update yolox_py

Author: Ar-Ray-code

yolox_ros_py/launch/yolox_lite_tflite_camera.launch.py

@@ -37,8 +37,8 @@ def generate_launch_description():
     yolox_tflite = launch_ros.actions.Node(
         package="yolox_ros_py", executable="yolox_tflite",output="screen",
         parameters=[
-            {"model" : yolox_ros_share_dir+"/model.tflite"},
-            {"score_th" : 0.4},
+            {"model_path" : yolox_ros_share_dir+"/model.tflite"},
+            {"conf" : 0.4},
             {"nms_th" : 0.5},
             {"input_shape/height" : 192},
             {"input_shape/width" : 192}

yolox_ros_py/launch/yolox_nano_onnx_camera.launch.py

@@ -28,9 +28,6 @@ def generate_launch_description():
     yolox_onnx = launch_ros.actions.Node(
         package="yolox_ros_py", executable="yolox_onnx",output="screen",
         parameters=[
-            {"image_size/width": 640},
-            {"image_size/height": 480},
-            
             {"input_shape/width": 416},
             {"input_shape/height": 416},
 

yolox_ros_py/launch/yolox_nano_onnx_gazebo.launch.py

@@ -34,9 +34,6 @@ def generate_launch_description():
     yolox_onnx = launch_ros.actions.Node(
         package="yolox_ros_py", executable="yolox_onnx",output="screen",
         parameters=[
-            {"image_size/width": 640},
-            {"image_size/height": 480},
-            
             {"input_shape/width": 416},
             {"input_shape/height": 416},
 

yolox_ros_py/launch/yolox_nano_onnx_picture.launch.py

@@ -34,9 +34,6 @@ def generate_launch_description():
     yolox_onnx = launch_ros.actions.Node(
         package="yolox_ros_py", executable="yolox_onnx",output="screen",
         parameters=[
-            {"image_size/width": 640},
-            {"image_size/height": 480},
-            
             {"input_shape/width": 416},
             {"input_shape/height": 416},
 

yolox_ros_py/launch/yolox_nano_onnx_youtube.launch.py

@@ -36,9 +36,6 @@ def generate_launch_description():
     yolox_onnx = launch_ros.actions.Node(
         package="yolox_ros_py", executable="yolox_onnx",output="screen",
         parameters=[
-            {"image_size/width": 640},
-            {"image_size/height": 360},
-            
             {"input_shape/width": 416},
             {"input_shape/height": 416},
 

yolox_ros_py/launch/yolox_nano_openvino.launch.py

@@ -29,8 +29,6 @@ def generate_launch_description():
     yolox_openvino = launch_ros.actions.Node(
         package="yolox_ros_py", executable="yolox_openvino",output="screen",
         parameters=[
-            {"image_size/width": 640},
-            {"image_size/height": 480},
             {"device" : 'CPU'},
             {"model_path" : yolox_ros_share_dir+"/yolox_nano.onnx"},
             {"conf" : 0.3},

yolox_ros_py/launch/yolox_nano_torch_cpu_camera.launch.py

@@ -26,8 +26,6 @@ def generate_launch_description():
     yolox_ros = launch_ros.actions.Node(
         package="yolox_ros_py", executable="yolox_ros",
         parameters=[
-            {"image_size/width": 640},
-            {"image_size/height": 480},
             {"yolox_exp_py" : yolox_ros_share_dir+'/yolox_nano.py'},
             {"device" : 'cpu'},
             {"fp16" : True},

yolox_ros_py/launch/yolox_nano_torch_gpu_camera.launch.py

@@ -26,8 +26,6 @@ def generate_launch_description():
     yolox_ros = launch_ros.actions.Node(
         package="yolox_ros_py", executable="yolox_ros",
         parameters=[
-            {"image_size/width": 640},
-            {"image_size/height": 480},
             {"yolox_exp_py" : yolox_ros_share_dir+'/yolox_nano.py'},
             {"device" : 'gpu'},
             {"fp16" : True},

yolox_ros_py/yolox_ros_py/yolox_onnx.py

@@ -15,6 +15,8 @@
 from yolox.data.datasets import COCO_CLASSES
 from yolox.utils import mkdir, multiclass_nms, demo_postprocess, vis
 
