2019-12-08 03:13:45 +00:00
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Basic opencv\n",
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"=================="
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]
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},
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{
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"cell_type": "code",
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2019-12-08 22:19:30 +00:00
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"execution_count": 10,
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2019-12-08 03:13:45 +00:00
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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2019-12-08 22:19:30 +00:00
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"<matplotlib.image.AxesImage at 0x7fe60c10f810>"
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2019-12-08 03:13:45 +00:00
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]
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},
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2019-12-08 22:19:30 +00:00
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"execution_count": 10,
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2019-12-08 03:13:45 +00:00
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"import cv2\n",
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"import numpy as np\n",
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"from matplotlib import pyplot as plt\n",
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"\n",
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"img = cv2.imread('sheep.png')\n",
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"\n",
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"plt.imshow(img[:,:,::-1])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"load images (matlab equiv)"
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]
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},
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{
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"cell_type": "code",
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2019-12-08 22:19:30 +00:00
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"execution_count": 20,
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2019-12-08 03:13:45 +00:00
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"numpy.ndarray"
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]
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},
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2019-12-08 22:19:30 +00:00
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"execution_count": 20,
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2019-12-08 03:13:45 +00:00
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"type(img)"
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]
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},
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{
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"cell_type": "code",
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2019-12-08 22:19:30 +00:00
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"execution_count": 21,
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2019-12-08 03:13:45 +00:00
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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2019-12-08 22:19:30 +00:00
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"array([82, 88, 87], dtype=uint8)"
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2019-12-08 03:13:45 +00:00
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]
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},
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2019-12-08 22:19:30 +00:00
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"execution_count": 21,
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2019-12-08 03:13:45 +00:00
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"px = img[100, 100]\n",
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"px"
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]
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},
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{
