Update RPS_Part2_VGG16.py

RPS_Part2_VGG16.py

@@ -1,61 +1,58 @@
-import sys
-import os
-import numpy as np
-import tensorflow as tf
-from keras_preprocessing import image
-from matplotlib import pyplot as plt
-
-# Part 2
-# a) The tested image is to be supplied via the arguments list
-# b) visualisation of the supplied image with the prediction score and predicted label
-
-# 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)
+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)