Update app.py

app.py

@@ -27,19 +27,24 @@ def get_image(path):
         img = np.array(img.convert('RGB'))
     return img
     
+
+
 def preprocess(img):
-    img = img / 255.
-    img = cv2.resize(img, (256, 256))
-    h, w = img.shape[0], img.shape[1]
-    y0 = (h - 224) // 2
-    x0 = (w - 224) // 2
-    img = img[y0 : y0+224, x0 : x0+224, :]
-    img = (img - [0.485, 0.456, 0.406]) / [0.229, 0.224, 0.225]
-    img = np.transpose(img, axes=[2, 0, 1])
-    img = img.astype(np.float32)
+    '''
+    Preprocessing required on the images for inference with mxnet gluon
+    The function takes loaded image and returns processed tensor
+    '''
+    img = np.array(Image.fromarray(img).resize((224, 224))).astype(np.float32)
+    img[:, :, 0] -= 123.68
+    img[:, :, 1] -= 116.779
+    img[:, :, 2] -= 103.939
+    img[:,:,[0,1,2]] = img[:,:,[2,1,0]]
+    img = img.transpose((2, 0, 1))
     img = np.expand_dims(img, axis=0)
+
     return img
 
+
 def predict(path):
     img = get_image(path)
     img = preprocess(img)