--- a
+++ b/YOLO/yolo3/utils.py
@@ -0,0 +1,121 @@
+"""Miscellaneous utility functions."""
+
+from functools import reduce
+
+from PIL import Image
+import numpy as np
+from matplotlib.colors import rgb_to_hsv, hsv_to_rgb
+
+def compose(*funcs):
+    """Compose arbitrarily many functions, evaluated left to right.
+
+    Reference: https://mathieularose.com/function-composition-in-python/
+    """
+    # return lambda x: reduce(lambda v, f: f(v), funcs, x)
+    if funcs:
+        return reduce(lambda f, g: lambda *a, **kw: g(f(*a, **kw)), funcs)
+    else:
+        raise ValueError('Composition of empty sequence not supported.')
+
+def letterbox_image(image, size):
+    '''resize image with unchanged aspect ratio using padding'''
+    iw, ih = image.size
+    w, h = size
+    scale = min(w/iw, h/ih)
+    nw = int(iw*scale)
+    nh = int(ih*scale)
+
+    image = image.resize((nw,nh), Image.BICUBIC)
+    new_image = Image.new('RGB', size, (128,128,128))
+    new_image.paste(image, ((w-nw)//2, (h-nh)//2))
+    return new_image
+
+def rand(a=0, b=1):
+    return np.random.rand()*(b-a) + a
+
+def get_random_data(annotation_line, input_shape, random=True, max_boxes=20, jitter=.3, hue=.1, sat=1.5, val=1.5, proc_img=True):
+    '''random preprocessing for real-time data augmentation'''
+    line = annotation_line.split()
+    image = Image.open(line[0])
+    iw, ih = image.size
+    h, w = input_shape
+    box = np.array([np.array(list(map(int,box.split(',')))) for box in line[1:]])
+
+    if not random:
+        # resize image
+        scale = min(w/iw, h/ih)
+        nw = int(iw*scale)
+        nh = int(ih*scale)
+        dx = (w-nw)//2
+        dy = (h-nh)//2
+        image_data=0
+        if proc_img:
+            image = image.resize((nw,nh), Image.BICUBIC)
+            new_image = Image.new('RGB', (w,h), (128,128,128))
+            new_image.paste(image, (dx, dy))
+            image_data = np.array(new_image)/255.
+
+        # correct boxes
+        box_data = np.zeros((max_boxes,5))
+        if len(box)>0:
+            np.random.shuffle(box)
+            if len(box)>max_boxes: box = box[:max_boxes]
+            box[:, [0,2]] = box[:, [0,2]]*scale + dx
+            box[:, [1,3]] = box[:, [1,3]]*scale + dy
+            box_data[:len(box)] = box
+
+        return image_data, box_data
+
+    # resize image
+    new_ar = w/h * rand(1-jitter,1+jitter)/rand(1-jitter,1+jitter)
+    scale = rand(.25, 2)
+    if new_ar < 1:
+        nh = int(scale*h)
+        nw = int(nh*new_ar)
+    else:
+        nw = int(scale*w)
+        nh = int(nw/new_ar)
+    image = image.resize((nw,nh), Image.BICUBIC)
+
+    # place image
+    dx = int(rand(0, w-nw))
+    dy = int(rand(0, h-nh))
+    new_image = Image.new('RGB', (w,h), (128,128,128))
+    new_image.paste(image, (dx, dy))
+    image = new_image
+
+    # flip image or not
+    flip = rand()<.5
+    if flip: image = image.transpose(Image.FLIP_LEFT_RIGHT)
+
+    # distort image
+    hue = rand(-hue, hue)
+    sat = rand(1, sat) if rand()<.5 else 1/rand(1, sat)
+    val = rand(1, val) if rand()<.5 else 1/rand(1, val)
+    x = rgb_to_hsv(np.array(image)/255.)
+    x[..., 0] += hue
+    x[..., 0][x[..., 0]>1] -= 1
+    x[..., 0][x[..., 0]<0] += 1
+    x[..., 1] *= sat
+    x[..., 2] *= val
+    x[x>1] = 1
+    x[x<0] = 0
+    image_data = hsv_to_rgb(x) # numpy array, 0 to 1
+
+    # correct boxes
+    box_data = np.zeros((max_boxes,5))
+    if len(box)>0:
+        np.random.shuffle(box)
+        box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx
+        box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy
+        if flip: box[:, [0,2]] = w - box[:, [2,0]]
+        box[:, 0:2][box[:, 0:2]<0] = 0
+        box[:, 2][box[:, 2]>w] = w
+        box[:, 3][box[:, 3]>h] = h
+        box_w = box[:, 2] - box[:, 0]
+        box_h = box[:, 3] - box[:, 1]
+        box = box[np.logical_and(box_w>1, box_h>1)] # discard invalid box
+        if len(box)>max_boxes: box = box[:max_boxes]
+        box_data[:len(box)] = box
+
+    return image_data, box_data