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import rclpy
from rclpy.node import Node
from rclpy.qos import QoSProfile, QoSReliabilityPolicy, QoSHistoryPolicy
# from realsense2_camera_msgs.msg import RGBD
from sensor_msgs.msg import Image
from std_msgs.msg import Float32
from std_msgs.msg import Int32
from autoboat_msgs.msg import ObjectDetectionResultsList
from cv_bridge import CvBridge
from ultralytics import YOLO
import cv2
import numpy as np
from math import sqrt
import os
current_directory_path = os.path.dirname(os.path.realpath(__file__))
TRAINED_IMAGE_SIZE = (640, 640) # pixel width and height of the images that the model was trained on
IMAGE_CONFIDENCE = 0.3
SHOULD_SAVE_IMAGES = False
class BuoyDetectionNode(Node):
def __init__(self):
super().__init__("buoy_detection")
self.model = YOLO(f"{current_directory_path}/weights/pretty_good_11l_train32.pt")
self.cv_bridge = CvBridge()
self.current_image_rgb = None
self.depth_image = None
self.image_to_save_index = 0 # images are saved in the format name[index].jpg so this just keeps track of the current index of the image so that we don't overwrite other images
sensor_qos_profile = QoSProfile(reliability=QoSReliabilityPolicy.BEST_EFFORT, history=QoSHistoryPolicy.KEEP_LAST, depth=1)
self.buoy_angle_pub = self.create_publisher(Float32, "/buoy_angle", 10)
self.buoy_depth_pixel_pub = self.create_publisher(Float32, "/buoy_depth_pixel", 10)
# self.depth_image_listener = self.create_subscription(
# msg_type=Image,
# topic="/camera/camera/aligned_depth_to_color/image_raw",
# callback=self.depth_image_callback,
# qos_profile=sensor_qos_profile,
# )
# self.depth_image_listener = self.create_subscription(msg_type=Image, topic="/camera/depth/image_rect_raw", callback=self.depth_image_callback, qos_profile=sensor_qos_profile)
self.rgb_image_listener = self.create_subscription(
msg_type=Image, topic="/camera/camera/color/image_raw", callback=self.rgb_image_callback, qos_profile=sensor_qos_profile
)
# self.object_detection_results_publisher = self.create_publisher(msg_type=ObjectDetectionResults, topic="/object_detection_results", qos_profile=sensor_qos_profile)
self.create_timer(timer_period_sec=0.001, callback=self.perform_inference)
def depth_image_callback(self, depth_image: Image):
self.get_logger().info("got here depth") # print(f"hihihihi")
# top = 0
# bottom = smaller_size
# TODO downscale the image so that the smallest dimension is 640p
# TODO: crop the image properly
# cropped_depth_image_cv = depth_image_cv[left:right, top:bottom]
# cropped_rgb_image_cv = rgb_image_cv
# depth_image_cv.resize(TRAINED_IMAGE_SIZE)f"The type of the cv image is
# rgb_image_cv.resize(TRAINED_IMAGE_SIZE)
depth_image_cv = self.cv_bridge.imgmsg_to_cv2(depth_image, desired_encoding=depth_image.encoding)
print(f"cropped image shape: {depth_image_cv.shape}")
# cv2.imwrite('depth_image.jpg', depth_image_cv)
print(f"The type of the cv image is {type(depth_image_cv)}")
self.depth_image = depth_image_cv
# np.where(depth_image_cv > 0)
# print(depth_image_cv)
def rgb_image_callback(self, rgb_image: Image):
self.get_logger().info("got here rgb")
self.current_image_rgb = self.cv_bridge.imgmsg_to_cv2(rgb_image, "bgr8")
self.current_image_rgb = self.current_image_rgb[80:1200, 40:680] # crop the image to 640,640
def perform_inference(self):
# https://docs.ultralytics.com/modes/predict/#inference-sources
if self.current_image_rgb is None:
return
results = self.model.predict(
[
self.current_image_rgb,
],
conf=IMAGE_CONFIDENCE,
) # return a list of Results objects
# Added variable for real-time inference
DIAGONAL_FIELD_OF_VIEW = 89
# Process results list
print(f"The length of the results object is {len(results)}")
result = results[0]
boxes = result.boxes # Boxes object for bounding box outputs
# print(f"boxes: {boxes}")
# masks = result.masks # Masks object for segmentation masks outputs
# keypoints = result.keypoints # Keypoints object for pose outputs
# probs = result.probs # Probs object for classification outputs
# obb = result.obb # Oriented boxes object for OBB outputs
height = result.orig_shape[0]
width = result.orig_shape[1]
diagonal = sqrt(height**2 + width**2)
deg_per_pixel = DIAGONAL_FIELD_OF_VIEW / diagonal
boxes = result.boxes
# conf_angle = {}
angle_list = []
conf_list = []
x_list = []
y_list = []
box_y_center = 0
box_x_center = 0
if boxes.shape[0] == 0:
return
self.get_logger().info("We are finally getting something")
for box in boxes:
print(box.conf.item())
box_location = box.xywh
# The Y stuff is only for trying to get depth image values
# box_centerx_location = box_location[0][0].item() + box_location[0][2].item()/2
# box_centery_location = box_location[0][1].item() + box_location[0][3].item()/2
box_centerx_location = box_location[0][0].item()
