def load_dialogs():
""" Load dialogs from json file """
global DIALOGS
if not DIALOGS:
with open(ros_utils.get_fullpath(__file__, 'static/dialogs.json')) as f:
DIALOGS = json.loads(f.read())
return DIALOGS
def test_segmentation_rate_on_two_frames(self):
""" Loads images and posts the corresponding segmentation rates """
test_images = helper.load_test_images(
__file__, ros_utils.get_fullpath(__file__, "test.json"))
for image in test_images:
self.assertFalse(rospy.is_shutdown())
rospy.loginfo("### Test image %s ###", image[helper.FILENAME])
self.segmentation_should_find_most_of_the_objects(image)
def load_test_images(file, test_filepath):
""" Loads test data and images"""
test_images = []
with open(test_filepath) as json_data:
file_data = json.load(json_data)
for test in file_data["tests"]:
test_images.append({
IMAGE_MSG: image_utils.image_file_to_ros_msg(ros_utils.get_fullpath(file, test["imageName"])),
FILENAME: test["imageName"],
EXPECTED_OBJECTS: test["refData"]["objects"]
})
return test_images
def test_image_size_should_not_affect_segmentation_too_much(self):
""" Tests image size """
test_images = helper.load_test_images(
__file__, ros_utils.get_fullpath(__file__, "test2.json"))
expected_objects = test_images[0][helper.EXPECTED_OBJECTS]
images_objects_missed = []
for image in test_images:
self.assertFalse(rospy.is_shutdown())
object_found = self.get_objects_found(image)
images_objects_missed.append(
helper.get_missed_objects(expected_objects, object_found))
for object_missed in images_objects_missed:
self.assertLessEqual(
len(object_missed), 1,
"Too much expected objects were not detected.")
objects_missed_from_smaller_image = images_objects_missed[0]
for i in range(2, len(images_objects_missed)):
self.assertGreaterEqual(objects_missed_from_smaller_image,
len(images_objects_missed[i]) + 1)
def test_image_quality_should_not_affect_segmentation(self):
""" Tests image quality """
test_images = helper.load_test_images(
__file__, ros_utils.get_fullpath(__file__, "test1.json"))
expected_objects = test_images[0][helper.EXPECTED_OBJECTS]
images_objects_found = []
for image in test_images:
self.assertFalse(rospy.is_shutdown())
images_objects_found.append(self.get_objects_found(image))
kinect_missed_objects = len(
helper.get_missed_objects(expected_objects,
images_objects_found[0]))
s8_missed_objects = len(
helper.get_missed_objects(expected_objects,
images_objects_found[1]))
self.assertAlmostEqual(kinect_missed_objects,
s8_missed_objects,
delta=1)
self.assertLessEqual(kinect_missed_objects, 1)
self.assertLessEqual(s8_missed_objects, 1)
from cv_bridge import CvBridge
sys.path.append(
os.path.join(os.path.dirname(os.path.realpath(__file__)),
'../../Mask_RCNN'))
import coco
import model as modellib
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
from devine_config import topicname
from ros_image_processor import ImageProcessor, ROSImageProcessingWrapper
from devine_image_processing.msg import SegmentedImage, SceneObject
# Paths
COCO_MODEL_PATH = ros_utils.get_fullpath(__file__, '../../mask_rcnn_coco.h5')
MODEL_DIR = ros_utils.get_fullpath(__file__, 'logs')
# Topics
IMAGE_TOPIC = topicname('segmentation_image')
SEGMENTATION_TOPIC = topicname('objects')
# This threshold removes objects with a lower confidence level
SEGMENTATION_THRESHOLD = 0.7
class RCNNSegmentation(ImageProcessor):
""" RCNN segmentation wrapper of Mask_RCNN for use in guesswhat """
