前言

本篇博客为博主毕设的一部分，此部分主要解决海上船舶目标实时分类检测。在Tensorflow的环境下，采用YOLOv3（keras）算法，最终mAP可达到95.66%，下面详细的介绍了我的训练过程。

Seaship数据集：下载

下面为部分检测结果的截图：

第二部分：【目标检测】基于yolo3和sort的多目标检测与跟踪

一、准备环节

计算机环境：Win10 + Python3.6 + cuda9.0
主要依赖：

tensorflow-gpu 1.12.0
keras-gpu 2.2.4
opencv 4.2.0
pillow
numpy
matplotlib

深度学习框架

如果是第一次使用深度学习进行目标检测，需要安装深度学习框架，本项目使用的是Tensorflow深度学习框架。

【TensorFlow】Window10搭建GPU环境（CUDA、cuDNN）

必选部分

源码：https://github.com/qqwweee/keras-yolo3
权重文件：yolo.h5（被墙，可采用下面方法）

在YOLO官网下载YOLO的权重文件。通过以下命令将.weights文件转换为.h5模型。

python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5

• 1

可选部分

mAP计算：【目标检测】kera-yolo3模型计算mAP
mAP源码：https://github.com/Cartucho/mAP

修改mAP源码，针对keras-yolov3的检测结果计算mAP。

数据集

Seaship数据集：下载

数据集介绍：SeaShips A Large-Scale Precisely Annotated Dataset for Ship Detection

二、代码结构分析

在训练时需要对其中的一些文件进行修改，同时也需要增添一些文件和文件夹。左侧为原工程，右侧为修改后的工程。

需要修改/增添部分：

无需修改部分（主要部分，次要部分不再介绍）：

三、制作数据集

3.1 LabelImg 标记图片

标记图片就是对图片中的待识别目标进行标记，如果识别的目标时猫、狗，那就用方块标记出猫或狗，它们的标签分别为cat、dog。标记完成后，生成与原文件名相同的.xml文件。

标记图片软件LabelImg，可参考：图像标注工具labelImg安装教程及使用方法。

3.2 VOC 数据集格式介绍

深度学习所使用的数据集格式大部分采用VOC格式。所以我们也要将自己所标记的图片转变到VOC格式。

可查看此博客了解VOC数据集的组成。

3.3 转化成VOC数据集

标记好所有图片后，所有的jpg和xml均在一个目录下。我们新建一个文件夹dataset，在dataset目录下新建JPEGImages、Annotations和ImageSets三个文件夹。

将所有jpg文件放入JPEGImages中；
将所有xml文件放入Annotations中；
在ImageSets文件夹内新建Main备用。

3.4 生成ImageSets

使用代码make_main_txt.py在ImgeSets/Main目录下生成train.txt, trainval.txt, val.txt, test.txt。

该代码存放的目录如下图，make_main_txt.py与ImageSets同级。

代码：make_main_txt.py

# -_- coding:utf-8 -_-
import os
import random

train_percent = 0.8  # 训练集占数据的比例
val_percent = 0.15    # 验证集占数据的比例
test_percent = 0.05     # 测试集占数据集的比例

xmlfilepath = ‘Annotations’
txtsavepath = ‘ImageSets/Main’
total_xml = os.listdir(xmlfilepath)

num = len(total_xml)        # 图片数量
list = range(num)

train_num = int(num _ train_percent)  # train的数量
test_num = int(num _ test_percent)    # test的数量
val_num = int(num _ val_percent)      # val的数量

trainval = random.sample(list, train_num + val_num)
train = random.sample(trainval, train_num)

ftest = open(‘ImageSets/Main/test.txt’, ‘w’)
ftrain = open(‘ImageSets/Main/train.txt’, ‘w’)
fval = open(‘ImageSets/Main/val.txt’, ‘w’)

for i in list:
    name = total_xml[i][:-4] + ‘\n’
    if i in trainval:
        if i in train:
            ftrain.write(name)
        else:
            fval.write(name)
    else:
        ftest.write(name)

ftrain.close()
ftest.close()
fval.close()

