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"""app.py |
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Automatically generated by Colab. |
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Original file is located at |
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https://colab.research.google.com/drive/15qyKzD_stNTm2T02dcfigCUwBCWCvhC9 |
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""" |
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import boto3 |
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import gradio as gr |
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import os |
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from PIL import Image, ImageDraw, ImageFont |
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from io import BytesIO |
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aws_access_key_id='ASIA47CRXKELALROPM2U' |
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aws_secret_access_key='IIsyEKSpnAVhhDCim2r0L5B/tct2SC5XLfOGpxml' |
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aws_session_token='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' |
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aws_region = 'us-east-1' |
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bucket_nom = "rekognitioneac5" |
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bucket_name = bucket_nom |
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client = boto3.client( |
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's3', |
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aws_access_key_id= aws_access_key_id, |
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aws_secret_access_key= aws_secret_access_key, |
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aws_session_token= aws_session_token, |
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region_name = aws_region |
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) |
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rekognition = boto3.client( |
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'rekognition', |
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aws_access_key_id= aws_access_key_id, |
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aws_secret_access_key= aws_secret_access_key, |
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aws_session_token= aws_session_token, |
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region_name = aws_region |
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) |
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def pujar_imatge_procesar_capses_s3(ruta_imatge): |
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resultats = [] |
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objectName = os.path.basename(ruta_imatge) |
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client.upload_file( |
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ruta_imatge, |
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bucket_name, |
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objectName |
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) |
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response = rekognition.detect_faces( |
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Image={ |
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'S3Object': { |
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'Bucket': bucket_name, |
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'Name': objectName |
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} |
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}, |
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Attributes=['ALL'] |
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) |
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image_file = client.get_object( |
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Bucket=bucket_name, |
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Key=objectName) |
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image_bytes = image_file['Body'].read() |
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image = Image.open(BytesIO(image_bytes)) |
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dibuix = ImageDraw.Draw(image) |
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font_text = ImageFont.load_default() |
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for i, face in enumerate(response['FaceDetails'], 1): |
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resultats.append(f"\nCara {i}:") |
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resultats.append(f"Edat: {face['AgeRange']['Low']} - {face['AgeRange']['High']} anys") |
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resultats.append(f"Sexe: {face['Gender']['Value']}") |
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resultats.append(f"Emocio: {face['Emotions'][0]['Type']}") |
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bounding_box = face["BoundingBox"] |
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left = int(bounding_box["Left"] * image.width) |
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top = int(bounding_box["Top"] * image.height) |
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width = int(bounding_box["Width"] * image.width) |
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height = int(bounding_box["Height"] * image.height) |
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dibuix.rectangle([left, top, left + width, top + height], outline="red", width=2) |
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dibuix.text((left, top - 20), f"Cara {i}", font=font_text, fill="blue") |
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return "\n".join(resultats),image |
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with gr.Blocks() as demo: |
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gr.Markdown("# Analisi de cares amb Amazon Rekognition") |
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with gr.Row(): |
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with gr.Column(): |
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imatge_entrada = gr.Image(type="filepath",label="Carreguem la imatge") |
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with gr.Column(): |
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text_sortida = gr.Textbox(label="Resultat") |
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imatge_resultant = gr.Image(type="pil",label="Imatges amb rectangles") |
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imatge_entrada.change(fn=pujar_imatge_procesar_capses_s3, inputs=imatge_entrada, |
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outputs=[text_sortida,imatge_resultant]) |
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demo.launch() |
