self.__yolo_loss = self.__yolo.loss(self.__yolo_output, self.__labels)
with tf.name_scope('learn'):
self.__learn_rate = tf.Variable(self.__learn_rate_base,
trainable=False,
name='learn_rate_base')
moving_ave = tf.train.ExponentialMovingAverage(
self.__moving_ave_decay).apply(tf.trainable_variables())
optimize = tf.train.AdamOptimizer(self.__learn_rate).minimize(
self.__yolo_loss)
with tf.control_dependencies([optimize]):
with tf.control_dependencies([moving_ave]):
self.__train_op = tf.no_op()
with tf.name_scope('load'):
self.__load = tf.train.Saver(tf.trainable_variables())
self.__save = tf.train.Saver(tf.global_variables(), max_to_keep=50)
with tf.name_scope('summary'):
tf.summary.scalar('batch_loss', self.__yolo_loss)
self.__summary_op = tf.summary.merge_all()
self.__summary_writer = tf.summary.FileWriter(self.__log_dir)
self.__summary_writer.add_graph(tf.get_default_graph())
self.__sess = tf.Session()
def train(self):
self.__sess.run(tf.global_variables_initializer())
ckpt_path = os.path.join(self.__model_dir, self.__model_file)
print 'Restoring weights from:\t %s' % ckpt_path
self.__load.restore(self.__sess, ckpt_path)
for period in range(self.__max_periods):
if period in [20, 50, 80]:
learning_rate_value = self.__sess.run(
tf.assign(self.__learn_rate,
self.__sess.run(self.__learn_rate) / 10.0))
print 'The value of learn rate is:\t%f' % learning_rate_value
for step, (batch_sample,
batch_label) in enumerate(self.__train_data):
_, summary_value, yolo_loss_value = self.__sess.run(
[self.__train_op, self.__summary_op, self.__yolo_loss],
feed_dict={
self.__samples: batch_sample,
self.__labels: batch_label,
self.__is_training: True
})
if np.isnan(yolo_loss_value):
raise ArithmeticError('The gradient is exploded')
if step % 10:
continue
self.__summary_writer.add_summary(
summary_value,
period * len(self.__train_data) + step)
print 'Period:\t%d\tstep:\t%d\ttrain loss:\t%.4f' % (
period, step, yolo_loss_value)
if period % self.__save_iter:
continue
total_test_loss = 0.0
for batch_sample, batch_label in self.__test_data:
yolo_loss_value = self.__sess.run(self.__yolo_loss,
feed_dict={
self.__samples:
batch_sample,
self.__labels:
batch_label,
self.__is_training: False
})
total_test_loss += yolo_loss_value
test_loss = total_test_loss / len(self.__test_data)
print 'Period:\t%d\ttest loss:\t%.4f' % (period, test_loss)
saved_model_name = os.path.join(
self.__model_dir, 'yolo.ckpt-%d-%.4f' % (period, test_loss))
self.__save.save(self.__sess, saved_model_name)
print 'Saved model:\t%s' % saved_model_name
self.__summary_writer.close()
class Semantic_Labelling:
def __init__(self):
rospy.init_node('Semantic_Labelling')
self.yolo = Yolo()
self.bridge = CvBridge()
self.depth_finder = Depth_Finder()
self.img_pub = rospy.Publisher('/semantic_labels/img',
Image,
queue_size=10)
self.nav_pub = rospy.Publisher('/azm_nav/semantic_label_additions',
String,
queue_size=10)
info_subscriber = rospy.Subscriber(
'/hsrb/head_rgbd_sensor/rgb/camera_info',
CameraInfo,
self.info_callback,
queue_size=None)
img_subscriber = rospy.Subscriber(
'/hsrb/head_rgbd_sensor/rgb/image_color',
Image,
self.img_callback,
queue_size=1,
buff_size=52428800)
# Gets camera info
def info_callback(self, msg):
self.cam_info = msg
# Takes the current image from the camera feed and searches for the target object, returning coordinates
def img_callback(self, msg):
cv_image = self.bridge.imgmsg_to_cv2(msg,
desired_encoding='passthrough')
#Respond to attribute error if subscribers haven't ran yet
try:
objects, img = self.yolo.search_for_objects(cv_image)
self.img_pub.publish(
self.bridge.cv2_to_imgmsg(img, encoding='passthrough'))
model = PinholeCameraModel()
model.fromCameraInfo(self.cam_info)
except AttributeError:
print("waiting")
return
if (len(objects) != 0):
for obj in objects:
print(obj)
xy = obj["Point"]
dist = self.depth_finder.get_depth(int(xy[0]), int(xy[1]))
depth = dist[0]
vect = model.projectPixelTo3dRay((xy[0], xy[1]))
xyz = [el / vect[2] for el in vect]
stampedPoint = PointStamped()
stampedPoint.header.frame_id = "head_rgbd_sensor_rgb_frame"
stampedPoint.point.x = xyz[0] * depth
stampedPoint.point.y = xyz[1] * depth
stampedPoint.point.z = depth
rospy.wait_for_service('transform_point')
get_3d_points = rospy.ServiceProxy('transform_point',
LocalizePoint)
resp = get_3d_points(stampedPoint)
#print(resp.localizedPointMap)
threeDPoint = resp.localizedPointMap
dictMsg = {}
dictMsg["name"] = obj["Label"]
dictMsg["type"] = "object"
dictMsg["coords"] = [
threeDPoint.point.x, threeDPoint.point.y,
threeDPoint.point.z
]
dictMsg["others"] = {}
self.nav_pub.publish(json.dumps(dictMsg))
else:
print("No objects found.")
