Image analysis lab

analyze-image.py

from dotenv import load_dotenv
import os
from array import array
from PIL import Image, ImageDraw
import sys
import time
from matplotlib import pyplot as plt
import numpy as np

# import namespaces
import azure.ai.vision as sdk



def main():
    global cv_client

    try:
        # Get Configuration Settings
        load_dotenv()
        ai_endpoint = os.getenv('AI_SERVICE_ENDPOINT')
        ai_key = os.getenv('AI_SERVICE_KEY')

        # Get image
        image_file = 'images/street.jpg'
        if len(sys.argv) > 1:
            image_file = sys.argv[1]

        # Authenticate Azure AI Vision client
        cv_client = sdk.VisionServiceOptions(ai_endpoint, ai_key)
        
        # Analyze image
        AnalyzeImage(image_file, cv_client)

        # Generate thumbnail
        BackgroundForeground(image_file, cv_client)

    except Exception as ex:
        print(ex)


def AnalyzeImage(image_file, cv_client):
    print('\nAnalyzing', image_file)

    # Specify features to be retrieved
    analysis_options = sdk.ImageAnalysisOptions()

    features = analysis_options.features = (
        sdk.ImageAnalysisFeature.CAPTION |
        sdk.ImageAnalysisFeature.DENSE_CAPTIONS |
        sdk.ImageAnalysisFeature.TAGS |
        sdk.ImageAnalysisFeature.OBJECTS |
        sdk.ImageAnalysisFeature.PEOPLE
    )

    # Get image analysis
    image = sdk.VisionSource(image_file)

    image_analyzer = sdk.ImageAnalyzer(cv_client, image, analysis_options)

    result = image_analyzer.analyze()

    if result.reason == sdk.ImageAnalysisResultReason.ANALYZED:
        # Get image captions
        if result.caption is not None:
            print("\nCaption:")
            print(" Caption: '{}' (confidence: {:.2f}%)".format(result.caption.content, result.caption.confidence * 100))

        # Get image dense captions
        if result.dense_captions is not None:
            print("\nDense Captions:")
            for caption in result.dense_captions:
                print(" Caption: '{}' (confidence: {:.2f}%)".format(caption.content, caption.confidence * 100))

        # Get image tags
        if result.tags is not None:
            print("\nTags:")
            for tag in result.tags:
                print(" Tag: '{}' (confidence: {:.2f}%)".format(tag.name, tag.confidence * 100))

        # Get objects in the image
        if result.objects is not None:
            print("\nObjects in image:")

            # Prepare image for drawing
            image = Image.open(image_file)
            fig = plt.figure(figsize=(image.width/100, image.height/100))
            plt.axis('off')
            draw = ImageDraw.Draw(image)
            color = 'cyan'

            for detected_object in result.objects:
                # Print object name
                print(" {} (confidence: {:.2f}%)".format(detected_object.name, detected_object.confidence * 100))

                # Draw object bounding box
                r = detected_object.bounding_box
                bounding_box = ((r.x, r.y), (r.x + r.w, r.y + r.h))
                draw.rectangle(bounding_box, outline=color, width=3)
                plt.annotate(detected_object.name,(r.x, r.y), backgroundcolor=color)

            # Save annotated image
            plt.imshow(image)
            plt.tight_layout(pad=0)
            outputfile = 'objects.jpg'
            fig.savefig(outputfile)
            print('  Results saved in', outputfile)

        # Get people in the image
    # Get people in the image
    if result.people is not None:
        print("\nPeople in image:")

        # Prepare image for drawing
        image = Image.open(image_file)
        fig = plt.figure(figsize=(image.width/100, image.height/100))
        plt.axis('off')
        draw = ImageDraw.Draw(image)
        color = 'cyan'

        for detected_people in result.people:
            # Draw object bounding box
            r = detected_people.bounding_box
            bounding_box = ((r.x, r.y), (r.x + r.w, r.y + r.h))
            draw.rectangle(bounding_box, outline=color, width=3)

            # Return the confidence of the person detected
            #print(" {} (confidence: {:.2f}%)".format(detected_people.bounding_box, detected_people.confidence * 100))

