MOT17数据集转Yolov5格式的数据集,并可视化。
【摘要】 @[toc] 1、Mot17转Yolov5、Yolov6、Yolov7等数据集'''创建以下四个目录,用于存放图片和标签images/trainimages/vallabels/trainlabels/val'''import osimport shutilimport numpy as npimport configparserif not os.path.exists('images')...
@[toc]
1、Mot17转Yolov5、Yolov6、Yolov7等数据集
'''
创建以下四个目录,用于存放图片和标签
images/train
images/val
labels/train
labels/val
'''
import os
import shutil
import numpy as np
import configparser
if not os.path.exists('images'):
os.makedirs('images/train')
os.makedirs('images/val')
if not os.path.exists('labels'):
os.makedirs('labels/train')
os.makedirs('labels/val')
def convert(imgWidth, imgHeight, left, top, width, height):
x = (left + width / 2.0) / imgWidth
y = (top + height / 2.0) / imgHeight
w = width / imgWidth
h = height / imgHeight
return ('%.6f'%x, '%.6f'%y, '%.6f'%w, '%.6f'%h) # 保留6位小数
list_mot17=['train','test']
for dir in list_mot17:
for mot_dir in os.listdir(dir): # mot_dir是例如MOT17-02-FRCNN这种
det_path = os.path.join(dir, mot_dir, 'det/det.txt') # det.txt路径
dets = np.loadtxt(det_path, delimiter=',') # 读取det.txt文件
ini_path = os.path.join(dir, mot_dir, 'seqinfo.ini') # seqinfo.ini路径
conf = configparser.ConfigParser()
conf.read(ini_path) # 读取seqinfo.ini文件
seqLength = int(conf['Sequence']['seqLength']) # MOT17-02-FRCNN序列的长度
imgWidth = int(conf['Sequence']['imWidth']) # 图片宽度
imgHeight = int(conf['Sequence']['imHeight']) # 图片长度
for det in dets:
frame_id, _, left, top, width, height = int(det[0]), det[1], det[2], det[3], det[4], det[5]
box = convert(imgWidth, imgHeight, left, top, width, height)
if '-' in ''.join(box) or float(box[0]) > 1.0 or float(box[1]) > 1.0 or float(box[2]) > 1.0 or float(
box[3]) > 1.0:
print(imgWidth, imgHeight, left, top, width, height)
print(box)
continue
image_name = mot_dir + '-' + '%06d' % frame_id + '.jpg' # MOT17-02-FRCNN-000001.jpg
label_name = mot_dir + '-' + '%06d' % frame_id + '.txt' # MOT17-02-FRCNN-000001.txt
oldimgpath = os.path.join(dir, mot_dir, 'img1',
'%06d' % frame_id + '.jpg') # train/MOT17-02-FRCNN/img1/000001.jpg
newimgpath = os.path.join('images', 'train', image_name) # images/train/MOT17-02-FRCNN-000001.jpg
labelpath = os.path.join('labels', 'train', label_name) # labels/train/MOT17-02-FRCNN-000001.txt
if not os.path.exists(newimgpath): # 如果图片没复制过去,就复制,
shutil.copyfile(oldimgpath, newimgpath) # 把旧图片复制到新的地方
with open(labelpath, 'a') as f: # 写label文件
f.write(f'0 {box[0]} {box[1]} {box[2]} {box[3]}\n')
2、显示转化后的图片。
import cv2
import os
def draw_box_in_single_image(image_path, txt_path):
# 读取图像
image = cv2.imread(image_path)
# 读取txt文件信息
def read_list(txt_path):
pos = []
with open(txt_path, 'r') as file_to_read:
while True:
lines = file_to_read.readline() # 整行读取数据
if not lines:
break
# 将整行数据分割处理,如果分割符是空格,括号里就不用传入参数,如果是逗号, 则传入‘,'字符。
p_tmp = [float(i) for i in lines.split(' ')]
pos.append(p_tmp) # 添加新读取的数据
# Efield.append(E_tmp)
pass
return pos
# txt转换为box
def convert(size, box):
xmin = (box[1]-box[3]/2.)*size[1]
xmax = (box[1]+box[3]/2.)*size[1]
ymin = (box[2]-box[4]/2.)*size[0]
ymax = (box[2]+box[4]/2.)*size[0]
box = (int(xmin), int(ymin), int(xmax), int(ymax))
return box
pos = read_list(txt_path)
print(pos)
tl = int((image.shape[0]+image.shape[1])/2)
lf = max(tl-1,1)
for i in range(len(pos)):
label = str(int(pos[i][0]))
print('label is '+label)
box = convert(image.shape, pos[i])
image = cv2.rectangle(image,(box[0], box[1]),(box[2],box[3]),(0,0,255),2)
cv2.putText(image,label,(box[0],box[1]-2), 0, 1, [0,0,255], thickness=2, lineType=cv2.LINE_AA)
pass
if pos:
cv2.imwrite('./VOCData/see_images/{}.png'.format(image_path.split('\\')[-1][:-4]), image)
else:
print('None')
print('./VOCData/see_images/{}.png'.format(image_path.split('\\')[-1][:-4]))
# cv2.imshow("images", image)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
img_folder = "./images/train"
img_list = os.listdir(img_folder)
img_list.sort()
label_folder = "./labels/train"
label_list = os.listdir(label_folder)
label_list.sort()
if not os.path.exists('./VOCData/see_images'):
os.makedirs('./VOCData/see_images')
for i in range(len(img_list)):
image_path = img_folder + "\\" + img_list[i]
txt_path = label_folder + "\\" + label_list[i]
draw_box_in_single_image(image_path, txt_path)
MOT17里面很多框标错了,服了!
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