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Diffstat (limited to 'megapixels/notebooks/face_analysis/mtcnn_comparison.ipynb')
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diff --git a/megapixels/notebooks/face_analysis/mtcnn_comparison.ipynb b/megapixels/notebooks/face_analysis/mtcnn_comparison.ipynb new file mode 100644 index 00000000..a342e3fc --- /dev/null +++ b/megapixels/notebooks/face_analysis/mtcnn_comparison.ipynb @@ -0,0 +1,5232 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Face Recognition Demo" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "%reload_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import os\n", + "from os.path import join\n", + "from glob import glob\n", + "from random import randint\n", + "from pathlib import Path\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import cv2 as cv\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import sys\n", + "sys.path.append('/work/megapixels_dev/megapixels/')\n", + "from app.settings import app_cfg as cfg\n", + "from app.utils import im_utils, file_utils\n", + "from app.models.bbox import BBox\n", + "from app.settings import types\n", + "from app.utils import draw_utils\n", + "from app.models.data_store import DataStore" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "data_store = DataStore(types.DataStore.HDD, types.Dataset.VGG_FACE2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "fp_records = data_store.metadata(types.Metadata.FILE_RECORD)\n", + "fp_roi = data_store.metadata(types.Metadata.FACE_ROI)\n", + "fp_roi = fp_roi.replace('face_roi.csv', 'face_roi_0_1000.csv')\n", + "fp_bbox_vgg = '/data_store/datasets/people/vgg_face2/downloads/bb_landmark/loose_bb_test.csv'\n", + "\n", + "df_records = pd.read_csv(fp_records).set_index('index')\n", + "df_roi = pd.read_csv(fp_roi).set_index('index')\n", + "df_bbox_vgg = pd.read_csv(fp_bbox_vgg).set_index('NAME_ID')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compare ROI sizes" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "rois = df_roi.to_dict()\n", + "records = df_records.to_dict()" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [], + "source": [ + "import math" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [], + "source": [ + "from app.models.bbox import BBox\n", + "\n", + "deltas_cxy = []\n", + "deltas_width = []\n", + "deltas_height = []\n", + "\n", + "for roi_idx, ds_roi in df_roi.iterrows():\n", + " bbox = BBox.from_xywh(ds_roi.x, ds_roi.y, ds_roi.w, ds_roi.h)\n", + " file_record = df_records.iloc[int(ds_roi.record_index)]\n", + " dim = (file_record.width, file_record.height)\n", + " bbox_dim = bbox.to_dim(dim)\n", + " name_id = join(file_record.subdir.split('/')[1], file_record.fn)\n", + " ds_vgg_bbox = df_bbox_vgg.loc[name_id]\n", + " roi_vgg = (ds_vgg_bbox.X, ds_vgg_bbox.Y, ds_vgg_bbox.W, ds_vgg_bbox.H)\n", + " bbox_vgg = BBox.from_xywh_dim(ds_vgg_bbox.X, ds_vgg_bbox.Y, ds_vgg_bbox.W, ds_vgg_bbox.H, dim)\n", + " bbox_vgg_dim = bbox_vgg.to_dim(dim)\n", + " w,h = dim\n", + " dx, dy = ((bbox_vgg_dim.cx - bbox_vgg_dim.cx)/w, (bbox_vgg_dim.cy - bbox_dim.cy)/h)\n", + " dw, dh = (abs(bbox_vgg_dim.w - bbox_dim.w)/w, abs(bbox_vgg_dim.h - bbox_dim.h)/h)\n", + " delta_center = math.sqrt(math.pow(dx/dim[0],2) + math.pow(dy/dim[1],2))\n", + " deltas_width.append(dw)\n", + " deltas_height.append(dh)\n", + " deltas_cxy.append(delta_center)" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "average c offset: 0.0002713261298150514\n", + "average w offset: 0.03440484078308657\n", + "average h offset: 0.03306552311371902\n" + ] + } + ], + "source": [ + "mu_c = np.mean(np.array(deltas_cxy))\n", + "mu_w = np.mean(np.array(deltas_width))\n", + "mu_h = np.mean(np.array(deltas_height))\n", + "print(f'average c offset: {mu_c}')\n", + "print(f'average w offset: {mu_w}')\n", + "print(f'average h offset: {mu_h}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cv.INTER_" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'h': {0: 0.4681763648986816,\n", + " 1: 0.4431796967983246,\n", + " 2: 0.4864671230316162,\n", + " 3: 0.5021313428878784,\n", + " 4: 0.4792057275772095,\n", + " 5: 0.5172641277313232,\n", + " 6: 0.5529294013977051,\n", + " 7: 0.5571120381355286,\n", + " 8: 0.4725810289382935,\n", + " 9: 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color=(0,0,255))\n", + "# # expand bbox\n", + "# bbox_expanded = bbox.expand(0.3)\n", + "# bbox_expanded_dim = bbox_expanded.to_dim(dim)\n", + "# im = draw_utils.draw_bbox(im, bbox_expanded_dim, color=(255,0,0))\n", + "# plt.imshow(cv.cvtColor(im, cv.COLOR_BGR2RGB))\n", + "# # load image and draw bbox" + ] + }, + { + "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 +} |