image manipulation (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "inimg=Image.open(r'C:\\Users\\Home\\Pictures\\gvr.jpg')\n",
    "img=np.asarray(inimg)\n",
    "opimg=Image.fromarray(img)\n",
    "opimg.show()\n",
    "#author-@_._hello_world._._\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "#crop image\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "inimg=Image.open(r'C:\\Users\\Home\\Pictures\\gvr.jpg')\n",
    "img=np.asarray(inimg)\n",
    "x,y,z=img.shape\n",
    "#remove 30 pixels from all sides\n",
    "img=img[30:x-30,30:y-30]\n",
    "opimg=Image.fromarray(img)\n",
    "opimg.show()\n",
    "#author-@_._hello_world._._\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "#negative image\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "inimg=Image.open(r'C:\\Users\\Home\\Pictures\\gvr.jpg')\n",
    "img=np.asarray(inimg)\n",
    "img=255-img\n",
    "opimg=Image.fromarray(img)\n",
    "opimg.show()\n",
    "#author-@_._hello_world._._\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "#seperate R,G,B from images\n",
    "opimg=[]#to store output images\n",
    "for i in range(3):\n",
    "    #copying only one colour to temporary variable \n",
    "    #for each iteration and displaying\n",
    "    temp=np.zeros(img.shape,dtype=\"uint8\")\n",
    "    temp[:,:,i]=img[:,:,i]\n",
    "    im=Image.fromarray(temp)\n",
    "    opimg.append(im)\n",
    "    opimg[i].show()\n",
    "\n",
    "#author-@_._hello_world._._\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#RGB to greyscale image\n",
    "\n",
    "opimg=[]#to store output images\n",
    "for i in range(3):\n",
    "    grey=img[:,:,i]\n",
    "    im=Image.fromarray(grey)\n",
    "    opimg.append(im)\n",
    "    opimg[i].show()\n",
    "#author-@_._hello_world._._\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "#adjust saturation\n",
    "\n",
    "x,y,z=img.shape\n",
    "img1=np.copy(img)\n",
    "s=50\n",
    "for i in range(x):\n",
    "    for j in range(y):\n",
    "        #0,1,2 for R,G,B respectively\n",
    "        if img[i][j][2]+s<255:\n",
    "            img1[i][j][2]=img[i][j][2]+s\n",
    "opimg=Image.fromarray(img1)\n",
    "opimg.show()\n",
    "#author-@_._hello_world._._\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#adjust brightness\n",
    "\n",
    "img1=np.copy(img)\n",
    "x,y,z=img.shape\n",
    "def bright(img1,b):\n",
    "    if max(img1[i][j])+b>=255:\n",
    "        img1[i][j]=255\n",
    "    else:\n",
    "        img1[i][j]+=b\n",
    "def dull(img1,d):\n",
    "    if min(img1[i][j])-d<=0:\n",
    "        img1[i][j]=0\n",
    "    else:\n",
    "        img1[i][j]-=d\n",
    "\n",
    "for i in range(x):\n",
    "    for j in range(y):\n",
    "        #place either dull or bright function here\n",
    "        bright(img1,100)\n",
    "opimg=Image.fromarray(img1)\n",
    "opimg.show()\n",
    "#author-@_._hello_world._._\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rotate images 90 degree\n",
    "\n",
    "inimg=Image.open(r'C:\\Users\\Home\\Pictures\\curvedarrow.jpg')\n",
    "img=np.asarray(inimg)\n",
    "x,y,z=img.shape\n",
    "\n",
