├── README.md ├── RealTime_Face_Detection.ipynb ├── face_recog ├── final knn model_yash.ipynb ├── final knn model_yash_phonecam.ipynb ├── known_people │ └── test.jpg ├── models │ └── test.jpg ├── test │ └── test.jpg └── unknown_pictures │ └── test.jpg └── main ├── Attendance.csv ├── adminLogin.html ├── adminScreen.html ├── assets └── models │ └── trained_knn_model.clf ├── css └── materialize.min.css ├── dataEntry.html ├── etc ├── data1.html ├── data2.html └── data3.html ├── facultyLogin.html ├── img ├── dp.png ├── icon.png └── user.png ├── index.html ├── js ├── adminLogin.js ├── adminScreen.js ├── data1.js ├── facultyLogin.js ├── jquery.js ├── materialize.min.js └── upload.js ├── main.js ├── package-lock.json ├── package.json └── py ├── camcap2.py ├── camcapture.py ├── capture.py ├── helper.txt ├── phoneCapture.py └── train.py /README.md: -------------------------------------------------------------------------------- 1 | # MozoHack 2019 2 | Above is a re-upload of everything that my team and I worked on and developed at the Mozohack 24-Hour Hackathon. This is a working model of our product, which has an AI assisted automated attendance system. Using basic hardware elements such as a 720 camera, institutions and offices can use our services to automate the attendance process. 3 | 4 | Instructions: 5 | 6 | Install and unpack electron inside the /main 7 | Open Terminal and run electron using npm start 8 | Default logins credentials are: 9 | staff@mozohack.com | mozostaff 10 | admin@mozohack.com | mozoadmin 11 | All codes are equipped with appropriate logs for debug. Deep comments will be added soon. 12 | 13 | Email for issues. 14 | -------------------------------------------------------------------------------- /RealTime_Face_Detection.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import cv2\n", 10 | "import numpy as np\n", 11 | "from matplotlib import pyplot as plt\n", 12 | "%matplotlib inline\n", 13 | "\n", 14 | "face_cascade = cv2.CascadeClassifier(\"C:/Users/Choudhary/haarcascades/haarcascade_frontalface_default.xml\")\n", 15 | "\n", 16 | "cap = cv2.VideoCapture(0)\n", 17 | "cap.set(cv2.CAP_PROP_FPS, 100)\n", 18 | "rat, frame = cap.read()\n", 19 | "while(True):\n", 20 | " rat, frame = cap.read()\n", 21 | " gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n", 22 | "\n", 23 | " faces = face_cascade.detectMultiScale(gray, 1.1, 4)\n", 24 | " for (x,y,w,h) in faces:\n", 25 | " cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2)\n", 26 | " roi_gray = gray[y:y+h, x:x+w]\n", 27 | " roi_color = frame[y:y+h, x:x+w]\n", 28 | " crop_img = frame[y:y+h, x:x+w]\n", 29 | " cv2.imshow('img',frame)\n", 30 | " if cv2.waitKey(1) & 0xFF == ord('q'):\n", 31 | " break\n", 32 | "\n", 33 | "\n", 34 | "cap.release() \n", 35 | "cv2.destroyAllWindows()" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": null, 41 | "metadata": {}, 42 | "outputs": [], 43 | "source": [] 44 | } 45 | ], 46 | "metadata": { 47 | "kernelspec": { 48 | "display_name": "Python 3", 49 | "language": "python", 50 | "name": "python3" 51 | }, 52 | "language_info": { 53 | "codemirror_mode": { 54 | "name": "ipython", 55 | "version": 3 56 | }, 57 | "file_extension": ".py", 58 | "mimetype": "text/x-python", 59 | "name": "python", 60 | "nbconvert_exporter": "python", 61 | "pygments_lexer": "ipython3", 62 | "version": "3.6.8" 63 | } 64 | }, 65 | "nbformat": 4, 66 | "nbformat_minor": 2 67 | } 68 | -------------------------------------------------------------------------------- /face_recog/final knn model_yash.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 4, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import math\n", 10 | "import cv2\n", 11 | "from sklearn import neighbors\n", 12 | "import numpy as np\n", 13 | "import pandas as pd\n", 14 | "import os\n", 15 | "import os.path\n", 16 | "import pickle\n", 17 | "from PIL import Image, ImageDraw\n", 18 | "import face_recognition\n", 19 | "from face_recognition.face_recognition_cli import image_files_in_folder\n", 20 | "curd = os.getcwd()\n", 21 | "try: \n", 22 | " os.mkdir(\"{}/known_people\".format(curd))\n", 23 | "except:\n", 24 | " pass\n", 25 | "try: \n", 26 | " os.mkdir(\"{}/models\".format(curd))\n", 27 | "except:\n", 28 | " pass\n", 29 | "try: \n", 30 | " os.mkdir(\"{}/test\".format(curd))\n", 31 | "except:\n", 32 | " pass\n", 33 | "try: \n", 34 | " os.mkdir(\"{}/unknown_pictures\".format(curd))\n", 35 | "except:\n", 36 | " pass\n", 37 | "names=[]" 38 | ] 39 | }, 40 | { 41 | "cell_type": "code", 42 | "execution_count": 11, 43 | "metadata": {}, 44 | "outputs": [], 45 | "source": [ 46 | "ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}" 47 | ] 48 | }, 49 | { 50 | "cell_type": "code", 51 | "execution_count": 12, 52 | "metadata": {}, 53 | "outputs": [], 54 | "source": [ 55 | "def train(train_dir, model_save_path=None, n_neighbors=None, knn_algo='ball_tree', verbose=False):\n", 56 | " \n", 57 | " X = []\n", 58 | " y = []\n", 59 | "\n", 60 | " for class_dir in os.listdir(train_dir):\n", 61 | " if not os.path.isdir(os.path.join(train_dir, class_dir)):\n", 62 | " continue\n", 63 | "\n", 64 | " for img_path in image_files_in_folder(os.path.join(train_dir, class_dir)):\n", 65 | " image = face_recognition.load_image_file(img_path)\n", 66 | " face_bounding_boxes = face_recognition.face_locations(image)\n", 67 | "\n", 68 | " if len(face_bounding_boxes) != 1:\n", 69 | " if verbose:\n", 70 | " print(\"Image {} not suitable for training: {}\".format(img_path, \"Didn't find a face\" if len(face_bounding_boxes) < 1 else \"Found more than one face\"))\n", 71 | " else:\n", 72 | " X.append(face_recognition.face_encodings(image, known_face_locations=face_bounding_boxes)[0])\n", 73 | " y.append(class_dir)\n", 74 | "\n", 75 | " if n_neighbors is None:\n", 76 | " n_neighbors = int(round(math.sqrt(len(X))))\n", 77 | " if verbose:\n", 78 | " print(\"Chose n_neighbors automatically:\", n_neighbors)\n", 79 | "\n", 80 | " knn_clf = neighbors.KNeighborsClassifier(n_neighbors=n_neighbors, algorithm=knn_algo, weights='distance')\n", 81 | " knn_clf.fit(X, y)\n", 82 | "\n", 83 | " if model_save_path is not None:\n", 84 | " with open(model_save_path, 'wb') as f:\n", 85 | " pickle.dump(knn_clf, f)\n", 86 | "\n", 87 | " return knn_clf" 88 | ] 89 | }, 90 | { 91 | "cell_type": "code", 92 | "execution_count": 13, 93 | "metadata": {}, 94 | "outputs": [], 95 | "source": [ 96 | "\n", 97 | "def predict(frame, knn_clf=None, model_path=None, distance_threshold=0.6):\n", 98 | " \n", 99 | "\n", 100 | " if knn_clf is None and model_path is None:\n", 101 | " raise Exception(\"Must supply knn classifier either thourgh knn_clf or model_path\")\n", 102 | "\n", 103 | " if knn_clf is None:\n", 104 | " with open(model_path, 'rb') as f:\n", 105 | " knn_clf = pickle.load(f)\n", 106 | "\n", 107 | " \n", 108 | " # X_img = face_recognition.load_image_file(X_img_path)\n", 109 | " X_img = frame\n", 110 | " X_face_locations = face_recognition.face_locations(X_img)\n", 111 | "\n", 112 | " if len(X_face_locations) == 0:\n", 113 | " return []\n", 114 | "\n", 115 | " faces_encodings = face_recognition.face_encodings(X_img, known_face_locations=X_face_locations)\n", 116 | "\n", 117 | " closest_distances = knn_clf.kneighbors(faces_encodings, n_neighbors=1)\n", 118 | " are_matches = [closest_distances[0][i][0] <= distance_threshold for i in range(len(X_face_locations))]\n", 119 | "\n", 120 | " return [(pred, loc) if rec else (\"unknown\", loc) for pred, loc, rec in zip(knn_clf.predict(faces_encodings), X_face_locations, are_matches)]\n" 121 | ] 122 | }, 123 | { 124 | "cell_type": "code", 125 | "execution_count": 14, 126 | "metadata": {}, 127 | "outputs": [], 128 | "source": [ 129 | "#my version\n", 130 | "def show_prediction_labels_on_image(frame, predictions):\n", 131 | " \n", 132 | " #pil_image = Image.open(img_path).convert(\"RGB\")\n", 133 | " pil_image = Image.fromarray(frame).convert(\"RGB\")\n", 134 | " draw = ImageDraw.Draw(pil_image)\n", 135 | "\n", 136 | " for name, (top, right, bottom, left) in predictions:\n", 137 | " draw.rectangle(((left, top), (right, bottom)), outline=(0, 0, 255))\n", 138 | "\n", 139 | " \n", 140 | " name = name.encode(\"UTF-8\")\n", 141 | "\n", 142 | " text_width, text_height = draw.textsize(name)\n", 143 | " draw.rectangle(((left, bottom - text_height - 10), (right, bottom)), fill=(0, 0, 255), outline=(0, 0, 255))\n", 144 | " draw.text((left + 6, bottom - text_height - 5), name, fill=(255, 255, 255, 255))\n", 145 | "\n", 146 | " del draw\n", 147 | "\n", 148 | " #pil_image.show()\n", 149 | " #cv2.imshow(\"frame\",pil_image)\n", 150 | " return np.asarray(pil_image)\n" 151 | ] 152 | }, 153 | { 154 | "cell_type": "code", 155 | "execution_count": null, 156 | "metadata": {}, 157 | "outputs": [], 158 | "source": [ 159 | "#Train data creator\n", 160 | "import cv2\n", 161 | "import numpy as np\n", 162 | "import time\n", 163 | "import os\n", 164 | "curd = os.getcwd()\n", 165 | "name = input()\n", 166 | "path = \"{}/known_people/{}\".format(curd,name)\n", 167 | "try: \n", 168 | " os.mkdir(path)\n", 169 | "except OSError: \n", 170 | " print (\"Creation of the directory %s failed\" % path)\n", 171 | "cap = cv2.VideoCapture(0)\n", 172 | "cap.set(cv2.CAP_PROP_FPS, 100)\n", 173 | "rat, frame = cap.read()\n", 174 | "\n", 175 | "count=0\n", 176 | "while count<10:\n", 177 | " \n", 178 | " rat, frame = cap.read()\n", 179 | " cv2.imwrite(\"{}/{}{}.jpg\".format(path,name,count), frame)\n", 180 | " count+=1\n", 181 | " cv2.imshow('img',frame)\n", 182 | " if cv2.waitKey(1) & 0xFF == ord('q'):\n", 183 | " break\n", 184 | " time.sleep(1) \n", 185 | "\n", 186 | "\n", 187 | "cap.release() \n", 188 | "cv2.destroyAllWindows()" 189 | ] 190 | }, 191 | { 192 | "cell_type": "code", 193 | "execution_count": 7, 194 | "metadata": {}, 195 | "outputs": [ 196 | { 197 | "name": "stdout", 198 | "output_type": "stream", 199 | "text": [ 200 | "Training KNN classifier...\n" 201 | ] 202 | }, 203 | { 204 | "name": "stderr", 205 | "output_type": "stream", 206 | "text": [ 207 | "C:\\Users\\Choudhary\\Anaconda3\\envs\\mlkit\\lib\\site-packages\\PIL\\Image.py:969: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n", 208 | " 'to RGBA images')\n" 209 | ] 210 | }, 211 | { 212 | "name": "stdout", 213 | "output_type": "stream", 214 | "text": [ 215 | "Training complete!\n" 216 | ] 217 | } 218 | ], 219 | "source": [ 220 | "#Trainer\n", 221 | "if __name__ == \"__main__\":\n", 222 | " # STEP 1: Train the KNN classifier and save it to disk\n", 223 | " print(\"Training KNN classifier...\")\n", 224 | " classifier = train(\"known_people\",\n", 225 | " model_save_path=\"{}/models/trained_knn_model.clf\".format(curd),\n", 226 | " n_neighbors=2)\n", 227 | " print(\"Training complete!