１．先把識別結果保存爲.txt文件。
##保存測量結果爲XML文件
from ctypes import *
import math
import random
"""The Python implementation of the GRPC helloworld.Greeter server."""
from concurrent import futures
import time
import logging
import numpy as np
import cv2
from ctypes import *
import math
import socket
import sys
import threading
import json
import random
import time
import gc
import objgraph
#import darknetmain
import psutil
import os
from multiprocessing import Process,Lock
import weakref
def sample(probs):
s = sum(probs)
probs = [a/s for a in probs]
r = random.uniform(0, 1)
for i in range(len(probs)):
r = r - probs[i]
if r <= 0:
return i
return len(probs)-1
def c_array(ctype, values):
arr = (ctype*len(values))()
arr[:] = values
return arr
class BOX(Structure):
_fields_ = [("x", c_float),
("y", c_float),
("w", c_float),
("h", c_float)]
class DETECTION(Structure):
_fields_ = [("bbox", BOX),
("classes", c_int),
("prob", POINTER(c_float)),
("mask", POINTER(c_float)),
("objectness", c_float),
("sort_class", c_int)]
class IMAGE(Structure):
_fields_ = [("w", c_int),
("h", c_int),
("c", c_int),
("data", POINTER(c_float))]
class METADATA(Structure):
_fields_ = [("classes", c_int),
("names", POINTER(c_char_p))]
#lib = CDLL("/home/pjreddie/documents/darknet/libdarknet.so", RTLD_GLOBAL)
lib = CDLL("libdarknet.so", RTLD_GLOBAL)
lib.network_width.argtypes = [c_void_p]
lib.network_width.restype = c_int
lib.network_height.argtypes = [c_void_p]
lib.network_height.restype = c_int
predict = lib.network_predict
predict.argtypes = [c_void_p, POINTER(c_float)]
predict.restype = POINTER(c_float)
set_gpu = lib.cuda_set_device
set_gpu.argtypes = [c_int]
make_image = lib.make_image
make_image.argtypes = [c_int, c_int, c_int]
make_image.restype = IMAGE
get_network_boxes = lib.get_network_boxes
get_network_boxes.argtypes = [c_void_p, c_int, c_int, c_float, c_float, POINTER(c_int), c_int, POINTER(c_int)]
get_network_boxes.restype = POINTER(DETECTION)
make_network_boxes = lib.make_network_boxes
make_network_boxes.argtypes = [c_void_p]
make_network_boxes.restype = POINTER(DETECTION)
free_detections = lib.free_detections
free_detections.argtypes = [POINTER(DETECTION), c_int]
free_ptrs = lib.free_ptrs
free_ptrs.argtypes = [POINTER(c_void_p), c_int]
network_predict = lib.network_predict
network_predict.argtypes = [c_void_p, POINTER(c_float)]
reset_rnn = lib.reset_rnn
reset_rnn.argtypes = [c_void_p]
load_net = lib.load_network
load_net.argtypes = [c_char_p, c_char_p, c_int]
load_net.restype = c_void_p
do_nms_obj = lib.do_nms_obj
do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]
do_nms_sort = lib.do_nms_sort
do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]
free_image = lib.free_image
free_image.argtypes = [IMAGE]
letterbox_image = lib.letterbox_image
letterbox_image.argtypes = [IMAGE, c_int, c_int]
letterbox_image.restype = IMAGE
load_meta = lib.get_metadata
lib.get_metadata.argtypes = [c_char_p]
lib.get_metadata.restype = METADATA
load_image = lib.load_image_color
load_image.argtypes = [c_char_p, c_int, c_int]
load_image.restype = IMAGE
rgbgr_image = lib.rgbgr_image
rgbgr_image.argtypes = [IMAGE]
predict_image = lib.network_predict_image
predict_image.argtypes = [c_void_p, IMAGE]
predict_image.restype = POINTER(c_float)
