【目标检测】kera-yolo3模型计算mAP

这些是GitHub上的源码，下载之后均要修改一下。
keras-yolo v3 源码：https://github.com/qqwweee/keras-yolo3
mAP计算代码：https://github.com/Cartucho/mAP

---

【目标检测】交并比IoU、准确率precision、查全率recall、mAP

上一篇博客【目标检测】基于YOLOv3的海上船舶目标检测分类（Tensorflow/keras）记录了我训练模型的过程。

计算mAP，直接运行项目中main.py就好了。唯一难点就是要生成符合格式要求的detection-results和ground-truth。

---

一、mAP项目构成

mAP项目如下：

我们所需要了解的就是input文件，input目录下包含：detection-results、ground-truth、images-optional。

文件夹	用途
detection-results	模型预测的检测结果
ground-truth	图片本身的标记信息
images-optional	原始图片

detection-results 格式
每张图片保存在一个txt文件内，文件命名为图片的名称，每一行代表一个检测结果，格式为：class score left top right bottom。

ground-truth 格式
与detection-results 格式基本相同，只是缺少score这一项。

二、 批量测试图片

在keras-yolo3项目内建立新的python文件test_yolo.py（随便起的名，里面的代码是在yolo.py的基础上修改的）。该代码实现了对test.txt内图片的批量测试，并将结果保存在results目录下。

result.txt文件内记录预测的结果。每一行代表一张图片的信息，依次代表图片名称、种类、得分、左、上、右、下。

代码同时复制一份原始图片到mAP/input/images-optional目录下。

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


# 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": 'logs/000/trained_weights.h5',
        "anchors_path": 'model_data/yolo_anchors.txt',
        "classes_path": 'model_data/voc_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("A05_helmet/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 = 'A05_helmet/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 = 'E:/Activities/fwwb2019/code/mAP-master/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()

三、计算mAP

首先在mAP项目中建立名为cal_mAP文件夹，在其中分别建立如下两个python文件。分别用于生成符合detection-result和ground-truth的格式要求的txt文件。

3.1 生成detection-results

从第二部分介绍的result，我们可以生成符合detection-results文件。下图为result.txt：

通过如下代码：

import re
import os

dir_project = os.path.abspath(os.path.join(os.getcwd(), "../.."))  # 获取上上级目录
dir_result = '/result/result.txt'  # yolo批量处理结果的目录
dir_detection_results = '/mAP/input/detection-results'  # detection-results目录
surplus = 'A05_helmet/JPEGImages/'  # result.txt文件中图片名称多余的部分

if __name__ == '__main__':
    with open(dir_project + dir_result, 'r') as f:  # 打开文件
        filename = f.readlines()  # 读取文件

    for i in range(len(filename)):
        filename[i] = re.sub(surplus, '', filename[i])        # 去除文件名多余的部分

    for i in range(len(filename)):  # 中按行存放的检测内容，为列表的形式
        r = filename[i].split('.jpg ')
        file = open(dir_project + dir_detection_results + '/' + r[0] + '.txt', 'w')
        t = r[1].split(';')
        # 去除空格和换行
        t.remove('\n')

        if len(t) == 0:            # 如果没有对象
            file.write('')
        else:
            for k in range(len(t)):
                file.write(t[k] + '\n')

将其转化为如下txt文件，每一张图片用一个txt文本表示：

每一个txt文本内容如下图样式，以图片00242为例：

3.2 生成ground-truth

在上一篇博客中我们生成了test.txt文件，我们则要将它转化为符合ground-truth格式要求的txt文本。

此文件中，没有score，其中“1”代表“hat”，“2”代表“person”。

我们通过如下代码进行转换：

import re
import os

dir_project = os.path.abspath(os.path.join(os.getcwd(), "../.."))  # 获取上上级目录
dir_ground_truth = '/mAP/input/ground-truth'  # detection-results目录
surplus = 'A05_helmet/JPEGImages/'  # result.txt文件中图片名称多余的部分

if __name__ == '__main__':
    with open(dir_project + '/test.txt', 'r') as f:  # 打开文件
        filename = f.readlines()  # 读取文件
        # print(filename)

    for i in range(len(filename)):
        filename[i] = re.sub(surplus, '', filename[i])    # 去除文件名多余的部分

    for i in range(len(filename)):  # 中按行存放的检测内容，为列表的形式
        r = filename[i].split('.jpg ')
        print(r[0])
        file = open(dir_project + dir_ground_truth + '/' + r[0] + '.txt', 'w')
        t = r[1].split(' ')

        for j in range(len(t)):
            class_t = t[j].split(',')[-1]
            pos_t = t[j].split(',')
            if class_t == '0' or class_t == '0\n':
                file.write('person ' + pos_t[0] + ' ' + pos_t[1] + ' '+ pos_t[2] + ' '+ pos_t[3] + '\n')
            elif class_t == '1' or class_t == '1\n':
                file.write('hat ' + pos_t[0] + ' ' + pos_t[1] + ' '+ pos_t[2] + ' '+ pos_t[3] + '\n')

转换成功后生成如下文件：

以图片00242为例：

3.3 计算mAP

如果你的类中包含空格（如cargo ship、ore carrier），则需要进行小小的修改，参考本篇博客：计算mAP去除类之间空格（remove_space）。

之后可直接运行mAP项目中的main.py，可生成result文件夹，在该文件夹内保存各类结果。

---

如果你的类中不包含空格(如person、hat），可直接运行mAP项目中的main.py，可生成result文件夹，在该文件夹内保存各类结果。

单一图像，预测和实际的对比：

各类结果都比较齐全，可慢慢分析。

参考：【YOLOV3-keras-MAP】YOLOV3-keras版本的mAP计算