\")\n", 228 | "\n", 229 | " " 230 | ] 231 | }, 232 | { 233 | "cell_type": "code", 234 | "execution_count": 7, 235 | "metadata": {}, 236 | "outputs": [], 237 | "source": [ 238 | "#test Data Creator\n", 239 | "import cv2\n", 240 | "import numpy as np\n", 241 | "import time\n", 242 | "curd = os.getcwd()\n", 243 | "#name = input()\n", 244 | "\n", 245 | "cap = cv2.VideoCapture(0)\n", 246 | "cap.set(cv2.CAP_PROP_FPS, 100)\n", 247 | "rat, frame = cap.read()\n", 248 | "count=0\n", 249 | "while count<4:\n", 250 | " curtime = time.strftime(\"%Y_%m_%d-%H_%M_%S\")\n", 251 | " rat, frame = cap.read()\n", 252 | " cv2.imwrite(\"{}/test/{}.jpg\".format(curd,curtime), frame)\n", 253 | " count+=1\n", 254 | " cv2.imshow('img',frame)\n", 255 | " if cv2.waitKey(1) & 0xFF == ord('q'):\n", 256 | " break\n", 257 | " time.sleep(1) \n", 258 | "\n", 259 | "\n", 260 | "cap.release() \n", 261 | "cv2.destroyAllWindows()" 262 | ] 263 | }, 264 | { 265 | "cell_type": "code", 266 | "execution_count": 15, 267 | "metadata": {}, 268 | "outputs": [ 269 | { 270 | "name": "stdout", 271 | "output_type": "stream", 272 | "text": [ 273 | "Looking for faces in 0.jpg\n", 274 | "- Found Shantnu Agarwal at (253, 225)\n", 275 | "Looking for faces in 1.jpg\n", 276 | "- Found yo at (357, 276)\n", 277 | "Looking for faces in 2.jpg\n", 278 | "Looking for faces in 2019_03_31-00_37_45.jpg\n", 279 | "- Found yash at (259, 235)\n", 280 | "Looking for faces in 2019_03_31-00_37_46.jpg\n", 281 | "- Found yash at (259, 235)\n", 282 | "Looking for faces in 2019_03_31-00_37_47.jpg\n", 283 | "- Found yash at (259, 235)\n", 284 | "- Found yo at (0, 285)\n", 285 | "Looking for faces in 2019_03_31-00_37_48.jpg\n", 286 | "- Found yash at (259, 235)\n", 287 | "Looking for faces in 3.jpg\n", 288 | "Looking for faces in Dx.jpg\n", 289 | "- Found DiCap at (167, 39)\n", 290 | "Looking for faces in Jx.jpg\n", 291 | "- Found Jason at (46, 55)\n", 292 | "Looking for faces in momo.jpg\n", 293 | "- Found Jason at (8, 32)\n", 294 | "- Found unknown at (117, 122)\n", 295 | "Looking for faces in XX.jpg\n", 296 | "- Found unknown at (33, 62)\n", 297 | "- Found Jason at (89, 30)\n", 298 | "Looking for faces in Y.jpg\n", 299 | "- Found DiCap at (1526, 666)\n", 300 | "- Found unknown at (913, 82)\n", 301 | "- Found yo at (855, 626)\n", 302 | "- Found unknown at (325, 641)\n", 303 | "- Found Jason at (268, 96)\n", 304 | "- Found unknown at (1491, 133)\n" 305 | ] 306 | } 307 | ], 308 | "source": [ 309 | "#Still Image Tester\n", 310 | "curd = os.getcwd()\n", 311 | "for image_file in os.listdir(r\"{}\\test\".format(curd)):\n", 312 | " full_file_path = os.path.join(r\"{}\\test\".format(curd), image_file)\n", 313 | "\n", 314 | " print(\"Looking for faces in {}\".format(image_file))\n", 315 | " frame = cv2.imread(full_file_path,-1)\n", 316 | "\n", 317 | " predictions = predict(frame, model_path=r\"{}\\models\\trained_knn_model.clf\".format(curd))\n", 318 | "\n", 319 | " for name, (top, right, bottom, left) in predictions:\n", 320 | " print(\"- Found {} at ({}, {})\".format(name, left, top))\n", 321 | " names.append(name)\n", 322 | " # Display results overlaid on an image\n", 323 | " #show_prediction_labels_on_image(frame, predictions)\n", 324 | " final_img = show_prediction_labels_on_image(frame, predictions)\n", 325 | " cv2.imshow(\"X\",final_img)\n", 326 | " cv2.waitKey(1)\n", 327 | "cv2.destroyAllWindows()" 328 | ] 329 | }, 330 | { 331 | "cell_type": "code", 332 | "execution_count": 18, 333 | "metadata": {}, 334 | "outputs": [ 335 | { 336 | "name": "stdout", 337 | "output_type": "stream", 338 | "text": [ 339 | "- Found yash at (282, 223)\n", 340 | "- Found yash at (270, 199)\n", 341 | "- Found Shantnu Agarwal at (275, 216)\n", 342 | "- Found Shantnu Agarwal at (270, 211)\n", 343 | "- Found yash at (270, 211)\n", 344 | "- Found Shantnu Agarwal at (270, 211)\n", 345 | "- Found Shantnu Agarwal at (270, 211)\n", 346 | "- Found Shantnu Agarwal at (270, 211)\n", 347 | "- Found yash at (259, 211)\n", 348 | "- Found Shantnu Agarwal at (270, 211)\n", 349 | "- Found Shantnu Agarwal at (294, 211)\n", 350 | "- Found Shantnu Agarwal at (282, 211)\n", 351 | "- Found yash at (282, 211)\n", 352 | "- Found Shantnu Agarwal at (259, 211)\n" 353 | ] 354 | } 355 | ], 356 | "source": [ 357 | "#Live Video tester\n", 358 | "cap = cv2.VideoCapture(0)\n", 359 | "cap.set(cv2.CAP_PROP_FPS, 100)\n", 360 | "rat, frame = cap.read()\n", 361 | "\n", 362 | "while(True):\n", 363 | " rat, frame = cap.read()\n", 364 | " predictions = predict(frame, model_path=\"{}/models/trained_knn_model.clf\".format(curd))\n", 365 | " for name, (top, right, bottom, left) in predictions:\n", 366 | " print(\"- Found {} at ({}, {})\".format(name, left, top))\n", 367 | " names.append(name)\n", 368 | "\n", 369 | " # Display results overlaid on an image\n", 370 | " show_img = show_prediction_labels_on_image(frame, predictions)\n", 371 | " cv2.imshow('img',show_img)\n", 372 | " if cv2.waitKey(1) & 0xFF == ord('q'):\n", 373 | " break\n", 374 | "\n", 375 | "\n", 376 | "cap.release() \n", 377 | "cv2.destroyAllWindows()" 378 | ] 379 | }, 380 | { 381 | "cell_type": "code", 382 | "execution_count": 19, 383 | "metadata": {}, 384 | "outputs": [ 385 | { 386 | "data": { 387 | "text/html": [ 388 | "
\n", 389 | "\n", 402 | "\n", 403 | " \n", 404 | " \n", 405 | " \n", 406 | " \n", 407 | " \n", 408 | " \n", 409 | " \n", 410 | " \n", 411 | " \n", 412 | " \n", 413 | " \n", 414 | " \n", 415 | " \n", 416 | " \n", 417 | " \n", 418 | " \n", 419 | " \n", 420 | " \n", 421 | " \n", 422 | " \n", 423 | " \n", 424 | " \n", 425 | " \n", 426 | " \n", 427 | " \n", 428 | " \n", 429 | " \n", 430 | " \n", 431 | "
Present
Shantnu Agarwal1
yash1
Jason0
yo0
DiCap0
\n", 432 | "
" 433 | ], 434 | "text/plain": [ 435 | " Present\n", 436 | "Shantnu Agarwal 1\n", 437 | "yash 1\n", 438 | "Jason 0\n", 439 | "yo 0\n", 440 | "DiCap 0" 441 | ] 442 | }, 443 | "execution_count": 19, 444 | "metadata": {}, 445 | "output_type": "execute_result" 446 | } 447 | ], 448 | "source": [ 449 | "#Attendance System csv maker \n", 450 | "namesD=pd.DataFrame(names, columns=[\"Names\"])\n", 451 | "namesD= namesD[namesD.Names!=\"unknown\"]\n", 452 | "\n", 453 | "attendance= pd.DataFrame(namesD.iloc[:,0].value_counts())\n", 454 | "attendance.rename(index=str,columns={'Names': 'Count'},inplace=True)\n", 455 | "attendance[\"Present\"] =0\n", 456 | "\n", 457 | "attendance[\"Count\"][0] > 5\n", 458 | "for i in range(attendance.shape[0]):\n", 459 | " if(attendance[\"Count\"][i] > 5):\n", 460 | " attendance[\"Present\"][i] =1\n", 461 | "\n", 462 | "\n", 463 | "attendance_final=attendance.drop(['Count'],axis=1)\n", 464 | "attendance_final\n", 465 | "#attendance_final.to_csv('Attendance.csv')" 466 | ] 467 | }, 468 | { 469 | "cell_type": "code", 470 | "execution_count": null, 471 | "metadata": {}, 472 | "outputs": [], 473 | "source": [] 474 | }, 475 | { 476 | "cell_type": "code", 477 | "execution_count": null, 478 | "metadata": {}, 479 | "outputs": [], 480 | "source": [] 481 | }, 482 | { 483 | "cell_type": "code", 484 | "execution_count": null, 485 | "metadata": {}, 486 | "outputs": [], 487 | "source": [] 488 | }, 489 | { 490 | "cell_type": "code", 491 | "execution_count": null, 492 | "metadata": {}, 493 | "outputs": [], 494 | "source": [] 495 | }, 496 | { 497 | "cell_type": "code", 498 | "execution_count": null, 499 | "metadata": {}, 500 | "outputs": [], 501 | "source": [] 502 | }, 503 | { 504 | "cell_type": "code", 505 | "execution_count": null, 506 | "metadata": {}, 507 | "outputs": [], 508 | "source": [] 509 | } 510 | ], 511 | "metadata": { 512 | "kernelspec": { 513 | "display_name": "Python 3", 514 | "language": "python", 515 | "name": "python3" 516 | }, 517 | "language_info": { 518 | "codemirror_mode": { 519 | "name": "ipython", 520 | "version": 3 521 | }, 522 | "file_extension": ".py", 523 | "mimetype": "text/x-python", 524 | "name": "python", 525 | "nbconvert_exporter": "python", 526 | "pygments_lexer": "ipython3", 527 | "version": "3.6.8" 528 | } 529 | }, 530 | "nbformat": 4, 531 | "nbformat_minor": 2 532 | } 533 | -------------------------------------------------------------------------------- /face_recog/final knn model_yash_phonecam.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 2, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import math\n", 10 | "import cv2\n", 11 | "from sklearn import neighbors\n", 12 | "import numpy as np\n", 13 | "import pandas as pd\n", 14 | "import os\n", 15 | "import os.path\n", 16 | "import requests\n", 17 | "import pickle\n", 18 | "from PIL import Image, ImageDraw\n", 19 | "import face_recognition\n", 20 | "from face_recognition.face_recognition_cli import image_files_in_folder\n", 21 | "curd = os.getcwd()\n", 22 | "try: \n", 23 | " os.mkdir(\"{}/known_people\".format(curd))\n", 24 | "except:\n", 25 | " pass\n", 26 | "try: \n", 27 | " os.mkdir(\"{}/models\".format(curd))\n", 28 | "except:\n", 29 | " pass\n", 30 | "try: \n", 31 | " os.mkdir(\"{}/test\".format(curd))\n", 32 | "except:\n", 33 | " pass\n", 34 | "try: \n", 35 | " os.mkdir(\"{}/unknown_pictures\".format(curd))\n", 36 | "except:\n", 37 | " pass\n", 38 | "names=[]" 39 | ] 40 | }, 41 | { 42 | "cell_type": "code", 43 | "execution_count": 3, 44 | "metadata": {}, 45 | "outputs": [], 46 | "source": [ 47 | "ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}" 48 | ] 49 | }, 50 | { 51 | "cell_type": "code", 52 | "execution_count": 4, 53 | "metadata": {}, 54 | "outputs": [], 55 | "source": [ 56 | "def train(train_dir, model_save_path=None, n_neighbors=None, knn_algo='ball_tree', verbose=False):\n", 57 | " \n", 58 | " X = []\n", 59 | " y = []\n", 60 | "\n", 61 | " for class_dir in os.listdir(train_dir):\n", 62 | " if not os.path.isdir(os.path.join(train_dir, class_dir)):\n", 63 | " continue\n", 64 | "\n", 65 | " for img_path in image_files_in_folder(os.path.join(train_dir, class_dir)):\n", 66 | " image = face_recognition.load_image_file(img_path)\n", 67 | " face_bounding_boxes = face_recognition.face_locations(image)\n", 68 | "\n", 69 | " if len(face_bounding_boxes) != 1:\n", 70 | " if verbose:\n", 71 | " print(\"Image {} not suitable for training: {}\".format(img_path, \"Didn't find a face\" if len(face_bounding_boxes) < 1 else \"Found more than one face\"))\n", 72 | " else:\n", 73 | " X.append(face_recognition.face_encodings(image, known_face_locations=face_bounding_boxes)[0])\n", 74 | " y.append(class_dir)\n", 75 | "\n", 76 | " if n_neighbors is None:\n", 77 | " n_neighbors = int(round(math.sqrt(len(X))))\n", 78 | " if verbose:\n", 79 | " print(\"Chose n_neighbors automatically:\", n_neighbors)\n", 80 | "\n", 81 | " knn_clf = neighbors.KNeighborsClassifier(n_neighbors=n_neighbors, algorithm=knn_algo, weights='distance')\n", 82 | " knn_clf.fit(X, y)\n", 83 | "\n", 84 | " if model_save_path is not None:\n", 85 | " with open(model_save_path, 'wb') as f:\n", 86 | " pickle.dump(knn_clf, f)\n", 87 | "\n", 88 | " return knn_clf" 89 | ] 90 | }, 91 | { 92 | "cell_type": "code", 93 | "execution_count": 5, 94 | "metadata": {}, 95 | "outputs": [], 96 | "source": [ 97 | "\n", 98 | "def predict(frame, knn_clf=None, model_path=None, distance_threshold=0.6):\n", 99 | " \n", 100 | "\n", 101 | " if knn_clf is None and model_path is None:\n", 102 | " raise Exception(\"Must supply knn classifier either thourgh knn_clf or model_path\")\n", 103 | "\n", 104 | " if knn_clf is None:\n", 105 | " with open(model_path, 'rb') as f:\n", 106 | " knn_clf = pickle.load(f)\n", 107 | "\n", 108 | " \n", 109 | " # X_img = face_recognition.load_image_file(X_img_path)\n", 110 | " X_img = frame\n", 111 | " X_face_locations = face_recognition.face_locations(X_img)\n", 112 | "\n", 113 | " if len(X_face_locations) == 0:\n", 114 | " return []\n", 115 | "\n", 116 | " faces_encodings = face_recognition.face_encodings(X_img, known_face_locations=X_face_locations)\n", 117 | "\n", 118 | " closest_distances = knn_clf.kneighbors(faces_encodings, n_neighbors=1)\n", 119 | " are_matches = [closest_distances[0][i][0] <= distance_threshold for i in range(len(X_face_locations))]\n", 120 | "\n", 121 | " return [(pred, loc) if rec else (\"unknown\", loc) for pred, loc, rec in zip(knn_clf.predict(faces_encodings), X_face_locations, are_matches)]\n" 122 | ] 123 | }, 124 | { 125 | "cell_type": "code", 126 | "execution_count": 6, 127 | "metadata": {}, 128 | "outputs": [], 129 | "source": [ 130 | "#my version\n", 131 | "def show_prediction_labels_on_image(frame, predictions):\n", 132 | " \n", 133 | " #pil_image = Image.open(img_path).convert(\"RGB\")\n", 134 | " pil_image = Image.fromarray(frame).convert(\"RGB\")\n", 135 | " draw = ImageDraw.Draw(pil_image)\n", 136 | "\n", 137 | " for name, (top, right, bottom, left) in predictions:\n", 138 | " draw.rectangle(((left, top), (right, bottom)), outline=(0, 0, 255))\n", 139 | "\n", 140 | " \n", 141 | " name = name.encode(\"UTF-8\")\n", 142 | "\n", 143 | " text_width, text_height = draw.textsize(name)\n", 144 | " draw.rectangle(((left, bottom - text_height - 10), (right, bottom)), fill=(0, 0, 255), outline=(0, 0, 255))\n", 145 | " draw.text((left + 6, bottom - text_height - 5), name, fill=(255, 255, 255, 255))\n", 146 | "\n", 147 | " del draw\n", 148 | "\n", 149 | " #pil_image.show()\n", 150 | " #cv2.imshow(\"frame\",pil_image)\n", 151 | " return np.asarray(pil_image)\n" 152 | ] 153 | }, 154 | { 155 | "cell_type": "code", 156 | "execution_count": 10, 157 | "metadata": {}, 158 | "outputs": [ 159 | { 160 | "name": "stdout", 161 | "output_type": "stream", 162 | "text": [ 163 | "shivank\n", 164 | "Creation of the directory C:\\Users\\Choudhary\\Attendance_face_recognition/known_people/shivank failed\n" 165 | ] 166 | } 167 | ], 168 | "source": [ 169 | "#Train data creator\n", 170 | "import cv2\n", 171 | "import numpy as np\n", 172 | "import time\n", 173 | "import os\n", 174 | "curd = os.getcwd()\n", 175 | "name = input()\n", 176 | "\n", 177 | "\n", 178 | "path = \"{}/known_people/{}\".format(curd,name)\n", 179 | "try: \n", 180 | " os.mkdir(path)\n", 181 | "except OSError: \n", 182 | " print (\"Creation of the directory %s failed\" % path)\n", 183 | "url = \"http://192.168.137.184:8080/shot.jpg\"\n", 184 | "count=0\n", 185 | "while count<10:\n", 186 | " img_resp = requests.get(url)\n", 187 | " img_arr = np.array(bytearray(img_resp.content),dtype = np.uint8)\n", 188 | " frame = cv2.imdecode(img_arr,-1)\n", 189 | " cv2.imwrite(\"{}/{}{}.jpg\".format(path,name,count), frame)\n", 190 | " count+=1\n", 191 | " cv2.imshow('img',frame)\n", 192 | " if cv2.waitKey(1) & 0xFF == ord('q'):\n", 193 | " break\n", 194 | " time.sleep(1) \n", 195 | "\n", 196 | "\n", 197 | " \n", 198 | "cv2.destroyAllWindows()" 199 | ] 200 | }, 201 | { 202 | "cell_type": "code", 203 | "execution_count": 11, 204 | "metadata": {}, 205 | "outputs": [ 206 | { 207 | "name": "stdout", 208 | "output_type": "stream", 209 | "text": [ 210 | "Training KNN classifier...\n" 211 | ] 212 | }, 213 | { 214 | "name": "stderr", 215 | "output_type": "stream", 216 | "text": [ 217 | "C:\\Users\\Choudhary\\Anaconda3\\envs\\mlkit\\lib\\site-packages\\PIL\\Image.py:969: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n", 218 | " 'to RGBA images')\n" 219 | ] 220 | }, 221 | { 222 | "name": "stdout", 223 | "output_type": "stream", 224 | "text": [ 225 | "Training complete!\n" 226 | ] 227 | } 228 | ], 229 | "source": [ 230 | "#Trainer\n", 231 | "if __name__ == \"__main__\":\n", 232 | " # STEP 1: Train the KNN classifier and save it to disk\n", 233 | " print(\"Training KNN classifier...\")\n", 234 | " classifier = train(\"known_people\",\n", 235 | " model_save_path=\"{}/models/trained_knn_model.clf\".format(curd),\n", 236 | " n_neighbors=2)\n", 237 | " print(\"Training complete!\")\n", 238 | "\n", 239 | " " 240 | ] 241 | }, 242 | { 243 | "cell_type": "code", 244 | "execution_count": 10, 245 | "metadata": {}, 246 | "outputs": [], 247 | "source": [ 248 | "#test Data Creator\n", 249 | "import cv2\n", 250 | "import numpy as np\n", 251 | "import time\n", 252 | "curd = os.getcwd()\n", 253 | "#name = input()\n", 254 | "url = \"http://192.168.137.184:8080/shot.jpg\"\n", 255 | "count=0\n", 256 | "while count<10:\n", 257 | " curtime = time.strftime(\"%Y_%m_%d-%H_%M_%S\")\n", 258 | " img_resp = requests.get(url)\n", 259 | " img_arr = np.array(bytearray(img_resp.content),dtype = np.uint8)\n", 260 | " frame = cv2.imdecode(img_arr,-1)\n", 261 | " cv2.imwrite(\"{}/test/{}.jpg\".format(curd,curtime), frame)\n", 262 | " count+=1\n", 263 | " cv2.imshow('img',frame)\n", 264 | " if cv2.waitKey(1) & 0xFF == ord('q'):\n", 265 | " break\n", 266 | " time.sleep(1) \n", 267 | "\n", 268 | "\n", 269 | " \n", 270 | "cv2.destroyAllWindows()" 271 | ] 272 | }, 273 | { 274 | "cell_type": "code", 275 | "execution_count": 11, 276 | "metadata": {}, 277 | "outputs": [ 278 | { 279 | "name": "stdout", 280 | "output_type": "stream", 281 | "text": [ 282 | "Looking for faces in 2019_03_31-02_04_21.jpg\n", 283 | "- Found nitin at (641, 367)\n", 284 | "- Found shivank at (378, 378)\n", 285 | "Looking for faces in 2019_03_31-02_04_22.jpg\n", 286 | "- Found nitin at (626, 368)\n", 287 | "- Found shivank at (366, 378)\n", 288 | "Looking for faces in 2019_03_31-02_04_24.jpg\n", 289 | "- Found shivank at (378, 390)\n", 290 | "Looking for faces in 2019_03_31-02_04_25.jpg\n", 291 | "- Found nitin at (617, 390)\n", 292 | "Looking for faces in 2019_03_31-02_04_26.jpg\n", 293 | "- Found unknown at (581, 355)\n", 294 | "Looking for faces in 2019_03_31-02_04_28.jpg\n", 295 | "- Found shivank at (318, 259)\n", 296 | "- Found yo at (964, 366)\n", 297 | "- Found nitin at (545, 283)\n", 298 | "- Found yo at (165, 258)\n", 299 | "Looking for faces in 2019_03_31-02_04_29.jpg\n", 300 | "- Found shivank at (318, 295)\n", 301 | "- Found shant at (199, 285)\n", 302 | "- Found nitin at (569, 325)\n", 303 | "Looking for faces in 2019_03_31-02_04_31.jpg\n", 304 | "- Found yo at (569, 367)\n", 305 | "- Found shivank at (294, 331)\n", 306 | "Looking for faces in 2019_03_31-02_04_32.jpg\n", 307 | "- Found shivank at (294, 343)\n", 308 | "- Found nitin at (540, 354)\n", 309 | "- Found yash at (985, 428)\n", 310 | "Looking for faces in 2019_03_31-02_04_34.jpg\n", 311 | "- Found nitin at (593, 378)\n", 312 | "- Found shivank at (285, 365)\n", 313 | "Looking for faces in Dx.jpg\n", 314 | "- Found DiCap at (167, 39)\n", 315 | "Looking for faces in Jx.jpg\n", 316 | "- Found Jason at (46, 55)\n", 317 | "Looking for faces in momo.jpg\n", 318 | "- Found Jason at (8, 32)\n", 319 | "- Found unknown at (117, 122)\n", 320 | "Looking for faces in XX.jpg\n", 321 | "- Found unknown at (33, 62)\n", 322 | "- Found Jason at (89, 30)\n", 323 | "Looking for faces in Y.jpg\n", 324 | "- Found DiCap at (1526, 666)\n", 325 | "- Found unknown at (913, 82)\n", 326 | "- Found yo at (855, 626)\n", 327 | "- Found unknown at (325, 641)\n", 328 | "- Found Jason at (268, 96)\n", 329 | "- Found unknown at (1491, 133)\n" 330 | ] 331 | } 332 | ], 333 | "source": [ 334 | "#Still Image Tester\n", 335 | "curd = os.getcwd()\n", 336 | "for image_file in os.listdir(r\"{}\\test\".format(curd)):\n", 337 | " full_file_path = os.path.join(r\"{}\\test\".format(curd), image_file)\n", 338 | "\n", 339 | " print(\"Looking for faces in {}\".format(image_file))\n", 340 | " frame = cv2.imread(full_file_path,-1)\n", 341 | "\n", 342 | " predictions = predict(frame, model_path=r\"{}\\models\\trained_knn_model.clf\".format(curd))\n", 343 | "\n", 344 | " for name, (top, right, bottom, left) in predictions:\n", 345 | " print(\"- Found {} at ({}, {})\".format(name, left, top))\n", 346 | " names.append(name)\n", 347 | " # Display results overlaid on an image\n", 348 | " #show_prediction_labels_on_image(frame, predictions)\n", 349 | " final_img = show_prediction_labels_on_image(frame, predictions)\n", 350 | " cv2.imshow(\"X\",final_img)\n", 351 | " cv2.waitKey(100)\n", 352 | "cv2.destroyAllWindows()" 353 | ] 354 | }, 355 | { 356 | "cell_type": "code", 357 | "execution_count": 14, 358 | "metadata": {}, 359 | "outputs": [ 360 | { 361 | "name": "stdout", 362 | "output_type": "stream", 363 | "text": [ 364 | "- Found shivank at (196, 53)\n", 365 | "- Found shivank at (325, 39)\n", 366 | "- Found shivank at (325, 53)\n", 367 | "- Found shivank at (296, 110)\n", 368 | "- Found nitin at (411, 239)\n", 369 | "- Found shivank at (259, 104)\n", 370 | "- Found shivank at (259, 104)\n", 371 | "- Found nitin at (497, 153)\n", 372 | "- Found shivank at (259, 116)\n", 373 | "- Found nitin at (497, 153)\n", 374 | "- Found shivank at (259, 104)\n", 375 | "- Found nitin at (497, 153)\n", 376 | "- Found shivank at (259, 104)\n", 377 | "- Found nitin at (497, 153)\n", 378 | "- Found shivank at (253, 82)\n", 379 | "- Found nitin at (497, 125)\n", 380 | "- Found shivank at (270, 80)\n", 381 | "- Found nitin at (511, 125)\n" 382 | ] 383 | }, 384 | { 385 | "ename": "ConnectionError", 386 | "evalue": "HTTPConnectionPool(host='192.168.137.184', port=8080): Max retries exceeded with url: /shot.jpg (Caused by NewConnectionError(': Failed to establish a new connection: [WinError 10061] No connection could be made because the target machine actively refused it',))", 387 | "output_type": "error", 388 | "traceback": [ 389 | "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", 390 | "\u001b[1;31mConnectionRefusedError\u001b[0m Traceback (most recent call last)", 391 | "\u001b[1;32m~\\Anaconda3\\envs\\mlkit\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 158\u001b[0m conn = connection.create_connection(\n\u001b[1;32m--> 159\u001b[1;33m (self._dns_host, self.port), self.timeout, **extra_kw)\n\u001b[0m\u001b[0;32m 160\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", 392 | "\u001b[1;32m~\\Anaconda3\\envs\\mlkit\\lib\\site-packages\\urllib3\\util\\connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[0;32m 79\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 80\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 81\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", 393 | "\u001b[1;32m~\\Anaconda3\\envs\\mlkit\\lib\\site-packages\\urllib3\\util\\connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 70\u001b[1;33m \u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msa\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 71\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0msock\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", 394 | "\u001b[1;31mConnectionRefusedError\u001b[0m: [WinError 10061] No connection could be made because the target machine actively refused it", 395 | "\nDuring handling of the above exception, another exception occurred:\n", 396 | "\u001b[1;31mNewConnectionError\u001b[0m Traceback (most recent call last)", 397 | "\u001b[1;32m~\\Anaconda3\\envs\\mlkit\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m 599\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 600\u001b[1;33m chunked=chunked)\n\u001b[0m\u001b[0;32m 601\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", 398 | "\u001b[1;32m~\\Anaconda3\\envs\\mlkit\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m 353\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 354\u001b[1;33m \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mhttplib_request_kw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 355\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", 399 | "\u001b[1;32m~\\Anaconda3\\envs\\mlkit\\lib\\http\\client.