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‎celeba_dataset_face_croped_[64_64].ipynb

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{
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"metadata": {
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"kernelspec": {
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"language": "python",
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"display_name": "Python 3",
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"name": "python3"
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},
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"language_info": {
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"name": "python",
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"version": "3.10.12",
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"mimetype": "text/x-python",
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"pygments_lexer": "ipython3",
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"nbconvert_exporter": "python",
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"file_extension": ".py"
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},
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"kaggle": {
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"accelerator": "gpu",
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"dataSources": [
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{
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"sourceId": 37705,
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"sourceType": "datasetVersion",
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"datasetId": 29561
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}
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],
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"dockerImageVersionId": 30822,
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"isInternetEnabled": true,
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"language": "python",
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"sourceType": "notebook",
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"isGpuEnabled": true
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},
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"colab": {
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"name": "celeba-dataset-face-croped-[64*64]",
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"provenance": [],
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"include_colab_link": true
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}
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},
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"nbformat_minor": 0,
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"nbformat": 4,
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/masoudshahrian/DeepLearning-Code/blob/main/celeba_dataset_face_croped_%5B64_64%5D.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"source": [
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"# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES,\n",
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"# THEN FEEL FREE TO DELETE THIS CELL.\n",
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"# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON\n",
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"# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR\n",
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"# NOTEBOOK.\n",
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"import kagglehub\n",
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"jessicali9530_celeba_dataset_path = kagglehub.dataset_download('jessicali9530/celeba-dataset')\n",
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"\n",
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"print('Data source import complete.')\n"
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],
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"metadata": {
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"id": "VzQ60JUpVJ2y"
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},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "code",
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"source": [
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"# # This Python 3 environment comes with many helpful analytics libraries installed\n",
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"# # It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
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"# # For example, here's several helpful packages to load\n",
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"\n",
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"# import numpy as np # linear algebra\n",
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"# import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
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"\n",
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"# # Input data files are available in the read-only \"../input/\" directory\n",
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"# # For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
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"\n",
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"# import os\n",
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"# for dirname, _, filenames in os.walk('/kaggle/input'):\n",
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"# for filename in filenames:\n",
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"# print(os.path.join(dirname, filename))\n",
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"\n",
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"# # 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",
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"# # You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
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],
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"metadata": {
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"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
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"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
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"trusted": true,
