WIP

fix: regenerate event struct with recent version of protobuf compiler
2022-08-10 12:28:41 +02:00 · 2022-08-02 12:27:15 +02:00
19 changed files with 1169 additions and 1955 deletions
--- a/.Dockerignore
+++ b/.Dockerignore
@@ -1,6 +1,2 @@
 venv
 dist/
 build-docker.sh
 Dockerfile
--- a/.gitignore
+++ b/.gitignore
@@ -3,6 +3,3 @@
 *.egg-info
 .idea
 */__pycache__/
 /dist/
 build
 __pycache__
--- a/.pylintrc
+++ b/.pylintrc
@@ -1,618 +0,0 @@
 [MAIN]
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 analyse-fallback-blocks=no
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 ignore=CVS
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 logging-modules=logging
 [SPELLING]
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 spelling-dict=
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 spelling-ignore-comment-directives=fmt: on,fmt: off,noqa:,noqa,nosec,isort:skip,mypy:
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 # List of note tags to take in consideration, separated by a comma.
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 callbacks=cb_,
          _cb
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 ignore-long-lines=^\s*(# )?<?https?://\S+>?$
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 [METHOD_ARGS]
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 [SIMILARITIES]
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 class-const-naming-style=UPPER_CASE
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 # const-naming-style. If left empty, class constant names will be checked with
 # the set naming style.
 #class-const-rgx=
 # Naming style matching correct class names.
 class-naming-style=PascalCase
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 # style. If left empty, class names will be checked with the set naming style.
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 # Naming style matching correct constant names.
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 # style. If left empty, method names will be checked with the set naming style.
 #method-rgx=
 # Naming style matching correct module names.
 module-naming-style=snake_case
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 # style. If left empty, module names will be checked with the set naming style.
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--- a/34
+++ b/34
@@ -1,37 +1,23 @@
-FROM docker.io/library/python:3.10-slim as base
+FROM docker.io/library/python:3.9-slim
 # Configure piwheels repo to use pre-compiled numpy wheels for arm
 RUN echo -n "[global]\nextra-index-url=https://www.piwheels.org/simple\n" >> /etc/pip.conf
 RUN apt-get update && apt-get install -y libgl1 libglib2.0-0
-#################
+RUN pip3 install numpy
 FROM base as model-builder
-RUN python3 -m pip install blobconverter
+ADD requirements.txt requirements.txt
-RUN mkdir -p /models
+RUN pip3 install -r requirements.txt
-RUN blobconverter --zoo-name mobile_object_localizer_192x192 --zoo-type depthai --shaves 6 --version 2021.4 --output-dir /models || echo ""
+ADD events events
 ADD camera camera
 ADD setup.cfg setup.cfg
 ADD setup.py setup.py
-#################
+ENV PYTHON_EGG_CACHE=/tmp/cache
-FROM base as builder
+RUN python3 setup.py install
 RUN apt-get install -y git && \
    pip3 install poetry==1.2.0 && \
    poetry self add "poetry-dynamic-versioning[plugin]"
 ADD . .
 RUN poetry build
 #################
 FROM base
 RUN mkdir /models
 COPY --from=model-builder /models/mobile_object_localizer_192x192_openvino_2021.4_6shave.blob /models/mobile_object_localizer_192x192_openvino_2021.4_6shave.blob
 COPY --from=builder dist/*.whl /tmp/
 RUN pip3 install /tmp/*whl
 WORKDIR /tmp
 USER 1234
--- a/build-docker.sh
+++ b/build-docker.sh
@@ -2,11 +2,11 @@
 IMAGE_NAME=robocar-oak-camera
 TAG=$(git describe)
-FULL_IMAGE_NAME=git.cyrilix.bzh/robocars/${IMAGE_NAME}:${TAG}
+FULL_IMAGE_NAME=docker.io/cyrilix/${IMAGE_NAME}:${TAG}
 PLATFORM="linux/amd64,linux/arm64"
 #PLATFORM="linux/amd64,linux/arm64,linux/arm/v7"
 podman build . --platform "${PLATFORM}" --manifest "${IMAGE_NAME}:${TAG}"
-podman manifest push --all --format v2s2 "localhost/${IMAGE_NAME}:${TAG}" "docker://${FULL_IMAGE_NAME}"
+podman manifest push --format v2s2 "localhost/${IMAGE_NAME}:${TAG}" "docker://${FULL_IMAGE_NAME}"
 printf "\nImage %s published" "docker://${FULL_IMAGE_NAME}"
--- a/camera/cli.py
+++ b/camera/cli.py
@@ -1,114 +1,66 @@
 """
-Mqtt gateway for oak-lite device
+Publish data from oak-lite device
 Usage: rc-oak-camera [-u USERNAME | --mqtt-username=USERNAME] [--mqtt-password=PASSWORD] [--mqtt-broker=HOSTNAME] \
    [--mqtt-topic-robocar-oak-camera="TOPIC_CAMERA"] [--mqtt-client-id=CLIENT_ID] \
    [-H IMG_HEIGHT | --image-height=IMG_HEIGHT] [-W IMG_WIDTH | --image-width=IMG_width]
 Options:
 -h --help                                               Show this screen.
 -u USERID --mqtt-username=USERNAME                      MQTT user
 -p PASSWORD --mqtt-password=PASSWORD                    MQTT password
 -b HOSTNAME --mqtt-broker=HOSTNAME                      MQTT broker host
 -C CLIENT_ID --mqtt-client-id=CLIENT_ID                 MQTT client id
 -c TOPIC_CAMERA --mqtt-topic-robocar-oak-camera=TOPIC_CAMERA        MQTT topic where to publish robocar-oak-camera frames
 -H IMG_HEIGHT --image-height=IMG_HEIGHT                 IMG_HEIGHT image height
 -W IMG_WIDTH --image-width=IMG_width                    IMG_WIDTH image width
 """
 import argparse
 import logging
 import os
 import signal
 import paho.mqtt.client as mqtt
 from . import depthai as cam
 from docopt import docopt
 import paho.mqtt.client as mqtt
 logger = logging.getLogger(__name__)
 logging.basicConfig(level=logging.INFO)
-_DEFAULT_CLIENT_ID = "robocar-depthai"
+default_client_id = "robocar-depthai"
-def _parse_args_cli() -> argparse.Namespace:
+def init_mqtt_client(broker_host: str, user: str, password: str, client_id: str) -> mqtt.Client:
    parser = argparse.ArgumentParser()
    parser.add_argument("-u", "--mqtt-username",
                        help="MQTT user",
                        default=_get_env_value("MQTT_USERNAME", ""))
    parser.add_argument("-p", "--mqtt-password",
                        help="MQTT password",
                        default=_get_env_value("MQTT_PASSWORD", ""))
    parser.add_argument("-b", "--mqtt-broker-host",
                        help="MQTT broker host",
                        default=_get_env_value("MQTT_BROKER_HOST", "localhost"))
    parser.add_argument("-P", "--mqtt-broker-port",
                        help="MQTT broker port",
                        type=int,
                        default=_get_env_int_value("MQTT_BROKER_PORT", 1883))
    parser.add_argument("-C", "--mqtt-client-id",
                        help="MQTT client id",
                        default=_get_env_value("MQTT_CLIENT_ID", _DEFAULT_CLIENT_ID))
    parser.add_argument("-c", "--mqtt-topic-robocar-oak-camera",
                        help="MQTT topic where to publish robocar-oak-camera frames",
                        default=_get_env_value("MQTT_TOPIC_CAMERA", "/oak/camera_rgb"))
    parser.add_argument("-o", "---mqtt-topic-robocar-objects",
                        help="MQTT topic where to publish objects detection results",
                        default=_get_env_value("MQTT_TOPIC_OBJECTS", "/objects"))
    parser.add_argument("-t", "--objects-threshold",
                        help="threshold to filter detected objects",
                        type=float,
                        default=_get_env_float_value("OBJECTS_THRESHOLD", 0.2))
    parser.add_argument("-H", "--image-height", help="image height",
                        type=int,
                        default=_get_env_int_value("IMAGE_HEIGHT", 120))
    parser.add_argument("-W", "--image-width", help="image width",
                        type=int,
                        default=_get_env_int_value("IMAGE_WIDTH", 126))
    args = parser.parse_args()
    return args
 def _init_mqtt_client(broker_host: str, broker_port, user: str, password: str, client_id: str) -> mqtt.Client:
    logger.info("Start part.py-robocar-oak-camera")
    client = mqtt.Client(client_id=client_id, clean_session=True, userdata=None, protocol=mqtt.MQTTv311)
    client.username_pw_set(user, password)
-    logger.info("Connect to mqtt broker %s", broker_host)
+    logger.info("Connect to mqtt broker "+ broker_host)
-    client.connect(host=broker_host, port=broker_port, keepalive=60)
+    client.connect(host=broker_host, port=1883, keepalive=60)
    logger.info("Connected to mqtt broker")
    return client
-def execute_from_command_line() -> None:
+def execute_from_command_line():
    """
    Cli entrypoint
    :return:
    """
    logging.basicConfig(level=logging.INFO)
-    args = _parse_args_cli()
+    args = docopt(__doc__)
-    client = _init_mqtt_client(broker_host=args.mqtt_broker_host,
+    client = init_mqtt_client(broker_host=get_default_value(args["--mqtt-broker"], "MQTT_BROKER", "localhost"),
-                               broker_port=args.mqtt_broker_port,
+                              user=get_default_value(args["--mqtt-username"], "MQTT_USERNAME", ""),
-                               user=args.mqtt_username,
+                              password=get_default_value(args["--mqtt-password"], "MQTT_PASSWORD", ""),
-                               password=args.mqtt_password,
+                              client_id=get_default_value(args["--mqtt-client-id"], "MQTT_CLIENT_ID",
-                               client_id=args.mqtt_client_id,
+                                                          default_client_id),
-                               )
+                              )
-    frame_processor = cam.FrameProcessor(mqtt_client=client, frame_topic=args.mqtt_topic_robocar_oak_camera)
+    frame_topic = get_default_value(args["--mqtt-topic-robocar-oak-camera"], "MQTT_TOPIC_CAMERA", "/oak/camera_rgb")
    object_processor = cam.ObjectProcessor(mqtt_client=client,
                                           objects_topic=args.mqtt_topic_robocar_objects,
                                           objects_threshold=args.objects_threshold)
-    pipeline_controller = cam.PipelineController(img_width=args.image_width,