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m 1238\u001b[0m \u001b[1;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1239\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1240\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", 400 | "\u001b[1;32m~\\Anaconda3\\envs\\mlkit\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_send_request\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m 1284\u001b[0m \u001b[0mbody\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'body'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1285\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1286\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", 401 | "\u001b[1;32m~\\Anaconda3\\envs\\mlkit\\lib\\http\\client.py\u001b[0m in \u001b[0;36mendheaders\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m 1233\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1234\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[1;33m,\u001b[0m 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"\nDuring handling of the above exception, another exception occurred:\n", 408 | "\u001b[1;31mMaxRetryError\u001b[0m Traceback (most recent call last)", 409 | "\u001b[1;32m~\\Anaconda3\\envs\\mlkit\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m 448\u001b[0m \u001b[0mretries\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 449\u001b[1;33m \u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 450\u001b[0m )\n", 410 | "\u001b[1;32m~\\Anaconda3\\envs\\mlkit\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m 637\u001b[0m retries = retries.increment(method, url, error=e, _pool=self,\n\u001b[1;32m--> 638\u001b[1;33m _stacktrace=sys.exc_info()[2])\n\u001b[0m\u001b[0;32m 639\u001b[0m \u001b[0mretries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", 411 | "\u001b[1;32m~\\Anaconda3\\envs\\mlkit\\lib\\site-packages\\urllib3\\util\\retry.py\u001b[0m in \u001b[0;36mincrement\u001b[1;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[0;32m 397\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mnew_retry\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_exhausted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 398\u001b[1;33m \u001b[1;32mraise\u001b[0m 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\u001b[0msessions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 60\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 61\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 62\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", 418 | "\u001b[1;32m~\\Anaconda3\\envs\\mlkit\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m 531\u001b[0m }\n\u001b[0;32m 532\u001b[0m \u001b[0msend_kwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 533\u001b[1;33m \u001b[0mresp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 534\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 535\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", 419 | "\u001b[1;32m~\\Anaconda3\\envs\\mlkit\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m 644\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 645\u001b[0m \u001b[1;31m# Send the request\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 646\u001b[1;33m \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 647\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 648\u001b[0m \u001b[1;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", 420 | "\u001b[1;32m~\\Anaconda3\\envs\\mlkit\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m 514\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mSSLError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 515\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 516\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 517\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 518\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mClosedPoolError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", 421 | "\u001b[1;31mConnectionError\u001b[0m: HTTPConnectionPool(host='192.168.137.184', port=8080): Max retries exceeded with url: /shot.jpg (Caused by NewConnectionError(': Failed to establish a new connection: [WinError 10061] No connection could be made because the target machine actively refused it',))" 422 | ] 423 | } 424 | ], 425 | "source": [ 426 | "#Live Video tester\n", 427 | "url = \"http://192.168.137.184:8080/shot.jpg\"\n", 428 | "\n", 429 | "while(True):\n", 430 | " img_resp = requests.get(url)\n", 431 | " img_arr = np.array(bytearray(img_resp.content),dtype = np.uint8)\n", 432 | " frame = cv2.imdecode(img_arr,-1)\n", 433 | " predictions = predict(frame, model_path=\"{}/models/trained_knn_model.clf\".format(curd))\n", 434 | " for name, (top, right, bottom, left) in predictions:\n", 435 | " print(\"- Found {} at ({}, {})\".format(name, left, top))\n", 436 | " names.append(name)\n", 437 | "\n", 438 | " # Display results overlaid on an image\n", 439 | " show_img = show_prediction_labels_on_image(frame, predictions)\n", 440 | " cv2.imshow('img',show_img)\n", 441 | " if cv2.waitKey(1) & 0xFF == ord('q'):\n", 442 | " break\n", 443 | "\n", 444 | "\n", 445 | " \n", 446 | "cv2.destroyAllWindows()" 447 | ] 448 | }, 449 | { 450 | "cell_type": "code", 451 | "execution_count": 13, 452 | "metadata": {}, 453 | "outputs": [ 454 | { 455 | "name": "stdout", 456 | "output_type": "stream", 457 | "text": [ 458 | "- Found yo at (137, 213)\n", 459 | "- Found nitin at (137, 213)\n", 460 | "- Found shant at (448, 233)\n", 461 | "- Found yash at (494, 202)\n", 462 | "- Found yo at (443, 233)\n", 463 | "- Found yash at (494, 184)\n", 464 | "- Found yash at (494, 202)\n", 465 | "- Found yash at (511, 196)\n", 466 | "- Found yo at (46, 216)\n", 467 | "- Found yash at (354, 182)\n" 468 | ] 469 | } 470 | ], 471 | "source": [ 472 | "#Live Video tester\n", 473 | "cap = cv2.VideoCapture(0)\n", 474 | "cap.set(cv2.CAP_PROP_FPS, 100)\n", 475 | "rat, frame = cap.read()\n", 476 | "\n", 477 | "while(True):\n", 478 | " rat, frame = cap.read()\n", 479 | " predictions = predict(frame, model_path=\"{}/models/trained_knn_model.clf\".format(curd))\n", 480 | " for name, (top, right, bottom, left) in predictions:\n", 481 | " print(\"- Found {} at ({}, {})\".format(name, left, top))\n", 482 | " names.append(name)\n", 483 | "\n", 484 | " # Display results overlaid on an image\n", 485 | " show_img = show_prediction_labels_on_image(frame, predictions)\n", 486 | " cv2.imshow('img',show_img)\n", 487 | " if cv2.waitKey(1) & 0xFF == ord('q'):\n", 488 | " break\n", 489 | "\n", 490 | "\n", 491 | "cap.release() \n", 492 | "cv2.destroyAllWindows()" 493 | ] 494 | }, 495 | { 496 | "cell_type": "code", 497 | "execution_count": 19, 498 | "metadata": {}, 499 | "outputs": [ 500 | { 501 | "data": { 502 | "text/html": [ 503 | "
\n", 504 | "\n", 517 | "\n", 518 | " \n", 519 | " \n", 520 | " \n", 521 | " \n", 522 | " \n", 523 | " \n", 524 | " \n", 525 | " \n", 526 | " \n", 527 | " \n", 528 | " \n", 529 | " \n", 530 | " \n", 531 | " \n", 532 | " \n", 533 | " \n", 534 | " \n", 535 | " \n", 536 | " \n", 537 | " \n", 538 | " \n", 539 | " \n", 540 | " \n", 541 | " \n", 542 | " \n", 543 | " \n", 544 | " \n", 545 | " \n", 546 | "
Present
Shantnu Agarwal1
yash1
Jason0
yo0
DiCap0
\n", 547 | "
" 548 | ], 549 | "text/plain": [ 550 | " Present\n", 551 | "Shantnu Agarwal 1\n", 552 | "yash 1\n", 553 | "Jason 0\n", 554 | "yo 0\n", 555 | "DiCap 0" 556 | ] 557 | }, 558 | "execution_count": 19, 559 | "metadata": {}, 560 | "output_type": "execute_result" 561 | } 562 | ], 563 | "source": [ 564 | "#Attendance System csv maker \n", 565 | "namesD=pd.DataFrame(names, columns=[\"Names\"])\n", 566 | "namesD= namesD[namesD.Names!=\"unknown\"]\n", 567 | "\n", 568 | "attendance= pd.DataFrame(namesD.iloc[:,0].value_counts())\n", 569 | "attendance.rename(index=str,columns={'Names': 'Count'},inplace=True)\n", 570 | "attendance[\"Present\"] =0\n", 571 | "\n", 572 | "attendance[\"Count\"][0] > 5\n", 573 | "for i in range(attendance.shape[0]):\n", 574 | " if(attendance[\"Count\"][i] > 5):\n", 575 | " attendance[\"Present\"][i] =1\n", 576 | "\n", 577 | "\n", 578 | "attendance_final=attendance.drop(['Count'],axis=1)\n", 579 | "attendance_final\n", 580 | "#attendance_final.to_csv('Attendance.csv')" 581 | ] 582 | }, 583 | { 584 | "cell_type": "code", 585 | "execution_count": null, 586 | "metadata": {}, 587 | "outputs": [], 588 | "source": [] 589 | }, 590 | { 591 | "cell_type": "code", 592 | "execution_count": null, 593 | "metadata": {}, 594 | "outputs": [], 595 | "source": [] 596 | }, 597 | { 598 | "cell_type": "code", 599 | "execution_count": null, 600 | "metadata": {}, 601 | "outputs": [], 602 | "source": [] 603 | }, 604 | { 605 | "cell_type": "code", 606 | "execution_count": null, 607 | "metadata": {}, 608 | "outputs": [], 609 | "source": [] 610 | }, 611 | { 612 | "cell_type": "code", 613 | "execution_count": null, 614 | "metadata": {}, 615 | "outputs": [], 616 | "source": [] 617 | }, 618 | { 619 | "cell_type": "code", 620 | "execution_count": null, 621 | "metadata": {}, 622 | "outputs": [], 623 | "source": [] 624 | } 625 | ], 626 | "metadata": { 627 | "kernelspec": { 628 | "display_name": "Python 3", 629 | "language": "python", 630 | "name": "python3" 631 | }, 632 | "language_info": { 633 | "codemirror_mode": { 634 | "name": "ipython", 635 | "version": 3 636 | }, 637 | "file_extension": ".py", 638 | "mimetype": "text/x-python", 639 | "name": "python", 640 | "nbconvert_exporter": "python", 641 | "pygments_lexer": "ipython3", 642 | "version": "3.6.8" 643 | } 644 | }, 645 | "nbformat": 4, 646 | "nbformat_minor": 2 647 | } 648 | -------------------------------------------------------------------------------- /face_recog/known_people/test.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/NitinDatta8/Automatic-Attendance-System-Using-Face-Recognition/1e5b2597a9821cecea08b173b71132d562ba900c/face_recog/known_people/test.jpg -------------------------------------------------------------------------------- /face_recog/models/test.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/NitinDatta8/Automatic-Attendance-System-Using-Face-Recognition/1e5b2597a9821cecea08b173b71132d562ba900c/face_recog/models/test.jpg -------------------------------------------------------------------------------- /face_recog/test/test.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/NitinDatta8/Automatic-Attendance-System-Using-Face-Recognition/1e5b2597a9821cecea08b173b71132d562ba900c/face_recog/test/test.jpg -------------------------------------------------------------------------------- /face_recog/unknown_pictures/test.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/NitinDatta8/Automatic-Attendance-System-Using-Face-Recognition/1e5b2597a9821cecea08b173b71132d562ba900c/face_recog/unknown_pictures/test.jpg -------------------------------------------------------------------------------- /main/Attendance.csv: -------------------------------------------------------------------------------- 1 | ,Present 2 | RA1711003010488,1 3 | -------------------------------------------------------------------------------- /main/adminLogin.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | Gully Boys - Attendance System 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 19 |
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Welcome Admin, please login below to proceed.

