lecture-2 filters.ipynb

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    "Image = \"/mnt/Study_Storage/Documents/Datasets/Images/standard_test_images/lena_gray_256.tif\" #gray\n",
    "\n",
    "import cv2 \n",
    "import skimage  \n",
    "import numpy as np \n",
    "\n",
    "\n",
    "def show(img):\n",
    "    cv2.imshow(\"image\", img)\n",
    "    cv2.waitKey(0)\n",
    "    cv2.destroyAllWindows()"
   ]
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    "img = cv2.imread(Image, 0)\n",
    "show(img)"
   ]
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    "filter_x = [[-1,0,1],\n",
    "            [-1,0,1],\n",
    "            [-1,0,1]]\n",
    "\n",
    "filter_x = np.array(filter_x)/3\n",
    "\n",
    "der_x = skimage.filters.edges.convolve(img, weights= filter_x)\n",
    "show(der_x)\n",
    "\n",
    "filter_y = [[-1,-1,-1],\n",
    "            [0,0,0],\n",
    "            [1,1,1]]\n",
    "\n",
    "filter_y =  np.array(filter_y)/3\n",
    "\n",
    "der_y = skimage.filters.edges.convolve(img, weights=filter_y)\n",
    "show(der_y)"
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    "\n",
    "filter_shift = np.array([[0,0,0],\n",
    "                         [0,0,1],\n",
    "                         [0,0,0]])\n",
    "\n",
    "show(skimage.filters.edges.convolve(img, weights= filter_shift, mode = \"constant\"))"
   ]
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   "source": [
    "filter_mean = (1/9)*np.array([[1,1,1],\n",
    "                              [1,1,1],\n",
    "                              [1,1,1]])\n",
    "\n",
    "show(skimage.filters.edges.convolve(img, filter_mean))"
   ]
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   "outputs": [],
   "source": [
    "show(skimage.filters.gaussian(img, sigma= 2))"
   ]
  },
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   "execution_count": 7,
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   "source": [
    "show(skimage.filters.prewitt(img))"
   ]
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   "source": [
    "from skimage import data\n",
    "camera = data.camera()\n",
    "show(camera)\n",
    "\n",
    "from skimage import filters\n",
    "edges = filters.prewitt(camera)\n",
    "show(edges)"
   ]
  },
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   "execution_count": 13,
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   "source": [
    "show(filters.median(img))"
   ]
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   "source": [
    "show(filters.edges.laplace(image=img))"
   ]
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   "source": [
    "\"\"\" sharpening filter\"\"\"\n",
    "\n",
    "filter1 = [[0,0,0],\n",
    "           [0,2,0],\n",
    "           [0,0,0]]\n",
    "\n",
    "filter1 = np.array(filter1)\n",
    "filter2 = (1/9)*np.ones(shape = [3,3])\n",
    "\n",
    "img1 = filters.edges.convolve(img, weights= filter1)\n",
    "img2 = filters.edges.convolve(img, weights= filter2)\n",
    "show(img1-img2)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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