Update RPS_Part3_VGG16.py

RPS_Part3_VGG16.py

@@ -1,87 +1,83 @@
-import os
-import random
-import matplotlib.pyplot as plt
-import numpy as np
-import tensorflow as tf
-from keras_preprocessing import image
-
-# Part 3
-# a) Read two images of hand signs provided as script arguments
-# b) Predict the labels of the two images
-# c) Output which image won the rock, paper, scissor game
-
-# Loading the pre-trained model/best saved weight and perform Prediction
-# vgg16_model = tf.keras.models.load_model('../Rock_Paper_Scissors_VGG16/RPS_Model.hdf5')
-vgg16_model = tf.keras.models.load_model('../Rock_Paper_Scissors_VGG16/best_weights.hdf5')
-img_width, img_height = 224, 224
-# Define labels and Image addresses
-class_labels = ['paper', 'rock', 'scissors']
-folder_path = '../rps/test'
-
-
-# Get a random Image from Folder
-def selectRandomPicture(folder_path):
-    files = os.listdir(folder_path)
-    image_files = [file for file in files if file.lower().endswith(('.png', '.jpg', '.jpeg'))]
-    random_photo = random.choice(image_files)
-    return os.path.join(folder_path, random_photo)
-
-
-# Function to load and preprocess images
-def load_and_preprocess_image(image_path):
-    img = image.load_img(image_path, target_size=(img_width, img_height))
-    img_array = image.img_to_array(img)
-    img_array /= 255.0  # Normalize pixel values
-
-    return img_array
-
-
-# A simple condition/rules to decide who’s the winner
-def whos_winner(first_image, second_image):
-    winner = ''
-    if first_image == second_image:
-        winner = "Tie!!"
-
-    elif (first_image == 'rock' and second_image == 'scissors' or
-          first_image == 'scissors' and second_image == 'rock'):
-        winner = "Rock wins"
-
-    elif (first_image == 'rock' and second_image == 'paper' or
-          first_image == 'paper' and second_image == 'rock'):
-        winner = "Paper wins"
-
-    elif (first_image == 'paper' and second_image == 'scissors' or
-          first_image == 'scissors' and second_image == 'paper'):
-        winner = "Scissors wins"
-
-    return winner
-
-
-# Read and preprocess the images and put 2 images in separate variables
-image1 = load_and_preprocess_image(selectRandomPicture(folder_path))
-image2 = load_and_preprocess_image(selectRandomPicture(folder_path))
-
-# Predict the labels of the images
-images = np.array([image1, image2])
-predictions = vgg16_model.predict(images)
-predicted_classes = np.argmax(predictions, axis=1)
-
-firs_img = class_labels[predicted_classes[0]]
-sec_img = class_labels[predicted_classes[1]]
-
-# Plot the images
-plt.figure(figsize=(8, 5))
-plt.subplot(1, 2, 1)
-plt.imshow(image1)
-plt.title(class_labels[predicted_classes[0]])
-plt.axis('off')
-# Plot/Display the last result
-plt.subplot(1, 2, 2)
-plt.imshow(image2)
-plt.title(class_labels[predicted_classes[1]])
-plt.axis('off')
-plt.tight_layout()
-plt.suptitle(f'{whos_winner(firs_img, sec_img)}!')
-plt.show()
-
-print(f'The winner is:{whos_winner(firs_img, sec_img)}')
+import os
+import random
+import matplotlib.pyplot as plt
+import numpy as np
+import tensorflow as tf
+from keras_preprocessing import image
+
+
+# Loading the pre-trained model/best saved weight and perform Prediction
+# vgg16_model = tf.keras.models.load_model('../Rock_Paper_Scissors_VGG16/RPS_Model.hdf5')
+vgg16_model = tf.keras.models.load_model('../Rock_Paper_Scissors_VGG16/best_weights.hdf5')
+img_width, img_height = 224, 224
+# Define labels and Image addresses
+class_labels = ['paper', 'rock', 'scissors']
+folder_path = '../rps/test'
+
+
+# Get a random Image from Folder
+def selectRandomPicture(folder_path):
+    files = os.listdir(folder_path)
+    image_files = [file for file in files if file.lower().endswith(('.png', '.jpg', '.jpeg'))]
+    random_photo = random.choice(image_files)
+    return os.path.join(folder_path, random_photo)
+
+
+# Function to load and preprocess images
+def load_and_preprocess_image(image_path):
+    img = image.load_img(image_path, target_size=(img_width, img_height))
+    img_array = image.img_to_array(img)
+    img_array /= 255.0  # Normalize pixel values
+
+    return img_array
+
+
+# A simple condition/rules to decide who’s the winner
+def whos_winner(first_image, second_image):
+    winner = ''
+    if first_image == second_image:
+        winner = "Tie!!"
+
+    elif (first_image == 'rock' and second_image == 'scissors' or
+          first_image == 'scissors' and second_image == 'rock'):
+        winner = "Rock wins"
+
+    elif (first_image == 'rock' and second_image == 'paper' or
+          first_image == 'paper' and second_image == 'rock'):
+        winner = "Paper wins"
+
+    elif (first_image == 'paper' and second_image == 'scissors' or
+          first_image == 'scissors' and second_image == 'paper'):
+        winner = "Scissors wins"
+
+    return winner
+
+
+# Read and preprocess the images and put 2 images in separate variables
+image1 = load_and_preprocess_image(selectRandomPicture(folder_path))
+image2 = load_and_preprocess_image(selectRandomPicture(folder_path))
+
+# Predict the labels of the images
+images = np.array([image1, image2])
+predictions = vgg16_model.predict(images)
+predicted_classes = np.argmax(predictions, axis=1)
+
+firs_img = class_labels[predicted_classes[0]]
+sec_img = class_labels[predicted_classes[1]]
+
+# Plot the images
+plt.figure(figsize=(8, 5))
+plt.subplot(1, 2, 1)
+plt.imshow(image1)
+plt.title(class_labels[predicted_classes[0]])
+plt.axis('off')
+# Plot/Display the last result
+plt.subplot(1, 2, 2)
+plt.imshow(image2)
+plt.title(class_labels[predicted_classes[1]])
+plt.axis('off')
+plt.tight_layout()
+plt.suptitle(f'{whos_winner(firs_img, sec_img)}!')
+plt.show()
+
+print(f'The winner is:{whos_winner(firs_img, sec_img)}')