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import gradio as gr
from PIL import Image, ImageDraw
from io import BytesIO
import boto3
# Inicieu un laboratori a AWS i copieu les credencials de la sessió (AWS CLI)
ACCESS_KEY='ASIA47CRWWK5XKZMPTTZ'
SECRET_KEY='Xn/8P+HXMq8aQF2ZjKQw78vPOQ4eQFTA7x+dppOE'
SESSION_TOKEN='IQoJb3JpZ2luX2VjENb//////////wEaCXVzLXdlc3QtMiJIMEYCIQDYWsAhaEVjsOLcA0MIzQ2qRnLojp4GF2PpAJvK3NSCNgIhALfrOzfHBkfhpe2j/8N3r4ojUY8EVTSdmx4wMLrJj2LbKqMCCG8QABoMODkxMzc3MDcwNzc5IgyEYmwqgac9JNnYvyQqgALV5+iV04/MXnA9qCZCT87zVoUtq9xyYoAUh2T7PiitBU5dN+nEYojC1C8r4TD0Lbfe/9S20V8srAMwet6XEsybHIHu92FjVFHlzTlPYM3w0PiJg1km/X/4BEUo1J5htZSDThEL00euy4zOnXH93Xyia+tezh79kXYlVYkD7AC3WLxvQoPvfgOjiQXrSp6qYpdSvMCDZPSG2Sw48OpYH8BVyBaGhkan2pVmBKFWl2Jwh3HdTuNaoQff5XaW+syNXglWgeI7I1Mty0IbbiF3qCVAFiqtX2dHu6ol7TcMAUWcI1wubkDsPUVMbPOSWjiDOH/Gxr/gnPem4mveAWGqrgRKMOmwvMAGOpwB7K2oqI+KxwP01iHMT4GsI6b+L/a/HarsJr7kML43zlxQz53gFxmlD55HPDvlDBiKm7lEm8PGSOXzOkn28mW5Ro0Yd4W+z5vHpddWMDUnE/2V9Q9vvoKfXHOGUAQVjhqCZPINn8g39TUAyK35v1HgBVxQy/anthk7KSSJR9cSbluOBp47gPDSNKEyepRXJps4TZDn2tVUfPUrTMBd'
s3_client = boto3.client('s3', aws_access_key_id=ACCESS_KEY, aws_secret_access_key=SECRET_KEY, aws_session_token=SESSION_TOKEN)
rekognition = boto3.client('rekognition', region_name='us-east-1', aws_access_key_id=ACCESS_KEY, aws_secret_access_key=SECRET_KEY, aws_session_token=SESSION_TOKEN)
s3BucketName = 'eac5'
rutes = ["biblioteca.jpg", "skyline_tarragona.jpg"]
def procesar(input_image):
#s3_client = boto3.client('s3')
#rekognition = boto3.client('rekognition')
s3_client.upload_file(input_image, s3BucketName, input_image)
#Font exercici de detecció de cares
image_file = s3_client.get_object(
Bucket= "eac5",
Key= input_image)
image_bytes = image_file['Body'].read()
image = Image.open(BytesIO(image_bytes))
response = rekognition.detect_labels(
Image={
'S3Object': {
'Bucket': s3BucketName,
'Name': input_image
}
})
resultats = []
# Crear un objecte ImageDraw
draw = ImageDraw.Draw(image)
for instance in response["Labels"]:
#Busquem les noms i la confiança i les imprimim
nom = instance["Name"]
grau = instance["Confidence"]
resultats.append('%s %d%%' % (nom, grau))
#Busquem les capses i les dibuixem
for instance_label in instance["Instances"]:
bounding_box = instance_label["BoundingBox"]
# Calcular les coordenades de píxels per al quadre delimitador
left = int(bounding_box["Left"] * image.width)
top = int(bounding_box["Top"] * image.height)
width = int(bounding_box["Width"] * image.width)
height = int(bounding_box["Height"] * image.height)
# Dibuixar el quadre delimitador a la imatge
draw.rectangle([left, top, left + width, top + height], outline="red", width=2)
return "\n".join(resultats), image
example1 = s3_client.get_object(
Bucket= s3BucketName,
Key= "biblioteca.jpg")
example1_bytes = example1['Body'].read()
image1 = Image.open(BytesIO(example1_bytes))
example2 = s3_client.get_object(
Bucket= s3BucketName,
Key= "skyline_tarragona.jpg")
example2_bytes = example2['Body'].read()
image2 = Image.open(BytesIO(example2_bytes))
text_output = gr.Textbox(lines=10, label="Results")
with gr.Blocks() as demo:
with gr.Row():
gr.Markdown(
"""
Label Detection
""")
with gr.Row():
with gr.Column():
image_input = gr.Image(type='filepath')
with gr.Row():
examples = gr.Examples(rutes, inputs=[image_input], fn=procesar, outputs=[text_output, image_input], cache_examples=True, label="Choose a sample image")
upload_button = gr.UploadButton("Use your own image", file_types=["image"], file_count="single")
with gr.Column():
text_output.render()
upload_button.upload(procesar, upload_button, [text_output, image_input])
demo.launch() |