class WebCamBis(object):
"""
The class WebCamBis goal is to take a picture and send it back to CameraManager.
It's a duplicate from WebCam class, it will be replace by a Kinect class in the future.
"""
def __init__(self):
self.queue_manager = QueueManager(
[self.__class__.__name__, "CameraManager"])
logger.info(f"{self.__class__.__name__} ready")
def callback(self, body, **_):
# avoid conflict over opencv between WebCam class
time.sleep(2)
cap = cv2.VideoCapture(0)
_, image = cap.read()
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
image = cv2.resize(image, (640, 480), cv2.INTER_AREA)
pprint(image.shape)
body["picture"] = {
"data": encode(image),
"shape": image.shape,
"from": self.__class__.__name__,
}
self.queue_manager.publish("CameraManager", body)
def run(self):
self.queue_manager.start_consuming(self.__class__.__name__,
self.callback)
class PositionDetection(object):
"""
The class PositionDetection purpose provide the global position of the detected objects.
"""
def __init__(self):
self.queue_manager = QueueManager([self.__class__.__name__, "Interpreter"])
logger.info(f"{self.__class__.__name__} ready")
def get_pos_str(self, obj, max_pos=(1, 1)):
step = (max_pos[0] / 3, max_pos[1] / 3)
center = (
(obj["bbox"][0] + obj["bbox"][2]) / 2,
(obj["bbox"][1] + obj["bbox"][3]) / 2,
)
if center[1] > 2 * step[1]:
return " on the left"
elif center[1] > step[1]:
return " in the center"
return " on the right"
def callback(self, body, **_):
pprint(body)
for i, obj in enumerate(body["objects"]):
body["objects"][i]["lateral_position"] = self.get_pos_str(obj)
body["path_done"].append(self.__class__.__name__)
next_service = body["vision_path"].pop(0)
self.queue_manager.publish(next_service, body)
def run(self):
self.queue_manager.start_consuming(self.__class__.__name__, self.callback)
class WebCam(object):
"""
The class WebCam goal is to take a picture and send it back to CameraManager.
"""
def __init__(self):
self.queue_manager = QueueManager(
[self.__class__.__name__, "CameraManager"])
logger.info(f"{self.__class__.__name__} ready")
def callback(self, body, **_):
cap = cv2.VideoCapture(0)
_, image = cap.read()
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
image = cv2.resize(image, (640, 480), cv2.INTER_AREA)
body["picture"] = {
"data": encode(image),
"shape": image.shape,
"from": self.__class__.__name__,
}
self.queue_manager.publish("CameraManager", body)
def run(self):
self.queue_manager.start_consuming(self.__class__.__name__,
self.callback)
class OCR(object):
"""
The class OCR purpose is to detect all the possible text in the picture.
"""
def __init__(self):
self.queue_manager = QueueManager(
[self.__class__.__name__, "Interpreter"])
self.pipeline = keras_ocr.pipeline.Pipeline()
def callback(self, body, **_):
image = [
decode(body["pictures"][0]["data"], body["pictures"][0]["shape"],
np.uint8)
]
predictions = self.pipeline.recognize(image)[0]
"""fig, axs = plt.subplots(nrows=len(image), figsize=(20, 20))
keras_ocr.tools.drawAnnotations(image=image[0], predictions=predictions, ax=axs)
plt.show()"""
pprint(predictions)
text = bb_to_text(predictions)
body["texts"] = text
body["path_done"].append(self.__class__.__name__)
del body["pictures"]
pprint(body)
next_service = body["vision_path"].pop(0)
self.queue_manager.publish(next_service, body)
logger.info(f"{self.__class__.__name__} ready")
def run(self):
self.queue_manager.start_consuming(self.__class__.__name__,
self.callback)
def __init__(self):
self.queue_manager = QueueManager([
self.__class__.__name__,
"Interpreter",
"ColourDetection",
"PositionDetection",
])
self.models = [{
"name":
"coco",
"model_path":
config.directory.data.joinpath("coco_resnet"),
"category_index":
coco_category_index
}, {
"name":
"epic-kitchens",
"model_path":
config.directory.data.joinpath("epic_kitchens"),
"category_index":
epic_kitchens_category_index
}]
for model in self.models:
tf_model = tf.saved_model.load(str(model["model_path"]))
model["model"] = tf_model.signatures["serving_default"]
logger.info(f"{self.__class__.__name__} ready")
def __init__(self):
self.queue_manager = QueueManager(
[self.__class__.__name__, "NaturalLanguageGenerator"]
)
self.memory = {}
logger.info(f"{self.__class__.__name__} ready")
def __init__(self):
self.queue_manager = QueueManager(
[self.__class__.__name__, "ExternalInterface"])
self.answers = {}
self.description_types = [
"DESCRIPTION_NOTHING", "DESCRIPTION_ANSWER_S",
"DESCRIPTION_ANSWER_P", "DESCRIPTION_UNKNOWN"
]
self.build_generator()
logger.info(f"{self.__class__.__name__} ready")
def __init__(self):
self.queue_manager = QueueManager([
self.__class__.__name__, "WebCam", "WebCamBis", "ObjectDetection",
"OCR"
])
self.camera_names = ["WebCam"]
self.pictures = []
self.waiting_cameras = 0
self.save_body = None
logger.info(f"{self.__class__.__name__} ready")
def __init__(self):
self.queue_manager = QueueManager([self.__class__.__name__, "Manager"])
self.previous_query = None
model_path = str(config.directory.data.joinpath("rasa", "nlu"))
dirs = [f for f in listdir(model_path) if isdir(join(model_path, f))]
dirs.sort(reverse=True)
model = join(model_path, dirs[0])
self.interpreter = Interpreter.load(model)
logger.info(f"{self.__class__.__name__} ready")
class Kinect(object):
"""
The class Kinect goal is to take a picture and send it back to CameraManager.