+    frame_processor = cam.FramePublisher(mqtt_client=client,
-                                                 img_height=args.image_height,
+                                         frame_topic=frame_topic,
-                                                 frame_processor=frame_processor,
+                                         img_width=int(get_default_value(args["--image-width"], "IMAGE_WIDTH", 160)),
-                                                 object_processor=object_processor)
+                                         img_height=int(get_default_value(args["--image-height"], "IMAGE_HEIGHT", 120)))
-
+    frame_processor.run()
    def sigterm_handler():
        logger.info("exit on SIGTERM")
        pipeline_controller.stop()
    signal.signal(signal.SIGTERM, sigterm_handler)
    pipeline_controller.run()
-def _get_env_value(env_var: str, default_value: str) -> str:
+def get_default_value(value, env_var: str, default_value) -> str:
    if value:
        return value
    if env_var in os.environ:
        return os.environ[env_var]
    return default_value
 def _get_env_int_value(env_var: str, default_value: int) -> int:
    value = _get_env_value(env_var, str(default_value))
    return int(value)
 def _get_env_float_value(env_var: str, default_value: float) -> float:
    value = _get_env_value(env_var, str(default_value))
    return float(value)
--- a/camera/depthai.py
+++ b/camera/depthai.py
@@ -1,153 +1,107 @@
 """
 Camera event loop
 """
 import datetime
 import logging
 import typing
 import cv2
 import depthai as dai
 import numpy as np
 import paho.mqtt.client as mqtt
 import events.events_pb2
 import depthai as dai
 import cv2
 logger = logging.getLogger(__name__)
-_NN_PATH = "/models/mobile_object_localizer_192x192_openvino_2021.4_6shave.blob"
+
-_NN_WIDTH = 192
+def to_tensor_result(packet):
-_NN_HEIGHT = 192
+    return {
        name: np.array(packet.getLayerFp16(name))
        for name in [tensor.name for tensor in packet.getRaw().tensors]
    }
-class ObjectProcessor:
+def to_planar(frame):
-    """
+    return frame.transpose(2, 0, 1).flatten()
    Processor for Object detection
    """
    def __init__(self, mqtt_client: mqtt.Client, objects_topic: str, objects_threshold: float):
        self._mqtt_client = mqtt_client
        self._objects_topic = objects_topic
        self._objects_threshold = objects_threshold
    def process(self, in_nn: dai.NNData, frame_ref) -> None:
        """
        Parse and publish result of NeuralNetwork result
        :param in_nn: NeuralNetwork result read from device
        :param frame_ref: Id of the frame where objects are been detected
        :return:
        """
        detection_boxes = np.array(in_nn.getLayerFp16("ExpandDims")).reshape((100, 4))
        detection_scores = np.array(in_nn.getLayerFp16("ExpandDims_2")).reshape((100,))
        # keep boxes bigger than threshold
        mask = detection_scores >= self._objects_threshold
        boxes = detection_boxes[mask]
        scores = detection_scores[mask]
        if boxes.shape[0] > 0:
            self._publish_objects(boxes, frame_ref, scores)
    def _publish_objects(self, boxes: np.array, frame_ref, scores: np.array) -> None:
        objects_msg = events.events_pb2.ObjectsMessage()
        objs = []
        for i in range(boxes.shape[0]):
            logger.debug("new object detected: %s", str(boxes[i]))
            objs.append(_bbox_to_object(boxes[i], scores[i].astype(float)))
        objects_msg.objects.extend(objs)
        objects_msg.frame_ref.name = frame_ref.name
        objects_msg.frame_ref.id = frame_ref.id
        objects_msg.frame_ref.created_at.FromDatetime(frame_ref.created_at.ToDatetime())
        logger.debug("publish object event to %s", self._objects_topic)
        self._mqtt_client.publish(topic=self._objects_topic,
                                  payload=objects_msg.SerializeToString(),
                                  qos=0,
                                  retain=False)
-class FrameProcessError(Exception):
+class FramePublisher:
-    """
+    def __init__(self, mqtt_client: mqtt.Client, frame_topic: str, img_width: int, img_height: int):
    Error base for invalid frame processing
    Attributes:
        message -- explanation of the error
    """
    def __init__(self, message: str):
        """
        :param message: explanation of the error
        """
        self.message = message
 class FrameProcessor:
    """
    Processor for camera frames
    """
    def __init__(self, mqtt_client: mqtt.Client, frame_topic: str):
        self._mqtt_client = mqtt_client
        self._frame_topic = frame_topic
    def process(self, img: dai.ImgFrame) -> typing.Any:
        """
        Publish camera frames
        :param img:
        :return:
            id frame reference
        :raise:
            FrameProcessError if frame can't be processed
        """
        im_resize = img.getCvFrame()
        is_success, im_buf_arr = cv2.imencode(".jpg", im_resize)
        if not is_success:
            raise FrameProcessError("unable to process to encode frame to jpg")
        byte_im = im_buf_arr.tobytes()
        now = datetime.datetime.now()
        frame_msg = events.events_pb2.FrameMessage()
        frame_msg.id.name = "robocar-oak-camera-oak"
        frame_msg.id.id = str(int(now.timestamp() * 1000))
        frame_msg.id.created_at.FromDatetime(now)
        frame_msg.frame = byte_im
        logger.debug("publish frame event to %s", self._frame_topic)
        self._mqtt_client.publish(topic=self._frame_topic,
                                  payload=frame_msg.SerializeToString(),
                                  qos=0,
                                  retain=False)
        return frame_msg.id
 class PipelineController:
    """
    Pipeline controller that drive camera device
    """
    def __init__(self, img_width: int, img_height: int, frame_processor: FrameProcessor,
                 object_processor: ObjectProcessor):
        self._img_width = img_width
        self._img_height = img_height
        self._pipeline = self._configure_pipeline()
        self._frame_processor = frame_processor
        self._object_processor = object_processor
        self._stop = False
    def _configure_pipeline(self) -> dai.Pipeline:
        logger.info("configure pipeline")
        pipeline = dai.Pipeline()
-        pipeline.setOpenVINOVersion(version=dai.OpenVINO.VERSION_2021_4)
+        version = "2021.2"
        pipeline.setOpenVINOVersion(version=dai.OpenVINO.Version.VERSION_2021_2)
-        detection_nn = self._configure_detection_nn(pipeline)
+        # colorCam = pipeline.create(dai.node.ColorCamera)
-        xout_nn = self._configure_xout_nn(pipeline)
+        # colorCam.setPreviewSize(256, 256)
        # colorCam.setVideoSize(1024, 1024)  # 4 times larger in both axis
        # colorCam.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
        # colorCam.setInterleaved(False)
        # colorCam.setBoardSocket(dai.CameraBoardSocket.RGB)
        # colorCam.setFps(10)
        #
        # controlIn = pipeline.create(dai.node.XLinkIn)
        # controlIn.setStreamName('control')
        # controlIn.out.link(colorCam.inputControl)
        #
        # cam_xout = pipeline.create(dai.node.XLinkOut)
        # cam_xout.setStreamName('video')
        # colorCam.video.link(cam_xout.input)
        # ---------------------------------------
        # 1st stage NN - text-detection
        # ---------------------------------------
        nn = pipeline.create(dai.node.NeuralNetwork)
        nn.setBlobPath(
            blobconverter.from_zoo(name="east_text_detection_256x256", zoo_type="depthai", shaves=6, version=version))
        colorCam.preview.link(nn.input)
        nn_xout = pipeline.create(dai.node.XLinkOut)
        nn_xout.setStreamName('detections')
        nn.out.link(nn_xout.input)
        # ---------------------------------------
        # 2nd stage NN - text-recognition-0012
        # ---------------------------------------
        # Resize image
        manip = pipeline.create(dai.node.ImageManip)
-        manip.initialConfig.setResize(_NN_WIDTH, _NN_HEIGHT)
+        manip.setWaitForConfigInput(True)
-        manip.initialConfig.setFrameType(dai.ImgFrame.Type.RGB888p)
+
-        manip.initialConfig.setKeepAspectRatio(False)
+        manip_img = pipeline.create(dai.node.XLinkIn)
        manip_img.setStreamName('manip_img')
        manip_img.out.link(manip.inputImage)
        manip_cfg = pipeline.create(dai.node.XLinkIn)
        manip_cfg.setStreamName('manip_cfg')
        manip_cfg.out.link(manip.inputConfig)
        manip_xout = pipeline.create(dai.node.XLinkOut)
        manip_xout.setStreamName('manip_out')
        nn2 = pipeline.create(dai.node.NeuralNetwork)
        nn2.setBlobPath(blobconverter.from_zoo(name="text-recognition-0012", shaves=6, version=version))
        nn2.setNumInferenceThreads(2)
        manip.out.link(nn2.input)
        manip.out.link(manip_xout.input)
        nn2_xout = pipeline.create(dai.node.XLinkOut)
        nn2_xout.setStreamName("recognitions")
        nn2.out.link(nn2_xout.input)
        cam_rgb = pipeline.create(dai.node.ColorCamera)
        xout_rgb = pipeline.create(dai.node.XLinkOut)
        xout_rgb.setStreamName("rgb")
        # Properties
@@ -157,90 +111,191 @@ class PipelineController:
        cam_rgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.RGB)
        cam_rgb.setFps(30)
-        # Link preview to manip and manip to nn
+        # Linking
        cam_rgb.preview.link(manip.inputImage)
        manip.out.link(detection_nn.input)
        # Linking to output
        cam_rgb.preview.link(xout_rgb.input)
        detection_nn.out.link(xout_nn.input)
        logger.info("pipeline configured")
        return pipeline
-    @staticmethod
+    def run(self):
-    def _configure_xout_nn(pipeline: dai.Pipeline) -> dai.node.XLinkOut:
+
-        xout_nn = pipeline.create(dai.node.XLinkOut)
+        with dai.Device(self._pipeline) as device:
-        xout_nn.setStreamName("nn")
+            q_vid = device.getOutputQueue("video", 4, blocking=False)
-        xout_nn.input.setBlocking(False)
+            # This should be set to block, but would get to some extreme queuing/latency!
