def main(FLAG):
Model = SimpleModel(FLAG.input_dim,
FLAG.hidden_dim,
FLAG.output_dim,
optimizer=tf.train.RMSPropOptimizer(
FLAG.learning_rate))
image, label = load_dataset()
image, label = image_augmentation(image,
label,
horizon_flip=True,
control_brightness=True)
label = label / 96.
(train_X, train_y), (valid_X,
valid_y), (test_X, test_y) = split_data(image, label)
if FLAG.Mode == "validation":
lr_list = 10**np.random.uniform(-6, -2, 20)
Model.validation(train_X, train_y, valid_X, valid_y, lr_list)
elif FLAG.Mode == "train":
Model.train(train_X, train_y, valid_X, valid_y, FLAG.batch_size,
FLAG.Epoch, FLAG.save_graph, FLAG.save_model)
pred_Y = Model.predict(test_X[123])
print(pred_Y)
print(test_y[123])
print(np.mean(np.square(pred_Y - test_y[123])))
def main(FLAG):
Model = SimpleModel(FLAG.input_dim,
FLAG.hidden_dim,
FLAG.output_dim,
optimizer=tf.train.RMSPropOptimizer(
FLAG.learning_rate),
using_gpu=False)
image_path = sys.argv[1]
cascPath = "./haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascPath)
image = cv2.imread(image_path)
src_height, src_width, src_channels = image.shape
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30),
flags=cv2.CASCADE_SCALE_IMAGE)
for x, y, w, h in faces:
print("faceLocation : ({},{}), width={}, height={}".format(x, y, w, h))
cropped_image = gray[x:x + w, y:y + h]
resized_image = imresize(cropped_image, (FLAG.Width, FLAG.Height))
resized_image = resized_image.flatten() / 255
pred_feature = Model.predict(resized_image).flatten()
pred_feature[::2] = pred_feature[::2] * w + x
pred_feature[1::2] = pred_feature[1::2] * h + y
result_img = draw_features_point_on_image(image, [pred_feature], src_width,
src_height)
print(pred_feature)
for (x, y, w, h) in faces:
cv2.rectangle(result_img, (x, y), (x + w, y + h), (0, 255, 0), 1)
cv2.imshow('Result', result_img)
cv2.imwrite("./result_img.png", result_img)
cv2.waitKey(0)
cv2.destroyAllWindows()