text: # ------------------------------------------------------------------------------
  # Copyright (c) Microsoft
  # Licensed under the MIT License.
  # Written by Bin Xiao (Bin.Xiao@microsoft.com)
  # Modified by Xingyi Zhou
  # ------------------------------------------------------------------------------
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  import cv2
  import random
  import torch
  
  PI = np.pi
  
  
  def flip(img):
    return img[:, :, ::-1].copy()  
  
  
  def transform_preds(coords, center, scale, output_size):
      target_coords = np.zeros(coords.shape)
      trans = get_affine_transform(center, scale, 0, output_size, inv=1)
      for p in range(coords.shape[0]):
          target_coords[p, 0:2] = affine_transform(coords[p, 0:2], trans)
      return target_coords
  
  
  def get_affine_transform(center,
                           scale,
                           rot,
                           output_size,
                           shift=np.array([0, 0], dtype=np.float32),
                           inv=0):
      if not isinstance(scale, np.ndarray) and not isinstance(scale, list):
          scale = np.array([scale, scale], dtype=np.float32)
  
      scale_tmp = scale
      src_w = scale_tmp[0]
      dst_w = output_size[0]
      dst_h = output_size[1]
  
      rot_rad = np.pi * rot / 180
      src_dir = get_dir([0, src_w * -0.5], rot_rad)
      dst_dir = np.array([0, dst_w * -0.5], np.float32)
  
      src = np.zeros((3, 2), dtype=np.float32)
      dst = np.zeros((3, 2), dtype=np.float32)
      src[0, :] = center + scale_tmp * shift
      src[1, :] = center + src_dir + scale_tmp * shift
      dst[0, :] = [dst_w * 0.5, dst_h * 0.5]
      dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5], np.float32) + dst_dir
  
      src[2:, :] = get_3rd_point(src[0, :], src[1, :])
      dst[2:, :] = get_3rd_point(dst[0, :], dst[1, :])
  
      if inv:
          trans = cv2.getAffineTransform(np.float32(dst), np.float32(src))
      else:
          trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))
  
      return trans
  
  
  def affine_transform(pt, t):
      new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32).T
      new_pt = np.dot(t, new_pt)
      return new_pt[:2]
  
  
  def get_3rd_point(a, b):
      direct = a - b
      return b + np.array([-direct[1], direct[0]], dtype=np.float32)
  
  
  def get_dir(src_point, rot_rad):
      sn, cs = np.sin(rot_rad), np.cos(rot_rad)
  
      src_result = [0, 0]
      src_result[0] = src_point[0] * cs - src_point[1] * sn
      src_result[1] = src_point[0] * sn + src_point[1] * cs
  
      return src_result
  
  
  def crop(img, center, scale, output_size, rot=0):
      trans = get_affine_transform(center, scale, rot, output_size)
  
      dst_img = cv2.warpAffine(img,
                               trans,
                               (int(output_size[0]), int(output_size[1])),
                               flags=cv2.INTER_LINEAR)
  
      return dst_img
  
  
  def gaussian_radius(det_size, min_overlap=0.7):
    height, width = det_size
  
    a1  = 1
    b1  = (height + width)
    c1  = width * height * (1 - min_overlap) / (1 + min_overlap)
    sq1 = np.sqrt(b1 ** 2 - 4 * a1 * c1)
    r1  = (b1 + sq1) / 2
  
    a2  = 4
    b2  = 2 * (height + width)
    c2  = (1 - min_overlap) * width * height
    sq2 = np.sqrt(b2 ** 2 - 4 * a2 * c2)
    r2  = (b2 + sq2) / 2
  
    a3  = 4 * min_overlap
    b3  = -2 * min_overlap * (height + width)
    c3  = (min_overlap - 1) * width * height
    sq3 = np.sqrt(b3 ** 2 - 4 * a3 * c3)
    r3  = (b3 + sq3) / 2
    return min(r1, r2, r3)
  
  
  def gaussian2D(shape, sigma=1):
      m, n = [(ss - 1.) / 2. for ss in shape]
      y, x = np.ogrid[-m:m+1,-n:n+1]
  
      h = np.exp(-(x * x + y * y) / (2 * sigma * sigma))
      h[h < np.finfo(h.dtype).eps * h.max()] = 0
      return h
  
  def draw_umich_gaussian(heatmap, center, radius, k=1):
    diameter = 2 * radius + 1
    gaussian = gaussian2D((diameter, diameter), sigma=diameter / 6)
    
    x, y = int(center[0]), int(center[1])
  
    height, width = heatmap.shape[0:2]
      
    left, right = min(x, radius), min(width - x, radius + 1)
    top, bottom = min(y, radius), min(height - y, radius + 1)
  
