image_process/opencv/SIFT/match_by_2_images_navie_ave.ipynb

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   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "img1 = cv2.imread('dataset/0012_3_1.bmp',cv2.IMREAD_GRAYSCALE) \n",
    "img2 = cv2.imread('dataset/012_8_1.bmp',cv2.IMREAD_GRAYSCALE)\n",
    "img3 = cv2.imread('dataset/0012_3_2.bmp',cv2.IMREAD_GRAYSCALE)# img1 and img3 are similar\n",
    "\n",
    "# Initiate ORB detector\n",
    "orb = cv2.ORB_create()\n",
    "\n",
    "# find the keypoints and descriptors with SIFT\n",
    "kp1, des1 = orb.detectAndCompute(img1,None)\n",
    "kp2, des2 = orb.detectAndCompute(img2,None)\n",
    "kp3, des3 = orb.detectAndCompute(img3,None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 500)"
      ]
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "len(des1),len(des2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "# get the navie average distance\n",
    "def get_ave_dist(matches):\n",
    "    ave=0    \n",
    "    for x in matches:\n",
    "        ave+=x.distance\n",
    "    return ave/len(matches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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     "execution_count": 4,
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   "source": [
    "# match 1 and 2\n",
    "\n",
    "# create BFMatcher object\n",
    "bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)\n",
    "\n",
    "# Match descriptors.\n",
    "matches = bf.match(des1,des2)\n",
    "\n",
    "get_ave_dist(matches) # the average distance"
   ]
  },
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   "cell_type": "code",
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     "execution_count": 5,
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   "source": [
    "# match 1 and 3\n",
    "\n",
    "# create BFMatcher object\n",
    "bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)\n",
    "\n",
    "# Match descriptors.\n",
    "matches = bf.match(des1,des3)\n",
    "\n",
    "get_ave_dist(matches) # the average distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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     "execution_count": 6,
     "metadata": {},
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   "source": [
    "# match 2 and 3\n",
    "\n",
    "# create BFMatcher object\n",
    "bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)\n",
    "\n",
    "# Match descriptors.\n",
    "matches = bf.match(des2,des3)\n",
    "\n",
    "get_ave_dist(matches) # the average distance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary\n",
    "\n",
    "* distance of img1 and img3 are the shortest\n",
    "* so img1 and img3 are similar, which is the truth"
   ]
  }
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