diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..4bf62b6ad6cebaeedd2d5901ab2e638f0b043ee3 Binary files /dev/null and b/.DS_Store differ diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..6e19794038b61a15f5706203fcb4c3d10e49771e 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +embedding_modelv2.keras filter=lfs diff=lfs merge=lfs -text diff --git a/.github/workflows/update_space.yml b/.github/workflows/update_space.yml new file mode 100644 index 0000000000000000000000000000000000000000..67dbc84e4e59320a7c98b94460eb976e5cd2984f --- /dev/null +++ b/.github/workflows/update_space.yml @@ -0,0 +1,28 @@ +name: Run Python script + +on: + push: + branches: + - main + +jobs: + build: + runs-on: ubuntu-latest + + steps: + - name: Checkout + uses: actions/checkout@v2 + + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: '3.9' + + - name: Install Gradio + run: python -m pip install gradio + + - name: Log in to Hugging Face + run: python -c 'import huggingface_hub; huggingface_hub.login(token="${{ secrets.hf_token }}")' + + - name: Deploy to Spaces + run: gradio deploy diff --git a/.ipynb_checkpoints/CGI_Classification_by_Fourier_Embeddings-checkpoint.ipynb b/.ipynb_checkpoints/CGI_Classification_by_Fourier_Embeddings-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..97eeceb8e1414cf4273461e3ab544574a0c2e0d2 --- /dev/null +++ b/.ipynb_checkpoints/CGI_Classification_by_Fourier_Embeddings-checkpoint.ipynb @@ -0,0 +1,725 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "private_outputs": true, + "provenance": [], + "machine_shape": "hm" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "### Data Preprocessing" + ], + "metadata": { + "id": "-dt9JrHpxRNH" + } + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "import cv2\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.manifold import TSNE\n", + "from sklearn.model_selection import train_test_split, cross_val_score, StratifiedKFold\n", + "from sklearn.metrics import accuracy_score, f1_score, confusion_matrix\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from xgboost import XGBClassifier\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.decomposition import PCA\n", + "from scipy.spatial import distance\n", + "from collections import Counter\n", + "import seaborn as sns\n", + "import joblib" + ], + "metadata": { + "id": "dHy-E-RQlDoj" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Evaluate classifiers\n", + "def evaluate_classifier(y_true, y_pred, classifier_name):\n", + " acc = accuracy_score(y_true, y_pred)\n", + " f1 = f1_score(y_true, y_pred)\n", + " cm = confusion_matrix(y_true, y_pred)\n", + " print(f\"{classifier_name} - Accuracy: {acc:.4f}, F1 Score: {f1:.4f}\")\n", + " print(f\"Confusion Matrix:\\n{cm}\\n\")\n", + "\n", + " plt.figure(figsize=(8, 6))\n", + " sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['Real Photo', 'CGI'], yticklabels=['Real Photo', 'CGI'])\n", + " plt.title(f'Confusion Matrix for {classifier_name}')\n", + " plt.xlabel('Predicted Labels')\n", + " plt.ylabel('True Labels')\n", + " plt.show()" + ], + "metadata": { + "id": "60Rkg6uR5oyS" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "from PIL import Image\n", + "from scipy.fftpack import fft2\n", + "from tensorflow.keras.models import load_model, Model\n", + "\n", + "# Function to apply Fourier transform\n", + "def apply_fourier_transform(image):\n", + " image = np.array(image)\n", + " fft_image = fft2(image)\n", + " return np.abs(fft_image)\n", + "\n", + "# Function to preprocess image\n", + "def preprocess_image(image_path):\n", + " try:\n", + " image = Image.open(image_path).convert('L')\n", + " image = image.resize((256, 256))\n", + " image = apply_fourier_transform(image)\n", + " image = np.expand_dims(image, axis=-1) # Expand dimensions to match model input shape\n", + " image = np.expand_dims(image, axis=0) # Expand to add batch dimension\n", + " return image\n", + " except Exception as e:\n", + " print(f\"Error processing image {image_path}: {e}\")\n", + " return None\n", + "\n", + "# Function to load embedding model and calculate embeddings\n", + "def calculate_embeddings(image_path, model_path='embedding_modelv2.keras'):\n", + " # Load the trained model\n", + " model = load_model(model_path)\n", + "\n", + " # Remove the final classification layer to get embeddings\n", + " embedding_model = Model(inputs=model.input, outputs=model.output)\n", + "\n", + " # Preprocess the image\n", + " preprocessed_image = preprocess_image(image_path)\n", + "\n", + " # Calculate embeddings\n", + " embeddings = embedding_model.predict(preprocessed_image)\n", + "\n", + " return embeddings\n", + "\n", + "\n", + "def calculate_embeddings_folder(folder_path, model_path='embedding_modelv2.keras'):\n", + " embeddings = []\n", + " labels = []\n", + " for filename in os.listdir(folder_path):\n", + " if filename.endswith(\".jpg\") or filename.endswith(\".png\"):\n", + " image_path = os.path.join(folder_path, filename)\n", + " embedding = calculate_embeddings(image_path, model_path)\n", + " embeddings.append(embedding)\n", + " if \"CGI\" in folder_path:\n", + " labels.append(1)\n", + " else:\n", + " labels.append(0)\n", + " return embeddings, labels" + ], + "metadata": { + "id": "oIsM1ilT5cQC" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "embeddings = np.load('embeddings.npy')\n", + "labels = np.load('labels.npy')" + ], + "metadata": { + "id": "1lzKxl_gJUEg" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(embeddings, labels, test_size=0.2, random_state=42, stratify=labels)" + ], + "metadata": { + "id": "12-KegWL3ZZh" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "X_test.shape" + ], + "metadata": { + "id": "8YY8_59Lmb1N" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "xgb_clf = XGBClassifier(use_label_encoder=False, eval_metric='logloss', early_stopping_rounds=10)\n", + "xgb_clf.fit(X_train, y_train, eval_set=[(X_test, y_test)], verbose=False)\n", + "y_pred_xgb = xgb_clf.predict(X_test)\n", + "evaluate_classifier(y_test, y_pred_xgb, \"XGBoost Classifier\")" + ], + "metadata": { + "id": "fSosG_aU3o67" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from sklearn.neural_network import MLPClassifier as MLP\n", + "from sklearn.svm import SVC" + ], + "metadata": { + "id": "YLhckFv8JYK0" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Naive random classifier\n", + "class RandomClassifier:\n", + " def fit(self, X, y):\n", + " pass\n", + "\n", + " def predict(self, X):\n", + " return np.random.choice([0, 1], size=X.shape[0])\n", + "\n", + "class MeanClassifier:\n", + " def fit(self, X, y):\n", + " self.mean_0 = np.mean(X[y == 0], axis=0) if np.any(y == 0) else None\n", + " self.mean_1 = np.mean(X[y == 1], axis=0) if np.any(y == 1) else None\n", + "\n", + " def predict(self, X):\n", + " preds = []\n", + " for x in X:\n", + " dist_0 = distance.euclidean(x, self.mean_0) if self.mean_0 is not None else np.inf\n", + " dist_1 = distance.euclidean(x, self.mean_1) if self.mean_1 is not None else np.inf\n", + " preds.append(1 if dist_1 < dist_0 else 0)\n", + " return np.array(preds)\n", + "\n", + " def predict_proba(self, X):\n", + " # An implementation of probability prediction which uses a softmax function to determine the probability of each class based on the distance to the mean for each prototype\n", + " preds = []\n", + " for x in X:\n", + " dist_0 = distance.euclidean(x, self.mean_0) if self.mean_0 is not None else np\n", + " dist_1 = distance.euclidean(x, self.mean_1) if self.mean_1 is not None else np.inf\n", + " prob_0 = np.exp(-dist_0) / (np.exp(-dist_0) + np.exp(-dist_1))\n", + " prob_1 = np.exp(-dist_1) / (np.exp(-dist_0) + np.exp(-dist_1))\n", + " preds.append([prob_0, prob_1])\n", + " return np.array(preds)\n", + "\n", + " def mean_distance(self, x):\n", + " dist_mean_0 = distance.euclidean(x, self.mean_0) if self.mean_0 is not None else np.inf\n", + " dist_mean_1 = distance.euclidean(x, self.mean_1) if self.mean_1 is not None else np.inf\n", + " return dist_mean_0, dist_mean_1\n", + "\n", + "# Initialize classifiers\n", + "random_clf = RandomClassifier()\n", + "mean_clf = MeanClassifier()\n", + "knn_clf = KNeighborsClassifier(n_neighbors=10)\n", + "rf_clf = RandomForestClassifier(max_depth=10, random_state=42)\n", + "mlp_clf = MLP(hidden_layer_sizes=(128,), max_iter=1000, random_state=42)\n", + "svc_clf = SVC()\n", + "\n", + "# Train classifiers\n", + "random_clf.fit(X_train, y_train)\n", + "mean_clf.fit(X_train, y_train)\n", + "knn_clf.fit(X_train, y_train)\n", + "#xgb_clf.fit(X_train, y_train, eval_set=[(X_test, y_test)], verbose=False)\n", + "rf_clf.fit(X_train, y_train)\n", + "mlp_clf.fit(X_train, y_train)\n", + "svc_clf.fit(X_train, y_train)\n", + "\n", + "# Make predictions\n", + "y_pred_random = random_clf.predict(X_test)\n", + "y_pred_mean = mean_clf.predict(X_test)\n", + "y_pred_knn = knn_clf.predict(X_test)\n", + "#y_pred_xgb = xgb_clf.predict(X_test)\n", + "y_pred_rf = rf_clf.predict(X_test)\n", + "y_pred_mlp = mlp_clf.predict(X_test)\n", + "y_pred_svc = svc_clf.predict(X_test)" + ], + "metadata": { + "id": "MXsnZFDXlNrT" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "evaluate_classifier(y_test, y_pred_random, \"Random Classifier\")\n", + "evaluate_classifier(y_test, y_pred_mean, \"Mean Classifier\")\n", + "evaluate_classifier(y_test, y_pred_knn, \"KNN Classifier\")" + ], + "metadata": { + "id": "sJ52bzdJmDvn" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "evaluate_classifier(y_test, y_pred_xgb, \"XGBoost Classifier\")\n", + "evaluate_classifier(y_test, y_pred_rf, \"Random Forest Classifier\")\n", + "evaluate_classifier(y_test, y_pred_svc, \"SVC Classifier\")" + ], + "metadata": { + "id": "DqyF_6STHW7o" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "evaluate_classifier(y_test, y_pred_mlp, \"MLP Classifier\")" + ], + "metadata": { + "id": "QfrAONS-DLau" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "test_filename = \"neytiri.png\"" + ], + "metadata": { + "id": "awoV0KS8_3Bi" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "test_embeddings = calculate_embeddings(test_filename, model_path='embedding_modelv2.keras')" + ], + "metadata": { + "id": "ddV4s5IUAaCc" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def print_prob(model, image_path):\n", + " test_embeddings = calculate_embeddings(image_path, model_path='embedding_modelv2.keras')\n", + " probs = model.predict_proba(test_embeddings)\n", + " print(f\"Real Photo Probability: {probs[0][0]:.4f}\")\n", + " print(f\"CGI Probability: {probs[0][1]:.4f}\")" + ], + "metadata": { + "id": "9yEk_X2rEH4K" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "print_prob(mlp_clf, test_filename)" + ], + "metadata": { + "id": "yD2JCKyJROb6" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "print_prob(mean_clf, test_filename)" + ], + "metadata": { + "id": "A7Nu_ABnRpT8" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "print_prob(xgb_clf, test_filename)" + ], + "metadata": { + "id": "AFJJuPG6Rpdz" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "print_prob(rf_clf, test_filename)" + ], + "metadata": { + "id": "Wil3P5JcRYNX" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "print_prob(knn_clf, test_filename)" + ], + "metadata": { + "id": "14O37IoKZCEW" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "dist = np.round(mean_clf.mean_distance(test_embeddings[0]), 2)\n", + "print(f\"Dist to real mean {dist[0]}\")\n", + "print(f\"Dist to CGI mean {dist[1]}\")" + ], + "metadata": { + "id": "gi5Vdf-bQElG" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def embedding_distance(image_path_1, image_path_2):\n", + " embedding_1 = calculate_embeddings(image_path_1)\n", + " embedding_2 = calculate_embeddings(image_path_2)\n", + " distance = np.linalg.norm(embedding_1 - embedding_2)\n", + " return distance" + ], + "metadata": { + "id": "3RkM68Li8Kh0" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Visualizing Feature Space" + ], + "metadata": { + "id": "x5GprsHRwkEX" + } + }, + { + "cell_type": "code", + "source": [ + "# prompt: How can I plot embeddings on a t-SNE scatter plot and colored by the label? A label of 1 should be \"CGI\" in the legend and 0 should be \"Real Photo\"\n", + "\n", + "import matplotlib.pyplot as plt\n", + "# Apply t-SNE\n", + "tsne = TSNE(n_components=2, random_state=42)\n", + "embeddings_2d = tsne.fit_transform(embeddings)\n", + "\n", + "# Plot the embeddings\n", + "plt.figure(figsize=(10, 7))\n", + "sns.scatterplot(\n", + " x=embeddings_2d[:, 0],\n", + " y=embeddings_2d[:, 1],\n", + " hue=['CGI' if label == 1 else 'Real Photo' for label in labels], # Map labels to strings\n", + " palette=sns.color_palette(\"hsv\", 2),\n", + " legend=\"full\"\n", + ")\n", + "plt.title(\"t-SNE of Image Embeddings\")\n", + "plt.xlabel(\"t-SNE component 1\")\n", + "plt.ylabel(\"t-SNE component 2\")\n", + "plt.show()" + ], + "metadata": { + "id": "oDx-07WfOd-2" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# prompt: Can you write a function that visualizes the embeddings using t-sne with the labels but allows a parameter which is an image path and preprocesses the image and calculates the embeddings and plots this embedding as well?\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "def visualize_embeddings_with_new_image(image_path, embeddings, labels):\n", + " \"\"\"\n", + " Visualizes embeddings using t-SNE, including a new image's embedding.\n", + "\n", + " Args:\n", + " image_path: Path to the new image.\n", + " embeddings: Existing embeddings.\n", + " labels: Corresponding labels for existing embeddings.\n", + " \"\"\"\n", + "\n", + " # Calculate embedding for the new image\n", + " new_embedding = calculate_embeddings(image_path, model_path='embedding_modelv2.keras')\n", + "\n", + " # Append new embedding and label to existing data\n", + " all_embeddings = np.concatenate((embeddings, new_embedding), axis=0)\n", + " all_labels = np.concatenate((labels, [2]), axis=0) # Assuming 2 is a new label for the new image\n", + "\n", + " # Apply t-SNE\n", + " tsne = TSNE(n_components=2, random_state=42)\n", + " embeddings_2d = tsne.fit_transform(all_embeddings)\n", + "\n", + " # Plot the embeddings\n", + " plt.figure(figsize=(10, 7))\n", + " sns.scatterplot(\n", + " x=embeddings_2d[:-1, 0], # Plot existing embeddings\n", + " y=embeddings_2d[:-1, 1],\n", + " hue=['CGI' if label == 1 else 'Real Photo' for label in all_labels[:-1]],\n", + " palette=sns.color_palette(\"hsv\", 2),\n", + " legend=\"full\"\n", + " )\n", + "\n", + " # Plot the new image's embedding\n", + " plt.scatter(\n", + " x=embeddings_2d[-1, 0],\n", + " y=embeddings_2d[-1, 1],\n", + " color='black',\n", + " marker='*',\n", + " s=200,\n", + " label='New Image'\n", + " )\n", + "\n", + " plt.title(\"t-SNE of Image Embeddings with New Image\")\n", + " plt.xlabel(\"t-SNE component 1\")\n", + " plt.ylabel(\"t-SNE component 2\")\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + "# Example usage:\n", + "# visualize_embeddings_with_new_image(\"path/to/your/new/image.jpg\", embeddings, labels)\n" + ], + "metadata": { + "id": "BKyYu-8won0l" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "visualize_embeddings_with_new_image(\"neytiri.png\", embeddings, labels)" + ], + "metadata": { + "id": "v6jrK3Auo-eM" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Testing Validation" + ], + "metadata": { + "id": "JokVT8QNCOCm" + } + }, + { + "cell_type": "code", + "source": [ + "!unzip Validation.zip" + ], + "metadata": { + "id": "QzkDffzBDGce" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "cgi_val_images, cgi_val_labels = calculate_embeddings_folder('Validation/CGI')\n", + "photo_val_images, photo_val_labels = calculate_embeddings_folder('Validation/Photo')\n", + "\n", + "print(f\"CGI shape {np.array(cgi_val_images).shape}\")\n", + "print(f\"Photo shape {np.array(photo_val_images).shape}\")" + ], + "metadata": { + "id": "UkuPOZXKCNd5" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# prompt: Can you test the validation images and labels against the XGB, Mean, and KNN classifiers?\n", + "\n", + "import numpy as np\n", + "# Combine validation data\n", + "X_val = np.concatenate((cgi_val_images, photo_val_images), axis=0)\n", + "y_val = np.concatenate((cgi_val_labels, photo_val_labels), axis=0)\n", + "\n", + "# Reshape validation data to match model input\n", + "X_val = X_val.reshape(X_val.shape[0], -1)\n", + "\n", + "# Predict using classifiers\n", + "y_pred_xgb_val = xgb_clf.predict(X_val)\n", + "y_pred_mean_val = mean_clf.predict(X_val)\n", + "y_pred_knn_val = knn_clf.predict(X_val)\n", + "y_pred_svc_val = svc_clf.predict(X_val)\n", + "y_pred_rf_val = rf_clf.predict(X_val)\n", + "y_pred_mlp_val = mlp_clf.predict(X_val)\n", + "\n", + "# Evaluate classifiers on validation set\n", + "evaluate_classifier(y_val, y_pred_xgb_val, \"XGBoost Classifier (Validation)\")\n", + "evaluate_classifier(y_val, y_pred_mean_val, \"Mean Classifier (Validation)\")\n", + "evaluate_classifier(y_val, y_pred_knn_val, \"KNN Classifier (Validation)\")\n", + "evaluate_classifier(y_val, y_pred_svc_val, \"SVC Classifier (Validation)\")\n", + "evaluate_classifier(y_val, y_pred_rf_val, \"Random Forest Classifier (Validation)\")\n" + ], + "metadata": { + "id": "pUE8siFEDF0h" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Old Preprocessing" + ], + "metadata": { + "id": "KFvqq8di5QnS" + } + }, + { + "cell_type": "code", + "source": [ + "# Function to load and preprocess images\n", + "def load_images(folder, label):\n", + " images = []\n", + " labels = []\n", + " for filename in os.listdir(folder):\n", + " if filename.endswith(\".jpg\") or filename.endswith(\".png\") or filename.endswith(\".jpeg\"):\n", + " img = cv2.imread(os.path.join(folder, filename), cv2.IMREAD_GRAYSCALE)\n", + " if img is not None:\n", + " img = cv2.resize(img, (256, 256))\n", + " images.append(img)\n", + " labels.append(label)\n", + " return images, labels\n", + "\n", + "pca = PCA(n_components=128)\n", + "# Function to perform Fourier transform and extract features\n", + "def extract_features(images):\n", + " features = []\n", + " for img in images:\n", + " f_transform = np.fft.fft2(img)\n", + " f_shift = np.fft.fftshift(f_transform)\n", + " magnitude_spectrum = 20 * np.log(np.abs(f_shift))\n", + " features.append(magnitude_spectrum.flatten())\n", + " features = pca.fit_transform(features)\n", + " return np.array(features)\n", + "\n", + "# Load and preprocess images from both folders\n", + "cgi_images, cgi_labels = load_images('CGI', 1) # 1 for CGI\n", + "photo_images, photo_labels = load_images('Photo', 0) # 0 for Real Photo\n", + "\n", + "min_length = min(len(cgi_images), len(photo_images))\n", + "cgi_images = cgi_images[:min_length]\n", + "cgi_labels = cgi_labels[:min_length]\n", + "photo_images = photo_images[:min_length]\n", + "photo_labels = photo_labels[:min_length]\n", + "\n", + "# Combine datasets\n", + "images = cgi_images + photo_images\n", + "labels = cgi_labels + photo_labels\n", + "\n", + "print(f\"Number of CGI images: {len(cgi_images)}\")\n", + "print(f\"Number of Photo images: {len(photo_images)}\")\n", + "\n", + "# Extract features\n", + "features = extract_features(images)\n", + "\n", + "# Encode labels\n", + "labels = np.array(labels)\n", + "\n", + "# Split data into training and testing sets\n", + "X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42, stratify=labels)" + ], + "metadata": { + "id": "5-M_iFWC5SOk" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "X_train.shape" + ], + "metadata": { + "id": "yAqmOxpp-iin" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "embeddings.shape" + ], + "metadata": { + "id": 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\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m95.1/95.1 MB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n", + "\u001b[?25h Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: numpy>=1.17.0 in /Users/nhradek/Library/jupyterlab-desktop/jlab_server/lib/python3.8/site-packages (from opencv-python) (1.24.2)\n", + "Building wheels for collected packages: opencv-python\n", + " Building wheel for opencv-python (pyproject.toml) ... \u001b[?25l/" + ] + } + ], + "source": [ + "!pip3 install opencv-python" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dHy-E-RQlDoj", + "tags": [] + }, + "outputs": [], + "source": [ + "import os\n", + "import cv2\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.manifold import TSNE\n", + "from sklearn.model_selection import train_test_split, cross_val_score, StratifiedKFold\n", + "from sklearn.metrics import accuracy_score, f1_score, confusion_matrix\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from xgboost import XGBClassifier\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.decomposition import PCA\n", + "from scipy.spatial import distance\n", + "from collections import Counter\n", + "import seaborn as sns\n", + "import joblib" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "60Rkg6uR5oyS", + "tags": [] + }, + "outputs": [], + "source": [ + "# Evaluate classifiers\n", + "def evaluate_classifier(y_true, y_pred, classifier_name):\n", + " acc = accuracy_score(y_true, y_pred)\n", + " f1 = f1_score(y_true, y_pred)\n", + " cm = confusion_matrix(y_true, y_pred)\n", + " print(f\"{classifier_name} - Accuracy: {acc:.4f}, F1 Score: {f1:.4f}\")\n", + " print(f\"Confusion Matrix:\\n{cm}\\n\")\n", + "\n", + " plt.figure(figsize=(8, 6))\n", + " sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['Real Photo', 'CGI'], yticklabels=['Real Photo', 'CGI'])\n", + " plt.title(f'Confusion Matrix for {classifier_name}')\n", + " plt.xlabel('Predicted Labels')\n", + " plt.ylabel('True Labels')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "oIsM1ilT5cQC", + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from PIL import Image\n", + "from scipy.fftpack import fft2\n", + "from tensorflow.keras.models import load_model, Model\n", + "\n", + "# Function to apply Fourier transform\n", + "def apply_fourier_transform(image):\n", + " image = np.array(image)\n", + " fft_image = fft2(image)\n", + " return np.abs(fft_image)\n", + "\n", + "# Function to preprocess image\n", + "def preprocess_image(image_path):\n", + " try:\n", + " image = Image.open(image_path).convert('L')\n", + " image = image.resize((256, 256))\n", + " image = apply_fourier_transform(image)\n", + " image = np.expand_dims(image, axis=-1) # Expand dimensions to match model input shape\n", + " image = np.expand_dims(image, axis=0) # Expand to add batch dimension\n", + " return image\n", + " except Exception as e:\n", + " print(f\"Error processing image {image_path}: {e}\")\n", + " return None\n", + "\n", + "# Function to load embedding model and calculate embeddings\n", + "def calculate_embeddings(image_path, model_path='embedding_modelv2.keras'):\n", + " # Load the trained model\n", + " model = load_model(model_path)\n", + "\n", + " # Remove the final classification layer to get