        # Save annotated image
        plt.imshow(image)
        plt.tight_layout(pad=0)
        outputfile = 'people.jpg'
        fig.savefig(outputfile)
        print('  Results saved in', outputfile)

    else:
        error_details = sdk.ImageAnalysisErrorDetails.from_result(result)
        print(" Analysis failed.")
        print("   Error reason: {}".format(error_details.reason))
        print("   Error code: {}".format(error_details.error_code))
        print("   Error message: {}".format(error_details.message))



def BackgroundForeground(image_file, cv_client):
    # Remove the background from the image or generate a foreground matte
    print('\nRemove the background from the image or generate a foreground matte')

    image = sdk.VisionSource(image_file)

    analysis_options = sdk.ImageAnalysisOptions()

    # Set the image analysis segmentation mode to background or foreground
    analysis_options.segmentation_mode = sdk.ImageSegmentationMode.BACKGROUND_REMOVAL

    image_analyzer = sdk.ImageAnalyzer(cv_client, image, analysis_options)

    result = image_analyzer.analyze()

    if result.reason == sdk.ImageAnalysisResultReason.ANALYZED:

        image_buffer = result.segmentation_result.image_buffer
        print(" Segmentation result:")
        print("   Output image buffer size (bytes) = {}".format(len(image_buffer)))
        print("   Output image height = {}".format(result.segmentation_result.image_height))
        print("   Output image width = {}".format(result.segmentation_result.image_width))

        output_image_file = "newimage.jpg"
        with open(output_image_file, 'wb') as binary_file:
            binary_file.write(image_buffer)
        print("   File {} written to disk".format(output_image_file))

    else:

        error_details = sdk.ImageAnalysisErrorDetails.from_result(result)
        print(" Analysis failed.")
        print("   Error reason: {}".format(error_details.reason))
        print("   Error code: {}".format(error_details.error_code))
        print("   Error message: {}".format(error_details.message))
        print(" Did you set the computer vision endpoint and key?")



if __name__ == "__main__":
    main()

analyze-text.py

from dotenv import load_dotenv
import os

# Import namespaces
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient

def main():
    try:
        # Get Configuration Settings
        load_dotenv()
        ai_endpoint = os.getenv('AI_SERVICE_ENDPOINT')
        ai_key = os.getenv('AI_SERVICE_KEY')

        # Create client using endpoint and key
        credential = AzureKeyCredential(ai_key)
        ai_client = TextAnalyticsClient(endpoint=ai_endpoint, credential=credential)

        # Analyze each text file in the reviews folder
        reviews_folder = 'reviews'
        for file_name in os.listdir(reviews_folder):
            # Read the file contents
            print('\n-------------\n' + file_name)
            text = open(os.path.join(reviews_folder, file_name), encoding='utf8').read()
            print('\n' + text)

            # Get language
            detectedLanguage = ai_client.detect_language(documents=[text])[0]
            print('\nLanguage: {}'.format(detectedLanguage.primary_language.name))

            # Get sentiment
            sentimentAnalysis = ai_client.analyze_sentiment(documents=[text])[0]
            print("\nSentiment: {}".format(sentimentAnalysis.sentiment))

            # Get key phrases
            phrases = ai_client.extract_key_phrases(documents=[text])[0].key_phrases
            if len(phrases) > 0:
                print("\nKey Phrases:")
                for phrase in phrases:
                    print('\t{}'.format(phrase))


            # Get entities
            entities = ai_client.recognize_entities(documents=[text])[0].entities
            if len(entities) > 0:
                print("\nEntities")
                for entity in entities:
                    print('\t{} ({})'.format(entity.text, entity.category))


            # Get linked entities
            entities = ai_client.recognize_linked_entities(documents=[text])[0].entities
            if len(entities) > 0:
                print("\nLinks")
                for linked_entity in entities:
                    print('\t{} ({})'.format(linked_entity.name, linked_entity.url))



    except Exception as ex:
        print(ex)


if __name__ == "__main__":
    main()

nlp-azure-openai.py

import os
from dotenv import load_dotenv

# Add Azure OpenAI package
from openai import AzureOpenAI

def main(): 
        
    try: 
    
        # Get configuration settings 
        load_dotenv()
        azure_oai_endpoint = os.getenv("AZURE_OAI_ENDPOINT")
        azure_oai_key = os.getenv("AZURE_OAI_KEY")
        azure_oai_model = os.getenv("AZURE_OAI_MODEL")
        
        # Read text from file
        text = open(file="../text-files/sample-text.txt", encoding="utf8").read()
        
        print("\nSending request for summary to Azure OpenAI endpoint...\n\n")
        
        client = AzureOpenAI(
                azure_endpoint = azure_oai_endpoint, 
                api_key=azure_oai_key,  
                api_version="2023-05-15"
        )

