Source: https://github.com/MicrosoftLearning/mslearn-ai-vision
1. Azure AI services multi-service account - Create Azure resource.
Detect Faces:
Detect Faces:
C# Code:
using System;
using System.Drawing;
using Microsoft.Extensions.Configuration;
using Azure;
using System.IO;
// dotnet add package Azure.AI.Vision.ImageAnalysis -v 1.0.0-beta.3
// Import namespaces
using Azure.AI.Vision.ImageAnalysis;
namespace detect_people
{
class Program
{
static void Main(string[] args)
{
try
{
// Get config settings from AppSettings
IConfigurationBuilder builder =
new ConfigurationBuilder().AddJsonFile("appsettings.json");
IConfigurationRoot configuration = builder.Build();
string aiSvcEndpoint =
"https://sreemultiserviceaccount1.cognitiveservices.azure.com/";
//configuration["AIServicesEndpoint"];
string aiSvcKey = "2D9XtWQ0Yfuw3AAAEACOGFMV1"; //configuration["AIServiceKey"];
// Get image
string imageFile = "images/people.jpg";
if (args.Length > 0)
{
imageFile = args[0];
}
// Authenticate Azure AI Vision client
ImageAnalysisClient cvClient = new ImageAnalysisClient(
new Uri(aiSvcEndpoint),
new AzureKeyCredential(aiSvcKey));
// Analyze image
AnalyzeImage(imageFile, cvClient);
}
catch (Exception ex)
{
Console.WriteLine(ex.Message);
}
}
static void AnalyzeImage(string imageFile, ImageAnalysisClient client)
{
Console.WriteLine($"\nAnalyzing {imageFile} \n");
// Use a file stream to pass the image data to the analyze call
using FileStream stream = new FileStream(imageFile, FileMode.Open);
// Get result with specified features to be retrieved (PEOPLE)
ImageAnalysisResult result = client.Analyze(
BinaryData.FromStream(stream),
VisualFeatures.People);
// Close the stream
stream.Close();
// Get people in the image
if (result.People.Values.Count > 0)
{
Console.WriteLine($" People:");
// Prepare image for drawing
System.Drawing.Image image = System.Drawing.Image.FromFile(imageFile);
Graphics graphics = Graphics.FromImage(image);
Pen pen = new Pen(Color.Cyan, 3);
// Draw bounding box around detected people
foreach (DetectedPerson person in result.People.Values)
{
if (person.Confidence > 0.5)
{
// Draw object bounding box
var r = person.BoundingBox;
Rectangle rect = new Rectangle(r.X, r.Y, r.Width, r.Height);
graphics.DrawRectangle(pen, rect);
}
// Return the confidence of the person detected
Console.WriteLine($" Bounding box {person.BoundingBox.ToString()},
Confidence: {person.Confidence:F2}");
}
// Save annotated image
String output_file = "people.jpg";
image.Save(output_file);
Console.WriteLine(" Results saved in " + output_file + "\n");
}
}
}
}
OutPut:
Analyze Faces:
Analyze Faces:
C# Code:
using System;
using System.IO;
using System.Linq;
using System.Drawing;
using System.Collections.Generic;
using System.Threading.Tasks;
using Microsoft.Extensions.Configuration;
// dotnet add package Azure.AI.Vision.Face -v 1.0.0-beta.2
// Import namespaces
using Azure;
using Azure.AI.Vision.Face;
namespace analyze_faces
{
class Program
{
private static FaceClient faceClient;
static async Task Main(string[] args)
{
try
{
// Get config settings from AppSettings
IConfigurationBuilder builder =
new ConfigurationBuilder().AddJsonFile("appsettings.json");
IConfigurationRoot configuration = builder.Build();
string cogSvcEndpoint =
"https://sreemultiserviceaccount1.cognitiveservices.azure.com/";
//configuration["AIServicesEndpoint"];
string cogSvcKey = "2D9XtWQ0Yfuw3AAAEACOGFMV1"; //configuration["AIServiceKey"];
// Authenticate Face client
faceClient = new FaceClient(
new Uri(cogSvcEndpoint),
new AzureKeyCredential(cogSvcKey));