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"cell_type": "code",
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2019-12-08 22:19:30 +00:00
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"execution_count": 22,
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2019-12-08 03:13:45 +00:00
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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2019-12-08 22:19:30 +00:00
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"<matplotlib.image.AxesImage at 0x7fd67a6bc490>"
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2019-12-08 03:13:45 +00:00
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]
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},
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2019-12-08 22:19:30 +00:00
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"execution_count": 22,
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2019-12-08 03:13:45 +00:00
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "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
|
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|
"text/plain": [
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|
|
|
|
"<Figure size 432x288 with 1 Axes>"
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|
]
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},
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"metadata": {
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|
"needs_background": "light"
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},
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"output_type": "display_data"
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}
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|
],
|
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"source": [
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|
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|
"img[100:300, 100:200] = [255, 0, 0]\n",
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"img[:, :, 2] = 0\n",
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|
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"plt.imshow(img[:,:,::-1])"
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]
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|
},
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{
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|
"cell_type": "markdown",
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|
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|
"metadata": {},
|
|
|
|
|
"source": [
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|
|
|
"set pixels red"
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|
]
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|
},
|
|
|
|
|
{
|
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|
|
|
"cell_type": "code",
|
2019-12-08 22:19:30 +00:00
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|
|
"execution_count": 23,
|
2019-12-08 03:13:45 +00:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
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|
|
{
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
|
|
|
|
"(213, 320, 3)\n",
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|
|
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|
"uint8\n"
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|
|
|
|
]
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"print(img.shape)\n",
|
|
|
|
|
"print(img.dtype)"
|
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|
|
]
|
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|
},
|
|
|
|
|
{
|
|
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|
|
"cell_type": "code",
|
2019-12-08 22:19:30 +00:00
|
|
|
"execution_count": 24,
|
2019-12-08 03:13:45 +00:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"(array([[0, 0, 0, ..., 0, 0, 0],\n",
|
|
|
|
|
" [0, 0, 0, ..., 0, 0, 0],\n",
|
|
|
|
|
" [0, 0, 0, ..., 0, 0, 0],\n",
|
|
|
|
|
" ...,\n",
|
|
|
|
|
" [0, 0, 0, ..., 0, 0, 0],\n",
|
|
|
|
|
" [0, 0, 0, ..., 0, 0, 0],\n",
|
|
|
|
|
" [0, 0, 0, ..., 0, 0, 0]], dtype=uint8),\n",
|
|
|
|
|
" array([[103, 103, 104, ..., 104, 103, 102],\n",
|
|
|
|
|
" [103, 102, 104, ..., 104, 105, 102],\n",
|
|
|
|
|
" [101, 101, 103, ..., 105, 103, 102],\n",
|
|
|
|
|
" ...,\n",