embeddings\n", + " embedding_model = Model(inputs=model.input, outputs=model.output)\n", + "\n", + " # Preprocess the image\n", + " preprocessed_image = preprocess_image(image_path)\n", + "\n", + " # Calculate embeddings\n", + " embeddings = embedding_model.predict(preprocessed_image)\n", + "\n", + " return embeddings\n", + "\n", + "\n", + "def calculate_embeddings_folder(folder_path, model_path='embedding_modelv2.keras'):\n", + " embeddings = []\n", + " labels = []\n", + " for filename in os.listdir(folder_path):\n", + " if filename.endswith(\".jpg\") or filename.endswith(\".png\"):\n", + " image_path = os.path.join(folder_path, filename)\n", + " embedding = calculate_embeddings(image_path, model_path)\n", + " embeddings.append(embedding)\n", + " if \"CGI\" in folder_path:\n", + " labels.append(1)\n", + " else:\n", + " labels.append(0)\n", + " return embeddings, labels" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "1lzKxl_gJUEg", + "tags": [] + }, + "outputs": [], + "source": [ + "embeddings = np.load('embeddings.npy')\n", + "labels = np.load('labels.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "12-KegWL3ZZh", + "tags": [] + }, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(embeddings, labels, test_size=0.2, random_state=42, stratify=labels)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "8YY8_59Lmb1N" + }, + "outputs": [], + "source": [ + "X_test.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "fSosG_aU3o67" + }, + "outputs": [], + "source": [ + "xgb_clf = XGBClassifier(use_label_encoder=False, eval_metric='logloss', early_stopping_rounds=10)\n", + "xgb_clf.fit(X_train, y_train, eval_set=[(X_test, y_test)], verbose=False)\n", + "y_pred_xgb = xgb_clf.predict(X_test)\n", + "evaluate_classifier(y_test, y_pred_xgb, \"XGBoost Classifier\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "YLhckFv8JYK0" + }, + "outputs": [], + "source": [ + "from sklearn.neural_network import MLPClassifier as MLP\n", + "from sklearn.svm import SVC" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "MXsnZFDXlNrT" + }, + "outputs": [], + "source": [ + "# Naive random classifier\n", + "class RandomClassifier:\n", + " def fit(self, X, y):\n", + " pass\n", + "\n", + " def predict(self, X):\n", + " return np.random.choice([0, 1], size=X.shape[0])\n", + "\n", + "class MeanClassifier:\n", + " def fit(self, X, y):\n", + " self.mean_0 = np.mean(X[y == 0], axis=0) if np.any(y == 0) else None\n", + " self.mean_1 = np.mean(X[y == 1], axis=0) if np.any(y == 1) else None\n", + "\n", + " def predict(self, X):\n", + " preds = []\n", + " for x in X:\n", + " dist_0 = distance.euclidean(x, self.mean_0) if self.mean_0 is not None else np.inf\n", + " dist_1 = distance.euclidean(x, self.mean_1) if self.mean_1 is not None else np.inf\n", + " preds.append(1 if dist_1 < dist_0 else 0)\n", + " return np.array(preds)\n", + "\n", + " def predict_proba(self, X):\n", + " # An implementation of probability prediction which uses a softmax function to determine the probability of each class based on the distance to the mean for each prototype\n", + " preds = []\n", + " for x in X:\n", + " dist_0 = distance.euclidean(x, self.mean_0) if self.mean_0 is not None else np\n", + " dist_1 = distance.euclidean(x, self.mean_1) if self.mean_1 is not None else np.inf\n", + " prob_0 = np.exp(-dist_0) / (np.exp(-dist_0) + np.exp(-dist_1))\n", + " prob_1 = np.exp(-dist_1) / (np.exp(-dist_0) + np.exp(-dist_1))\n", + " preds.append([prob_0, prob_1])\n", + " return np.array(preds)\n", + "\n", + " def mean_distance(self, x):\n", + " dist_mean_0 = distance.euclidean(x, self.mean_0) if self.mean_0 is not None else np.inf\n", + " dist_mean_1 = distance.euclidean(x, self.mean_1) if self.mean_1 is not None else np.inf\n", + " return dist_mean_0, dist_mean_1\n", + "\n", + "# Initialize classifiers\n", + "random_clf = RandomClassifier()\n", + "mean_clf = MeanClassifier()\n", + "knn_clf = KNeighborsClassifier(n_neighbors=10)\n", + "rf_clf = RandomForestClassifier(max_depth=10, random_state=42)\n", + "mlp_clf = MLP(hidden_layer_sizes=(128,), max_iter=1000, random_state=42)\n", + "svc_clf = SVC()\n", + "\n", + "# Train classifiers\n", + "random_clf.fit(X_train, y_train)\n", + "mean_clf.fit(X_train, y_train)\n", + "knn_clf.fit(X_train, y_train)\n", + "#xgb_clf.fit(X_train, y_train, eval_set=[(X_test, y_test)], verbose=False)\n", + "rf_clf.fit(X_train, y_train)\n", + "mlp_clf.fit(X_train, y_train)\n", + "svc_clf.fit(X_train, y_train)\n", + "\n", + "# Make predictions\n", + "y_pred_random = random_clf.predict(X_test)\n", + "y_pred_mean = mean_clf.predict(X_test)\n", + "y_pred_knn = knn_clf.predict(X_test)\n", + "#y_pred_xgb = xgb_clf.predict(X_test)\n", + "y_pred_rf = rf_clf.predict(X_test)\n", + "y_pred_mlp = mlp_clf.predict(X_test)\n", + "y_pred_svc = svc_clf.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "sJ52bzdJmDvn" + }, + "outputs": [], + "source": [ + "evaluate_classifier(y_test, y_pred_random, \"Random Classifier\")\n", + "evaluate_classifier(y_test, y_pred_mean, \"Mean Classifier\")\n", + "evaluate_classifier(y_test, y_pred_knn, \"KNN Classifier\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DqyF_6STHW7o" + }, + "outputs": [], + "source": [ + "evaluate_classifier(y_test, y_pred_xgb, \"XGBoost Classifier\")\n", + "evaluate_classifier(y_test, y_pred_rf, \"Random Forest Classifier\")\n", + "evaluate_classifier(y_test, y_pred_svc, \"SVC Classifier\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "QfrAONS-DLau" + }, + "outputs": [], + "source": [ + "evaluate_classifier(y_test, y_pred_mlp, \"MLP Classifier\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "awoV0KS8_3Bi" + }, + "outputs": [], + "source": [ + "test_filename = \"neytiri.png\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ddV4s5IUAaCc" + }, + "outputs": [], + "source": [ + "test_embeddings = calculate_embeddings(test_filename, model_path='embedding_modelv2.keras')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9yEk_X2rEH4K" + }, + "outputs": [], + "source": [ + "def print_prob(model, image_path):\n", + " test_embeddings = calculate_embeddings(image_path, model_path='embedding_modelv2.keras')\n", + " probs = model.predict_proba(test_embeddings)\n", + " print(f\"Real Photo Probability: {probs[0][0]:.4f}\")\n", + " print(f\"CGI Probability: {probs[0][1]:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "yD2JCKyJROb6" + }, + "outputs": [], + "source": [ + "print_prob(mlp_clf, test_filename)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "A7Nu_ABnRpT8" + }, + "outputs": [], + "source": [ + "print_prob(mean_clf, test_filename)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "AFJJuPG6Rpdz" + }, + "outputs": [], + "source": [ + "print_prob(xgb_clf, test_filename)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Wil3P5JcRYNX" + }, + "outputs": [], + "source": [ + "print_prob(rf_clf, test_filename)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "14O37IoKZCEW" + }, + "outputs": [], + "source": [ + "print_prob(knn_clf, test_filename)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "gi5Vdf-bQElG" + }, + "outputs": [], + "source": [ + "dist = np.round(mean_clf.mean_distance(test_embeddings[0]), 2)\n", + "print(f\"Dist to real mean {dist[0]}\")\n", + "print(f\"Dist to CGI mean {dist[1]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "3RkM68Li8Kh0" + }, + "outputs": [], + "source": [ + "def embedding_distance(image_path_1, image_path_2):\n", + " embedding_1 = calculate_embeddings(image_path_1)\n", + " embedding_2 = calculate_embeddings(image_path_2)\n", + " distance = np.linalg.norm(embedding_1 - embedding_2)\n", + " return distance" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "x5GprsHRwkEX" + }, + "source": [ + "## Visualizing Feature Space" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "oDx-07WfOd-2" + }, + "outputs": [], + "source": [ + "# prompt: How can I plot embeddings on a t-SNE scatter plot and colored by the label? A label of 1 should be \"CGI\" in the legend and 0 should be \"Real Photo\"\n", + "\n", + "import matplotlib.pyplot as plt\n", + "# Apply t-SNE\n", + "tsne = TSNE(n_components=2, random_state=42)\n", + "embeddings_2d = tsne.fit_transform(embeddings)\n", + "\n", + "# Plot the embeddings\n", + "plt.figure(figsize=(10, 7))\n", + "sns.scatterplot(\n", + " x=embeddings_2d[:, 0],\n", + " y=embeddings_2d[:, 1],\n", + " hue=['CGI' if label == 1 else 'Real Photo' for label in labels], # Map labels to strings\n", + " palette=sns.color_palette(\"hsv\", 2),\n", + " legend=\"full\"\n", + ")\n", + "plt.title(\"t-SNE of Image Embeddings\")\n", + "plt.xlabel(\"t-SNE component 1\")\n", + "plt.ylabel(\"t-SNE component 2\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "BKyYu-8won0l" + }, + "outputs": [], + "source": [ + "# prompt: Can you write a function that visualizes the embeddings using t-sne with the labels but allows a parameter which is an image path and preprocesses the image and calculates the embeddings and plots this embedding as well?\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "def visualize_embeddings_with_new_image(image_path, embeddings, labels):\n", + " \"\"\"\n", + " Visualizes embeddings using t-SNE, including a new image's embedding.\n", + "\n", + " Args:\n", + " image_path: Path to the new image.\n", + " embeddings: Existing embeddings.\n", + " labels: Corresponding labels for existing embeddings.\n", + " \"\"\"\n", + "\n", + " # Calculate embedding for the new image\n", + " new_embedding = calculate_embeddings(image_path, model_path='embedding_modelv2.keras')\n", + "\n", + " # Append new embedding and label to existing data\n", + " all_embeddings = np.concatenate((embeddings, new_embedding), axis=0)\n", + " all_labels = np.concatenate((labels, [2]), axis=0) # Assuming 2 is a new label for the new image\n", + "\n", + " # Apply t-SNE\n", + " tsne = TSNE(n_components=2, random_state=42)\n", + " embeddings_2d = tsne.fit_transform(all_embeddings)\n", + "\n", + " # Plot the embeddings\n", + " plt.figure(figsize=(10, 7))\n", + " sns.scatterplot(\n", + " x=embeddings_2d[:-1, 0], # Plot existing embeddings\n", + " y=embeddings_2d[:-1, 1],\n", + " hue=['CGI' if label == 1 else 'Real Photo' for label in all_labels[:-1]],\n", + " palette=sns.color_palette(\"hsv\", 2),\n", + " legend=\"full\"\n", + " )\n", + "\n", + " # Plot the new image's embedding\n", + " plt.scatter(\n", + " x=embeddings_2d[-1, 0],\n", + " y=embeddings_2d[-1, 1],\n", + " color='black',\n", + " marker='*',\n", + " s=200,\n", + " label='New Image'\n", + " )\n", + "\n", + " plt.title(\"t-SNE of Image Embeddings with New Image\")\n", + " plt.xlabel(\"t-SNE component 1\")\n", + " plt.ylabel(\"t-SNE component 2\")\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + "# Example usage:\n", + "# visualize_embeddings_with_new_image(\"path/to/your/new/image.jpg\", embeddings, labels)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "v6jrK3Auo-eM" + }, + "outputs": [], + "source": [ + "visualize_embeddings_with_new_image(\"neytiri.png\", embeddings, labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JokVT8QNCOCm" + }, + "source": [ + "### Testing Validation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "QzkDffzBDGce" + }, + "outputs": [], + "source": [ + "!unzip Validation.zip" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "UkuPOZXKCNd5" + }, + "outputs": [], + "source": [ + "cgi_val_images, cgi_val_labels = calculate_embeddings_folder('Validation/CGI')\n", + "photo_val_images, photo_val_labels = calculate_embeddings_folder('Validation/Photo')\n", + "\n", + "print(f\"CGI shape {np.array(cgi_val_images).shape}\")\n", + "print(f\"Photo shape {np.array(photo_val_images).shape}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "pUE8siFEDF0h" + }, + "outputs": [], + "source": [ + "# prompt: Can you test the validation images and labels against the XGB, Mean, and KNN classifiers?