app.py

from fastapi import FastAPI, File, UploadFile, HTTPException
from PIL import Image
import numpy as np
import pickle
from io import BytesIO
import math
import base64

app = FastAPI()

# Cargar el modelo SOM previamente entrenado
with open("som.pkl", "rb") as tf:
    som = pickle.load(tf)

M = np.array([
    [ 0., -1., -1., -1., -1.,  2., -1., -1., -1.,  3.],
    [-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.],
    [-1., -1., -1.,  1., -1., -1., -1., -1., -1., -1.],
    [ 1., -1., -1., -1., -1., -1., -1., -1., -1.,  0.],
    [-1., -1., -1., -1.,  1., -1., -1., -1., -1., -1.],
    [-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.],
    [ 3., -1., -1., -1., -1., -1., -1., -1., -1.,  3.],
    [-1., -1., -1.,  0., -1., -1.,  3., -1., -1., -1.],
    [-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.],
    [ 2., -1., -1., -1.,  1., -1., -1., -1., -1.,  2.]
])

# Función para realizar la predicción de huellas dactilares
def predict_fingerprint(image):
    try:
        # Preprocesar la imagen para que coincida con las dimensiones esperadas por el SOM
        processed_image = preprocess_image(image)
        # Obtener la ubicación del nodo ganador en el SOM
        winner = som.winner(processed_image)
        # Asignar la etiqueta correspondiente a la ubicación ganadora en el SOM
        fingerprint_type = get_fingerprint_type(winner)
        return fingerprint_type
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

def preprocess_image(image):
    # Guardar la imagen en formato TIFF
    image.save("temp_image.tif")
    # Aplicar el mismo preprocesamiento que a arco1.tif
    processed_image = representativo("temp_image.tif")
    # Redimensionar la imagen procesada para que coincida con las dimensiones esperadas por el modelo SOM
    processed_image_resized = processed_image.reshape(1, -1)
    return processed_image_resized

def get_fingerprint_type(winner):
    # Usar la matriz M del código SOM para asignar la etiqueta correspondiente
    labels = {0: "LL", 1: "RL", 2: "WH", 3: "AR"}  # Mapa de etiquetas
    fingerprint_type = labels[int(M[winner[0], winner[1]])]
    return fingerprint_type

@app.post("/predict/")
async def predict_fingerprint_api(file: UploadFile = File(...)):
    try:
        contents = await file.read()
        print(f"contents:\n{contents}")
        image = Image.open(BytesIO(base64.decodebytes(bytes(base64_str, "utf-8"))))
        print(f"image:\n{image}")
        fingerprint_type = predict_fingerprint(image)
        return {"prediction": fingerprint_type}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

def sobel(I):
    m,n = I.shape# I de 254x254
    Gx = np.zeros([m-2,n-2],np.float32)# Gx de 252x252
    Gy = np.zeros([m-2,n-2],np.float32)# Gy de 252x252
    gx = [[-1,0,1],[ -2,0,2],[ -1,0,1]]
    gy = [[1,2,1],[ 0,0,0],[ -1,-2,-1]]
    for j in range(1,m-2):
        for i in range(1,n-2):
            Gx[j-1,i-1] = sum(sum(I[j-1:j+2,i-1:i+2]*gx))
            Gy[j-1,i-1] = sum(sum(I[j-1:j+2,i-1:i+2]*gy))
    return Gx,Gy

def medfilt2(G,d=3):
    m,n = G.shape
    temp = np.zeros([m+2*(d//2),n+2*(d//2)],np.float32)
    salida = np.zeros([m,n],np.float32)
    temp[1:m+1,1:n+1] = G
    for i in range(1,m):
        for j in range(1,n):
            A = np.asarray(temp[i-1:i+2,j-1:j+2]).reshape(-1)
            salida[i-1,j-1] = np.sort(A)[d+1]
    return salida

def orientacion(patron,w):
    Gx,Gy = sobel(patron)# patron de 254x254
    Gx = medfilt2(Gx)# Gx de 252x252
    Gy = medfilt2(Gy)# Gy de 252x252
    m,n = Gx.shape
    mOrientaciones = np.zeros([m//w,n//w],np.float32)# de una matriz de 18x18
    for i in range(m//w):
        for j in range(n//w):
            YY = sum(sum(2*Gx[i*w:(i+1)*w,j:j+1]*Gy[i*w:(i+1)*w,j:j+1]))
            XX = sum(sum(Gx[i*w:(i+1)*w,j:j+1]**2-Gy[i*w:(i+1)*w,j:j+1]**2))
            #YY = sum(sum(2*Gx[i*w:(i+1)*w,0:1]*Gy[i*w:(i+1)*w,0:1]))
            #XX = sum(sum(Gx[i*w:(i+1)*w,0:1]**2-Gy[i*w:(i+1)*w,0:1]**2))
            mOrientaciones[i,j] = (0.5*math.atan2(YY,XX) + math.pi/2.0)*(180.0/math.pi)
    return mOrientaciones

def representativo(archivo):
    im = Image.open(archivo).convert('L')
    m,n = im.size
    imarray = np.array(im,np.float32)
    patron = imarray[1:m-1,1:n-1]# de 256x256  a 254x254
    EE = orientacion(patron,14)# retorna EE de 18x18
    return np.asarray(EE).reshape(-1)