os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import time
import cv2
import json
import glob
import numpy as np
import matplotlib.pyplot as plt
import random
import keras
import tensorflow as tf
from tensorflow.python.keras import backend as K
from model_wrapper.utils import voc, sub_plot, Tick, new_session
from deeplab.warpper import Deeplab_Wrapper
sess = new_session()
xception = Deeplab_Wrapper(sess, 'xception')
mobilenet = Deeplab_Wrapper(sess, 'mobilenetv2')
from mask_rcnn.warpper import predict, plot
cap = cv2.VideoCapture('tmp/videos/9_Very_Close_Takeoffs_Landings.mp4')
fourcc = cv2.VideoWriter_fourcc(*'XVID')
font = cv2.FONT_HERSHEY_SIMPLEX
out = cv2.VideoWriter('tmp/deeplab_cmp.avi', fourcc, 30.0, (640 * 2, 360 * 2))
counter, filter1, filter2, filter3 = 0, 0, 0, 0
while (cap.isOpened()):
with Tick(str(counter)):
ret, frame_bgr = cap.read()
frame_bgr = cv2.resize(frame_bgr, (0, 0), fx=0.5, fy=0.5)
frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)
cv2.waitKey(1)
if self.output_compressed:
msg = CompressedImage()
msg.header = ros_data.header
msg.format = "jpeg"
msg.data = np.array(cv2.imencode('.jpg', detections)[1]).tostring()
self.pub.publish(msg)
else:
msg = Image()
msg.header = ros_data.header
msg.data = detections.tostring()
self.pub.publish(msg)
def shutdownFunction(signalnum, frame):
print('Exit')
rospy.signal_shutdown(0)
signal.signal(signal.SIGINT, shutdownFunction)
signal.signal(signal.SIGTERM, shutdownFunction)
if __name__ == '__main__':
assert sys.argv[1] in ['mobilenetv2', 'xception'], sys.argv[1]
wrapper = Deeplab_Wrapper(new_session(), sys.argv[1])
input_topic = sys.argv[2]
output_topic = sys.argv[3]
node = Deeplab_Node(wrapper, input_topic, output_topic)
rospy.spin()
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import cv2
import time
import glob
import numpy as np
import matplotlib.pyplot as plt
from model_wrapper.utils import Tick,new_session
from model_wrapper.classification import Model_Wrapper,top_n
wrapper = Model_Wrapper(new_session(),'mobilenet_v2_1.0')
wrapper.print_support_models()
for fn in glob.glob('data/ImageNet/*'):
with Tick('interference'):
prediction = wrapper.predict(fn)
print('\n'+top_n(prediction,n=5))