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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()
som = load_model()
MM = 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.]
])
@app.post("/predict/")
async def predict_fingerprint_api(file: UploadFile = File(...)):
try:
contents = await file.read()
image = Image.open(BytesIO(contents)).convert('L')
image = np.asarray(image)
print(f"ARRAY{image.size}:\n\n\n{image}")
image = np.array(image.array).reshape(256, 256, 1)
representative_data = representativo(image)
representative_data = representative_data.reshape(1, -1)
w = som.winner(representative_data)
prediction = MM[w]
return {"prediction": prediction}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
def load_model():
with open('som.pkl', 'rb') as fid:
som = pickle.load(fid)
return som
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 = [[-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):
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):
mOrientaciones = np.zeros([m//w, n//w], np.float32)
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))
mOrientaciones[i, j] = (0.5 * math.atan2(YY, XX) + math.pi / 2.0) * (180.0 / math.pi)
return mOrientaciones
def representativo(imarray):
imarray = np.squeeze(imarray)
m, n = imarray.shape
patron = imarray[1:m-1, 1:n-1]
EE = orientacion(patron, 14)
return np.asarray(EE).reshape(-1) |