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Face-Clock
Commit 564d3055 authored by YONG-LIN SU's avatar YONG-LIN SU
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add save error detention somebody to the local folder for retrain

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notebook/demo-images.ipynb 0 → 100644
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notebook/demo-svm.ipynb 0 → 100644
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notebook/demo-webcam.ipynb 0 → 100644
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Reference\n",
"- https://medium.com/@neotheicebird/webcam-based-image-processing-in-ipython-notebooks-47c75a022514"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-27T10:43:38.815479Z",
"start_time": "2017-12-27T10:43:38.352970Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"import cv2\n",
"import matplotlib.pyplot as plt\n",
"import signal\n",
"from IPython import display\n",
"\n",
"from sklearn.svm import SVC\n",
"from sklearn.preprocessing import LabelEncoder\n",
"from skimage.transform import resize\n",
"from keras.models import load_model"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-27T10:43:38.829958Z",
"start_time": "2017-12-27T10:43:38.828130Z"
}
},
"outputs": [],
"source": [
"cascade_path = '../model/cv2/haarcascade_frontalface_alt2.xml'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-27T10:43:44.388891Z",
"start_time": "2017-12-27T10:43:39.027900Z"
}
},
"outputs": [],
"source": [
"model_path = '../model/keras/facenet_keras.h5'\n",
"model = load_model(model_path)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-27T10:43:44.419587Z",
"start_time": "2017-12-27T10:43:44.404326Z"
}
},
"outputs": [],
"source": [
"def prewhiten(x):\n",
" if x.ndim == 4:\n",
" axis = (1, 2, 3)\n",
" size = x[0].size\n",
" elif x.ndim == 3:\n",
" axis = (0, 1, 2)\n",
" size = x.size\n",
" else:\n",
" raise ValueError('Dimension should be 3 or 4')\n",
"\n",
" mean = np.mean(x, axis=axis, keepdims=True)\n",
" std = np.std(x, axis=axis, keepdims=True)\n",
" std_adj = np.maximum(std, 1.0/np.sqrt(size))\n",
" y = (x - mean) / std_adj\n",
" return y\n",
"\n",
"def l2_normalize(x, axis=-1, epsilon=1e-10):\n",
" output = x / np.sqrt(np.maximum(np.sum(np.square(x), axis=axis, keepdims=True), epsilon))\n",
" return output"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-27T10:43:44.447934Z",
"start_time": "2017-12-27T10:43:44.439987Z"
}
},
"outputs": [],
"source": [
"def calc_embs(imgs, margin, batch_size):\n",
" aligned_images = prewhiten(imgs)\n",
" pd = []\n",
" for start in range(0, len(aligned_images), batch_size):\n",
" pd.append(model.predict_on_batch(aligned_images[start:start+batch_size]))\n",
" embs = l2_normalize(np.concatenate(pd))\n",
"\n",
" return embs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-27T10:43:44.843761Z",
"start_time": "2017-12-27T10:43:44.469101Z"
}
},
"outputs": [],
"source": [
"class FaceDemo(object):\n",
" def __init__(self, cascade_path):\n",
" self.vc = None\n",
" self.cascade = cv2.CascadeClassifier(cascade_path)\n",
" self.margin = 10\n",
" self.batch_size = 1\n",
" self.n_img_per_person = 10\n",
" self.is_interrupted = False\n",
" self.data = {}\n",
" self.le = None\n",
" self.clf = None\n",
" \n",
" def _signal_handler(self, signal, frame):\n",
" self.is_interrupted = True\n",
" \n",
" def capture_images(self, name='Unknown'):\n",
" vc = cv2.VideoCapture(0)\n",
" self.vc = vc\n",
" if vc.isOpened():\n",
" is_capturing, _ = vc.read()\n",
" else:\n",
" is_capturing = False\n",
"\n",
" imgs = []\n",
" signal.signal(signal.SIGINT, self._signal_handler)\n",
" self.is_interrupted = False\n",
" while is_capturing:\n",
" is_capturing, frame = vc.read()\n",
" frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
" faces = self.cascade.detectMultiScale(frame,\n",
" scaleFactor=1.1,\n",
" minNeighbors=3,\n",
" minSize=(100, 100))\n",
" if len(faces) != 0:\n",
" face = faces[0]\n",
" (x, y, w, h) = face\n",
" left = x - self.margin // 2\n",
" right = x + w + self.margin // 2\n",
" bottom = y - self.margin // 2\n",
" top = y + h + self.margin // 2\n",
" img = resize(frame[bottom:top, left:right, :],\n",
" (160, 160), mode='reflect')\n",
" imgs.append(img)\n",
" cv2.rectangle(frame,\n",
" (left-1, bottom-1),\n",
" (right+1, top+1),\n",
" (255, 0, 0), thickness=2)\n",
