cv2-calibrate-realtime.ipynb

gistfile1.txt

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calibrate camera with checker board"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Also see https://youtu.be/TXdOZwAE9lc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import cv2\n",
    "cv2.startWindowThread() # needed to use highgui from opencv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## capture calibration images (total number=num_capture_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "#cv2.startWindowThread() # needed to use highgui from opencv\n",
    "cv2.namedWindow('frame',cv2.WINDOW_NORMAL)\n",
    "\n",
    "num_capture_images = 35\n",
    "# termination criteria\n",
    "criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)\n",
    "\n",
    "# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)\n",
    "objp = np.zeros((6*7,3), np.float32)\n",
    "objp[:,:2] = np.mgrid[0:7,0:6].T.reshape(-1,2)\n",
    "\n",
    "# Arrays to store object points and image points from all the images.\n",
    "objpoints = [] # 3d point in real world space\n",
    "imgpoints = [] # 2d points in image plane.\n",
    "\n",
    "\n",
    "cap = cv2.VideoCapture(0)\n",
    "count = 0\n",
    "\n",
    "while(True):\n",
    "    # Capture frame-by-frame\n",
    "    ret, img = cap.read()\n",
    "#    img = cv2.resize(img,(640,480))\n",
    "    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "    \n",
    "    # Find the chess board corners\n",
    "    ret, corners = cv2.findChessboardCorners(gray, (7,6),None)\n",
    "\n",
    "    font = cv2.FONT_HERSHEY_SIMPLEX\n",
    "    cv2.putText(img,str(count),(10,500), font, 10,(255,255,255),2,cv2.LINE_AA)\n",
    "    cv2.imshow('img',img)\n",
    "    # If found, add object points, image points (after refining them)\n",
    "    if ret == True:\n",
    "        objpoints.append(objp)\n",
    "\n",
    "        corners2 = cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)\n",
    "        imgpoints.append(corners2)\n",
    "\n",
    "        # Draw and display the corners\n",
    "        img = cv2.drawChessboardCorners(img, (7,6), corners2,ret)\n",
    "        cv2.imshow('img',img)\n",
    "        cv2.waitKey(500)\n",
    "        count=count+1\n",
    "\n",
    "    if count > num_capture_images:\n",
    "        break\n",
    "    if cv2.waitKey(50) & 0xFF == ord('q'):\n",
    "        break\n",
    "\n",
    "# When everything done, release the capture\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## computer camera matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1],None,None)\n",
    "\n",
    "h,  w = img.shape[:2]\n",
    "newcameramtx, roi=cv2.getOptimalNewCameraMatrix(mtx,dist,(w,h),1,(w,h))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Capture video stream and display it with radial distortion removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "#cv2.startWindowThread() # needed to use highgui from opencv\n",
    "cv2.namedWindow('frame',cv2.WINDOW_NORMAL)\n",
    "\n",
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "while(True):\n",
    "    # Capture frame-by-frame\n",
    "    ret, img = cap.read()\n",
    "\n",
    "    # undistort\n",
    "    dst = cv2.undistort(img, mtx, dist, None, newcameramtx)\n",
    "\n",
    "    # crop the image\n",
    "    x,y,w,h = roi # getOptimalNewCameraMatrix seems to have trouble getting the correct roi\n",
    "    dst = dst[y:y+h, x:x+w]\n",
    "\n",
    "    # Display the resulting frame\n",
    "    cv2.imshow('frame',dst)\n",
    "    cv2.setWindowProperty(\"frame\", cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN) #cv2.WND_PROP_FULLSCREEN,cv2.WINDOW_FULLSCREEN)\n",
    "    if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "        break\n",
    "\n",
    "# When everything done, release the capture\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Capture video and display it unprocessed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import platform\n",
    "\n",
    "cap=cv2.VideoCapture(0)\n",
    "\n",
    "while (True):\n",
    "    \n",
    "    ret,frame=cap.read()\n",
    "    \n",
    "    cv2.namedWindow(\"camera\",cv2.WND_PROP_FULLSCREEN)\n",
    "    cv2.setWindowProperty(\"camera\",cv2.WND_PROP_FULLSCREEN,cv2.WINDOW_FULLSCREEN)\n",
    "    cv2.imshow('camera',frame)\n",
    "    \n",
    "    if cv2.waitKey(1) &0xFF == ord('q'): # press q or ESC to quit. You probably need to hit the screen first\n",
    "        break\n",
    "\n",
    "        \n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}