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97cffe2f · Usman Shahid · 4e1d64d7 · 97cffe2f
Commit 97cffe2f authored Apr 22, 2021 by Usman Shahid
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Querying an Azure Custom Vision endpoint from Python.ipynb ...uerying an Azure Custom Vision endpoint from Python.ipynb +172 -0

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--- a/Querying Azure Custom Vision in Python/Querying an Azure Custom Vision endpoint from Python.ipynb
+++ b/Querying Azure Custom Vision in Python/Querying an Azure Custom Vision endpoint from Python.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Querying an Azure Custom Vision endpoint from Python\n",
+    "\n",
+    "This is the companion notebook to the talk on [Azure Custom Vison for Beginners](https://www.youtube.com/watch?v=OzMRNVolrKE&lc=UgwYIaTgEIkRtacBGI54AaABAg) hosted by [Data Science Dojo](https://datasciencedojo.com/) and delivered by [Usman Shahid](https://www.linkedin.com/in/shahidusmanm/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import functions as fn\n",
+    "import nest_asyncio\n",
+    "nest_asyncio.apply()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#These have already been imported in functions.py. Re-imported them here to show during the webinar.\n",
+    "\n",
+    "# Other libraries used\n",
+    "import os\n",
+    "\n",
+    "# for converting the frames into bytes\n",
+    "import cv2 \n",
+    "\n",
+    "# and for processing arrays   \n",
+    "import numpy as np\n",
+    "\n",
+    "# for encoding and decoding Custom Vision predictions \n",
+    "import json\n",
+    "\n",
+    "# for converting the Custom Vision predictions to dataframe   \n",
+    "import pandas as pd\n",
+    "\n",
+    "# import async packages\n",
+    "import asyncio\n",
+    "import aiohttp\n",
+    "\n",
+    "# for file name pattern matching   \n",
+    "import fnmatch  \n",
+    "\n",
+    "# for displaying images from the processes output video   \n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# importing other required libraries\n",
+    "import random\n",
+    "import textwrap\n",
+    "import datetime \n",
+    "from PIL import Image\n",
+    "import time "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#These have already been declared in functions.py. Declared them here to show during the webinar.\n",
+    "\n",
+    "# web service end-point for the Custom Vision model    \n",
+    "# we will process video frames (which are images)   \n",
+    "POST_URL = \"Your custom vision endpoint\"\n",
+    "\n",
+    "# providing prediction key\n",
+    "HEADERS = {'Prediction-Key': \"Your custom vision prediction key\", \"Content-Type\":\"application/json\"}\n",
+    "\n",
+    "# number of API calls per pool of request   \n",
+    "MAX_CONNECTIONS = 100 \n",
+    "\n",
+    "# initializing the height and width for frames in the video \n",
+    "WIDTH = 0\n",
+    "HEIGHT = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# creating Output and Stats directories for saving processed videos \n",
+    "if not os.path.isdir(\"Output\"):\n",
+    "    os.mkdir(\"Output\")\n",
+    "if not os.path.isdir(\"Stats\"):\n",
+    "    os.mkdir(\"Stats\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# converting raw input video to processed video complete with tags and stats\n",
+    "threshold=0.3\n",
+    "fn.ConvertVideo(\"input/input_video.mov\", \"Output/output_video.mp4\", threshold, nframes=70)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# saving processed video as images inside the frames directory\n",
+    "images = []\n",
+    "byteImages = []\n",
+    "vidObj = cv2.VideoCapture(\"Output/output_video.mp4\")\n",
+    "count = 0\n",
+    "success = 1\n",
+    "currentDir = os.getcwd()\n",
+    "if not os.path.isdir(\"frames\"):\n",
+    "    os.mkdir(\"frames\")\n",
+    "while success:\n",
+    "      success, image = vidObj.read()\n",
+    "        if success:\n",
+    "            cv2.imwrite(\"frames/frame%d.jpg\" % count, image) \n",
+    "            count += 1\n",
+    "\n",
+    "images_num=(len(fnmatch.filter(os.listdir(os.getcwd() + \"/frames\"), '*.jpg')))\n",
+    "\n",
+    "# displaying images from frames directory\n",
+    "i=0\n",
+    "for i in range(images_num):\n",
+    "    if i%75==0:\n",
+    "        a = plt.imread(\"frames/frame%d.jpg\" % i)\n",
+    "        plt.imshow(a)\n",
+    "        plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}