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from fastapi import FastAPI, File, UploadFile, HTTPException
from PIL import Image
import numpy as np
import pickle
from io import BytesIO
import math
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.]
])
def predict_fingerprint(image):
try:
processed_image = preprocess_image(image)
winner = som.winner(processed_image)
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.resize((256,256))
image.save("temp_image.tiff")
processed_image = representativo("temp_image.tiff")
processed_image_resized = processed_image.reshape(1, -1)
return processed_image_resized
def get_fingerprint_type(winner):
labels = {0: "LL", 1: "RL", 2: "WH", 3: "AR"}
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()
image = Image.open(BytesIO(contents)).convert('L')
print(f"IMAGEN\n\n{list(image.getdata())}")
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
Gx = np.zeros([m-2, n-2], np.float32)
Gy = np.zeros([m-2, n-2], np.float32)
gx = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])
gy = np.array([[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] = np.sum(I[j-1:j+2, i-1:i+2] * gx)
Gy[j-1, i-1] = np.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.sort(temp[i-1:i+2, j-1:j+2].reshape(-1))
salida[i-1, j-1] = A[d+1]
return salida
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], np.float32)
for i in range(m//w):
for j in range(n//w):
YY = np.sum(2 * Gx[i*w:(i+1)*w, j:j+1] * Gy[i*w:(i+1)*w, j:j+1])
XX = np.sum(Gx[i*w:(i+1)*w, j:j+1]**2 - Gy[i*w:(i+1)*w, j:j+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)
m, n = im.size
imarray = np.array(im, np.float32)
patron = imarray[1:m-1, 1:n-1]
EE = orientacion(patron, 14)
return np.asarray(EE).reshape(-1) |