32 lines
1.0 KiB
Python
32 lines
1.0 KiB
Python
import tensorflow as tf
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import numpy as np
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from tensorflow import keras
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import os
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import random
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from PIL import Image
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img_height = 180
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img_width = 180
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class_names=['glass','metal','paper','plastic']
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model = tf.keras.models.load_model('C:/Users/Natalia/Desktop/lsm04/smieciara/saved_model_vers2')
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def predict():
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path="C:/Users/Natalia/Documents/dane_testowe"
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files=os.listdir(path)
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d=random.choice(files)
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im = Image.open("C:/Users/Natalia/Documents/dane_testowe/" + d)
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im.show()
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img = keras.preprocessing.image.load_img(
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"C:/Users/Natalia/Documents/dane_testowe/" + d, target_size=(img_height, img_width)
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)
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img_array = keras.preprocessing.image.img_to_array(img)
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img_array = tf.expand_dims(img_array, 0) # Create a batch
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predictions = model.predict(img_array)
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score = tf.nn.softmax(predictions[0])
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print(
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"This image most likely belongs to {} with a {:.2f} percent confidence."
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.format(class_names[np.argmax(score)], 100 * np.max(score))
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)
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