Update app.py

app.py

@@ -1,5 +1,4 @@
 import pickle
-from minisom import MiniSom
 import numpy as np
 from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
@@ -14,30 +13,29 @@ app = FastAPI()
 # Cargar el modelo SOM
 def load_model():
     with open('som.pkl', 'rb') as fid:
-        somecoli = pickle.load(fid)
-    return somecoli
+        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))
+    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.zeros([m+2*(d//2), n+2*(d//2)], np.float32)
+    temp = np.pad(G, pad_width=d//2, mode='constant', constant_values=0)
     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]
+    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):
@@ -48,8 +46,8 @@ 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))
+            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
 
@@ -78,13 +76,13 @@ MM = np.array([
 @app.post("/predict/")
 async def predict(data: InputData):
     try:
-        input_data = np.array(data.array).reshape(256, 256, 1)
+        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}
+        return {"prediction": prediction.tolist()}
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))