51 lines
1.8 KiB
Python
51 lines
1.8 KiB
Python
import os
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import random
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import logging
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import numpy as np
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import tensorflow as tf
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from tensorflow import keras
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from termcolor import colored
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class Predictor:
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def __init__(self):
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# Turn off interactive logging
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tf.get_logger().setLevel(logging.ERROR)
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# Load the trained model
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self.model = keras.models.load_model('Network/trained_model.h5')
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# Load the class names
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self.class_names = ['table', 'table', 'order']
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# Path to the folder containing test images
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self.test_images_folder = 'Network/Testing/'
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def predict(self, image_path):
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# Load and preprocess the test image
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test_image = keras.preprocessing.image.load_img(
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image_path, target_size=(100, 100))
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test_image = keras.preprocessing.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 = self.model.predict(test_image, verbose=None)
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predicted_class_index = np.argmax(predictions[0])
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predicted_class = self.class_names[predicted_class_index]
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print(colored("Predicted class: ", "yellow")+f"{predicted_class}")
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return predicted_class
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def random_path_img(self) -> str:
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folder_name = random.choice(os.listdir(self.test_images_folder))
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folder_path = os.path.join(self.test_images_folder, folder_name)
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filename = ""
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while not (filename.endswith('.jpg') or filename.endswith('.jpeg')):
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filename = random.choice(os.listdir(folder_path))
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image_path = os.path.join(folder_path, filename)
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return image_path
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