# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------

"""Blob helper functions."""

import numpy as np
import cv2
import torch
import torch.nn as nn
import random

def chromatic_transform(im, label=None, d_h=None, d_s=None, d_l=None):
    """
    Given an image array, add the hue, saturation and luminosity to the image
    """
    # Set random hue, luminosity and saturation which ranges from -0.1 to 0.1
    if d_h is None:
        d_h = (np.random.rand(1) - 0.5) * 0.02 * 180
    if d_l is None:
        d_l = (np.random.rand(1) - 0.5) * 0.2 * 256
    if d_s is None:
        d_s = (np.random.rand(1) - 0.5) * 0.2 * 256
    # Convert the BGR to HLS
    hls = cv2.cvtColor(im, cv2.COLOR_BGR2HLS)
    h, l, s = cv2.split(hls)
    # Add the values to the image H, L, S
    new_h = (h + d_h) % 180
    new_l = np.clip(l + d_l, 0, 255)
    new_s = np.clip(s + d_s, 0, 255)
    # Convert the HLS to BGR
    new_hls = cv2.merge((new_h, new_l, new_s)).astype('uint8')
    new_im = cv2.cvtColor(new_hls, cv2.COLOR_HLS2BGR)

    if label is not None:
        I = np.where(label > 0)
        new_im[I[0], I[1], :] = im[I[0], I[1], :]
    return new_im


def add_noise(image):

    # random number
    r = np.random.rand(1)

    # gaussian noise
    if r < 0.9:
        row,col,ch= image.shape
        mean = 0
        var = np.random.rand(1) * 0.3 * 256
        sigma = var**0.5
        gauss = sigma * np.random.randn(row,col) + mean
        gauss = np.repeat(gauss[:, :, np.newaxis], ch, axis=2)
        noisy = image + gauss
        noisy = np.clip(noisy, 0, 255)
    else:
        # motion blur
        sizes = [3, 5, 7, 9, 11, 15]
        size = sizes[int(np.random.randint(len(sizes), size=1))]
        kernel_motion_blur = np.zeros((size, size))
        if np.random.rand(1) < 0.5:
            kernel_motion_blur[int((size-1)/2), :] = np.ones(size)
        else:
            kernel_motion_blur[:, int((size-1)/2)] = np.ones(size)
        kernel_motion_blur = kernel_motion_blur / size
        noisy = cv2.filter2D(image, -1, kernel_motion_blur)

    return noisy


def add_noise_depth(image, level = 0.01):
    row,col,ch= image.shape
    noise_level = random.uniform(0, level)
    gauss = noise_level * np.random.randn(row,col)
    gauss = np.repeat(gauss[:, :, np.newaxis], ch, axis=2)
    noisy = image + gauss
    return noisy