"\n",
" plt.imshow(frame)\n",
" plt.title('{}/{}'.format(len(imgs), self.n_img_per_person))\n",
" plt.xticks([])\n",
" plt.yticks([])\n",
" display.clear_output(wait=True)\n",
" if len(imgs) == self.n_img_per_person:\n",
" vc.release()\n",
" self.data[name] = np.array(imgs)\n",
" break\n",
" try:\n",
" plt.pause(0.1)\n",
" except Exception:\n",
" pass\n",
" if self.is_interrupted:\n",
" vc.release()\n",
" break\n",
" \n",
" def train(self):\n",
" labels = []\n",
" embs = []\n",
" names = self.data.keys()\n",
" for name, imgs in self.data.items():\n",
" embs_ = calc_embs(imgs, self.margin, self.batch_size) \n",
" labels.extend([name] * len(embs_))\n",
" embs.append(embs_)\n",
"\n",
" embs = np.concatenate(embs)\n",
" le = LabelEncoder().fit(labels)\n",
" y = le.transform(labels)\n",
" clf = SVC(kernel='linear', probability=True).fit(embs, y)\n",
" \n",
" self.le = le\n",
" self.clf = clf\n",
" \n",
" def infer(self):\n",
" vc = cv2.VideoCapture(0)\n",
" self.vc = vc\n",
" if vc.isOpened():\n",
" is_capturing, _ = vc.read()\n",
" else:\n",
" is_capturing = False\n",
"\n",
" signal.signal(signal.SIGINT, self._signal_handler)\n",
" self.is_interrupted = False\n",
" while is_capturing:\n",
" is_capturing, frame = vc.read()\n",
" frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
" faces = self.cascade.detectMultiScale(frame,\n",
" scaleFactor=1.1,\n",
" minNeighbors=3,\n",
" minSize=(100, 100))\n",
" pred = None\n",
" if len(faces) != 0:\n",
" face = faces[0]\n",
" (x, y, w, h) = face\n",
" left = x - self.margin // 2\n",
" right = x + w + self.margin // 2\n",
" bottom = y - self.margin // 2\n",
" top = y + h + self.margin // 2\n",
" img = resize(frame[bottom:top, left:right, :],\n",
" (160, 160), mode='reflect')\n",
" embs = calc_embs(img[np.newaxis], self.margin, 1)\n",
" pred = self.le.inverse_transform(self.clf.predict(embs))\n",
" cv2.rectangle(frame,\n",
" (left-1, bottom-1),\n",
" (right+1, top+1),\n",
" (255, 0, 0), thickness=2)\n",
" plt.imshow(frame)\n",
" plt.title(pred)\n",
" plt.xticks([])\n",
" plt.yticks([])\n",
" display.clear_output(wait=True)\n",
" try:\n",
" plt.pause(0.1)\n",
" except Exception:\n",
" pass\n",
" if self.is_interrupted:\n",
" vc.release()\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-27T10:43:44.873391Z",
"start_time": "2017-12-27T10:43:44.861742Z"
}
},
"outputs": [],
"source": [
"f = FaceDemo(cascade_path)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-27T10:44:10.714335Z",
"start_time": "2017-12-27T10:44:06.972864Z"
}
},
"outputs": [],
"source": [
"# Train with two or more people\n",
"f.capture_images('nyoki-mtl')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-27T10:44:17.284697Z",
"start_time": "2017-12-27T10:44:15.933323Z"
}
},
"outputs": [],
"source": [
"f.train()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-27T10:44:36.348569Z",
"start_time": "2017-12-27T10:44:17.930893Z"
}
},
"outputs": [],
"source": [
"f.infer()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.6"
},
"varInspector": {
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
"module",
"function",
"builtin_function_or_method",
"instance",
"_Feature"
],
"window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
notebook/keras_to_coreml.ipynb 0 → 100644
View file @ 564d3055
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Reference\n",
"\n",
"- http://machinethink.net/blog/coreml-custom-layers/"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-27T10:47:01.059894Z",
"start_time": "2017-12-27T10:46:55.443001Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"import sys\n",
"sys.path.append('../code/')\n",
"\n",
"from inception_resnet_v1 import *\n",
"model = InceptionResNetV1(weights_path='../model/keras/weights/facenet_keras_weights.h5')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-27T10:47:02.740150Z",
"start_time": "2017-12-27T10:47:02.677541Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:root:Keras version 2.1.2 detected. Last version known to be fully compatible of Keras is 2.0.6 .\n",
"WARNING:root:TensorFlow version 1.4.1 detected. Last version known to be fully compatible is 1.2.1 .\n"
]
}
],
"source": [
"from coremltools.proto import NeuralNetwork_pb2\n",
"\n",
"# The conversion function for Lambda layers.\n",
"def convert_lambda(layer):\n",
" if layer.function == scaling:\n",
