Face Detect

Face.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3b9ee3f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e83447d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def assure_path_exists(path):\n",
+    "    dir = os.path.dirname(path)\n",
+    "    if not os.path.exists(dir):\n",
+    "        os.makedirs(dir)\n",
+    "        \n",
+    "face_id = input('enter Id')\n",
+    "vid_cam = cv2.VideoCapture(0)\n",
+    "face_detector = cv2.CascadeClassifier(\"C:/Users/Thila/AppData/Local/Programs/Python/Python39/Scripts/face/haarcascade_frontalface_default.xml\")\n",
+    "count = 0\n",
+    "assure_path_exists(\"C:/Users/Thila/AppData/Local/Programs/Python/Python39/Scripts/face/data\")\n",
+    "\n",
+    "while True:\n",
+    "    _, image_frame = vid_cam.read()\n",
+    "    gray = cv2.cvtColor(image_frame, cv2.COLOR_BGR2GRAY)\n",
+    "    faces = face_detector.detectMultiScale(gray,1.3,5)\n",
+    "    for (x,y,w,h) in faces:\n",
+    "        cv2.rectangle(image_frame, (x, y), (x + w, y + h), (255, 0, 0),2)\n",
+    "        count +=1\n",
+    "        cv2.imwrite(\"C:/Users/Thila/AppData/Local/Programs/Python/Python39/Scripts/face/data/User.\" + str(face_id) + \".\"  + str(count) + \".jpg\", gray [y:y+h, x:x+w])\n",
+    "        cv2.imshow(\"frame\", image_frame)\n",
+    "        \n",
+    "    if cv2.waitKey(1)==13 or int(count)==100:\n",
+    "        break\n",
+    "        \n",
+    "    elif count >=50:\n",
+    "        print(\"Suceesfully Captured\")\n",
+    "        break\n",
+    "        \n",
+    "vid_cam.release()\n",
+    "cv2.destroyAllWindows()\n",
+    "\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e8c70d36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os, cv2\n",
+    "import numpy as np\n",
+    "from PIL import Image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4416305d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "recognizer = cv2.face.LBPHFaceRecognizer_create()\n",
+    "detector = cv2.CascadeClassifier(\"C:/Users/Thila/AppData/Local/Programs/Python/Python39/Scripts/face/haarcascade_frontalface_default.xml\")\n",
+    "\n",
+    "def getImagesAndLabels(path):\n",
+    "    imagePaths=[os.path.join(path,f) for f in os.listdir(path)]\n",
+    "    faceSamples=[]\n",
+    "    Ids=[]\n",
+    "    for imagePath in imagePaths:\n",
+    "        pilImage=Image.open(imagePath).convert('L')\n",
+    "        imageNp=np.array(pilImage,'uint8')\n",
+    "        Id=int(os.path.split(imagePath)[-1].split(\".\")[1])\n",
+    "        faces=detector.detectMultiScale(imageNp)\n",
+    "        for(x,y,w,h) in faces:\n",
+    "            faceSamples.append(imageNp[y:y+h,x:x+w])\n",
+    "            Ids.append(Id)\n",
+    "    return faceSamples,Ids\n",
+    "\n",
+    "faces,Ids = getImagesAndLabels(\"C:/Users/Thila/AppData/Local/Programs/Python/Python39/Scripts/face/data\")\n",
+    "s = recognizer.train(faces, np.array(Ids))\n",
+    "print(\"Successfully trained\")\n",
+    "recognizer.write(\"C:/Users/Thila/AppData/Local/Programs/Python/Python39/Scripts/face/trainer.yml\")\n",
+    "            \n",
+    "         "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c2cc4f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "343b84ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "face_detector =cv2.CascadeClassifier(\"C:/Users/Thila/AppData/Local/Programs/Python/Python39/Scripts/face/haarcascade_frontalface_default.xml\")\n",
+    "cap = cv2.VideoCapture(0)\n",
+    "recognizer = cv2.face.LBPHFaceRecognizer_create()\n",
+    "recognizer.read(\"C:/Users/Thila/AppData/Local/Programs/Python/Python39/Scripts/face/trainer.yml\")\n",
+    "id =0\n",
+    "font = cv2.FONT_HERSHEY_SIMPLEX\n",
+    "while True:\n",
+    "    ret, img = cap.read()\n",
+    "    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
+    "    faces = face_detector.detectMultiScale(gray,1.3,5)\n",
+    "    for(x,y,w,h) in faces:\n",
+    "        roi_gray =gray[y:y+h, x:x+w]\n",
+    "        cv2.rectangle(img, (x,y), (x+w, y+h),(255,0,0),2)\n",
+    "        id, conf = recognizer.predict(roi_gray)\n",
+    "        if(id==1):\n",
+    "            id=\"Gihan\"\n",
+    "        elif(id==2):\n",
+    "            id=\"Gayumi\"\n",
+    "        else:\n",
+    "            id= \"Unknow face\"\n",
+    "        cv2.putText(img, str(id) + \"\" + str(conf), (x, y - 10), font, 0.55, (120,255,120),1)\n",
+    "        #cv2.cv.PutText(cv2.cv.fromarray(img),str(id),(x,y+h),font,255)\n",
+    "        \n",
+    "    cv2.imshow(\"frame\", img)\n",
+    "    if cv2.waitKey(100) & 0xFF == ord(\"q\"):\n",
+    "        break\n",
+    "vid_cam.release()\n",
+    "cv2.destroyAllWindows()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cdfb3133",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
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