class VideoCamera(object):
def __init__(self):
self.video = cv2.VideoCapture(0)
self.nn = CNN()
self.model = None
if os.path.exists('./model.h5'):
print 'Model Already Exist'
print '--------------------'
self.model = self.nn.load_model()
else:
print 'Model Does Not Exist'
print '--------------------'
self.model = self.nn.create_cnn_model()
def __del__(self):
self.video.release()
def get_frame(self):
_, fr = self.video.read()
frame = np.copy(fr)
frame = self.nn.data_frame(frame)
self.nn.predict_frame(self.model, frame)
_, jpeg = cv2.imencode('.jpg', fr)
return jpeg.tobytes()
from cnn import CNN
import os
import time
import threading
import tensorflow as tf
nn = CNN()
if os.path.exists('./model2.h5'):
print 'Model Already Exist'
print '--------------------'
model = nn.load_model()
else:
print 'Model Does Not Exist'
print '--------------------'
model = nn.create_cnn_model()
input = int(raw_input('Enter 0 to train:\nEnter 1 to predict:'))
if input == 0:
list = []
for i in xrange(65, 91):
print 'Training: ' + str(chr(i))
print '-----------------------------------------------------'
images = nn.fetch_data('../asl_alphabet_train/' + chr(i))
nn.prepare_data(images, letter=chr(i))
#Nothing
images = nn.fetch_data('../asl_alphabet_train/' + 'nothing')
nn.prepare_data(images, letter='nothing')
#Space
images = nn.fetch_data('../asl_alphabet_train/' + 'space')