" params = NeuralNetwork_pb2.CustomLayerParams()\n",
" params.className = \"scaling\"\n",
" return params\n",
" else:\n",
" return None"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2017-12-27T10:47:20.149309Z",
"start_time": "2017-12-27T10:47:07.053856Z"
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 : input_1, <keras.engine.topology.InputLayer object at 0x7fd8e4406410>\n",
"1 : Conv2d_1a_3x3, <keras.layers.convolutional.Conv2D object at 0x7fd8ad70b750>\n",
"2 : Conv2d_1a_3x3_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8ad70b850>\n",
"3 : Conv2d_1a_3x3_Activation, <keras.layers.core.Activation object at 0x7fd8ad70b910>\n",
"4 : Conv2d_2a_3x3, <keras.layers.convolutional.Conv2D object at 0x7fd8ad70bb10>\n",
"5 : Conv2d_2a_3x3_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8ad688d90>\n",
"6 : Conv2d_2a_3x3_Activation, <keras.layers.core.Activation object at 0x7fd8ad63da50>\n",
"7 : Conv2d_2b_3x3, <keras.layers.convolutional.Conv2D object at 0x7fd8ad63d450>\n",
"8 : Conv2d_2b_3x3_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8ad619850>\n",
"9 : Conv2d_2b_3x3_Activation, <keras.layers.core.Activation object at 0x7fd8ad5ce510>\n",
"10 : MaxPool_3a_3x3, <keras.layers.pooling.MaxPooling2D object at 0x7fd8ad5cecd0>\n",
"11 : Conv2d_3b_1x1, <keras.layers.convolutional.Conv2D object at 0x7fd8ad5dead0>\n",
"12 : Conv2d_3b_1x1_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8ad5a7950>\n",
"13 : Conv2d_3b_1x1_Activation, <keras.layers.core.Activation object at 0x7fd8ad555a50>\n",
"14 : Conv2d_4a_3x3, <keras.layers.convolutional.Conv2D object at 0x7fd8ad555610>\n",
"15 : Conv2d_4a_3x3_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8ad51afd0>\n",
"16 : Conv2d_4a_3x3_Activation, <keras.layers.core.Activation object at 0x7fd8ad4e2510>\n",
"17 : Conv2d_4b_3x3, <keras.layers.convolutional.Conv2D object at 0x7fd8ad4cbd10>\n",
"18 : Conv2d_4b_3x3_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8ad4a7b10>\n",
"19 : Conv2d_4b_3x3_Activation, <keras.layers.core.Activation object at 0x7fd8ad4557d0>\n",
"20 : Block35_1_Branch_2_Conv2d_0a_1x1, <keras.layers.convolutional.Conv2D object at 0x7fd8ad287990>\n",
"21 : Block35_1_Branch_2_Conv2d_0a_1x1_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8ad2f2490>\n",
"22 : Block35_1_Branch_2_Conv2d_0a_1x1_Activation, <keras.layers.core.Activation object at 0x7fd8ad239690>\n",
"23 : Block35_1_Branch_1_Conv2d_0a_1x1, <keras.layers.convolutional.Conv2D object at 0x7fd8ad379750>\n",
"24 : Block35_1_Branch_2_Conv2d_0b_3x3, <keras.layers.convolutional.Conv2D object at 0x7fd8ad2192d0>\n",
"25 : Block35_1_Branch_1_Conv2d_0a_1x1_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8ad3e5650>\n",
"26 : Block35_1_Branch_2_Conv2d_0b_3x3_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8ad253150>\n",
"27 : Block35_1_Branch_1_Conv2d_0a_1x1_Activation, <keras.layers.core.Activation object at 0x7fd8ad3b0850>\n",
"28 : Block35_1_Branch_2_Conv2d_0b_3x3_Activation, <keras.layers.core.Activation object at 0x7fd8ad1cda90>\n",
"29 : Block35_1_Branch_0_Conv2d_1x1, <keras.layers.convolutional.Conv2D object at 0x7fd8ad46cd90>\n",
"30 : Block35_1_Branch_1_Conv2d_0b_3x3, <keras.layers.convolutional.Conv2D object at 0x7fd8ad34a0d0>\n",
"31 : Block35_1_Branch_2_Conv2d_0c_3x3, <keras.layers.convolutional.Conv2D object at 0x7fd8ad195a50>\n",
"32 : Block35_1_Branch_0_Conv2d_1x1_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8ad46ce90>\n",
"33 : Block35_1_Branch_1_Conv2d_0b_3x3_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8ad30a090>\n",
"34 : Block35_1_Branch_2_Conv2d_0c_3x3_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8ad17c6d0>\n",
"35 : Block35_1_Branch_0_Conv2d_1x1_Activation, <keras.layers.core.Activation object at 0x7fd8ad434c90>\n",
"36 : Block35_1_Branch_1_Conv2d_0b_3x3_Activation, <keras.layers.core.Activation object at 0x7fd8ad325d50>\n",
"37 : Block35_1_Branch_2_Conv2d_0c_3x3_Activation, <keras.layers.core.Activation object at 0x7fd8ad1458d0>\n",
"38 : Block35_1_Concatenate, <keras.layers.merge.Concatenate object at 0x7fd8ad125510>\n",
"39 : Block35_1_Conv2d_1x1, <keras.layers.convolutional.Conv2D object at 0x7fd8ad0d7210>\n",
"40 : lambda_1, <keras.layers.core.Lambda object at 0x7fd8ad0d7190>\n",
"41 : add_1, <keras.layers.merge.Add object at 0x7fd8ad0878d0>\n",
