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| from fastapi import FastAPI, File, UploadFile, HTTPException | |
| from minisom import MiniSom | |
| import numpy as np | |
| from PIL import Image | |
| import pickle | |
| app = FastAPI() | |
| # Cargar el modelo SOM entrenado | |
| with open('som.pkl', 'rb') as infile: | |
| som_model = pickle.load(infile) | |
| # Etiquetas para los tipos de huellas dactilares | |
| LABELS = {0: 'LL', 1: 'RL', 2: 'WH', 3: 'AR'} | |
| # Funci贸n para procesar y clasificar una imagen de huella dactilar | |
| def classify_fingerprint(image_path): | |
| # Funci贸n para calcular las orientaciones de la imagen | |
| 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) | |
| 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) | |
| # Obtener el vector representativo de la imagen de huella dactilar | |
| fingerprint_vector = representativo(image_path) | |
| # Calcular la ubicaci贸n ganadora en el SOM para el vector de huella dactilar | |
| winner_position = som_model.winner(fingerprint_vector) | |
| # Obtener la etiqueta correspondiente a la ubicaci贸n ganadora | |
| label = LABELS[som_model[winner_position[0], winner_position[1]]] | |
| return label | |
| # Ruta de predicci贸n | |
| async def predict(file: UploadFile = File(...)): | |
| try: | |
| # Guardar la imagen subida | |
| with open("temp_image.tif", "wb") as buffer: | |
| buffer.write(await file.read()) | |
| # Clasificar la imagen de huella dactilar | |
| prediction = classify_fingerprint("temp_image.tif") | |
| return {"prediction": prediction} | |
| except Exception as e: | |
| raise HTTPException(status_code=500, detail=str(e)) | |