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import pickle
from minisom import MiniSom
import numpy as np
import cv2
import urllib.request
import uuid
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List
class InputData(BaseModel):
data: str # image url
app = FastAPI()
# Funci贸n para construir el modelo manualmente
def build_model():
with open('somlucuma.pkl', 'rb') as fid:
somecoli = pickle.load(fid)
MM = np.loadtxt('matrizMM.txt', delimiter=" ")
return somecoli,MM
som,MM = build_model() # Construir el modelo al iniciar la aplicaci贸n
from scipy.ndimage import median_filter
from scipy.signal import convolve2d
def sobel(patron):
gx = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], dtype=np.float32)
gy = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]], dtype=np.float32)
Gx = convolve2d(patron, gx, mode='valid')
Gy = convolve2d(patron, gy, mode='valid')
return Gx, Gy
def medfilt2(G, d=3):
return median_filter(G, size=d)
def orientacion(patron, w):
Gx, Gy = sobel(patron)
Gx = medfilt2(Gx)
Gy = medfilt2(Gy)
m, n = Gx.shape
mOrientaciones = np.zeros((m // w, n // w), dtype=np.float32)
for i in range(m // w):
for j in range(n // w):
Gx_patch = Gx[i*w:(i+1)*w, j*w:(j+1)*w]
Gy_patch = Gy[i*w:(i+1)*w, j*w:(j+1)*w]
YY = np.sum(2 * Gx_patch * Gy_patch)
XX = np.sum(Gx_patch**2 - Gy_patch**2)
mOrientaciones[i, j] = (0.5 * np.arctan2(YY, XX) + np.pi / 2.0) * (18.0 / np.pi)
return mOrientaciones
def redimensionar(img, h, v):
return cv2.resize(img, (h, v), interpolation=cv2.INTER_AREA)
def prediction(som, imgurl):
archivo = f"/tmp/test-{uuid.uuid4()}.jpg"
urllib.request.urlretrieve(imgurl, archivo)
Xtest = redimensionar(cv2.imread(archivo),256,256)
Xtest = np.array(Xtest)
Xtest = cv2.cvtColor(Xtest, cv2.COLOR_BGR2GRAY)
orientaciones = orientacion(Xtest, w=14)
Xtest = Xtest.astype('float32') / 255.0
orientaciones = orientaciones.reshape(-1)
return som.winner(orientaciones)
# Ruta de predicci贸n
@app.post("/predict/")
async def predict(data: InputData):
print(f"Data: {data}")
global som
global MM
try:
# Convertir la lista de entrada a un array de NumPy para la predicci贸n
imgurl = data.data
print(type(data.data))
w = prediction(som, imgurl)
return {"prediction": MM[w]}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e)) |