def run_my_code(self, images):
sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)),'src'))
sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)),'models','research'))
#print(sys.path)
import object_detection_lib
# Create the instance of ObjectDetection
odc = object_detection_lib.ObjectDetection(0.5)
#print("odc defined")
predictions = []
for image in images: #image is already a cvimg
print(image)
#print(odc.run_inference_for_single_image(image))
output_dict_filtered = odc.run_inference_for_single_image(image)[1]
labels = output_dict_filtered["detection_labels"] #unicode strings
boxes = output_dict_filtered["detection_boxes"]
confidences = output_dict_filtered["detection_scores"]
#Write JSON files
prediction = []
for i in range(0, len(labels)):
prediction.append({"label":labels[i],"confidence":confidences[i],"x":boxes[i][0],"y":boxes[i][1],"w":boxes[i][2],"h":boxes[i][3]})
predictions.append(prediction)
print(predictions)
return predictions #list of [{'confidence': 0.71, 'label': 'person'}, {'label1': 'duckie', 'confidence': 0.6}] elements
def __init__(self):
self.__bridge = CvBridge()
# Publisher to publish predicted image
self.__image_pub = rospy.Publisher('/' + os.environ['DUCKIEBOT_NAME'] +
'/prediction_images',
Image,
queue_size=1)
# Publisher to publish the result of classes
# self.__result_pub = rospy.Publisher('/'+os.environ['DUCKIEBOT_NAME'] +"/predicted_result", detection_results, queue_size=1)
# Subscribe to topic which will kick off object detection in the next image
# self.__command_sub = rospy.Subscriber('/'+os.environ['DUCKIEBOT_NAME'] + "/start", Empty, self.StartCallback)
# Subscribe to the topic which will supply the image fom the camera
self.__image_sub = rospy.Subscriber(
'/' + os.environ['DUCKIEBOT_NAME'] + "/camera_node/image/raw",
Image, self.Imagecallback)
# Flag to indicate that we have been requested to use the next image
self.__scan_next = True
# Read the confidence level, any object with a level below this will not be used
# This parameter is set in the config.yaml file
confidence_level = rospy.get_param(
'/object_detection/confidence_level', 0.50)
# Create the object_detection_lib class instance
self.__odc = object_detection_lib.ObjectDetection(confidence_level)
def __init__(self):
self.__bridge = CvBridge()
# Publisher to publish update image
self.__image_pub = rospy.Publisher(
'tf_object_detection_node/image/compressed',
CompressedImage,
queue_size=1)
# Subscribe to the topic which will supply the image fom the camera
self.__image_sub = rospy.Subscriber('raspicam_node/image/compressed',
CompressedImage,
self.Imagecallback)
# Read the path for models/research/object_detection directory from the parameter server or use this default
object_detection_path = rospy.get_param(
'/object_detection/path',
'/home/ubuntu/git/models/research/object_detection')
# Read the confidence level, any object with a level below this will not be used
confidence_level = rospy.get_param(
'/object_detection/confidence_level', 0.50)
# Create the object_detection_lib class instance
self.__odc = object_detection_lib.ObjectDetection(
object_detection_path, confidence_level)
# Create the Action server
self.__as = actionlib.SimpleActionServer('object_detection',
scan_for_objectsAction,
self.do_action, False)
self.__as.start()
def __init__(self):
self.__bridge = CvBridge()
# Publisher to publish update image
#self.__image_pub = rospy.Publisher("test1", Image, queue_size=1)
# Subscribe to the topic which will supply the image fom the camera
self.__image_sub = rospy.Subscriber("/bebop/image_raw",Image, self.Imagecallback,queue_size=None,buff_size=2**24)
# Flag to indicate that we have been requested to use the next image
#self.__scan_next = False
# Read the path for models/research/object_detection directory from the parameter server or use this default
object_detection_path = rospy.get_param('/object_detection/path', '/home/lee_ming_wei/models/research/object_detection')
# Read the confidence level, any object with a level below this will not be used
confidence_level = rospy.get_param('/object_detection/confidence_level', 0.70)
# Create the object_detection_lib class instance
self.__odc = object_detection_lib.ObjectDetection(object_detection_path, confidence_level)
#!/usr/bin/env python
import sys
import os
import cv2
sys.path.append('../src')
sys.path.append("../models/research/")
import object_detection_lib
# Create the instance of ObjectDetection
odc = object_detection_lib.ObjectDetection(0.5)
cvimg = cv2.imread("/Users/zhou/Desktop/duckietown/duckietown_raw_dataset/all_images/good/b_BR_doort_frame00380.jpg")
# Detect the objects
object_names = odc.scan_for_objects(cvimg)
print(object_names)
cv2.imshow('object detection', cvimg)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.imwrite('adjusted_test_image.jpg', cvimg)
#!/usr/bin/env python
import sys
import os
import cv2
sys.path.append('/home/ubuntu/git/tf_object_detection/src')
import object_detection_lib
# Create the instance of ObjectDetection
odc = object_detection_lib.ObjectDetection(
'/home/samuelc/dev/recyclables_detector_wss/models/research/object_detection',
0.5)
cvimg = cv2.imread('../img/images.jpeg')
# Detect the objects
object_names = odc.scan_for_objects(cvimg)
print(object_names)
cv2.imshow('object detection', cvimg)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.imwrite('adjusted_test_image.jpg', cvimg)
#!/usr/bin/env python
import sys
import os
import cv2
sys.path.append('/home/ubuntu/git/tf_object_detection/src')
import object_detection_lib
# Create the instance of ObjectDetection
odc = object_detection_lib.ObjectDetection(
'/home/ubuntu/git/models/research/object_detection', 0.5)
cvimg = cv2.imread("test_image.jpg")
# Detect the objects
object_names = odc.scan_for_objects(cvimg)
print(object_names)
cv2.imshow('object detection', cvimg)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.imwrite('adjusted_test_image.jpg', cvimg)