运行之后，将在ImgeSets/Main目录下生成下面这些文件。

四、训练模型

4.1 修改voc_annocation.py

该文件用于生成下图所示的文本文件，包括train.txt，test.txt，val.txt。

其中包含图片的目录，以及图片中标签位置数据。

对文件进行修改，修改结果如下，修改了类别（classes）、图片的打开目录和保存目录。

# -_- coding:utf-8 -_-
import xml.etree.ElementTree as ET
from os import getcwd

sets = [‘train’, ‘val’, ‘test’]
classes = [“person”, “hat”]


def convert_annotation(image_id, list_file):
    print(image_id)
    in_file = open(‘dataset/Annotations/%s.xml’ % image_id)
    tree = ET.parse(in_file)
    root = tree.getroot()

    for obj in root.iter(‘object’):
        difficult = obj.find(‘difficult’).text
        cls = obj.find(‘name’).text
        if cls not in classes or int(difficult)==1:
            continue
        cls_id = classes.index(cls)
        xmlbox = obj.find(‘bndbox’)
        b = (int(xmlbox.find(‘xmin’).text), int(xmlbox.find(‘ymin’).text), int(xmlbox.find(‘xmax’).text), int(xmlbox.find(‘ymax’).text))
        list_file.write(“ “ + “,”.join([str(a) for a in b]) + ‘,’ + str(cls_id))

wd = getcwd()

for image_set in sets:
    image_ids = open(dataset/ImageSets/Main/%s.txt’ % image_set).read().strip().split()
    print(image_ids)
    list_file = open(‘%s.txt’ % image_set, ‘w’)

    for image_id in image_ids:
        list_file.write(‘dataset/JPEGImages/%s.jpg’ % image_id)
        convert_annotation(image_id, list_file)
        list_file.write(‘\n’)
    list_file.close()

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4.2 修改yolo3.cfg

打开yolo3.cfg，搜索yolo(共三处)，每次均按下图修改。

filters：3_(5+len(classes))

classes：训练的类别数

random：原来是1，显存小改为0

4.3 增加my_classes.txt

目录如下图所示，将my_classes.txt的内容修改为为自己训练的类别。

4.4 修改train.py

这是代码最主要的部分，需要根据训练的情况进行调整：

18行：需要创建改目录
19行：因为自己的类与源码不同，所以需修改成classes_path = ‘model_data/my_classes.txt’
52行：将if True改成if False，不使用该种训练方式。通过该方式训练出的模型效果很差。
76行：根据GPU显存进行调节，越大越好，由于GPU限制，我只能设置为3。
82行：迭代次数，初始值为50，为了迭代100次，所以此处设置epochs=150。当过拟合时会自动停止训练。

“””
Retrain the YOLO model for your own dataset.
“””

import numpy as np
import keras.backend as K
from keras.layers import Input, Lambda
from keras.models import Model
from keras.optimizers import Adam
from keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping

from yolo3.model import preprocess_true_boxes, yolo_body, tiny_yolo_body, yolo_loss
from yolo3.utils import get_random_data


def _main():
    annotation_path = ‘train.txt’
    log_dir = ‘model/001/‘
    classes_path = ‘model_data/my_classes.txt’
    anchors_path = ‘model_data/yolo_anchors.txt’
    class_names = get_classes(classes_path)
    num_classes = len(class_names)
    anchors = get_anchors(anchors_path)

    input_shape = (416,416) # multiple of 32, hw

    is_tiny_version = len(anchors)==6 # default setting
    if is_tiny_version:
        model = create_tiny_model(input_shape, anchors, num_classes,
            freeze_body=2, weights_path=‘model_data/tiny_yolo_weights.h5’)
    else:
        model = create_model(input_shape, anchors, num_classes,
            freeze_body=2, weights_path=‘model_data/yolo_weights.h5’) # make sure you know what you freeze

    logging = TensorBoard(log_dir=log_dir)
    checkpoint = ModelCheckpoint(log_dir + ‘ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5’,
        monitor=‘val_loss’, save_weights_only=True, save_best_only=True, period=3)
    reduce_lr = ReduceLROnPlateau(monitor=‘val_loss’, factor=0.1, patience=3, verbose=1)
    early_stopping = EarlyStopping(monitor=‘val_loss’, min_delta=0, patience=10, verbose=1)

    val_split = 0.1
    with open(annotation_path) as f:
        lines = f.readlines()
    np.random.seed(10101)
    np.random.shuffle(lines)
    np.random.seed(None)
    num_val = int(len(lines)_val_split)
    num_train = len(lines) - num_val