def classify(net, meta, im):
out = predict_image(net, im)
res = []
for i in range(meta.classes):
res.append((meta.names[i], out[i]))
res = sorted(res, key=lambda x: -x[1])
return res
#def detect(net, meta, image, thresh=.3, hier_thresh=.3, nms=.55):
def detect(net, meta, image, thresh=.3, hier_thresh=.3, nms=.35):
im = load_image(image, 0, 0)
num = c_int(0)
pnum = pointer(num)
predict_image(net, im)
dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum)
num = pnum[0]
if (nms): do_nms_obj(dets, num, meta.classes, nms);
res = []
for j in range(num):
for i in range(meta.classes):
if dets[j].prob[i] > 0:
b = dets[j].bbox
res.append((meta.names[i].decode(), dets[j].prob[i], (b.x, b.y, b.w, b.h)))
res = sorted(res, key=lambda x: -x[1])
free_image(im)
free_detections(dets, num)
return res
if __name__ == "__main__":
#net = load_net("cfg/densenet201.cfg", "/home/pjreddie/trained/densenet201.weights", 0)
#im = load_image("data/wolf.jpg", 0, 0)
#meta = load_meta("cfg/imagenet1k.data")
#r = classify(net, meta, im)
#print r[:10]
#net = load_net("cfg/tiny-yolo.cfg", "tiny-yolo.weights", 0)
#meta = load_meta("cfg/coco.data")
net = load_net(b"cfg/yolov3-voc.cfg", b"yolov3-voc_10000.weights", 0)
meta = load_meta(b"cfg/voc.data")
#print (r)
p0="/media/yll/ylw/yll/work/bag/9.25data"
#p0="/media/yll/ylw/yll/work/bag/11"
#p0="/media/yll/ylw/yll/work/bag/record9.25/10"
oldfile=os.listdir(p0)
for ii in oldfile:
path1=os.path.join(p0,ii)
print("開始開始開始",path1)
teimage=cv2.imread(path1)
boxes = detect(net, meta, path1.encode('utf-8'))
#boxes=retun(teimage)
#print("shape shape shape",teimage.shape)
#print(boxes,len(boxes))
fw = open('./result117.txt','a')
print("11111111",ii,boxes)
if len(boxes) > 0:
#for i in range(len(boxes)):
#score=boxes[i][1]
#label=boxes[i][0]
#aa=int(boxes[1])
print("222222222",boxes)
fw.write(str(ii)+""+str(boxes)+"\n")
#fw.write("\n")
#fw = open('./result.txt','a')
fw.close()
"""
if (label==b"package"):
#print("ddd")
fw = open('./result.txt','a') #保存結果的文件，下同
fw.write(str(ii)+" "+str(boxes)+"\n")
#fw.close()
elif (str(label)=="person"):
fw = open('./result.txt','a') #保存結果的文件，下同
fw.write(str(ii)+""+str(boxes)+"\n")
#fw.close()
"""
２，將ｔｘｔ改爲ＸＭＬ文件
from skimage import io
import shutil
import random
import os
import string
headstr = """\
<annotation>
<folder>VOC2007</folder>
<filename>%s.jpg</filename>
<path>%s</path>
<source>
<database>Unknown</database>
<annotation>PASCAL VOC2007</annotation>
<image>flickr</image>
</source>
<size>
<width>%d</width>
<height>%d</height>
<depth>%d</depth>
</size>
<segmented>0</segmented>
"""
objstr = """\
<object>
<name>%s</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>%d</xmin>
<ymin>%d</ymin>
<xmax>%d</xmax>
<ymax>%d</ymax>
</bndbox>
</object>
"""
tailstr = '''\
</annotation>
'''
def writexml(idx, head, bbxes, tail):
filename = "Annotations/%06d.xml" % (idx)
f = open(filename, "w")
f.write(head)
for bbx in bbxes:
f.write(objstr % ('face', bbx[0], bbx[1], bbx[0] + bbx[2], bbx[1] + bbx[3]))
f.write(tail)
f.close()
#def excute_datasets(idx, datatype):
i=0
f_bbx = open('/media/yll/ylw/yll/work/bag/darknet1test/result117.txt', 'r')
for filename in f_bbx:
#filename = f_bbx.readline().strip('\n')
#print(",,,,,,,,,,,,,,,,,",filename[:7])
if not filename:
break