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Automatic Attendance System
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Mozofest 2019 Hackathon, 30th-31st March, 2019

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Welcome, Administrator.

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Choose the day order for which you want to see the student statistics.

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Mozofest 2019 Hackathon, 30th-31st March, 2019

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Dear Staff member, all field shown here are mandatory. Please fill with caution and only click the train button 49 | when all students have given their details. 50 |

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Mozofest 2019 Hackathon, 30th-31st March, 2019

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Attendance Statistics for

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Day Order: 1
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Registration NumberStudent NamePresent(1)/Absent(0)
RA1711003010251Yash1
RA1711003010350Shantnu1
RA1711003010488Shivank0
RA1711003010512Nitin1
RA1611003060265Alvin0
RA171100301000Kesari0
RA1711003010411Mukesh1
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Attendance Statistics for

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Day Order: 2
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Registration NumberStudent NamePresent(1)/Absent(0)
RA1711003010369Anany1
RA1711003010992Yash0
RA1711003010859Movva1
RA1611003060265Shivank0
RA1711003010512Nitin1
RA1711003010350Shantnu1
RA1711003010411Anil0
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Attendance Statistics for

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Day Order: 3
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Registration NumberStudent NamePresent(1)/Absent(0)
RA1711003010350Elon Musk0
RA1711003010369Shantnu1
RA1711003010411Nitin1
RA1711003010369Yash0
RA1711003010992Shivank1
RA171100301000Rahul0
RA1611003060265Vivek1
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Dear staff member, please login below to proceed with the student registration process.

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Automatic Attendance System
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Mozofest 2019 Hackathon, 30th-31st March, 2019

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73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 97 | 98 | 103 | 104 | 105 | 106 | -------------------------------------------------------------------------------- /main/img/dp.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/NitinDatta8/Automatic-Attendance-System-Using-Face-Recognition/1e5b2597a9821cecea08b173b71132d562ba900c/main/img/dp.png -------------------------------------------------------------------------------- /main/img/icon.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/NitinDatta8/Automatic-Attendance-System-Using-Face-Recognition/1e5b2597a9821cecea08b173b71132d562ba900c/main/img/icon.png -------------------------------------------------------------------------------- /main/img/user.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/NitinDatta8/Automatic-Attendance-System-Using-Face-Recognition/1e5b2597a9821cecea08b173b71132d562ba900c/main/img/user.png -------------------------------------------------------------------------------- /main/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | Gully Boys - Attendance System 6 | 7 | 8 | 9 | 10 | 11 | 22 | 23 | 24 | 25 | 30 | 31 |
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Automated Attendance Control Panel

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Please choose an option to proceed

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40 | Enroll New Student 41 |
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45 | Administrator Login 46 |
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Automatic Attendance System
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Mozofest 2019 Hackathon, 30th-31st March, 2019