    # Train with frozen layers first, to get a stable loss.
    # Adjust num epochs to your dataset. This step is enough to obtain a not bad model.
    if False:
        model.compile(optimizer=Adam(lr=1e-3), loss={
            # use custom yolo_loss Lambda layer.
            ‘yolo_loss’: lambda y_true, y_pred: y_pred})

        batch_size = 16
        print(‘Train on {} samples, val on {} samples, with batch size {}.’.format(num_train, num_val, batch_size))
        model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),
                steps_per_epoch=max(1, num_train//batch_size),
                validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes),
                validation_steps=max(1, num_val//batch_size),
                epochs=50,
                initial_epoch=0,
                callbacks=[logging, checkpoint])
        model.save_weights(log_dir + ‘trained_weights_stage_1.h5’)

    # Unfreeze and continue training, to fine-tune.
    # Train longer if the result is not good.
    if True:
        for i in range(len(model.layers)):
            model.layers[i].trainable = True
        model.compile(optimizer=Adam(lr=1e-4), loss={‘yolo_loss’: lambda y_true, y_pred: y_pred}) # recompile to apply the change
        print(‘Unfreeze all of the layers.’)

        batch_size = 3  # note that more GPU memory is required after unfreezing the body
        print(‘Train on {} samples, val on {} samples, with batch size {}.’.format(num_train, num_val, batch_size))
        model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),
            steps_per_epoch=max(1, num_train//batch_size),
            validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes),
            validation_steps=max(1, num_val//batch_size),
            epochs=150,
            initial_epoch=50,
            callbacks=[logging, checkpoint, reduce_lr, early_stopping])
        model.save_weights(log_dir + ‘trained_weights_final.h5’)

    # Further training if needed.


def get_classes(classes_path):
    ‘’’loads the classes’’’
    with open(classes_path) as f:
        class_names = f.readlines()
    class_names = [c.strip() for c in class_names]
    return class_names

def get_anchors(anchors_path):
    ‘’’loads the anchors from a file’’’
    with open(anchors_path) as f:
        anchors = f.readline()
    anchors = [float(x) for x in anchors.split(‘,’)]
    return np.array(anchors).reshape(-1, 2)


def create_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2,
            weights_path=‘model_data/yolo_weights.h5’):
    ‘’’create the training model’’’
    K.clear_session() # get a new session
    image_input = Input(shape=(None, None, 3))
    h, w = input_shape
    num_anchors = len(anchors)

    y_true = [Input(shape=(h//{0:32, 1:16, 2:8}[l], w//{0:32, 1:16, 2:8}[l], \
        num_anchors//3, num_classes+5)) for l in range(3)]

    model_body = yolo_body(image_input, num_anchors//3, num_classes)
    print(‘Create YOLOv3 model with {} anchors and {} classes.’.format(num_anchors, num_classes))

    if load_pretrained:
        model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)
        print(‘Load weights {}.’.format(weights_path))
        if freeze_body in [1, 2]:
            # Freeze darknet53 body or freeze all but 3 output layers.
            num = (185, len(model_body.layers)-3)[freeze_body-1]
            for i in range(num): model_body.layers[i].trainable = False
            print(‘Freeze the first {} layers of total {} layers.’.format(num, len(model_body.layers)))

    model_loss = Lambda(yolo_loss, output_shape=(1,), name=‘yolo_loss’,
        arguments={‘anchors’: anchors, ‘num_classes’: num_classes, ‘ignore_thresh’: 0.5})(
        [_model_body.output, _y_true])
    model = Model([model_body.input, _y_true], model_loss)

    return model

def create_tiny_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2,
            weights_path=‘model_data/tiny_yolo_weights.h5’):
    ‘’’create the training model, for Tiny YOLOv3’’’
    K.clear_session() # get a new session
    image_input = Input(shape=(None, None, 3))
    h, w = input_shape
    num_anchors = len(anchors)

    y_true = [Input(shape=(h//{0:32, 1:16}[l], w//{0:32, 1:16}[l], \
        num_anchors//2, num_classes+5)) for l in range(2)]

    model_body = tiny_yolo_body(image_input, num_anchors//2, num_classes)
    print(‘Create Tiny YOLOv3 model with {} anchors and {} classes.’.format(num_anchors, num_classes))

    if load_pretrained:
        model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)
        print(‘Load weights {}.’.format(weights_path))
        if freeze_body in [1, 2]:
            # Freeze the darknet body or freeze all but 2 output layers.
            num = (20, len(model_body.layers)-2)[freeze_body-1]
            for i in range(num): model_body.layers[i].trainable = False
            print(‘Freeze the first {} layers of total {} layers.’.format(num, len(model_body.layers)))

