test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "from matplotlib import image\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from math import sqrt, floor, ceil\n",
    "from PIL import Image\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[255 255 255]\n",
      "  [254 254 254]\n",
      "  [255 255 255]\n",
      "  ...\n",
      "  [255 255 255]\n",
      "  [255 255 255]\n",
      "  [255 255 255]]\n",
      "\n",
      " [[254 254 254]\n",
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      "  ...\n",
      "  [254 254 254]\n",
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      "  [255 255 255]]\n",
      "\n",
      " [[254 254 254]\n",
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      "  ...\n",
      "  [255 255 255]\n",
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      "  [255 255 255]]\n",
      "\n",
      " ...\n",
      "\n",
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      "  ...\n",
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      "\n",
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      "  ...\n",
      "  [255 255 255]\n",
      "  [255 255 255]\n",
      "  [255 255 255]]\n",
      "\n",
      " [[255 255 255]\n",
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      "  [255 255 255]\n",
      "  ...\n",
      "  [255 255 255]\n",
      "  [255 255 255]\n",
      "  [255 255 255]]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nimg = Image.open(\"img.png\")\\nimg = np.array(img)\\ndi = img.shape\\n'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img= cv2.imread(\"./pikachu.png\")\n",
    "\n",
    "cv2.imwrite(\"pipi.png\", img)\n",
    "print(img)\n",
    "\n",
    "'''\n",
    "img = Image.open(\"img.png\")\n",
    "img = np.array(img)\n",
    "di = img.shape\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[0 0 0]\n",
      "  [0 0 0]\n",
      "  [0 0 0]\n",
      "  ...\n",
      "  [0 0 0]\n",
      "  [0 0 0]\n",
      "  [0 0 0]]\n",
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      "\n",
      " ...\n",
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      "  ...\n",
      "  [0 0 0]\n",
      "  [0 0 0]\n",
      "  [0 0 0]]]\n"
     ]
    }
   ],
   "source": [
    "print(cv2.imread(\"./pil.png\") - cv2.imread(\"./opencv.png\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_image(path):\n",
    "\n",
    "    img = cv2.imread(path)  # path , cv2.IMREAD_GRAYSCALE\n",
    "    \n",
    "    \n",
    "    cv2.imwrite(\"./opencv.png\", img)\n",
    "    cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "    \n",
    "    '''\n",
    "    dimg = Image.fromarray(img)\n",
    "    dimg.save(\"./asda.png\")\n",
    "    dimg.show()\n",
    "    '''\n",
    "    \n",
    "    size = img.shape\n",
    "    dimension = (size[0], size[1])\n",
    "\n",
    "    return img, size, dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img, size, dimension = read_image(\"./image.png\")\n",
    "print(f\"Image size is: {size}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def image_change_scale(img, dimension, scale=100, interpolation=cv2.INTER_LINEAR):\n",
    "\n",
    "    scale /= 100\n",
    "    new_dimension = (int(dimension[1]*scale), int(dimension[0]*scale))\n",
    "    resized_img = cv2.resize(img, new_dimension, interpolation=interpolation)\n",
    "    return resized_img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale_percent = 25  # percent of original image size\n",
    "resized_img = image_change_scale(img, dimension, scale_percent)\n",
    "print(f\"Smalled Image size is: {resized_img.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nn_img = image_change_scale(\n",
    "resized_img, dimension, interpolation=cv2.INTER_NEAREST)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def testimage(img1, img2):\n",
    "    print(img1.shape)\n",
    "    print(img2.shape)\n",
    "    print(img2.ndim)\n",
    "    print(img1.ndim)\n",
    "    if img1.shape == img2.shape:\n",
    "        return img1.ndim\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "testlist = []\n",
    "\n",
    "testlist.append(testimage(nn_img, img))\n",
    "print(testlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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