    "def rotate90():\n",
    "    global img,img1,y\n",
    "    img1=np.zeros((y,x,z),dtype='uint8')\n",
    "    for i in range(y):\n",
    "        img1[i,:]=img[:,i]\n",
    "rotate90()\n",
    "opimg=Image.fromarray(img1)\n",
    "opimg.show()\n",
    "#author-@_._hello_world._._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "#flip images\n",
    "\n",
    "inimg=Image.open(r'C:\\Users\\Home\\Pictures\\curvedarrow.jpg')\n",
    "img=np.asarray(inimg)\n",
    "x,y,z=img.shape\n",
    "img1=img.copy()\n",
    "def flip_vertical():\n",
    "    global img,img1,x\n",
    "    for i in range(x):\n",
    "        img1[i,:]=img[x-i-1,:]\n",
    "def flip_horizontal():\n",
    "    global img,img1,y\n",
    "    for i in range(y):\n",
    "        img1[:,i]=img[:,y-i-1]\n",
    "flip_vertical()\n",
    "opimg=Image.fromarray(img1)\n",
    "opimg.show()\n",
    "#author-@_._hello_world._._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "inimg=Image.open(r'C:\\Users\\Home\\Pictures\\curvedarrow.jpg')\n",
    "img=np.asarray(inimg)\n",
    "x,y,z=img.shape\n",
    "img1=img.copy()\n",
    "def flip_vertical():\n",
    "    global img,img1,x\n",
    "    for i in range(x):\n",
    "        img1[i,:]=img[x-i-1,:]\n",
    "def flip_horizontal():\n",
    "    global img,img1,y\n",
    "    for i in range(y):\n",
    "        img1[:,i]=img[:,y-i-1]\n",
    "def rotate90():\n",
    "    global img,img1,y\n",
    "    img1=np.zeros((y,x,z),dtype='uint8')\n",
    "    for i in range(y):\n",
    "        img1[i,:]=img[:,i] \n",
    "def rotate180():\n",
    "    global img,img1,x,y\n",
    "    flip_vertical()\n",
    "    img=img1.copy()\n",
    "    flip_horizontal()\n",
    "def rotate270():\n",
    "    global img,img1,x,y,z\n",
    "    rotate90()\n",
    "    img=img1.copy()\n",
    "    x,y=y,x\n",
    "    flip_horizontal()\n",
    "    img=img1.copy()\n",
    "    flip_vertical()\n",
    "    \n",
    "rotate180()\n",
    "opimg=Image.fromarray(img1)\n",
    "opimg.show()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "#box blur\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "inimg=Image.open(r'C:\\Users\\Home\\Pictures\\gvr.jpg')\n",
    "img=np.asarray(inimg)\n",
    "x,y,z=img.shape\n",
    "img1=np.zeros((img.shape),dtype='uint8')\n",
    "def box_blur(n):\n",
    "    global img,img1\n",
    "    for i in range(0,x-n,n):\n",
    "        for j in range(0,y-n,n):\n",
    "            img1[i:i+n,j:j+n]+=img[i][j]\n",
    "box_blur(10)\n",
    "opimg=Image.fromarray(img1)\n",
    "opimg.show()\n",
    "#author-@_._hello_world._._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "#smooth blur\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "inimg=Image.open(r'C:\\Users\\Home\\Pictures\\gvr.jpg')\n",
    "img=np.asarray(inimg)\n",
    "x,y,z=img.shape\n",
    "img1=img.copy()\n",
    "smooth =  [[1,1,1],\n",
    "         [1,1,1],\n",
    "         [1,1,1]]\n",
    "\n",
    "def blur(kernel):\n",
    "    global img,img1,x,y,z\n",
    "    for i in range(x-3):\n",
    "        for j in range(y-3):\n",
    "            for k in range(3):\n",
    "                img1[i][j][k]=(np.sum(img[i:i+3,j:j+3,k]*kernel))//np.sum(kernel)\n",