"""
def __init__(self, mode=COLOR_MODE):
self.kinect = PyKinectRuntime.PyKinectRuntime(mode)
if mode & DEPTH_MODE:
self.kinect_frame_size = (
self.kinect.depth_frame_desc.Height,
self.kinect.depth_frame_desc.Width,
)
if mode & COLOR_MODE:
self.kinect_frame_size = (
self.kinect.color_frame_desc.Height,
self.kinect.color_frame_desc.Width,
-1,
)
self.transform = mode & DEPTH_MODE and cv2.COLOR_GRAY2RGB or cv2.COLOR_RGBA2RGB
self.queue_manager = QueueManager(
[self.__class__.__name__, "CameraManager"])
logger.info(f"{self.__class__.__name__} ready")
def get_colored_frame(self, size=None):
frame = self.kinect.get_last_color_frame()
frame = frame.reshape(self.kinect_frame_size).astype(np.uint8)
frame = cv2.cvtColor(frame, self.transform)
if size:
return cv2.resize(frame, size)
return frame
def callback(self, body, **_):
cap = cv2.VideoCapture(0)
_, image_np = cap.read()
# if mode & DEPTH_MODE:
# frame = _kinect.get_last_depth_frame()
# frameD = _kinect._depth_frame_data
# draw = True
# if mode & COLOR_MODE and _kinect.has_new_color_frame():
frame = self.get_colored_frame()
body["picture"] = {
"data": encode(frame),
"shape": frame.shape,
"from": self.__class__.__name__,
}
self.queue_manager.publish("CameraManager", body)
def run(self):
self.queue_manager.start_consuming(self.__class__.__name__,
self.callback)
class Manager(object):
"""
The class Manager purpose is to create a path inside the dialogue manager depending on the goal of the query.
"""
def __init__(self):
self.queue_manager = QueueManager(
[self.__class__.__name__, "CameraManager", "Interpreter"])
# TODO: Missing intent for lateral position
self.intents_to_path = {
"read_text": [["CameraManager", "OCR", "Interpreter"]],
"detect_colour": [[
"CameraManager", "ObjectDetection", "ColourDetection",
"Interpreter"
]],
"identify": [
["CameraManager", "OCR", "Interpreter"],
["CameraManager", "ObjectDetection", "Interpreter"],
],
"recognise": [["CameraManager", "ObjectDetection", "Interpreter"]],
"locate": [[
"CameraManager", "ObjectDetection", "PositionDetection",
"Interpreter"
]],
"count": [["CameraManager", "ObjectDetection", "Interpreter"]],
"confidence": [["Interpreter"]],
}
logger.info(f"{self.__class__.__name__} ready")
def callback(self, body, **_):
intern_token = token_hex(8)
intent = body["intents"]["intent_ranking"][0]["name"]
body["intern_token"] = intern_token
body["wait_package"] = (len(self.intents_to_path[intent])
if intent in self.intents_to_path else 0)
body["path_done"].append(self.__class__.__name__)
intents_path = (copy.deepcopy(self.intents_to_path[intent])
if intent in self.intents_to_path else [])
for path in intents_path:
pprint(path)
body_ = copy.deepcopy(body)
body_["vision_path"] = path
next_service = body_["vision_path"].pop(0)
self.queue_manager.publish(next_service, body_)
if "run_as_webservice" not in body:
time.sleep(1)
def run(self):
self.queue_manager.start_consuming(self.__class__.__name__,
self.callback)
def __init__(self):
self.queue_manager = QueueManager([
self.__class__.__name__,
"Interpreter",
"ColourDetection",
"PositionDetection",
])
self.category_index = coco_category_index
self.model_path = config.directory.data.joinpath("resnet")
model = tf.saved_model.load(str(self.model_path))
self.model = model.signatures["serving_default"]
logger.info(f"{self.__class__.__name__} ready")
def __init__(self):
self.queue_manager = QueueManager(
[self.__class__.__name__, "Interpreter"])
data_file = config.directory.data.joinpath("colour", "lab.txt")
colour_list = pd.read_csv(data_file,
skiprows=28,
header=None,
names=["l", "a", "b", "name"])
colour_list = colour_list.values.tolist()[1:]
self.colour_list_names = [x[3] for x in colour_list]
self.colour_list_values = np.asarray(
[np.asarray(x[:3], dtype=np.float32) for x in colour_list])
logger.info(f"{self.__class__.__name__} ready")
class NaturalLanguageUnderstanding(object):
"""
The class NaturalLanguageUnderstanding purpose is to sense the objectives of the
query.
"""
def __init__(self):
self.queue_manager = QueueManager([self.__class__.__name__, "Manager"])
self.previous_query = None
model_path = str(config.directory.data.joinpath("rasa", "nlu"))
dirs = [f for f in listdir(model_path) if isdir(join(model_path, f))]
dirs.sort(reverse=True)
model = join(model_path, dirs[0])
logger.info(f"Model loading: " + model)
self.interpreter = Interpreter.load(model)
logger.info(f"{self.__class__.__name__} ready")
def callback(self, body, **_):
body["asking"] = body["query"].split()
intents = self.interpreter.parse(body["query"])
try:
if (intents["intent"]["name"] == "same_intent"
and self.previous_query != None):
intents["intent"]["name"] = self.previous_query["intent"][
"name"]
if (intents["intent"]["name"] != "recognise"
and intents["intent"]["name"] != "identify"
and check_followup(body["query"]) == True):
intents["entities"].extend(self.previous_query["entities"])
except IndexError as error:
logger.error(error)
except Exception as exception:
logger.warn(exception)
self.previous_query = intents
body["intents"] = intents
body["path_done"].append(self.__class__.__name__)
logger.info(body)
self.queue_manager.publish("Manager", body)
def run(self):
self.queue_manager.start_consuming(self.__class__.__name__,
self.callback)
def __init__(self, mode=COLOR_MODE):
self.kinect = PyKinectRuntime.PyKinectRuntime(mode)
if mode & DEPTH_MODE:
self.kinect_frame_size = (
self.kinect.depth_frame_desc.Height,
self.kinect.depth_frame_desc.Width,
)
if mode & COLOR_MODE:
self.kinect_frame_size = (
self.kinect.color_frame_desc.Height,
self.kinect.color_frame_desc.Width,
-1,
)
self.transform = mode & DEPTH_MODE and cv2.COLOR_GRAY2RGB or cv2.COLOR_RGBA2RGB
self.queue_manager = QueueManager(
[self.__class__.__name__, "CameraManager"])
logger.info(f"{self.__class__.__name__} ready")
def __init__(self):
self.queue_manager = QueueManager(
[self.__class__.__name__, "CameraManager"])
# TODO: Missing intent for lateral position
self.intents_to_path = {
"read_text": [["CameraManager", "OCR", "Interpreter"]],
"detect_colour": [[
"CameraManager", "ObjectDetection", "ColourDetection",
"Interpreter"
]],
"identify": [
["CameraManager", "OCR", "Interpreter"],
["CameraManager", "ObjectDetection", "Interpreter"],
],
"recognise": [["CameraManager", "ObjectDetection", "Interpreter"]],
"locate": [["CameraManager", "ObjectDetection", "Interpreter"]],
}
logger.info(f"{self.__class__.__name__} ready")
def submit():
service_if_audio = "AutomaticSpeechRecognition"
service_if_text = "NaturalLanguageUnderstanding"
# Parse user request
user_request = UserRequest(
service_if_audio=service_if_audio, service_if_text=service_if_text
)
# Create queue for Ayesaac and send it
ayesaac_queue_manager = QueueManager([user_request.first_service])
ayesaac_queue_manager.publish(user_request.first_service, user_request.body)
status_url = url_for("submit_status", task_id=user_request.uid)
return (
status_url,
202,
{"Location": status_url},
)
class Interpreter(object):
"""
The Interpreter class purpose is a simple comparison with what the vision part find
and what the user asked for.
