def train_validation_set_2(model_input=None, model_output=None):
data_v = DataSources.load_validation_dataset2()
data = DataSources.load_naive_augmented_dataset(DataSources.DataSources.VALIDATION_SET2_NG)
# data = data[:]
run_train(data, data_v, model_input=model_input, model_output=model_output, epochs=50)
def train_300w_3d_helen_naive_augmentations(data_sources: [DataSources.DataSources],
model_input, model_output,
limit=-1):
log.info('train_300w_3d_helen_naive_augmentations::')
data_v: [Data] = DataSources.load_validation_dataset2(recalc_pose=True)
data: [Data] = []
for data_source in data_sources:
data += DataSources.load_naive_augmented_dataset(data_source, limit=limit)
if limit > -1:
np.random.shuffle(data)
data = data[:limit]
run_train(data, data_v, model_input=model_input, model_output=model_output, epochs=30)
def validate_load_image():
import DataSources
from detect_face import DetectFace
from keras.preprocessing import image
# from keras.applications.resnet50 import preprocess_input
data = DataSources.load_validation_dataset2()
data: [Data] = DetectFace.get_face_bboxes(data[:1])
image_array = load_image(data[0])
image_array = preprocess_input(image_array, mode='tf')
img = image.load_img(data[0].image, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x, mode='tf')
print('done')
def validate_pose_vs_landmarks():
import DataSources
import GenerateTrainingSet
data = DataSources.load_naive_augmented_dataset(
DataSources.DataSources.VALIDATION_SET2_NG)
data = DataSources.load_validation_dataset2(
DataSources.DataSources.VALIDATION_2, recalc_pose=False)
total_theta = 0
for data_ in data:
face_model = GenerateTrainingSet.get_face_model()
rot_mat_orig, _ = cv2.Rodrigues(data_.pose[:3])
rotation_vecs, translation_vecs = GenerateTrainingSet.solve_pnp(
data_.landmarks_2d, face_model.model_TD, face_model)
rot_mat_land, _ = cv2.Rodrigues(rotation_vecs)
theta = Utils.get_theta_between_rot_mats(rot_mat_orig, rot_mat_land)
total_theta += theta
print(np.rad2deg(total_theta / len(data)))
def naive_augment_validation_set2():
output_folder = '../augmented/validation_set2'
data: [Data] = DataSources.load_validation_dataset2()
gen_naive_augmentations(data, output_folder)
def run_validation_set2(model_input=None, limit=-1, is_6pos=False, model_name='c2_net'):
log.info('run_validation_set2::')
data: [Data] = DataSources.load_validation_dataset2()
predict(data, model_input, limit, is_6pos, model_name=model_name)