-        return xout_nn
+            q_det = device.getOutputQueue("detections", 4, blocking=False)
            q_rec = device.getOutputQueue("recognitions", 4, blocking=True)
            q_manip_img = device.getInputQueue("manip_img")
            q_manip_cfg = device.getInputQueue("manip_cfg")
            q_manip_out = device.getOutputQueue("manip_out", 4, blocking=False)
            controlQueue = device.getInputQueue('control')
            frame = None
            cropped_stacked = None
            rotated_rectangles = []
            rec_pushed = 0
            rec_received = 0
            host_sync = HostSeqSync()
            characters = '0123456789abcdefghijklmnopqrstuvwxyz#'
            codec = CTCCodec(characters)
            ctrl = dai.CameraControl()
            ctrl.setAutoFocusMode(dai.CameraControl.AutoFocusMode.CONTINUOUS_VIDEO)
            ctrl.setAutoFocusTrigger()
            controlQueue.send(ctrl)
            while True:
                vid_in = q_vid.tryGet()
                if vid_in is not None:
                    host_sync.add_msg(vid_in)
                # Multiple recognition results may be available, read until queue is empty
                while True:
                    in_rec = q_rec.tryGet()
                    if in_rec is None:
                        break
                    rec_data = bboxes = np.array(in_rec.getFirstLayerFp16()).reshape(30, 1, 37)
                    decoded_text = codec.decode(rec_data)[0]
                    pos = rotated_rectangles[rec_received]
                    print("{:2}: {:20}".format(rec_received, decoded_text),
                          "center({:3},{:3}) size({:3},{:3}) angle{:5.1f} deg".format(
                              int(pos[0][0]), int(pos[0][1]), pos[1][0], pos[1][1], pos[2]))
                    # Draw the text on the right side of 'cropped_stacked' - placeholder
                    if cropped_stacked is not None:
                        cv2.putText(cropped_stacked, decoded_text,
                                    (120 + 10, 32 * rec_received + 24),
                                    cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
                        cv2.imshow('cropped_stacked', cropped_stacked)
                    rec_received += 1
                if cv2.waitKey(1) == ord('q'):
                    break
                if rec_received >= rec_pushed:
                    in_det = q_det.tryGet()
                    if in_det is not None:
                        frame = host_sync.get_msg(in_det.getSequenceNum()).getCvFrame().copy()
                        scores, geom1, geom2 = to_tensor_result(in_det).values()
                        scores = np.reshape(scores, (1, 1, 64, 64))
                        geom1 = np.reshape(geom1, (1, 4, 64, 64))
                        geom2 = np.reshape(geom2, (1, 1, 64, 64))
                        bboxes, confs, angles = east.decode_predictions(scores, geom1, geom2)
                        boxes, angles = east.non_max_suppression(np.array(bboxes), probs=confs, angles=np.array(angles))
                        rotated_rectangles = [
                            east.get_cv_rotated_rect(bbox, angle * -1)
                            for (bbox, angle) in zip(boxes, angles)
                        ]
                        rec_received = 0
                        rec_pushed = len(rotated_rectangles)
                        if rec_pushed:
                            print("====== Pushing for recognition, count:", rec_pushed)
                        cropped_stacked = None
                        for idx, rotated_rect in enumerate(rotated_rectangles):
                            # Detections are done on 256x256 frames, we are sending back 1024x1024
                            # That's why we multiply center and size values by 4
                            rotated_rect[0][0] = rotated_rect[0][0] * 4
                            rotated_rect[0][1] = rotated_rect[0][1] * 4
                            rotated_rect[1][0] = rotated_rect[1][0] * 4
                            rotated_rect[1][1] = rotated_rect[1][1] * 4
                            # Draw detection crop area on input frame
                            points = np.int0(cv2.boxPoints(rotated_rect))
                            print(rotated_rect)
                            cv2.polylines(frame, [points], isClosed=True, color=(255, 0, 0), thickness=1,
                                          lineType=cv2.LINE_8)
                            # TODO make it work taking args like in OpenCV:
                            # rr = ((256, 256), (128, 64), 30)
                            rr = dai.RotatedRect()
                            rr.center.x = rotated_rect[0][0]
                            rr.center.y = rotated_rect[0][1]
                            rr.size.width = rotated_rect[1][0]
                            rr.size.height = rotated_rect[1][1]
                            rr.angle = rotated_rect[2]
                            cfg = dai.ImageManipConfig()
                            cfg.setCropRotatedRect(rr, False)
                            cfg.setResize(120, 32)
                            # Send frame and config to device
                            if idx == 0:
                                w, h, c = frame.shape
                                imgFrame = dai.ImgFrame()
                                imgFrame.setData(to_planar(frame))
                                imgFrame.setType(dai.ImgFrame.Type.BGR888p)
                                imgFrame.setWidth(w)
                                imgFrame.setHeight(h)
                                q_manip_img.send(imgFrame)
                            else:
                                cfg.setReusePreviousImage(True)
                            q_manip_cfg.send(cfg)
                            # Get manipulated image from the device
                            transformed = q_manip_out.get().getCvFrame()
                            rec_placeholder_img = np.zeros((32, 200, 3), np.uint8)
                            transformed = np.hstack((transformed, rec_placeholder_img))
                            if cropped_stacked is None:
                                cropped_stacked = transformed
                            else:
                                cropped_stacked = np.vstack((cropped_stacked, transformed))
                if cropped_stacked is not None:
                    cv2.imshow('cropped_stacked', cropped_stacked)
                if frame is not None:
                    cv2.imshow('frame', frame)
                key = cv2.waitKey(1)
                if key == ord('q'):
                    break
                elif key == ord('t'):
                    print("Autofocus trigger (and disable continuous)")
                    ctrl = dai.CameraControl()
                    ctrl.setAutoFocusMode(dai.CameraControl.AutoFocusMode.AUTO)
                    ctrl.setAutoFocusTrigger()
                    controlQueue.send(ctrl)
    @staticmethod
    def _configure_detection_nn(pipeline: dai.Pipeline) -> dai.node.NeuralNetwork:
        # Define a neural network that will make predictions based on the source frames
        detection_nn = pipeline.create(dai.node.NeuralNetwork)
        detection_nn.setBlobPath(_NN_PATH)
        detection_nn.setNumPoolFrames(4)
        detection_nn.input.setBlocking(False)
        detection_nn.setNumInferenceThreads(2)
        return detection_nn
    def run(self) -> None:
        """
        Start event loop
        :return:
        """
        # Connect to device and start pipeline
        with dai.Device(self._pipeline) as device:
            logger.info('MxId: %s', device.getDeviceInfo().getMxId())
            logger.info('USB speed: %s', device.getUsbSpeed())
            logger.info('Connected cameras: %s', device.getConnectedCameras())
            logger.info("output queues found: %s", device.getOutputQueueNames())
            device.startPipeline()
            # Queues
            queue_size = 4
-            q_rgb = device.getOutputQueue(name="rgb", maxSize=queue_size, blocking=False)
+            q_rgb = device.getOutputQueue("rgb", maxSize=queue_size, blocking=False)
            q_nn = device.getOutputQueue(name="nn", maxSize=queue_size, blocking=False)
            self._stop = False
            while True:
                if self._stop:
                    logger.info("stop loop event")
                    return
                try:
-                    self._loop_on_camera_events(q_nn, q_rgb)
+                    logger.debug("wait for new frame")
