diff options
Diffstat (limited to 'megapixels')
| -rw-r--r-- | megapixels/notebooks/face_analysis/05b_detect_face_dlib_pose.ipynb | 6 | ||||
| -rw-r--r-- | megapixels/notebooks/face_analysis/beauty.ipynb | 181 |
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 +} |