(Which object was found and not found)
"""
def __init__(self):
self.queue_manager = QueueManager(
[self.__class__.__name__, "NaturalLanguageGenerator"]
)
self.memory = {}
logger.info(f"{self.__class__.__name__} ready")
def filter_objects(self, body):
return body["objects"]
def filter_texts(self, body):
return body["texts"]
def callback(self, body, **_):
data = None
key = ""
if "objects" in body:
key = "objects"
data = self.filter_objects(body)
body["objects"] = data
elif "texts" in body:
key = "texts"
data = self.filter_texts(body)
body["texts"] = data
if body["wait_package"] == 1:
body["path_done"].append(self.__class__.__name__)
del body["vision_path"]
pprint(body)
# TODO: uncomment if you wanna test the NLG, it could be text, objects,
# objects + colour, objects + lateral position
self.queue_manager.publish("NaturalLanguageGenerator", body)
else:
if body["intern_token"] not in self.memory:
self.memory[body["intern_token"]] = {key: data}
elif (
body["intern_token"] in self.memory
and body["wait_package"] < len(self.memory[body["intern_token"]]) - 1
):
self.memory[body["intern_token"]][key] = data
else:
for key in self.memory[body["intern_token"]]:
body[key] = self.memory[body["intern_token"]][key]
del self.memory[body["intern_token"]][key]
pprint(body)
# TODO: uncomment if you wanna test the NLG
self.queue_manager.publish("NaturalLanguageGenerator", body)
def run(self):
self.queue_manager.start_consuming(self.__class__.__name__, self.callback)
class PositionDetection(object):
"""
The class PositionDetection purpose provide the global position of the detected objects.
Methods within use bounding boxes to determine positions relative to hands/anchors.
A little reminder about bounding boxes returned from TensorFlow...
The bounding box coordinates are (top, left, bottom and right) and are normalised
to the image width/height (between 0 and 1).
Example coordinates:
* top = 0.2
* bottom = 0.4
* left = 0.3
* right = 0.6
The bounding box will Look like this:
left (0.3) right (0.6)
| |
top (0.2) -----|----------|----
| |
| |
| |
bottom (0.4) -----|----------|----
| |
"""
def __init__(self):
self.queue_manager = QueueManager(
[self.__class__.__name__, "Interpreter"])
logger.info(f"{self.__class__.__name__} ready")
def get_lateral_position(self, obj, max_pos=(1, 1)):
step = (max_pos[0] / 3, max_pos[1] / 3)
center = (
(obj["bbox"][0] + obj["bbox"][2]) / 2,
(obj["bbox"][1] + obj["bbox"][3]) / 2,
)
if center[1] > 2 * step[1]:
return " on the right"
elif center[1] > step[1]:
return " in the center"
return " on the left"
def get_hand_position(self, obj, objects):
'''Method identifies position relative to hands or people using bounding boxes'''
# Set default to ""
position_str = ""
# Set the hand classes
hand_classes = ["hand", "person"]
# If the object is a hand itself, it does not need to be positioned
if obj["name"] in hand_classes:
return position_str
# Determine if there are hands in the image: use "hand" or "person" class name
hand_objects = copy.deepcopy([
o for o in objects
if o["from"] == obj["from"] and o["name"] in hand_classes
])
if len(hand_objects) == 0:
return position_str
# Not going to be able to use hands to position if there are more than two in this image
if len(hand_objects) > 2:
return position_str
# Now we have hands in the image, find the positioning
# Get the bounding box normalised coords
top_obj, left_obj, bottom_obj, right_obj = tuple(obj["bbox"])
# get the central coords
x_obj = (left_obj + right_obj) / 2
y_obj = (top_obj + bottom_obj) / 2
# If there are two objects, see if the object is positioned between the hands
if len(hand_objects) == 2 and hand_objects[0][
"name"] == "hand" and hand_objects[1]["name"] == "hand":
if self.__obj_is_between_hands__(x_obj, y_obj, hand_objects):
position_str = " is between hands"
elif len(hand_objects) == 1:
# Get the bounding box normalised coords
top_hand, left_hand, bottom_hand, right_hand = tuple(
hand_objects[0]["bbox"])
# Get the midpoints
x_hand = (left_hand + right_hand) / 2
y_hand = (top_hand + bottom_hand) / 2
if self.__obj_is_right_of_hand__(x_obj, x_hand):
position_str = " is to the right of a " + hand_objects[0][
"name"]
if self.__obj_is_left_of_hand__(x_obj, x_hand):
position_str = " is to the left of a " + hand_objects[0]["name"]
return position_str
def __obj_is_between_hands__(self, x_obj, y_obj, hand_objects):
'''Method to determine if the object is between the hands'''
is_between = False
# Get the bounding boxes of the hands
top_hand1, left_hand1, bottom_hand1, right_hand1 = tuple(
hand_objects[0]["bbox"])
top_hand2, left_hand2, bottom_hand2, right_hand2 = tuple(
hand_objects[1]["bbox"])
# get the central coords
x_hand1 = (left_hand1 + right_hand1) / 2
x_hand2 = (left_hand2 + right_hand2) / 2
y_hand1 = (top_hand1 + bottom_hand1) / 2
y_hand2 = (top_hand2 + bottom_hand2) / 2
# determine if object is between hands
if (x_hand1 < x_obj < x_hand2) or (x_hand1 > x_obj > x_hand2):
is_between = True
elif (y_hand1 < y_obj < y_hand2) or (y_hand1 > y_obj > y_hand2):
is_between = True
return is_between
def __obj_is_right_of_hand__(self, x_obj, x_hand):
'''Method to determine if the object is to the right of the hand'''
if (x_obj > x_hand):
return True
else:
return False
def __obj_is_left_of_hand__(self, x_obj, x_hand):
'''Method to determine if the object is to the left of the hand'''
if (x_hand > x_obj):
return True
else:
return False
def get_anchored_position(self, obj, objects):
'''Method identifies position relative to anchors, e.g. "next to fridge"'''
# Set default to [] because anchored position may not be possible
position_str_list = []
# List the anchors in the image
anchors = copy.deepcopy([
o for o in objects
if o["from"] == obj["from"] and o["name"] in ANCHORS.keys()
])
# Remove self from anchors
if obj["name"] in [anchor["name"] for anchor in anchors]:
anchors.remove(obj)
# Determine if there are anchors in the image, if not return None
if len(anchors) == 0:
return position_str_list
# Append the relationship info to anchors
for anchor in anchors:
anchor["relationships"] = ANCHORS[anchor["name"]]["relationships"]
# Now we have anchors in the image, find the positioning relationship between the object and the anchor
for anchor in anchors:
