text: # -*- encoding: utf-8 -*-
  
  
  from PIL import Image
  import PIL.ImageEnhance as ImageEnhance
  import random
  import numpy as np
  
  class RandomCrop(object):
      def __init__(self, size, *args, **kwargs):
          self.size = size
  
      def __call__(self, im_lb):
          im = im_lb['im']
          lb = im_lb['lb']
          assert im.size == lb.size
          W, H = self.size
          w, h = im.size
  
          if (W, H) == (w, h): return dict(im=im, lb=lb)
          if w < W or h < H:
              scale = float(W) / w if w < h else float(H) / h
              w, h = int(scale * w + 1), int(scale * h + 1)
              im = im.resize((w, h), Image.BILINEAR)
              lb = lb.resize((w, h), Image.NEAREST)
          sw, sh = random.random() * (w - W), random.random() * (h - H)
          crop = int(sw), int(sh), int(sw) + W, int(sh) + H
          return dict(
                  im = im.crop(crop),
                  lb = lb.crop(crop)
                      )
  
  
  class HorizontalFlip(object):
      def __init__(self, p=0.5, *args, **kwargs):
          self.p = p
  
      def __call__(self, im_lb):
          if random.random() > self.p:
              return im_lb
          else:
              im = im_lb['im']
              lb = im_lb['lb']
  
              # atts = [1 'skin', 2 'l_brow', 3 'r_brow', 4 'l_eye', 5 'r_eye', 6 'eye_g', 7 'l_ear', 8 'r_ear', 9 'ear_r',
              #         10 'nose', 11 'mouth', 12 'u_lip', 13 'l_lip', 14 'neck', 15 'neck_l', 16 'cloth', 17 'hair', 18 'hat']
  
              flip_lb = np.array(lb)
              flip_lb[lb == 2] = 3
              flip_lb[lb == 3] = 2
              flip_lb[lb == 4] = 5
              flip_lb[lb == 5] = 4
              flip_lb[lb == 7] = 8
              flip_lb[lb == 8] = 7
              flip_lb = Image.fromarray(flip_lb)
              return dict(im = im.transpose(Image.FLIP_LEFT_RIGHT),
                          lb = flip_lb.transpose(Image.FLIP_LEFT_RIGHT),
                      )
  
  
  class RandomScale(object):
      def __init__(self, scales=(1, ), *args, **kwargs):
          self.scales = scales
  
      def __call__(self, im_lb):
          im = im_lb['im']
          lb = im_lb['lb']
          W, H = im.size
          scale = random.choice(self.scales)
          w, h = int(W * scale), int(H * scale)
          return dict(im = im.resize((w, h), Image.BILINEAR),
                      lb = lb.resize((w, h), Image.NEAREST),
                  )
  
  
  class ColorJitter(object):
      def __init__(self, brightness=None, contrast=None, saturation=None, *args, **kwargs):
          if not brightness is None and brightness>0:
              self.brightness = [max(1-brightness, 0), 1+brightness]
          if not contrast is None and contrast>0:
              self.contrast = [max(1-contrast, 0), 1+contrast]
          if not saturation is None and saturation>0:
              self.saturation = [max(1-saturation, 0), 1+saturation]
  
      def __call__(self, im_lb):
          im = im_lb['im']
          lb = im_lb['lb']
          r_brightness = random.uniform(self.brightness[0], self.brightness[1])
          r_contrast = random.uniform(self.contrast[0], self.contrast[1])
          r_saturation = random.uniform(self.saturation[0], self.saturation[1])
          im = ImageEnhance.Brightness(im).enhance(r_brightness)
          im = ImageEnhance.Contrast(im).enhance(r_contrast)
          im = ImageEnhance.Color(im).enhance(r_saturation)
          return dict(im = im,
                      lb = lb,
                  )
  
  
  class MultiScale(object):
      def __init__(self, scales):
          self.scales = scales
  
      def __call__(self, img):
          W, H = img.size
          sizes = [(int(W*ratio), int(H*ratio)) for ratio in self.scales]
          imgs = []
          [imgs.append(img.resize(size, Image.BILINEAR)) for size in sizes]
          return imgs
  
  
  class Compose(object):
      def __init__(self, do_list):
          self.do_list = do_list
  
      def __call__(self, im_lb):
          for comp in self.do_list:
              im_lb = comp(im_lb)
          return im_lb
  
  
  
  
  if __name__ == '__main__':
      flip = HorizontalFlip(p = 1)
      crop = RandomCrop((321, 321))
      rscales = RandomScale((0.75, 1.0, 1.5, 1.75, 2.0))
      img = Image.open('data/img.jpg')
      lb = Image.open('data/label.png')
  