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
代码语言：javascript复制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
modelsresearchobjectdetectiondata
目录下，实现代码如下：
代码语言：javascript复制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：
代码语言：javascript复制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：
代码语言：javascript复制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：
检测运行结果如下：
带mask分割效果如下：
官方测试图像运行结果：