class_names = [
'BG', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
import numpy as np
from guesswhat.models.guesser.guesser_network import GuesserNetwork
from guesswhat.models.qgen.qgen_lstm_network import QGenNetworkLSTM
from guesswhat.models.qgen.qgen_wrapper import QGenWrapper
from guesswhat.models.looper.basic_looper import BasicLooper
from guesswhat.data_provider.guesswhat_dataset import Game
from guesswhat.data_provider.guesswhat_tokenizer import GWTokenizer
from guesswhat.data_provider.questioner_batchifier import QuestionerBatchifier
from modelwrappers import GuesserROSWrapper, OracleROSWrapper
from devine_config import topicname
from devine_common import ros_utils
from devine_image_processing.msg import SegmentedImage, VGG16Features
EVAL_CONF_PATH = ros_utils.get_fullpath(__file__, '../../config/eval.json')
GUESS_CONF_PATH = ros_utils.get_fullpath(__file__, '../../config/guesser.json')
QGEN_CONF_PATH = ros_utils.get_fullpath(__file__, '../../config/qgen.json')
GUESS_NTW_PATH = ros_utils.get_fullpath(__file__, '../../data/guesser.ckpt')
QGEN_NTW_PATH = ros_utils.get_fullpath(__file__, '../../data/qgen.ckpt')
TOKENS_PATH = ros_utils.get_fullpath(__file__, '../../data/tokens.json')
# topics
SEGMENTATION_TOPIC = topicname('objects')
FEATURES_TOPIC = topicname('image_features')
STATUS_TOPIC = topicname('guesswhat_status')
# TODO: merge these topics to one
OBJECT_TOPIC = topicname('guess_location_image')
CATEGORY_TOPIC = topicname('guess_category')
__email__ = "*****@*****.**"
__status__ = "Production"
import rospy
import cv2
import tensorflow as tf
from std_msgs.msg import String
from bson import json_util
from tf_pose.estimator import TfPoseEstimator
from keras.backend.tensorflow_backend import set_session
from devine_config import topicname
from devine_common import ros_utils
from ros_image_processor import ImageProcessor, ROSImageProcessingWrapper
# Originally in './models/graph/mobilenet_thin/graph_opt.pb'
MODEL_DIR = ros_utils.get_fullpath(__file__, '../../mobilenet_thin.pb')
# Topics
IMAGE_TOPIC = topicname('body_tracking_image')
PUBLISH_TOPIC = topicname('body_tracking')
class BodyTracking(ImageProcessor):
""" Body Tracking wrapper of tf_pose for use in guesswhat """
body_parts_names = [
'Nose', 'Neck', 'RShoulder', 'RElbow', 'RWrist', 'LShoulder', 'LElbow', 'LWrist', 'RHip', 'RKnee',
'RAnkle', 'LHip', 'LKnee', 'LAnkle', 'REye', 'LEye', 'REar', 'LEar', 'Background'
]
def __init__(self):
config = tf.ConfigProto(log_device_placement=False)
__email__ = "*****@*****.**"
__status__ = "Production"
import cv2
import tensorflow as tf
import numpy as np
import rospy
from PIL import Image
from rospy.numpy_msg import numpy_msg
from tensorflow.contrib.slim.python.slim.nets import vgg
from devine_image_processing.msg import VGG16Features
from devine_config import topicname
from devine_common import ros_utils
from ros_image_processor import ImageProcessor, ROSImageProcessingWrapper
VGG16_NTW_PATH = ros_utils.get_fullpath(__file__, '../../vgg_16.ckpt')
FEATURES_TOPIC = topicname('image_features')
IMAGE_TOPIC = topicname('features_extraction_image')
IMAGE_SIZE = 224
CHANNEL_MEAN = np.array([123.68, 116.779, 103.939])
class ROSVgg16(ImageProcessor):
""" VGG-16 wrapper for ROS """
def __init__(self):
self.holder = tf.placeholder(
tf.float32, [None, IMAGE_SIZE, IMAGE_SIZE,
len(CHANNEL_MEAN)],
name='image')