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Opencv
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‎Linkedin/Opencv/0.jpeg

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‎Linkedin/Opencv/Untitled.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"import cv2\n",
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"img=cv2.imread('0.jpeg')\n",
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"# initilization of window\n",
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"cv2.namedWindow('IMAGE',0)\n",
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"\n",
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"cv2.imshow('IMAGE',img)\n",
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"cv2.imwrite('output.jpeg',img)\n",
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"cv2.waitKey()\n",
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"cv2.destroyAllWindows()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[[ 24 146 162]\n",
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" [ 21 146 166]\n",
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" [ 16 147 174]\n",
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" ...\n",
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" [ 7 146 172]\n",
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" [ 16 149 169]\n",
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" [ 23 155 172]]\n",
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"\n",
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" [[ 24 151 166]\n",
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" [ 22 151 170]\n",
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" [ 15 151 177]\n",
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" ...\n",
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" [ 7 151 176]\n",
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" [ 13 152 171]\n",
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" [ 17 156 172]]\n",
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"\n",
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" [[ 22 159 175]\n",
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" [ 19 158 177]\n",
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" [ 14 157 184]\n",
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" ...\n",
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" [ 13 165 189]\n",
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" [ 14 164 181]\n",
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" [ 17 166 180]]\n",
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"\n",
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" ...\n",
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"\n",
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" [[ 6 176 194]\n",
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" [ 2 174 192]\n",
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" [ 8 179 201]\n",
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" ...\n",
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" [ 1 186 196]\n",
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" [ 0 183 188]\n",
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" [ 0 175 178]]\n",
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"\n",
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" [[ 0 167 185]\n",
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" [ 0 167 185]\n",
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" [ 0 168 191]\n",
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" ...\n",
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" [ 0 177 188]\n",
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" [ 0 180 188]\n",
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" [ 0 180 186]]\n",
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"\n",
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" [[ 0 162 180]\n",
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" [ 0 163 181]\n",
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" [ 0 161 184]\n",
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" ...\n",
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" [ 0 168 182]\n",
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" [ 0 173 183]\n",
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" [ 0 181 189]]]\n",
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"<class 'numpy.ndarray'>\n",
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"920\n",
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"752\n",
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"3\n",
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"(920, 752, 3)\n",
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"uint8\n",
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"[[24 21 16 ... 7 16 23]\n",
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" [24 22 15 ... 7 13 17]\n",
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" [22 19 14 ... 13 14 17]\n",
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" ...\n",
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" [ 6 2 8 ... 1 0 0]\n",
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" [ 0 0 0 ... 0 0 0]\n",
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" [ 0 0 0 ... 0 0 0]]\n",
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"[ 86 162 214]\n"
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]
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}
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],
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"source": [
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"#access and understand pixel data\n",
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"import cv2\n",
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"import numpy as np\n",
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"img=cv2.imread('0.jpeg')\n",
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"\n",
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"print(img)\n",
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"print(type(img))\n",
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"print(len(img))\n",
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"print(len(img[0]))\n",
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"print(len(img[0][0]))\n",
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"print(img.shape)\n",
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"print(img.dtype)\n",
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"print(img[:,:,0]) #red channel\n",
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"print(img[10][15])\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[0]\n",
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"[1 1 1]\n",
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"[65535 65535 65535]\n",
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"[255 0 0]\n"
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]
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}
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],
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"source": [
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"# Data types and structures\n",
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"import numpy as np\n",
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"import cv2\n",
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"\n",
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"black=np.zeros([150,150,1],'uint8')\n",
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"cv2.imshow('black',black)\n",
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"print(black[0,0,:])\n",
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"\n",
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"ones=np.ones([150,150,3],'uint8')\n",
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"# ones*=(2**8-1)\n",
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"cv2.imshow('ones',ones)\n",
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"print(ones[0,0,:])\n",
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"\n",
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"white=np.ones([150,150,3],'uint16')\n",
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"white*=(2**16-1)\n",
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"cv2.imshow('white',white)\n",
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"print(white[0,0,:])\n",
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"\n",
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"#\n",
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"color=ones.copy()\n",
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"color[:,:]=(255,0,0) #blue\n",
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"cv2.imshow('blue',color)\n",
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"print(color[0,0,:])\n",
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"\n",
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"cv2.waitKey(0)\n",
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"cv2.destroyAllWindows()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(920, 752, 3)\n"
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]
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}
