Initial Commit

FriendFacialRecognition/.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

FriendFacialRecognition/.idea/bot.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

FriendFacialRecognition/.idea/encodings.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Encoding">
+    <file url="PROJECT" charset="UTF-8" />
+  </component>
+</project>

FriendFacialRecognition/.idea/inspectionProfiles/Project_Default.xml

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+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyInterpreterInspection" enabled="false" level="WARNING" enabled_by_default="false" />
+    <inspection_tool class="PyPep8Inspection" enabled="false" level="WEAK WARNING" enabled_by_default="false" />
+    <inspection_tool class="PyPep8NamingInspection" enabled="false" level="WEAK WARNING" enabled_by_default="false" />
+  </profile>
+</component>

FriendFacialRecognition/.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

FriendFacialRecognition/.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11 (bot)" project-jdk-type="Python SDK" />
+</project>

FriendFacialRecognition/.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/bot.iml" filepath="$PROJECT_DIR$/.idea/bot.iml" />
+    </modules>
+  </component>
+</project>

FriendFacialRecognition/.idea/vcs.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>

FriendFacialRecognition/CNN.py

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+from image_manipulation import *
+from keras import layers
+from keras import models
+import matplotlib.pyplot as plt
+import tensorflow as tf
+import keras
+import numpy as np
+
+def data_process():
+    train_datagen = ImageDataGenerator(rescale = 1./255)
+    test_datagen = ImageDataGenerator(rescale = 1./255)
+    train_generator = train_datagen.flow_from_directory(train_dir, target_size = (300, 350), batch_size = 8, class_mode = 'sparse')
+    validation_generator = test_datagen.flow_from_directory(validation_dir, target_size = (300, 350), batch_size = 8, class_mode = 'sparse')
+    return train_generator, validation_generator
+
+def CNN_model():
+    # creating CNN model
+    model = models.Sequential()
+    model.add(layers.Conv2D(32, (3, 3), activation = 'relu', input_shape = (300, 350, 3)))
+    model.add(layers.MaxPooling2D((2, 2)))
+    model.add(layers.Conv2D(64, (3, 3), activation = 'relu'))
+    model.add(layers.MaxPooling2D((2, 2)))
+    model.add(layers.Conv2D(128, (3, 3), activation = 'relu'))
+    model.add(layers.MaxPooling2D((2, 2)))
+    model.add(layers.Conv2D(128, (3, 3), activation = 'relu'))
+    model.add(layers.MaxPooling2D((2, 2)))
+    model.add(layers.Dropout(0.2))
+    model.add(layers.Flatten())
+    model.add(layers.Dense(256, activation = 'relu'))
+    model.add(layers.Dense(2, activation = 'softmax'))
+
+    return model
+
+def fit():
+    model = CNN_model()
+    model.compile(loss = 'sparse_categorical_crossentropy',
+                  optimizer = keras.optimizers.RMSprop(learning_rate = 1e-5), metrics = ['acc'])
+    train_generator, validation_generator = data_process()
+    return model.fit(train_generator, steps_per_epoch = 25, epochs = 50,
+                     validation_data = validation_generator, validation_steps = 6)
+
+def plot(history):
+    acc = history.history['acc']
+    val_acc = history.history['val_acc']
+    loss = history.history['loss']
+    val_loss = history.history['val_loss']
+    epochs = range(1, len(acc) + 1)
+    plt.plot(epochs, acc, 'r', label = 'Training acc')
+    plt.plot(epochs, val_acc, 'b', label = 'Validation acc')
+    plt.title('Training and validation accuracy')
+    plt.legend()
+    plt.figure()
+    plt.plot(epochs, loss, 'r', label = 'Training loss')
+    plt.plot(epochs, val_loss, 'b', label = 'Validation loss')
+    plt.title('Training and validation loss')
+    plt.legend()
+    plt.show()
+
+def test_image(model):
+    image_dir = "C:\\Ryan\\PersonalProject\\FriendRecog\\bot\\resized_images"
+    class_names = ["Brandon", "Manuel"]
+    target_size = (300, 350)
+
+    # List all image files in directory
+    image_files = [os.path.join(image_dir, file) for file in os.listdir(image_dir) if file.endswith('.png')]
+
+    # Loop over each image file
+    for image_path in image_files:
+        # Load the image
+        img = keras.utils.load_img(image_path, target_size = target_size)
+
+        # Preprocess the image
+        img_array = keras.utils.img_to_array(img)
+        img_array = tf.expand_dims(img_array, 0) # create a batch
+
+        # Use the pre-trained model to predict
+        predictions = model.predict(img_array)
+        score = tf.nn.softmax(predictions[0])
+
+        predicted_class =  class_names[np.argmax(score)]
+        confidence = np.max(score)
+
+        return predicted_class, confidence

