使用yolo3自己训练的模型加上deep_sort实现多目标跟踪

使用yolo3自己训练的模型加上deep_sort实现多目标跟踪

本文章用于记录自己多目标跟踪调试代码的过程。
我的部分环境如下

python                    3.6.10
keras                     2.3.1
scikit-learn              0.19.0
tensorflow                1.14.0
opencv                    3.3.1

1.使用yolo3模型训练自己的数据集

可以参照训练自己的yolo3模型用于识别机动车及部分道路信息或者训练自己的yolo3模型用于识别机动车及部分道路信息
得到自己训练出来的模型

2.下载yolo3+deep_sort的模型deep_sort_yolov3

1. 下载地址https://github.com/Qidian213/deep_sort_yolov3
   将自己训练的模型weights.h5复制到model_data文件夹中。
2. 生成mars-small128.pb文件，下载地址https://github.com/nwojke/deep_sort，下载下来，在cmd中cd到下载的文件目录，运行命令python tools/freeze_model.py
   生成mars-small128.pb文件，把mars-small128.pb文件复制到deep_sort_yolov3模型的model_data文件夹中。如果运行命令失败也可以就使用原mars-small128.pb文件。
3. 在deep_sort_yolov3中找到yolo3.cfg文件并打开，ctrl+f搜 yolo, 总共会搜出3个含有yolo的地方，将classes改为自己模型识别类别的个数，filters改为如图的样子。一共要改三处。
   
   在model_data文件夹中找到voc_classes.txt将内容改为自己的类别如图所示。
   
4. 找到yolo.py将内容替换为如下代码 。目的是为了更改权重trained_weights.h5的打开方式，model_path改为自己的权重名称。

import colorsys
import os
import random
from timeit import time
from timeit import default_timer as timer  ### to calculate FPS

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

from yolo3.model import yolo_eval, yolo_body, tiny_yolo_body
from yolo3.utils import letterbox_image

class YOLO(object):
    def __init__(self):
        self.model_path = 'model_data/trained_weights.h5'
        self.anchors_path = 'model_data/yolo_anchors.txt'
        self.classes_path = 'model_data/coco_classes.txt'
        self.score = 0.5
        self.iou = 0.5
        self.class_names = self._get_class()
        self.anchors = self._get_anchors()
        self.sess = K.get_session()
        self.model_image_size = (416, 416) # fixed size or (None, None)
        self.is_fixed_size = self.model_image_size != (None, None)
        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(',')]
            anchors = np.array(anchors).reshape(-1, 2)
        return anchors

    def generate(self):
        model_path = os.path.expanduser(self.model_path)
        assert model_path.endswith('.h5'), 'Keras model 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))
        random.seed(10101)  # Fixed seed for consistent colors across runs.
        random.shuffle(self.colors)  # Shuffle colors to decorrelate adjacent classes.
        random.seed(None)  # Reset seed to default.

        # Generate output tensor targets for filtered bounding boxes.
        self.input_image_shape = K.placeholder(shape=(2, ))
        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):
        if self.is_fixed_size:
            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
            })
        return_boxs = []
        return_scores = []
        for i, c in reversed(list(enumerate(out_classes))):
            predicted_class = self.class_names[c]
            if predicted_class != 'person' :
                continue
            box = out_boxes[i]
            score = out_scores[i]
            x = int(box[1])  
            y = int(box[0])  
            w = int(box[3]-box[1])
            h = int(box[2]-box[0])
            if x < 0 :
                w = w + x
                x = 0
            if y < 0 :
                h = h + y
                y = 0 
            return_boxs.append([x,y,w,h])
            return_scores.append(score)

        return return_boxs, return_scores

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

找到yolo3文件夹下的model.py在最后增加一个函数，代码如下。

def tiny_yolo_body(inputs, num_anchors, num_classes):
    '''Create Tiny YOLO_v3 model CNN body in keras.'''
    x1 = compose(
            DarknetConv2D_BN_Leaky(16, (3,3)),
            MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
            DarknetConv2D_BN_Leaky(32, (3,3)),
            MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
            DarknetConv2D_BN_Leaky(64, (3,3)),
            MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
            DarknetConv2D_BN_Leaky(128, (3,3)),
            MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
            DarknetConv2D_BN_Leaky(256, (3,3)))(inputs)
    x2 = compose(
            MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
            DarknetConv2D_BN_Leaky(512, (3,3)),
            MaxPooling2D(pool_size=(2,2), strides=(1,1), padding='same'),
            DarknetConv2D_BN_Leaky(1024, (3,3)),
            DarknetConv2D_BN_Leaky(256, (1,1)))(x1)
    y1 = compose(
            DarknetConv2D_BN_Leaky(512, (3,3)),
            DarknetConv2D(num_anchors*(num_classes+5), (1,1)))(x2)

    x2 = compose(
            DarknetConv2D_BN_Leaky(128, (1,1)),
            UpSampling2D(2))(x2)
    y2 = compose(
            Concatenate(),
            DarknetConv2D_BN_Leaky(256, (3,3)),
            DarknetConv2D(num_anchors*(num_classes+5), (1,1)))([x2,x1])

    return Model(inputs, [y1,y2])

5. 最后在demo.py里面修改一下视频路径，直接运行即可。
6. 过程中可能会遇见版本不兼容的问题，耐心一点百度就好了根据错误提示具体解决。其中如果scikit-learn版本>0.19，from sklearn.utils.linear_assignment_ import linear_assignment代码报错如下

No module named 'sklearn.utils.linear_assignment_'

有人说，linear_assignment 函数从0.21开始被弃用了，并且将在0.23版本中移除，官方提升将用scipy.optimize.linear_sum_assignment 进行替代。但相同的函数名称linear_assignment用法却完全不相同，所以必须将scikit-learn库版本降到0.19或以下。

from scipy.optimize import linear_sum_assignment as linear_assignment

与

from sklearn.utils.linear_assignment_ import linear_assignment