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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import cv2 as cv\n",
"import numpy as np\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"from glob import glob\n",
"from os.path import join\n",
"from pathlib import Path\n",
"sys.path.append('/work/megapixels_dev/megapixels')\n",
"from app.models.bbox import BBox\n",
"#from app.utils import im_utils\n",
"import random"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"dir_ims = '/data_store_ssd/apps/megapixels/datasets/umd_faces/faces/'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n"
]
}
],
"source": [
"fp_ims = glob(join(dir_ims, '*.png'))\n",
"print(len(fp_ims))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function choice in module random:\n",
"\n",
"choice(self, seq)\n",
" Choose a random element from a non-empty sequence.\n",
"\n"
]
}
],
"source": [
"help(random.sample)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 8, 0, 6, 3] True\n"
]
}
],
"source": [
"a = list(range(0,10))\n",
"b = random.sample(a, 5)\n",
"print(b, len(set(b))==5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from random import randint\n",
"imu"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import face_alignment\n",
"from skimage import io\n",
"\n",
"fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._3D, flip_input=False, device='cuda')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fp_im = np.random.choice(fp_ims)\n",
"im = io.imread(fp_im)\n",
"preds = fa.get_landmarks(im)\n",
"print(preds[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import json"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(len(preds[0]))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with open('test.json', 'w') as fp:\n",
" json.dump(preds[0].tolist(), fp)"
]
},
{
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