    masked_heatmap  = heatmap[y - top:y + bottom, x - left:x + right]
    masked_gaussian = gaussian[radius - top:radius + bottom, radius - left:radius + right]
    if min(masked_gaussian.shape) > 0 and min(masked_heatmap.shape) > 0: # TODO debug
      np.maximum(masked_heatmap, masked_gaussian * k, out=masked_heatmap)
    return heatmap
  
  def draw_dense_reg(regmap, heatmap, center, value, radius, is_offset=False):
    diameter = 2 * radius + 1
    gaussian = gaussian2D((diameter, diameter), sigma=diameter / 6)
    value = np.array(value, dtype=np.float32).reshape(-1, 1, 1)
    dim = value.shape[0]
    reg = np.ones((dim, diameter*2+1, diameter*2+1), dtype=np.float32) * value
    if is_offset and dim == 2:
      delta = np.arange(diameter*2+1) - radius
      reg[0] = reg[0] - delta.reshape(1, -1)
      reg[1] = reg[1] - delta.reshape(-1, 1)
    
    x, y = int(center[0]), int(center[1])
  
    height, width = heatmap.shape[0:2]
      
    left, right = min(x, radius), min(width - x, radius + 1)
    top, bottom = min(y, radius), min(height - y, radius + 1)
  
    masked_heatmap = heatmap[y - top:y + bottom, x - left:x + right]
    masked_regmap = regmap[:, y - top:y + bottom, x - left:x + right]
    masked_gaussian = gaussian[radius - top:radius + bottom,
                               radius - left:radius + right]
    masked_reg = reg[:, radius - top:radius + bottom,
                        radius - left:radius + right]
    if min(masked_gaussian.shape) > 0 and min(masked_heatmap.shape) > 0: # TODO debug
      idx = (masked_gaussian >= masked_heatmap).reshape(
        1, masked_gaussian.shape[0], masked_gaussian.shape[1])
      masked_regmap = (1-idx) * masked_regmap + idx * masked_reg
    regmap[:, y - top:y + bottom, x - left:x + right] = masked_regmap
    return regmap
  
  
  def draw_msra_gaussian(heatmap, center, sigma):
    tmp_size = sigma * 3
    mu_x = int(center[0] + 0.5)
    mu_y = int(center[1] + 0.5)
    w, h = heatmap.shape[0], heatmap.shape[1]
    ul = [int(mu_x - tmp_size), int(mu_y - tmp_size)]
    br = [int(mu_x + tmp_size + 1), int(mu_y + tmp_size + 1)]
    if ul[0] >= h or ul[1] >= w or br[0] < 0 or br[1] < 0:
      return heatmap
    size = 2 * tmp_size + 1
    x = np.arange(0, size, 1, np.float32)
    y = x[:, np.newaxis]
    x0 = y0 = size // 2
    g = np.exp(- ((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma ** 2))
    g_x = max(0, -ul[0]), min(br[0], h) - ul[0]
    g_y = max(0, -ul[1]), min(br[1], w) - ul[1]
    img_x = max(0, ul[0]), min(br[0], h)
    img_y = max(0, ul[1]), min(br[1], w)
    heatmap[img_y[0]:img_y[1], img_x[0]:img_x[1]] = np.maximum(
      heatmap[img_y[0]:img_y[1], img_x[0]:img_x[1]],
      g[g_y[0]:g_y[1], g_x[0]:g_x[1]])
    return heatmap
  
  def grayscale(image):
      return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
  
  def lighting_(data_rng, image, alphastd, eigval, eigvec):
      alpha = data_rng.normal(scale=alphastd, size=(3, ))
      image += np.dot(eigvec, eigval * alpha)
  
  def blend_(alpha, image1, image2):
      image1 *= alpha
      image2 *= (1 - alpha)
      image1 += image2
  
  def saturation_(data_rng, image, gs, gs_mean, var):
      alpha = 1. + data_rng.uniform(low=-var, high=var)
      blend_(alpha, image, gs[:, :, None])
  
  def brightness_(data_rng, image, gs, gs_mean, var):
      alpha = 1. + data_rng.uniform(low=-var, high=var)
      image *= alpha
  
  def contrast_(data_rng, image, gs, gs_mean, var):
      alpha = 1. + data_rng.uniform(low=-var, high=var)
      blend_(alpha, image, gs_mean)
  
  def color_aug(data_rng, image, eig_val, eig_vec):
      functions = [brightness_, contrast_, saturation_]
      random.shuffle(functions)
  
      gs = grayscale(image)
      gs_mean = gs.mean()
      for f in functions:
          f(data_rng, image, gs, gs_mean, 0.4)
      lighting_(data_rng, image, 0.1, eig_val, eig_vec)
  
  
  
  