def read_cam():
detector = ObjectDetector()
# OpenCV main loop
vid_center = VideoStream(src=0).start()
while True:
windowName = "car detection"
cv2.namedWindow(windowName, cv2.WINDOW_NORMAL)
cv2.resizeWindow(windowName, 416, 416)
cv2.moveWindow(windowName, 0, 0)
cv2.setWindowTitle(windowName, "car detection")
while True:
# -----------------------------------------------------
# Check to see if the user closed the window
if cv2.getWindowProperty(windowName, 0) < 0:
# This will fail if the user closed the window; get printed to the console
break
image = vid_center.read()
# -----------------------------------------------------
# run the network and detection
result = detector.detect_object(image, visualize=True)
displayBuf = np.array(result)
# show the stuff
# -----------------------------------------------------
cv2.imshow(windowName, displayBuf)
key = cv2.waitKey(10)
if key == 27: # ESC key
cv2.destroyAllWindows()
break
class ObjectDetectionNode:
def __init__(self):
self.camera_topic = rospy.get_param("/object_detection_node/camera_topic")
self.obj_detect_results_topic = rospy.get_param("/object_detection_node/obj_detect_results_topic")
self.obj_detect_viz_topic = rospy.get_param("/object_detection_node/obj_detect_viz_topic")
rospy.init_node('object_detection')
self.camera_sub = rospy.Subscriber(self.camera_topic,
Image, self.image_update_callback, queue_size=1)
self.current_frame = None
self.bridge = CvBridge()
rospy.loginfo("Object Detection Initializing")
rospy.loginfo("Tiny Yolo V3")
self.model_path = rospy.get_param("/object_detection_node/model_path")
self.classes_path = rospy.get_param("/object_detection_node/classes_path")
self.anchors_path = rospy.get_param("/object_detection_node/anchors_path")
self.iou_threshold = rospy.get_param("/object_detection_node/iou_threshold")
self.score_threshold = rospy.get_param("/object_detection_node/score_threshold")
self.input_size = (416, 416)
self.detector = ObjectDetector(model_path=self.model_path,
classes_path=self.classes_path,
anchors_path=self.anchors_path,
score_threshold=self.score_threshold,
iou_threshold=self.iou_threshold,
size=self.input_size)
labels_visualizer = utils.LabelsVisualizer(self.detector.class_names)
detection_image_pub = rospy.Publisher(self.obj_detect_viz_topic + "/compressed",
CompressedImage, queue_size=1)
detection_results_pub = rospy.Publisher(self.obj_detect_results_topic,
DetectionResults, queue_size=1)
rate = rospy.Rate(15)
while not rospy.is_shutdown():
time = rospy.get_time()
if self.current_frame is not None:
print("new image at time %.2f" % time)
out_boxes, out_scores, out_classes = \
self.detector.detect_object(self.current_frame)
inference_end_time = rospy.get_time()
print("*** inference time: %.3f" % (inference_end_time - time))
if detection_image_pub.get_num_connections() > 0:
image = labels_visualizer.draw_bboxes(self.current_frame,
out_boxes, out_scores, out_classes)
print("*** drawing time: %.3f" % (rospy.get_time() - inference_end_time))
# img_msg = self.bridge.cv2_to_imgmsg(image, "bgr8")
msg = utils.cv2_to_compressed_imgmsg(image)
detection_image_pub.publish(msg)
msg = self.convert_results_to_message(out_boxes, out_scores, out_classes)
msg.is_green = self.check_traffic_segmentation(out_boxes, out_classes)
if detection_results_pub.get_num_connections() > 0:
detection_results_pub.publish(msg)
else:
print("waiting for image at time %.2f" % time)
rate.sleep()
def check_traffic_segmentation(self, out_boxes, out_classes):
labels = [self.detector.class_names[i] for i in out_classes]
if 'TRAFFIC_LIGHT' not in labels:
return True
ind = labels.index('TRAFFIC_LIGHT')
bbox = out_boxes[ind]
top, left, bottom, right = bbox
top = max(0, np.floor(top + 0.5).astype('int32'))
left = max(0, np.floor(left + 0.5).astype('int32'))
bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))
right = min(image.size[0], np.floor(right + 0.5).astype('int32'))
bbox = ((left, top), (right, bottom))
print '************************** BBOX', bbox
'''if (x2 - x1) * (y2 - y1) < 13500:
return True'''
return not check_traffic_light(self.current_frame, bbox)['detected_traffic_stop']
def image_update_callback(self, data):
try:
cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8")
except CvBridgeError as e:
raise e
# cv_image = utils.compressed_imgmsg_to_cv2(data)
self.current_frame = cv_image
@staticmethod
def convert_results_to_message(out_boxes, out_scores, out_classes):
msgs = DetectionResults()
for i in range(len(out_scores)):
msg = DetectionResult()
msg.out_class = out_classes[i]
msg.out_score = out_scores[i]
msg.location = out_boxes[i, :]
msgs.results.append(msg)
return msgs
