# construct string by attributes
formated_str = ' {gender} - age {lowage}-{highage},'
# mustache and beard detection
if details['Mustache']['Value'] and facedeets['Beard']['Value']:
formated_str += ' with beard and mustache,'
elif details['Mustache']['Value']:
formated_str += ' with mustache,'
elif details['Beard']['Value']:
formated_str += ' with beard,'
# Sunglasses/eyeglasses detection
if details['Sunglasses']['Value']:
formated_str += ' wearing sunglasses,'
elif details['Eyeglasses']['Value']:
formated_str += ' wearing glasses,'
formated_str += ' looks {emotion}'
print(
formated_str.format(
gender=details['Gender']['Value'],
lowage=details['AgeRange']['Low'],
highage=details['AgeRange']['High'],
emotion=details['Emotions'][0]['Type'].lower()
)
)
image = get_image_from_file('images/team1.jpeg')
facial_analysis(image)
# facial_analysis_data(image)
import boto3
from pprint import pprint
from image_helper import get_image_from_file, get_image_from_url
def compare_faces_data(image_source, image_target):
client = boto3.client('rekognition')
response = client.compare_faces(SimilarityThreshold=80,
SourceImage={'Bytes': image_source},
TargetImage={'Bytes': image_target})
pprint(response)
def compare_faces(image_source, image_target):
client = boto3.client('rekognition')
response = client.compare_faces(SimilarityThreshold=80,
SourceImage={'Bytes': image_source},
TargetImage={'Bytes': image_target})
for faceMatch in response['FaceMatches']:
similarity = faceMatch['Similarity']
print(f'Matches with {round(similarity, 2)}% confidence')
url_img_src = 'images/eu.jpg'
url_img_target = 'images/role1.jpeg'
img_src = get_image_from_file(url_img_src)
img_target = get_image_from_file(url_img_target)
# compare_faces(img_src, img_target)
compare_faces_data(img_src, img_target)
import boto3
from pprint import pprint
from image_helper import get_image_from_file, get_image_from_url
def detect_text_data(image_bytes):
client = boto3.client('rekognition')
response = client.detect_text(Image={'Bytes': image_bytes})
pprint(response)
def detect_text(image_bytes):
client = boto3.client('rekognition')
response = client.detect_text(Image={'Bytes': image_bytes})
textDetections = response['TextDetections']
for text in textDetections:
print('Detected text:' + text['DetectedText'])
print('Confidence: ' + "{:.2f}".format(text['Confidence']) + "%")
print()
return len(textDetections)
document = get_image_from_file('images/cnh.jpg')
card = get_image_from_file('images/card.png')
detect_text(card)
# detect_text_data(document)
import boto3
from pprint import pprint
from image_helper import get_image_from_file, get_image_from_url
def detect_labels_data(image_bytes):
client = boto3.client('rekognition')
response = client.detect_labels(Image={'Bytes': image_bytes}, MinConfidence=50)
pprint(response)
def detect_labels(image_bytes):
client = boto3.client('rekognition')
response = client.detect_labels(Image={'Bytes': image_bytes}, MinConfidence=50)
print('\n\nLabels:')
for label in response['Labels']:
label_name = label['Name']
label_confidence = label['Confidence']
print(f' {label_name} -- {round(label_confidence, 2)}%')
url = 'https://upload.wikimedia.org/wikipedia/commons/thumb/a/aa/Av_Francisco_Glic%C3%A9rio_-_Campinas_SP_-_panoramio.jpg/1200px-Av_Francisco_Glic%C3%A9rio_-_Campinas_SP_-_panoramio.jpg'
image = get_image_from_file('images/role1.jpeg')
detect_labels(image)
# detect_labels_data(image)