def main(_argv):
# Preparing the gpu for use
physical_devices = tf.config.list_physical_devices('GPU')
if len(physical_devices) > 0:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
# Choice of model, NN, used by Yolo. Load of weights and classes used.
if FLAGS.tiny:
yolo = YoloV3Tiny(classes=FLAGS.num_classes)
else:
yolo = YoloV3(classes=FLAGS.num_classes)
yolo.load_weights(FLAGS.weights).expect_partial()
logging.info('weights loaded')
class_names = [c.strip() for c in open(FLAGS.classes).readlines()]
logging.info('classes loaded')
# Link is established with the server
mySocket = bind2server()
# Image processing
try:
ind = 0
t_timelose = 0
while os.path.exists(f'{FLAGS.image}/RGB/{ind}.jpg'):
time_step = t.time()
# Preparation of the images to be processed
img_raw = tf.image.decode_image(open(f'{FLAGS.image}/RGB/{ind}.jpg', 'rb').read(), channels=3)
img = tf.expand_dims(img_raw, 0)
img = transform_images(img, FLAGS.size)
imgDepths = cv2.imread(f'{FLAGS.image}/depths/{ind}.jpg')
t1 = time.time()
# Yolo results for the processed image
boxes, scores, classes, nums = yolo(img)
img = cv2.cvtColor(img_raw.numpy(), cv2.COLOR_RGB2BGR)
# Information processing
listObjects = yoloT.get_objects(img, imgDepths, boxes, scores, classes, nums, class_names,
FLAGS.x_resolution, FLAGS.y_resolution)
if len(listObjects) > 0:
orientationT.add_orientations(img, imgDepths, listObjects)
msg = occlusionT.solve_occlusion(img, listObjects)
else:
msg = 'empty'
t2 = time.time()
logging.info('time: {}'.format(t2 - t1))
# Sending the message
mySocket.sendall(msg.encode())
# Painting of the image processed by yolo
cv2.imshow('output', img)
if cv2.waitKey(1) == ord('q'):
break
# Wait for the message from the server, to process the following image
mySocket.recv(1024)
time_step = t.time() - time_step
f_step = FLAGS.fps_record * time_step
i_step = int(f_step)
t_timelose += f_step - i_step
i_timelose = int(t_timelose)
t_timelose -= i_timelose
ind += i_step + i_timelose
mySocket.close()
finally:
mySocket.close()
import time, cv2
import numpy as np
from PIL import Image
from myTools.my_client import bind2server
import agent
'''
Main script, where messages are received from the server and agent actions are sent.
'''
if __name__ == '__main__':
# Link is established with the server
mySocket = bind2server()
msg_size, channels, parameters_size = 8, 3, 29
verbose = 1 # 0 = no output, 1 = only telemetry, 2 = all message and paint images
# Message format [imageWidth, imageHeigth, numberofCameras, decimalAccuracy, throttle, speed, steer, brake, image]
index = [4, 8, 12, 16, 20, 24, 28, 29]
# Image processing
try:
mySocket.sendall("ready".encode())
print("Established connection")
while True:
# Message reconstruction
msg = mySocket.recv(msg_size)
imageWidth = int.from_bytes(msg[:index[0]],
byteorder='little',
signed=False)
imageHeigth = int.from_bytes(msg[index[0]:index[1]],
byteorder='little',
signed=False)