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from configuration import DatasetName, WflwConf, W300Conf, DatasetType, LearningConfig, InputDataSize
import tensorflow as tf
import cv2
import os.path
import scipy.io as sio
from cnn_model import CNNModel
from tqdm import tqdm
import numpy as np
from os import listdir
from os.path import isfile, join
from scipy.integrate import simps
from scipy.integrate import trapz
import matplotlib.pyplot as plt
from skimage.io import imread
class Test:
def test_model(self, pretrained_model_path, ds_name):
if ds_name == DatasetName.w300:
test_annotation_path = W300Conf.test_annotation_path
test_image_path = W300Conf.test_image_path
elif ds_name == DatasetName.wflw:
test_annotation_path = WflwConf.test_annotation_path
test_image_path = WflwConf.test_image_path
model = tf.keras.models.load_model(pretrained_model_path)
for i, file in tqdm(enumerate(os.listdir(test_image_path))):
# load image and then normalize it
img = imread(test_image_path + file)/255.0
# prediction
prediction = model.predict(np.expand_dims(img, axis=0))
# the first dimension is landmark point
landmark_predicted = prediction[0][0]
# the second dimension is the pose
pose_predicted = prediction[1][0]
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