零基础入门CV - 街景字符编码识别 用yolov3和yolov3_tiny教程

Posted 2021-06-24 星代码119

一、从官方依次下载文件并解压：

对于新手稍微提一下，这个数据集是通过下载下来的链接下载的。直接输入网址，打开既开始下载！.json文件不是下载，是复制。点开链接，创建一个.json文件，复制到里面即可。（我也不知道为啥，反正我下载的时候就这样）

我们会发现训练集为30000张图片，验证集为10000张图片。
注意：（.json文件不是下载，是复制。点开链接，创建一个.json文件，复制到里面即可）
数据集下载好，首先要把.json文件转换为我们所需要的的txt文件。

为了防止图片不连续，这里先生成图片的序号（名称）：
代码：

import os
train_file=open('D:\\competition\\mchar_train/train.txt','w')

for _,_,train_files in os.walk('D:/competition/mchar_train/mchar_train'):
    continue
i = 0
for file in train_files:
    print(file.split('.')[0])
    i = i + 1
    train_file.write(file.split('.')[0]+'\\n')
print(i)

会在指定文件夹下生成一个train.txt文件
然后运行：

import os
import cv2
import json

def process(dict1,shape,image_path):
    annotation = image_path
    for i in range(len(dict1['left'])):
        xmin = str(int(dict1['left'][i]))
        xmax = str(int(dict1['left'][i]+dict1['width'][i]))
        ymin = str(int(dict1['top'][i]))
        ymax = str(int(dict1['top'][i]+dict1['height'][i]))
        annotation += ' ' + ','.join([xmin, ymin, xmax, ymax, str(int(dict1['label'][i]))])       
    return annotation
 #修改相对应的路径即可
f = open(
    "D:/competiton/mchar/mchar_train.json",
    encoding='utf-8')
data = json.load(f)
with open('D:\\competition\\mchar_train/train.txt', 'r') as f:
        txt = f.readlines()
        image_inds = [line.strip() for line in txt]
with open('D:/competition/mchar_train//voc_train.txt', 'a') as f:
    for i in image_inds:
        print('D:/competition/mchar_train/mchar_train'+'/'+i+'.png')
        img = cv2.imread(r'D:/competition/mchar_train/mchar_train'+'/'+i+'.png')
        shape = img.shape
        image_path = 'D:/competition/mchar_train/mchar_train'+'/'+i+'.png'
        i = i+'.png'
        f.write(process(data[i],shape,image_path)+'\\n')
    f.close()

结果如下：

二、训练模型

其实做到这一步就可以了，但是为了新手更好的入门，这里还是把训练过程介绍一下（这里是针对的yolov3_tiny，我实在yolov3的基础上改的，几乎流程一样）。
yolov3代码来源：https://github.com/YunYang1994/tensorflow-yolov3
yolov3_tiny代码来源为：https://download.csdn.net/download/qq_40214464/19159417
在保存的文件夹就会有voc_train.txt，直接复制到dataset文件下：

这里是打比赛，按道理应该把val的数据集也转换过来，我觉得没必要，如果真要转换过来，你到不如，直接转换成训练集，一起训练。怎么转换就不说了，如果需要，请在下方留言。其实完全不用测试集的哈。

首先运行，获得锚框，由于yolov3_tiny只需要6个锚框，所以选择后6个锚框，复制到tiny

然后把对应的类别改了

点击训练即可！

三、准换为我们需要的.csv文件
代码：

import cv2
import os,glob
import shutil
import numpy as np
import tensorflow as tf
import core.utils as utils
from core.config_tiny import cfg
from core.yolov3_tiny import YOLOV3
import matplotlib.pyplot as plt
import pandas as pd


class YoloTest(object):
    def __init__(self):
        self.input_size = cfg.TEST.INPUT_SIZE
        self.anchor_per_scale = cfg.YOLO.ANCHOR_PER_SCALE
        self.classes = utils.read_class_names(cfg.YOLO.CLASSES)
        self.num_classes = len(self.classes)
        self.anchors = np.array(utils.get_anchors(cfg.YOLO.ANCHORS, is_tiny=True))
        self.score_threshold = cfg.TEST.SCORE_THRESHOLD
        self.iou_threshold = cfg.TEST.IOU_THRESHOLD
        self.moving_ave_decay = cfg.YOLO.MOVING_AVE_DECAY
        self.annotation_path = cfg.TEST.ANNOT_PATH
        self.weight_file = cfg.TEST.WEIGHT_FILE
        self.write_image = cfg.TEST.WRITE_IMAGE
        self.write_image_path = cfg.TEST.WRITE_IMAGE_PATH
        self.show_label = cfg.TEST.SHOW_LABEL

        with tf.name_scope('input'):
            self.input_data = tf.placeholder(dtype=tf.float32, name='input_data')
            self.trainable = tf.placeholder(dtype=tf.bool, name='trainable')

        model = YOLOV3(self.input_data, self.trainable)
        self.pred_mbbox, self.pred_lbbox = model.pred_mbbox, model.pred_lbbox



        self.saver = tf.train.Saver()

        self.sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))

        self.saver.restore(self.sess, self.weight_file)

    def predict(self, image_path,df_submit):
        imageDir = os.path.abspath(image_path)

        # 通过glob.glob来获取第一个文件夹下，所有'.jpg'文件
        imageList = glob.glob(os.path.join(imageDir, '*.png'))

        imgs_num = len(imageList)
        for item in imageList:

            image_path = item
            image = cv2.imdecode(np.fromfile(image_path, dtype=np.uint8), cv2.IMREAD_COLOR)
            image_name = image_path.split("\\\\")[-1]

            org_image = np.copy(image)
            org_h, org_w, _ = org_image.shape

            image_data = utils.image_preporcess(image, [self.input_size, self.input_size])
            image_data = image_data[np.newaxis, ...]

            pred_mbbox, pred_lbbox = self.sess.run([self.pred_mbbox, self.pred_lbbox],
                                                   feed_dict={
                                                       self.input_data: image_data,
                                                       self.trainable: False
                                                   }
                                                   )

            pred_bbox = np.concatenate([np.reshape(pred_mbbox, (-1, 5 + self.num_classes)),
                                        np.reshape(pred_lbbox, (-1, 5 + self.num_classes))], axis=0)

            bboxes = utils.postprocess_boxes(pred_bbox, (org_h, org_w), self.input_size, self.score_threshold)
            bboxes = utils.nms(bboxes, self.iou_threshold)

            b = np.array(bboxes)
            p = []
            if b.shape[0]==0:
                c = [2]
                xin = [2]
            else:
                c = b[:, 5]
                x_min = b[:,1]
            num = len(x_min)
            for i in range(num):
                wh = np.argmin(x_min)
                p.append(c[wh])
                x_min= np.delete(x_min, [wh])
                c = np.delete(c, [wh])
            c = np.array(p)




            #image = utils.draw_bbox(image, bboxes, show_label=self.show_label)
            # plt.imshow(image)
            # plt.show()


            test_label_pred = []
            for x in c:
                test_label_pred.append(str(int(x)))
            a = '\\t'
            i=len(test_label_pred)-1
            while i+1:
                a = a + test_label_pred[i]
                i = i-1

            n = []
            n.append(a)
            df_submit['file_code'] = n
            df_submit['file_name'] = image_name
            df_submit.to_csv('D:/Competiton/test_A_sample_submit.csv', mode='a',header=False,index=None)


if __name__ == "__main__":
    img_dir = "D:/Competiton/mchar_test_a"
    df_submit = pd.read_csv('D:/Competiton/test_A_sample_submit.csv')
    YoloTest().predict(img_dir ,df_submit)