-                # pylint: disable=broad-except # bad frame or event must not stop loop
+                    inRgb = q_rgb.get()  # blocking call, will wait until a new data has arrived
                except Exception as ex:
                    logger.exception("unexpected error: %s", str(ex))
-    def _loop_on_camera_events(self, q_nn: dai.DataOutputQueue, q_rgb: dai.DataOutputQueue):
+                    im_resize = inRgb.getCvFrame()
        logger.debug("wait for new frame")
-        # Wait for frame
+                    is_success, im_buf_arr = cv2.imencode(".jpg", im_resize)
-        in_rgb: dai.ImgFrame = q_rgb.get()  # blocking call, will wait until a new data has arrived
+                    byte_im = im_buf_arr.tobytes()
        try:
            frame_ref = self._frame_processor.process(in_rgb)
        except FrameProcessError as ex:
            logger.error("unable to process frame: %s", str(ex))
        # Read NN result
        in_nn: dai.NNData = q_nn.get()
        self._object_processor.process(in_nn, frame_ref)
-    def stop(self):
+                    now = datetime.datetime.now()
-        """
+                    frame_msg = events.events_pb2.FrameMessage()
-        Stop event loop, if loop is not running, do nothing
+                    frame_msg.id.name = "robocar-oak-camera-oak"
-        :return:
+                    frame_msg.id.id = str(int(now.timestamp() * 1000))
-        """
+                    frame_msg.id.created_at.FromDatetime(now)
-        self._stop = True
+                    frame_msg.frame = byte_im
                    logger.debug("publish frame event to %s", self._frame_topic)
                    self._mqtt_client.publish(topic=self._frame_topic,
                                              payload=frame_msg.SerializeToString(),
                                              qos=0,
                                              retain=False)
-def _bbox_to_object(bbox: np.array, score: float) -> events.events_pb2.Object:
+                except Exception as e:
-    obj = events.events_pb2.Object()
+                    logger.exception("unexpected error: %s", str(e))
    obj.type = events.events_pb2.TypeObject.ANY
    obj.top = bbox[0].astype(float)
    obj.right = bbox[3].astype(float)
    obj.bottom = bbox[2].astype(float)
    obj.left = bbox[1].astype(float)
    obj.confidence = score
    return obj
--- a/camera/east.py
+++ b/camera/east.py
@@ -0,0 +1,232 @@
 import cv2
 import depthai
 import numpy as np
 _conf_threshold = 0.5
 def get_cv_rotated_rect(bbox, angle):
    x0, y0, x1, y1 = bbox
    width = abs(x0 - x1)
    height = abs(y0 - y1)
    x = x0 + width * 0.5
    y = y0 + height * 0.5
    return [x.tolist(), y.tolist()], [width.tolist(), height.tolist()], np.rad2deg(angle)
 def rotated_Rectangle(bbox, angle):
    X0, Y0, X1, Y1 = bbox
    width = abs(X0 - X1)
    height = abs(Y0 - Y1)
    x = int(X0 + width * 0.5)
    y = int(Y0 + height * 0.5)
    pt1_1 = (int(x + width / 2), int(y + height / 2))
    pt2_1 = (int(x + width / 2), int(y - height / 2))
    pt3_1 = (int(x - width / 2), int(y - height / 2))
    pt4_1 = (int(x - width / 2), int(y + height / 2))
    t = np.array([[np.cos(angle), -np.sin(angle), x - x * np.cos(angle) + y * np.sin(angle)],
                  [np.sin(angle), np.cos(angle), y - x * np.sin(angle) - y * np.cos(angle)],
                  [0, 0, 1]])
    tmp_pt1_1 = np.array([[pt1_1[0]], [pt1_1[1]], [1]])
    tmp_pt1_2 = np.dot(t, tmp_pt1_1)
    pt1_2 = (int(tmp_pt1_2[0][0]), int(tmp_pt1_2[1][0]))
    tmp_pt2_1 = np.array([[pt2_1[0]], [pt2_1[1]], [1]])
    tmp_pt2_2 = np.dot(t, tmp_pt2_1)
    pt2_2 = (int(tmp_pt2_2[0][0]), int(tmp_pt2_2[1][0]))
    tmp_pt3_1 = np.array([[pt3_1[0]], [pt3_1[1]], [1]])
    tmp_pt3_2 = np.dot(t, tmp_pt3_1)
    pt3_2 = (int(tmp_pt3_2[0][0]), int(tmp_pt3_2[1][0]))
    tmp_pt4_1 = np.array([[pt4_1[0]], [pt4_1[1]], [1]])
    tmp_pt4_2 = np.dot(t, tmp_pt4_1)
    pt4_2 = (int(tmp_pt4_2[0][0]), int(tmp_pt4_2[1][0]))
    points = np.array([pt1_2, pt2_2, pt3_2, pt4_2])
    return points
 def non_max_suppression(boxes, probs=None, angles=None, overlapThresh=0.3):
    # if there are no boxes, return an empty list
    if len(boxes) == 0:
        return [], []
    # if the bounding boxes are integers, convert them to floats -- this
    # is important since we'll be doing a bunch of divisions
    if boxes.dtype.kind == "i":
        boxes = boxes.astype("float")
    # initialize the list of picked indexes
    pick = []
    # grab the coordinates of the bounding boxes
    x1 = boxes[:, 0]
    y1 = boxes[:, 1]
    x2 = boxes[:, 2]
    y2 = boxes[:, 3]
    # compute the area of the bounding boxes and grab the indexes to sort
    # (in the case that no probabilities are provided, simply sort on the bottom-left y-coordinate)
    area = (x2 - x1 + 1) * (y2 - y1 + 1)
    idxs = y2
    # if probabilities are provided, sort on them instead
    if probs is not None:
        idxs = probs
    # sort the indexes
    idxs = np.argsort(idxs)
    # keep looping while some indexes still remain in the indexes list
    while len(idxs) > 0:
        # grab the last index in the indexes list and add the index value to the list of picked indexes
        last = len(idxs) - 1
        i = idxs[last]
        pick.append(i)
        # find the largest (x, y) coordinates for the start of the bounding box and the smallest (x, y) coordinates
        # for the end of the bounding box
        xx1 = np.maximum(x1[i], x1[idxs[:last]])
        yy1 = np.maximum(y1[i], y1[idxs[:last]])
        xx2 = np.minimum(x2[i], x2[idxs[:last]])
        yy2 = np.minimum(y2[i], y2[idxs[:last]])
        # compute the width and height of the bounding box
        w = np.maximum(0, xx2 - xx1 + 1)
        h = np.maximum(0, yy2 - yy1 + 1)
        # compute the ratio of overlap
        overlap = (w * h) / area[idxs[:last]]
        # delete all indexes from the index list that have overlap greater than the provided overlap threshold
        idxs = np.delete(idxs, np.concatenate(([last], np.where(overlap > overlapThresh)[0])))
    # return only the bounding boxes that were picked
    return boxes[pick].astype("int"), angles[pick]
 def decode_predictions(scores, geometry1, geometry2):
    # grab the number of rows and columns from the scores volume, then
    # initialize our set of bounding box rectangles and corresponding
    # confidence scores
    (numRows, numCols) = scores.shape[2:4]
    rects = []
    confidences = []
    angles = []
    # loop over the number of rows
    for y in range(0, numRows):
        # extract the scores (probabilities), followed by the
        # geometrical data used to derive potential bounding box
        # coordinates that surround text
        scoresData = scores[0, 0, y]
        xData0 = geometry1[0, 0, y]
        xData1 = geometry1[0, 1, y]
        xData2 = geometry1[0, 2, y]
        xData3 = geometry1[0, 3, y]
        anglesData = geometry2[0, 0, y]
        # loop over the number of columns
        for x in range(0, numCols):
            # if our score does not have sufficient probability,
            # ignore it
            if scoresData[x] < _conf_threshold:
                continue
            # compute the offset factor as our resulting feature
            # maps will be 4x smaller than the input image
            (offsetX, offsetY) = (x * 4.0, y * 4.0)
            # extract the rotation angle for the prediction and
            # then compute the sin and cosine
            angle = anglesData[x]
            cos = np.cos(angle)
            sin = np.sin(angle)
            # use the geometry volume to derive the width and height
            # of the bounding box
            h = xData0[x] + xData2[x]
            w = xData1[x] + xData3[x]
            # compute both the starting and ending (x, y)-coordinates
            # for the text prediction bounding box
            endX = int(offsetX + (cos * xData1[x]) + (sin * xData2[x]))
            endY = int(offsetY - (sin * xData1[x]) + (cos * xData2[x]))
            startX = int(endX - w)
            startY = int(endY - h)
            # add the bounding box coordinates and probability score
            # to our respective lists
            rects.append((startX, startY, endX, endY))
            confidences.append(scoresData[x])
            angles.append(angle)
    # return a tuple of the bounding boxes and associated confidences
    return (rects, confidences, angles)
 def decode_east(nnet_packet, **kwargs):
    scores = nnet_packet.get_tensor(0)
    geometry1 = nnet_packet.get_tensor(1)
    geometry2 = nnet_packet.get_tensor(2)
