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aleksejalex/encrypting_via_img.ipynb

Created May 3, 2024 10:30
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encrypting_via_img.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/aleksejalex/1dad3b249ee314f908707a901c1754d7/encrypting_via_img.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OWirxiXCAaoM"
},
"source": [
"# Bonus: a funny way to encrypt text\n",
"\n",
" > Do you need to store some very important note/password in visible place and yet still be sure it won't be easily found?\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "fc1E-wVqAaoR"
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3zzw_nGVAaoV",
"outputId": "4cb7673e-e7df-4851-af20-f4cf3b6bdd92"
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import cv2\n",
"import numpy as np\n",
"\n",
"text = \"Hello World!\"\n",
"\n",
"# Create a black image\n",
"img = np.zeros((1, len(text), 3), dtype=np.uint8)\n",
"\n",
"# Fill the image with text\n",
"for i in range(len(text)):\n",
" img[0, i] = (ord(text[i]), 0, 0) # ord(letter) returns number of a letter\n",
"\n",
"# Save the image\n",
"cv2.imwrite('out.png', img)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "O176-PWeAaoX",
"outputId": "4f7d43e8-0134-4e4e-aa63-081af42976b7"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7fb7fad69250>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(img)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "SLIsb7j5Aaoa",
"outputId": "fbf69dfe-b7ad-4d8c-d8e1-ee45e7e8744f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello World!\n"
]
}
],
"source": [
"#decoder\n",
"text = \"\"\n",
"\n",
"# Read the image\n",
"img = cv2.imread('out.png')\n",
"\n",
"# Get the width of the image\n",
"width = img.shape[1]\n",
"\n",
"# Iterate through each pixel in the first row of the image\n",
"for i in range(width):\n",
" # Get the pixel value\n",
" px = img[0, i]\n",
" # Extract the first channel value (red channel)\n",
" channel_value = px[0]\n",
" # Convert the channel value to a character and append it to the text\n",
" text = text + chr(channel_value)\n",
"\n",
"print(text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "l-uQrKHaAaob"
},
"outputs": [],
"source": [
"# ####################################################################################"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UDBO76hNAaod"
},
"source": [
"An illustration of `ord()` and `chr()` re-type:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "TMgNh-ncAaod",
"outputId": "62a10487-7362-41d5-a7db-51db3fab92e1"
},
"outputs": [
{
"data": {
"text/plain": [
"97"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ord('a')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "RApypBfiAaof",
"outputId": "8ce0dd66-2e01-4517-c6d7-43d62f22c2d3"
},
"outputs": [
{
"data": {
"text/plain": [
"98"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ord('b')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "iJg7_s9WAaoh",
"outputId": "25df8b9b-ef8b-451e-b805-9bb1bab0bc9d"
},
"outputs": [
{
"data": {
"text/plain": [
"'a'"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chr(97)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "81ovwBOIAaoi",
"outputId": "352afa3b-16bb-4908-b28f-b7c01ce9ab00"
},
"outputs": [
{
"data": {
"text/plain": [
"'b'"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chr(98)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "fhof41KsAaoj"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "GW4e6pDgAaoj"
},
"outputs": [],
"source": [
"# ####################################################################################"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6S2aOLXDAaok"
},
"source": [
"Same functionality with different library - `PIL`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "RfLXHaK8Aaok"
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "8kSiBQ7cAaol"
},
"outputs": [],
"source": [
"#package with similar functionality to opencv\n",
"from PIL import Image"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ggfFiYxvAaol"
},
"outputs": [],
"source": [
"# encoder (string to image)\n",
"text = \"Hello World!\"\n",
"\n",
"img = Image.new(mode = 'RGB', size = (len(text),1))\n",
"\n",
"for i in range(len(text)):\n",
" img.putpixel((i,0), (ord(text[i]),0,0)) # ord(letter) returns number of a letter\n",
"\n",
"img.save('out.png')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "4LlVI-L8Aaol",
"outputId": "cb18de6f-4a21-463a-ec26-97b566497b29"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f17ac06c620>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(img)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ABbGMUE9Aaol",
"outputId": "94a2fc05-9be2-4d04-bfb5-8771eb14f025"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello World!\n"
]
}
],
"source": [
"# decoder\n",
"text = \"\"\n",
"\n",
"img = Image.open('out.png')\n",
"width, height = img.size\n",
"\n",
"for i in range(width):\n",
" px = img.getpixel((i,0))\n",
" text += chr(px[0])\n",
"\n",
"print(text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cP3EGy6SAaon"
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.12.2"
},
"colab": {
"provenance": [],
"include_colab_link": true
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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