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Face-Clock
Commit 50f8415a authored by YONG-LIN SU's avatar YONG-LIN SU
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demo new version

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import tkinter as tk\n",
"import cv2\n",
"from PIL import Image,ImageTk\n",
"import time\n",
"from datetime import datetime \n",
"from face_predict import *\n",
"from influxdb import InfluxDBClient"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Set up GUI\n",
"window = tk.Tk() #Makes main window\n",
"window.wm_title(\"人臉辨識打卡系統\")\n",
"window.config(background=\"#FFFFFF\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Graphics window\n",
"imageFrame = tk.Frame(window, width=600, height=500)\n",
"imageFrame.grid(row=1, column=0,rowspan=4, padx=10, pady=2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Capture video frames\n",
"lmain = tk.Label(imageFrame)\n",
"lmain.grid(row=0, column=0)\n",
"cap = cv2.VideoCapture(0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# result\n",
"status_label=tk.Label(window,text='歡迎使用本系統',font=(\"標楷體\", 20))\n",
"status_label.grid(row=0,column=0,columnspan=3)\n",
"time_label=tk.Label(window,text='時間:',font=(\"標楷體\", 20))\n",
"time_label.grid(row=1,column=1)\n",
"t=tk.Label(window,text='2019-06-19',font=(\"Times New Roman\", 20))\n",
"t.grid(row=2,column=1)\n",
"name_label=tk.Label(window,text='姓名: ',font=(\"標楷體\", 20))\n",
"name_label.grid(row=3,column=1)\n",
"name=tk.Label(window,text='Allen',font=(\"Times New Roman\", 20))\n",
"name.grid(row=4,column=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def clock(status):\n",
" if(status=='login_'):\n",
" msg='開始上班辨識打卡'\n",
" else:\n",
" msg='開始下班辨識打卡'\n",
" status_label['text']=msg\n",
" login_time=time.time()\n",
" ret,frame=cap.read()\n",
" if(ret):\n",
" frame = cv2.flip(frame, 1)\n",
" result=infer(le,clf,frame)\n",
" if(len(result)==2):\n",
" status_label['text']='辨識完成 請確認資料'\n",
" t['text']=time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(login_time))\n",
" name['text']=str(result[1])\n",
" login_button.configure(text=\"是\",command=lambda:submit(status,result[1],login_time))\n",
" logout_button.configure(text=\"否\",command=retry)\n",
" else:\n",
" status_label['text']='偵測不到請重新調整位置' \n",
"\n",
"def submit(measurments,name,time):\n",
" if(measurments=='login_'):\n",
" msg='上班加油!!'\n",
" influxdb_insert(measurments,name,time)\n",
" else:\n",
" msg='上班辛苦了 下班好好休息吧!!'\n",
" status_label['text']=msg\n",
" status_label.after(3000,func=empty_result)\n",
"def empty_result():\n",
" status_label['text']='歡迎使用本系統'\n",
" t['text']=''\n",
" name['text']=''\n",
" login_button.configure(text=\"上班打卡\",command=lambda:clock('login_'))\n",
" logout_button.configure(text=\"下班打卡\",command=lambda:clock('logout_'))\n",
"def retry():\n",
" error_window=tk.Toplevel(window)\n",
" error_window.geometry('300x200')\n",
" error_window.title('請輸入您的姓名 以利後續訓練')\n",
" tk.Label(error_window,text='姓名: ',font=(\"標楷體\", 20)).grid(row=0,column=0)\n",
" currect_name=tk.StringVar()\n",
" entry_currect_name = tk.Entry(error_window, textvariable=currect_name)\n",
" entry_currect_name.grid(row=0,column=1)\n",
" button_submit=tk.Button(error_window,text='提交',font=(\"標楷體\", 20))\n",
" button_submit.grid(row=1,column=0,columnspan=2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def influxdb_insert(measurments,name,time):\n",
" # 建立資料庫連線\n",
" client=InfluxDBClient(host='192.168.5.17',port=8086,username='root',password='root',database='RD')\n",