"42 : Block35_1_Activation, <keras.layers.core.Activation object at 0x7fd8ad0a00d0>\n",
"43 : Block35_2_Branch_2_Conv2d_0a_1x1, <keras.layers.convolutional.Conv2D object at 0x7fd8acf72590>\n",
"44 : Block35_2_Branch_2_Conv2d_0a_1x1_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8acf5e490>\n",
"45 : Block35_2_Branch_2_Conv2d_0a_1x1_Activation, <keras.layers.core.Activation object at 0x7fd8aced7d50>\n",
"46 : Block35_2_Branch_1_Conv2d_0a_1x1, <keras.layers.convolutional.Conv2D object at 0x7fd8ad066350>\n",
"47 : Block35_2_Branch_2_Conv2d_0b_3x3, <keras.layers.convolutional.Conv2D object at 0x7fd8acef3b90>\n",
"48 : Block35_2_Branch_1_Conv2d_0a_1x1_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8ad036790>\n",
"49 : Block35_2_Branch_2_Conv2d_0b_3x3_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8acf26d10>\n",
"50 : Block35_2_Branch_1_Conv2d_0a_1x1_Activation, <keras.layers.core.Activation object at 0x7fd8ad018b10>\n",
"51 : Block35_2_Branch_2_Conv2d_0b_3x3_Activation, <keras.layers.core.Activation object at 0x7fd8ace3a690>\n",
"52 : Block35_2_Branch_0_Conv2d_1x1, <keras.layers.convolutional.Conv2D object at 0x7fd8ad0a0a90>\n",
"53 : Block35_2_Branch_1_Conv2d_0b_3x3, <keras.layers.convolutional.Conv2D object at 0x7fd8acfe6ad0>\n",
"54 : Block35_2_Branch_2_Conv2d_0c_3x3, <keras.layers.convolutional.Conv2D object at 0x7fd8acdfeb50>\n",
"55 : Block35_2_Branch_0_Conv2d_1x1_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8ad0a0bd0>\n",
"56 : Block35_2_Branch_1_Conv2d_0b_3x3_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8acfcb750>\n",
"57 : Block35_2_Branch_2_Conv2d_0c_3x3_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8ace6b350>\n",
"58 : Block35_2_Branch_0_Conv2d_1x1_Activation, <keras.layers.core.Activation object at 0x7fd8ad036410>\n",
"59 : Block35_2_Branch_1_Conv2d_0b_3x3_Activation, <keras.layers.core.Activation object at 0x7fd8acf92950>\n",
"60 : Block35_2_Branch_2_Conv2d_0c_3x3_Activation, <keras.layers.core.Activation object at 0x7fd8ace33f90>\n",
"61 : Block35_2_Concatenate, <keras.layers.merge.Concatenate object at 0x7fd8acdcf110>\n",
"62 : Block35_2_Conv2d_1x1, <keras.layers.convolutional.Conv2D object at 0x7fd8acdabf90>\n",
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"141 : Mixed_6a_Branch_0_Conv2d_1a_3x3, <keras.layers.convolutional.Conv2D object at 0x7fd8ad455190>\n",
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]
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{
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"224 : Block17_5_Branch_1_Conv2d_0c_7x1, <keras.layers.convolutional.Conv2D object at 0x7fd8ab66ebd0>\n",
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"226 : Block17_5_Branch_1_Conv2d_0c_7x1_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8ab6593d0>\n",
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{
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"311 : Block17_10_Branch_1_Conv2d_0c_7x1_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8aaa77490>\n",
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]
},
{
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"383 : Block8_3_Branch_1_Conv2d_0c_3x1, <keras.layers.convolutional.Conv2D object at 0x7fd8a9fb9cd0>\n",
"384 : Block8_3_Branch_0_Conv2d_1x1_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8aa16ef10>\n",
"385 : Block8_3_Branch_1_Conv2d_0c_3x1_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8aa020650>\n",
"386 : Block8_3_Branch_0_Conv2d_1x1_Activation, <keras.layers.core.Activation object at 0x7fd8aa101690>\n",
"387 : Block8_3_Branch_1_Conv2d_0c_3x1_Activation, <keras.layers.core.Activation object at 0x7fd8a9f9bd10>\n",
"388 : Block8_3_Concatenate, <keras.layers.merge.Concatenate object at 0x7fd8a9f375d0>\n",
"389 : Block8_3_Conv2d_1x1, <keras.layers.convolutional.Conv2D object at 0x7fd8a9f370d0>\n",
"390 : lambda_18, <keras.layers.core.Lambda object at 0x7fd8a9efbd90>\n",
"391 : add_18, <keras.layers.merge.Add object at 0x7fd8a9f16510>\n",
"392 : Block8_3_Activation, <keras.layers.core.Activation object at 0x7fd8a9f16110>\n",
"393 : Block8_4_Branch_1_Conv2d_0a_1x1, <keras.layers.convolutional.Conv2D object at 0x7fd8a9e85790>\n",
"394 : Block8_4_Branch_1_Conv2d_0a_1x1_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8a9edc910>\n",
"395 : Block8_4_Branch_1_Conv2d_0a_1x1_Activation, <keras.layers.core.Activation object at 0x7fd8a9e63fd0>\n",