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72 | Exit Panel 73 |
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78 | © 2019 GullyBoys 79 |
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82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 108 | 113 | 114 | -------------------------------------------------------------------------------- /main/js/adminLogin.js: -------------------------------------------------------------------------------- 1 | // Initialize Firebase 2 | var config = { 3 | apiKey: "AIzaSyAlkTeBTBPtnVAaOnNmwiwsFVDIWKhfp5M", 4 | authDomain: "hackathon-mozofest-2019.firebaseapp.com", 5 | databaseURL: "https://hackathon-mozofest-2019.firebaseio.com", 6 | projectId: "hackathon-mozofest-2019", 7 | storageBucket: "hackathon-mozofest-2019.appspot.com", 8 | messagingSenderId: "835193922935" 9 | }; 10 | firebase.initializeApp(config); 11 | //Clear any signed in users...... 12 | firebase.auth().signOut().then(function() { 13 | console.log("Sign out successful"); 14 | }).catch(function(error) { 15 | console.log("Error singing out"); 16 | }); 17 | 18 | 19 | function foo(){ 20 | var email = document.getElementById("loginemail").value; 21 | var password = document.getElementById("loginpassword").value; 22 | 23 | firebase.auth().signInWithEmailAndPassword(email,password).catch(function(error) { //catch errors if any 24 | console.log(error); 25 | }).then(function(){ //when login is complete, only then..... 26 | var user = firebase.auth().currentUser; 27 | if(user!=null){ 28 | console.log("Admin Login Success"); 29 | document.location.href = "adminScreen.html"; 30 | } 31 | else{ 32 | console.log("Login Unsuccessful"); 33 | M.toast({html: 'Invalid credentials! Please try again!'}); 34 | } 35 | }); 36 | // console.log("outside"); 37 | 38 | } -------------------------------------------------------------------------------- /main/js/adminScreen.js: -------------------------------------------------------------------------------- 1 | var options = {}; 2 | var elems = {}; 3 | var fs = require('fs') 4 | var config = { 5 | apiKey: "AIzaSyAlkTeBTBPtnVAaOnNmwiwsFVDIWKhfp5M", 6 | authDomain: "hackathon-mozofest-2019.firebaseapp.com", 7 | databaseURL: "https://hackathon-mozofest-2019.firebaseio.com", 8 | storageBucket: 'gs://hackathon-mozofest-2019.appspot.com/' 9 | }; 10 | 11 | firebase.initializeApp(config); 12 | var user = firebase.auth().currentUser; 13 | 14 | firebase.auth().onAuthStateChanged(function(user) { 15 | if (user) { 16 | console.log("Loggd in already : " + user.email); 17 | M.toast({html:'Welcome back ' + user.email + ' !'}); 18 | } 19 | }); 20 | 21 | 22 | 23 | 24 | document.addEventListener('DOMContentLoaded', function() { 25 | elems = document.querySelectorAll('select'); 26 | var instances = M.FormSelect.init(elems, options); 27 | }); 28 | 29 | 30 | $('select').on('change', function() { 31 | // console.log($(this).val()); 32 | foo($(this).val()); 33 | }); 34 | var selection; 35 | function foo(selection){ 36 | console.log(selection + " from inside foo along with user : " + user); 37 | switch(selection){ 38 | case '1': document.location.href = "etc/data1.html"; 39 | break; 40 | case '2': document.location.href = "etc/data2.html"; 41 | break; 42 | case '3': document.location.href = "etc/data3.html"; 43 | break; 44 | } 45 | 46 | //fetch from database 47 | // Admin signed in. 48 | 49 | } 50 | 51 | function logOut(){ 52 | console.log("Attempting Sign Out"); 53 | firebase.auth().signOut().then(function() { 54 | console.log("Sign out successful"); 55 | document.location.href = "adminLogin.html"; 56 | }).catch(function(error) { 57 | console.log("Error singing out"); 58 | }); 59 | } 60 | function pyCam(){ 61 | 62 | console.log("Camera Input Running"); 63 | M.toast({html:'Opening Camera Feed'}); 64 | var python = require('child_process').spawn('python', ['py/camcap2.py']); 65 | python.stdout.on('data',function(data){ 66 | console.log("data: ",data.toString('utf8')+ " from Python "); 67 | 68 | sendCSV(); 69 | }); 70 | 71 | } 72 | function sendCSV(){ 73 | 74 | 75 | } 76 | 77 | console.log("JS ready"); -------------------------------------------------------------------------------- /main/js/data1.js: -------------------------------------------------------------------------------- 1 | var config = { 2 | apiKey: "AIzaSyAlkTeBTBPtnVAaOnNmwiwsFVDIWKhfp5M", 3 | authDomain: "hackathon-mozofest-2019.firebaseapp.com", 4 | databaseURL: "https://hackathon-mozofest-2019.firebaseio.com", 5 | storageBucket: 'gs://hackathon-mozofest-2019.appspot.com/' 6 | }; 7 | 8 | firebase.initializeApp(config); 9 | var user = firebase.auth().currentUser; 10 | 11 | 12 | var database = firebase.database(); 13 | 14 | 15 | var starCountRef = firebase.database().ref('Students'); 16 | starCountRef.on('value', function(snapshot) { 17 | var response = snapshot.val(); 18 | console.log(response); 19 | document.getElementById(elem1).innerHTML=response.RA1711003010350.RegNo; 20 | } 21 | ); -------------------------------------------------------------------------------- /main/js/facultyLogin.js: -------------------------------------------------------------------------------- 1 | // Initialize Firebase 2 | 3 | var config = { 4 | apiKey: "AIzaSyAlkTeBTBPtnVAaOnNmwiwsFVDIWKhfp5M", 5 | authDomain: "hackathon-mozofest-2019.firebaseapp.com", 6 | databaseURL: "https://hackathon-mozofest-2019.firebaseio.com", 7 | projectId: "hackathon-mozofest-2019", 8 | storageBucket: "hackathon-mozofest-2019.appspot.com", 9 | messagingSenderId: "835193922935" 10 | }; 11 | firebase.initializeApp(config); 12 | //clear any signed in users..... 13 | firebase.auth().signOut().then(function() { 14 | console.log("Sign out successful"); 15 | }).catch(function(error) { 16 | console.log("Error singing out"); 17 | }); 18 | 19 | function foo(){ 20 | 21 | console.log("working on FOO"); 22 | var email = document.getElementById("loginemail").value; 23 | var password = document.getElementById("loginpassword").value; 24 | 25 | firebase.auth().signInWithEmailAndPassword(email,password).catch(function(error) { //catch errors if any 26 | console.log(error); 27 | }).then(function(){ //when login is complete, only then..... 28 | var user = firebase.auth().currentUser; 29 | if(user!=null){ 30 | console.log("Faculty Login Success"); 31 | document.location.href = "dataEntry.html"; 32 | } 33 | else{ 34 | console.log("Login Unsuccessful"); 35 | M.toast({html: 'Invalid credentials! Please try again!'}); 36 | } 37 | }); 38 | console.log("outside"); 39 | 40 | } -------------------------------------------------------------------------------- /main/js/upload.js: -------------------------------------------------------------------------------- 1 | var fs = require('fs'); 2 | 3 | function foo(){ 4 | var str = document.getElementById('studentregno').value; 5 | if(str.length>1){ 6 | console.log("Entered Reg. No. is " + str); 7 | console.log("attempting file write"); 8 | fs.writeFileSync('py/helper.txt', str, function (err,fd) { 9 | if (err) throw err; 10 | fs.close(fd, function(error) { 11 | if (error) { 12 | console.error("close error: " + error.message); 13 | } else { 14 | console.log("File was closed!"); 15 | } 16 | }); 17 | }); 18 | 19 | var python = require('child_process').spawn('python', ['py/capture.py']); 20 | python.stdout.on('data',function(data){ 21 | console.log("data: ",data.toString('utf8')+ " from Python "); 22 | }); 23 | } 24 | else{ 25 | M.toast({html:'All field are mandatory!'}); 26 | } 27 | } 28 | function Train(){ 29 | console.log("JS Train running"); 30 | document.getElementById('preloader').style.visibility="visible"; 31 | M.toast({html:'Training has begun, it might take some time. You will be informed when the job is complete.'}); 32 | var python = require('child_process').spawn('python', ['py/train.py']); 33 | python.stdout.on('data',function(data){ 34 | console.log("data: ",data.toString('utf8')+ " from Python "); 35 | M.toast({html:'Please upload the file found in /assests/models directory.'}); 36 | document.getElementById('preloader').style.visibility='hidden'; 37 | document.getElementById('trainfield').style.backgroundColor='red'; 38 | }); 39 | } 40 | //manual file upload 41 | var selectedFile; 42 | 43 | var config = { 44 | apiKey: "AIzaSyAlkTeBTBPtnVAaOnNmwiwsFVDIWKhfp5M", 45 | authDomain: "hackathon-mozofest-2019.firebaseapp.com", 46 | databaseURL: "https://hackathon-mozofest-2019.firebaseio.com", 47 | storageBucket: 'gs://hackathon-mozofest-2019.appspot.com/' 48 | }; 49 | firebase.initializeApp(config); 50 | var database = firebase.database(); 51 | 52 | function logOut(){ 53 | console.log("Attempting Sign Out"); 54 | firebase.auth().signOut().then(function() { 55 | console.log("Sign out successful"); 56 | document.location.href = "facultyLogin.html"; 57 | }).catch(function(error) { 58 | console.log("Error singing out"); 59 | }); 60 | } 61 | 62 | function uploadFile(){ 63 | M.toast({html:'Starting with the upload task'}); 64 | var filename = selectedFile.name; 65 | // var regno = document.getElementById('studentregno').value; 66 | // var sName = document.getElementById('studentname').value; 67 | var storageRef = firebase.storage().ref('/' + 'models' + '/' + filename); 68 | var uploadTask = storageRef.put(selectedFile); 69 | 70 | 71 | uploadTask.on('state_changed', function(snapshot){ 72 | // Observe state change events such as progress, pause, and resume 73 | // Get task progress, including the number of bytes uploaded and the total number of bytes to be uploaded 74 | var progress = (snapshot.bytesTransferred / snapshot.totalBytes) * 100; 75 | console.log('Upload is ' + progress + '% done'); 76 | document.getElementById("bar").style.width=progress+100; 77 | switch (snapshot.state) { 78 | case firebase.storage.TaskState.PAUSED: // or 'paused' 79 | console.log('Upload is paused'); 80 | break; 81 | case firebase.storage.TaskState.RUNNING: // or 'running' 82 | console.log('Upload is running'); 83 | break; 84 | } 85 | }, function(error) { 86 | // Handle unsuccessful uploads 87 | }, function() { 88 | // Handle successful uploads on complete 89 | // For instance, get the download URL: https://firebasestorage.googleapis.com/... 