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目标检测 YOLO v3 验证 COCO 模型

96
SpikeKing 595a1b60 08f6 4beb 998f 2bf55e230555
2018.07.04 15:59* 字数 584

YOLO,是You Only Look Once的缩写,一种基于深度卷积神经网络的物体检测算法,YOLO v3是YOLO的第3个版本,检测算法更快更准,2018年4月8日。

本文源码https://github.com/SpikeKing/keras-yolo3-detection

欢迎Follow我的GitHubhttps://github.com/SpikeKing

YOLO

数据集

YOLO v3已经提供COCO(Common Objects in Context)数据集的模型参数,支持直接用于物体检测,模型248M,下载:

wget https://pjreddie.com/media/files/yolov3.weights

将模型参数转换为Keras的模型参数,模型248.6M,转换:

python convert.py -w yolov3.cfg model_data/yolov3.weights model_data/yolo_weights.h5

画出网络结构:

plot_model(model, to_file='./model_data/model.png', show_shapes=True, show_layer_names=True)  # 网络图

COCO含有80个类别:

person(人)  

bicycle(自行车)  car(汽车)  motorbike(摩托车)  aeroplane(飞机)  bus(公共汽车)  train(火车)  truck(卡车)  boat(船)  

traffic light(信号灯)  fire hydrant(消防栓)  stop sign(停车标志)  parking meter(停车计费器)  bench(长凳)  

bird(鸟)  cat(猫)  dog(狗)  horse(马)  sheep(羊)  cow(牛)  elephant(大象)  bear(熊)  zebra(斑马)  giraffe(长颈鹿)  

backpack(背包)  umbrella(雨伞)  handbag(手提包)  tie(领带)  suitcase(手提箱)  

frisbee(飞盘)  skis(滑雪板双脚)  snowboard(滑雪板)  sports ball(运动球)  kite(风筝) baseball bat(棒球棒)  baseball glove(棒球手套)  skateboard(滑板)  surfboard(冲浪板)  tennis racket(网球拍)  

bottle(瓶子)  wine glass(高脚杯)  cup(茶杯)  fork(叉子)  knife(刀)
spoon(勺子)  bowl(碗)  

banana(香蕉)  apple(苹果)  sandwich(三明治)  orange(橘子)  broccoli(西兰花)  carrot(胡萝卜)  hot dog(热狗)  pizza(披萨)  donut(甜甜圈)  cake(蛋糕)

chair(椅子)  sofa(沙发)  pottedplant(盆栽植物)  bed(床)  diningtable(餐桌)  toilet(厕所)  tvmonitor(电视机)  

laptop(笔记本)  mouse(鼠标)  remote(遥控器)  keyboard(键盘)  cell phone(电话)  

microwave(微波炉)  oven(烤箱)  toaster(烤面包器)  sink(水槽)  refrigerator(冰箱)

book(书)  clock(闹钟)  vase(花瓶)  scissors(剪刀)  teddy bear(泰迪熊)  hair drier(吹风机)  toothbrush(牙刷)

YOLO的默认anchors是9个,对应三个尺度,每个尺度含有3个anchors,如下:

10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326

检测器

YOLO检测类的构造器:

  1. anchors、model、classes是参数文件,其中,anchors可以使用默认,但是model与classes必须相互匹配;
  2. score和iou是检测参数,即置信度阈值和交叉区域阈值,置信度阈值避免误检,交叉区域阈值避免物体重叠;
  3. self.class_namesself.anchors,读取类别和anchors;
  4. self.sess是TensorFlow的上下文环境;
  5. self.model_image_size,检测图片尺寸,将原图片同比例resize检测尺寸,空白填充;
  6. self.generate()是参数流程,输出框(boxes)、置信度(scores)和类别(classes);

源码:

class YOLO(object):
    def __init__(self):
        self.anchors_path = 'configs/yolo_anchors.txt'  # anchors
        self.model_path = 'model_data/yolo_weights.h5'  # 模型文件
        self.classes_path = 'configs/coco_classes.txt'  # 类别文件

        self.score = 0.3  # 置信度阈值
        # self.iou = 0.45
        self.iou = 0.20  # 交叉区域阈值

        self.class_names = self._get_class()  # 获取类别
        self.anchors = self._get_anchors()  # 获取anchor
        self.sess = K.get_session()
        self.model_image_size = (416, 416)  # fixed size or (None, None), hw
        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)

参数流程:输出框(boxes)、置信度(scores)和类别(classes)

  1. yolo_body网络中,加载yolo_model参数;
  2. 为不同的框,生成不同的颜色,随机;
  3. 将模型的输出,经过置信度和交叉区域,过滤框;

源码:

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

    num_anchors = len(self.anchors)  # anchors的数量
    num_classes = len(self.class_names)  # 类别数

    # 加载模型参数
    self.yolo_model = yolo_body(Input(shape=(None, None, 3)), 3, num_classes)
    self.yolo_model.load_weights(model_path)

    print('{} model, {} anchors, and {} classes loaded.'.format(model_path, num_anchors, num_classes))

    # 不同的框,不同的颜色
    hsv_tuples = [(float(x) / len(self.class_names), 1., 1.)
                  for x in range(len(self.class_names))]  # 不同颜色
    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))  # RGB
    np.random.seed(10101)
    np.random.shuffle(self.colors)
    np.random.seed(None)

    # 根据检测参数,过滤框
    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

检测方法detect_image

第1步,图像处理:

  1. 将图像等比例转换为检测尺寸,检测尺寸需要是32的倍数,周围进行填充;
  2. 将图片增加1维,符合输入参数格式;
if self.model_image_size != (None, None):  # 416x416, 416=32*13,必须为32的倍数,最小尺度是除以32
    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)))  # 填充图像
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('detector size {}'.format(image_data.shape))
image_data /= 255.  # 转换0~1
image_data = np.expand_dims(image_data, 0)  # 添加批次维度,将图片增加1维

第2步,feed数据,图像,图像尺寸;

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
    })

第3步,绘制边框,自动设置边框宽度,绘制边框和类别文字,使用Pillow。

font = ImageFont.truetype(font='font/FiraMono-Medium.otf',
                          size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))  # 字体
thickness = (image.size[0] + image.size[1]) // 512  # 厚度
for i, c in reversed(list(enumerate(out_classes))):
    predicted_class = self.class_names[c]  # 类别
    box = out_boxes[i]  # 框
    score = out_scores[i]  # 执行度

    label = '{} {:.2f}'.format(predicted_class, score)  # 标签
    draw = ImageDraw.Draw(image)  # 画图
    label_size = draw.textsize(label, font)  # 标签文字

    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))  # 边框

    if top - label_size[1] >= 0:  # 标签文字
        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):  # 画框
        draw.rectangle(
            [left + i, top + i, right - i, bottom - i],
            outline=self.colors[c])
    draw.rectangle(  # 文字背景
        [tuple(text_origin), tuple(text_origin + label_size)],
        fill=self.colors[c])
    draw.text(text_origin, label, fill=(0, 0, 0), font=font)  # 文案
    del draw

目标检测

使用YOLO检测器,检测图像:

def detect_img_for_test(yolo):
    img_path = './dataset/a4386X6Te9ajq866zgOtWKLx18XGW.jpg'
    image = Image.open(img_path)
    r_image = yolo.detect_image(image)
    r_image.show()
    yolo.close_session()
    
if __name__ == '__main__':
    detect_img_for_test(YOLO())

效果:

output

参考1参考2参考3,Thx@qqwweee

OK, that‘s all! Enjoy it!

深度学习📒
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