// Menu for face functions
Console.WriteLine("1: Detect faces\nAny other key to quit");
Console.WriteLine("Enter a number:");
string command = Console.ReadLine();
switch (command)
{
case "1":
await DetectFaces("images/people.jpg");
break;
default:
break;
}
}
catch (Exception ex)
{
Console.WriteLine(ex.Message);
}
}
static async Task DetectFaces(string imageFile)
{
Console.WriteLine($"Detecting faces in {imageFile}");
// Specify facial features to be retrieved
FaceAttributeType[] features = new FaceAttributeType[]
{
FaceAttributeType.Detection03.HeadPose,
FaceAttributeType.Detection03.Blur,
FaceAttributeType.Detection03.Mask
};
// Get faces
using (var imageData = File.OpenRead(imageFile))
{
var response = await faceClient.DetectAsync(
BinaryData.FromStream(imageData),
FaceDetectionModel.Detection03,
FaceRecognitionModel.Recognition04,
returnFaceId: false,
returnFaceAttributes: features);
IReadOnlyList<FaceDetectionResult> detected_faces = response.Value;
if (detected_faces.Count() > 0)
{
Console.WriteLine($"{detected_faces.Count()} faces detected.");
// Prepare image for drawing
Image image = Image.FromFile(imageFile);
Graphics graphics = Graphics.FromImage(image);
Pen pen = new Pen(Color.LightGreen, 3);
Font font = new Font("Arial", 4);
SolidBrush brush = new SolidBrush(Color.White);
int faceCount = 0;
// Draw and annotate each face
foreach (var face in detected_faces)
{
faceCount++;
Console.WriteLine($"\nFace number {faceCount}");
// Get face properties
Console.WriteLine($" - Head Pose (Yaw): {face.FaceAttributes.HeadPose.Yaw}");
Console.WriteLine($" - Head Pose (Pitch):
{face.FaceAttributes.HeadPose.Pitch}");
Console.WriteLine($" - Head Pose (Roll):
{face.FaceAttributes.HeadPose.Roll}");
Console.WriteLine($" - Blur: {face.FaceAttributes.Blur.BlurLevel}");
Console.WriteLine($" - Mask: {face.FaceAttributes.Mask.Type}");
// Draw and annotate face
var r = face.FaceRectangle;
Rectangle rect = new Rectangle(r.Left, r.Top, r.Width, r.Height);
graphics.DrawRectangle(pen, rect);
string annotation = $"Face number {faceCount}";
graphics.DrawString(annotation, font, brush, r.Left, r.Top);
}
// Save annotated image
String output_file = "detected_faces.jpg";
image.Save(output_file);
Console.WriteLine(" Results saved in " + output_file);
}
}
}
}
}
OutPut:
Detect Faces:
Python Code:
OutPut:
from dotenv import load_dotenv
import os
from PIL import Image, ImageDraw
import sys
from matplotlib import pyplot as plt
import numpy as np
#pip install azure-ai-vision-imageanalysis==1.0.0b3
# import namespaces
from azure.ai.vision.imageanalysis import ImageAnalysisClient
from azure.ai.vision.imageanalysis.models import VisualFeatures
from azure.core.credentials import AzureKeyCredential
def main():
global cv_client
try:
# Get Configuration Settings
load_dotenv()
ai_endpoint = 'https://sreemultiserviceaccount1.cognitiveservices.azure.com/'
#os.getenv('AI_SERVICE_ENDPOINT')
ai_key = '2D9XtWQ0Yfuw3AAAEACOGFMV1' #os.getenv('AI_SERVICE_KEY')
# Get image
image_file = 'images/people.jpg'
if len(sys.argv) > 1:
image_file = sys.argv[1]
with open(image_file, "rb") as f:
image_data = f.read()
# Authenticate Azure AI Vision client
cv_client = ImageAnalysisClient(
endpoint=ai_endpoint,
credential=AzureKeyCredential(ai_key)
)
# Analyze image
AnalyzeImage(image_file, image_data, cv_client)
except Exception as ex:
print(ex)
def AnalyzeImage(filename, image_data, cv_client):
print('\nAnalyzing ', filename)
# Get result with specified features to be retrieved (PEOPLE)
result = cv_client.analyze(
image_data=image_data,