    "    img=img1.copy()\n",
    "for i in range(1):\n",
    "    blur(smooth)\n",
    "opimg=Image.fromarray(img1)\n",
    "opimg.show()\n",
    "#author-@_._hello_world._._\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "#with generalized kernel\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "inimg=Image.open(r'C:\\Users\\Home\\Pictures\\gvr.jpg')\n",
    "img=np.asarray(inimg)\n",
    "x,y,z=img.shape\n",
    "img1=img.copy()\n",
    "n=20\n",
    "smooth_blur=np.ones((n,n))\n",
    "\n",
    "def blur(kernel):\n",
    "    global img,img1,x,y,z,n\n",
    "    n=len(kernel)\n",
    "    for i in range(n,x-n):\n",
    "        for j in range(n,y-n):\n",
    "            for k in range(3):\n",
    "                img1[i][j][k]=(np.sum(img[i:i+n,j:j+n,k]*kernel))//np.sum(kernel)\n",
    "    \n",
    "blur(smooth_blur)\n",
    "opimg=Image.fromarray(img1)\n",
    "opimg.show()\n",
    "opimg=Image.fromarray(img)\n",
    "opimg.show()\n",
    "#author-@_._hello_world._._\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "#increase blur efficiency with temp\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "inimg=Image.open(r'C:\\Users\\Home\\Pictures\\gvr.jpg')\n",
    "img=np.asarray(inimg)\n",
    "x,y,z=img.shape\n",
    "img1=img.copy()\n",
    "n=20\n",
    "smooth_blur=np.ones((n,n))\n",
    "temp=np.ones((x+n*2,y+n*2,3),dtype='uint8')\n",
    "#copying center\n",
    "temp[n:x+n,n:y+n]=img[:,:]\n",
    "#copying corners\n",
    "temp[:n,:n]=img[:n,:n]\n",
    "temp[:n,-n:]=img[:n,-n:]\n",
    "temp[-n:,:n]=img[-n:,:n]\n",
    "temp[-n:,-n:]=img[-n:,-n:]\n",
    "#copying edges\n",
    "temp[:n,n:y+n]=img[:n]\n",
    "temp[-n:,n:y+n]=img[-n:]\n",
    "temp[n:x+n,-n:]=img[:,-n:]\n",
    "temp[n:x+n,:n]=img[:,:n]\n",
    "\n",
    "temp1=temp.copy()\n",
    "def blur(kernel):\n",
    "    global img,img1,x,y,z,n\n",
    "    n=len(kernel)\n",
    "    for i in range(n,x+n):\n",
    "        for j in range(n,y+n):\n",
    "            for k in range(3):\n",
    "                temp1[i][j][k]=(np.sum(temp[i:i+n,j:j+n,k]*kernel))//np.sum(kernel)\n",
    "    img1=temp1[n:x+n,n:y+n]\n",
    "blur(smooth_blur)\n",
    "opimg=Image.fromarray(temp)\n",
    "opimg.show()\n",
    "opimg=Image.fromarray(temp1)\n",
    "opimg.show()\n",
    "#author-@_._hello_world._._\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sharpen=[[0,-0.5,0],[-0.5,3,-0.5],[0,-0.5,0]]\n",
    "\n",
    "gaussian=[[1,2,1],\n",
    "        [2,4,2],\n",
    "        [1,2,1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  4  8  4  2  1]\n",
      " [ 2  4  8 16  8  4  2]\n",
      " [ 4  8 16 32 16  8  4]\n",
      " [ 8 16 32 64 32 16  8]\n",
      " [ 4  8 16 32 16  8  4]\n",
      " [ 2  4  8 16  8  4  2]\n",
      " [ 1  2  4  8  4  2  1]]\n"
     ]
    }
   ],
   "source": [
    "#gaussian kernel\n",
    "import numpy as np\n",
    "n=7 #use only odd number\n",
    "g=np.ones((n,n),dtype='int')\n",
    "\n",
    "for i in range(1,n):\n",
    "    #row\n",
    "    if i<=n//2:\n",
    "        g[0][i]=g[0][i-1]*2\n",
    "    else:\n",
    "        g[0][i]=g[0][i-1]//2\n",
    "    #coloumn\n",
