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import sys
import os
import numpy as np
import tensorflow as tf
from keras_preprocessing import image
from matplotlib import pyplot as plt
# Loading the pre-trained model/best saved weight and perform Prediction
# model = tf.keras.models.load_model('../Rock_Paper_Scissors_VGG16/RPS_Model.hdf5')
model = tf.keras.models.load_model('../Rock_Paper_Scissors_VGG16/best_weights.hdf5')
img_width, img_height = 224, 224
# Predict function
def predict_image(image_input, model):
if image_input is None or image_input == '':
print("Invalid type")
return None
# putting the images in an array
img_array = image.img_to_array(image_input)
processed_img = tf.reshape(img_array, shape=[1, img_width, img_height, 3])
# It uses the model to predict the class probabilities for the processed image.
predict_proba = np.max(model.predict(processed_img)[0])
# It identifies the predicted class index and its corresponding label.
predict_class = np.argmax(model.predict(processed_img))
# Map predicted class index to label
class_labels = ['Paper', 'Rock', 'Scissors']
predict_label = class_labels[predict_class]
# It plots the input image with its predicted class label and displays the image without axis ticks.
plt.figure(figsize=(4, 4))
plt.imshow(img)
plt.axis('off')
plt.title(f'Predicted Class: {predict_label}')
plt.show()
# Print prediction result and probability
print("\nImage prediction result:", predict_label)
print("Probability:", round(predict_proba * 100, 2), "%")
print('\n')
# asking the user for their desired folder location
if __name__ == "__main__":
image_path = ''
if len(sys.argv) != 2:
image_path = input("Enter the path to the image file: ")
if input() == '':
image_path = '../rps/test'
# it collects 21 random images from the folder
for filename in os.listdir(image_path)[0:20]:
filepath = os.path.join(image_path, filename)
# it sends the images and loaded model to prediction function
img = image.load_img(filepath, target_size=(img_width, img_height))
predict_image(img, model)
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