\n", + "\n", + "import numpy as np\n", + "# Combine validation data\n", + "X_val = np.concatenate((cgi_val_images, photo_val_images), axis=0)\n", + "y_val = np.concatenate((cgi_val_labels, photo_val_labels), axis=0)\n", + "\n", + "# Reshape validation data to match model input\n", + "X_val = X_val.reshape(X_val.shape[0], -1)\n", + "\n", + "# Predict using classifiers\n", + "y_pred_xgb_val = xgb_clf.predict(X_val)\n", + "y_pred_mean_val = mean_clf.predict(X_val)\n", + "y_pred_knn_val = knn_clf.predict(X_val)\n", + "y_pred_svc_val = svc_clf.predict(X_val)\n", + "y_pred_rf_val = rf_clf.predict(X_val)\n", + "y_pred_mlp_val = mlp_clf.predict(X_val)\n", + "\n", + "# Evaluate classifiers on validation set\n", + "evaluate_classifier(y_val, y_pred_xgb_val, \"XGBoost Classifier (Validation)\")\n", + "evaluate_classifier(y_val, y_pred_mean_val, \"Mean Classifier (Validation)\")\n", + "evaluate_classifier(y_val, y_pred_knn_val, \"KNN Classifier (Validation)\")\n", + "evaluate_classifier(y_val, y_pred_svc_val, \"SVC Classifier (Validation)\")\n", + "evaluate_classifier(y_val, y_pred_rf_val, \"Random Forest Classifier (Validation)\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KFvqq8di5QnS" + }, + "source": [ + "### Old Preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "5-M_iFWC5SOk" + }, + "outputs": [], + "source": [ + "# Function to load and preprocess images\n", + "def load_images(folder, label):\n", + " images = []\n", + " labels = []\n", + " for filename in os.listdir(folder):\n", + " if filename.endswith(\".jpg\") or filename.endswith(\".png\") or filename.endswith(\".jpeg\"):\n", + " img = cv2.imread(os.path.join(folder, filename), cv2.IMREAD_GRAYSCALE)\n", + " if img is not None:\n", + " img = cv2.resize(img, (256, 256))\n", + " images.append(img)\n", + " labels.append(label)\n", + " return images, labels\n", + "\n", + "pca = PCA(n_components=128)\n", + "# Function to perform Fourier transform and extract features\n", + "def extract_features(images):\n", + " features = []\n", + " for img in images:\n", + " f_transform = np.fft.fft2(img)\n", + " f_shift = np.fft.fftshift(f_transform)\n", + " magnitude_spectrum = 20 * np.log(np.abs(f_shift))\n", + " features.append(magnitude_spectrum.flatten())\n", + " features = pca.fit_transform(features)\n", + " return np.array(features)\n", + "\n", + "# Load and preprocess images from both folders\n", + "cgi_images, cgi_labels = load_images('CGI', 1) # 1 for CGI\n", + "photo_images, photo_labels = load_images('Photo', 0) # 0 for Real Photo\n", + "\n", + "min_length = min(len(cgi_images), len(photo_images))\n", + "cgi_images = cgi_images[:min_length]\n", + "cgi_labels = cgi_labels[:min_length]\n", + "photo_images = photo_images[:min_length]\n", + "photo_labels = photo_labels[:min_length]\n", + "\n", + "# Combine datasets\n", + "images = cgi_images + photo_images\n", + "labels = cgi_labels + photo_labels\n", + "\n", + "print(f\"Number of CGI images: {len(cgi_images)}\")\n", + "print(f\"Number of Photo images: {len(photo_images)}\")\n", + "\n", + "# Extract features\n", + "features = extract_features(images)\n", + "\n", + "# Encode labels\n", + "labels = np.array(labels)\n", + "\n", + "# Split data into training and testing sets\n", + "X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42, stratify=labels)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "yAqmOxpp-iin" + }, + "outputs": [], + "source": [ + "X_train.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Dm1lretJBbKs" + }, + "outputs": [], + "source": [ + "embeddings.shape" + ] + }, + { + "cell_type": "code", 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a/Validation/Photo/tim-mossholder-KSep6RcaBC4-unsplash.jpg b/Validation/Photo/tim-mossholder-KSep6RcaBC4-unsplash.jpg new file mode 100644 index 0000000000000000000000000000000000000000..efd4a8f986f2c2396c8b9434e58b4935e3d946c0 Binary files /dev/null and b/Validation/Photo/tim-mossholder-KSep6RcaBC4-unsplash.jpg differ diff --git a/Validation/Photo/zhao-yangyang-UFBx9WQYAFI-unsplash.jpg b/Validation/Photo/zhao-yangyang-UFBx9WQYAFI-unsplash.jpg new file mode 100644 index 0000000000000000000000000000000000000000..82ed16f1cff6468d914361b3ab21f295dc0d6a98 Binary files /dev/null and b/Validation/Photo/zhao-yangyang-UFBx9WQYAFI-unsplash.jpg differ diff --git a/cgi_classification_app.py b/cgi_classification_app.py new file mode 100644 index 0000000000000000000000000000000000000000..8dd2908f65e9e31c705d93dcf303e8f6425b090b --- /dev/null +++ b/cgi_classification_app.py @@ -0,0 +1,106 @@ +# -*- coding: utf-8 -*- +"""CGI Classification App.ipynb + +Automatically generated by Colab. + +Original file is located at + https://colab.research.google.com/drive/1ckzOtXUiFW_NqlIandwoH07lnsLGKTLB +""" + +!pip install gradio + +from scipy.spatial import distance +import numpy as np + +class MeanClassifier: + def fit(self, X, y): + self.mean_0 = np.mean(X[y == 0], axis=0) if np.any(y == 0) else None + self.mean_1 = np.mean(X[y == 1], axis=0) if np.any(y == 1) else None + + def predict(self, X): + preds = [] + for x in X: + dist_0 = distance.euclidean(x, self.mean_0) if self.mean_0 is not None else np.inf + dist_1 = distance.euclidean(x, self.mean_1) if self.mean_1 is not None else np.inf + preds.append(1 if dist_1 < dist_0 else 0) + return np.array(preds) + + def