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