FriendFacialRecognition/bot.py

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+from CNN import CNN_model, test_image
+from image_manipulation import resize
+import discord
+from discord.ext import commands
+import uuid
+import requests
+import shutil
+import os
+
+intents = discord.Intents.default()
+intents.message_content = True
+client = commands.Bot(command_prefix= '.', intents = intents)
+
+@client.event
+async def on_ready():
+    print("Bot is up and running!")
+@client.command()
+async def save(ctx):
+    try:
+        url = ctx.message.attachments[0].url
+    except IndexError:
+        print("Error: No attachments")
+        await ctx.send("No attachments detected. Please try again.")
+    else:
+        if url[0:26] == "https://cdn.discordapp.com":
+            r = requests.get(url, stream = True)
+            imageName = str(uuid.uuid4()) + '.png'
+            folder_path = "C:\Ryan\PersonalProject\\FriendRecog\\bot\images"
+            full_path = os.path.join(folder_path, imageName)
+            with open(full_path, 'wb') as file:
+                print('Saving Image: ' + imageName)
+                shutil.copyfileobj(r.raw, file)
+
+            resize()
+            model = CNN_model()
+            predicted_class, confidence = test_image(model)
+            await ctx.send("Prediction: "  + str(predicted_class) + " with a " + 100 * str(confidence) + "% confidence level.")

FriendFacialRecognition/image_manipulation.py

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+import os
+from PIL import Image
+from keras.preprocessing import image
+from keras.preprocessing.image import ImageDataGenerator
+
+BRANDON_ORIGINAL_DATASET_DIR = "C:\Ryan\PP stuff\\try1\Classification Data-20240212T032009Z-001\Classification Data\\Brandon"
+MANUEL_ORIGINAL_DATASET_DIR = "C:\\Ryan\\PP stuff\\try1\\Classification Data-20240212T032009Z-001\\Classification Data\\Manuel"
+BASE_DIR = "C:\\Ryan\\PP stuff\\try1\\face_recog"
+
+#create directories for train/validation/test sets
+train_dir = os.path.join(BASE_DIR, 'train')
+validation_dir = os.path.join(BASE_DIR, 'validation')
+test_dir = os.path.join(BASE_DIR, 'test')
+
+train_bran_dir = os.path.join(train_dir, 'brandon')
+train_man_dir = os.path.join(train_dir, 'manuel')
+
+validation_bran_dir = os.path.join(validation_dir, 'brandon')
+validation_man_dir = os.path.join(validation_dir, 'manuel')
+
+test_bran_dir = os.path.join(test_dir, 'brandon')
+test_man_dir = os.path.join(test_dir, 'manuel')
+
+def resize():
+    target_size = (300, 350)
+
+    input_dir = "C:\Ryan\PersonalProject\\FriendRecog\\bot\images"
+    output_dir = "C:\\Ryan\\PersonalProject\\FriendRecog\\bot\\resized_images"
+
+    try:
+        for filename in os.listdir(input_dir):
+            # Construct the full path to the image file
+            input_path = os.path.join(input_dir, filename)
+
+            # Open the image
+            with Image.open(input_path) as img:
+                # Resize the image
+                resized_img = img.resize(target_size)
+
+                # Construct the output path
+                output_path = os.path.join(output_dir, filename)
+
+                # Save the resized image
+                resized_img.save(output_path)
+    finally:
+        pass
+
+def data_augmentation():
+    augmented_datagen = ImageDataGenerator(
+        rescale = 1. / 255,
+        rotation_range = 40,
+        width_shift_range = 0.2,
+        height_shift_range = 0.2,
+        shear_range = 0.2,
+        zoom_range = 0.2,
+        horizontal_flip = True,
+        fill_mode = "nearest")
+    augmented_generator = augmented_datagen.flow_from_directory(train_dir, target_size = (300, 350),
+                                                                batch_size = 20,
+                                                                class_mode = 'sparse')
+
+    augmented_dir = os.path.join(BASE_DIR, "augmented")
+    augmented_all = os.path.join(augmented_dir, "all")
+    os.mkdir(augmented_dir)
+    os.mkdir(augmented_all)
+
+    for i, (images, labels) in enumerate(augmented_generator):
+        if i >= 5:
+            break
+        for j in range(len(images)):
+            augmented_image = image.array_to_img(images[j])
+            filename = f"{i * len(images) + j}.png"
+            augmented_image_path = os.path.join(augmented_all, filename)
+            augmented_image.save(augmented_image_path)

FriendFacialRecognition/main.py

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+from CNN import fit
+from bot import client
+
+if __name__ == '__main__':
+    model = fit()
+    client.run("MTIwOTcxMDMxNDQwOTU2NjIwOA.GWFDG0.qNkiHf_SfG-Jj4pMsD7P1D1urhq9RaTGLLIpTg")