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],
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"source": [
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"# image types and color channels\n",
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"import numpy as np\n",
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"import cv2\n",
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"img=cv2.imread('0.jpeg',1)\n",
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"cv2.imshow('image',img)\n",
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"cv2.moveWindow('image',0,0)\n",
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"\n",
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"print(img.shape)\n",
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"width,height,channel=img.shape\n",
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"\n",
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"cv2.waitKey(0)\n",
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"cv2.destroyAllWindows()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"# pixel manipulation and filtering\n",
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"import cv2\n",
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"img=cv2.imread('0.jpeg',1)\n",
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"\n",
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"gray=cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)\n",
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"cv2.imshow('KESHAV',gray)\n",
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"\n",
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"r=img[:,:,0]\n",
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"g=img[:,:,1]\n",
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"b=img[:,:,2]\n",
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"\n",
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"rgba=cv2.merge((r,g,b,g))\n",
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"cv2.imshow('rgba',rgba)\n",
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"\n",
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"\n",
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"cv2.waitKey(0)\n",
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"cv2.destroyAllWindows()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Gussian Blur,dilation and erosion\n",
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"import numpy as np\n",
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"import cv2\n",
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"img=cv2.imread('0.jpeg',1)\n",
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"cv2.imshow('original',img)\n",
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"\n",
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"# blur\n",
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"blur=cv2.GaussianBlur(img,(5,55),0)\n",
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"cv2.imshow('Gaussian Blur',blur)\n",
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"\n",
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"kernel=np.ones((5,5),'uint8')\n",
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"dilate=cv2.dilate(img,kernel,iterations=1)\n",
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"erode=cv2.erode(img,kernel,iterations=1)\n",
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"\n",
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"cv2.imshow('dilate',dilate)\n",
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"cv2.imshow('erode',erode)\n",
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"\n",
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"cv2.waitKey(0)\n",
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"cv2.destroyAllWindows()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# scale and rotate image\n",
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"import numpy as np\n",
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"import cv2\n",
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"img=cv2.imread('0.jpeg',1)\n",
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"cv2.imshow('original',img)\n",
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"\n",
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"# scale\n",
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"half=cv2.resize(img,(0,0),fx=0.5,fy=0.5,)\n",
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"cv2.imshow('half',half)\n",
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"\n",
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"stretch_=cv2.resize(img,(600,600),fx=0.5,fy=0.5)\n",
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"cv2.imshow('stretch_',stretch_)\n",
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"\n",
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"stretch_near_=cv2.resize(img,(600,600),fx=0.5,fy=0.5,interpolation = cv2.INTER_NEAREST)\n",
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"cv2.imshow('stretch_near_',stretch_near_)\n",
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"\n",
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"# rotate\n",
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"M=cv2.getRotationMatrix2D((0,0),-30,1)\n",
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"rotated=cv2.warpAffine(img,M,(img.shape[1],img.shape[0]))\n",
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"cv2.imshow('rotated',rotated)\n",
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"\n",
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"cv2.waitKey(0)\n",
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"cv2.destroyAllWindows()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"# video inputs\n",
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"import numpy as np\n",
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"import cv2\n",
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"cap=cv2.VideoCapture(0)\n",
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"while True:\n",
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" ret,frame=cap.read()\n",
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" cv2.imshow('keshav',frame)\n",
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" if cv2.waitKey(1)&0xff==ord('q'):\n",
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" break\n",
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"cap.release()\n",
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"cv2.destroyAllWindows()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# create custom interface\n",
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"import numpy as np\n",
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"import cv2\n",
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"cap=cv2.VideoCapture(0)\n",
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"# circle\n",
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"color=(0,255,0)\n",
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"point=(200,200)\n",
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"radius=40\n",
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"border_width=6\n",
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"\n",
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"while True:\n",
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" ret,frame=cap.read()\n",
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" cv2.circle(frame,point,radius,color,border_width)\n",
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" cv2.imshow('keshav',frame)\n",
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" if cv2.waitKey(1)&0xff==ord('q'):\n",
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" break\n",
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"cap.release()\n",
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"cv2.destroyAllWindows()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import cv2\n",
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"cap=cv2.VideoCapture(0)\n",
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"# circle\n",
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"color=(0,255,0)\n",
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"point=(200,200)\n",
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"radius=40\n",
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"border_width=6\n",
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"def click(event,x,y)\n",
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"while True:\n",
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" ret,frame=cap.read()\n",
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" cv2.circle(frame,point,radius,color,border_width)\n",
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" cv2.imshow('keshav',frame)\n",
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" if cv2.waitKey(1)&0xff==ord('q'):\n",
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" break\n",
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"cap.release()\n",
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"cv2.destroyAllWindows()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎Linkedin/Opencv/output.jpeg

330 KB
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