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Neural Network - Testers (edit)

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s473609 2023-06-01 23:59:47 +02:00
parent c006fc128e
commit b1880d61cb
2 changed files with 9 additions and 10 deletions
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17
Network/TesterRandom.py
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@ -1,14 +1,13 @@
import os
import numpy as np
import tensorflow as tf import tensorflow as tf
from tensorflow import keras from tensorflow import keras
from tensorflow.keras.preprocessing import image
import numpy as np
import os
# Load the trained model # Load the trained model
model = keras.models.load_model('trained_model.h5') model = keras.models.load_model('trained_model.h5')
# Load the class names # Load the class names
class_names = ['Empty', 'Food','People'] class_names = ['Table', 'Done','Order']
# Path to the folder containing test images # Path to the folder containing test images
test_images_folder = 'Testing/' test_images_folder = 'Testing/'
@ -23,11 +22,11 @@ for folder_name in os.listdir(test_images_folder):
# True class based on folder name # True class based on folder name
if folder_name == 'Empty': if folder_name == 'Empty':
true_class = 'Empty' true_class = 'Table'
elif folder_name == 'Food': elif folder_name == 'Food':
true_class = 'Food' true_class = 'Done'
elif folder_name == 'People': elif folder_name == 'People':
true_class = 'People' true_class = 'Order'
true_class = folder_name true_class = folder_name
# Iterate over the files in the subfolder # Iterate over the files in the subfolder
@ -36,8 +35,8 @@ for folder_name in os.listdir(test_images_folder):
i+=1 i+=1
# Load and preprocess the test image # Load and preprocess the test image
image_path = os.path.join(folder_path, filename) image_path = os.path.join(folder_path, filename)
test_image = image.load_img(image_path, target_size=(100, 100)) test_image = keras.preprocessingimage.load_img(image_path, target_size=(100, 100))
test_image = image.img_to_array(test_image) test_image = keras.preprocessingimage.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis=0) test_image = np.expand_dims(test_image, axis=0)
test_image = test_image / 255.0 # Normalize the image test_image = test_image / 255.0 # Normalize the image

2
Network/testerVal.py
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@ -7,7 +7,7 @@ from tensorflow import keras
model = keras.models.load_model('trained_model.h5') model = keras.models.load_model('trained_model.h5')
# Load the class names # Load the class names
class_names = ['Empty', 'Food','People'] class_names = ['Table', 'Done','Order']
# Load and preprocess the validation dataset # Load and preprocess the validation dataset
data_dir = "Training/" data_dir = "Training/"