目录
检验灰度图
制作语义分割数据集或用训练好模型测试图像时,得到的结果是灰度图像,如下:
检验代码
上面图像灰度值不是全是全为0,灰度范围在[0,1]之间,使用下面脚本测试灰度图像的灰度值是否全为0:
python
import cv2
img = cv2.imread("output/result/Result_2023.9.18_Int8/Val_Predict/BlockImage/1.png")
min_val = img.min()
max_val= img.max()
print("min_val",min_val)
print("max_val",max_val)
print("dtype",img.dtype)
print("shape",img.shape)
print("img = ",img)
cv2.imshow("1",img)
cv2.waitKey()
cv2.destroyWindow()
通过上面脚本检测结果如下:
灰度图转伪彩色图代码
上面的灰度图直观的看不了测试结果怎样,得将[0,1]区间的灰度值映射到[0,255],详解代码见下:
python
from __future__ import print_function
import argparse
import os
import os.path as osp
import sys
import numpy as np
from PIL import Image
def parse_args():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
'dir_or_file', help='input gray label directory or file list path')
parser.add_argument('output_dir', help='output colorful label directory')
parser.add_argument('--dataset_dir', help='dataset directory')
parser.add_argument('--file_separator', help='file list separator')
return parser.parse_args()
def get_color_map_list(num_classes):
"""
Returns the color map for visualizing the segmentation mask,
which can support arbitrary number of classes.
Args:
num_classes (int): Number of classes.
Returns:
(list). The color map.
"""
num_classes += 1
color_map = num_classes * [0, 0, 0]
for i in range(0, num_classes):
j = 0
lab = i
while lab:
color_map[i * 3] |= (((lab >> 0) & 1) << (7 - j))
color_map[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j))
color_map[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j))
j += 1
lab >>= 3
color_map = color_map[3:]
return color_map
def gray2pseudo_color(args):
"""将灰度标注图片转换为伪彩色图片"""
input = args.dir_or_file
output_dir = args.output_dir
if not osp.exists(output_dir):
os.makedirs(output_dir)
print('Creating colorful label directory:', output_dir)
color_map = get_color_map_list(256)
if os.path.isdir(input):
for fpath, dirs, fs in os.walk(input):
for f in fs:
try:
grt_path = osp.join(fpath, f)
_output_dir = fpath.replace(input, '')
_output_dir = _output_dir.lstrip(os.path.sep)
im = Image.open(grt_path)
lbl = np.asarray(im)
lbl_pil = Image.fromarray(lbl.astype(np.uint8), mode='P')
lbl_pil.putpalette(color_map)
real_dir = osp.join(output_dir, _output_dir)
if not osp.exists(real_dir):
os.makedirs(real_dir)
new_grt_path = osp.join(real_dir, f)
lbl_pil.save(new_grt_path)
print('New label path:', new_grt_path)
except:
continue
elif os.path.isfile(input):
if args.dataset_dir is None or args.file_separator is None:
print('No dataset_dir or file_separator input!')
sys.exit()
with open(input) as f:
for line in f:
parts = line.strip().split(args.file_separator)
grt_name = parts[1]
grt_path = os.path.join(args.dataset_dir, grt_name)
im = Image.open(grt_path)
lbl = np.asarray(im)
lbl_pil = Image.fromarray(lbl.astype(np.uint8), mode='P')
lbl_pil.putpalette(color_map)
grt_dir, _ = osp.split(grt_name)
new_dir = osp.join(output_dir, grt_dir)
if not osp.exists(new_dir):
os.makedirs(new_dir)
new_grt_path = osp.join(output_dir, grt_name)
lbl_pil.save(new_grt_path)
print('New label path:', new_grt_path)
else:
print('It\'s neither a dir nor a file')
if __name__ == '__main__':
args = parse_args()
gray2pseudo_color(args)
转换代码使用细则
使用该代码,只需要在终端去到该文件所在路径下,添加灰度图像文件夹路径和转换后的保存路径即可。
终端中输入的命令为:
python
python gray2pseudo_color.py <dir_or_file> <output_dir>
上面命令中:
dir_or_file为灰度图所在的路径
output_dir为转换后伪彩色图像的保存路径
具体的使用方法见下图:
示例转换结果
转换后的对比结果如下图:
总结
以上就是语义分割中灰度图像转伪彩色图像的方法,希望能帮到你,多多支持,谢谢!