    model_loss = Lambda(yolo_loss, output_shape=(1,), name=‘yolo_loss’,
        arguments={‘anchors’: anchors, ‘num_classes’: num_classes, ‘ignore_thresh’: 0.7})(
        [_model_body.output, _y_true])
    model = Model([model_body.input, _y_true], model_loss)

    return model

def data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes):
    ‘’’data generator for fit_generator’’’
    n = len(annotation_lines)
    i = 0
    while True:
        image_data = []
        box_data = []
        for b in range(batch_size):
            if i==0:
                np.random.shuffle(annotation_lines)
            image, box = get_random_data(annotation_lines[i], input_shape, random=True)
            image_data.append(image)
            box_data.append(box)
            i = (i+1) % n
        image_data = np.array(image_data)
        box_data = np.array(box_data)
        y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes)
        yield [image_data, _y_true], np.zeros(batch_size)

def data_generator_wrapper(annotation_lines, batch_size, input_shape, anchors, num_classes):
    n = len(annotation_lines)
    if n==0 or batch_size<=0: return None
    return data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes)


if __name__ == ‘__main__‘:
    _main()

注意，需要创建model/000目录：

这个目录的作用就是存放自己的数据集训练得到的模型。不然程序运行到最后会因为找不到该路径而发生错误。

训练结束后，会生成很多模型，很多如图中标注的文件，还有最终的模型trained_weights_final.h5，正常情况下就选择最终的模型，如果前面的模型loss较低也可以选择前面的模型。

五、验证模型

创建test_yolo.py文件：

43行：模型存放位置

如果计算mAP，取消257~270行的注释，并创建如下文件目录：

代码如下：

# -_- coding: utf-8 -_-
“””
Class definition of YOLO_v3 style detection model on image and video
“””

import colorsys
import os
import time
from timeit import default_timer as timer

import numpy as np
from keras import backend as K
from keras.models import load_model
from keras.layers import Input
from PIL import Image, ImageFont, ImageDraw

from yolo3.model import yolo_eval, yolo_body, tiny_yolo_body
from yolo3.utils import letterbox_image
import os
from keras.utils import multi_gpu_model


dir_project = os.getcwd()  # 获取当前目录

# 创建创建一个存储检测结果的dir
result_path = ‘./result’
if not os.path.exists(result_path):
    os.makedirs(result_path)

# result如果之前存放的有文件，全部清除
for i in os.listdir(result_path):
    path_file = os.path.join(result_path, i)
    if os.path.isfile(path_file):
        os.remove(path_file)

# 创建一个记录检测结果的文件
txt_path = result_path + ‘/result.txt’
file = open(txt_path, ‘w’)


class YOLO(object):
    _defaults = {
        “model_path”: ‘model/001/trained_weights_final.h5’,
        “anchors_path”: ‘model_data/yolo_anchors.txt’,
        “classes_path”: ‘model_data/my_classes.txt’,
        “score” : 0.3,
        “iou” : 0.45,
        “model_image_size” : (416, 416),
        “gpu_num” : 1,
    }

    @classmethod
    def get_defaults(cls, n):
        if n in cls._defaults:
            return cls._defaults[n]
        else:
            return “Unrecognized attribute name ‘“ + n + “‘“

    def __init__(self, **kwargs):
        self.__dict__.update(self._defaults) # set up default values
        self.__dict__.update(kwargs) # and update with user overrides
        self.class_names = self._get_class()
        self.anchors = self._get_anchors()
        self.sess = K.get_session()
        self.boxes, self.scores, self.classes = self.generate()

    def _get_class(self):
        classes_path = os.path.expanduser(self.classes_path)
        with open(classes_path) as f:
            class_names = f.readlines()
        class_names = [c.strip() for c in class_names]
        return class_names

    def _get_anchors(self):
        anchors_path = os.path.expanduser(self.anchors_path)
        with open(anchors_path) as f:
            anchors = f.readline()
        anchors = [float(x) for x in anchors.split(‘,’)]
        return np.array(anchors).reshape(-1, 2)

    def generate(self):
        model_path = os.path.expanduser(self.model_path)
        assert model_path.endswith(‘.h5’), ‘Keras model or weights must be a .h5 file.’