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"execution": {
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"iopub.status.busy": "2024-12-31T14:00:22.340417Z",
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"iopub.execute_input": "2024-12-31T14:00:22.340718Z",
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"iopub.status.idle": "2024-12-31T14:00:22.344574Z",
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"shell.execute_reply.started": "2024-12-31T14:00:22.340692Z",
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"shell.execute_reply": "2024-12-31T14:00:22.343643Z"
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},
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"id": "SBWyO33hVJ20"
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},
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "code",
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"source": [
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"!pip install dlib"
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],
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"metadata": {
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"trusted": true,
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"execution": {
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"iopub.status.busy": "2024-12-31T19:23:53.286376Z",
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"iopub.execute_input": "2024-12-31T19:23:53.286655Z",
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"iopub.status.idle": "2024-12-31T19:23:57.60329Z",
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"shell.execute_reply.started": "2024-12-31T19:23:53.286634Z",
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"shell.execute_reply": "2024-12-31T19:23:57.602309Z"
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},
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"id": "vssMfka4VJ21",
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"outputId": "507f001c-17aa-4b08-fb40-666f61a32bc9"
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},
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"outputs": [
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{
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"name": "stdout",
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"text": "Requirement already satisfied: dlib in /usr/local/lib/python3.10/dist-packages (19.24.2)\n",
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"output_type": "stream"
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}
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],
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"execution_count": null
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},
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{
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"cell_type": "code",
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"source": [
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"import cv2\n",
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"import dlib\n",
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"import os\n",
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"\n",
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"# مسیر به دایرکتوری تصاویر CelebA\n",
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"input_dir = '/kaggle/input/celeba-dataset/img_align_celeba/img_align_celeba/' # مسیر دایرکتوری تصاویر\n",
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"output_dir_64 = '/kaggle/working/img_align_celeba/CelebA_Image_Cropped_64/' # مسیر دایرکتوری خروجی برای تصاویر 64x64\n",
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"\n",
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"# ایجاد دایرکتوری خروجی در صورت عدم وجود\n",
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"os.makedirs(output_dir_64, exist_ok=True)\n",
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"\n",
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"# بارگذاری مدل شناسایی صورت\n",
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"detector = dlib.get_frontal_face_detector()\n",
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"\n",
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"# لیست کردن تصاویر در دایرکتوری ورودی\n",
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"for filename in os.listdir(input_dir):\n",
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" if filename.endswith('.jpg') or filename.endswith('.png'):\n",
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" # بارگذاری تصویر\n",
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" image_path = os.path.join(input_dir, filename)\n",
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" image = cv2.imread(image_path)\n",
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"\n",
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" # تبدیل تصویر به خاکستری\n",
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" gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
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"\n",
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" # شناسایی صورت‌ها\n",
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" faces = detector(gray)\n",
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"\n",
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" # پردازش اولین صورت شناسایی شده\n",
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" for face in faces:\n",
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" # استخراج مختصات صورت\n",
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" x, y, w, h = (face.left(), face.top(), face.width(), face.height())\n",
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"\n",
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" # محاسبه مرکز صورت\n",
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" center_x = x + w // 2\n",
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" center_y = y + h // 2\n",
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"\n",
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" # محاسبه مختصات جدید برای کراپ\n",
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" crop_x1 = max(center_x - 32, 0)\n",
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" crop_x2 = min(center_x + 32, image.shape[1])\n",
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" crop_y1 = max(center_y - 32, 0)\n",
