Created using Colab

Author: masoudshahrian

celeba_dataset_face_croped.ipynb

@@ -0,0 +1,190 @@
+{
+  "metadata": {
+    "kernelspec": {
+      "language": "python",
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "pygments_lexer": "ipython3",
+      "nbconvert_exporter": "python",
+      "version": "3.6.4",
+      "file_extension": ".py",
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "name": "python",
+      "mimetype": "text/x-python"
+    },
+    "kaggle": {
+      "accelerator": "none",
+      "dataSources": [
+        {
+          "sourceId": 37705,
+          "sourceType": "datasetVersion",
+          "datasetId": 29561
+        }
+      ],
+      "isInternetEnabled": true,
+      "language": "python",
+      "sourceType": "notebook",
+      "isGpuEnabled": false
+    },
+    "colab": {
+      "name": "celeba-dataset-face-croped",
+      "provenance": [],
+      "include_colab_link": true
+    }
+  },
+  "nbformat_minor": 0,
+  "nbformat": 4,
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/masoudshahrian/DeepLearning-Code/blob/main/celeba_dataset_face_croped.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "source": [
+        "# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES,\n",
+        "# THEN FEEL FREE TO DELETE THIS CELL.\n",
+        "# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON\n",
+        "# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR\n",
+        "# NOTEBOOK.\n",
+        "import kagglehub\n",
+        "jessicali9530_celeba_dataset_path = kagglehub.dataset_download('jessicali9530/celeba-dataset')\n",
+        "\n",
+        "print('Data source import complete.')\n"
+      ],
+      "metadata": {
+        "id": "nW8T2OlPS23i"
+      },
+      "cell_type": "code",
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# # This Python 3 environment comes with many helpful analytics libraries installed\n",
+        "# # It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
+        "# # For example, here's several helpful packages to load\n",
+        "\n",
+        "# import numpy as np # linear algebra\n",
+        "# import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
+        "\n",
+        "# # Input data files are available in the read-only \"../input/\" directory\n",
+        "# # For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
+        "\n",
+        "# import os\n",
+        "# for dirname, _, filenames in os.walk('/kaggle/input'):\n",
+        "#     for filename in filenames:\n",
+        "#         print(os.path.join(dirname, filename))\n",
+        "\n",
+        "# # You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\"\n",
+        "# # You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
+      ],
+      "metadata": {
+        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+        "trusted": true,
+        "id": "eSYs8S5MS23m"
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install dlib"
+      ],
+      "metadata": {
+        "trusted": true,
+        "id": "bTkl19pJS23n"
+      },
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import cv2\n",
+        "import dlib\n",
+        "import os\n",
+        "\n",
+        "# Path to the CelebA images directory\n",
+        "input_dir = '/kaggle/input/celeba-dataset/img_align_celeba/img_align_celeba/'  # Path to the images directory\n",
+        "output_dir_128 = '/kaggle/working/img_align_celeba/CelebA_Image_Cropped_128/'  # Path to the output directory for 128x128 images\n",
+        "output_dir_64 = '/kaggle/working/img_align_celeba/CelebA_Image_Cropped_64/'  # Path to the output directory for 64x64 images\n",
+        "\n",
+        "# Create output directories if they do not exist\n",
+        "os.makedirs(output_dir_128, exist_ok=True)\n",
+        "os.makedirs(output_dir_64, exist_ok=True)\n",
+        "\n",
+        "# Load the face detection model\n",
+        "detector = dlib.get_frontal_face_detector()\n",
+        "\n",
+        "# List images in the input directory\n",
+        "image_count = 0  # Counter for the number of processed images\n",
+        "\n",
+        "for filename in os.listdir(input_dir):\n",
+        "    if filename.endswith('.jpg') or filename.endswith('.png'):\n",
+        "        # Load the image\n",
+        "        image_path = os.path.join(input_dir, filename)\n",
+        "        image = cv2.imread(image_path)\n",
+        "\n",
+        "        # Convert the image to grayscale\n",
+        "        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
+        "\n",
+        "        # Detect faces\n",
+        "        faces = detector(gray)\n",
+        "\n",
+        "        # Process each detected face\n",
+        "        for face in faces:\n",
+        "            # Extract face coordinates\n",
+        "            x, y, w, h = (face.left(), face.top(), face.width(), face.height())\n",
+        "\n",
+        "            # Calculate the center of the face\n",
+        "            center_x = x + w // 2\n",
+        "            center_y = y + h // 2\n",
+        "\n",
+        "            # Calculate new coordinates for cropping\n",
+        "            crop_x1 = max(center_x - 64, 0)\n",
+        "            crop_x2 = min(center_x + 64, image.shape[1])\n",
+        "            crop_y1 = max(center_y - 64, 0)\n",
+        "            crop_y2 = min(center_y + 64, image.shape[0])\n",
+        "\n",
+        "            # Crop the image\n",
+        "            cropped_image = image[crop_y1:crop_y2, crop_x1:crop_x2]\n",
+        "\n",
+        "            # Resize to 128x128\n",
+        "            resized_image_128 = cv2.resize(cropped_image, (128, 128))\n",
+        "            # Save the cropped image with dimensions 128x128\n",
+        "            output_path_128 = os.path.join(output_dir_128, f'cropped_128_{image_count}.jpg')\n",
+        "            cv2.imwrite(output_path_128, resized_image_128)\n",
+        "\n",
+        "            # Resize to 64x64\n",
+        "            resized_image_64 = cv2.resize(cropped_image, (64, 64))\n",
+        "            # Save the cropped image with dimensions 64x64\n",
+        "            output_path_64 = os.path.join(output_dir_64, f'cropped_64_{image_count}.jpg')\n",
+        "            cv2.imwrite(output_path_64, resized_image_64)\n",
+        "\n",
+        "            image_count += 1  # Increment the counter\n",
+        "\n",
+        "print(f\"All {image_count} images have been successfully cropped and saved.\")"
+      ],
+      "metadata": {
+        "trusted": true,
+        "id": "126soy8fS23n"
+      },
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
+}