def camera():
camera = PiCamera()
camera.capture('front_view.png')
now = t.time()
process_image('front_view.png')
print("Elapsed Time: {}".format(t.time() - now))
def scan(pic, camera, rotation_count):
GPIO.output(green_led_pin, GPIO.HIGH)
camera.capture('{}.png'.format(pic))
persons = process_image('{}.png'.format(pic))
if not persons:
wii = aplay_audio("pre_made/wii.mp3")
sleep(1)
rotate(2)
rotation_count += 1
return persons, rotation_count
def valGenerator():
while True:
batchx = []
batchy = []
for fname, cl in zip(X_val, y_val):
img = cv2.imread(fname)
for ggg in range(generate):
img = transform_image(img, 30, 20, 1)
processed = process_image(img)
batchx.append(processed)
batchy.append([cl])
if len(batchx) == batch_size:
bx = np.array(batchx)
by = np.array(batchy)
batchx = []
batchy = []
yield bx, by
def trainGenerator():
while True:
batchx = []
batchy = []
for iii, (fname, cl) in enumerate(list(zip(X_train, y_train))):
img = cv2.imread(fname)
for ggg in range(generate):
img = transform_image(img, 30, 20, 1)
# cv2.imshow('gen',img_)
# cv2.waitKey(0)
processed = process_image(img)
batchx.append(processed)
batchy.append([cl])
if len(batchx) == batch_size:
bx = np.array(batchx)
by = np.array(batchy)
batchx = []
batchy = []
yield bx, by
for fname, cl in zip(X_val, y_val):
img = cv2.imread(fname)
for ggg in range(generate):
img = transform_image(img, 30, 20, 1)
processed = process_image(img)
batchx.append(processed)
batchy.append([cl])
if len(batchx) == batch_size:
bx = np.array(batchx)
by = np.array(batchy)
batchx = []
batchy = []
yield bx, by
input_shape = process_image(np.zeros(shape=(64, 64, 3))).shape
print(input_shape, 'input shape')
for t in trainGenerator():
print(t[0].shape, t[1].shape)
break
kernel_size = (3, 3)
model = Sequential()
model.add(
Convolution2D(256,
kernel_size[0],
kernel_size[1],
border_mode='valid',
input_shape=input_shape))
model.add(Activation('relu'))