aws_rekognition_test.py

import boto3
import time
from datetime import timedelta
import sys
import json
import os


#We need to get our API credentials in the code for authentication that we have stored as Environment Variables locally.
os.environ.get("AWS_ACCESS_KEY_ID")
os.environ.get("AWS_SECRET_ACCESS_KEY")
AWS_REGION = os.environ.get("AWS_REGION")


#Following line is used to save all the console outputs in a text file.
sys.stdout = open('rekognition_output.txt','w')

start_time = time.monotonic()

BUCKET = "kontiki-amazon-rekognition"
KEY = "tom.jpg"


#Detect Labels
def detect_labels(bucket, key, max_labels = 10, min_confidence = 90, region = AWS_REGION):
	rekognition = boto3.client("rekognition", region) #Initialize amazon_rekognition client function
	response = rekognition.detect_labels(
		Image = {
			"S3Object": {
				"Bucket": bucket,
				"Name": key,
			}
		},
		MaxLabels = max_labels,
		MinConfidence = min_confidence,
	)
	return response['Labels']

print('Detected Labels for:' + KEY)
for label in detect_labels(BUCKET, KEY):
	print("{Name} - {Confidence}%".format(**label))
print('\n')


#Detect Faces
def detect_faces(bucket, key, attributes = ['ALL'], region = AWS_REGION):
	rekognition = boto3.client("rekognition", region) #Initialize amazon_rekognition client function
	response = rekognition.detect_faces(
	    Image = {
			"S3Object": {
				"Bucket": bucket,
				"Name": key,
			}
		},
	    Attributes = attributes,
	)
	return response['FaceDetails']

c = detect_faces(BUCKET, KEY)

print('Detected Faces for:' + KEY)
for face in detect_faces(BUCKET, KEY):
	print("Face ({Confidence}%)".format(**face))
	#Emotions
	for emotion in face['Emotions']:
		print("{Type} : {Confidence}%".format(**emotion))
	#Quality
	for quality, value in face['Quality'].items():
		print("{quality} : {value}".format(quality = quality, value = value))
	#Facial Features
	for faceDetail in c:
		print('Here are the other attributes:')
		print(json.dumps(faceDetail, indent = 4, sort_keys = True))
print('\n')


#Compare Faces
KEY_SOURCE = "tom.jpg"
KEY_TARGET = "tom_cruise.jpg"

def compare_faces(bucket, key, bucket_target, key_target, threshold = 80, region = AWS_REGION):
	rekognition = boto3.client("rekognition", region) #Initialize amazon_rekognition client function
	response = rekognition.compare_faces(
	    SourceImage = {
			"S3Object": {
				"Bucket": bucket,
				"Name": key,
			}
		},
		TargetImage = {
			"S3Object": {
				"Bucket": bucket_target,
				"Name": key_target,
			}
		},
	    SimilarityThreshold = threshold,
	)
	return response['SourceImageFace'], response['FaceMatches']


source_face, matches = compare_faces(BUCKET, KEY_SOURCE, BUCKET, KEY_TARGET)

print("Detected Similarity between", KEY_SOURCE + " and " + KEY_TARGET)
#Main Source Face
print("Source Face ({Confidence}%)".format(**source_face))

#One match for each Target Face
for match in matches:
	print("Target Face ({Confidence}%)".format(**match['Face']))
	print("Similarity : {}%".format(match['Similarity']))
print('\n')


#Index Faces
KEY = "tom.jpg"
IMAGE_ID = KEY  # S3 key as ImageId
COLLECTION = "mygallery"

# Note: you have to create the collection first!
def index_faces(bucket, key, collection_id, image_id = None, attributes = (), region = AWS_REGION):
	rekognition = boto3.client("rekognition", region) #Initialize amazon_rekognition client function
	#rekognition.create_collection(CollectionId=COLLECTION)  #If you're executing this code snippet to index faces in an image for the 'first time', just uncomment this line to create the collection first!
	response = rekognition.index_faces(
		Image = {
			"S3Object": {
				"Bucket": bucket,
				"Name": key,
			}
		},
		CollectionId = collection_id,
		ExternalImageId = image_id,
	    DetectionAttributes = attributes,
	)
	return response['FaceRecords']
print("Detected Faces in:", KEY)
count = 0
for record in index_faces(BUCKET, KEY, COLLECTION, IMAGE_ID):
	count += 1
	face = record['Face']
	
	print("Face ({}%)".format(face['Confidence']))
	print("FaceId: {}".format(face['FaceId']))
	print("ImageId: {}".format(face['ImageId']))
print("Total no. of Faces Detected in ", KEY , ":", count)
print('\n')


#Search Faces
KEY = "tom_cruise.jpg"
COLLECTION = "mygallery"

def search_faces_by_image(bucket, key, collection_id, threshold = 80, region = AWS_REGION):
	rekognition = boto3.client("rekognition", region)
	response = rekognition.search_faces_by_image(
		Image = {
			"S3Object": {
				"Bucket": bucket,
				"Name": key,
			}
		},
		CollectionId = collection_id,
		FaceMatchThreshold = threshold,
	)
	return response['FaceMatches']

print("Searched for:", KEY, "in", COLLECTION)
for record in search_faces_by_image(BUCKET, KEY, COLLECTION):
	face = record['Face']
	print("Matched Face ({}%)".format(record['Similarity']))
	print("FaceId : {}".format(face['FaceId']))
	print("ImageId : {}".format(face['ExternalImageId']))
print('\n')


end_time = time.monotonic()
print("Execution_Time:", timedelta(seconds = end_time - start_time))