        # Send request to Azure OpenAI model
        response = client.chat.completions.create(
            model=azure_oai_model,
            temperature=0.7,
            max_tokens=120,
            messages=[
                {"role": "system", "content": "You are a helpful assistant."},
                {"role": "user", "content": "Summarize the following text in 20 words or less:\n" + text}
            ]
        )

        print("Summary: " + response.choices[0].message.content + "\n")

    except Exception as ex:
        print(ex)

if __name__ == '__main__': 
    main()

pip-command.sh

pip install azure-ai-vision==0.15.1b1

prompt-engineering.py

import os
from dotenv import load_dotenv

# Add Azure OpenAI package
from openai import AzureOpenAI

# Set to True to print the full response from OpenAI for each call
printFullResponse = False

def main(): 
        
    try: 
    
        # Get configuration settings 
        load_dotenv()
        azure_oai_endpoint = os.getenv("AZURE_OAI_ENDPOINT")
        azure_oai_key = os.getenv("AZURE_OAI_KEY")
        azure_oai_model = os.getenv("AZURE_OAI_MODEL")
        
        # Configure the Azure OpenAI client
        client = AzureOpenAI(
                azure_endpoint = azure_oai_endpoint, 
                api_key=azure_oai_key,  
                api_version="2023-05-15"
                )   

        while True:
            print('1: Basic prompt (no prompt engineering)\n' +
                  '2: Prompt with email formatting and basic system message\n' +
                  '3: Prompt with formatting and specifying content\n' +
                  '4: Prompt adjusting system message to be light and use jokes\n' +
                  '\'quit\' to exit the program\n')
            command = input('Enter a number:')
            if command == '1':
                call_openai_model(messages="../prompts/basic.txt", model=azure_oai_model, client=client)
            elif command =='2':
                call_openai_model(messages="../prompts/email-format.txt", model=azure_oai_model, client=client)
            elif command =='3':
                call_openai_model(messages="../prompts/specify-content.txt", model=azure_oai_model, client=client)
            elif command =='4':
                call_openai_model(messages="../prompts/specify-tone.txt", model=azure_oai_model, client=client)
            elif command.lower() == 'quit':
                print('Exiting program...')
                break
            else :
                print("Invalid input. Please try again.")

    except Exception as ex:
        print(ex)

def call_openai_model(messages, model, client):
    # In this sample, each file contains both the system and user messages
    # First, read them into variables, strip whitespace, then build the messages array
    file = open(file=messages, encoding="utf8")
    system_message = file.readline().split(':', 1)[1].strip()
    user_message = file.readline().split(':', 1)[1].strip()

    # Print the messages to the console
    print("System message: " + system_message)
    print("User message: " + user_message)

    # Format and send the request to the model
    messages =[
        {"role": "system", "content": system_message},
        {"role": "user", "content": user_message},
    ]

    # Call the Azure OpenAI model
    response = client.chat.completions.create(
        model=model,
        messages=messages,
        temperature=0.7,
        max_tokens=800
    )

    if printFullResponse:
        print(response)

    print("Completion: \n\n" + response.choices[0].message.content + "\n")

if __name__ == '__main__': 
    main()

read-text-from-image.py

from dotenv import load_dotenv
import os
import time
from PIL import Image, ImageDraw
from matplotlib import pyplot as plt

# Import namespaces
import azure.ai.vision as sdk

def main():

    global cv_client

    try:
        # Get Configuration Settings
        load_dotenv()
        ai_endpoint = os.getenv('AI_SERVICE_ENDPOINT')
        ai_key = os.getenv('AI_SERVICE_KEY')

        # Authenticate Azure AI Vision client
        cv_client = sdk.VisionServiceOptions(ai_endpoint, ai_key)
        


        # Menu for text reading functions
        print('\n1: Use Read API for image (Lincoln.jpg)\n2: Read handwriting (Note.jpg)\nAny other key to quit\n')
        command = input('Enter a number:')
        if command == '1':
            image_file = os.path.join('images','Lincoln.jpg')
            GetTextRead(image_file)
        elif command =='2':
            image_file = os.path.join('images','Note.jpg')
            GetTextRead(image_file)
                

    except Exception as ex:
        print(ex)

def GetTextRead(image_file):
    print('\n')

