Windows下计算keras/yolov3模型map值

记yolov3模型map值计算

文件准备

计算代码

计算代码地址: mAP-master

准备数据集

准备若干用于计算map值的数据集

目录结构(供参考)

Windows下计算keras/yolov3模型map值

计算map

写入文件名

首先需要在VOC2007\ImageSets\Main目录下的test.txt文件中写入用于计算的数据集文件名
Windows下计算keras/yolov3模型map值
运行如下代码。(voc2ssd.py)

import os
import random 
 
xmlfilepath=r'VOC2007\Annotations'#xml文件所在地址
saveBasePath=r"VOC2007\ImageSets\Main"#test.txt文件所在地址
 
train_percent=1.0
trainval_percent=0#0表示以上目录中所有数据集都用于计算map,可自行修改

temp_xml = os.listdir(xmlfilepath)
total_xml = []
for xml in temp_xml:
    if xml.endswith(".xml"):
        total_xml.append(xml)

num=len(total_xml)  
list=range(num)  
tv=int(num*trainval_percent)  
tr=int(tv*train_percent)  
trainval= random.sample(list,tv)  
train=random.sample(trainval,tr)  
 
print("train and val size",tv)
print("traub suze",tr)
ftrainval = open(os.path.join(saveBasePath,'trainval.txt'), 'w')  
ftest = open(os.path.join(saveBasePath,'test.txt'), 'w')  
ftrain = open(os.path.join(saveBasePath,'train.txt'), 'w')  
fval = open(os.path.join(saveBasePath,'val.txt'), 'w')  
 
for i  in list:  
    name=total_xml[i][:-4]+'\n'  
    if i in trainval:  
        ftrainval.write(name)  
        if i in train:  
            ftrain.write(name)  
        else:  
            fval.write(name)  
    else:  
        ftest.write(name)  
  
ftrainval.close()  
ftrain.close()  
fval.close()  
ftest .close()

生成真实值文件

接下来我们需要准备一个保存真实值的文件夹(mAP-master\input\ground-truth,删除原有的)
Windows下计算keras/yolov3模型map值
运行如下代码(yolo_true.py)

import sys
import os
import glob
import xml.etree.ElementTree as ET

image_ids = open('VOC2007/ImageSets/Main/test.txt').read().strip().split()#之前生成的test.txt所在地址

if not os.path.exists("./input"):
    os.makedirs("./input")
if not os.path.exists("./input/ground-truth"):
    os.makedirs("./input/ground-truth")

for image_id in image_ids:
    with open("mAP-master/input/ground-truth/"+image_id+".txt", "a") as new_f:#用于存储真实值文件所用的文件目录
        root = ET.parse("VOCdevkit/VOC2007/Annotations/"+image_id+'.xml').getroot()#数据集中xml文件所在文件夹
        for obj in root.findall('object'):
            obj_name = obj.find('name').text
            bndbox = obj.find('bndbox')
            left = bndbox.find('xmin').text
            top = bndbox.find('ymin').text
            right = bndbox.find('xmax').text
            bottom = bndbox.find('ymax').text
            new_f.write("%s %s %s %s %s\n" % (obj_name, left, top, right, bottom))
print("Conversion completed!")

拷贝数据集图片(可选)

将用于计算的数据集图片拷贝到mAP-master\input\images-optional目录下,请手动删除原有图片,在计算时会显示预测情况,嫌麻烦可忽略此步骤。

生成预测值文件

修改原yolo.py文件,将预测结果保存到mAP-master\input\detection-results文件夹下(请手动删除原有的)
Windows下计算keras/yolov3模型map值

# -*- coding: utf-8 -*-
"""
Class definition of YOLO_v3 style detection model on image and video
"""

import colorsys
import os
import sys
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

class YOLO(object):
    _defaults = {
        "model_path": 'logs/000/ep450.h5', ##训练好的模型的路径
        "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" : 0
    }

    @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

        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'))
            print(label, (left, top), (right, bottom))
            new_f.write("%s %s %s %s %s\n" %  (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])

            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()

if __name__ == '__main__':
    yolo=YOLO()
    dirname="../VOC2007/JPEGImages/" ##该目录为测试照片的存储路径,每次测试照片的数量可以自己设定
    path=os.path.join(dirname)
    pic_list=os.listdir(path)
    count=0
    for filename in pic_list:
        tmp_file=pic_list[count]
        new_f=open("../mAP-master/input/detection-results/"+tmp_file.replace(".jpg", ".txt"), "a")  #预测坐标生成txt文件保存的路径
        abs_path=path+pic_list[count]
        image = Image.open(abs_path)
        r_image = yolo.detect_image(image)
        count=count+1
    print(count)
    yolo.close_session()

计算map值

运行mAP-master下的main.py文件

查看结果

Windows下计算keras/yolov3模型map值
Windows下计算keras/yolov3模型map值

后记

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