"396 : Block8_4_Branch_1_Conv2d_0b_1x3, <keras.layers.convolutional.Conv2D object at 0x7fd8a9dffb10>\n",
"397 : Block8_4_Branch_1_Conv2d_0b_1x3_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8a9e39d10>\n",
"398 : Block8_4_Branch_1_Conv2d_0b_1x3_Activation, <keras.layers.core.Activation object at 0x7fd8a9dc6690>\n",
"399 : Block8_4_Branch_0_Conv2d_1x1, <keras.layers.convolutional.Conv2D object at 0x7fd8a9ec78d0>\n",
"400 : Block8_4_Branch_1_Conv2d_0c_3x1, <keras.layers.convolutional.Conv2D object at 0x7fd8a9d90b50>\n",
"401 : Block8_4_Branch_0_Conv2d_1x1_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8a9ec7f10>\n",
"402 : Block8_4_Branch_1_Conv2d_0c_3x1_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8a9d79350>\n",
"403 : Block8_4_Branch_0_Conv2d_1x1_Activation, <keras.layers.core.Activation object at 0x7fd8a9edc390>\n",
"404 : Block8_4_Branch_1_Conv2d_0c_3x1_Activation, <keras.layers.core.Activation object at 0x7fd8a9d43f90>\n",
"405 : Block8_4_Concatenate, <keras.layers.merge.Concatenate object at 0x7fd8a9d5c110>\n",
"406 : Block8_4_Conv2d_1x1, <keras.layers.convolutional.Conv2D object at 0x7fd8a9d5c090>\n",
"407 : lambda_19, <keras.layers.core.Lambda object at 0x7fd8a9cbbf10>\n",
"408 : add_19, <keras.layers.merge.Add object at 0x7fd8a9cea210>\n",
"409 : Block8_4_Activation, <keras.layers.core.Activation object at 0x7fd8a9c81c50>\n",
"410 : Block8_5_Branch_1_Conv2d_0a_1x1, <keras.layers.convolutional.Conv2D object at 0x7fd8a9c5a490>\n",
"411 : Block8_5_Branch_1_Conv2d_0a_1x1_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8a9cad610>\n",
"412 : Block8_5_Branch_1_Conv2d_0a_1x1_Activation, <keras.layers.core.Activation object at 0x7fd8a9c0d990>\n",
"413 : Block8_5_Branch_1_Conv2d_0b_1x3, <keras.layers.convolutional.Conv2D object at 0x7fd8a9bd4cd0>\n",
"414 : Block8_5_Branch_1_Conv2d_0b_1x3_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8a9bbc650>\n",
"415 : Block8_5_Branch_1_Conv2d_0b_1x3_Activation, <keras.layers.core.Activation object at 0x7fd8a9b37d10>\n",
"416 : Block8_5_Branch_0_Conv2d_1x1, <keras.layers.convolutional.Conv2D object at 0x7fd8a9cd1b90>\n",
"417 : Block8_5_Branch_1_Conv2d_0c_3x1, <keras.layers.convolutional.Conv2D object at 0x7fd8a9b67850>\n",
"418 : Block8_5_Branch_0_Conv2d_1x1_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8a9c98c10>\n",
"419 : Block8_5_Branch_1_Conv2d_0c_3x1_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8a9b525d0>\n",
"420 : Block8_5_Branch_0_Conv2d_1x1_Activation, <keras.layers.core.Activation object at 0x7fd8a9cad090>\n",
"421 : Block8_5_Branch_1_Conv2d_0c_3x1_Activation, <keras.layers.core.Activation object at 0x7fd8a9b19250>\n",
"422 : Block8_5_Concatenate, <keras.layers.merge.Concatenate object at 0x7fd8a9a77250>\n",
"423 : Block8_5_Conv2d_1x1, <keras.layers.convolutional.Conv2D object at 0x7fd8a9ac7a10>\n",
"424 : lambda_20, <keras.layers.core.Lambda object at 0x7fd8a9a90c10>\n",
"425 : add_20, <keras.layers.merge.Add object at 0x7fd8a9aaaed0>\n",
"426 : Block8_5_Activation, <keras.layers.core.Activation object at 0x7fd8a9aaae50>\n",
"427 : Block8_6_Branch_1_Conv2d_0a_1x1, <keras.layers.convolutional.Conv2D object at 0x7fd8a9a1af90>\n",
"428 : Block8_6_Branch_1_Conv2d_0a_1x1_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8a9a080d0>\n",
"429 : Block8_6_Branch_1_Conv2d_0a_1x1_Activation, <keras.layers.core.Activation object at 0x7fd8a997efd0>\n",
"430 : Block8_6_Branch_1_Conv2d_0b_1x3, <keras.layers.convolutional.Conv2D object at 0x7fd8a99acb10>\n",
"431 : Block8_6_Branch_1_Conv2d_0b_1x3_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8a9996890>\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"432 : Block8_6_Branch_1_Conv2d_0b_1x3_Activation, <keras.layers.core.Activation object at 0x7fd8a995df50>\n",
"433 : Block8_6_Branch_0_Conv2d_1x1, <keras.layers.convolutional.Conv2D object at 0x7fd8aa620a90>\n",
"434 : Block8_6_Branch_1_Conv2d_0c_3x1, <keras.layers.convolutional.Conv2D object at 0x7fd8a98be510>\n",
"435 : Block8_6_Branch_0_Conv2d_1x1_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8a9a5bb50>\n",
"436 : Block8_6_Branch_1_Conv2d_0c_3x1_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8a98f80d0>\n",
"437 : Block8_6_Branch_0_Conv2d_1x1_Activation, <keras.layers.core.Activation object at 0x7fd8a9a71d50>\n",
