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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# HELEN Facial Landmarks"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [],
"source": [
"%reload_ext autoreload\n",
"%autoreload 2\n",
"\n",
"import os\n",
"from os.path import join\n",
"from glob import glob, iglob\n",
"from pathlib import Path\n",
"from tqdm import tqdm_notebook as tqdm\n",
"import random\n",
"from datetime import datetime\n",
"\n",
"import h5py\n",
"from scipy import misc\n",
"from io import BytesIO\n",
"from base64 import b64decode\n",
"\n",
"from PIL import Image, ImageDraw\n",
"import imutils\n",
"import cv2 as cv\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import scipy.io as sio\n",
"import h5py\n",
"import numpy as np\n",
"import pandas as pd\n",
"import sys\n",
"sys.path.append('/work/megapixels_dev/megapixels/')\n",
"from app.utils import file_utils, draw_utils, im_utils\n",
"from app.models.bbox import BBox"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load Data"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [],
"source": [
"fp_anno_dir = '/data_store/datasets/people/helen/downloads/annotation/'\n",
"fp_annos = glob(join(fp_anno_dir, '*.txt'))\n",
"fp_dir_ims = '/data_store/datasets/people/helen/media/original/'\n",
"fp_dir_out = '/data_store/datasets/people/helen/processed/'"
]
},
{
"cell_type": "code",
"execution_count": 226,
"metadata": {},
"outputs": [],
"source": [
"def create_landmark_im(fp_anno, fp_dir_ims, size=(640,640), margin=20, radius=2):\n",
" \n",
" # parse annotation\n",
" _annos = file_utils.load_text(fp_anno)\n",
" fn = _annos[0]\n",
" annos = [list(map(float, a.split(','))) for a in _annos[1:]]\n",
" fp_im = join(fp_dir_ims, f'{fn}.jpg')\n",
"\n",
" # load image\n",
" im = cv.imread(fp_im)\n",
" im_pil = Image.open(fp_im)\n",
" h, w = im.shape[:2]\n",
" \n",
" # normalize points and get min max and bbox\n",
" points_norm = [(x/w, y/h) for x,y in annos]\n",
" points_norm_np = np.array(points_norm)\n",
" points_x, points_y = (points_norm_np[:,0], points_norm_np[:,1])\n",
" x1, y1 = (min(points_x), min(points_y))\n",
" x2, y2 = (max(points_x), max(points_y))\n",
" bbox = BBox.from_xyxy(x1, y1, x2, y2)\n",
" #bbox_exp = bbox.expand(.2).to_square()\n",
" \n",
" # create offset and redraw\n",
" adjw, adjh = (1 + (2*margin)/im_size[0], 1 + (2*margin)/im_size[1])\n",
" points_norm_offset = [((x - bbox.x1) / (bbox.w * adjw), (y - bbox.y1) / (bbox.h * adjh)) for x, y in points_norm]\n",
" points_norm_offset = [(x + margin/im_size[0], y + margin/im_size[1]) for x, y in points_norm_offset]\n",
" im_border = np.zeros([size[0], size[1], 3], dtype=np.uint8)\n",
" #im_border = draw_utils.draw_landmarks2D(im_border, points_norm_offset, radius=radius, color=(255,255,255))\n",
" im_border = draw_utils.draw_landmarks2D_pil(im_border, points_norm_offset, radius=radius, color=(255,255,255))\n",
" return im_border\n",
"\n",
"def make_transparent(im, bg_color=(0,0,0)):\n",
" indices = np.all(im == bg_color, axis=-1)\n",
" im = cv.cvtColor(im, cv.COLOR_BGR2BGRA)\n",
" im[indices] = [0, 0, 0, 0]\n",
" return im"
]
},
{
"cell_type": "code",
"execution_count": 179,
"metadata": {},
"outputs": [],
"source": [
"# create a montage\n",
"for n_iter in range(20):\n",
" ims = []\n",
" im_size = (360,360)\n",
" cols, rows = (3, 4)\n",
" \n",
" for i in range(cols*rows):\n",
" fp_anno_rn = fp_annos[random.randint(0, len(fp_annos))]\n",
" im_lms = create_landmark_im(fp_anno_rn, fp_dir_ims, size=im_size, margin=50)\n",
" ims.append(im_lms)\n",
" \n",
" ims_montaged = imutils.build_montages(ims, im_size, (cols, rows))\n",
" im_montage = ims_montaged[0]\n",
" \n",
" # make transparent\n",
" im_montage = make_transparent(im_montage)\n",
" \n",
" # save\n",
" d = datetime.now()\n",
" fname = f'montage_lms_{d.day}_{d.hour}_{d.hour}_{d.minute}_{d.second}_{n_iter}.png'\n",
" fp_out = join(fp_dir_out, fname)\n",
" cv.imwrite(fp_out, im_montage)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Single Faces"
]
},
{
"cell_type": "code",
"execution_count": 228,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 228,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"im_size = (640, 640)\n",
"fp_anno_rn = fp_annos[random.randint(0, len(fp_annos))]\n",
"im_lms = create_landmark_im(fp_anno_rn, fp_dir_ims, size=im_size, margin=100, radius=4)\n",
"im_lms = make_transparent(im_lms)\n",
"d = datetime.now()\n",
"fname = f'single_{d.day}_{d.hour}_{d.hour}_{d.minute}_{d.second}_{n_iter}.png'\n",
"fname = 'single.png'\n",
"fp_out = join(fp_dir_out, fname)\n",
"cv.imwrite(fp_out, im_lms)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "megapixels",
"language": "python",
"name": "megapixels"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
