File size: 1,870 Bytes
4ba40aa 637eff3 4ba40aa 637eff3 4ba40aa 637eff3 4ba40aa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | import os
import numpy as np
import requests
import base64
from PIL import Image
import io
# URL del endpoint proporcionado por Hugging Face
# ENDPOINT_URL = "https://qh7glc3xj9iw4tk2.eu-west-1.aws.endpoints.huggingface.cloud"
ENDPOINT_URL = os.environ.get("ENDPOINT_URL", "")
# Token de API de Hugging Face
# API_TOKEN = "hf_..."
API_TOKEN = os.environ.get("API_TOKEN", "")
headers = {
"Authorization": f"Bearer {API_TOKEN}",
"Content-Type": "application/json"
}
# Cargar y codificar una imagen
# image = Image.open("mine.jpeg")
# buffered = io.BytesIO()
# image.save(buffered, format="JPEG")
# img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
# Preparar los datos para la solicitud
# payload = {
# "inputs" : {
#
# },
# "file" : img_str,
# "visualization": True
# }
#----------------------------------------------------------------------------------
points = [[0, 0]]
# Preparar los datos para la solicitud
payload = {
"inputs" : {
},
"url" : "https://images.unsplash.com/photo-1586023492125-27b2c045efd7?fm=jpg&q=60&w=3000&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxzZWFyY2h8Mnx8aW50ZXJpb3IlMjBkZXNpZ258ZW58MHx8MHx8fDA%3D",
# "visualization": False,
"points": points
}
# Enviar la solicitud
response = requests.post(ENDPOINT_URL, headers=headers, json=payload)
# Procesar la respuesta
if response.status_code == 200:
result = response.json()
if "visualization" in result:
# Decodificar y guardar la visualización
vis_bytes = base64.b64decode(result["visualization"])
with open("depth_visualization.png", "wb") as f:
f.write(vis_bytes)
print("Visualización guardada como 'depth_visualization.png'")
print(f"Profundidad: {result.get('deph')}")
else:
print(f"Error: {response.status_code}")
print(response.text)
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