{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# IMDB WIKI: Convert .mat to CSVs" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import os.path as osp\n", "from os.path import join\n", "from glob import glob\n", "import random\n", "import math\n", "from datetime import datetime\n", "\n", "import cv2 as cv\n", "import pandas as pd\n", "from scipy.io import loadmat\n", "import numpy as np\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "from tqdm import tqdm_notebook as tqdm\n", "%reload_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load Metadata" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "fp_mat = '/data_store_hdd/datasets/people/imdb_wiki/downloads/imdb.mat'\n", "dir_out = '/data_store_hdd/datasets/people/imdb_wiki/metadata/'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "mat_data = loadmat(fp_mat)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# row 3\n", "def load_parse_imdb_mat(mat):\n", " metadata = mat['imdb'][0][0]\n", " results = []\n", " num_records = len(metadata[0][0])\n", " print(f'loaded: {num_records} records')\n", " for i in tqdm(range(num_records), total=num_records):\n", " dob_matlab = metadata[0][0][i]\n", " dob = datetime.fromordinal(dob_matlab)\n", " dob_str = f'{dob.year}-{dob.month}-{dob.day}'\n", " year_photo = metadata[1][0][i]\n", " fp = metadata[2][0][i][0]\n", " gender_val = metadata[3][0][i]\n", " if gender_val == 0:\n", " gender = 'f'\n", " elif gender_val == 1:\n", " gender = 'm'\n", " else:\n", " gender = None\n", " name = metadata[4][0][i][0]\n", " roi = metadata[5][0][i][0]\n", " face_conf = metadata[6][0][i]\n", " face_conf_second = metadata[7][0][i]\n", " celeb_id = metadata[9][0][i]\n", " result = {\n", " 'dob': dob_str,\n", " 'year_photo': year_photo,\n", " 'filepath': fp,\n", " 'gender': gender,\n", " 'name': name,\n", " 'x1': roi[0],\n", " 'y1': roi[1],\n", " 'x2': roi[2],\n", " 'y2': roi[3],\n", " 'celeb_id': celeb_id\n", " }\n", " results.append(result)\n", " return results" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loaded: 460723 records\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8a4a106e3bee4fde89492ceef50b9c05", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=460723), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "results_meta = load_parse_imdb_mat(mat_data)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "df_meta = pd.DataFrame.from_dict(results_meta)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
celeb_iddobfilepathgendernamex1x2y1y2year_photo
064881900-5-1101/nm0000001_rm124825600_1899-5-10_1968.jpgmFred Astaire1072.9260001214.784000161.838000303.6960001968
164881900-5-1101/nm0000001_rm3343756032_1899-5-10_1970.jpgmFred Astaire477.184000622.592000100.352000245.7600001970
264881900-5-1101/nm0000001_rm577153792_1899-5-10_1968.jpgmFred Astaire114.969643451.686572114.969643451.6865721968
364881900-5-1101/nm0000001_rm946909184_1899-5-10_1968.jpgmFred Astaire622.885506844.339008424.217504645.6710061968
464881900-5-1101/nm0000001_rm980463616_1899-5-10_1968.jpgmFred Astaire1013.8590021201.586128233.882042421.6091681968
\n", "
" ], "text/plain": [ " celeb_id dob filepath gender \\\n", "0 6488 1900-5-11 01/nm0000001_rm124825600_1899-5-10_1968.jpg m \n", "1 6488 1900-5-11 01/nm0000001_rm3343756032_1899-5-10_1970.jpg m \n", "2 6488 1900-5-11 01/nm0000001_rm577153792_1899-5-10_1968.jpg m \n", "3 6488 1900-5-11 01/nm0000001_rm946909184_1899-5-10_1968.jpg m \n", "4 6488 1900-5-11 01/nm0000001_rm980463616_1899-5-10_1968.jpg m \n", "\n", " name x1 x2 y1 y2 year_photo \n", "0 Fred Astaire 1072.926000 1214.784000 161.838000 303.696000 1968 \n", "1 Fred Astaire 477.184000 622.592000 100.352000 245.760000 1970 \n", "2 Fred Astaire 114.969643 451.686572 114.969643 451.686572 1968 \n", "3 Fred Astaire 622.885506 844.339008 424.217504 645.671006 1968 \n", "4 Fred Astaire 1013.859002 1201.586128 233.882042 421.609168 1968 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_meta.head()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "df_meta.index.name = 'index'\n", "df_meta.to_csv(join(dir_out,'imdb_mat.csv'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Count Images per Person" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# count images per person and save to CSV\n", "df_name_groups = df_meta.groupby('name')\n", "images_per_person = []\n", "for name, df_name in df_name_groups:\n", " images_per_person.append({'name': name, 'num_images': len(df_name)})\n", "df_images_per_person = pd.DataFrame.from_dict(images_per_person)\n", "df_images_per_person.index.name = 'index'\n", "df_images_per_person.to_csv(join(dir_out, 'imdb_images_per_person.csv'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Find Face Size" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "sizes = [(x['x2'] - x['x1']) for x in results_meta]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "buckets = list(range(0,500,50))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib import pyplot as plt \n", "import numpy as np \n", "bins = list(range(0,500,20))\n", "plt.figure(figsize=(12,8))\n", "plt.hist(sizes, bins=bins)\n", "plt.title(\"Face Image Sizes\") \n", "plt.ylabel(\"Images\")\n", "plt.xlabel(\"Width (px)\")\n", "plt.yticks(range(0, 60000, 10000))\n", "plt.title('IMDB-Wiki: Face Pixel Size')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "dob: date of birth (Matlab serial date number)\n", "photo_taken: year when the photo was taken\n", "full_path: path to file\n", "gender: 0 for female and 1 for male, NaN if unknown\n", "name: name of the celebrity\n", "face_location: location of the face. To crop the face in Matlab run\n", "\n", "img(face_location(2):face_location(4),face_location(1):face_location(3),:))\n", "\n", "face_score: detector score (the higher the better). Inf implies that no face was found in the image and the face_location then just returns the entire image\n", "second_face_score: detector score of the face with the second highest score. This is useful to ignore images with more than one face. second_face_score is NaN if no second face was detected.\n", "celeb_names (IMDB only): list of all celebrity names\n", "celeb_id (IMDB only): index of celebrity name\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:megapixels]", "language": "python", "name": "conda-env-megapixels-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 2 }