2019-12-08 03:13:45 +00:00
|
|
|
{
|
|
|
|
|
"cells": [
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"source": [
|
|
|
|
|
"<h2><center>edge detection</center></h2>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2019-12-08 12:02:15 +00:00
|
|
|
"execution_count": 3,
|
2019-12-08 03:13:45 +00:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
2019-12-08 12:02:15 +00:00
|
|
|
"<matplotlib.image.AxesImage at 0x7f6a9f8a01d0>"
|
2019-12-08 03:13:45 +00:00
|
|
|
]
|
|
|
|
|
},
|
2019-12-08 12:02:15 +00:00
|
|
|
"execution_count": 3,
|
2019-12-08 03:13:45 +00:00
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "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
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"import cv2\n",
|
|
|
|
|
"import math\n",
|
|
|
|
|
"import numpy as np\n",
|
|
|
|
|
"from scipy.signal import convolve2d\n",
|
|
|
|
|
"from matplotlib import pyplot as plt\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"img = cv2.imread('sheep.png')\n",
|
|
|
|
|
"plt.imshow(img[:,:,::-1])"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"source": [
|
|
|
|
|
"sobel filter"
|
|
|
|
|
]
|
|
|
|
|
},
|
2019-12-08 12:02:15 +00:00
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 4,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<matplotlib.image.AxesImage at 0x7f6a9c204bd0>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"execution_count": 4,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "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
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"kx = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]) / 4\n",
|
|
|
|
|
"ky = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]) / 4\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) / 256\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"blur = convolve2d(grey, np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]]) / 9)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"dx = convolve2d(blur, kx)\n",
|
|
|
|
|
"dy = convolve2d(blur, ky)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"x = np.concatenate((dx, dy), axis=1)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"plt.imshow(x)"
|
|
|
|
|
]
|
|
|
|
|
},
|
2019-12-08 03:13:45 +00:00
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 68,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<matplotlib.image.AxesImage at 0x7f758913b8d0>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"execution_count": 68,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "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
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"kx = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]) / 4\n",
|
|
|
|
|
"ky = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]) / 4\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) / 256\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"blur = convolve2d(grey, np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]]) / 9)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"dx_grey = convolve2d(grey, kx)\n",
|
|
|
|
|
"dy_grey = convolve2d(grey, ky)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"dx_blur = convolve2d(blur, kx)\n",
|
|
|
|
|
"dy_blur = convolve2d(blur, ky)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"dx = np.concatenate((dx_grey, dx_blur[1:-1, 1:-1]), axis=1)\n",
|
|
|
|
|
"dy = np.concatenate((dy_grey, dy_blur[1:-1, 1:-1]), axis=1)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"x = np.sqrt(pow(dx, 2) + pow(dy, 2))\n",
|
|
|
|
|
"plt.imshow(x>0.08)"
|
|
|
|
|
]
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"metadata": {
|
|
|
|
|
"kernelspec": {
|
|
|
|
|
"display_name": "Python 3",
|
|
|
|
|
"language": "python",
|
|
|
|
|
"name": "python3"
|
|
|
|
|
},
|
|
|
|
|
"language_info": {
|
|
|
|
|
"codemirror_mode": {
|
|
|
|
|
"name": "ipython",
|
|
|
|
|
"version": 3
|
|
|
|
|
},
|
|
|
|
|
"file_extension": ".py",
|
|
|
|
|
"mimetype": "text/x-python",
|
|
|
|
|
"name": "python",
|
|
|
|
|
"nbconvert_exporter": "python",
|
|
|
|
|
"pygments_lexer": "ipython3",
|
|
|
|
|
"version": "3.7.4"
|
|
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"nbformat": 4,
|
|
|
|
|
"nbformat_minor": 4
|
|
|
|
|
}
|