+from .yolox_ros_py_utils.utils import yolox_py
+
 # ROS2 =====================================
 import rclpy
 from rclpy.node import Node
@@ -31,100 +33,28 @@
 # from darkself.net_ros_msgs.msg import BoundingBoxes
 # from darkself.net_ros_msgs.msg import BoundingBox
 
-class yolox_ros(Node):
+class yolox_ros(yolox_py):
     def __init__(self) -> None:
 
         # ROS2 init
-        super().__init__('yolox_ros')
-
-        self.setting_yolox_exp()
+        super().__init__('yolox_ros', load_params=True)
 
         if (self.imshow_isshow):
             cv2.namedWindow("YOLOX")
 
         self.bridge = CvBridge()
 
         self.pub = self.create_publisher(BoundingBoxes,"yolox/bounding_boxes", 10)
-        self.pub_image = self.create_publisher(Image,"yolox/image_raw", 10)
-
-        if (self.sensor_qos_mode):
-            self.sub = self.create_subscription(Image,"image_raw",self.imageflow_callback, qos_profile_sensor_data)
-        else:
-            self.sub = self.create_subscription(Image,"image_raw",self.imageflow_callback, 10)
-
-    def setting_yolox_exp(self) -> None:
-        # set environment variables for distributed training
-        
-        # ==============================================================
-
-        ONNX_PATH = './install/yolox_ros_py/share/yolox_ros_py/yolox_nano.onnx'
-
-        self.declare_parameter('imshow_isshow',True)
-
-        self.declare_parameter('model_path', ONNX_PATH)
-        self.declare_parameter('conf', 0.3)
-        self.declare_parameter('with_p6', False)
-        self.declare_parameter('input_shape/width', 416)
-        self.declare_parameter('input_shape/height', 416)
-
-        self.declare_parameter('image_size/width', 640)
-        self.declare_parameter('image_size/height', 480)
-
-        self.declare_parameter('sensor_qos_mode', False)
-
-        # =============================================================
-        self.imshow_isshow = self.get_parameter('imshow_isshow').value
-
-        self.model_path = self.get_parameter('model_path').value
-        self.conf = self.get_parameter('conf').value
-
-        self.image_size_w = self.get_parameter('image_size/width').value
-        self.image_size_h = self.get_parameter('image_size/height').value
-        self.input_shape_w = self.get_parameter('input_shape/width').value
-        self.input_shape_h = self.get_parameter('input_shape/height').value
-
-        self.sensor_qos_mode = self.get_parameter('sensor_qos_mode').value
+        # self.pub_image = self.create_publisher(Image,"yolox/image_raw", 10)
 
-        # ==============================================================
-        self.with_p6 = self.get_parameter('with_p6').value
-
-        self.get_logger().info('model_path: {}'.format(self.model_path))
-        self.get_logger().info('conf: {}'.format(self.conf))
-        self.get_logger().info('input_shape: {}'.format((self.input_shape_w, self.input_shape_h)))
-        self.get_logger().info('image_size: {}'.format((self.image_size_w, self.image_size_h)))
-
-        self.get_logger().info('with_p6: {}'.format(self.with_p6))
-        self.get_logger().info('sensor_qos_mode: {}'.format(self.sensor_qos_mode))
-
-
-        self.input_shape = (self.input_shape_h, self.input_shape_w)
-
-
-    def yolox2bboxes_msgs(self, bboxes, scores, cls, cls_names, img_header:Header):
-        bboxes_msg = BoundingBoxes()
-        bboxes_msg.header = img_header
-        i = 0
-        for bbox in bboxes:
-            one_box = BoundingBox()
-            one_box.xmin = int(bbox[0])
-            one_box.ymin = int(bbox[1])
-            one_box.xmax = int(bbox[2])
-            one_box.ymax = int(bbox[3])
-            one_box.probability = float(scores[i])
-            one_box.class_id = str(cls_names[int(cls[i])])
-            bboxes_msg.bounding_boxes.append(one_box)
-            i = i+1
-
-        return bboxes_msg
+        self.sub = self.create_subscription(Image,"image_raw",self.imageflow_callback, self.qos_image_sub)
 
     def imageflow_callback(self,msg:Image) -> None:
         try:
             # fps start
             start_time = cv2.getTickCount()
             bboxes = BoundingBoxes()
             origin_img = self.bridge.imgmsg_to_cv2(msg,"bgr8")
-            # resize
-            origin_img = cv2.resize(origin_img, (self.image_size_w, self.image_size_h))
 