    bboxes, confs, angles = decode_predictions(scores, geometry1, geometry2
                                               )
    boxes, angles = non_max_suppression(np.array(bboxes), probs=confs, angles=np.array(angles))
    boxesangles = (boxes, angles)
    return boxesangles
 def show_east(boxesangles, frame, **kwargs):
    bboxes = boxesangles[0]
    angles = boxesangles[1]
    for ((X0, Y0, X1, Y1), angle) in zip(bboxes, angles):
        width = abs(X0 - X1)
        height = abs(Y0 - Y1)
        cX = int(X0 + width * 0.5)
        cY = int(Y0 + height * 0.5)
        rotRect = ((cX, cY), ((X1 - X0), (Y1 - Y0)), angle * (-1))
        points = rotated_Rectangle(frame, rotRect, color=(255, 0, 0), thickness=1)
        cv2.polylines(frame, [points], isClosed=True, color=(255, 0, 0), thickness=1, lineType=cv2.LINE_8)
    return frame
 def order_points(pts):
    rect = np.zeros((4, 2), dtype="float32")
    s = pts.sum(axis=1)
    rect[0] = pts[np.argmin(s)]
    rect[2] = pts[np.argmax(s)]
    diff = np.diff(pts, axis=1)
    rect[1] = pts[np.argmin(diff)]
    rect[3] = pts[np.argmax(diff)]
    return rect
 def four_point_transform(image, pts):
    rect = order_points(pts)
    (tl, tr, br, bl) = rect
    widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
    widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
    maxWidth = max(int(widthA), int(widthB))
    heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
    heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
    maxHeight = max(int(heightA), int(heightB))
    dst = np.array([
        [0, 0],
        [maxWidth - 1, 0],
        [maxWidth - 1, maxHeight - 1],
        [0, maxHeight - 1]], dtype="float32")
    M = cv2.getPerspectiveTransform(rect, dst)
    warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
    return warped
--- a/camera/tests/init.py
+++ b/camera/tests/init.py
--- a/camera/tests/test_depthai.py
+++ b/camera/tests/test_depthai.py
@@ -1,150 +0,0 @@
 import datetime
 import unittest.mock
 import depthai as dai
 import numpy as np
 import paho.mqtt.client as mqtt
 import pytest
 import pytest_mock
 import camera.depthai
 import events.events_pb2
 Object = dict[str, float]
@pytest.fixture
 def mqtt_client(mocker: pytest_mock.MockerFixture) -> mqtt.Client:
    return mocker.MagicMock()
 class TestObjectProcessor:
    @pytest.fixture
    def frame_ref(self):
        now = datetime.datetime.now()
        frame_msg = events.events_pb2.FrameMessage()
        frame_msg.id.name = "robocar-oak-camera-oak"
        frame_msg.id.id = str(int(now.timestamp() * 1000))
        frame_msg.id.created_at.FromDatetime(now)
        return frame_msg.id
    @pytest.fixture
    def object1(self) -> Object:
        return {
            "left": 0.3,
            "right": 0.7,
            "top": 0.1,
            "bottom": 0.6,
            "score": 0.8,
        }
    @pytest.fixture
    def raw_objects_empty(self, mocker: pytest_mock.MockerFixture) -> dai.NNData:
        raw_objects = mocker.MagicMock()
        def mock_return(name):
            if name == "ExpandDims":
                return [[0] * 4] * 100
            elif name == "ExpandDims_2":
                return [0] * 100
            else:
                raise ValueError(f"{name} is not a valid arg")
        m = mocker.patch(target='depthai.NNData.getLayerFp16', autospec=True)
        m.getLayerFp16 = mock_return
        return m
    @pytest.fixture
    def raw_objects_one(self, mocker: pytest_mock.MockerFixture, object1: Object) -> dai.NNData:
        def mock_return(name):
            if name == "ExpandDims":  # Detection boxes
                boxes = [[0] * 4] * 100
                boxes[0] = [object1["top"], object1["left"], object1["bottom"], object1["right"]]
                return np.array(boxes)
            elif name == "ExpandDims_2":  # Detection scores
                scores = [0] * 100
                scores[0] = object1["score"]
                return scores
            else:
                raise ValueError(f"{name} is not a valid arg")
        m = mocker.patch(target='depthai.NNData.getLayerFp16', autospec=True)
        m.getLayerFp16 = mock_return
        return m
    @pytest.fixture
    def object_processor(self, mqtt_client: mqtt.Client) -> camera.depthai.ObjectProcessor:
        return camera.depthai.ObjectProcessor(mqtt_client, "topic/object", 0.2)
    def test_process_without_object(self, object_processor: camera.depthai.ObjectProcessor, mqtt_client,
                                    raw_objects_empty, frame_ref):
        object_processor.process(raw_objects_empty, frame_ref)
        mqtt_client.publish.assert_not_called()
    def test_process_with_object_with_low_score(self, object_processor: camera.depthai.ObjectProcessor, mqtt_client,
                                                raw_objects_one, frame_ref):
        object_processor._objects_threshold = 0.9
        object_processor.process(raw_objects_one, frame_ref)
        mqtt_client.publish.assert_not_called()
    def test_process_with_one_object(self,
                                     object_processor: camera.depthai.ObjectProcessor, mqtt_client,
                                     raw_objects_one, frame_ref, object1: Object):
        object_processor.process(raw_objects_one, frame_ref)
        left = object1["left"]
        right = object1["right"]
        top = object1["top"]
        bottom = object1["bottom"]
        score = object1["score"]
        pub_mock: unittest.mock.MagicMock = mqtt_client.publish
        pub_mock.assert_called_once_with(payload=unittest.mock.ANY, qos=0, retain=False, topic="topic/object")
        payload = pub_mock.call_args.kwargs['payload']
        objects_msg = events.events_pb2.ObjectsMessage()
        objects_msg.ParseFromString(payload)
        assert len(objects_msg.objects) == 1
        assert left - 0.0001 < objects_msg.objects[0].left < left + 0.0001
        assert right - 0.0001 < objects_msg.objects[0].right < right + 0.0001
        assert top - 0.0001 < objects_msg.objects[0].top < top + 0.0001
        assert bottom - 0.0001 < objects_msg.objects[0].bottom < bottom + 0.0001
        assert score - 0.0001 < objects_msg.objects[0].confidence < score + 0.0001
        assert objects_msg.frame_ref == frame_ref
 class TestFrameProcessor:
    @pytest.fixture
    def frame_processor(self, mqtt_client: mqtt.Client) -> camera.depthai.FrameProcessor:
        return camera.depthai.FrameProcessor(mqtt_client, "topic/frame")
    def test_process(self, frame_processor: camera.depthai.FrameProcessor, mocker: pytest_mock.MockerFixture,
                     mqtt_client: mqtt.Client):
        img: dai.ImgFrame = mocker.MagicMock()
        mocker.patch(target="cv2.imencode").return_value = (True, np.array(b"img content"))
        frame_ref = frame_processor.process(img)
        pub_mock: unittest.mock.MagicMock = mqtt_client.publish
        pub_mock.assert_called_once_with(payload=unittest.mock.ANY, qos=0, retain=False, topic="topic/frame")
        payload = pub_mock.call_args.kwargs['payload']
        frame_msg = events.events_pb2.FrameMessage()
        frame_msg.ParseFromString(payload)
        assert frame_msg.id == frame_ref
        assert frame_msg.frame == b"img content"
        assert frame_msg.id.name == "robocar-oak-camera-oak"
        assert len(frame_msg.id.id) is 13
        now = datetime.datetime.now()
        assert now - datetime.timedelta(
            milliseconds=10) < frame_msg.id.created_at.ToDatetime() < now + datetime.timedelta(milliseconds=10)
    def test_process_error(self, frame_processor: camera.depthai.FrameProcessor, mocker: pytest_mock.MockerFixture,
                           mqtt_client: mqtt.Client):
        img: dai.ImgFrame = mocker.MagicMock()
        mocker.patch(target="cv2.imencode").return_value = (False, None)
        with pytest.raises(camera.depthai.FrameProcessError) as ex:
            _ = frame_processor.process(img)
        exception_raised = ex.value
        assert exception_raised.message == "unable to process to encode frame to jpg"
--- a/camera/text.py
+++ b/camera/text.py
@@ -0,0 +1,61 @@
 class HostSeqSync:
    def __init__(self):
        self.imfFrames = []
    def add_msg(self, msg):
        self.imfFrames.append(msg)
    def get_msg(self, target_seq):
        for i, imgFrame in enumerate(self.imfFrames):
            if target_seq == imgFrame.getSequenceNum():
                self.imfFrames = self.imfFrames[i:]
                break
        return self.imfFrames[0]
 class CTCCodec(object):
    """ Convert between text-label and text-index """
    def __init__(self, characters):