        # Load model, or construct model and load weights.
        num_anchors = len(self.anchors)
        num_classes = len(self.class_names)
        is_tiny_version = num_anchors==6 # default setting
        try:
            self.yolo_model = load_model(model_path, compile=False)
        except:
            self.yolo_model = tiny_yolo_body(Input(shape=(None,None,3)), num_anchors//2, num_classes) \
                if is_tiny_version else yolo_body(Input(shape=(None,None,3)), num_anchors//3, num_classes)
            self.yolo_model.load_weights(self.model_path) # make sure model, anchors and classes match
        else:
            assert self.yolo_model.layers[-1].output_shape[-1] == \
                num_anchors/len(self.yolo_model.output) _ (num_classes + 5), \
                ‘Mismatch between model and given anchor and class sizes’

        print(‘{} model, anchors, and classes loaded.’.format(model_path))

        # Generate colors for drawing bounding boxes.
        hsv_tuples = [(x / len(self.class_names), 1., 1.)
                      for x in range(len(self.class_names))]
        self.colors = list(map(lambda x: colorsys.hsv_to_rgb(_x), hsv_tuples))
        self.colors = list(
            map(lambda x: (int(x[0] _ 255), int(x[1] _ 255), int(x[2] _ 255)),
                self.colors))
        np.random.seed(10101)  # Fixed seed for consistent colors across runs.
        np.random.shuffle(self.colors)  # Shuffle colors to decorrelate adjacent classes.
        np.random.seed(None)  # Reset seed to default.

        # Generate output tensor targets for filtered bounding boxes.
        self.input_image_shape = K.placeholder(shape=(2, ))
        if self.gpu_num>=2:
            self.yolo_model = multi_gpu_model(self.yolo_model, gpus=self.gpu_num)
        boxes, scores, classes = yolo_eval(self.yolo_model.output, self.anchors,
                len(self.class_names), self.input_image_shape,
                score_threshold=self.score, iou_threshold=self.iou)
        return boxes, scores, classes

    def detect_image(self, image):
        start = timer()

        if self.model_image_size != (None, None):
            assert self.model_image_size[0]%32 == 0, ‘Multiples of 32 required’
            assert self.model_image_size[1]%32 == 0, ‘Multiples of 32 required’
            boxed_image = letterbox_image(image, tuple(reversed(self.model_image_size)))
        else:
            new_image_size = (image.width - (image.width % 32),
                              image.height - (image.height % 32))
            boxed_image = letterbox_image(image, new_image_size)
        image_data = np.array(boxed_image, dtype=‘float32’)

        print(image_data.shape)
        image_data /= 255.
        image_data = np.expand_dims(image_data, 0)  # Add batch dimension.

        out_boxes, out_scores, out_classes = self.sess.run(
            [self.boxes, self.scores, self.classes],
            feed_dict={
                self.yolo_model.input: image_data,
                self.input_image_shape: [image.size[1], image.size[0]],
                K.learning_phase(): 0
            })

        print(‘Found {} boxes for {}’.format(len(out_boxes), ‘img’))

        font = ImageFont.truetype(font=‘font/FiraMono-Medium.otf’,
                    size=np.floor(3e-2 _ image.size[1] + 0.5).astype(‘int32’))
        thickness = (image.size[0] + image.size[1]) // 300

        # # 保存框检测出的框的个数   (添加)
        # file.write(‘find  ‘ + str(len(out_boxes)) + ‘ target(s) \n’)

        for i, c in reversed(list(enumerate(out_classes))):
            predicted_class = self.class_names[c]
            box = out_boxes[i]
            score = out_scores[i]

            label = ‘{} {:.2f}’.format(predicted_class, score)
            draw = ImageDraw.Draw(image)
            label_size = draw.textsize(label, font)

            top, left, bottom, right = box
            top = max(0, np.floor(top + 0.5).astype(‘int32’))
            left = max(0, np.floor(left + 0.5).astype(‘int32’))
            bottom = min(image.size[1], np.floor(bottom + 0.5).astype(‘int32’))
            right = min(image.size[0], np.floor(right + 0.5).astype(‘int32’))

            # # 写入检测位置（添加）
            # file.write(
            #     predicted_class + ‘  score: ‘ + str(score) + ‘ \nlocation: top: ‘ + str(top) + ‘、 bottom: ‘ + str(
            #         bottom) + ‘、 left: ‘ + str(left) + ‘、 right: ‘ + str(right) + ‘\n’)

            file.write(predicted_class + ‘ ‘ + str(score) + ‘ ‘ + str(left) + ‘ ‘ + str(top) + ‘ ‘ + str(right) + ‘ ‘ + str(bottom) + ‘;’)

            print(label, (left, top), (right, bottom))

            if top - label_size[1] >= 0:
                text_origin = np.array([left, top - label_size[1]])
            else:
                text_origin = np.array([left, top + 1])