"438 : Block8_6_Branch_1_Conv2d_0c_3x1_Activation, <keras.layers.core.Activation object at 0x7fd8a98ef390>\n",
"439 : Block8_6_Concatenate, <keras.layers.merge.Concatenate object at 0x7fd8a98a36d0>\n",
"440 : Block8_6_Conv2d_1x1, <keras.layers.convolutional.Conv2D object at 0x7fd8a98a3c50>\n",
"441 : lambda_21, <keras.layers.core.Lambda object at 0x7fd8a98698d0>\n",
"442 : add_21, <keras.layers.merge.Add object at 0x7fd8a9801b90>\n",
"443 : AvgPool, <keras.layers.pooling.GlobalAveragePooling2D object at 0x7fd8a9801b10>\n",
"444 : Bottleneck, <keras.layers.core.Dense object at 0x7fd8a97cba90>\n",
"445 : Bottleneck_BatchNorm, <keras.layers.normalization.BatchNormalization object at 0x7fd8a97e23d0>\n"
]
}
],
"source": [
"import coremltools\n",
"\n",
"coreml_model = coremltools.converters.keras.convert(\n",
" model,\n",
" input_names=\"image\",\n",
" image_input_names=\"image\",\n",
" output_names=\"output\",\n",
" add_custom_layers=True,\n",
" custom_conversion_functions={ \"Lambda\": convert_lambda })"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "python2.7",
"language": "python",
"name": "python2.7"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.14"
},
"varInspector": {
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
"module",
"function",
"builtin_function_or_method",
"instance",
"_Feature"
],
"window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
notebook/predict.ipynb 0 → 100644
View file @ 564d3055
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"from sklearn.externals import joblib\n",
"from skimage.transform import resize\n",
"from sklearn.preprocessing import LabelEncoder\n",
"from keras.models import model_from_json\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 載入人臉偵測cascade分類器\n",
"face_cascade = cv2.CascadeClassifier(\"../model/cv2/haarcascade_frontalface_alt2.xml\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 載入Facenet預測模型\n",
"model=model_from_json(open(\"../model/keras/facenet_model.json\",\"r\").read())\n",
"model.load_weights(\"../model/keras/facenet_weights.h5\")\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 載入SVM分類器\n",
"clf=joblib.load('../model/20190618224324/20190618224324.pkl')\n",
"# 載入LabelEncoder\n",
"le=LabelEncoder()\n",
"le.classes_ =np.load('../model/20190618224324/classes.npy')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 影像預處理\n",
"def prewhiten(x):\n",
" if x.ndim == 4:\n",
" axis = (1, 2, 3)\n",
" size = x[0].size\n",
" elif x.ndim == 3:\n",
" axis = (0, 1, 2)\n",
" size = x.size\n",
" else:\n",
" print(x.ndim)\n",
" raise ValueError('Dimension should be 3 or 4')\n",
"\n",
" mean = np.mean(x, axis=axis, keepdims=True)\n",
" std = np.std(x, axis=axis, keepdims=True)\n",
" std_adj = np.maximum(std, 1.0/np.sqrt(size))\n",
" y = (x - mean) / std_adj\n",
" return y\n",
"\n",
"def l2_normalize(x, axis=-1, epsilon=1e-10):\n",
" output = x / np.sqrt(np.maximum(np.sum(np.square(x), axis=axis, keepdims=True), epsilon))\n",
" return output"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 人臉偵測處理回傳結果矩陣\n",
"image_size=160\n",
"def face_cropped(img,faces, margin): \n",
" aligned_images = []\n",
" for f in faces:\n",
" (x, y, w, h) = f\n",
" cropped = img[y-margin//2:y+h+margin//2,x-margin//2:x+w+margin//2, :]\n",
" aligned = resize(cropped, (image_size, image_size), mode='reflect')\n",
" aligned_images.append(aligned)\n",
" \n",
" return np.array(aligned_images)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 取得人臉Facenet預測之特徵值\n",
"def calc_embs(faces, margin=10, batch_size=1):\n",
" aligned_images = prewhiten(faces)\n",
" pd = []\n",
" for start in range(0, len(aligned_images), batch_size):\n",
" pd.append(model.predict_on_batch(aligned_images[start:start+batch_size]))\n",
" embs = l2_normalize(np.concatenate(pd))\n",
"\n",
" return embs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 人臉辨識推斷\n",
"def infer(le, clf, img):\n",
" faces = face_cascade.detectMultiScale(img,scaleFactor=1.1,minNeighbors=3)\n",
" if(len(faces)==0):\n",
" return '偵測不到人臉請重新調整'\n",
" \n",
" embs = calc_embs(face_cropped(img,faces,10))\n",
"# pred = le.inverse_transform(clf.predict(embs))\n",
" pred=get_labels(le,clf,embs)\n",
" return [faces,pred]\n",
"# Labels 解析\n",
"def get_labels(le,clf,embs):\n",
" socres=clf.predict_proba(embs)\n",
" print(socres)\n",
" results=[]\n",