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" crop_y2 = min(center_y + 32, image.shape[0])\n",
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"\n",
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" # کراپ کردن تصویر\n",
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" cropped_image = image[crop_y1:crop_y2, crop_x1:crop_x2]\n",
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"\n",
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" # تغییر اندازه به 64x64\n",
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" resized_image_64 = cv2.resize(cropped_image, (64, 64))\n",
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"\n",
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" # ذخیره تصویر کراپ شده با همان نام اصلی\n",
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" output_path_64 = os.path.join(output_dir_64, filename)\n",
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" cv2.imwrite(output_path_64, resized_image_64)\n",
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"\n",
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" break # پردازش فقط اولین صورت شناسایی شده در هر تصویر\n",
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"\n",
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"print(\"تمامی تصاویر با موفقیت کراپ و ذخیره شدند.\")\n"
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],
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"metadata": {
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"trusted": true,
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"execution": {
198+
"iopub.status.busy": "2024-12-31T19:24:03.333117Z",
199+
"iopub.execute_input": "2024-12-31T19:24:03.333419Z",
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"iopub.status.idle": "2024-12-31T20:01:08.073201Z",
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"shell.execute_reply.started": "2024-12-31T19:24:03.333398Z",
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"shell.execute_reply": "2024-12-31T20:01:08.072318Z"
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},
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"id": "_ct7E0wsVJ22",
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"outputId": "b1f88e19-bcf2-4ff3-95c5-29ec465de04c"
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},
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"outputs": [
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{
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"name": "stdout",
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"text": "تمامی تصاویر با موفقیت کراپ و ذخیره شدند.\n",
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"output_type": "stream"
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}
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],
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"execution_count": null
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},
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{
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"cell_type": "code",
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"source": [
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"# import cv2\n",
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"# import dlib\n",
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"# import os\n",
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"\n",
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"# # مسیر به دایرکتوری تصاویر CelebA\n",
224+
"# input_dir = '/kaggle/input/celeba-dataset/img_align_celeba/img_align_celeba/' # مسیر دایرکتوری تصاویر\n",
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"# output_dir_128 = '/kaggle/working/img_align_celeba/CelebA_Image_Cropped_128/' # مسیر دایرکتوری خروجی برای تصاویر 128x128\n",
226+
"# output_dir_64 = '/kaggle/working/img_align_celeba/CelebA_Image_Cropped_64/' # مسیر دایرکتوری خروجی برای تصاویر 64x64\n",
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"\n",
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"# # ایجاد دایرکتوری خروجی در صورت عدم وجود\n",
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"# os.makedirs(output_dir_128, exist_ok=True)\n",
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"# os.makedirs(output_dir_64, exist_ok=True)\n",
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"\n",
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"# # بارگذاری مدل شناسایی صورت\n",
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"# detector = dlib.get_frontal_face_detector()\n",
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"\n",
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"# # لیست کردن تصاویر در دایرکتوری ورودی\n",
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"# image_count = 0 # شمارنده برای تعداد تصاویر پردازش شده\n",
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"\n",
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"# for filename in os.listdir(input_dir):\n",
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"# if filename.endswith('.jpg') or filename.endswith('.png'):\n",
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"# # بارگذاری تصویر\n",
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"# image_path = os.path.join(input_dir, filename)\n",
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"# image = cv2.imread(image_path)\n",
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"\n",
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"# # تبدیل تصویر به خاکستری\n",
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"# gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
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"\n",
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"# # شناسایی صورت‌ها\n",
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"# faces = detector(gray)\n",
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"\n",
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"# # پردازش هر صورت شناسایی شده\n",
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"# for face in faces:\n",
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"# # استخراج مختصات صورت\n",
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"# x, y, w, h = (face.left(), face.top(), face.width(), face.height())\n",
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"\n",