# Get the bounding box normalised coords
top_obj, left_obj, bottom_obj, right_obj = tuple(obj["bbox"])
top_anchor, left_anchor, bottom_anchor, right_anchor = tuple(
anchor["bbox"])
if "in" in anchor["relationships"]:
if self.__obj_is_in_anchor__(left_obj, left_anchor, right_obj,
right_anchor, bottom_obj,
bottom_anchor, top_obj,
top_anchor):
position_str_list.append(" it's in the " + anchor["name"])
continue
if "on" in anchor["relationships"]:
if self.__obj_is_on_anchor__(left_obj, left_anchor, right_obj,
right_anchor, bottom_obj,
bottom_anchor):
position_str_list.append(" it's on the " + anchor["name"])
continue
if "next to" in anchor["relationships"]:
if self.__obj_is_left_of_anchor__(left_obj, left_anchor,
right_obj, right_anchor):
position_str_list.append(" it's left of the " +
anchor["name"])
continue
elif self.__obj_is_right_of_anchor__(left_obj, left_anchor,
right_obj, right_anchor):
position_str_list.append(" it's right of the " +
anchor["name"])
continue
if "below" in anchor["relationships"]:
if self.__obj_is_below_anchor__(left_obj, left_anchor,
right_obj, right_anchor,
top_obj, top_anchor):
position_str_list.append(" it's below the " +
anchor["name"])
continue
# Default position if the above conditions cannot be met
position_str_list.append(" it's near the " + anchor["name"])
return position_str_list
def __obj_is_in_anchor__(self, left_obj, left_anchor, right_obj,
right_anchor, bottom_obj, bottom_anchor, top_obj,
top_anchor):
'''Method to determine whether obj is in anchor.'''
is_on = False
if (left_obj > left_anchor and right_obj < right_anchor
and bottom_obj < bottom_anchor and top_obj > top_anchor):
is_on = True
return is_on
def __obj_is_on_anchor__(self, left_obj, left_anchor, right_obj,
right_anchor, bottom_obj, bottom_anchor):
'''Method to determine whether obj is on anchor.'''
is_on = False
if (left_obj > left_anchor and right_obj < right_anchor
and bottom_obj < bottom_anchor):
is_on = True
return is_on
def __obj_is_left_of_anchor__(self, left_obj, left_anchor, right_obj,
right_anchor):
'''Method to determine whether obj is left of anchor.'''
is_left_of = False
if (left_obj < left_anchor):
is_left_of = True
return is_left_of
def __obj_is_right_of_anchor__(self, left_obj, left_anchor, right_obj,
right_anchor):
'''Method to determine whether obj is right of anchor.'''
is_right_of = False
if (right_obj > right_anchor):
is_right_of = True
return is_right_of
def __obj_is_below_anchor__(self, left_obj, left_anchor, right_obj,
right_anchor, top_obj, top_anchor):
'''Method to determine whether obj is below anchor.'''
is_below = False
if (left_obj > left_anchor and right_obj < right_anchor
and top_obj > top_anchor):
is_below = True
return is_below
def callback(self, body, **_):
pprint(body)
for i, obj in enumerate(body["objects"]):
body["objects"][i]["lateral_position"] = self.get_lateral_position(
obj)
body["objects"][i][
"anchored_position"] = self.get_anchored_position(
obj, body["objects"])
body["objects"][i]["hand_position"] = self.get_hand_position(
obj, body["objects"])
print("lateral_position: " +
str(body["objects"][i]["lateral_position"]))
print("anchored_position: " +
str(body["objects"][i]["anchored_position"]))
print("hand_position: " + str(body["objects"][i]["hand_position"]))
body["path_done"].append(self.__class__.__name__)
next_service = body["vision_path"].pop(0)
self.queue_manager.publish(next_service, body)
def run(self):
self.queue_manager.start_consuming(self.__class__.__name__,
self.callback)
def __init__(self):
self.queue_manager = QueueManager(
[self.__class__.__name__, "Interpreter"])
self.pipeline = keras_ocr.pipeline.Pipeline()
class NaturalLanguageGenerator(object):
"""
The class NaturalLanguageGenerator purpose is to translate the results obtained to a nicely formatted sentence.
"""
def __init__(self):
self.queue_manager = QueueManager(
[self.__class__.__name__, "ExternalInterface"]
)
self.answers = {}
self.description_types = [
"DESCRIPTION_NOTHING",
"DESCRIPTION_ANSWER_S",
"DESCRIPTION_ANSWER_P",
]
self.build_generator()
logger.info(f"{self.__class__.__name__} ready")
def build_generator(self):
folder_path = config.directory.data.joinpath("sentence_templates")
for _, _, files in os.walk(folder_path):
for name in files:
with open(str(folder_path / name)) as f:
self.answers[name] = [line.strip() for line in f]
def get_det(self, word):
return str(word[1]) + " " if word[1] > 1 else "a "
def generate_text(self, words, context, obj_cnt):
answer = choice(self.answers[context])
if type(words) == str:
return answer.replace("*", words, 1)
elif len(words) > 1:
tmp = (
", ".join([self.get_det(w) + w[0] for w in words[:-1]])
+ " and "
+ self.get_det(words[-1])
+ words[-1][0]
)
return answer.replace("*", tmp, 1)
elif len(words):
return answer.replace(
"*",
((str(words[0][1]) + " ") if words[0][1] > 1 else "") + words[0][0],
1,
)
return answer
def identify(self, body):
pprint("identify")
objects = []
for o in body["objects"]:
if o["name"] != "person":
objects.append(
o["name"]
+ (o["lateral_position"] if o.get("lateral_position") else "")
)
objects = list(set([(o, objects.count(o)) for o in objects]))
obj_cnt = sum(n for _, n in objects)
context = self.description_types[obj_cnt if obj_cnt < 2 else 2]
return objects, context, obj_cnt
def recognise(self, body):
pprint("recognise")
objects = []
for o in body["objects"]:
for p in body["intents"]["entities"]:
if o["name"] == p["value"]:
objects.append(
o["name"]
+ (o["lateral_position"] if o.get("lateral_position") else "")
)
objects = list(set([(o, objects.count(o)) for o in objects]))
obj_cnt = sum(n for _, n in objects)
context = (
("POSITIVE" if obj_cnt > 0 else "NEGATIVE")
+ "_ANSWER_"
+ ("P" if obj_cnt > 1 else "S")
)
if not obj_cnt:
objects = [(p["value"], 1) for p in body["intents"]["entities"]]
obj_cnt = sum(n for _, n in objects)
return objects, context, obj_cnt
def REAL_read_text(self, body):
pprint("read_text")
objects = " ".join(" ".join(t) for t in body["texts"])
print(objects)
obj_cnt = 1 if len(objects) > 0 else 0
context = "READ_TEXT_" + ("POSITIVE" if obj_cnt > 0 else "NEGATIVE")
return objects, context, obj_cnt
'''
TEMPORARILY HIJACKING THE read_text FUNCTION ABOVE
THIS SHOULD BE CHANGED BACK TO extract_label ONCE
SUCH AN INTENT HAS BEEN TRAINED WITH THE NLU!!!