        # characters (str): set of the possible characters.
        dict_character = list(characters)
        self.dict = {}
        for i, char in enumerate(dict_character):
            self.dict[char] = i + 1
        self.characters = dict_character
        # print(self.characters)
        # input()
    def decode(self, preds):
        """ convert text-index into text-label. """
        texts = []
        index = 0
        # Select max probabilty (greedy decoding) then decode index to character
        preds = preds.astype(np.float16)
        preds_index = np.argmax(preds, 2)
        preds_index = preds_index.transpose(1, 0)
        preds_index_reshape = preds_index.reshape(-1)
        preds_sizes = np.array([preds_index.shape[1]] * preds_index.shape[0])
        for l in preds_sizes:
            t = preds_index_reshape[index:index + l]
            # NOTE: t might be zero size
            if t.shape[0] == 0:
                continue
            char_list = []
            for i in range(l):
                # removing repeated characters and blank.
                if not (i > 0 and t[i - 1] == t[i]):
                    if self.characters[t[i]] != '#':
                        char_list.append(self.characters[t[i]])
            text = ''.join(char_list)
            texts.append(text)
            index += l
        return texts
--- a/east.py
+++ b/east.py
@@ -0,0 +1,229 @@
 import cv2
 import depthai
 import numpy as np
 _conf_threshold = 0.5
 def get_cv_rotated_rect(bbox, angle):
    x0, y0, x1, y1 = bbox
    width = abs(x0 - x1)
    height = abs(y0 - y1)
    x = x0 + width * 0.5
    y = y0 + height * 0.5
    return ([x.tolist(), y.tolist()], [width.tolist(), height.tolist()], np.rad2deg(angle))
 def rotated_Rectangle(bbox, angle):
    X0, Y0, X1, Y1 = bbox
    width = abs(X0 - X1)
    height = abs(Y0 - Y1)
    x = int(X0 + width * 0.5)
    y = int(Y0 + height * 0.5)
    pt1_1 = (int(x + width / 2), int(y + height / 2))
    pt2_1 = (int(x + width / 2), int(y - height / 2))
    pt3_1 = (int(x - width / 2), int(y - height / 2))
    pt4_1 = (int(x - width / 2), int(y + height / 2))
    t = np.array([[np.cos(angle), -np.sin(angle), x - x * np.cos(angle) + y * np.sin(angle)],
                  [np.sin(angle), np.cos(angle), y - x * np.sin(angle) - y * np.cos(angle)],
                  [0, 0, 1]])
    tmp_pt1_1 = np.array([[pt1_1[0]], [pt1_1[1]], [1]])
    tmp_pt1_2 = np.dot(t, tmp_pt1_1)
    pt1_2 = (int(tmp_pt1_2[0][0]), int(tmp_pt1_2[1][0]))
    tmp_pt2_1 = np.array([[pt2_1[0]], [pt2_1[1]], [1]])
    tmp_pt2_2 = np.dot(t, tmp_pt2_1)
    pt2_2 = (int(tmp_pt2_2[0][0]), int(tmp_pt2_2[1][0]))
    tmp_pt3_1 = np.array([[pt3_1[0]], [pt3_1[1]], [1]])
    tmp_pt3_2 = np.dot(t, tmp_pt3_1)
    pt3_2 = (int(tmp_pt3_2[0][0]), int(tmp_pt3_2[1][0]))
    tmp_pt4_1 = np.array([[pt4_1[0]], [pt4_1[1]], [1]])
    tmp_pt4_2 = np.dot(t, tmp_pt4_1)
    pt4_2 = (int(tmp_pt4_2[0][0]), int(tmp_pt4_2[1][0]))
    points = np.array([pt1_2, pt2_2, pt3_2, pt4_2])
    return points
 def non_max_suppression(boxes, probs=None, angles=None, overlapThresh=0.3):
    # if there are no boxes, return an empty list
    if len(boxes) == 0:
        return [], []
    # if the bounding boxes are integers, convert them to floats -- this
    # is important since we'll be doing a bunch of divisions
    if boxes.dtype.kind == "i":
        boxes = boxes.astype("float")
    # initialize the list of picked indexes
    pick = []
    # grab the coordinates of the bounding boxes
    x1 = boxes[:, 0]
    y1 = boxes[:, 1]
    x2 = boxes[:, 2]
    y2 = boxes[:, 3]
    # compute the area of the bounding boxes and grab the indexes to sort
    # (in the case that no probabilities are provided, simply sort on the bottom-left y-coordinate)
    area = (x2 - x1 + 1) * (y2 - y1 + 1)
    idxs = y2
    # if probabilities are provided, sort on them instead
    if probs is not None:
        idxs = probs
    # sort the indexes
    idxs = np.argsort(idxs)
    # keep looping while some indexes still remain in the indexes list
    while len(idxs) > 0:
        # grab the last index in the indexes list and add the index value to the list of picked indexes
        last = len(idxs) - 1
        i = idxs[last]
        pick.append(i)
        # find the largest (x, y) coordinates for the start of the bounding box and the smallest (x, y) coordinates for the end of the bounding box
        xx1 = np.maximum(x1[i], x1[idxs[:last]])
        yy1 = np.maximum(y1[i], y1[idxs[:last]])
        xx2 = np.minimum(x2[i], x2[idxs[:last]])
        yy2 = np.minimum(y2[i], y2[idxs[:last]])
        # compute the width and height of the bounding box
        w = np.maximum(0, xx2 - xx1 + 1)
        h = np.maximum(0, yy2 - yy1 + 1)
        # compute the ratio of overlap
        overlap = (w * h) / area[idxs[:last]]
        # delete all indexes from the index list that have overlap greater than the provided overlap threshold
        idxs = np.delete(idxs, np.concatenate(([last], np.where(overlap > overlapThresh)[0])))
    # return only the bounding boxes that were picked
    return boxes[pick].astype("int"), angles[pick]
 def decode_predictions(scores, geometry1, geometry2):
    # grab the number of rows and columns from the scores volume, then
    # initialize our set of bounding box rectangles and corresponding
    # confidence scores
    (numRows, numCols) = scores.shape[2:4]
    rects = []
    confidences = []
    angles = []
    # loop over the number of rows
    for y in range(0, numRows):
        # extract the scores (probabilities), followed by the
        # geometrical data used to derive potential bounding box
        # coordinates that surround text
        scoresData = scores[0, 0, y]
        xData0 = geometry1[0, 0, y]
        xData1 = geometry1[0, 1, y]
        xData2 = geometry1[0, 2, y]
        xData3 = geometry1[0, 3, y]
        anglesData = geometry2[0, 0, y]
        # loop over the number of columns
        for x in range(0, numCols):
            # if our score does not have sufficient probability,
            # ignore it
            if scoresData[x] < _conf_threshold:
                continue
            # compute the offset factor as our resulting feature
            # maps will be 4x smaller than the input image
            (offsetX, offsetY) = (x * 4.0, y * 4.0)
            # extract the rotation angle for the prediction and
            # then compute the sin and cosine
            angle = anglesData[x]
            cos = np.cos(angle)
            sin = np.sin(angle)
            # use the geometry volume to derive the width and height
            # of the bounding box
            h = xData0[x] + xData2[x]
            w = xData1[x] + xData3[x]
            # compute both the starting and ending (x, y)-coordinates
            # for the text prediction bounding box
            endX = int(offsetX + (cos * xData1[x]) + (sin * xData2[x]))
            endY = int(offsetY - (sin * xData1[x]) + (cos * xData2[x]))
            startX = int(endX - w)
            startY = int(endY - h)
            # add the bounding box coordinates and probability score
            # to our respective lists
            rects.append((startX, startY, endX, endY))
            confidences.append(scoresData[x])
            angles.append(angle)
    # return a tuple of the bounding boxes and associated confidences
    return (rects, confidences, angles)
 def decode_east(nnet_packet, **kwargs):
    scores = nnet_packet.get_tensor(0)
    geometry1 = nnet_packet.get_tensor(1)
    geometry2 = nnet_packet.get_tensor(2)
    bboxes, confs, angles = decode_predictions(scores, geometry1, geometry2
                                               )
    boxes, angles = non_max_suppression(np.array(bboxes), probs=confs, angles=np.array(angles))
    boxesangles = (boxes, angles)
    return boxesangles
 def show_east(boxesangles, frame, **kwargs):
    bboxes = boxesangles[0]
    angles = boxesangles[1]
    for ((X0, Y0, X1, Y1), angle) in zip(bboxes, angles):
        width = abs(X0 - X1)
        height = abs(Y0 - Y1)
        cX = int(X0 + width * 0.5)
        cY = int(Y0 + height * 0.5)
