update fbp nb

2 files changed, 184 insertions, 3 deletions

diff --git a/megapixels/notebooks/face_analysis/05b_detect_face_dlib_pose.ipynb b/megapixels/notebooks/face_analysis/05b_detect_face_dlib_pose.ipynb
index 7ecc660a..f9379f1d 100644
--- a/megapixels/notebooks/face_analysis/05b_detect_face_dlib_pose.ipynb
+++ b/megapixels/notebooks/face_analysis/05b_detect_face_dlib_pose.ipynb
@@ -505,9 +505,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:vframe]",
+   "display_name": "Python [conda env:megapixels]",
    "language": "python",
-   "name": "conda-env-vframe-py"
+   "name": "conda-env-megapixels-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -519,7 +519,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.6"
+   "version": "3.6.5"
   }
  },
  "nbformat": 4,
diff --git a/megapixels/notebooks/face_analysis/beauty.ipynb b/megapixels/notebooks/face_analysis/beauty.ipynb
new file mode 100644
index 00000000..fc557c99
--- /dev/null
+++ b/megapixels/notebooks/face_analysis/beauty.ipynb
@@ -0,0 +1,181 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from os.path import join\n",
+    "import os.path as osp\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "from PIL import Image, ImageDraw\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import math\n",
+    "import dlib\n",
+    "from pathlib import Path\n",
+    "%reload_ext autoreload\n",
+    "%autoreload 2\n",
+    "#from utils import imx # Image function extensions\n",
+    "#from config import config as cfg\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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from keras.layers import Conv2D, Input, MaxPool2D,Flatten, Dense, Permute, GlobalAveragePooling2D\n",
+    "from keras.models import Model\n",
+    "from keras.optimizers import adam\n",
+    "import numpy as np\n",
+    "import pickle\n",
+    "import keras\n",
+    "import cv2\n",
+    "import sys\n",
+    "import dlib\n",
+    "import os.path\n",
+    "from keras.models import Sequential\n",
+    "from keras.applications.resnet50 import ResNet50\n",
+    "#from keras.applications.resnet50 import Dense\n",
+    "from keras.layers import Dense\n",
+    "from keras.optimizers import Adam\n",
+    "import pickle\n",
+    "import numpy as np\n",
+    "import cv2\n",
+    "import os\n",
+    "from keras.layers import Dropout"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#model_path = parent_path+\"/common/mmod_human_face_detector.dat\"\n",
+    "model_path = '/data_store_hdd/apps/megapixels/models/dlib/mmod_human_face_detector.dat'\n",
+    "#cnn_face_detector = dlib.cnn_face_detection_model_v1(model_path)\n",
+    "cnn_face_detector = dlib.get_frontal_face_detector()\n",
+    "\n",
+    "resnet = ResNet50(include_top=False, pooling='avg')\n",
+    "model = Sequential()\n",
+    "model.add(resnet)\n",
+    "model.add(Dense(5, activation='softmax'))\n",
+    "model.layers[0].trainable = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fp_model = join(cfg.DIR_MODELS, 'keras', 'model-ldl-resnet.h5')\n",
+    "print(fp_model)\n",
+    "model.load_weights(fp_model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def score_mapping(modelScore):\n",
+    "\n",
+    "    if modelScore <= 1.9:\n",
+    "        mappingScore = ((4 - 2.5) / (1.9 - 1.0)) * (modelScore-1.0) + 2.5\n",
+    "    elif modelScore <= 2.8:\n",
+    "        mappingScore = ((5.5 - 4) / (2.8 - 1.9)) * (modelScore-1.9) + 4\n",
+    "    elif modelScore <= 3.4:\n",
+    "        mappingScore = ((6.5 - 5.5) / (3.4 - 2.8)) * (modelScore-2.8) + 5.5\n",
+    "    elif modelScore <= 4:\n",
+    "        mappingScore = ((8 - 6.5) / (4 - 3.4)) * (modelScore-3.4) + 6.5\n",
+    "    elif modelScore < 5:\n",
+    "        mappingScore = ((9 - 8) / (5 - 4)) * (modelScore-4) + 8\n",
+    "\n",
+    "    return mappingScore\n",
+    "\n",
+    "def beauty_predict(fp_im):\n",
+    "    im = cv2.imread(fp_im)\n",
+    "\n",
+    "    if im.shape[0] > 1280:\n",
+    "      new_shape = (1280, im.shape[1] * 1280 / im.shape[0])\n",
+    "    elif im.shape[1] > 1280:\n",
+    "      new_shape = (im.shape[0] * 1280 / im.shape[1], 1280)\n",
+    "    elif im.shape[0] < 640 or im0.shape[1] < 640:\n",
+    "      new_shape = (im.shape[0] * 2, im.shape[1] * 2)\n",
+    "    else:\n",
+    "      new_shape = im.shape[0:2]\n",
+    "\n",
+    "    im = cv2.resize(im, (int(new_shape[1]), int(new_shape[0])))\n",
+    "    dets = cnn_face_detector(im, 0)\n",
+    "\n",
+    "    for i, d in enumerate(dets):\n",
+    "      #face = [d.rect.left(), d.rect.top(), d.rect.right(), d.rect.bottom()]\n",
+    "      face = [d.left(), d.top(), d.right(), d.bottom()]\n",
+    "      croped_im = im[face[1]:face[3], face[0]:face[2], :]\n",
+    "      resized_im = cv2.resize(croped_im, (224, 224))\n",
+    "      normed_im = np.array([(resized_im - 127.5) / 127.5])\n",
+    "\n",
+    "      import time\n",
+    "      st = time.time()\n",
+    "      pred = model.predict(normed_im)\n",
+    "      print(f'{(time.time()-st)}ms')\n",
+    "      ldList = pred[0]\n",
+    "      out = 1 * ldList[0] + 2 * ldList[1] + 3 * ldList[2] + 4 * ldList[3] + 5 * ldList[4]\n",
+    "\n",
+    "      out = score_mapping(out)\n",
+    "\n",
+    "      print(Path(fp_im).stem + \"Score:\" + str('%.2f' % (out)))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "beauty_predict('/home/adam/Downloads/faces/Arnold_Schwarzenegger_0014.jpg')"
+   ]
+  },
+  {
+   "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
+}