Neural Network - Testers

Network/TesterRandom.py

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+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras.preprocessing import image
+import numpy as np
+import os
+
+# Load the trained model
+model = keras.models.load_model('trained_model.h5')
+
+# Load the class names
+class_names = ['Empty', 'Food','People']
+
+# Path to the folder containing test images
+test_images_folder = 'Testing/'
+
+# Iterate over the test images
+i = 0
+errorcount = 0
+for folder_name in os.listdir(test_images_folder):
+    folder_path = os.path.join(test_images_folder, folder_name)
+    if os.path.isdir(folder_path):
+        print('Testing images in folder:', folder_name)
+        
+        # True class based on folder name
+        if folder_name == 'Empty':
+            true_class = 'Empty'
+        elif folder_name == 'Food':
+            true_class = 'Food'
+        elif folder_name == 'People':
+            true_class = 'People'
+        true_class = folder_name
+        
+        # Iterate over the files in the subfolder
+        for filename in os.listdir(folder_path):
+            if filename.endswith('.jpg') or filename.endswith('.jpeg'):
+                i+=1
+                # Load and preprocess the test image
+                image_path = os.path.join(folder_path, filename)
+                test_image = image.load_img(image_path, target_size=(100, 100))
+                test_image = image.img_to_array(test_image)
+                test_image = np.expand_dims(test_image, axis=0)
+                test_image = test_image / 255.0  # Normalize the image
+            
+                # Reshape the image array to (1, height, width, channels)
+                test_image = np.reshape(test_image, (1,100, 100, 3))
+            
+                # Make predictions
+                predictions = model.predict(test_image)
+                predicted_class_index = np.argmax(predictions[0])
+                predicted_class = class_names[predicted_class_index]
+                
+                direct = 'Results/'
+                filename = str(i) + predicted_class  + '.jpeg'
+                test_image = np.reshape(test_image, (100, 100, 3))
+                tf.keras.preprocessing.image.save_img(direct+filename, test_image)
+                if predicted_class != true_class:
+                    errorcount += 1
+                print('Image:', filename)
+                print('True class:', true_class)
+                print('Predicted class:', predicted_class)
+                print()
+print('Error count: ', errorcount)

Network/testerVal.py

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+import os
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+
+# Load the trained model
+model = keras.models.load_model('trained_model.h5')
+
+# Load the class names
+class_names = ['Empty', 'Food','People']
+
+# Load and preprocess the validation dataset
+data_dir = "Training/"
+image_size = (100, 100)
+batch_size = 32
+
+val_ds = tf.keras.preprocessing.image_dataset_from_directory(
+    data_dir,
+    validation_split=0.2,
+    subset="validation",
+    seed=123,
+    image_size=image_size,
+    batch_size=batch_size,
+)
+
+# Select 20 random images from the validation set
+val_images = []
+val_labels = []
+for images, labels in val_ds.unbatch().shuffle(1000).take(60):
+    val_images.append(images)
+    val_labels.append(labels)
+
+# Make predictions on the random images
+errorcount = 0
+for i in range(60):
+    test_image = val_images[i]
+    test_label = val_labels[i]
+    test_image = np.expand_dims(test_image, axis=0)
+    test_image = test_image / 255.0  # Normalize the image
+
+    # Make predictions
+    predictions = model.predict(test_image)
+    predicted_class_index = np.argmax(predictions[0])
+    predicted_class = class_names[predicted_class_index]
+
+    true_class = class_names[test_label]
+
+    direct = 'Results/'
+    filename = predicted_class + str(i) + '.jpeg'
+    tf.keras.preprocessing.image.save_img(direct+filename, test_image)
+    if predicted_class != true_class:
+        errorcount += 1
+    print('Image', i+1)
+    print('True class:', true_class)
+    print('Predicted class:', predicted_class)
+    print()
+print('Error count: ', errorcount)