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