|
|
|
|
|
" [104, 102, 86, ..., 116, 87, 86],\n",
|
|
|
|
|
" [117, 90, 79, ..., 83, 71, 100],\n",
|
|
|
|
|
" [210, 97, 87, ..., 84, 108, 101]], dtype=uint8),\n",
|
|
|
|
|
" array([[ 53, 53, 53, ..., 49, 52, 49],\n",
|
|
|
|
|
" [ 51, 50, 52, ..., 46, 46, 45],\n",
|
|
|
|
|
" [ 50, 48, 52, ..., 49, 49, 47],\n",
|
|
|
|
|
" ...,\n",
|
|
|
|
|
" [ 51, 42, 19, ..., 57, 35, 35],\n",
|
|
|
|
|
" [ 77, 37, 19, ..., 30, 28, 43],\n",
|
|
|
|
|
" [173, 52, 26, ..., 27, 56, 46]], dtype=uint8))"
|
|
|
|
|
]
|
|
|
|
|
},
|
2019-12-08 22:19:30 +00:00
|
|
|
"execution_count": 24,
|
2019-12-08 03:13:45 +00:00
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"b, g, r = cv2.split(img)\n",
|
|
|
|
|
"r, g, b"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"source": [
|
|
|
|
|
"fairly expensive"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2019-12-08 22:19:30 +00:00
|
|
|
"execution_count": 25,
|
2019-12-08 03:13:45 +00:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
|
|
|
|
"img = cv2.merge((b, g, r))"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2019-12-08 22:19:30 +00:00
|
|
|
"execution_count": 26,
|
2019-12-08 03:13:45 +00:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
|
|
|
|
"BLUE = [255, 0, 0]\n",
|
|
|
|
|
"constant = cv2.copyMakeBorder(img, 10, 10, 10, 10, cv2.BORDER_CONSTANT, value=BLUE)"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2019-12-08 22:19:30 +00:00
|
|
|
"execution_count": 27,
|
2019-12-08 03:13:45 +00:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"array([[255]], dtype=uint8)"
|
|
|
|
|
]
|
|
|
|
|
},
|
2019-12-08 22:19:30 +00:00
|
|
|
"execution_count": 27,
|
2019-12-08 03:13:45 +00:00
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"x = np.uint8([250])\n",
|
|
|
|
|
"y = np.uint8([10])\n",
|
|
|
|
|
"cv2.add(x, y)"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"source": [
|
|
|
|
|
"use numpy integers"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2019-12-08 22:19:30 +00:00
|
|
|
"execution_count": 28,
|
2019-12-08 03:13:45 +00:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
|
|
|
|
"blended = cv2.addWeighted(img[0:200, 0:200], 0.8, img[0:200, 0:200], 0.2, 0)"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2019-12-08 22:19:30 +00:00
|
|
|
"execution_count": 29,
|
2019-12-08 03:13:45 +00:00
|
|
|
"metadata": {},
|
2019-12-08 22:19:30 +00:00
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<matplotlib.image.AxesImage at 0x7fd67a432e90>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"execution_count": 29,
|
|
|
|
|
"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"
|
|
|
|
|
}
|
|
|
|
|
],
|
2019-12-08 03:13:45 +00:00
|
|
|
"source": [
|
2019-12-08 22:19:30 +00:00
|
|
|
"img = cv2.imread('sheep.png')\n",
|
|
|
|
|
"img = cv2.normalize(img.astype('float'), None, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F)\n",
|
|
|
|
|
"grayscale = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
|
|
|
|
|
"plt.imshow(grayscale)"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 5,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<matplotlib.image.AxesImage at 0x7fe60db583d0>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"execution_count": 5,
|
|
|
|
|
"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": [
|
|
|
|
|
"img = cv2.imread('sheep.png')\n",
|
|
|
|
|
"x = np.concatenate((img, img), axis=1)\n",
|
|
|
|
|
"plt.imshow(x)"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 6,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<matplotlib.image.AxesImage at 0x7fe60dac5890>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"execution_count": 6,
|
|
|
|
|
"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": [
|
|
|
|
|
"img = cv2.imread('sheep.png')\n",
|
|
|
|
|
"x = np.concatenate((img, img), axis=0)\n",
|
|
|
|
|
"plt.imshow(x)"
|
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|
|
|
]
|
|
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|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 18,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<matplotlib.image.AxesImage at 0x7fe5ffd8e810>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"execution_count": 18,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
