Adds CV recipe (#2)
* documents code * add cv readme * update main readme * update readme * update readme
This commit is contained in:
Adi Azulay
GitHub
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Коммит
e694b56f72
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"python.linting.flake8Enabled": true,
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"python.linting.enabled": true,
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"cSpell.ignoreWords": [
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"adafruit",
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"dotenv",
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"getenv"
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"getenv",
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"imshow"
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]
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}
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@ -59,6 +59,11 @@ First you'll provision the Azure resources needed for this sample. You're going
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1. Give your resource group a name
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1. Select a Location that is located near you
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1. Select *F1: Free* for the pricing tier
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> Note: You can only have one free tier active per account
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1. The IoT Hub should now appear in the *Azure IoT Hub* tab
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1. Open the command palette, search for and select **Azure IoT Hub: Copy IoT Hub Connection String**
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@ -96,7 +96,7 @@ Our provision the Azure resources we'll need for this sample. We're going to use
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1. In the client folder on your Pi type
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```sh
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source ./.venv/Scripts/activate
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source ./.venv/bin/activate
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```
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1. Then type
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{
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"python.linting.pylintEnabled": false,
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"python.linting.flake8Enabled": true,
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"python.linting.enabled": true
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}
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from azure.cognitiveservices.vision.computervision import ComputerVisionClient
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from msrest.authentication import CognitiveServicesCredentials
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import os
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import matplotlib.pyplot as plt
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import matplotlib.patches as patches
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from PIL import Image
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from time import sleep
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from dotenv import load_dotenv
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from picamera import PiCamera
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# Create a camera object
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camera = PiCamera()
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load_dotenv('.env')
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# Add your Computer Vision subscription key and endpoint to your environment
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# variables in the .env file.
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subscription_key = os.getenv('SUBSCRIPTION_KEY')
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endpoint = os.getenv('ENDPOINT')
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computervision_client = ComputerVisionClient(
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endpoint,
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CognitiveServicesCredentials(subscription_key)
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)
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def take_picture(image_path='./image.jpg'):
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'''Returns path to picture, stores picture in the same directory as code.
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Parameter:
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---------
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image_path: str
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path to save image to. Default is './image.jpg'
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Returns:
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--------
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image_path: str
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path to image
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'''
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camera.start_preview(alpha=200)
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# Pi Foundation recommends waiting 2s for light adjustment
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sleep(2)
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# Change or comment out as needed
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camera.rotation = 180
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# Input image file path here
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camera.capture(image_path)
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# Stop camera
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camera.stop_preview()
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return(image_path)
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def detect_objects(image_path):
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''' Prints objects detected in an image and returns the ImageDescription
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from computer vision API
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Parameters
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----------
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image_path: str
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path to the location of the image file to use in object detection
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Returns
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-------
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detected_objects: ImageDescription
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ImageDescription class created by the computer vision api
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'''
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# Open image from path
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local_image = open(image_path, "rb")
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# Call API with URL
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detected_objects = computervision_client.detect_objects_in_stream(
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local_image
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)
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# Print detected objects results with bounding boxes
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print("Detecting objects in image:")
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if len(detected_objects.objects) == 0:
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print("No objects detected.")
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for object in detected_objects.objects:
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print(object.object_property)
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return(detected_objects)
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def frame_objects(detected_objects, image_path):
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'''Creates frames around the object detected in an image and plots the
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frame over that image.
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Parameters
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----------
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detected_objects : ImageDescription
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ImageDescription that was returned from the computer vision api
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image_path: str
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path to the location of the image file that was used in object
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detection
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'''
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# Create figure and plot
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fig, ax = plt.subplots(1)
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if len(detected_objects.objects) == 0:
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print("No objects detected.")
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else:
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for object in detected_objects.objects:
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rect = patches.Rectangle(
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(object.rectangle.x, object.rectangle.y),
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object.rectangle.w, object.rectangle.h,
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linewidth=1,
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edgecolor='r',
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facecolor='none'
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)
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plt.text(
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object.rectangle.x,
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object.rectangle.y,
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object.object_property,
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color='w'
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)
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ax.add_patch(rect)
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local_image = open(image_path, "rb")
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image = Image.open(local_image)
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ax.imshow(image)
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plt.axis("off")
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plt.show()
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if __name__ == "__main__":
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try:
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path_to_image = take_picture()
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detected_objects = detect_objects(path_to_image)
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frame_objects(detected_objects, path_to_image)
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except KeyboardInterrupt:
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print('Script Stopped')
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#!/bin/bash
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# Setup Virtual Environment
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echo "Python virtual environment creation script"
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python -m venv ./.venv --system-site-packages
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echo "Virtual evnironment created"
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source ./.venv/bin/activate
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echo "Virtual enviornment activated"
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pip install -r requirements.txt
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echo SUBSCRIPTION_KEY= >> .env
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echo ENDPOINT= >>> .env
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echo "Dependencies installed"
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sleep 5
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# Using Azure Computer Vision on a Raspberry Pi
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## Overview
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## Prerequisites
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1. An active Azure account. If you don't have one, you can sign up for a [free account](https://azure.microsoft.com/free/).
