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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 | |
| processed_image = representativo("temp_image.tif") | |
| 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 | |
| async def predict_fingerprint_api(file: UploadFile = File(...)): | |
| try: | |
| contents = await file.read() | |
| image = Image.open(BytesIO(contents)).convert('L') | |
| image.resize((256,256)) | |
| image.save("temp_image.tif") | |
| image = Image.open("temp_image.tiff") | |
| print(f"\n\n\n\nSIZE:{image.size}") | |
| 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) |