'''
def read_text(self, body):
# def extract_label(self, body):
objects = " ".join(" ".join(t) for t in body["texts"])
print(objects)
objects = extract_label(objects)
obj_cnt = 1 if len(objects) > 0 else 0
context = "READ_TEXT_" + ("POSITIVE" if obj_cnt > 0 else "NEGATIVE")
return objects, context, obj_cnt
def detect_colour(self, body):
pprint("detect_colour")
obj_cnt = 0
objects = None
context = None
for o in body["objects"]:
for p in body["intents"]["entities"]:
if o["name"] == p["value"]:
objects = (p["value"], o["colour"])
break
else:
objects = (p["value"], None)
if objects:
obj_cnt = 1 if objects[1] else 0
objects = objects[obj_cnt]
context = "COLOR_DETECTION" if obj_cnt else "COLOR_DETECTION_N"
return objects, context, obj_cnt
def locate(self, body):
pprint("locate")
objects = []
for o in body["objects"]:
for p in body["intents"]["entities"]:
if o["name"] == p["value"]:
if (
not o.get("lateral_position")
and o.get("bbox")
and len(o["bbox"]) >= 4
):
bbox = o["bbox"]
yStart = bbox[0]
xStart = bbox[1]
yEnd = bbox[2]
xEnd = bbox[3]
xCenter = (xEnd + xStart) / 2
yCenter = (yEnd + yStart) / 2
pprint("xCenter")
pprint(xCenter)
if xCenter < 0.382:
o["lateral_position"] = " on the left"
elif xCenter >= 0.382 and xCenter <= 0.618:
o["lateral_position"] = " in front"
elif xCenter > 0.618:
o["lateral_position"] = " on the right"
objects.append(
o["name"]
+ (o["lateral_position"] if o.get("lateral_position") else "")
)
objects = list(set([(o, objects.count(o)) for o in objects]))
obj_cnt = sum(n for _, n in objects)
context = self.description_types[obj_cnt if obj_cnt < 2 else 2]
return objects, context, obj_cnt
def default(self, body):
pprint("default")
# Creates list of object detected in the scene
objects = [
o["name"] + (o["lateral_position"] if o.get("lateral_position") else "")
for o in body["objects"]
]
objects = list(set([(o, objects.count(o)) for o in objects]))
obj_cnt = sum(n for _, n in objects)
context = self.description_types[obj_cnt if obj_cnt < 2 else 2]
return objects, context, obj_cnt
def callback(self, body, **_):
pprint(body)
method = getattr(self, body["intents"]["intent"]["name"], self.default)
pprint("----- METHOD CALLED -----")
objects, context, obj_cnt = method(body)
print(objects)
print(context)
if objects != None and context != None:
response = self.generate_text(objects, context, obj_cnt)
else:
response = "I didn't understand the question, could you repeat please."
body["response"] = response
pprint(body["response"])
body["path_done"].append(self.__class__.__name__)
self.queue_manager.publish("ExternalInterface", body)
def run(self):
self.queue_manager.start_consuming(self.__class__.__name__, self.callback)
class CameraManager(object):
"""
The class CameraManager goal is to organise the collect of pictures from different
camera sources.
"""
def __init__(self):
self.queue_manager = QueueManager([
self.__class__.__name__, "WebCam", "WebCamBis", "ObjectDetection",
"OCR"
])
self.camera_names = ["WebCam"]
self.pictures = []
self.waiting_cameras = 0
self.save_body = None
logger.info(f"{self.__class__.__name__} ready")
def from_cameras(self, body):
logger.info("Receiving picture from: ", body["picture"]["from"])
logger.info(body)
self.pictures.append(body["picture"])
self.waiting_cameras -= 1
def request_pictures_from_all_concern_cameras(self):
logger.info("Request pictures !")
self.waiting_cameras = len(self.camera_names)
for camera_name in self.camera_names:
self.queue_manager.publish(camera_name, {"nb_picture": 1})
def callback(self, body, **_):
logger.info("Callback triggered")
if "run_as_webservice" in body:
# skip running cameras, we have an image!
logger.info(
"Camera management: don't use cameras as running in webservice mode."
)
assert "pictures" in body
next_service = body["vision_path"].pop(0)
body["path_done"].append(self.__class__.__name__)
self.queue_manager.publish(next_service, body)
elif self.waiting_cameras:
self.from_cameras(body)
if not self.waiting_cameras:
self.save_body["pictures"] = copy.deepcopy(self.pictures)
self.save_body["path_done"].append(self.__class__.__name__)
logger.info("Send pictures !")
# for path in self.save_body['vision_path']:
# body_ = self.save_body
# body_['vision_path'] = path
next_service = self.save_body["vision_path"].pop(0)
self.queue_manager.publish(next_service, self.save_body)
self.pictures = []
self.save_body = None
else:
self.request_pictures_from_all_concern_cameras()
self.save_body = body
def run(self):
self.queue_manager.start_consuming(self.__class__.__name__,
self.callback)
class ColourDetection(object):
"""
The class ColourDetection purpose is to detect every main colour from objects in
the given pictures.
