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January 2, 2024 21:34
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d8n-public.ipynb
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "provenance": [], | |
| "authorship_tag": "ABX9TyN8CVlpnGo6bPuO5/zzo4wF", | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| }, | |
| "language_info": { | |
| "name": "python" | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/ch-hristov/db6cde1547f918571cbd907d0146f06a/d8n-public.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## API key\n", | |
| "\n", | |
| "\n", | |
| "If you reached this notebook, it is likely that I also gave you an API key.\n", | |
| "In order to use it you must input it in the secrets tab on the left side of the screen (look for the key icon).\n", | |
| "\n", | |
| "" | |
| ], | |
| "metadata": { | |
| "id": "hBIhTFNkFIGL" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "First we install the d8n package from pypi. This includes a library to communicate with the API." | |
| ], | |
| "metadata": { | |
| "id": "y6HPRzLIFvp8" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [], | |
| "metadata": { | |
| "id": "WOdeS8Wdxnk8" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!pip install d8n\n", | |
| "\n", | |
| "import requests\n", | |
| "from google.colab import userdata\n", | |
| "from d8n.d8nClient import d8nClient\n", | |
| "\n", | |
| "api_key = userdata.get('api_key')\n", | |
| "client = d8nClient(api_key)\n", | |
| "\n", | |
| "url = \"https://media.licdn.com/dms/image/D5612AQHlEzNOh4bIbg/article-cover_image-shrink_720_1280/0/1673730667692?e=2147483647&v=beta&t=dUWIM4P6t15lohxqYepJe3-wfFH31PuIkznVUo36Y8U\"\n", | |
| "img_data = requests.get(url).content\n", | |
| "\n", | |
| "with open('image_name.jpg', 'wb') as handler:\n", | |
| " handler.write(img_data)" | |
| ], | |
| "metadata": { | |
| "id": "1xz9_lKFTYu1", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "dc8d9dec-f180-414f-9f48-6244557bad0e" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Collecting d8n\n", | |
| " Downloading d8n-0.2.6-py3-none-any.whl (2.2 kB)\n", | |
| "Installing collected packages: d8n\n", | |
| "Successfully installed d8n-0.2.6\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Code above will fetch a random image from the internet.\n", | |
| "\n", | |
| "You can see how the image looks like by opening the file image_name.jpg" | |
| ], | |
| "metadata": { | |
| "id": "zHkQUlyeF8pF" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Upload the image to the API and run analysis on it\n", | |
| "jsonid = client.from_local_file('/content/image_name.jpg')\n", | |
| "id = jsonid['id']\n", | |
| "# id is now a reference to the ID of your request\n", | |
| "display(id)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 37 | |
| }, | |
| "id": "9pBEDR9HOucD", | |
| "outputId": "e928cb9c-28be-43b3-fe8e-8d287ebb788c" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "'ca8fa272-408a-4d61-99d0-dc13b2f6a845'" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "string" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "status = client.fetch_status(id)\n", | |
| "\n", | |
| "print(\"Current status:\")\n", | |
| "display(status[\"status\"])\n", | |
| "\n", | |
| "print(\"Finished tasks:\")\n", | |
| "display(status[\"finished_tasks\"])" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 92 | |
| }, | |
| "id": "BMoeXBJxPWaZ", | |
| "outputId": "5725e545-f59a-42ad-83a4-5f979f0e7c22" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Current status:\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "'Status.Line_Detection'" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "string" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Finished tasks:\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "'Status(Started|Symbol_Detection|Text_Extraction)'" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "string" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "**Note**: wait until 'finished_tasks' is Completed. (shouln't take more than a minute)\n", | |
| "\n", | |
| "\n", | |
| "This will download the collected info into YOLO format which you can input into d8n sandbox to view.\n" | |
| ], | |
| "metadata": { | |
| "id": "52HhJWu_S3iF" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Download a zip file containing\n", | |
| "# 1. The image you uploaded\n", | |
| "# 2. The text file of labels of the identified objects\n", | |
| "# 3. A text file containing all the identified objects with their identified coordinates (normalized)\n", | |
| "# and the class predicted\n", | |
| "import zipfile\n", | |
| "\n", | |
| "zip_file = client.download_entry(id)\n", | |
| "save_file = './file.zip'\n", | |
| "\n", | |
| "with zipfile.ZipFile(save_file, 'w') as zip:\n", | |
| " zip.writestr('out.zip',zip_file)\n", | |
| "\n", | |
| "from google.colab import files\n", | |
| "files.download(\"/content/file.zip\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 17 | |
| }, | |
| "id": "j2o6MLUcXGns", | |
| "outputId": "153b5b70-12d9-4758-fbd0-6be242f604c0" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.Javascript object>" | |
| ], | |
| "application/javascript": [ | |
| "\n", | |
| " async function download(id, filename, size) {\n", | |
| " if (!google.colab.kernel.accessAllowed) {\n", | |
| " return;\n", | |
| " }\n", | |
| " const div = document.createElement('div');\n", | |
| " const label = document.createElement('label');\n", | |
| " label.textContent = `Downloading \"${filename}\": `;\n", | |
| " div.appendChild(label);\n", | |
| " const progress = document.createElement('progress');\n", | |
| " progress.max = size;\n", | |
| " div.appendChild(progress);\n", | |
| " document.body.appendChild(div);\n", | |
| "\n", | |
| " const buffers = [];\n", | |
| " let downloaded = 0;\n", | |
| "\n", | |
| " const channel = await google.colab.kernel.comms.open(id);\n", | |
| " // Send a message to notify the kernel that we're ready.\n", | |
| " channel.send({})\n", | |
| "\n", | |
| " for await (const message of channel.messages) {\n", | |
| " // Send a message to notify the kernel that we're ready.\n", | |
| " channel.send({})\n", | |
| " if (message.buffers) {\n", | |
| " for (const buffer of message.buffers) {\n", | |
| " buffers.push(buffer);\n", | |
| " downloaded += buffer.byteLength;\n", | |
| " progress.value = downloaded;\n", | |
| " }\n", | |
| " }\n", | |
| " }\n", | |
| " const blob = new Blob(buffers, {type: 'application/binary'});\n", | |
| " const a = document.createElement('a');\n", | |
| " a.href = window.URL.createObjectURL(blob);\n", | |
| " a.download = filename;\n", | |
| " div.appendChild(a);\n", | |
| " a.click();\n", | |
| " div.remove();\n", | |
| " }\n", | |
| " " | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.Javascript object>" | |
| ], | |
| "application/javascript": [ | |
| "download(\"download_712c5c96-76f6-49a3-890c-c013292e1629\", \"file.zip\", 390738)" | |
| ] | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "\n", | |
| "To do this unzip the downloaded file and then input the image into\n", | |
| "\n", | |
| "Sandbox url: https://sandbox.d8n.host/\n", | |
| "\n", | |
| "After that click on\n", | |
| "1. Object Detection\n", | |
| "2. Multiple- files in YOLO format.\n", | |
| "3. Select both the .txt file and the labels.txt file in the extracted folder" | |
| ], | |
| "metadata": { | |
| "id": "VItWn3o1TyFT" | |
| } | |
| } | |
| ] | |
| } |
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