        rotRect = ((cX, cY), ((X1 - X0), (Y1 - Y0)), angle * (-1))
        points = rotated_Rectangle(frame, rotRect, color=(255, 0, 0), thickness=1)
        cv2.polylines(frame, [points], isClosed=True, color=(255, 0, 0), thickness=1, lineType=cv2.LINE_8)
    return frame
 def order_points(pts):
    rect = np.zeros((4, 2), dtype="float32")
    s = pts.sum(axis=1)
    rect[0] = pts[np.argmin(s)]
    rect[2] = pts[np.argmax(s)]
    diff = np.diff(pts, axis=1)
    rect[1] = pts[np.argmin(diff)]
    rect[3] = pts[np.argmax(diff)]
    return rect
 def four_point_transform(image, pts):
    rect = order_points(pts)
    (tl, tr, br, bl) = rect
    widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
    widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
    maxWidth = max(int(widthA), int(widthB))
    heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
    heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
    maxHeight = max(int(heightA), int(heightB))
    dst = np.array([
        [0, 0],
        [maxWidth - 1, 0],
        [maxWidth - 1, maxHeight - 1],
        [0, maxHeight - 1]], dtype="float32")
    M = cv2.getPerspectiveTransform(rect, dst)
    warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
    return warped
--- a/events/events_pb2.py
+++ b/events/events_pb2.py
@@ -14,7 +14,7 @@ _sym_db = _symbol_database.Default()
 from google.protobuf import timestamp_pb2 as google_dot_protobuf_dot_timestamp__pb2
-DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x13\x65vents/events.proto\x12\x0erobocar.events\x1a\x1fgoogle/protobuf/timestamp.proto\"T\n\x08\x46rameRef\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\n\n\x02id\x18\x02 \x01(\t\x12.\n\ncreated_at\x18\x03 \x01(\x0b\x32\x1a.google.protobuf.Timestamp\"C\n\x0c\x46rameMessage\x12$\n\x02id\x18\x01 \x01(\x0b\x32\x18.robocar.events.FrameRef\x12\r\n\x05\x66rame\x18\x02 \x01(\x0c\"d\n\x0fSteeringMessage\x12\x10\n\x08steering\x18\x01 \x01(\x02\x12\x12\n\nconfidence\x18\x02 \x01(\x02\x12+\n\tframe_ref\x18\x03 \x01(\x0b\x32\x18.robocar.events.FrameRef\"d\n\x0fThrottleMessage\x12\x10\n\x08throttle\x18\x01 \x01(\x02\x12\x12\n\nconfidence\x18\x02 \x01(\x02\x12+\n\tframe_ref\x18\x03 \x01(\x0b\x32\x18.robocar.events.FrameRef\"A\n\x10\x44riveModeMessage\x12-\n\ndrive_mode\x18\x01 \x01(\x0e\x32\x19.robocar.events.DriveMode\"f\n\x0eObjectsMessage\x12\'\n\x07objects\x18\x01 \x03(\x0b\x32\x16.robocar.events.Object\x12+\n\tframe_ref\x18\x02 \x01(\x0b\x32\x18.robocar.events.FrameRef\"\x80\x01\n\x06Object\x12(\n\x04type\x18\x01 \x01(\x0e\x32\x1a.robocar.events.TypeObject\x12\x0c\n\x04left\x18\x02 \x01(\x02\x12\x0b\n\x03top\x18\x03 \x01(\x02\x12\r\n\x05right\x18\x04 \x01(\x02\x12\x0e\n\x06\x62ottom\x18\x05 \x01(\x02\x12\x12\n\nconfidence\x18\x06 \x01(\x02\"&\n\x13SwitchRecordMessage\x12\x0f\n\x07\x65nabled\x18\x01 \x01(\x08\"\x8c\x01\n\x0bRoadMessage\x12&\n\x07\x63ontour\x18\x01 \x03(\x0b\x32\x15.robocar.events.Point\x12(\n\x07\x65llipse\x18\x02 \x01(\x0b\x32\x17.robocar.events.Ellipse\x12+\n\tframe_ref\x18\x03 \x01(\x0b\x32\x18.robocar.events.FrameRef\"\x1d\n\x05Point\x12\t\n\x01x\x18\x01 \x01(\x05\x12\t\n\x01y\x18\x02 \x01(\x05\"r\n\x07\x45llipse\x12%\n\x06\x63\x65nter\x18\x01 \x01(\x0b\x32\x15.robocar.events.Point\x12\r\n\x05width\x18\x02 \x01(\x05\x12\x0e\n\x06height\x18\x03 \x01(\x05\x12\r\n\x05\x61ngle\x18\x04 \x01(\x02\x12\x12\n\nconfidence\x18\x05 \x01(\x02\"\x82\x01\n\rRecordMessage\x12+\n\x05\x66rame\x18\x01 \x01(\x0b\x32\x1c.robocar.events.FrameMessage\x12\x31\n\x08steering\x18\x02 \x01(\x0b\x32\x1f.robocar.events.SteeringMessage\x12\x11\n\trecordSet\x18\x03 \x01(\t*-\n\tDriveMode\x12\x0b\n\x07INVALID\x10\x00\x12\x08\n\x04USER\x10\x01\x12\t\n\x05PILOT\x10\x02*2\n\nTypeObject\x12\x07\n\x03\x41NY\x10\x00\x12\x07\n\x03\x43\x41R\x10\x01\x12\x08\n\x04\x42UMP\x10\x02\x12\x08\n\x04PLOT\x10\x03\x42\nZ\x08./eventsb\x06proto3')
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x13\x65vents/events.proto\x12\x0erobocar.events\x1a\x1fgoogle/protobuf/timestamp.proto\"T\n\x08\x46rameRef\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\n\n\x02id\x18\x02 \x01(\t\x12.\n\ncreated_at\x18\x03 \x01(\x0b\x32\x1a.google.protobuf.Timestamp\"C\n\x0c\x46rameMessage\x12$\n\x02id\x18\x01 \x01(\x0b\x32\x18.robocar.events.FrameRef\x12\r\n\x05\x66rame\x18\x02 \x01(\x0c\"d\n\x0fSteeringMessage\x12\x10\n\x08steering\x18\x01 \x01(\x02\x12\x12\n\nconfidence\x18\x02 \x01(\x02\x12+\n\tframe_ref\x18\x03 \x01(\x0b\x32\x18.robocar.events.FrameRef\"d\n\x0fThrottleMessage\x12\x10\n\x08throttle\x18\x01 \x01(\x02\x12\x12\n\nconfidence\x18\x02 \x01(\x02\x12+\n\tframe_ref\x18\x03 \x01(\x0b\x32\x18.robocar.events.FrameRef\"A\n\x10\x44riveModeMessage\x12-\n\ndrive_mode\x18\x01 \x01(\x0e\x32\x19.robocar.events.DriveMode\"f\n\x0eObjectsMessage\x12\'\n\x07objects\x18\x01 \x03(\x0b\x32\x16.robocar.events.Object\x12+\n\tframe_ref\x18\x02 \x01(\x0b\x32\x18.robocar.events.FrameRef\"\x80\x01\n\x06Object\x12(\n\x04type\x18\x01 \x01(\x0e\x32\x1a.robocar.events.TypeObject\x12\x0c\n\x04left\x18\x02 \x01(\x05\x12\x0b\n\x03top\x18\x03 \x01(\x05\x12\r\n\x05right\x18\x04 \x01(\x05\x12\x0e\n\x06\x62ottom\x18\x05 \x01(\x05\x12\x12\n\nconfidence\x18\x06 \x01(\x02\"&\n\x13SwitchRecordMessage\x12\x0f\n\x07\x65nabled\x18\x01 \x01(\x08\"\x8c\x01\n\x0bRoadMessage\x12&\n\x07\x63ontour\x18\x01 \x03(\x0b\x32\x15.robocar.events.Point\x12(\n\x07\x65llipse\x18\x02 \x01(\x0b\x32\x17.robocar.events.Ellipse\x12+\n\tframe_ref\x18\x03 \x01(\x0b\x32\x18.robocar.events.FrameRef\"\x1d\n\x05Point\x12\t\n\x01x\x18\x01 \x01(\x05\x12\t\n\x01y\x18\x02 \x01(\x05\"r\n\x07\x45llipse\x12%\n\x06\x63\x65nter\x18\x01 \x01(\x0b\x32\x15.robocar.events.Point\x12\r\n\x05width\x18\x02 \x01(\x05\x12\x0e\n\x06height\x18\x03 \x01(\x05\x12\r\n\x05\x61ngle\x18\x04 \x01(\x02\x12\x12\n\nconfidence\x18\x05 \x01(\x02\"\x82\x01\n\rRecordMessage\x12+\n\x05\x66rame\x18\x01 \x01(\x0b\x32\x1c.robocar.events.FrameMessage\x12\x31\n\x08steering\x18\x02 \x01(\x0b\x32\x1f.robocar.events.SteeringMessage\x12\x11\n\trecordSet\x18\x03 \x01(\t*-\n\tDriveMode\x12\x0b\n\x07INVALID\x10\x00\x12\x08\n\x04USER\x10\x01\x12\t\n\x05PILOT\x10\x02*2\n\nTypeObject\x12\x07\n\x03\x41NY\x10\x00\x12\x07\n\x03\x43\x41R\x10\x01\x12\x08\n\x04\x42UMP\x10\x02\x12\x08\n\x04PLOT\x10\x03\x42\nZ\x08./eventsb\x06proto3')
 _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
 _builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'events.events_pb2', globals())
--- a/main.py
+++ b/main.py
@@ -0,0 +1,274 @@
 #!/usr/bin/env python3
 from pathlib import Path
 import cv2
 import numpy as np
 import depthai as dai
 import east
 import blobconverter
 class HostSeqSync:
    def __init__(self):
        self.imfFrames = []
    def add_msg(self, msg):
        self.imfFrames.append(msg)
    def get_msg(self, target_seq):
        for i, imgFrame in enumerate(self.imfFrames):
            if target_seq == imgFrame.getSequenceNum():
                self.imfFrames = self.imfFrames[i:]
                break
        return self.imfFrames[0]
 pipeline = dai.Pipeline()
 version = "2021.2"
 pipeline.setOpenVINOVersion(version=dai.OpenVINO.Version.VERSION_2021_2)
 colorCam = pipeline.create(dai.node.ColorCamera)
 colorCam.setPreviewSize(256, 256)
 colorCam.setVideoSize(1024, 1024) # 4 times larger in both axis
 colorCam.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
 colorCam.setInterleaved(False)
 colorCam.setBoardSocket(dai.CameraBoardSocket.RGB)
 colorCam.setFps(10)
 controlIn = pipeline.create(dai.node.XLinkIn)
 controlIn.setStreamName('control')
 controlIn.out.link(colorCam.inputControl)
 cam_xout = pipeline.create(dai.node.XLinkOut)
 cam_xout.setStreamName('video')
 colorCam.video.link(cam_xout.input)
 # ---------------------------------------
 # 1st stage NN - text-detection
 # ---------------------------------------
 nn = pipeline.create(dai.node.NeuralNetwork)
 nn.setBlobPath(blobconverter.from_zoo(name="east_text_detection_256x256",zoo_type="depthai",shaves=6, version=version))
 colorCam.preview.link(nn.input)
 nn_xout = pipeline.create(dai.node.XLinkOut)
 nn_xout.setStreamName('detections')
