python图片目标检测_python目标检测给图画框,bbox画到图上并保存案例

我就废话不多说了，还是直接上代码吧！ import os

import xml.dom.minidom

import cv2 as cv

ImgPath = 'C:/Users/49691/Desktop/gangjin/gangjin_test/JPEGImages/'

AnnoPath = 'C:/Users/49691/Desktop/gangjin/gangjin_test/Annotations/' #xml文件地址

save_path = ''

def draw_anchor(ImgPath,AnnoPath,save_path):

imagelist = os.listdir(ImgPath)

for image in imagelist:

image_pre, ext = os.path.splitext(image)

imgfile = ImgPath + image

xmlfile = AnnoPath + image_pre + '.xml'

# print(image)

# 打开xml文档

DOMTree = xml.dom.minidom.parse(xmlfile)

# 得到文档元素对象

collection = DOMTree.documentElement

# 读取图片

img = cv.imread(imgfile)

filenamelist = collection.getElementsByTagName("filename")

filename = filenamelist[0].childNodes[0].data

print(filename)

# 得到标签名为object的信息

objectlist = collection.getElementsByTagName("object")

for objects in objectlist:

# 每个object中得到子标签名为name的信息

namelist = objects.getElementsByTagName('name')

# 通过此语句得到具体的某个name的值

objectname = namelist[0].childNodes[0].data

bndbox = objects.getElementsByTagName('bndbox')

# print(bndbox)

for box in bndbox:

x1_list = box.getElementsByTagName('xmin')

x1 = int(x1_list[0].childNodes[0].data)

y1_list = box.getElementsByTagName('ymin')

y1 = int(y1_list[0].childNodes[0].data)

x2_list = box.getElementsByTagName('xmax') #注意坐标，看是否需要转换

x2 = int(x2_list[0].childNodes[0].data)

y2_list = box.getElementsByTagName('ymax')

y2 = int(y2_list[0].childNodes[0].data)

cv.rectangle(img, (x1, y1), (x2, y2), (255, 255, 255), thickness=2)

cv.putText(img, objectname, (x1, y1), cv.FONT_HERSHEY_COMPLEX, 0.7, (0, 255, 0),

thickness=2)

# cv.imshow('head', img)

cv.imwrite(save_path+'/'+filename, img) #save picture

补充知识：深度学习python之用Faster-rcnn 检测结果(txt文件) 在原图画出box

使用Faster-rcnn 的test_net.py 检测网络的mAP等精度会生成一个检测结果(txt文件)，格式如下： 000004 0.972 302.8 94.5 512.0 150.0

000004 0.950 348.1 166.1 512.0 242.9

000004 0.875 1.0 25.7 292.6 126.3

000004 0.730 1.0 138.5 488.3 230.0

000004 0.699 1.0 120.9 145.5 139.9

000004 0.592 54.4 227.4 431.9 343.4

000004 0.588 1.0 159.8 18.8 231.6

000004 0.126 1.0 247.1 342.3 270.0

000004 0.120 1.0 225.4 185.7 309.3

每行分别为 名称 检测概率 xmin ymin xmax ymax

问题在于每一行只显示一个box数据，每幅图像可能包括多个box，需要判断提取的多行数据是不是属于同一图片

下面使用python提取这些数据，在原图上画出box并且保存起来 import os

import os.path

import numpy as np

import xml.etree.ElementTree as xmlET

from PIL import Image, ImageDraw

import cPickle as pickle

txt_name = 'comp4_8a226fd7-753d-40fc-8013-f68d2a465579_det_test_ship.txt'

file_path_img = '/home/JPEGImages'

save_file_path = '/home/detect_results'

source_file = open(txt_name)

img_names = []

for line in source_file:

staff = line.split()

img_name = staff[0]

img_names.append(img_name)

name_dict = {}

for i in img_names:

if img_names.count(i)>0:

name_dict[i] = img_names.count(i)

source_file.close()

source_file = open(txt_name)

for idx in name_dict:

img = Image.open(os.path.join(file_path_img, idx + '.jpg'))

draw = ImageDraw.Draw(img)

for i in xrange(name_dict[idx]):

line = source_file.readline()

staff = line.split()

score = staff[1]

box = staff[2:6]

draw.rectangle([int(np.round(float(box[0]))), int(np.round(float(box[1]))),

int(np.round(float(box[2]))), int(np.round(float(box[3])))], outline=(255, 0, 0))

img.save(os.path.join(save_file_path, idx + '.jpg'))

source_file.close()

运行完即可在保存文件夹中得到效果图。

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