|
|
| import tensorflow as tf |
| import tensorflow.keras as keras |
| import numpy as np |
| import matplotlib.pyplot as plt |
| import math |
| from datetime import datetime |
| from numpy import save, load, asarray |
| import csv |
| from skimage.io import imread |
| from skimage.transform import resize |
| import pickle |
| from PIL import Image |
|
|
| import os |
|
|
| |
|
|
| class TestModels: |
| def __init__(self, h5_address:str, GPU=True): |
| self.exps = ['Neutral', 'Happy', 'Sad', 'Surprise', 'Fear', 'Disgust', 'Anger'] |
|
|
| if GPU: |
| physical_devices = tf.config.list_physical_devices('GPU') |
| tf.config.experimental.set_memory_growth(physical_devices[0], True) |
|
|
| self.model = self.load_model(h5_address=h5_address) |
|
|
| def load_model(self, h5_address: str): |
| "load weight file and create the model once" |
| model = tf.keras.models.load_model(h5_address, custom_objects={'tf': tf}) |
| return model |
|
|
| def recognize_fer(self, img_path:str): |
| "create and image from the path and recognize expression" |
| img = imread(img_path) |
| |
| img = resize(img, (224, 224,3)) |
| img = np.expand_dims(img, axis=0) |
| |
| prediction = self.model.predict_on_batch([img]) |
| exp = np.array(prediction[0]) |
| '''in case you need the embeddings''' |
| |
| print(self.exps[np.argmax(exp)]) |
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