65 lines
2.5 KiB
Python
65 lines
2.5 KiB
Python
import os
|
|
from pathlib import Path
|
|
import numpy as np
|
|
import tensorflow as tf
|
|
from tensorflow import keras
|
|
|
|
# Load the trained model
|
|
model = keras.models.load_model('Network/trained_model.h5')
|
|
|
|
# Load the class names
|
|
class_names = ['table', 'done', 'order']
|
|
|
|
# Path to the folder containing test images
|
|
test_images_folder = 'Network/Testing/'
|
|
|
|
# Iterate over the test images
|
|
i = 0
|
|
errorcount = 0
|
|
for folder_name in os.listdir(test_images_folder):
|
|
folder_path = os.path.join(test_images_folder, folder_name)
|
|
if os.path.isdir(folder_path):
|
|
print('Testing images in folder:', folder_name)
|
|
|
|
# True class based on folder name
|
|
if folder_name == 'Empty':
|
|
true_class = 'table'
|
|
elif folder_name == 'Food':
|
|
true_class = 'done'
|
|
elif folder_name == 'People':
|
|
true_class = 'order'
|
|
|
|
# Iterate over the files in the subfolder
|
|
for filename in os.listdir(folder_path):
|
|
if filename.endswith('.jpg') or filename.endswith('.jpeg'):
|
|
i += 1
|
|
# Load and preprocess the test image
|
|
image_path = os.path.join(folder_path, filename)
|
|
test_image = keras.preprocessing.image.load_img(
|
|
image_path, target_size=(100, 100))
|
|
test_image = keras.preprocessing.image.img_to_array(test_image)
|
|
test_image = np.expand_dims(test_image, axis=0)
|
|
test_image = test_image / 255.0 # Normalize the image
|
|
|
|
# Reshape the image array to (1, height, width, channels)
|
|
test_image = np.reshape(test_image, (1, 100, 100, 3))
|
|
|
|
# Make predictions
|
|
predictions = model.predict(test_image)
|
|
predicted_class_index = np.argmax(predictions[0])
|
|
predicted_class = class_names[predicted_class_index]
|
|
|
|
direct = 'Network/Results/'
|
|
filename = str(i) + predicted_class + '.jpeg'
|
|
test_image = np.reshape(test_image, (100, 100, 3))
|
|
Path(direct).mkdir(parents=True, exist_ok=True)
|
|
tf.keras.preprocessing.image.save_img(
|
|
direct+filename, test_image)
|
|
if predicted_class != true_class:
|
|
errorcount += 1
|
|
print('Image:', filename)
|
|
print('True class:', true_class)
|
|
print('Predicted class:', predicted_class)
|
|
print()
|
|
print('Error count: ', errorcount)
|