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from config import DatasetName, W300Conf, DatasetType, LearningConfig, InputDataSize, CofwConf |
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import tensorflow as tf |
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import cv2 |
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import os.path |
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import scipy.io as sio |
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from cnn import CNNModel |
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from tqdm import tqdm |
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import numpy as np |
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from os import listdir |
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from os.path import isfile, join |
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from scipy.integrate import simps |
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from scipy.integrate import trapz |
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import matplotlib.pyplot as plt |
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from skimage.io import imread |
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class Test: |
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def test_model(self, pretrained_model_path, ds_name): |
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if ds_name == DatasetName.w300: |
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test_annotation_path = W300Conf.test_annotation_path |
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test_image_path = W300Conf.test_image_path |
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elif ds_name == DatasetName.cofw: |
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test_annotation_path = CofwConf.test_annotation_path |
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test_image_path = CofwConf.test_image_path |
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model = tf.keras.models.load_model(pretrained_model_path) |
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for i, file in tqdm(enumerate(os.listdir(test_image_path))): |
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img = imread(test_image_path + file) / 255.0 |
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prediction = model.predict(np.expand_dims(img, axis=0)) |
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landmark_predicted = prediction |
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