diff --git a/.gitignore b/.gitignore index a0ba551..2276e64 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,4 @@ -processedData/ -unprocessedData/ -experiment.py +.ipynb_checkpoints/ meta.csv imdb_crop/ wiki_crop/ diff --git a/Age and Gender Distribution.ipynb b/Age and Gender Distribution.ipynb new file mode 100644 index 0000000..06b457f --- /dev/null +++ b/Age and Gender Distribution.ipynb @@ -0,0 +1,233 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Importing dependencies\n", + "\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv('meta.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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agegenderpath
042maleimdb_crop/82/nm0005282_rm878739712_1966-10-31_...
132femaleimdb_crop/76/nm1411676_rm2490280960_1979-6-24_...
229maleimdb_crop/83/nm1145983_rm1406371840_1979-7-31_...
322femaleimdb_crop/02/nm3240202_rm2934473728_1986-2-28_...
478femaleimdb_crop/12/nm0326412_rm1823643648_1926-9-7_2...
\n", + "
" + ], + "text/plain": [ + " age gender path\n", + "0 42 male imdb_crop/82/nm0005282_rm878739712_1966-10-31_...\n", + "1 32 female imdb_crop/76/nm1411676_rm2490280960_1979-6-24_...\n", + "2 29 male imdb_crop/83/nm1145983_rm1406371840_1979-7-31_...\n", + "3 22 female imdb_crop/02/nm3240202_rm2934473728_1986-2-28_...\n", + "4 78 female imdb_crop/12/nm0326412_rm1823643648_1926-9-7_2..." + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['age', 'gender', 'path'], dtype='object')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(224840, 3)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "gender = []\n", + "for g in data['gender'].values:\n", + " if g == 'male':\n", + " gender.append(1)\n", + " else:\n", + " gender.append(0)\n", + " \n", + "\n", + "plt.hist(gender, range(3))\n", + "plt.title('There are total ' + str(len(gender) - sum(gender)) + ' female images and ' + str(sum(gender)) + ' male images')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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lH5KOBc4D3gssBb4macb4ih8REfuqqQAgaT5wFnBtWRdwKnBzybIOOLssLyvrlO2nlfzLgBttv277Gao5g09sRyUiIqJ1zbYAvgT8OfDLsn4EsNv2nrK+FZhXlucBWwDK9pdL/r3pI+yzl6QVkvok9Q0ODrZQlYiIaMWYAUDSR4AdtjdNQnmwvdp2j+2e2bNnT8YpIyJqqZnfAXwA+KikM4GDgH8DfBmYKWm/8i1/PjBQ8g8AC4CtkvYDDgNeakgf0rhPRERMsjFbALYvsT3f9kKqh7h32T4fuBs4p2TrBW4ty+vLOmX7XbZd0s8ro4QWAYuB+9tWk