Image resize

app.py

@@ -586,15 +586,6 @@ print(os.listdir(os.path.join("pretrained")))
 
 # plot_images(images, 5)
 
-def pixelate_rgb(img, window):
-    n, m, _ = img.shape
-    n, m = n - n % window, m - m % window
-    img1 = np.zeros((n, m, 3))
-    for x in range(0, n, window):
-        for y in range(0, m, window):
-            img1[x:x+window,y:y+window] = img[x:x+window,y:y+window].mean(axis=(0,1))
-    return img1
-
 class InferenceWrapper:
     def __init__(self, model):
         self.model = model
@@ -613,7 +604,13 @@ class InferenceWrapper:
             self.noise = self.style_gan.generate_noise(batch_size=batch_size)
         else:
             print(f"Model '{self.model}' already loaded, reusing it.")
-        return pixelate_rgb(self.style_gan({"style_code": self.w, "noise": self.noise, "alpha": 1.0})[0],64)
+
+        image = self.style_gan({"style_code": self.w, "noise": self.noise, "alpha": 1.0})[0]
+        res = 64
+        image = tf.image.resize(image, (res, res), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
+        image = tf.cast(image, tf.float32) / 127.5 - 1.0
+
+        return image
 
 
 wrapper = InferenceWrapper('celeba')