" json_body = [\n",
" {\n",
" \"measurement\": measurments,\n",
" \"time\": datetime.utcfromtimestamp(time),\n",
" \"fields\": {\n",
" \"name\": name[0],\n",
" 'snapshot':name[0]+'.jpg'\n",
" }\n",
" }\n",
" ]\n",
" client.write_points(json_body)\n",
" client.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# button\n",
"login_button= tk.Button(window,text=\"上班打卡\",command=lambda:clock('login_'),font=(\"標楷體\", 20))\n",
"login_button.grid(row=5,column=0)\n",
"logout_button= tk.Button(window,text=\"下班打卡\",command=lambda:clock('logout_'),font=(\"標楷體\", 20))\n",
"logout_button.grid(row=5,column=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def show_frame():\n",
" _, frame = cap.read()\n",
" frame = cv2.flip(frame, 1)\n",
" \n",
" faces = face_cascade.detectMultiScale(frame,scaleFactor=1.1,minNeighbors=3)\n",
" if(len(faces)>0):\n",
" for f in faces:\n",
" x,y,w,h=f\n",
" margin=10\n",
" cv2.rectangle(frame,(x-margin//2,y-margin//2),(x+w+margin//2,y+h+margin//2),(0,0,255))\n",
" cv2.putText(frame,time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime()),(0,30)\n",
" ,cv2.FONT_HERSHEY_TRIPLEX,1, (0, 255, 255), 1, cv2.LINE_AA) \n",
" cv2image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGBA)\n",
" img = Image.fromarray(cv2image)\n",
" imgtk = ImageTk.PhotoImage(image=img)\n",
" lmain.imgtk = imgtk\n",
" lmain.configure(image=imgtk)\n",
" lmain.after(10, show_frame)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"show_frame() #Display 2\n",
"window.mainloop() #Starts GUI"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Slider window (slider controls stage position)\n",
"# sliderFrame = tk.Frame(window, width=600, height=100)\n",
"# sliderFrame.grid(row = 600, column=0, padx=10, pady=2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"img=cv2.imread('../data/Test/test1.jpg')\n",
"cv2img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n",
"imgtk=ImageTk.PhotoImage(Image.fromarray(cv2img))\n",
"lmain.imgtk = imgtk\n",
"lmain.configure(image=imgtk)\n",
"root.mainloop()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from influxdb import InfluxDBClient"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 建立資料庫連線\n",
"client=InfluxDBClient(host='192.168.5.17',port=8086,username='root',password='root',database='RD')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"json_body = [\n",
" {\n",
" \"measurement\": \"login_\",\n",
" \"time\": datetime.utcfromtimestamp(time.time()),\n",
" \"fields\": {\n",
" \"name\": 'test2',\n",
" 'snapshot':\"test.jpg\"\n",
" }\n",
" }\n",
"]\n",
"client.write_points(json_body)\n",
"client.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from datetime import datetime"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"datetime.utcfromtimestamp(time.time())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(time.time()))"
]
},
{
"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
}
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notebook/.ipynb_checkpoints/demo-svm-checkpoint.ipynb deleted 100644 → 0
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notebook/.ipynb_checkpoints/predict-checkpoint.ipynb deleted 100644 → 0
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{
"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
}
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notebook/.ipynb_checkpoints/tf_to_keras-checkpoint.ipynb deleted 100644 → 0
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notebook/.ipynb_checkpoints/train-checkpoint.ipynb deleted 100644 → 0
