yolo

yolo

多线程调用yolo模型会出错(包括使用cv2载入yolo模型)

深度学习李魔佛 发表了文章 • 0 个评论 • 10 次浏览 • 2019-12-05 10:27 • 来自相关话题

占坑。
 
多线程调用yolo模型会出错(包括使用cv2载入yolo模型)
 
占坑。
 
多线程调用yolo模型会出错(包括使用cv2载入yolo模型)
 

keras yolo物体检测 入门教程

深度学习李魔佛 发表了文章 • 0 个评论 • 33 次浏览 • 2019-11-28 16:03 • 来自相关话题

占坑
占坑

yolo voc_label 源码分析

深度学习李魔佛 发表了文章 • 0 个评论 • 28 次浏览 • 2019-11-27 15:19 • 来自相关话题

作用读取每个xml文件,把坐标转化为相对坐标,对应文件名保存起来
 
import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join




sets = [('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test')]

# 20类
classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog",
"horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]

# size w,h
# box x-min,x-max,y-min,y-max
def convert(size, box):
dw = 1. / size[0]
dh = 1. / size[1]
x = (box[0] + box[1]) / 2.0 # 中心点位置
y = (box[2] + box[3]) / 2.0
w = box[1] - box[0]
h = box[3] - box[2]
x = x * dw
w = w * dw
y = y * dh
h = h * dh # 全部转化为相对坐标
return (x, y, w, h)


def convert_annotation(year, image_id):
# 找到2个同样的文件
in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml' % (year, image_id))
out_file = open('VOCdevkit/VOC%s/labels/%s.txt' % (year, image_id), 'w')

tree = ET.parse(in_file)
root = tree.getroot()
size = root.find('size')
w = int(size.find('width').text)
h = int(size.find('height').text)

for obj in root.iter('object'):
difficult = obj.find('difficult').text
cls = obj.find('name').text
if cls not in classes or int(difficult) == 1: # difficult ==1 的不要了
continue
cls_id = classes.index(cls) # 排在第几位
xmlbox = obj.find('bndbox')
b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),
float(xmlbox.find('ymax').text))
# 传入的是w,h 与框框的周边
bb = convert((w, h), b)
out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')


wd = getcwd()

for year, image_set in sets:
# ('2012', 'train') 循环5次
# 创建目录 一次性
if not os.path.exists('VOCdevkit/VOC%s/labels/' % (year)):
os.makedirs('VOCdevkit/VOC%s/labels/' % (year))

# 图片的id数据
image_ids = open('VOCdevkit/VOC%s/ImageSets/Main/%s.txt' % (year, image_set)).read().strip().split()

# 结果写入这个文件
list_file = open('%s_%s.txt' % (year, image_set), 'w')

for image_id in image_ids:
list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg\n' % (wd, year, image_id)) # 补全路径
convert_annotation(year, image_id)
list_file.close()

  查看全部
作用读取每个xml文件,把坐标转化为相对坐标,对应文件名保存起来
 
import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join




sets = [('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test')]

# 20类
classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog",
"horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]

# size w,h
# box x-min,x-max,y-min,y-max
def convert(size, box):
dw = 1. / size[0]
dh = 1. / size[1]
x = (box[0] + box[1]) / 2.0 # 中心点位置
y = (box[2] + box[3]) / 2.0
w = box[1] - box[0]
h = box[3] - box[2]
x = x * dw
w = w * dw
y = y * dh
h = h * dh # 全部转化为相对坐标
return (x, y, w, h)


def convert_annotation(year, image_id):
# 找到2个同样的文件
in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml' % (year, image_id))
out_file = open('VOCdevkit/VOC%s/labels/%s.txt' % (year, image_id), 'w')

tree = ET.parse(in_file)
root = tree.getroot()
size = root.find('size')
w = int(size.find('width').text)
h = int(size.find('height').text)

for obj in root.iter('object'):
difficult = obj.find('difficult').text
cls = obj.find('name').text
if cls not in classes or int(difficult) == 1: # difficult ==1 的不要了
continue
cls_id = classes.index(cls) # 排在第几位
xmlbox = obj.find('bndbox')
b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),
float(xmlbox.find('ymax').text))
# 传入的是w,h 与框框的周边
bb = convert((w, h), b)
out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')


wd = getcwd()

for year, image_set in sets:
# ('2012', 'train') 循环5次
# 创建目录 一次性
if not os.path.exists('VOCdevkit/VOC%s/labels/' % (year)):
os.makedirs('VOCdevkit/VOC%s/labels/' % (year))

# 图片的id数据
image_ids = open('VOCdevkit/VOC%s/ImageSets/Main/%s.txt' % (year, image_set)).read().strip().split()

# 结果写入这个文件
list_file = open('%s_%s.txt' % (year, image_set), 'w')

for image_id in image_ids:
list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg\n' % (wd, year, image_id)) # 补全路径
convert_annotation(year, image_id)
list_file.close()