print(filename[0:6],filename[0:10])
i+=1
#print(i)
p0="/media/yll/ylw/yll/work/bag/9.25data/"+str(filename[0:10])
path1="/media/yll/ylw/yll/work/bag/darknet1/rename/24/"+str(filename[0:6]+".xml")
#print(filename)
#print("ssssssssss",i,p0)
im = io.imread(p0)
idx=filename[0:6]
idpath="/media/yll/ylw/yll/work/bag/darknet1/VOCdevkit/VOC2007/JPEGImages/"+str(filename[0:10])
#idpath="/media/yll/ylw/yll/work/bag/darknet1/VOCdevkit/VOC2007/JPEGImages"+
head = headstr % (idx,idpath, im.shape[1], im.shape[0], im.shape[2])
f = open(path1, 'w')
f.write(head)
boxes=eval(filename[10:])
#print("aaaaaaaaaaaa",numss[0])
for i in range (len(boxes)):
xmin=boxes[i][2][0]-boxes[i][2][2]/2
ymin=boxes[i][2][1]-boxes[i][2][3]/2
xmax=boxes[i][2][0]+boxes[i][2][2]/2
ymax=boxes[i][2][1]+boxes[i][2][3]/2
#print("ddddqqqqq",ind,len(boxes))
f.write(objstr % (str(boxes[i][0]), xmin, ymin,xmax ,ymax))
f.write(tailstr)
f.close()
３．該圖片名字並修改ＸＭＬ文件
# -*- coding: UTF-8 -*-
##修改xml文件
import xml.etree.ElementTree as ET
import os
import cv2
def xmlre(cca,ccc,ccd,result2):
if ccc in result2:
tree=ET.parse(i)
root=tree.getroot()
filename = tree.find("filename")
filename.text=str(cca)
path = tree.find("path")
#c2=
aa=path4+str(cca)
#print(aa)
path.text =aa
tree.write(ccd,encoding='utf-8')
spath="/media/yll/ylw/yll/work/bag/darknet1/VOCdevkit/VOC2007/JPEGImages"
result=[]
path1="/media/yll/ylw/yll/work/bag/darknet1/VOCdevkit/VOC2007/Annotations"
path2="/media/yll/ylw/yll/work/bag/darknet1/VOCdevkit/VOC200711/Annotations"
path0="/media/yll/ylw/yll/work/bag/record9.25/data/xml (復件)"
path4="/media/yll/ylw/yll/work/bag/darknet1/VOCdevkit/VOC2007/JPEGImages/"
path5="/media/yll/ylw/yll/work/bag/darknet1/rename/22"
path6="/media/yll/ylw/yll/work/bag/9.25data"
#XML路徑#XML路徑
xml=os.listdir(path5)
for file in xml:
#filename=os.path.join(path0,file)
result.append(file)
#圖片路徑#圖片路徑
filelist = os.listdir(path6)
total_sum = len(filelist)
i = 1
for item in filelist:
# endswith() 方法用於判斷字符串是否以指定後綴結尾，如果以指定後綴結尾返回True，否則返回False。
if item.endswith('.jpg'):
# os.path.join 用於路徑拼接，src爲完整圖片路徑
src = os.path.join(os.path.abspath(path6),item)
str1 = str(i+16623)
cA=str1.zfill(6)+'.jpg'
cC=item[0:6]+'.xml'
cD=str1.zfill(6)+'.xml'
if cC in result:
path15=os.path.join(path5,cC)
#path5=path0+str(cC)
tree=ET.parse(path15)
root=tree.getroot()
filename = tree.find("filename")
filename.text=cA
path = tree.find("path")
#c2=
aa=path4+str(cA)
print(aa)
path.text =aa
tree.write(cD,encoding='utf-8')
dst = os.path.join(os.path.abspath(path6),str1.zfill(6)+'.jpg')
os.rename(src,dst)
４，給ＸＭＬ文件增加節點屬性
# -*- coding: UTF-8 -*-
import xml.etree.ElementTree as ET
import os
import cv2
path0="/media/yll/ylw/yll/work/bag/darknet/VOCdevkit/000001.xml"
tree=ET.parse(path0)
root=tree.getroot()
newNode = ET.Element('annotation')
newNodeName = ET.Element('path')
newNodeName.text = '/media/yll/ylw/yll/work/bag/darknet/VOCdevkit/VOC2007/JPEGImages/010765.jpg'
#newNode.append(newNodeName)
root.insert(2, newNodeName)
#root[3].set('na')
print(root[2])
ET.dump(root)
#insert
#tree.set(source, 'cc')
#root.set=mei
#rank = ET.SubElement(root,'source',{'rank':'2'})
#rank.text = '1'
tree.write("/media/yll/ylw/yll/work/bag/darknet/VOCdevkit/000002.xml")
#path.appendChild="ccc"
#c1=path.text
#print(root[3][0].text)