    "    if i<=n//2:\n",
    "        g[i][0]=g[i-1][0]*2\n",
    "    else:\n",
    "        g[i][0]=g[i-1][0]//2\n",
    "    \n",
    "for i in range(1,n):    \n",
    "    for j in range(1,n):\n",
    "        g[i][j]=g[0][j]*g[i][0]\n",
    "        \n",
    "print(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1 -1 -1]\n",
      " [-1  9 -1]\n",
      " [-1 -1 -1]]\n"
     ]
    }
   ],
   "source": [
    "#sharpen kernel\n",
    "import numpy as np\n",
    "n=3 #use only odd number\n",
    "sharpen=np.ones((n,n),dtype='int')\n",
    "sharpen*=-1\n",
    "sharpen[n//2][n//2]=n*n\n",
    "\n",
    "print(sharpen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.1 -0.2 -0.4 -0.8 -0.4 -0.2 -0.1]\n",
      " [-0.2 -0.4 -0.8 -1.6 -0.8 -0.4 -0.2]\n",
      " [-0.4 -0.8 -1.6 -3.2 -1.6 -0.8 -0.4]\n",
      " [-0.8 -1.6 -3.2 64.  -3.2 -1.6 -0.8]\n",
      " [-0.4 -0.8 -1.6 -3.2 -1.6 -0.8 -0.4]\n",
      " [-0.2 -0.4 -0.8 -1.6 -0.8 -0.4 -0.2]\n",
      " [-0.1 -0.2 -0.4 -0.8 -0.4 -0.2 -0.1]]\n"
     ]
    }
   ],
   "source": [
    "#gaussian kernel\n",
    "import numpy as np\n",
    "n=7 #use only odd number\n",
    "g=np.ones((n,n),dtype='float')\n",
    "\n",
    "for i in range(1,n):\n",
    "    #row\n",
    "    if i<=n//2:\n",
    "        g[0][i]=g[0][i-1]*2\n",
    "    else:\n",
    "        g[0][i]=g[0][i-1]//2\n",
    "    #coloumn\n",
    "    if i<=n//2:\n",
    "        g[i][0]=g[i-1][0]*2\n",
    "    else:\n",
    "        g[i][0]=g[i-1][0]//2\n",
    "    \n",
    "for i in range(1,n):    \n",
    "    for j in range(1,n):\n",
    "        g[i][j]=g[0][j]*g[i][0]\n",
    "t=g[n//2][n//2]\n",
    "g*=-1/10\n",
    "\n",
    "g[n//2][n//2]=t\n",
    "print(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  247/247 th iteration completed16/247 th iteration completed 81/247 th iteration completed 191/247 th iteration completed"
     ]
    }
   ],
   "source": [
    "#with generalized kernel\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "inimg=Image.open(r'C:\\Users\\Home\\Pictures\\impy23.jpg')\n",
    "img=np.asarray(inimg)\n",
    "x,y,z=img.shape\n",
    "img1=img.copy()\n",
    "n=10\n",
    "smooth_blur=np.ones((n,n))\n",
    "temp=np.ones((x+n*2,y+n*2,3),dtype='uint8')\n",
    "#copying center\n",
    "temp[n:x+n,n:y+n]=img[:,:]\n",
    "#copying corners\n",
    "temp[:n,:n]=img[:n,:n]\n",
    "temp[:n,-n:]=img[:n,-n:]\n",
    "temp[-n:,:n]=img[-n:,:n]\n",
    "temp[-n:,-n:]=img[-n:,-n:]\n",
    "#copying edges\n",
    "temp[:n,n:y+n]=img[:n]\n",
    "temp[-n:,n:y+n]=img[-n:]\n",
    "temp[n:x+n,-n:]=img[:,-n:]\n",
    "temp[n:x+n,:n]=img[:,:n]\n",
    "\n",
    "temp1=temp.copy()\n",
    "def blur(kernel):\n",
    "    global img,img1,x,y,z,n\n",
    "    n=len(kernel)\n",
    "    for i in range(n,x+n):\n",
    "        print('\\r',f' {i-n+1}/{x} th iteration completed',end='',flush=True)\n",
    "        for j in range(n,y+n):\n",
    "            for k in range(3):\n",
    "                temp1[i][j][k]=(np.sum(temp[i:i+n,j:j+n,k]*kernel))/np.sum(kernel)\n",
    "    img1=temp1[n:x+n,n:y+n]\n",
    "blur(g)\n",
    "opimg=Image.fromarray(img1)\n",
    "opimg.show()\n",
