implement load_data

load_test_data.py

@@ -0,0 +1,46 @@
+import numpy as np
+import os
+from skimage.io import imread
+import cv2 as cv
+from pathlib import Path
+
+def load_data(input_dir, newSize=(64,64)):
+    image_path = Path(input_dir)
+    file_names = os.listdir(image_path)
+    categories_name = []
+    categories_count=[]
+    count = 0
+    n = file_names[0]
+    for name in file_names:
+        if name != n:
+            categories_count.append(count)
+            n = name
+            count = 1
+        else:
+            count += 1
+        if not name in categories_name:
+            categories_name.append(name)
+    categories_count.append(count)
+    test_img = []
+    labels=[]
+
+    for n in file_names:
+        p = image_path / n
+        img = imread(p) # zwraca ndarry postaci xSize x ySize x colorDepth
+        img = cv.resize(img, newSize, interpolation=cv.INTER_AREA) # zwraca ndarray
+        img = img / 255 # type: ignore #normalizacja
+        test_img.append(img)
+        labels.append(n)
+
+    X={}
+    X["values"] = np.array(test_img)
+    X["categories_name"] = categories_name
+    X["categories_count"] = categories_count
+    X["labels"]=labels
+    return X
+
+data = load_data('test_set')
+print(data['categories_name'])
+print(data['categories_count'])
+print(data['labels'])
+print(list(data["values"]))