             # preprocess
             img, self.ratio = preprocess(origin_img, self.input_shape)
@@ -145,39 +75,36 @@ def imageflow_callback(self,msg:Image) -> None:
             boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2]/2.
             boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3]/2.
             boxes_xyxy /= self.ratio
-            dets = multiclass_nms(boxes_xyxy, scores, nms_thr=0.45, score_thr=self.conf)
+            dets = multiclass_nms(boxes_xyxy, scores, nms_thr=self.nms_th, score_thr=self.conf)
             if dets is not None:
                 self.final_boxes, self.final_scores, self.final_cls_inds = dets[:, :4], dets[:, 4], dets[:, 5]
                 origin_img = vis(origin_img, self.final_boxes, self.final_scores, self.final_cls_inds,
                          conf=self.conf, class_names=COCO_CLASSES)
-            
+
             end_time = cv2.getTickCount()
             time_took = (end_time - start_time) / cv2.getTickFrequency()
-                
+
             # rclpy log FPS
             self.get_logger().info(f'FPS: {1 / time_took}')
 
             try:
-                bboxes = self.yolox2bboxes_msgs(dets[:, :4], self.final_scores, self.final_cls_inds, COCO_CLASSES, msg.header)
-                # self.get_logger().info(f'bboxes: {bboxes}')
+                bboxes = self.yolox2bboxes_msgs(dets[:, :4], self.final_scores, self.final_cls_inds, COCO_CLASSES, msg.header, origin_img)
                 if (self.imshow_isshow):
                     cv2.imshow("YOLOX",origin_img)
                     cv2.waitKey(1)
-                
+
             except:
-                # self.get_logger().info('No object detected')
                 if (self.imshow_isshow):
                     cv2.imshow("YOLOX",origin_img)
                     cv2.waitKey(1)
 
             self.pub.publish(bboxes)
-            self.pub_image.publish(self.bridge.cv2_to_imgmsg(origin_img,"bgr8"))
+            # self.pub_image.publish(self.bridge.cv2_to_imgmsg(origin_img,"bgr8"))
 
         except Exception as e:
             self.get_logger().info(f'Error: {e}')
             pass
 
-
 def ros_main(args = None):
     rclpy.init(args=args)
     ros_class = yolox_ros()
@@ -189,6 +116,6 @@ def ros_main(args = None):
     finally:
         ros_class.destroy_node()
         rclpy.shutdown()
-    
+
 if __name__ == "__main__":
     ros_main()

yolox_ros_py/yolox_ros_py/yolox_openvino.py

@@ -15,6 +15,8 @@
 from yolox.data.datasets import COCO_CLASSES
 from yolox.utils import multiclass_nms, demo_postprocess, vis
 
+from .yolox_ros_py_utils.utils import yolox_py
+
 # ROS2 =====================================
 import rclpy
 from rclpy.node import Node
@@ -31,11 +33,11 @@
 # from darkself.net_ros_msgs.msg import BoundingBoxes
 # from darkself.net_ros_msgs.msg import BoundingBox
 
-class yolox_ros(Node):
+class yolox_ros(yolox_py):
     def __init__(self) -> None:
 
         # ROS2 init
-        super().__init__('yolox_ros')
+        super().__init__('yolox_ros', load_params=True)
 
         self.setting_yolox_exp()
 
@@ -45,43 +47,14 @@ def __init__(self) -> None:
         self.bridge = CvBridge()
 
         self.pub = self.create_publisher(BoundingBoxes,"yolox/bounding_boxes", 10)
-        self.pub_image = self.create_publisher(Image,"yolox/image_raw", 10)
-        if (self.sensor_qos_mode):
-            self.sub = self.create_subscription(Image,"image_raw",self.imageflow_callback, qos_profile_sensor_data)
-        else:
-            self.sub = self.create_subscription(Image,"image_raw",self.imageflow_callback, 10)
+        # self.pub_image = self.create_publisher(Image,"yolox/image_raw", 10)
+        
+        self.sub = self.create_subscription(Image,"image_raw",self.imageflow_callback, self.qos_image_sub)
 
     def setting_yolox_exp(self) -> None:
         # set environment variables for distributed training
 
