31 lines
948 B
Python
31 lines
948 B
Python
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# TensorFlow and tf.keras
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import tensorflow as tf
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from tensorflow.keras.applications.imagenet_utils import decode_predictions
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# Helper libraries
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import numpy as np
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import cv2
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# load an image
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Testing_img_path ="D:\ISE\Internet of Things\RPi_Lab 3\cat2.jpg"
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input_image = cv2.imread(Testing_img_path)
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# Preprae data
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input_image = cv2.resize(input_image, (224, 224))
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input_image = np.expand_dims(input_image, axis=0) # Change the shape of image array like input image when trained
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# Load the ResNet50 model
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loadpath = "Resnet50.h5"
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resnet_model = tf.keras.models.load_model(loadpath)
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# get the predicted probabilities for each class
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predictions = resnet_model.predict(input_image)
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print("Predicted Top 3 is:", decode_predictions(predictions, top=3)[0])
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Answer = decode_predictions(predictions, top=1)[0][0][1]
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print("Predicted = ", Answer)
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# Visualize, check the answer
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img = cv2.imread(Testing_img_path)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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