predict_proba(self, X): + # An implementation of probability prediction which uses a softmax function to determine the probability of each class based on the distance to the mean for each prototype + preds = [] + for x in X: + dist_0 = distance.euclidean(x, self.mean_0) if self.mean_0 is not None else np + dist_1 = distance.euclidean(x, self.mean_1) if self.mean_1 is not None else np.inf + prob_0 = np.exp(-dist_0) / (np.exp(-dist_0) + np.exp(-dist_1)) + prob_1 = np.exp(-dist_1) / (np.exp(-dist_0) + np.exp(-dist_1)) + preds.append([prob_0, prob_1]) + return np.array(preds) + + def mean_distance(self, x): + dist_mean_0 = distance.euclidean(x, self.mean_0) if self.mean_0 is not None else np.inf + dist_mean_1 = distance.euclidean(x, self.mean_1) if self.mean_1 is not None else np.inf + return dist_mean_0, dist_mean_1 + +import gradio as gr +from PIL import Image +import numpy as np +from PIL import Image +from scipy.fftpack import fft2 +from tensorflow.keras.models import load_model, Model +import pickle + +mean_clf = None +with open('mean_clf.pkl', 'rb') as f: + mean_clf = pickle.load(f) + + +# Function to apply Fourier transform +def apply_fourier_transform(image): + image = np.array(image) + fft_image = fft2(image) + return np.abs(fft_image) + +def preprocess_image(image): + try: + image = Image.fromarray(image) + image = image.convert('L') + image = image.resize((256, 256)) + image = apply_fourier_transform(image) + image = np.expand_dims(image, axis=-1) # Expand dimensions to match model input shape + image = np.expand_dims(image, axis=0) # Expand to add batch dimension + return image + except Exception as e: + print(f"Error processing image: {e}") + return None + +# Function to load embedding model and calculate embeddings +def calculate_embeddings(image, model_path='embedding_modelv2.keras'): + # Load the trained model + model = load_model(model_path) + + # Remove the final classification layer to get embeddings + embedding_model = Model(inputs=model.input, outputs=model.output) + + # Preprocess the image + preprocessed_image = preprocess_image(image) + # Calculate embeddings + embeddings = embedding_model.predict(preprocessed_image) + + return embeddings + + +def classify_image(image): + embeddings = calculate_embeddings(image) + # Convert to 2D array for model input + probabilities = mean_clf.predict_proba(embeddings)[0] + labels = ["Photo", "CGI"] + return {f"{labels[i]}": prob for i, prob in enumerate(probabilities)} + +interface = gr.Interface( + fn=classify_image, + inputs=["image"], + outputs=gr.Label(num_top_classes=2) +) + +interface.launch(share=True) + diff --git a/embedding_modelv2.keras b/embedding_modelv2.keras new file mode 100644 index 0000000000000000000000000000000000000000..76f693f217e2980d060ef5038adf7b7e399f9cdf --- /dev/null +++ b/embedding_modelv2.keras @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:58ad419087ae572213a0a1d98cb8f93125334ea071e6c0fce30f348ab1bf2bcd +size 229314822 diff --git a/embeddings.npy b/embeddings.npy new file mode 100644 index 0000000000000000000000000000000000000000..0ca9b57fbd46ea43bc1c01f319aa7fb539688712 --- /dev/null +++ b/embeddings.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6d59846dfddeadcbbc1594eb92067a3378e995e1e4c196dd75833016e9077008 +size 403584 diff --git a/labels.npy b/labels.npy new file mode 100644 index 0000000000000000000000000000000000000000..b1767ef53c58f120936526255270a5f23d62bd03 --- /dev/null +++ b/labels.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d109031800da667131cc5c07c0f0cdaf786973c85742d5583688384d654e00fb +size 6432 diff --git a/mean_clf.pkl b/mean_clf.pkl new file mode 100644 index 0000000000000000000000000000000000000000..51fb39018f09d4c07fb82445edf4a4915f1f59c4 --- /dev/null +++ b/mean_clf.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0b77ca9a1d0a10c8b2a688f3a1f7c1b31c0ccd3786dc37e7badb657e4f264a13 +size 1263 diff --git a/resize.py b/resize.py new file mode 100644 index 0000000000000000000000000000000000000000..3510caef8b221bc70fff4662222ed6a138e24a8c --- /dev/null +++ b/resize.py @@ -0,0 +1,45 @@ +import os +from PIL import Image +from tqdm import tqdm + + +def resize_images_in_directory(directory, size): + """ + Resizes all images in the specified directory to the given size. + + Parameters: + directory (str): The path to the directory containing images. + size (tuple): The desired size for the resized images, e.g., (512, 512). + """ + if not os.path.exists(directory): + print(f"The directory {directory} does not exist.") + return + + # Get list of all files in the directory + files = os.listdir(directory) + + # Filter out non-image files + image_files = [ + file + for file in files + if file.lower().endswith(("png", "jpg", "jpeg", "tiff", "bmp", "gif")) + ] + + if not image_files: + print("No image files found in the directory.") + return + + for image_file in tqdm(image_files): + image_path = os.path.join(directory, image_file) + try: + with Image.open(image_path) as img: + img_resized = img.resize(size, Image.ANTIALIAS) + img_resized.save(image_path) + except Exception as e: + print(f"Failed to process {image_file}: {e}") + + +if __name__ == "__main__": + directory = "Validation/CGI" # Replace with your directory path + size = (256, 256) # Replace with your desired size + resize_images_in_directory(directory, size) diff --git a/xgb_cgi_classifier.json b/xgb_cgi_classifier.json new file mode 100644 index 0000000000000000000000000000000000000000..835a88e246a5fb903b3ba4778948dd51fe76aac7 --- /dev/null +++ b/xgb_cgi_classifier.json @@ -0,0 +1 @@ +{"learner":{"attributes":{"best_iteration":"12","best_score":"0.20815285839894904","scikit_learn":"{\"_estimator_type\": 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