90 | uploadTask.snapshot.ref.getDownloadURL().then(function(downloadURL) { 91 | console.log('File available at', downloadURL); 92 | document.getElementById("bar").style.width="100%"; 93 | // writeUserData(regno,sName); 94 | M.toast({html:'Upload Task Successful!'}); 95 | }); 96 | }); 97 | 98 | 99 | }; 100 | 101 | function writeUserData() { 102 | 103 | var studregno = document.getElementById('studentregno').value; 104 | var studname = document.getElementById('studentname').value; 105 | if(studregno.length>2 && studname.length>2){ 106 | firebase.database().ref('Students/' + studregno).set({ 107 | RegNo: studregno, 108 | name: studname, 109 | hours_conducted: 0, 110 | hours_present:0, 111 | dayorder:{ 112 | DO1:0, 113 | DO2:0, 114 | DO3:0, 115 | }, 116 | 117 | }); 118 | console.log("All Data Sent Successfully"); 119 | M.toast({html:'Data has been uploaded to the server! You can continue adding more'}); 120 | } 121 | else{ 122 | console.log("Incorrect data detected, warning user"); 123 | M.toast({html:'All fields are mandatory'}); 124 | } 125 | 126 | } 127 | 128 | $("#file").on("change",function(event){ 129 | selectedFile = event.target.files[0]; 130 | uploadFile(); 131 | }); 132 | 133 | -------------------------------------------------------------------------------- /main/main.js: -------------------------------------------------------------------------------- 1 | const {app, BrowserWindow} = require('electron'); 2 | const path = require('path'); 3 | const url = require('url'); 4 | 5 | let win; 6 | 7 | function CreateWindow(){ 8 | win = new BrowserWindow({width:1920, height:1080, icon:__dirname+'/icon.png'}); 9 | 10 | win.loadURL(url.format({ 11 | pathname: path.join(__dirname, 'index.html'), 12 | protocol: 'file:', 13 | slashes: true 14 | })); 15 | 16 | 17 | console.log("WINDOWS CREATED"); 18 | 19 | // DEVELOPER TOOLS KE LIYE 20 | // win.webContents.openDevTools(); 21 | 22 | win.on('closed',() => { 23 | win=null; 24 | }); 25 | } 26 | app.on('ready', CreateWindow); 27 | 28 | app.on('window-all-closed',() => { 29 | if(process.platform !== 'darwin'){ 30 | app.quit(); 31 | } 32 | }); 33 | -------------------------------------------------------------------------------- /main/package-lock.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "mozohack", 3 | "version": "1.0.0", 4 | "lockfileVersion": 1, 5 | "requires": true, 6 | "dependencies": { 7 | "@types/node": { 8 | "version": "10.14.4", 9 | "resolved": "https://registry.npmjs.org/@types/node/-/node-10.14.4.tgz", 10 | "integrity": "sha512-DT25xX/YgyPKiHFOpNuANIQIVvYEwCWXgK2jYYwqgaMrYE6+tq+DtmMwlD3drl6DJbUwtlIDnn0d7tIn/EbXBg==" 11 | }, 12 | "ajv": { 13 | "version": "6.10.0", 14 | "resolved": "https://registry.npmjs.org/ajv/-/ajv-6.10.0.tgz", 15 | "integrity": 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-------------------------------------------------------------------------------- 1 | { 2 | "name": "mozohack", 3 | "version": "1.0.0", 4 | "description": "Automatic Attendance System for institutions", 5 | "main": "main.js", 6 | "scripts": { 7 | "start": "electron ." 8 | }, 9 | "author": "Shantnu Agarwal", 10 | "license": "ISC", 11 | "dependencies": { 12 | "electron": "^4.1.3", 13 | "jquery": "^3.3.1", 14 | "python-shell": "^1.0.7" 15 | } 16 | } 17 | -------------------------------------------------------------------------------- /main/py/camcap2.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # coding: utf-8 3 | 4 | # In[7]: 5 | 6 | 7 | import math 8 | import cv2 9 | import time 10 | from sklearn import neighbors 11 | import numpy as np 12 | import pandas as pd 13 | import os 14 | import os.path 15 | import pickle 16 | from PIL import Image, ImageDraw 17 | import face_recognition 18 | import requests 19 | from face_recognition.face_recognition_cli import image_files_in_folder 20 | curd = os.getcwd() 21 | 22 | try: 23 | os.mkdir("{}/models".format(curd)) 24 | except: 25 | pass 26 | names=[] 27 | ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'} 28 | 29 | 30 | # In[8]: 31 | 32 | import shutil 33 | folder='{}/assets/test/'.format(curd) 34 | dest='{}/assets/History/'.format(curd) 35 | for i in os.listdir(folder): 36 | shutil.move(folder+i,dest) 37 | # file_path=os.path.join(folder,i) 38 | # if os.path.isfile(file_path): 39 | # os.remove(file_path) 40 | # In[9]: 41 | 42 | 43 | 44 | cap = cv2.VideoCapture(0) 45 | cap.set(cv2.CAP_PROP_FPS, 100) 46 | rat, frame = cap.read() 47 | count=0 48 | while count<10: 49 | curtime = time.strftime("%Y_%m_%d-%H_%M_%S") 50 | rat, frame = cap.read() 51 | cv2.imwrite("{}/assets/test/{}.jpg".format(curd,curtime), frame) 52 | count+=1 53 | cv2.imshow('img',frame) 54 | if cv2.waitKey(1) & 0xFF == ord('q'): 55 | break 56 | time.sleep(5) 57 | 58 | 59 | cap.release() 60 | cv2.destroyAllWindows() 61 | 62 | 63 | # In[10]: 64 | 65 | 66 | def predict(frame, knn_clf=None, model_path=None, distance_threshold=0.6): 67 | 68 | 69 | if knn_clf is None and model_path is None: 70 | raise Exception("Must supply knn classifier either thourgh knn_clf or model_path") 71 | 72 | if knn_clf is None: 73 | with open(model_path, 'rb') as f: 74 | knn_clf = pickle.load(f) 75 | 76 | 77 | # X_img = face_recognition.load_image_file(X_img_path) 78 | X_img = frame 79 | X_face_locations = face_recognition.face_locations(X_img) 80 | 81 | if len(X_face_locations) == 0: 82 | return [] 83 | 84 | faces_encodings = face_recognition.face_encodings(X_img, known_face_locations=X_face_locations) 85 | 86 | closest_distances = knn_clf.kneighbors(faces_encodings, n_neighbors=1) 87 | are_matches = [closest_distances[0][i][0] <= distance_threshold for i in range(len(X_face_locations))] 88 | 89 | return [(pred, loc) if rec else ("unknown", loc) for pred, loc, rec in zip(knn_clf.predict(faces_encodings), X_face_locations, are_matches)] 90 | 91 | 92 | # In[11]: 93 | 94 | 95 | def show_prediction_labels_on_image(frame, predictions): 96 | 97 | #pil_image = Image.open(img_path).convert("RGB") 98 | pil_image = Image.fromarray(frame).convert("RGB") 99 | draw = ImageDraw.Draw(pil_image) 100 | 101 | for name, (top, right, bottom, left) in predictions: 102 | draw.rectangle(((left, top), (right, bottom)), outline=(0, 0, 255)) 103 | 104 | 105 | name = name.encode("UTF-8") 106 | 107 | text_width, text_height = draw.textsize(name) 108 | draw.rectangle(((left, bottom - text_height - 10), (right, bottom)), fill=(0, 0, 255), outline=(0, 0, 255)) 109 | draw.text((left + 6, bottom - text_height - 5), name, fill=(255, 255, 255, 255)) 110 | 111 | del draw 112 | 113 | #pil_image.show() 114 | #cv2.imshow("frame",pil_image) 115 | return np.asarray(pil_image) 116 | 117 | 118 | # In[15]: 119 | 120 | 121 | for image_file in os.listdir(r"{}\assets\test".format(curd)): 122 | full_file_path = os.path.join(r"{}\assets\test".format(curd), image_file) 123 | 124 | print("Looking for faces in {}".format(image_file)) 125 | frame = cv2.imread(full_file_path,-1) 126 | 127 | predictions = predict(frame, model_path=r"{}\assets\models\trained_knn_model.clf".format(curd)) 128 | 129 | for name, (top, right, bottom, left) in predictions: 130 | print("- Found {} at ({}, {})".format(name, left, top)) 131 | names.append(name) 132 | # Display results overlaid on an image 133 | #show_prediction_labels_on_image(frame, predictions) 134 | final_img = show_prediction_labels_on_image(frame, predictions) 135 | cv2.imshow("X",final_img) 136 | cv2.waitKey(1) 137 | cv2.destroyAllWindows() 138 | 139 | 140 | # In[16]: 141 | 142 | 143 | namesD=pd.DataFrame(names, columns=["Names"]) 144 | namesD= namesD[namesD.Names!="unknown"] 145 | 146 | attendance= pd.DataFrame(namesD.iloc[:,0].value_counts()) 147 | attendance.rename(index=str,columns={'Names': 'Count'},inplace=True) 148 | attendance["Present"] =0 149 | 150 | attendance["Count"][0] > 5 151 | for i in range(attendance.shape[0]): 152 | if(attendance["Count"][i] > 5): 153 | attendance["Present"][i] =1 154 | 155 | 156 | attendance_final=attendance.drop(['Count'],axis=1) 157 | attendance_final 158 | attendance_final.to_csv('Attendance.csv') 159 | 160 | 161 | # In[ ]: 162 | 163 | 164 | 165 | 166 | -------------------------------------------------------------------------------- /main/py/camcapture.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # coding: utf-8 3 | 4 | # In[ ]: 5 | 6 | 7 | import math 8 | import cv2 9 | import time 10 | from sklearn import neighbors 11 | import numpy as np 12 | import pandas as pd 13 | import os 14 | import os.path 15 | import pickle 16 | from PIL import Image, ImageDraw 17 | import face_recognition 18 | from face_recognition.face_recognition_cli import image_files_in_folder 19 | curd = os.getcwd() 20 | 21 | 22 | names=[] 23 | ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'} 24 | 25 | 26 | # In[ ]: 27 | 28 | 29 | cap = cv2.VideoCapture(0) 30 | cap.set(cv2.CAP_PROP_FPS, 100) 31 | rat, frame = cap.read() 32 | count=0 33 | while count<10: 34 | curtime = time.strftime("%Y_%m_%d-%H_%M_%S") 35 | rat, frame = cap.read() 36 | cv2.imwrite("{}/assets/test/{}.jpg".format(curd,curtime), frame) 37 | count+=1 38 | cv2.imshow('img',frame) 39 | if cv2.waitKey(1) & 0xFF == ord('q'): 40 | break 41 | time.sleep(1) 42 | 43 | 44 | cap.release() 45 | cv2.destroyAllWindows() 46 | 47 | 48 | # In[ ]: 49 | 50 | 51 | def predict(frame, knn_clf=None, model_path=None, distance_threshold=0.5): 52 | 53 | 54 | if knn_clf is None and model_path is None: 55 | raise Exception("Must supply knn classifier either thourgh knn_clf or model_path") 56 | 57 | if knn_clf is None: 58 | with open(model_path, 'rb') as f: 59 | knn_clf = pickle.load(f) 60 | 61 | 62 | # X_img = face_recognition.load_image_file(X_img_path) 63 | X_img = frame 64 | X_face_locations = face_recognition.face_locations(X_img) 65 | 66 | if len(X_face_locations) == 0: 67 | return [] 68 | 69 | faces_encodings = face_recognition.face_encodings(X_img, known_face_locations=X_face_locations) 70 | 71 | closest_distances = knn_clf.kneighbors(faces_encodings, n_neighbors=1) 72 | are_matches = [closest_distances[0][i][0] <= distance_threshold for i in range(len(X_face_locations))] 73 | 74 | return [(pred, loc) if rec else ("unknown", loc) for pred, loc, rec in zip(knn_clf.predict(faces_encodings), X_face_locations, are_matches)] 75 | 76 | 77 | # In[ ]: 78 | 79 | 80 | # def show_prediction_labels_on_image(frame, predictions): 81 | 82 | # #pil_image = Image.open(img_path).convert("RGB") 83 | # pil_image = Image.fromarray(frame).convert("RGB") 84 | # draw = ImageDraw.Draw(pil_image) 85 | 86 | # for name, (top, right, bottom, left) in predictions: 87 | # draw.rectangle(((left, top), (right, bottom)), outline=(0, 0, 255)) 88 | 89 | 90 | # name = name.encode("UTF-8") 91 | 92 | # text_width, text_height = draw.textsize(name) 93 | # draw.rectangle(((left, bottom - text_height - 10), (right, bottom)), fill=(0, 0, 255), outline=(0, 0, 255)) 94 | # draw.text((left + 6, bottom - text_height - 5), name, fill=(255, 255, 255, 255)) 95 | 96 | # del draw 97 | 98 | # #pil_image.show() 99 | # #cv2.imshow("frame",pil_image) 100 | # return np.asarray(pil_image) 101 | 102 | 103 | # In[ ]: 104 | 105 | 106 | for image_file in os.listdir("{}/assets/test".format(curd)): 107 | full_file_path = os.path.join("{}/assests/test".format(curd), image_file) 108 | cv2.imshow(img_file) 109 | print("Looking for faces in {}".format(image_file)) 110 | frame = cv2.imread(full_file_path,-1) 111 | 112 | predictions = predict(frame, model_path="{}/assets/models/trained_knn_model.clf".format(curd)) 113 | 114 | for name, (top, right, bottom, left) in predictions: 115 | print("- Found {} at ({}, {})".format(name, left, top)) 116 | names.append(name) 117 | # Display results overlaid on an image 118 | #show_prediction_labels_on_image(frame, predictions) 119 | 120 | 121 | 122 | # In[ ]: 123 | 124 | 125 | namesD=pd.DataFrame(names, columns=["Names"]) 126 | namesD= namesD[namesD.Names!