七、图像上的算数运算.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>图像上的算数运算</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>目标</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ul>\n",
    "    <li>学习图像上的算术 算 加法 减法 位 算等。</li>\n",
    "    <li>我们将 学习的函数有 cv2.add() cv2.addWeighted() 等。\n",
    "</li>\n",
    "    </ul>"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "<h3>7.1 图像加法</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "你可以使用OpenCv中的函数 cv2.add() 将两幅图像进行加法运算，当然也可以直接使用 numpy，如 res=img1+img。两幅图像的大小类型必 一致 或者第二个图像可以使一个简单的标量值。"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4> 注意 </h4>OpenCV 中的加法与 Numpy 的加法是有所不同的。OpenCV 的加法是一种饱和操作，而 Numpy 的加法是一种模操作。"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "例如，思考以下示例:"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.uint8([250])\n",
    "y = np.uint8([10])\n",
    "\n",
    "print (cv2.add(x,y)) # 250+10 = 260 => 255\n",
    "[[255]]\n",
    "\n",
    "print (x+y)         # 250+10 = 260 % 256 = 4\n",
    "[4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 这种差别在你对两幅图像进行加法时会更加明显。OpenCV 的结果会更好一点。所以我们尽量使用 OpenCV 中的函数。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>7.2 图像混合</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 其实也是加法，但是不同的是两幅图像的权重不同 ，就会给人一种混\n",
    "合或者透明的感觉。图像混合的运算公式如下：\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "g(x) = (1 - \\alpha)f_{0}(x) + \\alpha f_{1}(x)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  修改 α 的值（0 → 1 ）可以实现非常酷的混合。\n",
    "现在我们把两幅图混合在一起。第一幅图的权重是 0.7 第二幅图的权重是 0.3。函数 cv2.addWeighted() 可以按下面的公式对图片进行混合操作。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "dst = \\alpha \\cdot img1 + \\beta \\cdot img2 + \\gamma\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  这里γ 的取值为 0。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img1 = cv2.imread('ml.png')\n",
    "img2 = cv2.imread('opencv_logo.jpg')\n",
    "\n",
    "dst = cv2.addWeighted(img1,0.7,img2,0.3,0)\n",
    "\n",
    "cv2.imshow('dst',dst)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>7.3 按位运算</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  包括的按位操作有：AND OR NOT XOR 等。当我们提取图像的一部分选择非矩形 ROI 时这些操作会很有用（下一章你就会明白）。下面的例子就是教给我们如何改变一幅图的特定区域。\n",
    "我想把 OpenCV 的标志放到另一幅图像上。如果我使用加法颜色会改变，如果使用混合，会得到透明效果，但是我不想透明。如果他是矩形我可以像上一章一样使用 ROI。但是他不是矩形。但是我们可以通过下面的按位运算实现 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载两张图片\n",
    "img1 = cv2.imread('messi5.jpg')\n",
    "img2 = cv2.imread('opencv_logo.png')\n",
    "\n",
    "# 我想将logo 放到图片的左上角, 所以我创建一个 ROI\n",
    "rows,cols,channels = img2.shape\n",
    "roi = img1[0:rows, 0:cols ]\n",
    "\n",
    "# Now create a mask of logo and create its inverse mask also\n",
    "img2gray = cv2.cvtColor(img2,cv2.COLOR_BGR2GRAY)\n",
    "ret, mask = cv2.threshold(img2gray, 10, 255, cv2.THRESH_BINARY)\n",
    "mask_inv = cv2.bitwise_not(mask)\n",
    "\n",
    "# Now black-out the area of logo in ROI\n",
    "img1_bg = cv2.bitwise_and(roi,roi,mask = mask_inv)\n",
    "\n",
    "# Take only region of logo from logo image.\n",
    "img2_fg = cv2.bitwise_and(img2,img2,mask = mask)\n",
    "\n",
    "# Put logo in ROI and modify the main image\n",
    "dst = cv2.add(img1_bg,img2_fg)\n",
    "img1[0:rows, 0:cols ] = dst\n",
    "\n",
    "cv2.imshow('res',img1)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n"
   ]
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