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{
"cells": [
{
"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",
"from sklearn.externals import joblib\n",
"import os\n",
"import numpy as np\n",
"from keras.models import model_from_json\n",
"import time"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# loading facenet model\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": [
"# 人臉偵測casecade分類器\n",
"cascade_path = '../model/cv2/haarcascade_frontalface_alt2.xml'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 訓練圖片路徑\n",
"image_dir_basepath=\"../data/images/\"\n",
"image_size = 160\n",
"names=[]\n",
"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": [
"# 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",
"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": [
"# 取得人臉Facenet預測之特徵值\n",
"def calc_embs(filepaths, margin=10, batch_size=1):\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": [
"# 人臉辨識訓練主程序\n",
"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": [
"# 人臉辨識推斷\n",
"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": [
"# 模型保存SVM與laberencoder\n",
"model_name=time.strftime(\"%Y%m%d%H%M%S\", time.localtime())\n",
"full_path=os.path.join(os.path.abspath(\"../model/\"),model_name)\n",
"if(not os.path.exists(full_path)):\n",
" os.mkdir(full_path)\n",
"else:\n",
" print('is exist')\n",
"joblib.dump(clf,os.path.join(full_path,model_name+'.pkl'))\n",
"np.save(os.path.join(full_path,'classes.npy'),le.classes_)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 寫入所有classes以便查閱\n",
"f=open(os.path.join(full_path,'labels.txt'),'w')\n",
"for label in le.classes_:\n",
" f.write(label+'\\n')\n",
"f.close()"
]
},
{
"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
}
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import tkinter as tk\n",
"import cv2\n",
"from PIL import Image,ImageTk\n",
"import time\n",
"from datetime import datetime \n",
"from face_predict import *\n",
"from influxdb import InfluxDBClient"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Set up GUI\n",
"window = tk.Tk() #Makes main window\n",
"window.wm_title(\"人臉辨識打卡系統\")\n",
"window.config(background=\"#FFFFFF\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Graphics window\n",
"imageFrame = tk.Frame(window, width=600, height=500)\n",
"imageFrame.grid(row=1, column=0,rowspan=4, padx=10, pady=2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Capture video frames\n",
"lmain = tk.Label(imageFrame)\n",
"lmain.grid(row=0, column=0)\n",
"cap = cv2.VideoCapture(0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# result\n",
"status_label=tk.Label(window,text='歡迎使用本系統',font=(\"標楷體\", 20))\n",
"status_label.grid(row=0,column=0,columnspan=3)\n",
"time_label=tk.Label(window,text='時間:',font=(\"標楷體\", 20))\n",
"time_label.grid(row=1,column=1)\n",
"t=tk.Label(window,text='2019-06-19',font=(\"Times New Roman\", 20))\n",
"t.grid(row=2,column=1)\n",
"name_label=tk.Label(window,text='姓名: ',font=(\"標楷體\", 20))\n",
"name_label.grid(row=3,column=1)\n",
"name=tk.Label(window,text='Allen',font=(\"Times New Roman\", 20))\n",
"name.grid(row=4,column=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def clock(status):\n",
" if(status=='login_'):\n",
" msg='開始上班辨識打卡'\n",
" else:\n",
" msg='開始下班辨識打卡'\n",
" status_label['text']=msg\n",
" login_time=time.time()\n",
" ret,frame=cap.read()\n",
" if(ret):\n",
" frame = cv2.flip(frame, 1)\n",
" result=infer(le,clf,frame)\n",
" if(len(result)==2):\n",
" status_label['text']='辨識完成 請確認資料'\n",
