hsv 明度的范围_通过HSV转换的方式实现图片数据增强
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在我的上一篇文章中，我记录了自己将MOT17-Det数据集转换成VOC格式：
HUST小菜鸡：将MOT17-Det数据集转成VOC格式zhuanlan.zhihu.com
但是在后期的测试过程中，发现了一些小问题：
- 首先是train.txt里面写入的图片数和标注的数目不一致，这在训练的过程中会产生错误（训练集中存在的图片的大于有标注的图片数据）
- 数据集体量过小，一共只有5000多张有标注的数据，拆分成训练集和测试集之后数据集的体量更小，训练时容易产生过拟合
考虑到这些问题，今天对其进行一些对应的更新：
- train.txt的生成和标注生成同步完成保证数据集和标注的体量一致
- 通过HSV变换的方式实现数据增强
有关于HSV和RGB空间的转换及相关特性参照我的这篇文章：
HUST小菜鸡：HSV和RGB通道颜色的区别和转换zhuanlan.zhihu.com
首先先明确H，S，V分别表示什么
H表示色调，取值范围为（0，180）
S表示饱和度，取值范围为（0，255）
V表示亮度，取值范围为（0，255）
def gamma_transform_s(img, gamma):
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
illum = hsv[..., 1] / 255.
illum = np.power(illum, gamma)
v = illum * 255.
v[v > 255] = 255
v[v < 0] = 0
hsv[..., 1] = v.astype(np.uint8)
img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
return img
def gamma_transform_v(img, gamma):
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
illum = hsv[..., 2] / 255.
illum = np.power(illum, gamma)
v = illum * 255.
v[v > 255] = 255
v[v < 0] = 0
hsv[..., 2] = v.astype(np.uint8)
img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
return img
def gamma_transform_sv(img, gamma1,gamma2):
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
illum = hsv[..., 2] / 255.
illum = np.power(illum, gamma1)
v = illum * 255.
v[v > 255] = 255
v[v < 0] = 0
hsv[..., 2] = v.astype(np.uint8)
illum = hsv[..., 1] / 255.
illum = np.power(illum, gamma2)
v = illum * 255.
v[v > 255] = 255
v[v < 0] = 0
hsv[..., 1] = v.astype(np.uint8)
img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
return img这里定义了三个函数，一个是通过gamma变换进行饱和度变换，一个是通过gamma变换进行明度变换，一个是通过gamma变换进行饱和度和明度变换，这样就会产生原数据四倍体量的数据，并且所有的gamma都是随机生成的，使得数据集具有更多的普适性。
首先看一组测试的demo，看HSV转换后生成的数据
通过修改后的代码实现数据集的增强如下所示
import os
import cv2
import codecs
import time
import numpy as np
ori_gt_lists = ['D:/MOT17-Det/train/MOT17-02/gt/gt.txt',
'D:/MOT17-Det/train/MOT17-04/gt/gt.txt',
'D:/MOT17-Det/train/MOT17-05/gt/gt.txt',
'D:/MOT17-Det/train/MOT17-09/gt/gt.txt',
'D:/MOT17-Det/train/MOT17-10/gt/gt.txt',
'D:/MOT17-Det/train/MOT17-11/gt/gt.txt',
'D:/MOT17-Det/train/MOT17-13/gt/gt.txt']
img_dir = 'D:/MOT17-Det/voc/JPEGImages/'
annotation_dir = 'D:/MOT17-Det/voc/Annotations/'
root = 'D:/MOT17-Det/voc/ImageSets/Main/'
fp_trainlist = open(root + 'train_list.txt','w')
def replace_char(string,char,index):
string = list(string)
string[index] = char
return ''.join(string)
def gamma_transform_s(img, gamma):
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
illum = hsv[..., 1] / 255.
illum = np.power(illum, gamma)
v = illum * 255.
v[v > 255] = 255
v[v < 0] = 0
hsv[..., 1] = v.astype(np.uint8)
img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
return img
def gamma_transform_v(img, gamma):
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
illum = hsv[..., 2] / 255.
illum = np.power(illum, gamma)
v = illum * 255.
v[v > 255] = 255
v[v < 0] = 0
hsv[..., 2] = v.astype(np.uint8)
img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
return img
def gamma_transform_sv(img, gamma1,gamma2):
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
illum = hsv[..., 2] / 255.
illum = np.power(illum, gamma1)
v = illum * 255.
v[v > 255] = 255
v[v < 0] = 0
hsv[..., 2] = v.astype(np.uint8)
illum = hsv[..., 1] / 255.
