Lecture 2.6 - Color Spaces.ipynb

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    "## Let's take a closer look at color spaces\n",
    "\n",
    "You may have remembered we talked about images being stored in RGB (Red Green Blue) color Spaces. Let's take a look at that in OpenCV.\n",
    "\n",
    "### First thing to remember about OpenCV's RGB is that it's BGR (I know, this is annoying)\n",
    "\n",
    "Let's look at the image shape again. The '3L' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "image = cv2.imread('./images/input.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "### Let's look at the individual color levels for the first pixel (0,0)"
   ]
  },
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     "text": [
      "13 19 32\n",
      "(830L, 1245L, 3L)\n"
     ]
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   ],
   "source": [
    "# BGR Values for the first 0,0 pixel\n",
    "B, G, R = image[10, 50] \n",
    "print B, G, R\n",
    "print image.shape"
   ]
  },
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   "source": [
    "Let's see what happens when we convert it to grayscale"
   ]
  },
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      "(830L, 1245L)\n",
      "22\n"
     ]
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   ],
   "source": [
    "gray_img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "print gray_img.shape\n",
    "print gray_img[10, 50] "
   ]
  },
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    "It's now only 2 dimensions. Each pixel coordinate has only one value (previously 3) with a range of 0 to 255"
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       "22"
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   "source": [
    "gray_img[10, 50] "
   ]
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   "cell_type": "markdown",
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   "source": [
    "### Another useful color space is HSV \n",
    "Infact HSV is very useful in color filtering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(830L, 1245L, 3L)\n"
     ]
    }
   ],
   "source": [
    "#H: 0 - 180, S: 0 - 255, V: 0 - 255\n",
    "\n",
    "image = cv2.imread('./images/input.jpg')\n",
    "\n",
    "hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n",
    "\n",
    "print image.shape\n",
    "\n",
    "cv2.imshow('HSV image', hsv_image)\n",
    "cv2.imshow('Hue channel', hsv_image[:, :, 0])\n",
    "cv2.imshow('Saturation channel', hsv_image[:, :, 1])\n",
    "cv2.imshow('Value channel', hsv_image[:, :, 2])\n",
    "\n",
    "cv2.waitKey()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's now explore lookng at individual channels in an RGB image"
   ]
  },
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   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "(830L, 1245L)\n"
     ]
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   ],
   "source": [
    "image = cv2.imread('./images/input.jpg')\n",
    "\n",
    "# OpenCV's 'split' function splites the image into each color index\n",
    "B, G, R = cv2.split(image)\n",
    "\n",
    "print B.shape\n",
    "\n",
    "cv2.imshow(\"Original\", image)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n",
    "\n",
    "cv2.imshow(\"Red\", R)\n",
    "cv2.imshow(\"Green\", G)\n",
    "cv2.imshow(\"Blue\", B)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n",
    "\n",
    "# Let's re-make the original image, \n",
    "merged = cv2.merge([B, G, R]) \n",
    "cv2.imshow(\"Merged\", merged) \n",
    "\n",
    "# Let's amplify a particular color\n",
    "merged = cv2.merge([B, G, R+30])\n",
    "cv2.imshow(\"Merged with Blue Amplified\", merged) \n",
    "\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
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   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "B, G, R = cv2.split(image)\n",
    "\n",
    "# Let's create a matrix of zeros \n",
    "# with dimensions of the image h x w  \n",
    "zeros = np.zeros(image.shape[0:2], dtype = \"uint8\")\n",
    "\n",
    "cv2.imshow(\"Red\", cv2.merge([zeros, zeros, R]))\n",
    "cv2.imshow(\"Green\", cv2.merge([zeros, G, zeros]))\n",
    "cv2.imshow(\"Blue\", cv2.merge([B, zeros, zeros]))\n",
    "\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
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   "cell_type": "code",
   "execution_count": 20,
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       "(830L, 1245L)"
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   "source": [
    "image.shape[0:2]\n"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### You can view a list of color converisons here, but keep in mind you won't ever use or need many of these\n",
    "\n",
    "http://docs.opencv.org/trunk/d7/d1b/group__imgproc__misc.html#ga4e0972be5de079fed4e3a10e24ef5ef0"
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