" t['text']=time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(login_time))\n",
" name['text']=str(result[1])\n",
" login_button.configure(text=\"是\",command=lambda:submit(status,result[1],login_time))\n",
" logout_button.configure(text=\"否\",command=retry)\n",
" else:\n",
" status_label['text']='偵測不到請重新調整位置' \n",
"\n",
"def submit(measurments,name,time):\n",
" if(measurments=='login_'):\n",
" msg='上班加油!!'\n",
" influxdb_insert(measurments,name,time)\n",
" else:\n",
" msg='上班辛苦了 下班好好休息吧!!'\n",
" status_label['text']=msg\n",
" status_label.after(3000,func=empty_result)\n",
"def empty_result():\n",
" status_label['text']='歡迎使用本系統'\n",
" t['text']=''\n",
" name['text']=''\n",
" login_button.configure(text=\"上班打卡\",command=lambda:clock('login_'))\n",
" logout_button.configure(text=\"下班打卡\",command=lambda:clock('logout_'))\n",
"def retry():\n",
" error_window=tk.Toplevel(window)\n",
" error_window.geometry('300x200')\n",
" error_window.title('請輸入您的姓名 以利後續訓練')\n",
" tk.Label(error_window,text='姓名: ',font=(\"標楷體\", 20)).grid(row=0,column=0)\n",
" currect_name=tk.StringVar()\n",
" entry_currect_name = tk.Entry(error_window, textvariable=currect_name)\n",
" entry_currect_name.grid(row=0,column=1)\n",
" button_submit=tk.Button(error_window,text='提交',font=(\"標楷體\", 20))\n",
" button_submit.grid(row=1,column=0,columnspan=2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def influxdb_insert(measurments,name,time):\n",
" # 建立資料庫連線\n",
" client=InfluxDBClient(host='192.168.5.17',port=8086,username='root',password='root',database='RD')\n",
" json_body = [\n",
" {\n",
" \"measurement\": measurments,\n",
" \"time\": datetime.utcfromtimestamp(time),\n",
" \"fields\": {\n",
" \"name\": name[0],\n",
" 'snapshot':name[0]+'.jpg'\n",
" }\n",
" }\n",
" ]\n",
" client.write_points(json_body)\n",
" client.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# button\n",
"login_button= tk.Button(window,text=\"上班打卡\",command=lambda:clock('login_'),font=(\"標楷體\", 20))\n",
"login_button.grid(row=5,column=0)\n",
"logout_button= tk.Button(window,text=\"下班打卡\",command=lambda:clock('logout_'),font=(\"標楷體\", 20))\n",
"logout_button.grid(row=5,column=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def show_frame():\n",
" _, frame = cap.read()\n",
" frame = cv2.flip(frame, 1)\n",
" \n",
" faces = face_cascade.detectMultiScale(frame,scaleFactor=1.1,minNeighbors=3)\n",
" if(len(faces)>0):\n",
" for f in faces:\n",
" x,y,w,h=f\n",
" margin=10\n",
" cv2.rectangle(frame,(x-margin//2,y-margin//2),(x+w+margin//2,y+h+margin//2),(0,0,255))\n",
" cv2.putText(frame,time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime()),(0,30)\n",
" ,cv2.FONT_HERSHEY_TRIPLEX,1, (0, 255, 255), 1, cv2.LINE_AA) \n",
" cv2image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGBA)\n",
" img = Image.fromarray(cv2image)\n",
" imgtk = ImageTk.PhotoImage(image=img)\n",
" lmain.imgtk = imgtk\n",
" lmain.configure(image=imgtk)\n",
" lmain.after(10, show_frame)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"show_frame() #Display 2\n",
"window.mainloop() #Starts GUI"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Slider window (slider controls stage position)\n",
"# sliderFrame = tk.Frame(window, width=600, height=100)\n",
"# sliderFrame.grid(row = 600, column=0, padx=10, pady=2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"img=cv2.imread('../data/Test/test1.jpg')\n",
"cv2img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n",
"imgtk=ImageTk.PhotoImage(Image.fromarray(cv2img))\n",
"lmain.imgtk = imgtk\n",
"lmain.configure(image=imgtk)\n",