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|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAD8CAYAAAC4uSVNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nO2dfewm1XXfv6dkWYoNxRvoag2kC9biyk7pmq5YV3EoNcbYKM3GlUXhDwcSq2vXRorVVPU6rmI3VSQn9YtcJcJZxMpQ2YALdkwtUgzELo1UsBe8Xi8mwEKw2PWaTfwCJFRrXk7/eOYxs3fvnblvM/fOPN+P9NPveeaZuXNm7sx3zj333DuiqiCEEDIt/l5pAwghhIRD8SaEkAlC8SaEkAlC8SaEkAlC8SaEkAlC8SaEkAkymHiLyNtE5GER2S8iO4baDyGErCIyRJ63iBwH4BEAFwM4AOCbAK5Q1e9m3xkhhKwgQ3ne5wPYr6qPq+pPAdwEYNtA+yKEkJXj5wYq93QAT7a+HwCw1bXy8bJWT8ArBjKFEEKmybP48d+o6mm234YS715EZDuA7QBwAk7EVrmolCmEEFIld+kt33P9NlTY5CCAM1vfz2iW/QxV3amqW1R1yxqsHcgMQgiZJ0OJ9zcBbBKRs0TkeACXA7htoH0RQsjKMUjYRFVfEJGrAdwB4DgAu1T1wSH2RQghq8hgMW9VvR3A7UOVTwghqwxHWBJCyASheBNCyASheBNCyASheBNCyASheBNCyASheBNCyASheBNCyASheBNCyASheBNCyASheBNCyASheBNCyASheBNCyASheBNCyASheBNCyASheBNCyASheBNCyASheBNCyASJFm8ROVNEviYi3xWRB0Xkt5rlHxWRgyKyp/m7NJ+5hBBCgLTXoL0A4LdV9QEROQnA/SJyZ/Pbp1T14+nmEUIIsREt3qp6CMCh5vOzIvIQgNNzGUYIIcRNlpi3iGwE8AYA9zWLrhaRvSKyS0Re5dhmu4jsFpHdz+NIDjMIIWRlSBZvEXklgFsBfEBVnwFwDYDXANiMhWf+Cdt2qrpTVbeo6pY1WJtqBiGErBRJ4i0ia7AQ7s+p6hcBQFWfUtUXVfUlANcCOD/dTEIIIW1Ssk0EwHUAHlLVT7aWb2it9g4A++LNI4QQYiMl2+SXALwLwHdEZE+z7HcAXCEimwEogCcAvCfJQkIIIceQkm3yFwDE8tPt8eYQQgjxIcXzJoSQ2XPH9/cc9f2SV28uZMnRcHg8IYT0cMmrN1cj2kvoeRNCjsL0NIF6vM2S2M5LSSjehKw4PmEBl3Ctiqgvj7+m46V4ExLIEJ5pqFeXS0RsouR7fHd8f0+VopabWo+N4k1IByFC1vV7X9mhAtEl9j5ldYm2j+fdjgHXFk6IpdaOSRcUb0JaxIQQzM6sPm80h7faVbaPmIbYa/vtju/vqV7cYpjSA4niTYiFrpu4S9CXQt4non3C1+WVd4ltiKCmeP5zFG5gGqK9hOJNiIMQD7kt2KlpZaZX62oNpHQipnj/bfvm5IGHhLtqOGaKNyENtpsy5Ca1iWqMJ2yW4yqjLyyzXMcU267tp+R5joXtnNQg4KKqRQ0AgJNlnW6Vi0qbQVaclDDCcntXiKOrTFfnYa4MFt+HQag3PqVMk9jOSNcxjnXsd+kt96vqFttv9LwJafDplOsSAdPLbf/eDqnYBD3F43cR2hKI2ecUhHtJaGdkbMunr7yYbW3Q8yakRYhHZbsZfdLtSgieLXQSmnFilheyfmlCPe/c9RjbT0DPm5BAfG4wm1dtdly6tkm1LaW83OGYqRCbUZNrUFbuODnFm5AWths2RMiXn00Bb4dMUmPbuWPjtnJt5G72j0mKlxy6fdf+c3YIc1ZBQiy0Y9NmnLpr3fYyX0pmePgO6jHz2OeMmaef83hznr/kmLeIPAHgWQAvAnhBVbeIyDoANwPYiMXbdC5T1R+7ymDMm9SKT6zSJvJdnZ85vO8Q+9vEeJRTDZPMga6Ydy7P+1+q6ubWTnYAuFtVNwG4u/lOyOSweeDtv+Vy4FiB7htEk+pxhwyFt9k1V+E262euDBXz3gbgwubz9QC+DuCDA+2LkMExOyRdKX/mZ1s5y3VcaYK+tG3wFdnQbJIU+0rgG+qaAznEWwF8VUQUwJ+o6k4A61X1UPP7DwCsz7AfQopgxkDbuIay25a5ykoRR1snqM220NDMlATbZMq2h5BDvN+kqgdF5B8CuFNE/rL9o6pqI+xHISLbAWwHgBNwYgYzCBmOLoHuyve2bT/GaL25e51Afx+Ea5slUxf5ZPFW1YPN/8Mi8iUA5wN4SkQ2qOohEdkA4LBlu50AdgKLDstUOwgZC5/8bZ/QyXK9vtzwVDvnKOTmMbXDWn3bzCWkktRhKSKvEJGTlp8BvBXAPgC3AbiyWe1KAF9O2Q8hNdInuF0dlqbQ+HR0hjDEcPvasOXWh2zjWt/WKV0jqZ73egBfEpFlWZ9X1f8lIt8E8AUReTeA7wG4LHE/hFRBaLPbJx5tlhfaCdnHHIW7TYjAhs7zMlvxVtXHAfxTy/IfAmDi9kj4XmA13sSlUtH68p9dhHb69aULuuzKeV6mmO7XR1dOfdc2c4LD4ydEqBD4bh9TViol449dGSElb/DYmHefkM0lxmsyZF1N4XxRvCsl12Q4odunPiBCGFMo+4avDy0EKeWnDL0fkyEfgGNmidR6fk0o3hVRQxpTTOdbLEN7N7Wcz5TjtKUlDjmcPoWu1MkU5pYlkguKdyXU0GzvIlTU+7Yd2jsbah9D0SXIvqM3u+gazJOKLfXRXJ6CKdq1PrzGhuJdAWMJ9xAX/RihGF/Pa8o3tEtcQzvjSghb7jRHkyE6cW3lD1H2kPBNOoUY64Kp+cIMzZJZBY8r9hh9hG0Vzl8M5lw1NZ0jvkmnIkqIaU0XY5tQu2o9jlyEiGvfEPBS56pmZ2FuULxHotRTvS9WbZtngzdd/ZhpjjXV2RRbSjV63X3wTTqEEDJB6HmPQE1PdVvq2RQ9pVXElU3T1boqGT6ZyrU0FTtNKN4DU5twd82nMcXRhTXQNyVsyHH1nQefiZVKnschUxLJ0cw626S0cJbevy+82fJgnseY8xoyS2FIZgnreJqsZLaJmdQPjD9vx5j7NPffd2O3ifGWKAbHkmvIvevcmmLc1elM5s9sxRvwa2LmpqS33bVvV9O+HTdljDQPKSM9l/XgGz4x9+WadGsu55a8zKyzTZY3wSoI95KuIdbtocvLZV0enAtzUENO5uI1muc6dFvg2JGFJj4eOpkvs/W8S05ENCVCQyWx2/aVW9rzT6X9ALQ9IJef+8owW0Lm5779m7aQ+TJb8R6bkh5j6s3qK8oUAzddoSrfVkrXA6zv4ca49+oRLd4i8loAN7cWnQ3gdwGcAuDfAvjrZvnvqOrt0RZOgFKejhnHzp3dMBZ9+cq5SZ0/xIbPSFWf/ZpCb3r0FGWyJFq8VfVhAJsBQESOA3AQwJcA/AaAT6nqx7NYOBFKCGCsANma9aUFfAr4pvC5tg2diKtd/rKOOJ0BWZKrw/IiAI+p6vcylZdEiU7KsbBlF5jN874Orr6OsBKUsCnXdeLbaRjrOZted+lUVFIHucT7cgA3tr5fLSJ7RWSXiLzKtoGIbBeR3SKy+3kcyWTGuJ5IDR1D5o3tk+VQS3pgKYY+7q6HQkroI2bbmh7QJC/JHZYicjyAXwXwoWbRNQD+CwBt/n8CwG+a26nqTgA7gcUIy1Q7YonNoCgh3LYbMcSOmtPKarEjFz6ZJT7rhZabaxtSPzmyTd4O4AFVfQoAlv8BQESuBfCVDPsIItTbcA148N1uLGoW31yMdTw5Y8cxWSXt7YaCXve8ySHeV6AVMhGRDap6qPn6DgD7MuzDm9xZBK715yaaq4xt0FGsR1z6urB1SJN5kiTeIvIKABcDeE9r8R+KyGYswiZPGL9VxxAXt6+48yEQxhCDeXIJnW0iqL4yUx8YZLVJ6rBU1b9T1Z9X1adby96lqv9EVc9V1V9teeGzwNdLd2VPzLEp63NMIcf
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"img = cv2.imread('sheep.png')\n",
|
|
|
|
|
"edges = cv2.Canny(img, 150, 220)\n",
|
|
|
|
|
"plt.imshow(edges)"
|
2019-12-08 03:13:45 +00:00
|
|
|
]
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"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
|
|
|
|
|
}
|
