MaskRCNN(TensorFlow版)的项目地址:

https://github.com/matterport/Mask_RCNN

如果要用这个算法来训练自己的数据集，方法十分简单，只需修改代码的几个关键部分就好了。

准备数据集

首先将你的数据集分为两类，一类为训练集（train），一类为验证集(val)。

然后是标注数据，这里我用到的工具是VIA（VGG数据集的标注工具），操作起来十分方便。在网页上即可进行数据标注，VIA的链接为:

http://www.robots.ox.ac.uk/~vgg/software/via/

如下图：

修改源代码

数据集准备好之后，我们可以在源代码（balloon.py）的基础上进行修改，使之适应我们的数据集。

1、复制一份balloon.py的代码进行编辑，在class balloon_config下面修改分类个数，我这里是分成3类，所以是1+3，注意默认背景是一类：

class BalloonConfig(Config):
    """Configuration for training on the toy  dataset.
    Derives from the base Config class and overrides some values.
    """
    # Give the configuration a recognizable name
    NAME = "balloon"

    # We use a GPU with 12GB memory, which can fit two images.
    # Adjust down if you use a smaller GPU.
    IMAGES_PER_GPU = 2

    # Number of classes (including background)
    NUM_CLASSES = 1 + 3  # Background + 分类类别

    # Number of training steps per epoch
    STEPS_PER_EPOCH = 50

    # Skip detections with < 90% confidence
    DETECTION_MIN_CONFIDENCE = 0.9

2、解析VIA的标注信息

因为源码是用coco数据集，它的标注信息格式与VIA不同，所以我们要修改代码中解析标注信息(annotations)的部分。

在balloon.py中修改load_balloon函数：

def load_balloon(self, dataset_dir, subset):
    """Load a subset of the Balloon dataset.
    dataset_dir: Root directory of the dataset.
    subset: Subset to load: train or val
    """

    # Add classes.
    # self.add_class("大类名称",序号,"小类")
    self.add_class("balloon", 1, "helmet")
    self.add_class("balloon", 2, "person")
    self.add_class("balloon", 3, "reflector")

    # self.add_class("helmet",4,"cloth")

    # Train or validation dataset?
    assert subset in ["train", "val"]
    dataset_dir = os.path.join(dataset_dir, subset)

    # We mostly care about the x and y coordinates of each region
    annotations = json.load(open(os.path.join(dataset_dir, "via_region_data.json")))
    annotations = list(annotations.values())  # don't need the dict keys

    # The VIA tool saves images in the JSON even if they don't have any
    # annotations. Skip unannotated images.
    annotations = [a for a in annotations if a['regions']]

    # Add images
    for a in annotations:
        # Get the x, y coordinaets of points of the polygons that make up
        # the outline of each object instance. There are stores in the
        # shape_attributes (see json format above)
        polygons = [r['shape_attributes'] for r in a['regions'].values()]
        name = [r['region_attributes']['name'] for r in a['regions'].values()]
        # 序列字典
         name_dict = {"helmet":1,"person":2,"reflector":3}
        name_id = [name_dict[a] for a in name]
    
        # load_mask() needs the image size to convert polygons to masks.
        # Unfortunately, VIA doesn't include it in JSON, so we must read
        # the image. This is only managable since the dataset is tiny.
        image_path = os.path.join(dataset_dir, a['filename'])
        image = skimage.io.imread(image_path)
        height, width = image.shape[:2]

        # for i,j in enumerate(polygons):
        self.add_image(
            "balloon",
            image_id=a['filename'],
            # image_id='{}_{}'.format(a['filename'],i),  # use file name as a unique image id
            path=image_path,
            class_id=name_id,
            width=width, height=height,
            polygons=polygons)

注意 self.add_class的第一个参数要一致，表示的是这一个数据集的整体名字，第二个给你的类别标号，第三个参数是类别的具体名称。

3、修改load_mask函数：

def load_mask(self, image_id):
    """Generate instance masks for an image.
   Returns:
    masks: A bool array of shape [height, width, instance count] with
        one mask per instance.
    class_ids: a 1D array of class IDs of the instance masks.
    """
    # If not a balloon dataset image, delegate to parent class.
    image_info = self.image_info[image_id]
    if image_info["source"] != "balloon" :
        return super(self.__class__, self).load_mask(image_id)

    name_id = image_info["class_id"]
    print(name_id)
    # Convert polygons to a bitmap mask of shape
    # [height, width, instance_count]
    info = self.image_info[image_id]
    mask = np.zeros([info["height"], info["width"], len(info["polygons"])],
                    dtype=np.uint8)
    class_ids = np.array(name_id, dtype=np.int32)

    for i, p in enumerate(info["polygons"]):
        # Get indexes of pixels inside the polygon and set them to 1
        rr, cc = skimage.draw.polygon(p['all_points_y'], p['all_points_x'])
        mask[rr, cc, i] = 1
    # print( mask.astype(np.bool), name_id)

    # Return mask, and array of class IDs of each instance. Since we have
    # one class ID only, we return an array of 1s
    return (mask.astype(np.bool), class_ids)