"root.mainloop()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from influxdb import InfluxDBClient"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 建立資料庫連線\n",
"client=InfluxDBClient(host='192.168.5.17',port=8086,username='root',password='root',database='RD')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"json_body = [\n",
" {\n",
" \"measurement\": \"login_\",\n",
" \"time\": datetime.utcfromtimestamp(time.time()),\n",
" \"fields\": {\n",
" \"name\": 'test2',\n",
" 'snapshot':\"test.jpg\"\n",
" }\n",
" }\n",
"]\n",
"client.write_points(json_body)\n",
"client.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from datetime import datetime"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"datetime.utcfromtimestamp(time.time())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(time.time()))"
]
},
{
"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/demo.py
View file @ 50f8415a
# coding: utf-8 # coding: utf-8
# In[ ]: # In[1]:
import tkinter as tk import tkinter as tk
...@@ -11,9 +11,10 @@ import time ...@@ -11,9 +11,10 @@ import time
from datetime import datetime from datetime import datetime
from face_predict import * from face_predict import *
from influxdb import InfluxDBClient from influxdb import InfluxDBClient
import os
# In[ ]: # In[2]:
#Set up GUI #Set up GUI
...@@ -22,7 +23,7 @@ window.wm_title("人臉辨識打卡系統") ...@@ -22,7 +23,7 @@ window.wm_title("人臉辨識打卡系統")
window.config(background="#FFFFFF") window.config(background="#FFFFFF")
# In[ ]: # In[3]:
#Graphics window #Graphics window
...@@ -30,7 +31,7 @@ imageFrame = tk.Frame(window, width=600, height=500) ...@@ -30,7 +31,7 @@ imageFrame = tk.Frame(window, width=600, height=500)
imageFrame.grid(row=1, column=0,rowspan=4, padx=10, pady=2) imageFrame.grid(row=1, column=0,rowspan=4, padx=10, pady=2)
# In[ ]: # In[4]:
#Capture video frames #Capture video frames
...@@ -39,7 +40,7 @@ lmain.grid(row=0, column=0) ...@@ -39,7 +40,7 @@ lmain.grid(row=0, column=0)
cap = cv2.VideoCapture(0) cap = cv2.VideoCapture(0)
# In[ ]: # In[5]:
# result # result
...@@ -55,7 +56,7 @@ name=tk.Label(window,text='Allen',font=("Times New Roman", 20)) ...@@ -55,7 +56,7 @@ name=tk.Label(window,text='Allen',font=("Times New Roman", 20))
name.grid(row=4,column=1) name.grid(row=4,column=1)
# In[ ]: # In[6]:
def clock(status): def clock(status):
...@@ -69,30 +70,39 @@ def clock(status): ...@@ -69,30 +70,39 @@ def clock(status):
if(ret): if(ret):
frame = cv2.flip(frame, 1) frame = cv2.flip(frame, 1)
result=infer(le,clf,frame) result=infer(le,clf,frame)
if(len(result)==2): if(len(result)==3):
status_label['text']='辨識完成 請確認資料' status_label['text']='辨識完成 請確認資料'
t['text']=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(login_time)) t['text']=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(login_time))
name['text']=str(result[1]) name['text']=str(result[1]+result[2])
login_button.configure(text="是",command=lambda:submit(status,result[1],login_time)) login_button.configure(text="是",command=lambda:submit(frame,status,result[1],login_time))
logout_button.configure(text="否",command=retry) logout_button.configure(text="否",command=lambda:retry(frame,status,result[1],login_time))
else: else:
status_label['text']='偵測不到請重新調整位置' status_label['text']='偵測不到請重新調整位置'
def submit(measurments,name,time): def submit(frame,measurments,name,t):
if(measurments=='login_'): if(measurments=='login_'):
msg='上班加油!!' msg='上班加油!!'
influxdb_insert(measurments,name,time)
else: else:
msg='上班辛苦了 下班好好休息吧!!' msg='上班辛苦了 下班好好休息吧!!'
