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| import pickle | |
| import numpy as np | |
| from fastapi import FastAPI, HTTPException | |
| from pydantic import BaseModel | |
| from typing import List | |
| import math | |
| class InputData(BaseModel): | |
| array: List[List[int]] | |
| app = FastAPI() | |
| # Cargar el modelo SOM | |
| 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, n], np.float32) | |
| Gy = np.zeros([m, n], 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-1): | |
| for i in range(1, n-1): | |
| Gx[j, i] = np.sum(I[j-1:j+2, i-1:i+2] * gx) | |
| Gy[j, i] = 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.pad(G, pad_width=d//2, mode='constant', constant_values=0) | |
| salida = np.zeros([m, n], np.float32) | |
| for i in range(m): | |
| for j in range(n): | |
| A = temp[i:i+d, j:j+d].flatten() | |
| salida[i, j] = np.median(A) | |
| 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*w:(j+1)*w] * Gy[i*w:(i+1)*w, j*w:(j+1)*w]) | |
| XX = np.sum(Gx[i*w:(i+1)*w, j*w:(j+1)*w]**2 - Gy[i*w:(i+1)*w, j*w:(j+1)*w]**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) | |
| 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.] | |
| ]) | |
| async def predict(data: InputData): | |
| try: | |
| print(data.array) | |
| input_data = np.array(data.array).reshape(256, 256) | |
| representative_data = representativo(input_data) | |
| representative_data = representative_data.reshape(1, -1) | |
| w = som.winner(representative_data) | |
| prediction = MM[w] | |
| return {"prediction": prediction.tolist()} | |
| except Exception as e: | |
| raise HTTPException(status_code=500, detail=str(e)) | |