--- a
+++ b/Classifier/Classes/visualizer.py
@@ -0,0 +1,43 @@
+
+import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+import torch
+import torchvision
+
+mean_nums = [0.485, 0.456, 0.406]
+std_nums = [0.229, 0.224, 0.225]
+
+
+def load_image(img_path, resize=True):
+  img = cv2.cvtColor(cv2.imread(img_path), cv2.COLOR_BGR2RGB)
+
+  if resize:
+    img = cv2.resize(img, (64, 64), interpolation = cv2.INTER_AREA)
+
+  return img
+
+def show_image(img_path):
+  img = load_image(img_path)
+  plt.imshow(img)
+  plt.axis('off')
+
+def show_grid_image(image_paths):
+  images = [load_image(img) for img in image_paths]
+  images = torch.as_tensor(images)
+  images = images.permute(0, 3, 1, 2)
+  grid_img = torchvision.utils.make_grid(images, nrow=11)
+  plt.figure(figsize=(24, 12))
+  plt.imshow(grid_img.permute(1, 2, 0))
+  plt.axis('off');
+
+def image_show(inp, title=None):
+  inp = inp.numpy().transpose((1, 2, 0))
+  mean = np.array([mean_nums])
+  std = np.array([std_nums])
+  inp = std * inp + mean
+  inp = np.clip(inp, 0, 1)
+  plt.imshow(inp)
+  if title is not None:
+    plt.title(title)
+  plt.axis('off')
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