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