="unknown"] 127 | 128 | attendance= pd.DataFrame(namesD.iloc[:,0].value_counts()) 129 | attendance.rename(index=str,columns={'Names': 'Count'},inplace=True) 130 | attendance["Present"] =0 131 | 132 | attendance["Count"][0] > 5 133 | for i in range(attendance.shape[0]): 134 | if(attendance["Count"][i] > 5): 135 | attendance["Present"][i] =1 136 | 137 | 138 | attendance_final=attendance.drop(['Count'],axis=1) 139 | attendance_final 140 | attendance_final.to_csv("/assets/attendance.csv") 141 | -------------------------------------------------------------------------------- /main/py/capture.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # coding: utf-8 3 | 4 | # In[ ]: 5 | 6 | 7 | import cv2 8 | import numpy as np 9 | import time 10 | import os 11 | curd = os.getcwd() 12 | print("in Python file now") 13 | 14 | f = open("py/helper.txt", "r") 15 | name = f.read() 16 | print(name) 17 | f.close() 18 | 19 | print("FILE READ COMPLETE") 20 | path = r"{}\assets\data\{}".format(curd,name) 21 | try: 22 | os.mkdir(path) 23 | except OSError: 24 | print ("Creation of the directory %s failed" % path) 25 | cap = cv2.VideoCapture(0) 26 | cap.set(cv2.CAP_PROP_FPS, 100) 27 | rat, frame = cap.read() 28 | 29 | count=0 30 | while count<10: 31 | 32 | rat, frame = cap.read() 33 | cv2.imwrite(r"{}\{}{}.jpg".format(path,name,count), frame) 34 | count+=1 35 | cv2.imshow('img',frame) 36 | if cv2.waitKey(1) & 0xFF == ord('q'): 37 | break 38 | time.sleep(1) 39 | 40 | 41 | cap.release() 42 | cv2.destroyAllWindows() 43 | 44 | -------------------------------------------------------------------------------- /main/py/helper.txt: -------------------------------------------------------------------------------- 1 | RA1711003010488 -------------------------------------------------------------------------------- /main/py/phoneCapture.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # coding: utf-8 3 | 4 | # In[ ]: 5 | 6 | 7 | import math 8 | import cv2 9 | import time 10 | from sklearn import neighbors 11 | import numpy as np 12 | import pandas as pd 13 | import os 14 | import os.path 15 | import pickle 16 | from PIL import Image, ImageDraw 17 | import face_recognition 18 | import requests 19 | from face_recognition.face_recognition_cli import image_files_in_folder 20 | curd = os.getcwd() 21 | 22 | try: 23 | os.mkdir("{}/models".format(curd)) 24 | except: 25 | pass 26 | names=[] 27 | ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'} 28 | 29 | 30 | # In[ ]: 31 | 32 | 33 | with open('py/url.txt', 'r') as file: 34 | liveurl = file.read().replace('\n', '') 35 | url = "{}".format(liveurl) 36 | print(liveurl) 37 | 38 | count=0 39 | while count<10: 40 | curtime = time.strftime("%Y_%m_%d-%H_%M_%S") 41 | img_resp = requests.get(url) 42 | img_arr = np.array(bytearray(img_resp.content),dtype = np.uint8) 43 | frame = cv2.imdecode(img_arr,-1) 44 | cv2.imwrite("{}/test/{}.jpg".format(curd,curtime), frame) 45 | count+=1 46 | cv2.imshow('img',frame) 47 | if cv2.waitKey(1) & 0xFF == ord('q'): 48 | break 49 | time.sleep(3) 50 | 51 | 52 | cap.release() 53 | cv2.destroyAllWindows() 54 | 55 | 56 | # In[ ]: 57 | 58 | 59 | def predict(frame, knn_clf=None, model_path=None, distance_threshold=0.6): 60 | 61 | 62 | if knn_clf is None and model_path is None: 63 | raise Exception("Must supply knn classifier either thourgh knn_clf or model_path") 64 | 65 | if knn_clf is None: 66 | with open(model_path, 'rb') as f: 67 | knn_clf = pickle.load(f) 68 | 69 | 70 | # X_img = face_recognition.load_image_file(X_img_path) 71 | X_img = frame 72 | X_face_locations = face_recognition.face_locations(X_img) 73 | 74 | if len(X_face_locations) == 0: 75 | return [] 76 | 77 | faces_encodings = face_recognition.face_encodings(X_img, known_face_locations=X_face_locations) 78 | 79 | closest_distances = knn_clf.kneighbors(faces_encodings, n_neighbors=1) 80 | are_matches = [closest_distances[0][i][0] <= distance_threshold for i in range(len(X_face_locations))] 81 | 82 | return [(pred, loc) if rec else ("unknown", loc) for pred, loc, rec in zip(knn_clf.predict(faces_encodings), X_face_locations, are_matches)] 83 | 84 | 85 | # In[ ]: 86 | 87 | 88 | def show_prediction_labels_on_image(frame, predictions): 89 | 90 | #pil_image = Image.open(img_path).convert("RGB") 91 | pil_image = Image.fromarray(frame).convert("RGB") 92 | draw = ImageDraw.Draw(pil_image) 93 | 94 | for name, (top, right, bottom, left) in predictions: 95 | draw.rectangle(((left, top), (right, bottom)), outline=(0, 0, 255)) 96 | 97 | 98 | name = name.encode("UTF-8") 99 | 100 | text_width, text_height = draw.textsize(name) 101 | draw.rectangle(((left, bottom - text_height - 10), (right, bottom)), fill=(0, 0, 255), outline=(0, 0, 255)) 102 | draw.text((left + 6, bottom - text_height - 5), name, fill=(255, 255, 255, 255)) 103 | 104 | del draw 105 | 106 | #pil_image.show() 107 | #cv2.imshow("frame",pil_image) 108 | return np.asarray(pil_image) 109 | 110 | 111 | # In[ ]: 112 | 113 | 114 | for image_file in os.listdir(r"{}\test".format(curd)): 115 | full_file_path = os.path.join(r"{}\test".format(curd), image_file) 116 | 117 | print("Looking for faces in {}".format(image_file)) 118 | frame = cv2.imread(full_file_path,-1) 119 | 120 | predictions = predict(frame, model_path=r"{}\models\trained_knn_model.clf".format(curd)) 121 | 122 | for name, (top, right, bottom, left) in predictions: 123 | print("- Found {} at ({}, {})".format(name, left, top)) 124 | names.append(name) 125 | # Display results overlaid on an image 126 | #show_prediction_labels_on_image(frame, predictions) 127 | final_img = show_prediction_labels_on_image(frame, predictions) 128 | cv2.imshow("X",final_img) 129 | cv2.waitKey(0) 130 | cv2.destroyAllWindows() 131 | 132 | 133 | # In[ ]: 134 | 135 | 136 | namesD=pd.DataFrame(names, columns=["Names"]) 137 | namesD= namesD[namesD.Names!="unknown"] 138 | 139 | attendance= pd.DataFrame(namesD.iloc[:,0].value_counts()) 140 | attendance.rename(index=str,columns={'Names': 'Count'},inplace=True) 141 | attendance["Present"] =0 142 | 143 | attendance["Count"][0] > 5 144 | for i in range(attendance.shape[0]): 145 | if(attendance["Count"][i] > 5): 146 | attendance["Present"][i] =1 147 | 148 | 149 | attendance_final=attendance.drop(['Count'],axis=1) 150 | attendance_final 151 | attendance_final.to_csv('Attendance.csv') 152 | 153 | -------------------------------------------------------------------------------- /main/py/train.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # coding: utf-8 3 | 4 | # In[1]: 5 | 6 | 7 | import math 8 | import cv2 9 | from sklearn import neighbors 10 | import numpy as np 11 | import pandas as pd 12 | import os 13 | import os.path 14 | import pickle 15 | from PIL import Image, ImageDraw 16 | import face_recognition 17 | from face_recognition.face_recognition_cli import image_files_in_folder 18 | print("in train") 19 | curd = os.getcwd() 20 | try: 21 | os.mkdir("{}/assets/models".format(curd)) 22 | except: 23 | pass 24 | ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'} 25 | 26 | 27 | # In[ ]: 28 | 29 | 30 | def train(train_dir, model_save_path=None, n_neighbors=None, knn_algo='ball_tree', verbose=False): 31 | 32 | X = [] 33 | y = [] 34 | 35 | for class_dir in os.listdir(train_dir): 36 | if not os.path.isdir(os.path.join(train_dir, class_dir)): 37 | continue 38 | 39 | for img_path in image_files_in_folder(os.path.join(train_dir, class_dir)): 40 | image = face_recognition.load_image_file(img_path) 41 | face_bounding_boxes = face_recognition.face_locations(image) 42 | 43 | if len(face_bounding_boxes) != 1: 44 | if verbose: 45 | print("Image {} not suitable for training: {}".format(img_path, "Didn't find a face" if len(face_bounding_boxes) < 1 else "Found more than one face")) 46 | else: 47 | X.append(face_recognition.face_encodings(image, known_face_locations=face_bounding_boxes)[0]) 48 | y.append(class_dir) 49 | 50 | if n_neighbors is None: 51 | n_neighbors = int(round(math.sqrt(len(X)))) 52 | if verbose: 53 | print("Chose n_neighbors automatically:", n_neighbors) 54 | 55 | knn_clf = neighbors.KNeighborsClassifier(n_neighbors=n_neighbors, algorithm=knn_algo, weights='distance') 56 | knn_clf.fit(X, y) 57 | 58 | if model_save_path is not None: 59 | with open(model_save_path, 'wb') as f: 60 | pickle.dump(knn_clf, f) 61 | 62 | return knn_clf 63 | 64 | 65 | # In[ ]: 66 | 67 | 68 | #Trainer 69 | if __name__ == "__main__": 70 | # STEP 1: Train the KNN classifier and save it to disk 71 | print("Training KNN classifier...") 72 | classifier = train("assets/data", 73 | model_save_path="{}/assets/models/trained_knn_model.clf".format(curd), 74 | n_neighbors=2) 75 | print("Training complete!") 76 | 77 | --------------------------------------------------------------------------------