-        # ==============================================================
-
-        WEIGHTS_PATH = '/home/ubuntu/ros2_ws/install/yolox_ros_py/share/yolox_ros_py/yolox_s.xml'
-
-        self.declare_parameter('imshow_isshow',True)
-
-        self.declare_parameter('model_path', WEIGHTS_PATH)
-        self.declare_parameter('conf', 0.3)
-        self.declare_parameter('device', "CPU")
-
-        self.declare_parameter('image_size/width', 640)
-        self.declare_parameter('image_size/height', 480)
-
-        self.declare_parameter('sensor_qos_mode', False)
-
-        # =============================================================
-        self.imshow_isshow = self.get_parameter('imshow_isshow').value
-
-        self.model_path = self.get_parameter('model_path').value
-        self.conf = self.get_parameter('conf').value
-        self.device = self.get_parameter('device').value
-
-        self.input_image_w = self.get_parameter('image_size/width').value
-        self.input_image_h = self.get_parameter('image_size/height').value
-
-        self.sensor_qos_mode = self.get_parameter('sensor_qos_mode').value
-
-        # ==============================================================
+        
 
         print('Creating Inference Engine')
         ie = IECore()
@@ -101,32 +74,12 @@ def setting_yolox_exp(self) -> None:
         print('Loading the model to the plugin')
         self.exec_net = ie.load_network(network=self.net, device_name=self.device)
 
-
-    def yolox2bboxes_msgs(self, bboxes, scores, cls, cls_names, img_header:Header):
-        bboxes_msg = BoundingBoxes()
-        bboxes_msg.header = img_header
-        i = 0
-        for bbox in bboxes:
-            one_box = BoundingBox()
-            one_box.xmin = int(bbox[0])
-            one_box.ymin = int(bbox[1])
-            one_box.xmax = int(bbox[2])
-            one_box.ymax = int(bbox[3])
-            one_box.probability = float(scores[i])
-            one_box.class_id = str(cls_names[int(cls[i])])
-            bboxes_msg.bounding_boxes.append(one_box)
-            i = i+1
-        
-        return bboxes_msg
-
     def imageflow_callback(self,msg:Image) -> None:
         try:
             # fps start
             start_time = cv2.getTickCount()
             bboxes = BoundingBoxes()
-            img_rgb = self.bridge.imgmsg_to_cv2(msg,"bgr8")
-            # resize
-            origin_img = cv2.resize(img_rgb, (self.input_image_w, self.input_image_h))
+            origin_img = self.bridge.imgmsg_to_cv2(msg,"bgr8")
             # deep copy
             nodetect_image = copy.deepcopy(origin_img)
 
@@ -165,24 +118,20 @@ def imageflow_callback(self,msg:Image) -> None:
 
             # rclpy log FPS
             self.get_logger().info(f'FPS: {1 / time_took}')
-
-            self.get_logger().info(f'Width: {self.input_image_w}, Height: {self.input_image_h}')
 
             try:
-                bboxes = self.yolox2bboxes_msgs(dets[:, :4], final_scores, final_cls_inds, COCO_CLASSES, msg.header)
-                # self.get_logger().info(f'bboxes: {bboxes}')
+                bboxes = self.yolox2bboxes_msgs(dets[:, :4], final_scores, final_cls_inds, COCO_CLASSES, msg.header,  origin_img)
                 if (self.imshow_isshow):
                     cv2.imshow("YOLOX",origin_img)
                     cv2.waitKey(1)
 
             except:
-                # self.get_logger().info('No object detected')
                 if (self.imshow_isshow):
                     cv2.imshow("YOLOX",origin_img)
                     cv2.waitKey(1)
 
             self.pub.publish(bboxes)
-            self.pub_image.publish(self.bridge.cv2_to_imgmsg(nodetect_image,"bgr8"))
+            # self.pub_image.publish(self.bridge.cv2_to_imgmsg(nodetect_image,"bgr8"))
 
         except Exception as e:
             self.get_logger().info(f'Error: {e}')