" for s in socres:\n",
" if(s[s.argmax()]>0.5):\n",
" results.append(le.inverse_transform([s.argmax()])[0])\n",
" else:\n",
" results.append('Unknow')\n",
" return results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"img=cv2.imread('../data/Test/Kevin04.jpg')\n",
"# img2=cv2.imread('../data/Test/test6.jpg')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"result=infer(le,clf,img)\n",
"# result2=infer(le,clf,img2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 顯示結果\n",
"if(len(result)==2): \n",
" fa=result[0]\n",
" na=result[1]\n",
" for f,n in zip(fa,na):\n",
" x,y,w,h=f\n",
" margin=10\n",
" cv2.rectangle(img,(x-margin//2,y-margin//2),(x+w+margin//2,y+h+margin//2),(0,0,255))\n",
" cv2.putText(img, n, (x-margin//2,y-margin//2), cv2.FONT_HERSHEY_PLAIN,1, (0, 255, 255), 1, cv2.LINE_AA)\n",
" cv2.imshow('frame',img)\n",
" cv2.waitKey(0)\n",
" cv2.destroyAllWindows()\n",
"else:\n",
" print(result)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"faces = face_cascade.detectMultiScale(img,scaleFactor=1.1,minNeighbors=3)\n",
"if(len(faces)!=0):\n",
" err,embs = calc_embs(face_cropped(faces,10))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"socre=clf.predict_proba(embs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"get_labels(le,clf,embs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fa=result1[0]\n",
"na=result1[1]\n",
"print(f,n)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for f,n in zip(fa,na):\n",
" print(str(f) + n)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
notebook/test.ipynb 0 → 100644
View file @ 564d3055
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import cv2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cascade_path = '../model/cv2/haarcascade_frontalface_alt2.xml'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"face_cascade=cv2.CascadeClassifier(cascade_path)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"img=cv2.imread(\"../data/images/Yutanek/Yutanek03.jpg\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"margin=10"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x,y,w,h=face_cascade.detectMultiScale(img)[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"result=cv2.rectangle(img,(x-margin//2,y-margin//2),(x+w+margin//2,y+h+margin//2),(0,0,255))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cv2.imshow(\"frame\",img[y:y+h,x:x+w])\n",
"cv2.imshow(\"result\",result)\n",
"cv2.waitKey(0)\n",
"cv2.destroyAllWindows()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# real time face detention"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cap=cv2.VideoCapture(0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"while(cap.isOpened()):\n",
" ret,frame=cap.read()\n",
" if(ret):\n",
" result=face_cascade.detectMultiScale(frame)\n",
" if(len(result)==0):\n",
" continue\n",
" x,y,w,h=result[0]\n",
" cv2.rectangle(frame,(x,y),(x+w,y+h),(0,0,255))\n",
" cv2.imshow(\"frame\",frame)\n",
" if(cv2.waitKey(33)==27):\n",
" break\n",
"cap.release()\n",
"cv2.destroyAllWindows()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# SVM People Traning"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"from sklearn.svm import SVC\n",
"from sklearn.preprocessing import LabelEncoder\n",
"from skimage.transform import resize\n",
"import os\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cascade_path = '../model/cv2/haarcascade_frontalface_alt2.xml'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"image_dir_basepath=\"../data/people/train/\"\n",
"image_size = 160\n",
"names=[]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for f in os.listdir(os.path.abspath(image_dir_basepath)):\n",
" if(os.path.isdir(os.path.join(os.path.abspath(image_dir_basepath),f))):\n",
" names.append(f)\n",
"print(names)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def prewhiten(x):\n",
" if x.ndim == 4:\n",
" axis = (1, 2, 3)\n",
" size = x[0].size\n",
" elif x.ndim == 3:\n",
" axis = (0, 1, 2)\n",
" size = x.size\n",
" else:\n",
" print(x.ndim)\n",
" raise ValueError('Dimension should be 3 or 4')\n",
"\n",
" mean = np.mean(x, axis=axis, keepdims=True)\n",
" std = np.std(x, axis=axis, keepdims=True)\n",
" std_adj = np.maximum(std, 1.0/np.sqrt(size))\n",
" y = (x - mean) / std_adj\n",
" return y\n",
"\n",
"def l2_normalize(x, axis=-1, epsilon=1e-10):\n",