4iIaMl4fgn8aeBGSZ8HHgLWlPQ1wPWS+oGdVEED249Jugl4HNgDXGT7jXGcPyIixqGlAGD7HuCesvw0I4zisf0z4GOj7H85cHmrhYzR5UVnEbGv8kvgiIiaSgCIiKipBICIiJpKAIiIqKkEgIiImsqEMBFFJoeJukkLICKiptICiBhBWgNRB2kBRETUVAJARERNJQBERNRUAkBERE0lAERE1FQCQERETWUYaMQYMiQ0ulVaABERNdXMpPAHSbpf0v+T9Jikz5X0RZLuk9Qv6duSDijpB5b1/rJ9YcOxLinpT0o6faIqFRERY2umBfA6cKrt44DjgaWSTgauBK6yfTSwC1he8i8HdpX0q0o+JB1LNT3ke4GlwNckzWhnZSIionnNTApv2z8pq/uXj4FTgZtL+jrg7LK8rKxTtp8mSSX9Rtuv234G6GeEKSUjImJyNPUMQNIMSQ8DO4ANwFPAbtt7SpatwLyyPA/YAlC2vwwc0Zg+wj4RETHJmgoAtt+wfTwwn+pb+zETVSBJKyT1SeobHBycqNNERNReS6OAbO8G7gZOAWZKGhpGOh8YKMsDwAKAsv0w4KXG9BH2aTzHats9tntmz57dSvEiIqIFzYwCmi1pZll+G/Bh4AmqQHBOydYL3FqW15d1yva7bLukn1dGCS0CFgP3t6siEZNh4crb934iprtmfgg2F1hXRuz8GnCT7dskPQ7cKOnzwEPAmpJ/DXC9pH5gJ9XIH2w/Jukm4HFgD3CR7TfaW52IiGjWmAHA9iPA+0ZIf5oRRvHY/hnwsVGOdTlweevFjIiIdsurICL2UV4REdNdXgUREVFTCQARETWVLqBpKCNQIqId0gKIiKipBICIiJpKF1BEG2REUExHaQFERNRUWgARbZbWQEwXaQFERNRUAkBERE0lAERE1FQCQERETSUARETUVAJARERNJQBERNRUM1NCLpB0t6THJT0m6RMl/XBJGyRtLn9nlXRJulpSv6RHJJ3QcKzekn+zpN7RzhnRjTKdZEw1zbQA9gCfsn0scDJwkaRjgZXARtuLgY1lHeAMqvl+FwMrgGugChjAKuAkqpnEVg0FjYiImHxjBgDb22w/WJZfpZoQfh6wDFhXsq0Dzi7Ly4DrXLkXmClpLnA6sMH2Ttu7gA3A0rbWJiIimtbSMwBJC6nmB74PmGN7W9m0HZhTlucBWxp221rSRkuPiIgOaPpdQJIOBb4DfNL2K5L2brNtSW5HgSStoOo64qijjmrHISM6Jv39MZU11QKQtD/Vzf+btm8pyS+Wrh3K3x0lfQBY0LD7/JI2Wvpb2F5tu8d2z+zZs1upS0REtGDMFoCqr/prgCds/03DpvVAL3BF+XtrQ/rFkm6keuD7su1tku4EvtDw4HcJcEl7qtH98k0yItqtmS6gDwB/DPxQ0sMl7S+obvw3SVoOPAecW7bdAZwJ9AOvARcC2N4p6TLggZLvUts721KLiIho2ZgBwPY/ARpl82kj5Ddw0SjHWgusbaWAERExMTIhTEQHZNKYmAryKoiIiJpKCyBiCknLICZTAkBEhzUzwiuBISZCAkDEFJWhvzHREgCmsNwAImIi5SFwRERNJQBERNRUAkBERE3lGUDENJMRQdEuaQFERNRUAkBERE0lAERE1FSeAURMY6P9ViTPBqIZCQBTTH78FRGTJV1AERE1lQAQEVFTYwYASWsl7ZD0aEPa4ZI2SNpc/s4q6ZJ0taR+SY9IOqFhn96Sf7Ok3ompTkRENKuZFsA3gKXD0lYCG20vBjaWdYAzgMXlswK4BqqAAayimiT+RGBVw+TwEdFmC1fevvcTMZpm5gT+gaSFw5KXAR8qy+uAe4BPl/TryrzA90qaKWluybthaBJ4SRuogsoN465BF8j/pDGR8svhGM2+PgOYY3tbWd4OzCnL84AtDfm2lrTR0n+FpBWS+iT1DQ4O7mPxIiJiLOMeBmrbktyOwpTjrQZWA/T09LTtuBGR1kC81b4GgBclzbW9rXTx7CjpA8CChnzzS9oAb3YZDaXfs4/n7grp9omITtvXLqD1wNBInl7g1ob0C8pooJOBl0tX0Z3AEkmzysPfJSUtIiI6ZMwWgKQbqL69HylpK9VoniuAmyQtB54Dzi3Z7wDOBPqB14ALAWzvlHQZ8EDJd+nQA+GIiOgMVQN2pqaenh739fV1uhgTIl1AMZXl+cD0JmmT7Z6x8uVdQJMoN/2ImEoSACLiX5WRQ90rAWCC5Vt/TEf5d1sPeRlcRERNpQUQEU1Ld1B3SQCIiH2SYDD9JQBMgPSfRsR0kAAQEeOW1sD0lIfAERE1lRZARLRVM12gaSVMDQkAbZJ+/4jmpctoakgAiIiOSjDonASAcci3/oj2Gu3/qQSGiZEA0KLc9CMmX1oJEyMBICKmlQSD9kkAiIhpK11G4zPpAUDSUuDLwAzgWttXTHYZWpVun4jpJUNRmzOpAUDSDOCrwIeBrcADktbbfnyiz91qszE3/Yju1sw9Yfh9oNuCxqROCSnpFOCztk8v65cA2P7iSPnHOyVkbuIRMVW0+sVzPMFmqk4JOQ/Y0rC+FTipMYOkFcCKsvoTSU+O43xHAj8ex/7TTd3qC6lzXUz7OuvKlvOPp87vaibTlHsIbHs1sLodx5LU10wU7BZ1qy+kznWROk+MyX4Z3ACwoGF9fkmLiIhJNtkB4AFgsaRFkg4AzgPWT3IZIiKCSe4Csr1H0sXAnVTDQNfafmwCT9mWrqRppG71hdS5LlLnCTCpo4AiImLqyIQwERE1lQAQEVFTXRkAJC2V9KSkfkkrO12eiSBpgaS7JT0u6TFJnyjph0vaIGlz+Tur02VtJ0kzJD0k6bayvkjSfeVaf7sMLugqkmZKulnSjyQ9IemUbr7Okv6s/Jt+VNINkg7qxussaa2kHZIebUgb8bqqcnWp/yOSTmhHGbouADS8buIM4FjgDyUd29lSTYg9wKdsHwucDFxU6rkS2Gh7MbCxrHeTTwBPNKxfCVxl+2hgF7C8I6WaWF8Gvmf7GOA4qvp35XWWNA/4U6DH9m9RDRY5j+68zt8Alg5LG+26ngEsLp8VwDXtKEDXBQDgRKDf9tO2fw7cCCzrcJnazvY22w+W5VepbgrzqOq6rmRbB5zdmRK2n6T5wFnAtWVdwKnAzSVLV9UXQNJhwO8CawBs/9z2brr4OlONTnybpP2Ag4FtdOF1tv0DYOew5NGu6zLgOlfuBWZKmjveMnRjABjpdRPzOlSWSSFpIfA+4D5gju1tZdN2YE6HijURvgT8OfDLsn4EsNv2nrLejdd6ETAIfL10fV0r6RC69DrbHgD+Gnie6sb/MrCJ7r/OQ0a7rhNyX+vGAFArkg4FvgN80vYrjdtcjfHtinG+kj4C7LC9qdNlmWT7AScA19h+H/BThnX3dNl1nkX1bXcR8E7gEH61m6QWJuO6dmMAqM3rJiTtT3Xz/6btW0ryi0NNw/J3R6fK12YfAD4q6Vmqbr1TqfrGZ5auAujOa70V2Gr7vrJ+M1VA6Nbr/PvAM7YHbf8CuIXq2nf7dR4y2nWdkPtaNwaAWrxuovR/rwGesP03DZvWA71luRe4dbLLNhFsX2J7vu2FVNf0LtvnA3cD55RsXVPfIba3A1skvacknQY8TpdeZ6qun5MlHVz+jQ/Vt6uvc4PRrut64IIyGuhk4OWGrqJ9Z7vrPsCZwD8DTwGf6XR5JqiOH6RqHj4CPFw+Z1L1i28ENgP/CBze6bJOQN0/BNxWlt8N3A/0A38PHNjp8k1AfY8H+sq1/p/ArG6+zsDngB8BjwLXAwd243UGbqB6zvELqpbe8tGuKyCq0Y1PAT+kGiU17jLkVRARETXVjV1AERHRhASAiIiaSgCIiKipBICIiJpKAIiIqKkEgIiImkoAiIioqf8P9mAs1ROFKUwAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(data['age'], range(101))\n", + "plt.title('Age Distribution')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/README.md b/README.md index 67b1588..793572f 100644 --- a/README.md +++ b/README.md @@ -39,7 +39,7 @@ The dataset is great for research purposes. It contains more than `500 thousand+ In this project, I filter all the images, resized them all to `128x128`, remove all the images with invalid age, fix the gender distribution problem, and save them in the proper format. Along with that, I’ve also processed the `.mat` files and converted them in `.csv` files also. ## File Structure -This repository contains 4 files +This repository contains 3 files - `mat.py` - `gender.py` - `age.py`