automatyczny_kelner/Tester2.py
2023-06-01 22:52:51 +02:00

38 lines
1.3 KiB
Python

import os
import numpy as np
import tensorflow as tf
from tensorflow import keras
train_data_dir = "Training/"
train_ds = tf.keras.utils.image_dataset_from_directory(train_data_dir, validation_split=0.2,
subset="training", seed=123, batch_size=32, image_size=(100, 100))
val_ds = tf.keras.utils.image_dataset_from_directory(train_data_dir, validation_split=0.2,
subset="validation", seed=123, batch_size=32, image_size=(100, 100))
model = keras.models.load_model("trained_model.h5")
predictions = model.predict(val_ds.take(32))
classNames = ['Empty', 'Food','People']
# Make predictions
direct = ''
i = 0
for image, _ in val_ds.take(32):
predicted_class_index = np.argmax(predictions[i])
predicted_class = classNames[predicted_class_index]
filename = predicted_class + str(i) + '.jpeg'
tf.keras.preprocessing.image.save_img(direct+filename, image[0])
print('Predicted class:', predicted_class)
i += 1
#direct = ''
#i = 0
#for image, _ in val_ds.take(32):
# predictedLabel = int(predictions[i] >= 0.5)
#
# filename = classNames[predictedLabel] + str(i) + '.jpeg'
# tf.keras.preprocessing.image.save_img(direct+filename, image[0])
# i += 1