    # Use Analyze image function to read text in image
    # Use Analyze image function to read text in image
    print('Reading text in {}\n'.format(image_file))

    analysis_options = sdk.ImageAnalysisOptions()

    features = analysis_options.features = (
        # Specify features to be retrieved
        sdk.ImageAnalysisFeature.TEXT

    )

    # Get image analysis
    image = sdk.VisionSource(image_file)

    image_analyzer = sdk.ImageAnalyzer(cv_client, image, analysis_options)

    result = image_analyzer.analyze()

    if result.reason == sdk.ImageAnalysisResultReason.ANALYZED:

        # Get image captions
        if result.text is not None:
            print("\nText:")

            # Prepare image for drawing
            image = Image.open(image_file)
            fig = plt.figure(figsize=(image.width/100, image.height/100))
            plt.axis('off')
            draw = ImageDraw.Draw(image)
            color = 'cyan'

            for line in result.text.lines:
                # Return the text detected in the image
                # Return the text detected in the image
                print(line.content)    

                drawLinePolygon = True

                r = line.bounding_polygon
                bounding_polygon = ((r[0], r[1]),(r[2], r[3]),(r[4], r[5]),(r[6], r[7]))

                # Return each line detected in the image and the position bounding box around each line



                # Return each word detected in the image and the position bounding box around each word with the confidence level of each word
                # Return each word detected in the image and the position bounding box around each word with the confidence level of each word
                for word in line.words:
                    r = word.bounding_polygon
                    bounding_polygon = ((r[0], r[1]),(r[2], r[3]),(r[4], r[5]),(r[6], r[7]))
                    print("  Word: '{}', Bounding Polygon: {}, Confidence: {}".format(word.content, bounding_polygon,word.confidence))

                    # Draw word bounding polygon
                    drawLinePolygon = False
                    draw.polygon(bounding_polygon, outline=color, width=3)


                # Draw line bounding polygon
                if drawLinePolygon:
                    draw.polygon(bounding_polygon, outline=color, width=3)


            # Save image
            plt.imshow(image)
            plt.tight_layout(pad=0)
            outputfile = 'text.jpg'
            fig.savefig(outputfile)
            print('\n  Results saved in', outputfile)



if __name__ == "__main__":
    main()

recognize-and-synthesize-speech.py

from dotenv import load_dotenv
from datetime import datetime
import os

# Import namespaces
import azure.cognitiveservices.speech as speech_sdk
from playsound import playsound

def main():
    try:
        global speech_config

        # Get Configuration Settings
        load_dotenv()
        ai_key = os.getenv('SPEECH_KEY')
        ai_region = os.getenv('SPEECH_REGION')

        # Configure speech service
        speech_config = speech_sdk.SpeechConfig(subscription=ai_key, region=ai_region)

        print('Ready to use speech service in:', speech_config.region)

        # Get spoken input
        command = TranscribeCommand()
        if command.lower() == 'what time is it?':
            TellTime()

    except Exception as ex:
        print(ex)

def TranscribeCommand():
    command = ''

    # Configure speech recognition
    current_dir = os.getcwd()
    audioFile = current_dir + '\\time.wav'
    playsound(audioFile)
    audio_config = speech_sdk.AudioConfig(filename=audioFile)
    speech_recognizer = speech_sdk.SpeechRecognizer(speech_config, audio_config)

    # Process speech input
    speech = speech_recognizer.recognize_once_async().get()

    print(speech.text)
    if speech.reason == speech_sdk.ResultReason.RecognizedSpeech:
        command = speech.text
        print(command)
    else:
        print(speech.reason)
        if speech.reason == speech_sdk.ResultReason.Canceled:
            cancellation = speech.cancellation_details
            print(cancellation.reason)
            print(cancellation.error_details)

    # Return the command
    return command


def TellTime():
    now = datetime.now()
    response_text = 'The time is {}:{:02d}'.format(now.hour,now.minute)


    # Configure speech synthesis
    speech_config.speech_synthesis_voice_name = "en-GB-RyanNeural"
    speech_synthesizer = speech_sdk.SpeechSynthesizer(speech_config)  

    # Synthesize spoken output
    speak = speech_synthesizer.speak_text_async(response_text).get()
    if speak.reason != speech_sdk.ResultReason.SynthesizingAudioCompleted:
        print(speak.reason)

    # Print the response
    print(response_text)


if __name__ == "__main__":
    main()

A dense enchanted forest where treehouses are connected by suspended bridges, and diverse elves and creatures celebrate under giant luminescent mushrooms