    "opimg=Image.fromarray(img)\n",
    "opimg.show()\n",
    "#author-@_._hello_world._._\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "sharpen=[[0,-0.5,0],[-0.5,3,-0.5],[0,-0.5,0]]\n",
    "\n",
    "gaussian=[[1,2,1],\n",
    "        [2,4,2],\n",
    "        [1,2,1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>create images</h1>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#random pattern greyscale\n",
    "from PIL import Image\n",
    "import random\n",
    "import numpy as np\n",
    "x,y=200,200\n",
    "num=np.zeros((x,y),dtype='uint8')\n",
    "for i in range(x):\n",
    "    for j in range(y):\n",
    "        num[i][j]=random.randint(0,255)\n",
    "opimg=Image.fromarray(num)\n",
    "opimg.show()\n",
    "#author-@_._hello_world._._\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#random pattern RGB\n",
    "from PIL import Image\n",
    "import random\n",
    "import numpy as np\n",
    "x,y=200,200\n",
    "num=np.zeros((x,y,3),dtype='uint8')\n",
    "for i in range(x):\n",
    "    for j in range(y):\n",
    "        for k in range(3):\n",
    "            num[i][j][k]=random.randint(0,255)\n",
    "opimg=Image.fromarray(num)\n",
    "opimg.show()\n",
    "#author-@_._hello_world._._\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#greyscale\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "x,y=100,200\n",
    "num=np.zeros((x,y),dtype='uint8')\n",
    "for i in range(x):\n",
    "    for j in range(y):\n",
    "        num[i][j]=j\n",
    "opimg=Image.fromarray(num)\n",
    "opimg.show()\n",
    "#author-@_._hello_world._._\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#RGB scale \n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "x,y=100,200\n",
    "num=np.zeros((x,y,3),dtype='uint8')\n",
    "for i in range(x):\n",
    "    for j in range(y):\n",
    "        #0,1,2 = r,g,b respectively\n",
    "        num[i][j][2]=j\n",
    "opimg=Image.fromarray(num)\n",
    "opimg.show()\n",
    "#author-@_._hello_world._._\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#resize scale\n",
    "#greyscale\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "d=10 #divisions\n",
    "x,y=100,200\n",
    "n=y//d +1\n",
    "num=np.zeros((x,y),dtype='uint8')\n",
    "for i in range(0,x-n,n):\n",
    "    for j in range(0,y-n,n):\n",
    "        num[i:i+n,j:j+n]=j\n",
    "opimg=Image.fromarray(num)\n",
    "opimg.show()\n",
    "#author-@_._hello_world._._\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#spray colour on sheet\n",
    "from PIL import Image\n",
    "import random\n",
    "import numpy as np\n",
    "x,y=200,200\n",
    "sheet_colour=[255,255,255]\n",
    "num=np.zeros((x,y,3),dtype='int')\n",
    "num+=sheet_colour\n",
    "dt=10 #dot_thickness\n",
    "for i in range(100):\n",
    "    v=random.randint(0,x-1-dt)\n",
    "    v1=random.randint(0,y-1-dt)\n",
    "    num[v:dt,v1:dt,2]=0  #random.randint(0,255)\n",
    "num=num.astype('uint8')\n",
    "opimg=Image.fromarray(num)\n",
    "opimg.show()\n",
    "#author-@_._hello_world._._\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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