官方给的模型可以检测80多个类别，这里我们只检测行人，就需要将代码进行稍微修改即可：

项目路径图：

tensorflow yolov3只检测行人_jj


查看./data/classes/coco.names 类别的索引：

tensorflow yolov3只检测行人_jj_02


这里person为第1个，即索引为0

更改：

./core/utils.py

draw_bbox函数(将roi_index设置为你想要的索引即可，我这里为0)：

def draw_bbox(image, bboxes, classes=read_class_names(cfg.YOLO.CLASSES), show_label=True):
    """
    bboxes: [x_min, y_min, x_max, y_max, probability, cls_id] format coordinates.
    """
    roi_index=0
    num_classes = len(classes)
    image_h, image_w, _ = image.shape
    hsv_tuples = [(1.0 * x / num_classes, 1., 1.) for x in range(num_classes)]
    colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
    colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), colors))

    random.seed(0)
    random.shuffle(colors)
    random.seed(None)

    for i, bbox in enumerate(bboxes):
        coor = np.array(bbox[:4], dtype=np.int32)
        fontScale = 0.5
        score = bbox[4]
        class_ind = int(bbox[5])
        if class_ind ==roi_index:
            bbox_color = colors[class_ind]
            bbox_thick = int(0.6 * (image_h + image_w) / 600)
            c1, c2 = (coor[0], coor[1]), (coor[2], coor[3])
            cv2.rectangle(image, c1, c2, bbox_color, bbox_thick)

            if show_label:
                bbox_mess = '%s: %.2f' % (classes[class_ind], score)
                t_size = cv2.getTextSize(bbox_mess, 0, fontScale, thickness=bbox_thick // 2)[0]
                cv2.rectangle(image, c1, (c1[0] + t_size[0], c1[1] - t_size[1] - 3), bbox_color, -1)  # filled

                cv2.putText(image, bbox_mess, (c1[0], c1[1] - 2), cv2.FONT_HERSHEY_SIMPLEX,
                            fontScale, (0, 0, 0), bbox_thick // 2, lineType=cv2.LINE_AA)

    return image

效果：

只框出了行人

tensorflow yolov3只检测行人_jj_03