使用大佬的tensorflow yolov3以及darknet该成shufflenet

https://github.com/YunYang1994/tensorflow-yolov3

这个是大佬的源代码，拿来直接用就可以，首先需要两个数据集，一个是测试用的，一个是训练用的。

然后我们对这两个数据集进行一些文档生成，主要是core/config文件里面所需要的一些文档

首先是voc_train.txt，其格式为文件路径左上角坐标右下角坐标类名

那么首先需要生成类名文件

需要一个脚本去读取annotations文件夹，生成类名文件保存在data/classes目录下

import os
import re
import xml.etree.cElementTree as et

annotations_path = "D:/Tensorflow/VOCtest_06-Nov-2007/VOCdevkit/VOC2007/Annotations/"

save_all_class_name = []


if __name__ == "__main__":
    for filanama in os.listdir(annotations_path):
        filename = annotations_path + filanama
        e = et.parse(filename).getroot()
        str_one_line = "D:/Tensorflow/VOCtest_06-Nov-2007/VOCdevkit/VOC2007/JPEGImages/"
        for atype_2 in e.findall('object'):
            for atype_3 in atype_2.findall("name"):
                if atype_3.text not in save_all_class_name:
                    save_all_class_name.append(atype_3.text)
    with open('D:/Tensorflow/tensorflow-yolov3-master/data/classes/my_voc.names', 'a+') as f_names:
        for i in save_all_class_name:
            f_names.writelines(i+"\n")
    f_names.close()

生成一个my_voc_train的文件，用一个脚本读取voc2007数据集里面的annotations文件夹，然后训练标注文件

annotations_path = "D:/Tensorflow/VOCtest_06-Nov-2007/VOCdevkit/VOC2007/Annotations/"

save_all_class_name = []
all_class_name = []
dict_all_class_name = {}
str_all_line = []
with open('D:/Tensorflow/tensorflow-yolov3-master/data/classes/voc_2007_nov/my_voc.names', 'r') as f:
    while True:
        buffer_text = f.readline()
        buffer_text = buffer_text.rstrip("\n")
        if buffer_text != '':
            all_class_name.append(buffer_text)
        if not buffer_text:
            break

count = 0
for i in all_class_name:
    dict_all_class_name[i] = count
    count += 1
print(dict_all_class_name)



if __name__ == "__main__":
    for filanama in os.listdir(annotations_path):
        filename = annotations_path + filanama
        e = et.parse(filename).getroot()
        str_one_line = "D:/Tensorflow/VOCtest_06-Nov-2007/VOCdevkit/VOC2007/JPEGImages/"
        for atype in e.findall('filename'):
            str_one_line += atype.text
            print(atype.text)
        str_one_line += " "
        for atype_2 in e.findall('object'):
            for atype_4 in atype_2.findall("bndbox"):
                atype_xmin = atype_4.findall("xmin")[0].text
                atype_ymin = atype_4.findall("ymin")[0].text
                atype_xmax = atype_4.findall("xmax")[0].text
                atype_ymax = atype_4.findall("ymax")[0].text
                str_one_line += atype_xmin
                str_one_line += ","
                str_one_line += atype_ymin
                str_one_line += ","
                str_one_line += atype_xmax
                str_one_line += ","
                str_one_line += atype_ymax
                str_one_line += ","
            for atype_3 in atype_2.findall("name"):
                str_one_line += str(dict_all_class_name[atype_3.text])
                str_one_line += " "
                if atype_3.text not in save_all_class_name:
                    save_all_class_name.append(atype_3.text)
        str_all_line.append(str_one_line)

    with open("D:/Tensorflow/tensorflow-yolov3-master/data/dataset/my_voc_test.txt", 'a+') as ff:
        for uu in str_all_line:
            ff.writelines(uu)
            ff.writelines("\n")
    ff.close()

同样用来生成测试集的标注文件，名字自命名，生成如下内容

如果想检测一下生成的标注文件是否正确，可以用脚本测试一下

import cv2
import os
import re
all_class_name = []
dict_all_class_name = {}
with open('D:/Tensorflow/tensorflow-yolov3-master/data/classes/voc_2007_nov/my_voc.names', 'r') as f:
    while True:
        buffer_text = f.readline()
        buffer_text = buffer_text.rstrip("\n")
        if buffer_text != '':
            all_class_name.append(buffer_text)
        if not buffer_text:
            break

count = 0
for i in all_class_name:
    dict_all_class_name[count] = i
    count += 1
print(dict_all_class_name)

with open('D:/Tensorflow/tensorflow-yolov3-master/data/dataset/my_voc_test.txt', 'r') as f:
    while True:
        a = f.readline()
        pattern = re.compile('(\d+.jpg).+', re.S)
        result = re.findall(pattern, a)
        #每张图片
        if len(result) != 0:
            image = cv2.imread("D:/Tensorflow/VOCtest_06-Nov-2007/VOCdevkit/VOC2007/JPEGImages/"
                               + result[0])
            pattert_2 = re.compile('\s?(\d+,\d+,\d+,\d+),\d+', re.S)
            pattert_2_2 = re.compile('\s?\d+,\d+,\d+,\d+,(\d+)', re.S)
            result_2 = re.findall(pattert_2, a)
            result_2_2 = re.findall(pattert_2_2, a)
            pic_count = 0
            for pt_data in result_2:
                pattert_3 = re.compile('(\d+,\d+)', re.S)
                oop = re.findall(pattert_3, pt_data)
                #每两个点
                pt1_x = int(oop[0].split(',')[0])
                pt1_y = int(oop[0].split(',')[1])
                pt2_x = int(oop[1].split(',')[0])
                pt2_y = int(oop[1].split(',')[1])
                # pt2 = oop.split(',')[1]
                in_class = int(result_2_2[pic_count])
                buffer_class = dict_all_class_name[in_class]
                pic_count += 1
                font = cv2.FONT_HERSHEY_SIMPLEX
                cv2.putText(image, buffer_class, (50, 300), font, 0.8, (0, 0, 255), 2)
                cv2.rectangle(image, (pt1_x, pt1_y), (pt2_x, pt2_y), (0, 255, 0), 2)
            cv2.imshow(str(result[0]), image)
            cv2.waitKey(2000)
            cv2.destroyAllWindows()
        else:
            pass
        if not a:
            break

效果如下