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trt_yolo_v4.py
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trt_yolo_v4.py
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#!/usr/bin/env python
import os
import time
import cv2
import pycuda.autoinit # For initializing CUDA driver
import pycuda.driver as cuda
from utils.yolo_classes import get_cls_dict
from utils.display import open_window, set_display, show_fps
from utils.visualization import BBoxVisualization
from utils.yolo_with_plugins import TrtYOLO
import rospy
import rospkg
from yolov4_trt_ros.msg import Detector2DArray
from yolov4_trt_ros.msg import Detector2D
from vision_msgs.msg import BoundingBox2D
from sensor_msgs.msg import Image
from cv_bridge import CvBridge, CvBridgeError
class yolov4(object):
def __init__(self):
""" Constructor """
self.bridge = CvBridge()
self.init_params()
self.init_yolo()
self.cuda_ctx = cuda.Device(0).make_context()
self.trt_yolo = TrtYOLO(
(self.model_path + self.model), (self.h, self.w), self.category_num)
def __del__(self):
""" Destructor """
self.cuda_ctx.pop()
del self.trt_yolo
del self.cuda_ctx
def clean_up(self):
""" Backup destructor: Release cuda memory """
if self.trt_yolo is not None:
self.cuda_ctx.pop()
del self.trt_yolo
del self.cuda_ctx
def init_params(self):
""" Initializes ros parameters """
rospack = rospkg.RosPack()
package_path = rospack.get_path("yolov4_trt_ros")
self.video_topic = rospy.get_param("/video_topic", "/video_source/raw")
self.model = rospy.get_param("/model", "yolov4Custom")
self.model_path = rospy.get_param(
"/model_path", package_path + "/yolo/")
self.category_num = rospy.get_param("/category_number", 10)
self.input_shape = rospy.get_param("/input_shape", "416")
self.conf_th = rospy.get_param("/confidence_threshold", 0.5)
self.show_img = rospy.get_param("/show_image", True)
self.image_sub = rospy.Subscriber(
self.video_topic, Image, self.img_callback, queue_size=1, buff_size=1920*1080*3)
self.detection_pub = rospy.Publisher(
"detections", Detector2DArray, queue_size=1)
self.overlay_pub = rospy.Publisher(
"/result/overlay", Image, queue_size=1)
def init_yolo(self):
""" Initialises yolo parameters required for trt engine """
if self.model.find('-') == -1:
self.model = self.model + "-" + self.input_shape
yolo_dim = self.model.split('-')[-1]
if 'x' in yolo_dim:
dim_split = yolo_dim.split('x')
if len(dim_split) != 2:
raise SystemExit('ERROR: bad yolo_dim (%s)!' % yolo_dim)
self.w, self.h = int(dim_split[0]), int(dim_split[1])
else:
self.h = self.w = int(yolo_dim)
if self.h % 32 != 0 or self.w % 32 != 0:
raise SystemExit('ERROR: bad yolo_dim (%s)!' % yolo_dim)
cls_dict = get_cls_dict(self.category_num)
self.vis = BBoxVisualization(cls_dict)
def img_callback(self, ros_img):
"""Continuously capture images from camera and do object detection """
tic = time.time()
# converts from ros_img to cv_img for processing
try:
cv_img = self.bridge.imgmsg_to_cv2(
ros_img, desired_encoding="bgr8")
rospy.logdebug("ROS Image converted for processing")
except CvBridgeError as e:
rospy.loginfo("Failed to convert image %s", str(e))
if cv_img is not None:
boxes, confs, clss = self.trt_yolo.detect(cv_img, self.conf_th)
cv_img = self.vis.draw_bboxes(cv_img, boxes, confs, clss)
toc = time.time()
fps = 1.0 / (toc - tic)
self.publisher(boxes, confs, clss)
if self.show_img:
cv_img = show_fps(cv_img, fps)
cv2.imshow("YOLOv4 DETECTION RESULTS", cv_img)
cv2.waitKey(1)
# converts back to ros_img type for publishing
try:
overlay_img = self.bridge.cv2_to_imgmsg(
cv_img, encoding="passthrough")
rospy.logdebug("CV Image converted for publishing")
self.overlay_pub.publish(overlay_img)
except CvBridgeError as e:
rospy.loginfo("Failed to convert image %s", str(e))
def publisher(self, boxes, confs, clss):
""" Publishes to detector_msgs
Parameters:
boxes (List(List(int))) : Bounding boxes of all objects
confs (List(double)) : Probability scores of all objects
clss (List(int)) : Class ID of all classes
"""
detection2d = Detector2DArray()
detection = Detector2D()
detection2d.header.stamp = rospy.Time.now()
detection2d.header.frame_id = "camera" # change accordingly
for i in range(len(boxes)):
# boxes : xmin, ymin, xmax, ymax
for _ in boxes:
detection.header.stamp = rospy.Time.now()
detection.header.frame_id = "camera" # change accordingly
detection.results.id = clss[i]
detection.results.score = confs[i]
detection.bbox.center.x = boxes[i][0] + (boxes[i][2] - boxes[i][0])/2
detection.bbox.center.y = boxes[i][1] + (boxes[i][3] - boxes[i][1])/2
detection.bbox.center.theta = 0.0 # change if required
detection.bbox.size_x = abs(boxes[i][0] - boxes[i][2])
detection.bbox.size_y = abs(boxes[i][1] - boxes[i][3])
detection2d.detections.append(detection)
self.detection_pub.publish(detection2d)
def main():
yolo = yolov4()
rospy.init_node('yolov4_trt_ros', anonymous=True)
try:
rospy.spin()
except KeyboardInterrupt:
rospy.on_shutdown(yolo.clean_up())
print("Shutting down")
if __name__ == '__main__':
main()