Update app.py

app.py

@@ -1,9 +1,15 @@
-from fastapi import FastAPI, File, UploadFile, HTTPException
-from PIL import Image
+import cv2
 import numpy as np
 import pickle
-from io import BytesIO
 import math
+from fastapi import FastAPI, File, UploadFile, HTTPException
+from PIL import Image
+from io import BytesIO
+from pydantic import BaseModel
+from typing import List
+from scipy.ndimage import median_filter
+from scipy.signal import convolve2d
+from minisom import MiniSom
 
 def load_model():
     with open('somlucuma.pkl', 'rb') as fid:
@@ -11,40 +17,36 @@ def load_model():
     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))
+def sobel(patron):
+    gx = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], dtype=np.float32)
+    gy = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]], dtype=np.float32)
+
+    Gx = convolve2d(patron, gx, mode='valid')
+    Gy = convolve2d(patron, gy, mode='valid')
+
     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
+    return median_filter(G, size=d)
 
 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)
+    mOrientaciones = np.zeros((m // w, n // w), dtype=np.float32)
+
+    for i in range(m // w):
+        for j in range(n // w):
+            Gx_patch = Gx[i*w:(i+1)*w, j*w:(j+1)*w]
+            Gy_patch = Gy[i*w:(i+1)*w, j*w:(j+1)*w]
+
+            YY = np.sum(2 * Gx_patch * Gy_patch)
+            XX = np.sum(Gx_patch**2 - Gy_patch**2)
+
+            mOrientaciones[i, j] = (0.5 * np.arctan2(YY, XX) + np.pi / 2.0) * (18.0 / np.pi)
+
     return mOrientaciones
 
 def representativo(imarray):
@@ -54,6 +56,9 @@ def representativo(imarray):
     EE = orientacion(patron, 14)
     return np.asarray(EE).reshape(-1)
 
+class InputData(BaseModel):
+    data: List[float]
+
 app = FastAPI()
 
 som, MM = load_model()