Lecture 5.5 - SIFT, SURF, FAST, BRIEF & ORB.ipynb

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   "source": [
    "# Feature Detection"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "#### The SIFT & SURF algorithms are patented by their respective creators, and while they are free to use in academic and research settings, you should technically be obtaining a license/permission from the creators if you are using them in a commercial (i.e. for-profit) application."
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## SIFT\n",
    "\n",
    "http://www.inf.fu-berlin.de/lehre/SS09/CV/uebungen/uebung09/SIFT.pdf"
   ]
  },
  {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1890\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "image = cv2.imread('images/input.jpg')\n",
    "gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "#create soft feature detector\n",
    "sift = cv2.SIFT()\n",
    "\n",
    "keypoints = sift.detect(gray, None)\n",
    "print len(keypoints)\n",
    "\n",
    "#image = cv2.drawKeypoints(image, keypoints, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n",
    "image = cv2.drawKeypoints(image, keypoints, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n",
    "\n",
    "cv2.imshow('image', image)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SURF\n",
    "\n",
    "http://www.vision.ee.ethz.ch/~surf/eccv06.pdf"
   ]
  },
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     "text": [
      "74\n"
     ]
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   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "image = cv2.imread('images/input.jpg')\n",
    "gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "surf = cv2.SURF()\n",
    "\n",
    "\n",
    "surf.hessianThreshold = 7550\n",
    "keypoints, descriptors = surf.detectAndCompute(gray,None)\n",
    "\n",
    "print len(keypoints)\n",
    "\n",
    "image = cv2.drawKeypoints(image,keypoints, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n",
    "\n",
    "cv2.imshow('image', image)\n",
    "cv2.waitKey()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FAST\n",
    "\n",
    "https://www.edwardrosten.com/work/rosten_2006_machine.pdf\n",
    "http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/AV1011/AV1FeaturefromAcceleratedSegmentTest.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8963\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "image = cv2.imread('images/input.jpg')\n",
    "gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "fast = cv2.FastFeatureDetector()\n",
    "\n",
    "keypoints = fast.detect(gray, None)\n",
    "print len(keypoints)\n",
    "\n",
    "image = cv2.drawKeypoints(image, keypoints, flags =cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n",
    "\n",
    "cv2.imshow('image', image)\n",
    "cv2.waitKey()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BRIEF\n",
    "\n",
    "http://cvlabwww.epfl.ch/~lepetit/papers/calonder_pami11.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of keypoints Detected:  8738\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "image = cv2.imread('images/input.jpg')\n",
    "gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "# Create FAST detector object\n",
    "fast = cv2.FastFeatureDetector()\n",
    "\n",
    "# Create BRIEF extractor object\n",
    "brief = cv2.DescriptorExtractor_create(\"BRIEF\")\n",
    "\n",
    "# Determine key points\n",
    "keypoints = fast.detect(gray, None)\n",
    "\n",
    "# Obtain descriptors and new final keypoints using BRIEF\n",
    "keypoints, descriptors = brief.compute(gray, keypoints)\n",
    "print \"Number of keypoints Detected: \", len(keypoints)\n",
    "\n",
    "# Draw rich keypoints on input image\n",
    "image = cv2.drawKeypoints(image, keypoints, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n",
    "                                    \n",
    "cv2.imshow('Feature Method - BRIEF', image)\n",
    "cv2.waitKey()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Oriented FAST and Rotated BRIEF (ORB)\n",
    "http://www.willowgarage.com/sites/default/files/orb_final.pdf"
   ]
  },
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   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "image = cv2.imread('images/input.jpg')\n",
    "gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "cv2.imshow('image', image)\n",
    "cv2.waitKey()\n",
    "\n",
    "\n",
    "orb = cv2.ORB(5)\n",
    "\n",
    "keypoints = orb.detect(gray, None)\n",
    "\n",
    "# Obtain the descriptors\n",
    "keypoints, descriptors = orb.compute(gray, keypoints)\n",
    "\n",
    "print len(keypoints)\n",
    "\n",
    "image = cv2.drawKeypoints(image, keypoints,flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n",
    "\n",
    "\n",
    "cv2.imshow('image', image)\n",
    "cv2.waitKey()\n",
    "cv2.destroyAllWindows()"
   ]
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