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"# # محاسبه مرکز صورت\n",
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"# center_x = x + w // 2\n",
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"# center_y = y + h // 2\n",
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"\n",
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"# # محاسبه مختصات جدید برای کراپ\n",
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"# crop_x1 = max(center_x - 64, 0)\n",
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"# crop_x2 = min(center_x + 64, image.shape[1])\n",
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"# crop_y1 = max(center_y - 64, 0)\n",
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"# crop_y2 = min(center_y + 64, image.shape[0])\n",
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"\n",
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"# # کراپ کردن تصویر\n",
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"# cropped_image = image[crop_y1:crop_y2, crop_x1:crop_x2]\n",
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"\n",
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"# # تغییر اندازه به 128x128\n",
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"# resized_image_128 = cv2.resize(cropped_image, (128, 128))\n",
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"# # ذخیره تصویر کراپ شده با ابعاد 128x128\n",
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"# output_path_128 = os.path.join(output_dir_128, f'cropped_128_{image_count}.jpg')\n",
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"# cv2.imwrite(output_path_128, resized_image_128)\n",
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"\n",
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"# # تغییر اندازه به 64x64\n",
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"# resized_image_64 = cv2.resize(cropped_image, (64, 64))\n",
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"# # ذخیره تصویر کراپ شده با ابعاد 64x64\n",
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"# output_path_64 = os.path.join(output_dir_64, f'cropped_64_{image_count}.jpg')\n",
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"# cv2.imwrite(output_path_64, resized_image_64)\n",
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"\n",
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"# image_count += 1 # افزایش شمارنده\n",
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"\n",
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"# print(f\"تمامی {image_count} تصویر با موفقیت کراپ و ذخیره شدند.\")"
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],
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"metadata": {
285+
"trusted": true,
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"execution": {
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},
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"id": "87JB2HbvVJ23"
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},
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"outputs": [],
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"execution_count": null
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},
298+
{
299+
"cell_type": "code",
300+
"source": [
301+
"# import cv2\n",
302+
"# import dlib\n",
303+
"# import os\n",
304+
"\n",
305+
"# # مسیر به دایرکتوری تصاویر CelebA\n",
306+
"# input_dir = '/kaggle/input/celeba-dataset/img_align_celeba/img_align_celeba/' # مسیر دایرکتوری تصاویر\n",
307+
"# output_dir_128 = '/kaggle/working/img_align_celeba/CelebA_Face_Cropped_128/' # مسیر دایرکتوری خروجی برای تصاویر 128x128\n",
308+
"# output_dir_64 = '/kaggle/working/img_align_celeba/CelebA_Face_Cropped_64/' # مسیر دایرکتوری خروجی برای تصاویر 64x64\n",
309+
"\n",
310+
"# # ایجاد دایرکتوری خروجی در صورت عدم وجود\n",
311+
"# os.makedirs(output_dir_128, exist_ok=True)\n",
312+
"# os.makedirs(output_dir_64, exist_ok=True)\n",
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"\n",
314+
"# # بارگذاری مدل شناسایی صورت\n",
315+
"# detector = dlib.get_frontal_face_detector()\n",
316+
"\n",
317+
"# # لیست کردن تصاویر در دایرکتوری ورودی\n",
318+
"# for filename in os.listdir(input_dir):\n",
319+
"# if filename.endswith('.jpg') or filename.endswith('.png'):\n",
320+
"# # بارگذاری تصویر\n",
321+
"# image_path = os.path.join(input_dir, filename)\n",
322+
"# image = cv2.imread(image_path)\n",
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"\n",
324+
"# # تبدیل تصویر به خاکستری\n",
325+
"# gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
326+
"\n",
327+
"# # شناسایی صورت‌ها\n",
328+
"# faces = detector(gray)\n",
329+
"\n",
330+
"# # پردازش هر صورت شناسایی شده\n",
331+
"# for i, face in enumerate(faces):\n",
332+
"# # استخراج مختصات صورت\n",
333+
"# x, y, w, h = (face.left(), face.top(), face.width(), face.height())\n",
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"\n",
335+
"# # محاسبه مرکز صورت\n",
336+
"# center_x = x + w // 2\n",
337+
"# center_y = y + h // 2\n",
338+
"\n",
339+
"# # محاسبه مختصات جدید برای کراپ\n",
340+
"# crop_x1 = max(center_x - 64, 0)\n",
341+
"# crop_x2 = min(center_x + 64, image.shape[1])\n",
342+
"# crop_y1 = max(center_y - 64, 0)\n",
343+
"# crop_y2 = min(center_y + 64, image.shape[0])\n",
344+
"\n",
345+
"# # کراپ کردن تصویر\n",
346+
"# cropped_image = image[crop_y1:crop_y2, crop_x1:crop_x2]\n",
347+
"\n",
348+
"# # تغییر اندازه به 128x128\n",
349+
"# resized_image_128 = cv2.resize(cropped_image, (128, 128))\n",
350+
"# # ذخیره تصویر کراپ شده با ابعاد 128x128\n",
351+
"# output_path_128 = os.path.join(output_dir_128, f'{os.path.splitext(filename)[0]}.jpg')\n",
352+
"# cv2.imwrite(output_path_128, resized_image_128)\n",
353+
"\n",
354+
"# # تغییر اندازه به 64x64\n",
355+
"# resized_image_64 = cv2.resize(cropped_image, (64, 64))\n",
356+
"# # ذخیره تصویر کراپ شده با ابعاد 64x64\n",
357+
"# output_path_64 = os.path.join(output_dir_64, f'{os.path.splitext(filename)[0]}.jpg')\n",
358+
"# cv2.imwrite(output_path_64, resized_image_64)\n",
359+
"\n",
360+
"# print(\"تمامی تصاویر با موفقیت کراپ و ذخیره شدند.\")"
361+
],
362+
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"execution_count": null
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}
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]
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}

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