# ------------------------------------------------------------------------------
# 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

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)