"""
def __init__(self):
self.queue_manager = QueueManager(
[self.__class__.__name__, "Interpreter"])
data_file = config.directory.data.joinpath("colour", "lab.txt")
colour_list = pd.read_csv(data_file,
skiprows=28,
header=None,
names=["l", "a", "b", "name"])
colour_list = colour_list.values.tolist()[1:]
self.colour_list_names = [x[3] for x in colour_list]
self.colour_list_values = np.asarray(
[np.asarray(x[:3], dtype=np.float32) for x in colour_list])
logger.info(f"{self.__class__.__name__} ready")
@staticmethod
def convert_rgb_to_lab(image: np.ndarray) -> np.ndarray:
return rgb2lab(image)
@staticmethod
def flatten_image(image: np.ndarray) -> np.ndarray:
dimensions = np.shape(image)
return np.reshape(image,
(dimensions[0] * dimensions[1], dimensions[2]))
@staticmethod
def remove_non_unique_pixels(image: np.ndarray) -> np.ndarray:
return np.unique(image, axis=0)
@staticmethod
def create_labelled_image(lab_image) -> np.ndarray:
return slic(
lab_image,
n_segments=200,
compactness=10,
sigma=0.1,
convert2lab=False,
enforce_connectivity=True,
)
@staticmethod
def create_regions(lab_image, labelled_image):
region_segments = regionprops(labelled_image)
image_dimensions = np.shape(labelled_image)
for region in region_segments:
region.is_boundary = ColourDetection.is_region_on_boundary(
region, image_dimensions)
region.average_colour = ColourDetection.get_region_average_colour(
region.label, labelled_image, lab_image)
return region_segments
@staticmethod
def is_region_on_boundary(region, image_dimensions):
if (region.bbox[0] == 0 or region.bbox[1] == 0
or region.bbox[2] == image_dimensions[0]
or region.bbox == image_dimensions[1]):
return True
return False
@staticmethod
def get_pixels_from_label_id(label_id, labelled_image, image):
label_mask = np.invert(np.isin(labelled_image, label_id))
label_mask = np.dstack((label_mask, label_mask, label_mask))
image_mask = np.ma.array(image, mask=label_mask)
return image_mask
@staticmethod
def get_region_average_colour(label_id, labelled_image, image):
masked_image = ColourDetection.get_pixels_from_label_id(
label_id, labelled_image, image)
flattened_masked_image = ColourDetection.flatten_image(masked_image)
average_colour = np.zeros(3, dtype=np.float32)
for channel in range(np.shape(image)[2]):
average_colour[channel] = np.mean(flattened_masked_image[:,
channel])
return average_colour
@staticmethod
def get_all_region_colours(region_list):
return [region.average_colour for region in region_list]
def detect_colours(self, crop_image):
lab_image = self.convert_rgb_to_lab(crop_image)
labelled_image = self.create_labelled_image(lab_image)
region_list = self.create_regions(lab_image, labelled_image)
colours = self.get_all_region_colours(region_list)
colours_found = {}
for colour in colours:
d = ((self.colour_list_values - colour)**2).sum(axis=1)
if not self.colour_list_names[d.argmin()] in colours_found:
colours_found[self.colour_list_names[d.argmin()]] = 0
colours_found[self.colour_list_names[d.argmin()]] += 1
sorted_colours = max(colours_found.items(), key=operator.itemgetter(1))
pprint(colours_found)
return sorted_colours[0]
def callback(self, body, **_):
body["path_done"].append(self.__class__.__name__)
for picture in body["pictures"]:
image = decode(picture["data"], picture["shape"], np.uint8)
for i, obj in enumerate(body["objects"]):
crop_img = image[int(picture["shape"][0] *
obj["bbox"][0]):int(picture["shape"][0] *
obj["bbox"][2]),
int(picture["shape"][1] *
obj["bbox"][1]):int(picture["shape"][1] *
obj["bbox"][3]), ]
colour_name = self.detect_colours(crop_img)
body["objects"][i]["colour"] = colour_name
del body["pictures"]
pprint(body)
next_service = body["vision_path"].pop(0)
self.queue_manager.publish(next_service, body)
def run(self):
self.queue_manager.start_consuming(self.__class__.__name__,
self.callback)
class ObjectDetection(object):
"""
The class ObjectDetection purpose is to detect every object in the given pictures.
"""
def __init__(self):
self.queue_manager = QueueManager([
self.__class__.__name__,
"Interpreter",
"ColourDetection",
"PositionDetection",
])
self.category_index = coco_category_index
self.model_path = config.directory.data.joinpath("resnet")
model = tf.saved_model.load(str(self.model_path))
self.model = model.signatures["serving_default"]
logger.info(f"{self.__class__.__name__} ready")
def run_inference_for_single_image(self, image):
input_tensor = tf.convert_to_tensor(image)
input_tensor = input_tensor[tf.newaxis, ...]
output_dict = self.model(input_tensor)
num_detections = int(output_dict.pop("num_detections"))
output_dict = {
key: value[0, :num_detections].numpy()
for key, value in output_dict.items()
}
output_dict["num_detections"] = num_detections
output_dict["detection_classes"] = output_dict[
"detection_classes"].astype(np.int32)
return output_dict
def callback(self, body, **_):
objects = []
for picture in body["pictures"]:
image = decode(picture["data"], picture["shape"], np.uint8)
output = self.run_inference_for_single_image(image)
for i in range(output["num_detections"]):
if float(output["detection_scores"][i]) >= 0.5:
objects.append({
"name":
self.category_index[output["detection_classes"][i]]
["name"],
"confidence":
float(output["detection_scores"][i]),
"bbox":
output["detection_boxes"][i].tolist(),
"from":
picture["from"],
})
pprint(objects)
body["objects"] = objects
body["path_done"].append(self.__class__.__name__)
if "ColourDetection" not in body["vision_path"]:
del body["pictures"]
next_service = body["vision_path"].pop(0)
self.queue_manager.publish(next_service, body)
def run(self):
self.queue_manager.start_consuming(self.__class__.__name__,
self.callback)
class NaturalLanguageGenerator(object):
"""
The class NaturalLanguageGenerator purpose is to translate the results obtained to a nicely formatted sentence.
"""
def __init__(self):
self.queue_manager = QueueManager(
[self.__class__.__name__, "ExternalInterface"])
self.answers = {}
self.description_types = [
"DESCRIPTION_NOTHING", "DESCRIPTION_ANSWER_S",
"DESCRIPTION_ANSWER_P", "DESCRIPTION_UNKNOWN"
]
self.build_generator()
logger.info(f"{self.__class__.__name__} ready")
def build_generator(self):
folder_path = config.directory.data.joinpath("sentence_templates")
for _, _, files in os.walk(folder_path):
for name in files:
with open(str(folder_path / name)) as f:
self.answers[name] = [line.strip() for line in f]
def get_det(self, word, context):
if context == "CONFIDENCE_SOMETHING":
return ""
if (word[1] > 1):
return str(word[1]) + " "
elif word[1] == 1:
return "a "
else:
return "no "
def compare_name_value(self, name, value):
if name == value:
return True
elif name == value[:-1] and value[-1] == 's':
return True
return False
def generate_text(self, words, context, obj_cnt):
answer = choice(self.answers[context])
if type(words) == str:
return answer.replace("*", words, 1)
elif len(words) > 1:
tmp = (", ".join(
[self.get_det(w, context) + w[0] for w in words[:-1]]) +
" and " + self.get_det(words[-1], context) + words[-1][0])
return answer.replace("*", tmp, 1)
elif len(words):
return answer.replace(
"*",
self.get_det(words[0], context) + words[0][0],
1,
)
return answer
def identify(self, body):
pprint("identify")
objects = []
for o in body["objects"]:
if o["name"] != "person":
objects.append(o["name"] + (o["lateral_position"] if o.