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1. [VS Code](https://code.visualstudio.com/Download)
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1. Hardware listed below
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### Hardware
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| Item | Description | Link |
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| Raspberry Pi 3 or 4 | Single board computer | [Adafruit](https://www.adafruit.com/product/4292) |
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| Raspberry Pi Camera V2 | Ribbon camera for the Pi | [Adafruit](https://www.adafruit.com/product/3099) |
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| USB C power supply (Pi 4) | Power cable for Raspberry Pi 4 | [Adafruit](https://www.adafruit.com/product/4298) |
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| Micro USB USB power supply (Pi 3) | Power cable for Raspberry Pi 3 | [Adafruit](https://www.adafruit.com/product/1995) |
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| SD Card with Raspberry Pi OS | Operating system for the Pi | [Adafruit](https://www.adafruit.com/product/2820) |
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## Setup Azure Resources
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| Resource | Description | Link |
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| Computer Vision | Computer vision API from Azure | [Azure](https://azure.microsoft.com/en-us/services/cognitive-services/computer-vision/) |
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### Preparing your environment
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1. If you haven't already, clone this repo to your computer
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1. Open command prompt or terminal and navigate to *pi-azure-recipes*
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1. In command prompt or terminal type and run ```code 03_cv```. This will open the project folder in VS Code
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### Create a computer vision resource
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1. First you'll need to create [a computer vision resource](https://ms.portal.azure.com/#create/Microsoft.CognitiveServicesComputerVision)
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1. Select your subscription
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1. For resource group select *Create new*, and give your resource group a name
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1. Select a Location that is near you
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1. Give your computer vision resource a name
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1. Select *Free F0* for the pricing tier
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> Note: You can only have one free tier active per account
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1. After your resource is deployed, select *Go to resource*
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1. You will need the key and endpoint from this resource to connect your Raspberry Pi to it. Select *Keys and Endpoints* from the left navigation.
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1. Make a note of the key and endpoint for your resource, you'll use these later on your Pi.
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> Rember to treat these like passwords
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### Setup you Raspberry Pi Device
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1. Connect your Raspberry Pi to a monitor and keyboard or use the the instructions [here](https://github.com/microsoft/rpi-resources/tree/master/headless-setup) to setup your pi for SSH
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1. Set up your Pi camera using [this guide](https://www.raspberrypi.org/documentation/configuration/camera.md)
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1. Using a USB drive or an SSH file transfer software copy the *client* folder to the Pi
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1. Run the *python_environment_setup.sh* shell script
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1. Once the script finishes navigate to the *client* folder press **Ctrl + H* to show hidden files.
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1. Open the newly created *.env* file in a text editor and fill in your key and endpoint
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```
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SUBSCRIPTION_KEY='YOUR-SUBSCRIPTION-KEY'
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ENDPOINT='YOUR-ENDPOINT'
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```
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1. Then type
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```sh
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python3 cv_pi_client.py
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```
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1. You should see the picture the camera took and what the Computer Vision service was able to identify in it.
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## Clean up Resources
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If you keep the resources you provisioned you'll continue to incur costs on them. The steps below will walk you through how to clean up your resources.
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1. In the Azure portal navigate to the resource group you created earlier
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1. In the Resource group page, review the list of included resources, and verify that they are the ones you want to delete
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1. Select Delete resource group, and follow the instructions.
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Deletion may take a couple of minutes. When it's done, a notification appears for a few seconds. You can also select the bell icon at the top of the page to view the notification.
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@ -0,0 +1,3 @@
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azure-cognitiveservices-vision-computervision
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python-dotenv
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matplotlib
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@ -20,7 +20,8 @@ If there is something you'd like to know how to do using Azure and a Raspberry P
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| Recipe | Description | Time | Prerequisites |
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|--------|-------------|------|---------------|
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| [01 IoT Hub d2c](./01_iot) | Send telemetry to Azure table storage using IoT Hub and Azure functions. | 25 Mins | None |
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| [02 IoT Hub c2d](./02_c2d_messages) | Trigger events on your Raspberry Pi using Azure functions and IoT Hub | 15 mins | None
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| [02 IoT Hub c2d](./02_c2d_messages) | Trigger events on your Raspberry Pi using Azure functions and IoT Hub | 15 mins | None |
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| [03 Computer Vision](./03_cv) | Use the computer vision cognitive service to detect objects in an image | 20 Mins | None |
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## Resources
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