ros2_ws/src/yolov3/CMakeLists.txt

@@ -1,46 +0,0 @@
-
-
-cmake_minimum_required(VERSION 3.5)
-project(yolov4_msg)
-
-# Find dependencies
-#find_package(ament_cmake REQUIRED)
-find_package(rclpy REQUIRED)
-find_package(std_msgs REQUIRED)
-
-# Create the executable
-#add_executable(yolov4_msg yolov4_msg.py)
-
-# Set the permissions of the Python script to be executable
-
-find_package(rosidl_default_generators REQUIRED)
-
-rosidl_generate_interfaces(yolov4_msg
-  "msg/BoundingBox.msg"
-  "msg/BoundingBoxes.msg"
-  DEPENDENCIES builtin_interfaces std_msgs 
-)
-
-# Link the executable with the necessary libraries
-#ament_target_dependencies(yolov4_msg rclpy)
-
-# Install the executable
-#install(TARGETS yolov4_msg DESTINATION lib/yolov4_msg)
-
-# Install the script, setup.py, and package.xml files
-#install(PROGRAMS yolov4_msg.py
- #       DESTINATION lib/yolov3_on_bag)
-#install(FILES package.xml
-  #      DESTINATION share/yolov3_on_bag)
-        
-# Export the package dependencies
-#ament_export_dependencies(rclpy)
-
-# Package information
-#ament_package()
-
-#add_executable(yolov4_msg yolov4_msg.py)
-
-ament_export_dependencies(rosidl_default_runtime)
-
-ament_package()

ros2_ws/src/yolov3/detect_on_bag.py

@@ -1,210 +0,0 @@
-
-# Subscribe to input image topic. 
-# object_detector ([std_msgs::Int8])
-
-# Publishes the number of detected objects.
-
-# bounding_boxes ([darknet_ros_msgs::BoundingBoxes])
-
-# Publishes an array of bounding boxes that gives information of the position and size of the bounding box in pixel coordinates.
-
-# detection_image ([sensor_msgs::Image])
-
-# Publishes an image of the detection image including the bounding boxes.
-
-import rclpy
-from rclpy.node import Node
-
-from std_msgs.msg import String
-from sensor_msgs.msg import Image
-from cv_bridge import CvBridge
-import argparse
-import os
-import sys
-from pathlib import Path
-
-import cv2
-import torch
-import torch.backends.cudnn as cudnn
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[0]  # root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
-
-from models.common import DetectMultiBackend
-from utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
-from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr,
-                           increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
-from utils.plots import Annotator, colors, save_one_box
-from utils.torch_utils import select_device, time_sync
-from utils.augmentations import Albumentations, augment_hsv, copy_paste, letterbox
-import numpy as np
-from yolov4_msg.msg import BoundingBox, BoundingBoxes
-from rclpy.clock import Clock
-
-@torch.no_grad()
-def run(weights=ROOT / 'yolov3.pt',  # model.pt path(s)
-        source=ROOT / 'data/images',  # file/dir/URL/glob, 0 for webcam
-        imgsz=1280,  # inference size (pixels)
-        conf_thres=0.25,  # confidence threshold
-        iou_thres=0.45,  # NMS IOU threshold
-        max_det=1000,  # maximum detections per image
-        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
-        view_img=False,  # show results
-        save_txt=False,  # save results to *.txt
-        save_conf=False,  # save confidences in --save-txt labels
-        save_crop=False,  # save cropped prediction boxes
-        nosave=False,  # do not save images/videos
-        classes=None,  # filter by class: --class 0, or --class 0 2 3
-        agnostic_nms=False,  # class-agnostic NMS
-        augment=False,  # augmented inference
-        visualize=False,  # visualize features
-        update=False,  # update all models
-        project=ROOT / 'runs/detect',  # save results to project/name
-        name='exp',  # save results to project/name
-        exist_ok=False,  # existing project/name ok, do not increment
-        line_thickness=3,  # bounding box thickness (pixels)
-        hide_labels=False,  # hide labels
-        hide_conf=False,  # hide confidences
-        half=False,  # use FP16 half-precision inference
-        dnn=False,  # use OpenCV DNN for ONNX inference
-        ):
-
-    # Directories
-    save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
-    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
-
-    # Load model
-    device = select_device(device)
-    model = DetectMultiBackend(weights, device=device, dnn=dnn)
-    stride, names, pt, jit, onnx = model.stride, model.names, model.pt, model.jit, model.onnx
-    imgsz = check_img_size(imgsz, s=stride)  # check image size
-
-    # Half
-    half &= pt and device.type != 'cpu'  # half precision only supported by PyTorch on CUDA
-    if pt:
-        model.model.half() if half else model.model.float()
-
-    # Padded resize
-    dataset_img = letterbox(source, imgsz, stride=stride, auto=pt)[0]
-
-    # Convert
-    dataset_img = dataset_img.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
-    dataset_img = np.ascontiguousarray(dataset_img)
-    # Run inference
-    if pt and device.type != 'cpu':
-        model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.model.parameters())))  # warmup
-    dt, seen = [0.0, 0.0, 0.0], 0
-    bs = 1
-    t1 = time_sync()
-    im = dataset_img
-    im = torch.from_numpy(im).to(device)
-    im = im.half() if half else im.float()  # uint8 to fp16/32
-    im /= 255  # 0 - 255 to 0.0 - 1.0
-    if len(im.shape) == 3:
-        im = im[None]  # expand for batch dim
-    t2 = time_sync()
-    dt[0] += t2 - t1
-    pred = model(im, augment=augment, visualize=visualize)
-    t3 = time_sync()
-    pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
-    dt[2] += time_sync() - t3
-
-    ## iterate through detections
-    for i, det in enumerate(pred):
-        seen += 1
-        path = '/home/parallels'
-        p, im0, frame = path, source.copy(), 'frame'
-        p = Path(p)  # to Path
-        save_path = '/home/parallels/predict.jpeg'
-        annotator = Annotator(im0, line_width=line_thickness, example=str(names))
-        
-        time_stamp = Clock().now()
-        bounding_boxes_msg = BoundingBoxes()
-        bounding_boxes_msg.header.stamp = time_stamp.to_msg()
-        bounding_boxes_msg.header.frame_id = 'detection'
-        s = ''
-        if len(det):
-            # Rescale boxes from img_size to im0 size
-            det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()
-            # Print results
-            for c in det[:, -1].unique():
-                n = (det[:, -1] == c).sum()  # detections per class
-                s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string
-            
-            # Write results
-            for *xyxy, conf, cls in reversed(det):
-                bounding_box = BoundingBox()
-                c = int(cls)  # integer class
-                label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
-                annotator.box_label(xyxy, label, color=colors(c, True))
-                bounding_box.probability = float(conf)
-                bounding_box.xmin = int(xyxy[0])
-                bounding_box.ymin = int(xyxy[1])
-                bounding_box.xmax = int(xyxy[2])
-                bounding_box.ymax = int(xyxy[3])
-                bounding_box.id = c
-                bounding_box.class_id = names[c]
-                bounding_boxes_msg.bounding_boxes.append(bounding_box)
-        # Print time (inference-only)
-        LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')
-        # Stream results
-        im0 = annotator.result()
-        # cv2.imwrite(save_path, im0)
-        return im0, bounding_boxes_msg
-
-class DetectOnBag(Node):
-
-    def __init__(self):
-        super().__init__('detect_on_bag')
-        self.subscription = self.create_subscription(
-            Image,
-            '/camera/color/image_raw',
-            self.image_callback,
-            10)
-        self.subscription  # prevent unused variable warning
-        self.detected_img_pub = self.create_publisher(Image, '/detection_image', 10)
-        self.bboxes_pub = self.create_publisher(BoundingBoxes, '/bboxes', 10)
-
-    def image_callback(self, msg):
-        self.get_logger().info('Image')
-        print(msg.header.stamp)
-
-        bridge = CvBridge()
-        cv_image = bridge.imgmsg_to_cv2(msg, desired_encoding='passthrough')
-        detected_img, bboxes = run('src/yolov3/runs/train/exp14/weights/best.pt', cv_image)
-        self.detected_img_pub.publish(self.numpy_array_to_image_msg(detected_img))
-        bboxes.image_header = msg.header
-        self.bboxes_pub.publish(bboxes)
-    
-    def numpy_array_to_image_msg(self, numpy_array):
-        # Create a CvBridge object
-        bridge = CvBridge()
-
-        # Convert the numpy array to a cv image
-        cv_image = cv2.cvtColor(numpy_array, cv2.COLOR_RGB2BGR)
-
-        # Convert the cv image to a ROS message
-        ros_image = bridge.cv2_to_imgmsg(cv_image, encoding="bgr8")
-
-        return ros_image
-
-
-def main(args=None):
-    rclpy.init(args=args)
-
-    detect_on_bag = DetectOnBag()
-
-    rclpy.spin(detect_on_bag)
-
-    # Destroy the node explicitly
-    # (optional - otherwise it will be done automatically
-    # when the garbage collector destroys the node object)
-    detect_on_bag.destroy_node()
-    rclpy.shutdown()
-
-
-if __name__ == '__main__':
-    main()

ros2_ws/src/yolov3/msg/BoundingBox.msg

@@ -1,7 +0,0 @@
-float64 probability
-int64 xmin
-int64 ymin
-int64 xmax
-int64 ymax
-int16 id
-string class_id

ros2_ws/src/yolov3/msg/BoundingBoxes.msg

@@ -1,3 +0,0 @@
-std_msgs/Header header
-std_msgs/Header image_header
-BoundingBox[] bounding_boxes

ros2_ws/src/yolov3/package.xml

@@ -1,26 +0,0 @@
-<?xml version="1.0"?>
-<package format="3">
-  <name>yolov4_msg</name>
-  <version>0.1.0</version>
-  <description>
-    This is a ROS2 package for running yolov3 on rosbag
-  </description>
-  <maintainer email="myname@myemail.com">My Name</maintainer>
-  <license>MIT License</license>
-  
-  <buildtool_depend>ament_cmake</buildtool_depend>
-  <build_depend>rclpy</build_depend>
-  <build_depend>std_msgs</build_depend>
-  
-  <exec_depend>rclpy</exec_depend>
-  <exec_depend>std_msgs</exec_depend>
-
-
-  <build_depend>rosidl_default_generators</build_depend>
-  <exec_depend>rosidl_default_runtime</exec_depend>
-  <member_of_group>rosidl_interface_packages</member_of_group>
-  
-  <export>
-    <build_type>ament_cmake</build_type>
-  </export>
-</package>