from video_capture import video_capture from renderer import renderer from openface_handler import openface_handler from data_saver import data_saver video_capture = video_capture() openface_handler = openface_handler() data_saver = data_saver() renderer = renderer(video_capture, openface_handler, data_saver) #renderer.show_frames() tick = 1 # every ten seconds! renderer.runopenface_on_frames_everytick(show=True, tick=tick) #renderer.runopenface_on_frames_nowaiting(show=True) #renderer.record_frames() video_capture.destroy()
trafic_G_lower = [93, 181, 235]
rafic_G_upper = [178, 241, 255]
trafic_G = track_bar('rafic_G', False, trafic_G_lower, rafic_G_upper)
cap = cv2.VideoCapture(0)
cap1 = cv2.VideoCapture(1)
cap.set(3, 320)
cap.set(4, 240)
cap1.set(3, 320)
cap1.set(4, 240)
pub = rospy.Publisher('/forwardVision', forwardVision, queue_size=10)
rospy.init_node('forward_vision')
msg = forwardVision()
dat = data_saver()
#========================================================================================#
while (cap.isOpened() and cap1.isOpened()):
ret, frame = cap.read()
ret, frame1 = cap1.read()
#================================ hsv ===================================================#
hsv1 = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
hsv2 = cv2.cvtColor(frame1, cv2.COLOR_BGR2HSV)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cv2.rectangle(frame, (0, 275), (600, 300), (0, 255, 0), 2)
roi1 = hsv1[150:240, 0:320]
roi1_trafic = hsv1[50:240, 160:320]
#algo_clustering = 'k-means'
#algo_clustering = 'agglomerative'
algo_clustering = 'spectral'
###########################################################################
# Preparing data for clustering
###########################################################################
training_data = data_loader(file_name, features_names)
logging.info('Data has been prepared')
###########################################################################
# Training the model and get the clusters
###########################################################################
results, model = clustering_algorithms(algo_clustering,
training_data,
nb_clusters,
random_state=seed,
affinity='nearest_neighbors')
logging.info('Training has been done')
###########################################################################
# Saving results
# csv file containing data and a new column : clusters
# a PNG of initial data and new clusters
###########################################################################
features_names.append("clusters")
data_clusters = pd.DataFrame(results, columns=features_names)
data_saver(file_name, data_clusters, algo_clustering)
logging.info('Results saved')
listener_1 = Listener(address1)
data_conn = listener_1.accept()
print("connected to data stream")
print("Waiting for real time plotter")
address2 = ('localhost', plotter_port)
listener_2 = Listener(address2)
plotter_conn = listener_2.accept()
print("Connected to plotter")
"""
Create data saver object
"""
print("Creating data saver object")
current_time = strftime(f"%b%d_%H%M", localtime())
current_dir = current_time + "test"
saver = data_saver(current_dir, program_state)
print(f"Current time is {current_time}. Successfully created saver")
program_mode = "DEV_MODE"
"""
Initialize pygame and the other visual programs
"""
pygame.init()
if pygame.mixer and not pygame.mixer.get_init():
print('Warning, no sound')
pygame.mixer = None
quit()
width, height = 600, 600
screen = pygame.display.set_mode((width, height))