 

多线程调用yolo模型会出错(包括使用cv2载入yolo模型)

深度学习李魔佛 发表了文章 • 0 个评论 • 10 次浏览 • 2019-12-05 10:27 • 来自相关话题

占坑。
 
多线程调用yolo模型会出错(包括使用cv2载入yolo模型)
 
占坑。
 
多线程调用yolo模型会出错(包括使用cv2载入yolo模型)
 

keras yolo物体检测 入门教程

深度学习李魔佛 发表了文章 • 0 个评论 • 33 次浏览 • 2019-11-28 16:03 • 来自相关话题

占坑
占坑

yolo voc_label 源码分析

深度学习李魔佛 发表了文章 • 0 个评论 • 28 次浏览 • 2019-11-27 15:19 • 来自相关话题

作用读取每个xml文件,把坐标转化为相对坐标,对应文件名保存起来
 
import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join




sets = [('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test')]

# 20类
classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog",
"horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]

# size w,h
# box x-min,x-max,y-min,y-max
def convert(size, box):
dw = 1. / size[0]
dh = 1. / size[1]
x = (box[0] + box[1]) / 2.0 # 中心点位置
y = (box[2] + box[3]) / 2.0
w = box[1] - box[0]
h = box[3] - box[2]
x = x * dw
w = w * dw
y = y * dh
h = h * dh # 全部转化为相对坐标
return (x, y, w, h)


def convert_annotation(year, image_id):
# 找到2个同样的文件
in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml' % (year, image_id))
out_file = open('VOCdevkit/VOC%s/labels/%s.txt' % (year, image_id), 'w')

tree = ET.parse(in_file)
root = tree.getroot()
size = root.find('size')
w = int(size.find('width').text)
h = int(size.find('height').text)

for obj in root.iter('object'):
difficult = obj.find('difficult').text
cls = obj.find('name').text
if cls not in classes or int(difficult) == 1: # difficult ==1 的不要了
continue
cls_id = classes.index(cls) # 排在第几位
xmlbox = obj.find('bndbox')
b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),
float(xmlbox.find('ymax').text))
# 传入的是w,h 与框框的周边
bb = convert((w, h), b)
out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')


wd = getcwd()

for year, image_set in sets:
# ('2012', 'train') 循环5次
# 创建目录 一次性
if not os.path.exists('VOCdevkit/VOC%s/labels/' % (year)):
os.makedirs('VOCdevkit/VOC%s/labels/' % (year))

# 图片的id数据
image_ids = open('VOCdevkit/VOC%s/ImageSets/Main/%s.txt' % (year, image_set)).read().strip().split()

# 结果写入这个文件
list_file = open('%s_%s.txt' % (year, image_set), 'w')

for image_id in image_ids:
list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg\n' % (wd, year, image_id)) # 补全路径
convert_annotation(year, image_id)
list_file.close()

  查看全部
作用读取每个xml文件,把坐标转化为相对坐标,对应文件名保存起来
 
import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join




sets = [('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test')]

# 20类
classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog",
"horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]

# size w,h
# box x-min,x-max,y-min,y-max
def convert(size, box):
dw = 1. / size[0]
dh = 1. / size[1]
x = (box[0] + box[1]) / 2.0 # 中心点位置
y = (box[2] + box[3]) / 2.0
w = box[1] - box[0]
h = box[3] - box[2]
x = x * dw
w = w * dw
y = y * dh
h = h * dh # 全部转化为相对坐标
return (x, y, w, h)


def convert_annotation(year, image_id):
# 找到2个同样的文件
in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml' % (year, image_id))
out_file = open('VOCdevkit/VOC%s/labels/%s.txt' % (year, image_id), 'w')

tree = ET.parse(in_file)
root = tree.getroot()
size = root.find('size')
w = int(size.find('width').text)
h = int(size.find('height').text)

for obj in root.iter('object'):
difficult = obj.find('difficult').text
cls = obj.find('name').text
if cls not in classes or int(difficult) == 1: # difficult ==1 的不要了
continue
cls_id = classes.index(cls) # 排在第几位
xmlbox = obj.find('bndbox')
b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),
float(xmlbox.find('ymax').text))
# 传入的是w,h 与框框的周边
bb = convert((w, h), b)
out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')


wd = getcwd()

for year, image_set in sets:
# ('2012', 'train') 循环5次
# 创建目录 一次性
if not os.path.exists('VOCdevkit/VOC%s/labels/' % (year)):
os.makedirs('VOCdevkit/VOC%s/labels/' % (year))

# 图片的id数据
image_ids = open('VOCdevkit/VOC%s/ImageSets/Main/%s.txt' % (year, image_set)).read().strip().split()

# 结果写入这个文件
list_file = open('%s_%s.txt' % (year, image_set), 'w')

for image_id in image_ids:
list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg\n' % (wd, year, image_id)) # 补全路径
convert_annotation(year, image_id)
list_file.close()