visual_features=[
VisualFeatures.PEOPLE],
)
# Identify people in the image
if result.people is not None:
print("\nPeople in image:")
# Prepare image for drawing
image = Image.open(filename)
fig = plt.figure(figsize=(image.width/100, image.height/100))
plt.axis('off')
draw = ImageDraw.Draw(image)
color = 'cyan'
# Draw bounding box around detected people
for detected_people in result.people.list:
if(detected_people.confidence > 0.5):
# Draw object bounding box
r = detected_people.bounding_box
bounding_box = ((r.x, r.y), (r.x + r.width, r.y + r.height))
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)
if __name__ == "__main__":
main()
OutPut:
Analyze Faces:
Python Code:
from dotenv import load_dotenv
import os
from PIL import Image, ImageDraw
from matplotlib import pyplot as plt
#pip install azure-cognitiveservices-vision-face==0.6.0
# Import namespaces
from azure.cognitiveservices.vision.face import FaceClient
from azure.cognitiveservices.vision.face.models import FaceAttributeType
from msrest.authentication import CognitiveServicesCredentials
def main():
global face_client
try:
# Get Configuration Settings
load_dotenv()
# cog_endpoint = os.getenv('AI_SERVICE_ENDPOINT')
# cog_key = os.getenv('AI_SERVICE_KEY')
cog_endpoint = 'https://sreemultiserviceaccount1.cognitiveservices.azure.com/'
cog_key ='2D9XtWQ0Yfuw3AAAEACOGFMV1'
# Authenticate Face client
credentials = CognitiveServicesCredentials(cog_key)
face_client = FaceClient(cog_endpoint, credentials)
# Menu for face functions
print('1: Detect faces\nAny other key to quit')
command = input('Enter a number:')
if command == '1':
DetectFaces(os.path.join('images','people.jpg'))
except Exception as ex:
print(ex)
def DetectFaces(image_file):
print('Detecting faces in', image_file)
# Specify facial features to be retrieved
features = [
FaceAttributeType.occlusion,
FaceAttributeType.blur,
FaceAttributeType.glasses
]
# Get faces
with open(image_file, mode="rb") as image_data:
detected_faces = face_client.face.detect_with_stream(
image=image_data,
return_face_attributes=features,
return_face_id=False
)
if len(detected_faces) > 0:
print(len(detected_faces), 'faces detected.')
# Prepare image for drawing
fig = plt.figure(figsize=(8, 6))
plt.axis('off')
image = Image.open(image_file)
draw = ImageDraw.Draw(image)
color = 'lightgreen'
face_count = 0
# Draw and annotate each face
for face in detected_faces:
# Get face properties
face_count += 1
print('\nFace number {}'.format(face_count))
detected_attributes = face.face_attributes.as_dict()
if 'blur' in detected_attributes:
print(' - Blur:')
for blur_name in detected_attributes['blur']:
print(' - {}: {}'.format(blur_name, detected_attributes['blur'][blur_name]))
if 'occlusion' in detected_attributes:
print(' - Occlusion:')
for occlusion_name in detected_attributes['occlusion']:
print(' - {}: {}'.format(occlusion_name,
detected_attributes['occlusion'][occlusion_name]))
if 'glasses' in detected_attributes:
print(' - Glasses: {}'.format(detected_attributes['glasses']))
# Draw and annotate face
r = face.face_rectangle
bounding_box = ((r.left, r.top), (r.left + r.width, r.top + r.height))
draw.rectangle(bounding_box, outline=color, width=5)
annotation = 'Face number {}'.format(face_count)
plt.annotate(annotation, (r.left, r.top), backgroundcolor=color)
# Save annotated image
plt.imshow(image)
outputfile = 'detected_faces.jpg'
fig.savefig(outputfile)
print('\nResults saved in', outputfile)
if __name__ == "__main__":
main()
OutPut:
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