illum = np.power(illum, gamma2)
v = illum * 255.
v[v > 255] = 255
v[v < 0] = 0
hsv[..., 1] = v.astype(np.uint8)
img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
return img
for each_dir in ori_gt_lists:
start_time = time.time()
fp = open(each_dir, 'r')
userlines = fp.readlines()
fp.close()
# 寻找gt中的对应的最大frame
# max_indx = 0
# for line in userlines:
# e_fram = int(line.split(',')[0])
# if e_fram > max_index:
# max_index = e_fram
# print(max_index)
fram_list = []
for line in userlines:
e_fram = int(line.split(',')[0])
fram_list.append(e_fram)
max_index = max(fram_list)
print(each_dir + 'max_index：', max_index)
for i in range(1, max_index):
clear_name = each_dir[-12:-10] + format(str(i), '0>6s')
format_name = clear_name + '.jpg'
detail_dir = img_dir + format_name
img = cv2.imread(detail_dir)
shape_img = img.shape
height = shape_img[0]
width = shape_img[1]
depth = shape_img[2]
gamma1 = np.random.uniform(0.5,1.5)
gamma2 = np.random.uniform(0.5, 1.5)
gamma3 = np.random.uniform(0.5, 1.5)
gamma4 = np.random.uniform(0.5, 1.5)
img1 = gamma_transform_s(img,gamma1)
img2 = gamma_transform_v(img, gamma2)
img3 = gamma_transform_sv(img, gamma3,gamma4)
format_name1 = replace_char(format_name,'1',2)
format_name2 = replace_char(format_name, '2', 2)
format_name3 = replace_char(format_name, '3', 2)
cv2.imwrite(img_dir+format_name1,img1)
cv2.imwrite(img_dir + format_name2, img2)
cv2.imwrite(img_dir + format_name3, img3)
txt_name = format_name[:-4]
txt_name1 = format_name1[:-4]
txt_name2 = format_name2[:-4]
txt_name3 = format_name3[:-4]
# fp.writelines(txt_name + 'n')
# fp.writelines(txt_name1 + 'n')
# fp.writelines(txt_name2 + 'n')
# fp.writelines(txt_name3 + 'n')
xml_list = [txt_name,txt_name1,txt_name2,txt_name3]
each_index = [num for num,x in enumerate(fram_list) if x == (i)]
for xml_name in xml_list:
fp_trainlist.writelines(xml_name + 'n')
with codecs.open(annotation_dir + xml_name + '.xml', 'w') as xml:
xml.write('<?xml version="1.0" encoding="UTF-8"?>n')
xml.write('<annotation>n')
xml.write('t<folder>' + 'voc' + '</folder>n')
xml.write('t<filename>' + xml_name + '.jpg' + '</filename>n')
# xml.write('t<path>' + path + "/" + info1 + '</path>n')
xml.write('t<source>n')
xml.write('tt<database> The MOT17-Det </database>n')
xml.write('t</source>n')
xml.write('t<size>n')
xml.write('tt<width>' + str(width) + '</width>n')
xml.write('tt<height>' + str(height) + '</height>n')
xml.write('tt<depth>' + str(depth) + '</depth>n')
xml.write('t</size>n')
xml.write('tt<segmented>0</segmented>n')
for j in range(len(each_index)):
num = each_index[j]
x1 = int(userlines[num].split(',')[2])
y1 = int(userlines[num].split(',')[3])
x2 = int(userlines[num].split(',')[4])
y2 = int(userlines[num].split(',')[5])
xml.write('t<object>n')
xml.write('tt<name>person</name>n')
xml.write('tt<pose>Unspecified</pose>n')
xml.write('tt<truncated>0</truncated>n')
xml.write('tt<difficult>0</difficult>n')
xml.write('tt<bndbox>n')
xml.write('ttt<xmin>' + str(x1) + '</xmin>n')
xml.write('ttt<ymin>' + str(y1) + '</ymin>n')
xml.write('ttt<xmax>' + str(x1 + x2) + '</xmax>n')
xml.write('ttt<ymax>' + str(y1 + y2) + '</ymax>n')
xml.write('tt</bndbox>n')
xml.write('t</object>n')
xml.write('</annotation>')
end_time = time.time()
print('process {} cost time:{}s'.format(each_dir,(end_time-start_time)))
fp_trainlist.close()
print('succeed in processing all gt files')
标注的数量和train.txt中的数量也一致实现了匹配，实现了数据增强的目的
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