box_centery_location = box_location[0][1].item()
print(f"X-coordinate: {box_centerx_location}")
print(f"Y-coordinate: {box_centery_location}")
# print(f"non-absolute-value-x-location: {(width/2)-box_centerx_location}")
# print(f"non-absolute-value-y-location: {(height/2)-box_centery_location}")
x_distance_from_center = box_centerx_location - (width / 2)
img_angle_from_center = x_distance_from_center * deg_per_pixel
# conf_angle[box.conf.item()]=img_angle_from_center
angle_list.append(img_angle_from_center)
conf_list.append(box.conf.item())
x_list.append(box_centerx_location)
y_list.append(box_centery_location)
# Trying to get the maximum confidence keys for the x and y locations for the conf box dictionaries -- FAILED, kept just in case
# Skip to after the returning of the angle to see this used
# max_conf_x_key = min(conf_x_box, key=conf_x_box.get)
# max_conf_y_key = min(conf_y_box, key=conf_y_box.get)
# print(f"The keys of the x dictionary are {conf_x_box.keys()}")
# print(f"The keys of the y dictionary are {conf_y_box.keys()}")
# print(f"max_conf_x_key: {max_conf_x_key}")
# print(f"max_conf_y_key: {max_conf_y_key}")
max_conf_index = np.argmax(conf_list)
# y_avg_distance_from_center = sum_y/count
# print(f"The confidence angle pairs sorted are {dict(sorted(conf_angle.items(), reverse=True))}")
# max_angle_key = max(conf_angle, key=conf_angle.get)
# print(f"The angle of the maximum confidence box is {conf_angle[max_angle_key]}")
max_conf_angle = angle_list[max_conf_index]
print(f"The most confident buoy angle is: {max_conf_angle}")
msg = Float32()
msg.data = max_conf_angle
self.buoy_angle_pub.publish(msg)
# Need to get the x,y location of the buoy/center of the bounding box
box_x_center = x_list[max_conf_index]
box_y_center = y_list[max_conf_index]
print(f"The type of the current image is {type(self.current_image_rgb)}")
print(f"The value at the index is {self.current_image_rgb[int(box_y_center), int(box_x_center)]}")
# self.current_image_rgb[int(box_x_center), int(box_y_center)]= (160, 32, 240)
# self.current_image_rgb[int(box_x_center+1), int(box_y_center)]= (160, 32, 240)
# self.current_image_rgb[int(box_x_center+2), int(box_y_center)]= (160, 32, 240)
# self.current_image_rgb[int(box_x_center-1), int(box_y_center)]= (160, 32, 240)
# self.current_image_rgb[int(box_x_center-2), int(box_y_center)]= (160, 32, 240)
# self.current_image_rgb[int(box_x_center), int(box_y_center-1)]= (160, 32, 240)
# self.current_image_rgb[int(box_x_center), int(box_y_center-2)]= (160, 32, 240)
# self.current_image_rgb[int(box_x_center), int(box_y_center+1)]= (160, 32, 240)
# self.current_image_rgb[int(box_x_center), int(box_y_center+2)]= (160, 32, 240)
# Then need to index into the depth image class variable saved as self.depth_image with the x,y coordinates
# And then print out the pixel value
##TODO: make sure that y-center and x-center are not out of bounds (return nothing if out of bounds)
if not self.depth_image is None:
print(type(self.depth_image))
print(f"The boxcenterx variable is {int(box_x_center)}")
print(f"The boxcentery variable is {int(box_y_center)}")
print(f"The pixel value at the box location is hopefully {self.depth_image[int(box_y_center), int(box_x_center)]}")
# print(type(self.depth_image[int(box_x_center)][int(box_y_center)]))
msg = Float32()
msg.data = (self.depth_image[int(box_y_center + 50), int(box_x_center + 30)].item()) / 1000
# msg.data = int(box_x_center)
self.buoy_depth_pixel_pub.publish(msg)
# generate purple boxes around the center of the detected buoy
for x_value in range(self.current_image_rgb.shape[1]):
for y_value in range(self.current_image_rgb.shape[0]):
difference_vector = [abs(y_value - int(box_y_center)), abs(x_value - int(box_x_center))]
max_value = max(difference_vector[0], difference_vector[1])
if max_value <= 20:
self.current_image_rgb[y_value, x_value] = [160, 32, 240]
# cv2.imwrite("rgb_image.jpg", self.current_image_rgb)
result.save("rgb_image.png")
# For Depth Image
# for x_value in range(self.depth_image.shape[1]):
# for y_value in range(self.depth_image.shape[0]):
# difference_vector = [abs(y_value - int(box_y_center + 50)), abs(x_value - int(box_x_center + 30))]
# max_value = max(difference_vector[0], difference_vector[1])
# if max_value <= 20:
# self.depth_image[y_value, x_value] = 0
# cv2.imwrite("depth_image.jpg", self.depth_image)
if SHOULD_SAVE_IMAGES:
print("GOT HERE")
result.save(f"cv_results2/result_{self.image_to_save_index}.png") # display to screen
self.image_to_save_index += 1
# TODO process these results properly
# self.object_detection_results_publisher.publish()
def main():
rclpy.init()
buoy_detection_node = BuoyDetectionNode()
rclpy.spin(buoy_detection_node)
if __name__ == "__main__":
main()
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