Object Detection Based On Yolo（一）

第一次写博客啦~

萌新请多指教

yolo是一个在速度上很优秀的object detection模型。

对yolo的算法理解请前往：https://blog.csdn.net/u014380165/article/details/72616238

我们的目标是要做到这个样子：

 

 

这回，我们先对label进行预处理吧orz

我们先对yolo的label进行预处理

raw_data的label就差不多长这个样子啦

如：

88.82,98.33,98.82,191.42,456.95,184.42,447.95,99.33,薄利汽配

代表四个坐标值，每个坐标值就是框住object的四个顶点啦

label是一个13x13x6的格式

其中，6即(类别概率,x,y,w,h,置信度)

图片的预训练打算用inception v4，处理成17x17x1024的矩阵

这个也基本完工了

时间太紧，暂且这么多吧

代码如下：

然后就是 

只要你有label和对应的图片

换成你的路径

就能生成yolo所需要的label格式啦

写的不太好请见谅啊~

import numpy as np
import os
import cv2
#运行该程序前请新建一个文件夹把generate_label_array放进去
class label_preprocessing:
    def __init__(self):
        self.raw_images_path='./raw_data/image_9000/'           #换成你自己的图片路径
        self.raw_label_path='./raw_data/txt_9000/'              #换成你自己的label路径
        self.generate_data_path_from_filter_label_1='./labels/'
        self.labels_matrix_path='./label_matrix/'
        self.image_size=299
        self.cell_size=13
    def filter_label_1(self):
        f=os.listdir(self.raw_label_path)
        for fs in f:
            print('*' * 20)
            with open(fs,'r',encoding='utf-8') as file:
                f_line=''
                for line in file:
                    if '#' not in line.replace('\n','').split(',')[-1]:
                        f_line=f_line+line+' '
            with open(self.generate_data_path_from_filter_label_1+fs,'w',encoding='utf-8') as label:
                label.write(f_line)
            print(fs,'done')
            print('*' * 20)
    def generate_float_label(self,label_):
        with open(self.generate_data_path_from_filter_label_1+label_,'r',encoding='utf-8') as labels:
            label_all_locations=[]
            for label_txt in labels:
                lo_str=label_txt.replace(' ', '').replace('\n', '').split(',')[:-1]
                if len(lo_str)!=0:
                    locations=[float(t) for t in lo_str]
                label_all_locations.append(locations)
            return np.array(label_all_locations)
    def load_annotation(self):
        imname = os.listdir(self.raw_images_path)
                                                                            # modify and delete
        for i_name in imname:
            try:
                im = cv2.imread(self.raw_images_path+i_name)
                im.shape
            except:
                continue
            h_ratio = 1.0 * self.image_size / im.shape[0]
            w_ratio = 1.0 * self.image_size / im.shape[1]
            label = np.zeros((self.cell_size, self.cell_size, 6))

            locations_matrix=self.generate_float_label(i_name[:-4]+'.txt')

            for location in locations_matrix:
                xmin=location[0]
                ymin=location[1]
                xmax=location[4]
                ymax=location[5]
                x1 = max(min((xmin - 1) * w_ratio, self.image_size - 1), 0)
                y1 = max(min((ymin - 1) * h_ratio, self.image_size - 1), 0)
                x2 = max(min((xmax - 1) * w_ratio, self.image_size - 1), 0)
                y2 = max(min((ymax - 1) * h_ratio, self.image_size - 1), 0)
                boxes = [(x2 + x1) / 2.0, (y2 + y1) / 2.0, x2 - x1, y2 - y1]
                x_ind = int(boxes[0] * self.cell_size / self.image_size)
                y_ind = int(boxes[1] * self.cell_size / self.image_size)
                if label[y_ind, x_ind, 0] == 1:
                    continue
                label[y_ind, x_ind, 0] = 1
                label[y_ind, x_ind, 1:5] = boxes
                label[y_ind, x_ind, 5] = 1
            yield label,i_name
    def check_LabelsFile_exist(self):
        build_filter_label=False
        try:
            os.listdir(self.generate_data_path_from_filter_label_1)
            print('[info]   ./labels exist,checking finished!')
        except:
            build_filter_label=True
        if build_filter_label:
            self.filter_label_1()
    def save_labels_matrix_13x13(self):
        label=self.load_annotation()
        for label_matrix,label_file_name in label:
            np.save(self.labels_matrix_path+label_file_name+'.npy',label_matrix)
            print('[info]   ','done===>',label_file_name)
if __name__ == '__main__':
    print('''
               ┏┓      ┏┓
              ┏┛┻━━━━━━┛┻┓
              ┃          ┃
              ┃ ┳┛    ┗┳ ┃
              ┃    ┻     ┃
              ┗━        ━┛            
                ''')
    lp=label_preprocessing()
    lp.check_LabelsFile_exist()
    try:                                                      #modify
        os.listdir(lp.labels_matrix_path)                     #modify
    except:                                                   #modify
        os.mkdir(lp.labels_matrix_path)                       #modify
    if len(os.listdir(lp.labels_matrix_path))==8878:
        print('[info]   ','The file called labels_matrix has been build and the data has been generated, escape the generating process...')
    else:
        print('[info]   ','Start to build and generate...')
        lp.save_labels_matrix_13x13()

下回写写自己对yolo的理解吧。。。