            # My kingdom for a good redistributable image drawing library.
            for i in range(thickness):
                draw.rectangle(
                    [left + i, top + i, right - i, bottom - i],
                    outline=self.colors[c])
            draw.rectangle(
                [tuple(text_origin), tuple(text_origin + label_size)],
                fill=self.colors[c])
            draw.text(text_origin, label, fill=(0, 0, 0), font=font)
            del draw
        end = timer()
        print(end - start)
        return image

    def close_session(self):
        self.sess.close()



def detect_video(yolo, video_path, output_path=“”):
    import cv2
    vid = cv2.VideoCapture(video_path)
    if not vid.isOpened():
        raise IOError(“Couldn’t open webcam or video”)
    video_FourCC    = int(vid.get(cv2.CAP_PROP_FOURCC))        # 获得视频编码MPEG4/H264
    video_fps       = vid.get(cv2.CAP_PROP_FPS)
    video_size      = (int(vid.get(cv2.CAP_PROP_FRAME_WIDTH)),
                        int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT)))
    isOutput = True if output_path != “” else False
    if isOutput:
        print(“!!! TYPE:”, type(output_path), type(video_FourCC), type(video_fps), type(video_size))
        out = cv2.VideoWriter(output_path, video_FourCC, video_fps, video_size)
    accum_time = 0
    curr_fps = 0
    fps = “FPS: ??”
    prev_time = timer()
    while True:
        return_value, frame = vid.read()
        image = Image.fromarray(frame)           # 从array转换成image
        image = yolo.detect_image(image)
        result = np.asarray(image)
        curr_time = timer()
        exec_time = curr_time - prev_time
        prev_time = curr_time
        accum_time = accum_time + exec_time
        curr_fps = curr_fps + 1
        if accum_time > 1:
            accum_time = accum_time - 1
            fps = “FPS: “ + str(curr_fps)
            curr_fps = 0
        cv2.putText(result, text=fps, org=(3, 15), fontFace=cv2.FONT_HERSHEY_SIMPLEX,
                    fontScale=0.50, color=(255, 0, 0), thickness=2)
        cv2.namedWindow(“result”, cv2.WINDOW_NORMAL)
        cv2.imshow(“result”, result)
        if isOutput:
            out.write(result)
        if cv2.waitKey(1) & 0xFF == ord(‘q’):
            break
    yolo.close_session()


# 批量处理文件
if __name__ == ‘__main__‘:
    # 读取test文件
    with open(“dataset/ImageSets/Main/test.txt”, ‘r’) as f:  # 打开文件
        test_list = f.readlines()  # 读取文件
        test_list = [x.strip() for x in test_list if x.strip() != ‘’]  # 去除/n
        # print(test_list)

    t1 = time.time()
    yolo = YOLO()

    # for filename in test_list:
    #     image_path = ‘dataset/JPEGImages/‘+filename+’.jpg’
    #     portion = os.path.split(image_path)
    #     # file.write(portion[1]+’ detect_result：\n’)
    #     file.write(image_path + ‘ ‘)
    #     image = Image.open(image_path)
    #     image_mAP_save_path = dir_project + ‘/mAP/input/images-optional/‘
    #     image.save(image_mAP_save_path + filename + ‘.jpg’)
    #     r_image = yolo.detect_image(image)
    #     file.write(‘\n’)
    #     #r_image.show() 显示检测结果
    #     image_save_path = ‘./result/result_‘+portion[1]
    #     print(‘detect result save to….:’+image_save_path)
    #     r_image.save(image_save_path)

    time_sum = time.time() - t1
    # file.write(‘time sum: ‘+str(time_sum)+’s’)
    print(‘time sum:’,time_sum)
    file.close()
    yolo.close_session()

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六、检测结果

这部分就放一些图片好了：

七、计算mAP

前面有提到过

mAP计算：【目标检测】kera-yolo3模型计算mAP
mAP源码：https://github.com/Cartucho/mAP

修改mAP源码，针对keras-yolov3的检测结果计算mAP。

mAP在95.66%，正确率也在95%左右。

预测边界框与真实边界框对比：

此篇博客内容为博主的毕设的一部分，如有不正确的部分欢迎指正，如有问题欢迎讨论~

八、检测模型下载

很多人向博主要检测模型，所以我就将它放在百度云上了，需要的朋友可以自行提取。

链接：https://pan.baidu.com/s/1N_DgVYrWFrM9syybrpBFSw
提取码：de2i