get("lateral_position") else ""))
objects = list(set([(o, objects.count(o)) for o in objects]))
obj_cnt = sum(n for _, n in objects)
context = self.description_types[obj_cnt if obj_cnt < 2 else 2]
return objects, context, obj_cnt
def recognise(self, body):
pprint("recognise")
objects = []
for o in body["objects"]:
for p in body["intents"]["entities"]:
if self.compare_name_value(o["name"], p["value"]):
objects.append(p["value"] +
(o["lateral_position"] if o.
get("lateral_position") else ""))
objects = list(set([(o, objects.count(o)) for o in objects]))
obj_cnt = sum(n for _, n in objects)
context = (("POSITIVE" if obj_cnt > 0 else "NEGATIVE") + "_ANSWER_" +
("P" if obj_cnt > 1 else "S"))
if not obj_cnt:
objects = [(p["value"], 1) for p in body["intents"]["entities"]]
obj_cnt = sum(n for _, n in objects)
return objects, context, obj_cnt
def read_text(self, body):
pprint("read_text")
objects = " ".join(" ".join(t) for t in body["texts"])
print(objects)
obj_cnt = 1 if len(objects) > 0 else 0
context = "READ_TEXT_" + ("POSITIVE" if obj_cnt > 0 else "NEGATIVE")
return objects, context, obj_cnt
def detect_colour(self, body):
pprint("detect_colour")
obj_cnt = 0
objects = None
context = None
for o in body["objects"]:
for p in body["intents"]["entities"]:
if self.compare_name_value(o["name"], p["value"]):
objects = (p["value"], o["colour"])
break
else:
objects = (p["value"], None)
if objects:
obj_cnt = 1 if objects[1] else 0
objects = objects[obj_cnt]
context = "COLOR_DETECTION" if obj_cnt else "COLOR_DETECTION_N"
return objects, context, obj_cnt
def count(self, body):
pprint("count")
obj_cnt = 0
objects = []
context = ""
for o in body["objects"]:
for p in body["intents"]["entities"]:
if self.compare_name_value(o["name"], p["value"]):
objects.append(p["value"])
objects = list(set([(o, objects.count(o)) for o in objects]))
obj_cnt = sum(n for _, n in objects)
for p in body["intents"]["entities"]:
elements = [x for x in objects if x[0] == p["value"]]
if not len(elements):
objects.append((p["value"], 0))
context = "DESCRIPTION_COUNT"
return objects, context, obj_cnt
def confidence(self, body):
pprint("confidence")
obj_cnt = 0
objects = []
can_answer = len(body["responses"]) > 0
previous_question = None
if can_answer:
previous_question = body["responses"][-1]
if can_answer and (not previous_question["intents"]["intent"]["name"]
in ["identify", "recognise", "locate", "count"]):
can_answer = False
if can_answer:
entities = previous_question["intents"]["entities"]
if len(entities) == 0:
entities = [{
"value": o["name"]
} for o in previous_question["objects"]]
for e in entities:
percentage = 0
nb_object = 0
for o in previous_question["objects"]:
if self.compare_name_value(o["name"], e["value"]):
percentage += o["confidence"]
nb_object += 1
if nb_object > 0:
percentage /= nb_object
objects.append(
(str(round(percentage * 100)) + "% that there is " +
str(nb_object) + " " + e["value"], nb_object))
else:
objects.append(
("more than 50% that there is no " + e["value"], 0))
obj_cnt = sum(n for _, n in objects)
context = "CONFIDENCE_SOMETHING" if can_answer else "CONFIDENCE_NOTHING"
return objects, context, obj_cnt
def locate(self, body):
pprint("locate")
objects = []
for o in body["objects"]:
for p in body["intents"]["entities"]:
if self.compare_name_value(o["name"], p["value"]):
pos_str = ""
if (len(o.get("anchored_position")) > 0):
pos_list = o.get("anchored_position")
for pos in pos_list:
if pos_list.index(pos) != (len(pos_list) - 1):
pos_str += ", " + pos
else:
pos_str += " and" + pos
elif (o.get("hand_position") != ""):
pos_str = o.get("hand_position")
else:
pos_str = o.get("lateral_position")
objects.append(p["value"] + pos_str)
objects = list(set([(o, objects.count(o)) for o in objects]))
obj_cnt = sum(n for _, n in objects)
context_index = 0
if len(objects) == 1:
context_index = 1
elif len(objects) > 1:
context_index = 2
elif len(body["objects"]) > 0:
context_index = 3
context = self.description_types[context_index]
return objects, context, obj_cnt
def default(self, body):
pprint("default")
# Creates list of object detected in the scene
objects = [
o["name"] +
(o["lateral_position"] if o.get("lateral_position") else "")
for o in body["objects"]
]
objects = list(set([(o, objects.count(o)) for o in objects]))
obj_cnt = sum(n for _, n in objects)
context = self.description_types[obj_cnt if obj_cnt < 2 else 2]
return objects, context, obj_cnt
def callback(self, body, **_):
pprint(body)
method = getattr(self, body["intents"]["intent"]["name"], self.default)
pprint("----- METHOD CALLED -----")
objects, context, obj_cnt = method(body)
print(objects)
print(context)
if objects != None and context != None:
response = self.generate_text(objects, context, obj_cnt)
else:
response = "I didn't understand the question, could you repeat please."
body["response"] = response
pprint(body["response"])
body["path_done"].append(self.__class__.__name__)
self.queue_manager.publish("ExternalInterface", body)
def run(self):
self.queue_manager.start_consuming(self.__class__.__name__,
self.callback)
class OCR(object):
"""
The class OCR purpose is to detect all the possible text in the picture.
