Lecture 3.13 - Edge Detection & Image Gradients.ipynb

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    "## Edge Detection & Image Gradients"
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    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "image = cv2.imread('images/input.jpg',0)\n",
    "height, width = image.shape\n",
    "\n",
    "sobel_x = cv2.Sobel(image, cv2.CV_64F,0,1,ksize=5)\n",
    "sobel_y = cv2.Sobel(image, cv2.CV_64F,1,0,ksize=5)\n",
    "sobel_both = cv2.bitwise_or(sobel_x, sobel_y)\n",
    "\n",
    "cv2.imshow('image', image)\n",
    "cv2.waitKey(0)\n",
    "\n",
    "cv2.imshow('sobelx', sobel_x)\n",
    "cv2.waitKey(0)\n",
    "\n",
    "cv2.imshow('sobely', sobel_y)\n",
    "cv2.waitKey(0)\n",
    "\n",
    "cv2.imshow('sobelCombined', sobel_both)\n",
    "cv2.waitKey(0)\n",
    "\n",
    "laplacian = cv2.Laplacian(image, cv2.CV_64F)\n",
    "cv2.imshow('laplacian', laplacian)\n",
    "cv2.waitKey(0)\n",
    "\n",
    "\n",
    "##  Then, we need to provide two values: threshold1 and threshold2. Any gradient value larger than threshold2\n",
    "# is considered to be an edge. Any value below threshold1 is considered not to be an edge. \n",
    "#Values in between threshold1 and threshold2 are either classiﬁed as edges or non-edges based on how their \n",
    "#intensities are “connected”. In this case, any gradient values below 60 are considered non-edges\n",
    "#whereas any values above 120 are considered edges.\n",
    "\n",
    "\n",
    "# Canny Edge Detection uses gradient values as thresholds\n",
    "# The first threshold gradient\n",
    "canny = cv2.Canny()\n",
    "cv2.destroyAllWindows()"
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