influxdb_insert(measurments,name[0],t)
cv2.imwrite(os.path.abspath(os.path.join('../save/',name[0],measurments,time.strftime("%Y-%m-%d-%H%M%S",time.localtime(t))+'.jpg')),frame)
status_label['text']=msg status_label['text']=msg
status_label.after(3000,func=empty_result) status_label.after(3000,func=empty_result)
def empty_result(): def empty_result():
status_label['text']='歡迎使用本系統' status_label['text']='歡迎使用本系統'
t['text']='' t['text']=''
name['text']='' name['text']=''
login_button.configure(text="上班打卡",command=lambda:clock('login_')) login_button.configure(text="上班打卡",command=lambda:clock('login_'))
logout_button.configure(text="下班打卡",command=lambda:clock('logout_')) logout_button.configure(text="下班打卡",command=lambda:clock('logout_'))
def retry():
# In[7]:
# 錯誤處理
def retry(frame,measurments,name,t):
error_window=tk.Toplevel(window) error_window=tk.Toplevel(window)
error_window.geometry('300x200') error_window.geometry('300x200')
error_window.title('請輸入您的姓名 以利後續訓練') error_window.title('請輸入您的姓名 以利後續訓練')
...@@ -100,23 +110,28 @@ def retry(): ...@@ -100,23 +110,28 @@ def retry():
currect_name=tk.StringVar() currect_name=tk.StringVar()
entry_currect_name = tk.Entry(error_window, textvariable=currect_name) entry_currect_name = tk.Entry(error_window, textvariable=currect_name)
entry_currect_name.grid(row=0,column=1) entry_currect_name.grid(row=0,column=1)
button_submit=tk.Button(error_window,text='提交',font=("標楷體", 20)) button_submit=tk.Button(error_window,text='提交',font=("標楷體", 20),command=lambda:retry_ok(frame,error_window,measurments,entry_currect_name.get(),t))
button_submit.grid(row=1,column=0,columnspan=2) button_submit.grid(row=1,column=0,columnspan=2)
def retry_ok(frame,error_window,measurments,name,t):
error_window.destroy()
influxdb_insert(measurments,name,t)
cv2.imwrite(os.path.abspath(os.path.join('../save/',name,'error_',time.strftime("%Y-%m-%d-%H%M%S",time.localtime(t))+'.jpg')),frame)
empty_result()
# In[ ]: # In[8]:
def influxdb_insert(measurments,name,time): def influxdb_insert(measurments,name,t):
# 建立資料庫連線 # 建立資料庫連線
client=InfluxDBClient(host='192.168.5.17',port=8086,username='root',password='root',database='RD') client=InfluxDBClient(host='192.168.5.17',port=8086,username='root',password='root',database='RD')
json_body = [ json_body = [
{ {
"measurement": measurments, "measurement": measurments,
"time": datetime.utcfromtimestamp(time), "time": datetime.utcfromtimestamp(t),
"fields": { "fields": {
"name": name[0], "name": name,
'snapshot':name[0]+'.jpg' 'snapshot':name+'.jpg'
} }
} }
] ]
...@@ -124,7 +139,7 @@ def influxdb_insert(measurments,name,time): ...@@ -124,7 +139,7 @@ def influxdb_insert(measurments,name,time):
client.close() client.close()
# In[ ]: # In[9]:
# button # button
...@@ -134,7 +149,7 @@ logout_button= tk.Button(window,text="下班打卡",command=lambda:clock('logout ...@@ -134,7 +149,7 @@ logout_button= tk.Button(window,text="下班打卡",command=lambda:clock('logout
logout_button.grid(row=5,column=1) logout_button.grid(row=5,column=1)
# In[ ]: # In[10]:
def show_frame(): def show_frame():
...@@ -157,7 +172,7 @@ def show_frame(): ...@@ -157,7 +172,7 @@ def show_frame():
lmain.after(10, show_frame) lmain.after(10, show_frame)
# In[ ]: # In[11]:
show_frame() #Display 2 show_frame() #Display 2
...@@ -228,5 +243,26 @@ datetime.utcfromtimestamp(time.time()) ...@@ -228,5 +243,26 @@ datetime.utcfromtimestamp(time.time())
# In[ ]: # In[ ]:
time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time())) time.strftime("%Y-%m-%d-%H%M%S", time.localtime(time.time()))
# In[ ]:
with open('../model/20190624135811//labels.txt') as f:
lines = f.readlines()
# In[ ]:
for i in range(len(lines)):
name=lines[i].split('\n')[0]
save_path=os.path.abspath('../save/')
name_path=os.path.join(save_path,name)
if(not os.path.isdir(name_path)):
os.mkdir(name_path)
os.mkdir(os.path.join(name_path,'login_'))
os.mkdir(os.path.join(name_path,'logout_'))
os.mkdir(os.path.join(name_path,'error_'))
notebook/keras_to_coreml.ipynb deleted 100644 → 0
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notebook/predict.ipynb deleted 100644 → 0
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{
"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
}
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