Change seginfo msg type definition (HiroIshida#16)

* Change Seginfo msg type definition

* Update readme

CMakeLists.txt

@@ -10,6 +10,7 @@ add_custom_target(${PROJECT_NAME}_setup_package ALL
 
 find_package(catkin REQUIRED
     std_msgs
+    sensor_msgs
     message_generation
 )
 
@@ -21,6 +22,7 @@ add_message_files(
 generate_messages(
     DEPENDENCIES
     std_msgs
+    sensor_msgs
 )
 
 if(CATKIN_ENABLE_TESTING)

README.md

@@ -49,12 +49,10 @@ Example for using the published topic from the node above is [masked_image_publi
   - Input image
 - `~debug_image` (`sensor_msgs/Image`)
   - debug image 
-- `~segmentation_image` (`sensor_msgs/Image` with `8UC1` encoding)
-  - Segmentation image. Suppose detected class number is 14, image is filled with 0~14 uint8 values. Note that 0 means background label.
 - `~debug_segmentation_image` (`sensor_msgs/Image` with `8UC1` encoding)
   - Say detected class number is 14, `~segmentation_image` in grayscale image is almost completely dark and not good for debugging. Therefore this topic scale the value to [0 ~ 255] so that grayscale image is human-friendly.
 - `~segmentation_info` (`detic_ros/SegmentationInfo`)
-  - class name list and confidence score list corresponding to `~segmentation_image`. Note that `score` of `background` class is always 1.0
+  - class name list, confidence score list and segmentation image with `8UC1` encoding. The image is filled by 0 and positive integers indicating segmented object number. These indexes correspond to those of class name list and confidence score list. Note that index 0 is always reserved for 'background' instance and the confidence of the that instance is always 1.0.
 
 As for rosparam, see [node_cofig.py](./node_script/node_config.py).
 

example/masked_image_publisher.py

@@ -17,16 +17,15 @@ class SampleNode:
 
     def __init__(self, mask_class_name='bottle'):
         sub_image = message_filters.Subscriber(rospy.get_param('~in_image'), Image)
-        sub_segmentation = message_filters.Subscriber(rospy.get_param('~segmentation'), Image)
         sub_info = message_filters.Subscriber(rospy.get_param('~seginfo'), SegmentationInfo)
-        sub_list = [sub_image, sub_segmentation, sub_info]
+        sub_list = [sub_image, sub_info]
         ts = message_filters.ApproximateTimeSynchronizer(sub_list, 100, 10.0)
         ts.registerCallback(self.callback)
 
         self.pub = rospy.Publisher(rospy.get_param('~out_image'), Image)
         self.class_name = mask_class_name
 
-    def callback(self, msg_image, msg_segmentation, msg_info: SegmentationInfo):
+    def callback(self, msg_image, msg_info: SegmentationInfo):
         rospy.loginfo('rec messages')
 
         # find label number corresponding to desired object class name
@@ -38,12 +37,13 @@ def callback(self, msg_image, msg_segmentation, msg_info: SegmentationInfo):
         except ValueError:
             return
 
-        assert msg_image.width == msg_segmentation.width
-        assert msg_image.height == msg_segmentation.height
+        seg_img = msg_info.segmentation
+        assert msg_image.width == seg_img.width
+        assert msg_image.height == seg_img.height
         bridge = CvBridge()
         img = bridge.imgmsg_to_cv2(msg_image, desired_encoding='passthrough')
 
-        seg_matrix = segmentation_image_to_nparray(msg_segmentation)
+        seg_matrix = segmentation_image_to_nparray(seg_img)
         mask_indexes = np.where(seg_matrix==label_index)
 
         masked_img = copy.deepcopy(img)

msg/SegmentationInfo.msg

@@ -1,3 +1,4 @@
 std_msgs/Header header
 string[] detected_classes
 float32[] scores
+sensor_msgs/Image segmentation

node_script/node.py

@@ -67,7 +67,6 @@ def __init__(self, node_config: Optional[NodeConfig]=None):
         # https://answers.ros.org/question/220502/image-subscriber-lag-despite-queue-1/?answer=220505?answer=220505#post-id-22050://answers.ros.org/question/220502/image-subscriber-lag-despite-queue-1/?answer=220505?answer=220505#post-id-220505
         self.sub = rospy.Subscriber('~input_image', Image, self.callback, queue_size=1, buff_size=2**24)
         self.pub_debug_image = rospy.Publisher('~debug_image', Image, queue_size=1)
-        self.pub_segmentation_image = rospy.Publisher('~segmentation_image', Image, queue_size=1)
         if node_config.out_debug_segimage:
             self.pub_debug_segmentation_image = rospy.Publisher('~debug_segmentation_image', Image, queue_size=10)
 
@@ -109,7 +108,6 @@ def callback(self, msg: Image):
         assert data.shape == (seg_img.height, seg_img.width)
         seg_img.data = data.flatten().astype(np.uint8).tolist()
         assert set(seg_img.data) == set(list(range(len(instances.pred_masks)+1)))
-        self.pub_segmentation_image.publish(seg_img)
 
         if self.node_config.out_debug_segimage:
             debug_data = copy.deepcopy(data)
@@ -127,6 +125,7 @@ def callback(self, msg: Image):
         seginfo.detected_classes = class_names_detected
         seginfo.scores = [1.0] + instances.scores.tolist() # confidence with 1.0 about background detection
         seginfo.header = msg.header
+        seginfo.segmentation = seg_img
         self.pub_info.publish(seginfo)
 
         time_elapsed_total = (rospy.Time.now() - time_start).to_sec()

test/test_detic_ros_node.py

@@ -24,12 +24,12 @@ def all_subscribed() -> bool:
             return all(bool_list)
 
         def cb_debug_image(msg): cb_data['msg1'] = msg
-        def cb_segimage(msg): cb_data['msg2'] = msg
+        def cb_debug_segimage(msg): cb_data['msg2'] = msg
         def cb_info(msg): cb_data['msg3'] = msg
         def cb_test_image(msg): cb_data['msg4'] = msg
 
         rospy.Subscriber('/docker/detic_segmentor/debug_image', Image, cb_debug_image)
-        rospy.Subscriber('/docker/detic_segmentor/debug_segmentation_image', Image, cb_segimage)
+        rospy.Subscriber('/docker/detic_segmentor/debug_segmentation_image', Image, cb_debug_segimage)
         rospy.Subscriber('/docker/detic_segmentor/segmentation_info', SegmentationInfo, cb_info)
         rospy.Subscriber('/test_out_image', Image, cb_test_image)