Spaces:
Sleeping
Sleeping
File size: 2,777 Bytes
8969de7 a2a680b 8969de7 1b09ccf 8969de7 1b09ccf 8969de7 1b09ccf 8969de7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
from fastapi import FastAPI, File, UploadFile, HTTPException
from PIL import Image
import numpy as np
import pickle
from io import BytesIO
import math
def load_model():
with open('somlucuma.pkl', 'rb') as fid:
som = pickle.load(fid)
MM = np.loadtxt('matrizMM.txt', delimiter=" ")
return som, MM
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):
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)
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 = 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)
app = FastAPI()
som, MM = load_model()
@app.post("/predict/")
async def predict(file: UploadFile = File(...)):
try:
contents = await file.read()
image = Image.open(BytesIO(contents)).convert('L')
image = np.asarray(image)
if image.shape != (256, 256):
raise ValueError("La imagen debe ser de tamaño 256x256.")
image = image.reshape(256, 256, 1)
print(f"Imagen convertida a matriz: {image.shape}")
representative_data = representativo(image)
print(f"Datos representativos de la imagen: {representative_data.shape}")
representative_data = representative_data.reshape(1, -1)
w = som.winner(representative_data)
print(f"Índice ganador del SOM: {w}")
prediction = MM[w]
print(f"Predicción: {prediction}")
return {"prediction": prediction}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
|