"""
default_ocr_model, supported_ocr_models = None, []
import json
with open("./group-6-config.json") as f:
data = json.load(f)
default_ocr_model = data["default-ocr-model"]
supported_ocr_models = data["supported-ocr-models"]
print("Using OCR model: " + default_ocr_model)
if (default_ocr_model == "keras-ocr"):
def __init__(self):
self.queue_manager = QueueManager(
[self.__class__.__name__, "Interpreter"])
self.pipeline = keras_ocr.pipeline.Pipeline()
def callback(self, body, **_):
image = [
decode(body["pictures"][0]["data"],
body["pictures"][0]["shape"], np.uint8)
]
predictions = self.pipeline.recognize(image)[0]
# Recomment this
# fig, axs = plt.subplots(nrows=len(image), figsize=(20, 20))
# keras_ocr.tools.drawAnnotations(image=image[0], predictions=predictions, ax=axs)
# plt.show()
pprint(predictions)
text = bb_to_text(predictions)
body["texts"] = text
body["path_done"].append(self.__class__.__name__)
del body["pictures"]
pprint(body)
next_service = body["vision_path"].pop(0)
self.queue_manager.publish(next_service, body)
logger.info(f"{self.__class__.__name__} ready")
def run(self):
self.queue_manager.start_consuming(self.__class__.__name__,
self.callback)
elif (default_ocr_model == "tesseract"):
def __init__(self):
self.queue_manager = QueueManager(
[self.__class__.__name__, "Interpreter"])
def callback(self, body, **_):
pytesseract.pytesseract.tesseract_cmd = r'../usr/bin/tesseract'
image = [
decode(body["pictures"][0]["data"],
body["pictures"][0]["shape"], np.uint8)
]
text = pytesseract.image_to_string(image[0])
body["texts"] = text
body["path_done"].append(self.__class__.__name__)
del body["pictures"]
pprint(body)
next_service = body["vision_path"].pop(0)
self.queue_manager.publish(next_service, body)
logger.info(f"{self.__class__.__name__} ready")
def run(self):
self.queue_manager.start_consuming(self.__class__.__name__,
self.callback)
def __init__(self):
self.queue_manager = QueueManager(
[self.__class__.__name__, "Interpreter"])
def __init__(self):
self.queue_manager = QueueManager(
[self.__class__.__name__, "CameraManager"])
logger.info(f"{self.__class__.__name__} ready")
class ObjectDetection(object):
"""
The class ObjectDetection purpose is to detect every object in the given pictures.
"""
# define constants
# confidence threshold for retaining object detection
CONFIDENCE_THRESHOLD = 0.5
# IoU threshold for determining whether detections are overlapping
IOU_THRESHOLD = 0.5
# list of model preferences for selecting detection
MODEL_PREFS = ["coco", "epic-kitchens"]
def __init__(self):
self.queue_manager = QueueManager([
self.__class__.__name__,
"Interpreter",
"ColourDetection",
"PositionDetection",
])
self.models = [{
"name":
"coco",
"model_path":
config.directory.data.joinpath("coco_resnet"),
"category_index":
coco_category_index
}, {
"name":
"epic-kitchens",
"model_path":
config.directory.data.joinpath("epic_kitchens"),
"category_index":
epic_kitchens_category_index
}]
for model in self.models:
tf_model = tf.saved_model.load(str(model["model_path"]))
model["model"] = tf_model.signatures["serving_default"]
logger.info(f"{self.__class__.__name__} ready")
def run_inference_for_single_image(self, image, model):
input_tensor = tf.convert_to_tensor(image)
input_tensor = input_tensor[tf.newaxis, ...]
output_dict = model(input_tensor)
num_detections = int(output_dict.pop("num_detections"))
output_dict = {
key: value[0, :num_detections].numpy()
for key, value in output_dict.items()
}
output_dict["num_detections"] = num_detections
output_dict["detection_classes"] = output_dict[
"detection_classes"].astype(np.int32)
return output_dict
def filter_objects(self, objects, img_height, img_width):
'''Method to filter duplicate detections from the output'''
retained_objects = []
for obj in objects:
retain = True
# duplicates are of the same class and have very high IoU
for other_obj in objects:
# ignore self
if obj == other_obj:
continue
else:
# calculate the IoU
iou = calculate_iou(obj["bbox"], other_obj["bbox"],
img_height, img_width)
# check if IoU is greater than threshold
if iou >= ObjectDetection.IOU_THRESHOLD:
# we have a duplicate, don't retain the object if the model preference is lower
if ObjectDetection.MODEL_PREFS.index(
obj["model"]
) > ObjectDetection.MODEL_PREFS.index(
other_obj["model"]):
retain = False
break
# append the object if it's okay
if retain:
retained_objects.append(obj)
return retained_objects
def callback(self, body, **_):
all_objects = []
for picture in body["pictures"]:
objects = []
image = decode(picture["data"], picture["shape"], np.uint8)
img_height = picture["shape"][0]
img_width = picture["shape"][1]
# iterate through the models, performing object detection
for model in self.models:
output = self.run_inference_for_single_image(
image, model["model"])
for i in range(output["num_detections"]):
if float(output["detection_scores"]
[i]) >= ObjectDetection.CONFIDENCE_THRESHOLD:
bbox = output["detection_boxes"][i].tolist()
objects.append({
"name":
model["category_index"][output["detection_classes"]
[i]]["name"],
"confidence":
float(output["detection_scores"][i]),
"bbox":
bbox,
"from":
picture["from"],
"model":
model["name"],
"img_height":
img_height,
"img_width":
img_width
})
bboxes = [obj["bbox"] for obj in objects]
class_names = [obj["name"] for obj in objects]
scores = [obj["confidence"] for obj in objects]
models = [obj["model"] for obj in objects]
# draw the bounding boxes
# (outputs image to docker/volumes/aye-saac_output_data/_data/bbox.[uid].png)
draw_bounding_boxes(image,
bboxes,
class_names,
scores,
models,
filename="bbox.{u}.png".format(u=body["uid"]))
# need to filter the results to remove massively overlapping object detections
# (this can arise when different models identify the same object for example)
objects = self.filter_objects(objects, img_height, img_width)
bboxes = [obj["bbox"] for obj in objects]
class_names = [obj["name"] for obj in objects]
scores = [obj["confidence"] for obj in objects]
models = [obj["model"] for obj in objects]
# draw the bounding boxes
# (outputs image to docker/volumes/aye-saac_output_data/_data/bbox_filtered.[uid].png)
draw_bounding_boxes(
image,
bboxes,
class_names,
scores,
models,
filename="bbox_filtered.{u}.png".format(u=body["uid"]))
# append the objects to all_objects
all_objects.extend(objects)
# pprint(objects)
body["objects"] = all_objects
body["path_done"].append(self.__class__.__name__)
if "ColourDetection" not in body["vision_path"]:
del body["pictures"]
next_service = body["vision_path"].pop(0)
self.queue_manager.publish(next_service, body)
def run(self):
self.queue_manager.start_consuming(self.__class__.__name__,
self.callback)
