tensorflow Object Detection API使用预训练模型mask r-cnn实现对象检测

OpenCV学堂

发布于 2018-09-29 18:04:25

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发布于 2018-09-29 18:04:25

Mask R-CNN模型下载

Mask R-CNN是何凯明大神在2017年整出来的新网络模型，在原有的R-CNN基础上实现了区域ROI的像素级别分割。关于Mask R-CNN模型本身的介绍与解释网络上面已经是铺天盖地了，论文也是到处可以看到。这里主要想介绍一下在tensorflow中如何使用预训练的Mask R-CNN模型实现对象检测与像素级别的分割。tensorflow框架有个扩展模块叫做models里面包含了很多预训练的网络模型，提供给tensorflow开发者直接使用或者迁移学习使用，首先需要下载Mask R-CNN网络模型，这个在tensorflow的models的github上面有详细的解释与model zoo的页面介绍， tensorflow models的github主页地址如下： https://github.com/tensorflow/models

我这里下载的是：

mask_rcnn_inception_v2_coco_2018_01_28.tar.gz

下载好模型之后可以解压缩为tar文件，然后通过下面的代码读入模型

MODEL_NAME = 'mask_rcnn_inception_v2_coco_2018_01_28'
MODEL_FILE = 'D:/tensorflow/' + MODEL_NAME + '.tar'

# Path to frozen detection graph
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'

# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('D:/tensorflow/models/research/object_detection/data', 'mscoco_label_map.pbtxt')

NUM_CLASSES = 90
tar_file = tarfile.open(MODEL_FILE)
for file in tar_file.getmembers():
    file_name = os.path.basename(file.name)
    if 'frozen_inference_graph.pb' in file_name:
        tar_file.extract(file, os.getcwd())

detection_graph = tf.Graph()
with detection_graph.as_default():
    od_graph_def = tf.GraphDef()
    with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
        serialized_graph = fid.read()
        od_graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(od_graph_def, name='')

模型使用coco数据集，可以检测与分割90个对象类别，所以下面需要把对应labelmap文件读进去，这个文件在

models\research\objectdetection\data

目录下，实现代码如下：

label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)

有了这个之后就需要从模型中取出如下几个tensor

num_detections 表示检测对象数目
detection_boxes 表示输出框BB
detection_scores 表示得分
detection_classes 表示对象类别索引
detection_masks 表示mask分割

然后在会话中运行这几个tensor即可，代码实现如下：

def run_inference_for_single_image(image, graph):
    with graph.as_default():
        with tf.Session() as sess:
            # Get handles to input and output tensors
            ops = tf.get_default_graph().get_operations()
            all_tensor_names = {output.name for op in ops for output in op.outputs}
            tensor_dict = {}
            for key in ['num_detections', 'detection_boxes', 'detection_scores', 'detection_classes', 'detection_masks']:
                tensor_name = key + ':0'
                if tensor_name in all_tensor_names:
                    tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(tensor_name)

            if 'detection_masks' in tensor_dict:
                # The following processing is only for single image
                detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
                detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
                # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
                real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)
                detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])
                detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])
                detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
                    detection_masks, detection_boxes, image.shape[0], image.shape[1])
                detection_masks_reframed = tf.cast(
                    tf.greater(detection_masks_reframed, 0.5), tf.uint8)
                # Follow the convention by adding back the batch dimension
                tensor_dict['detection_masks'] = tf.expand_dims(
                    detection_masks_reframed, 0)
            image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')

            # Run inference
            output_dict = sess.run(tensor_dict,
                                 feed_dict={image_tensor: np.expand_dims(image, 0)})

            # all outputs are float32 numpy arrays, so convert types as appropriate
            output_dict['num_detections'] = int(output_dict['num_detections'][0])
            output_dict['detection_classes'] = output_dict[
              'detection_classes'][0].astype(np.uint8)
            output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
            output_dict['detection_scores'] = output_dict['detection_scores'][0]
            if 'detection_masks' in output_dict:
                output_dict['detection_masks'] = output_dict['detection_masks'][0]
        return output_dict

下面就是通过opencv来读取一张彩色测试图像，然后调用模型进行检测与对象分割，代码实现如下：

image = cv2.imread("D:/apple.jpg");
# image = cv2.imread("D:/tensorflow/models/research/object_detection/test_images/image2.jpg");
cv2.imshow("input image", image)
print(image.shape)

# Actual detection.
output_dict = run_inference_for_single_image(image, detection_graph)

# Visualization of the results of a detection.
vis_util.visualize_boxes_and_labels_on_image_array(
    image,
    output_dict['detection_boxes'],
    output_dict['detection_classes'],
    output_dict['detection_scores'],
    category_index,
    instance_masks=output_dict.get('detection_masks'),
    use_normalized_coordinates=True,
    line_thickness=8)

原图如下：