" output = x / np.sqrt(np.maximum(np.sum(np.square(x), axis=axis, keepdims=True), epsilon))\n",
" return output"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def load_and_align_images(filepaths, margin):\n",
" cascade = cv2.CascadeClassifier(cascade_path)\n",
" \n",
" aligned_images = []\n",
" for filepath in filepaths:\n",
" img = cv2.imread(filepath)\n",
"\n",
" faces = cascade.detectMultiScale(img,\n",
" scaleFactor=1.1,\n",
" minNeighbors=3)\n",
" if(len(faces)==0):\n",
" continue\n",
" (x, y, w, h) = faces[0]\n",
" cropped = img[y-margin//2:y+h+margin//2,\n",
" x-margin//2:x+w+margin//2, :]\n",
" aligned = resize(cropped, (image_size, image_size), mode='reflect')\n",
" aligned_images.append(aligned)\n",
" \n",
" return np.array(aligned_images)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def calc_embs(filepaths, margin=10, batch_size=1):\n",
" print(load_and_align_images(filepaths, margin))\n",
" aligned_images = prewhiten(load_and_align_images(filepaths, margin))\n",
" pd = []\n",
" for start in range(0, len(aligned_images), batch_size):\n",
" pd.append(model.predict_on_batch(aligned_images[start:start+batch_size]))\n",
" embs = l2_normalize(np.concatenate(pd))\n",
"\n",
" return embs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def train(dir_basepath, names, max_num_img=10):\n",
" labels = []\n",
" embs = []\n",
" for name in names:\n",
" dirpath = os.path.abspath(dir_basepath + name)\n",
" filepaths = [os.path.join(dirpath, f) for f in os.listdir(dirpath)][:max_num_img]\n",
" embs_ = calc_embs(filepaths) \n",
" labels.extend([name] * len(embs_))\n",
" embs.append(embs_)\n",
" \n",
" embs = np.concatenate(embs)\n",
" le = LabelEncoder().fit(labels)\n",
" y = le.transform(labels)\n",
" clf = SVC(kernel='linear', probability=True).fit(embs, y)\n",
" return le, clf"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def infer(le, clf, filepaths):\n",
" embs = calc_embs(filepaths)\n",
" pred = le.inverse_transform(clf.predict(embs))\n",
" return embs,pred"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"le, clf = train(image_dir_basepath, names)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"test_dirpath = os.path.abspath(\"../data/images/Test/\")\n",
"test_filepaths = [os.path.join(test_dirpath, f) for f in os.listdir(test_dirpath)]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"embs_,pred_ = infer(le, clf, test_filepaths)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pred_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Somthing Test"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from keras.models import model_from_json"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model=model_from_json(open(\"../model/keras/facenet_model.json\",\"r\").read())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.load_weights(\"../model/keras/facenet_weights.h5\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.predict(img)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def load_and_align_images(filepaths, margin):\n",
" cascade = cv2.CascadeClassifier(cascade_path)\n",
" \n",
" aligned_images = []\n",
" for filepath in filepaths:\n",
" img = cv2.imread(filepath)\n",
"\n",
" faces = cascade.detectMultiScale(img,\n",
" scaleFactor=1.1,\n",
" minNeighbors=3)\n",
" (x, y, w, h) = faces[0]\n",
" cropped = img[y-margin//2:y+h+margin//2,\n",
" x-margin//2:x+w+margin//2, :]\n",
" aligned = resize(cropped, (image_size, image_size), mode='reflect')\n",
" aligned_images.append(aligned)\n",
" \n",
" return np.array(aligned_images)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"margin=10\n",
"cascade = cv2.CascadeClassifier(cascade_path)\n",
"img = cv2.imread(\"../data/images/BillGates/Bill_Gates_0003.jpg\")\n",
"faces = cascade.detectMultiScale(img,scaleFactor=1.1,minNeighbors=3)\n",
"(x, y, w, h) = faces[0]\n",
"cropped = img[y-margin//2:y+h+margin//2,x-margin//2:x+w+margin//2, :]\n",
"aligned = resize(cropped, (image_size, image_size), mode='reflect')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"aligned_images = prewhiten(aligned)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cv2.imshow(\"test\",aligned_images)\n",
"cv2.waitKey(0)\n",
"cv2.destroyAllWindows()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
notebook/tf_to_keras.ipynb 0 → 100644
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