 nn.out.link(nn_xout.input)
 # ---------------------------------------
 # 2nd stage NN - text-recognition-0012
 # ---------------------------------------
 manip = pipeline.create(dai.node.ImageManip)
 manip.setWaitForConfigInput(True)
 manip_img = pipeline.create(dai.node.XLinkIn)
 manip_img.setStreamName('manip_img')
 manip_img.out.link(manip.inputImage)
 manip_cfg = pipeline.create(dai.node.XLinkIn)
 manip_cfg.setStreamName('manip_cfg')
 manip_cfg.out.link(manip.inputConfig)
 manip_xout = pipeline.create(dai.node.XLinkOut)
 manip_xout.setStreamName('manip_out')
 nn2 = pipeline.create(dai.node.NeuralNetwork)
 nn2.setBlobPath(blobconverter.from_zoo(name="text-recognition-0012", shaves=6, version=version))
 nn2.setNumInferenceThreads(2)
 manip.out.link(nn2.input)
 manip.out.link(manip_xout.input)
 nn2_xout = pipeline.create(dai.node.XLinkOut)
 nn2_xout.setStreamName("recognitions")
 nn2.out.link(nn2_xout.input)
 def to_tensor_result(packet):
    return {
        name: np.array(packet.getLayerFp16(name))
        for name in [tensor.name for tensor in packet.getRaw().tensors]
    }
 def to_planar(frame):
    return frame.transpose(2, 0, 1).flatten()
 with dai.Device(pipeline) as device:
    q_vid = device.getOutputQueue("video", 4, blocking=False)
    # This should be set to block, but would get to some extreme queuing/latency!
    q_det = device.getOutputQueue("detections", 4, blocking=False)
    q_rec = device.getOutputQueue("recognitions", 4, blocking=True)
    q_manip_img = device.getInputQueue("manip_img")
    q_manip_cfg = device.getInputQueue("manip_cfg")
    q_manip_out = device.getOutputQueue("manip_out", 4, blocking=False)
    controlQueue = device.getInputQueue('control')
    frame = None
    cropped_stacked = None
    rotated_rectangles = []
    rec_pushed = 0
    rec_received = 0
    host_sync = HostSeqSync()
    class CTCCodec(object):
        """ Convert between text-label and text-index """
        def __init__(self, characters):
            # characters (str): set of the possible characters.
            dict_character = list(characters)
            self.dict = {}
            for i, char in enumerate(dict_character):
                self.dict[char] = i + 1
            self.characters = dict_character
            #print(self.characters)
            #input()
        def decode(self, preds):
            """ convert text-index into text-label. """
            texts = []
            index = 0
            # Select max probabilty (greedy decoding) then decode index to character
            preds = preds.astype(np.float16)
            preds_index = np.argmax(preds, 2)
            preds_index = preds_index.transpose(1, 0)
            preds_index_reshape = preds_index.reshape(-1)
            preds_sizes = np.array([preds_index.shape[1]] * preds_index.shape[0])
            for l in preds_sizes:
                t = preds_index_reshape[index:index + l]
                # NOTE: t might be zero size
                if t.shape[0] == 0:
                    continue
                char_list = []
                for i in range(l):
                    # removing repeated characters and blank.
                    if not (i > 0 and t[i - 1] == t[i]):
                        if self.characters[t[i]] != '#':
                            char_list.append(self.characters[t[i]])
                text = ''.join(char_list)
                texts.append(text)
                index += l
            return texts
    characters = '0123456789abcdefghijklmnopqrstuvwxyz#'
    codec = CTCCodec(characters)
    ctrl = dai.CameraControl()
    ctrl.setAutoFocusMode(dai.CameraControl.AutoFocusMode.CONTINUOUS_VIDEO)
    ctrl.setAutoFocusTrigger()
    controlQueue.send(ctrl)
    while True:
        vid_in = q_vid.tryGet()
        if vid_in is not None:
            host_sync.add_msg(vid_in)
        # Multiple recognition results may be available, read until queue is empty
        while True:
            in_rec = q_rec.tryGet()
            if in_rec is None:
                break
            rec_data = bboxes = np.array(in_rec.getFirstLayerFp16()).reshape(30,1,37)
            decoded_text = codec.decode(rec_data)[0]
            pos = rotated_rectangles[rec_received]
            print("{:2}: {:20}".format(rec_received, decoded_text),
                "center({:3},{:3}) size({:3},{:3}) angle{:5.1f} deg".format(
                    int(pos[0][0]), int(pos[0][1]), pos[1][0], pos[1][1], pos[2]))
            # Draw the text on the right side of 'cropped_stacked' - placeholder
            if cropped_stacked is not None:
                cv2.putText(cropped_stacked, decoded_text,
                                (120 + 10 , 32 * rec_received + 24),
                                cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0,255,0), 2)
                cv2.imshow('cropped_stacked', cropped_stacked)
            rec_received += 1
        if cv2.waitKey(1) == ord('q'):
            break
        if rec_received >= rec_pushed:
            in_det = q_det.tryGet()
            if in_det is not None:
                frame = host_sync.get_msg(in_det.getSequenceNum()).getCvFrame().copy()
                scores, geom1, geom2 = to_tensor_result(in_det).values()
                scores = np.reshape(scores, (1, 1, 64, 64))
                geom1 = np.reshape(geom1, (1, 4, 64, 64))
                geom2 = np.reshape(geom2, (1, 1, 64, 64))
                bboxes, confs, angles = east.decode_predictions(scores, geom1, geom2)
                boxes, angles = east.non_max_suppression(np.array(bboxes), probs=confs, angles=np.array(angles))
                rotated_rectangles = [
                    east.get_cv_rotated_rect(bbox, angle * -1)
                    for (bbox, angle) in zip(boxes, angles)
                ]
                rec_received = 0
                rec_pushed = len(rotated_rectangles)
                if rec_pushed:
                    print("====== Pushing for recognition, count:", rec_pushed)
                cropped_stacked = None
                for idx, rotated_rect in enumerate(rotated_rectangles):
                    # Detections are done on 256x256 frames, we are sending back 1024x1024
                    # That's why we multiply center and size values by 4
                    rotated_rect[0][0] = rotated_rect[0][0] * 4
                    rotated_rect[0][1] = rotated_rect[0][1] * 4
                    rotated_rect[1][0] = rotated_rect[1][0] * 4
                    rotated_rect[1][1] = rotated_rect[1][1] * 4
                    # Draw detection crop area on input frame
                    points = np.int0(cv2.boxPoints(rotated_rect))
                    print(rotated_rect)
                    cv2.polylines(frame, [points], isClosed=True, color=(255, 0, 0), thickness=1, lineType=cv2.LINE_8)
                    # TODO make it work taking args like in OpenCV:
                    # rr = ((256, 256), (128, 64), 30)
                    rr = dai.RotatedRect()
                    rr.center.x    = rotated_rect[0][0]
                    rr.center.y    = rotated_rect[0][1]
                    rr.size.width  = rotated_rect[1][0]
                    rr.size.height = rotated_rect[1][1]
                    rr.angle       = rotated_rect[2]
                    cfg = dai.ImageManipConfig()
                    cfg.setCropRotatedRect(rr, False)
                    cfg.setResize(120, 32)
                    # Send frame and config to device
                    if idx == 0:
                        w,h,c = frame.shape
                        imgFrame = dai.ImgFrame()
                        imgFrame.setData(to_planar(frame))
                        imgFrame.setType(dai.ImgFrame.Type.BGR888p)
                        imgFrame.setWidth(w)
                        imgFrame.setHeight(h)
                        q_manip_img.send(imgFrame)
                    else:
                        cfg.setReusePreviousImage(True)
                    q_manip_cfg.send(cfg)
                    # Get manipulated image from the device
                    transformed = q_manip_out.get().getCvFrame()
                    rec_placeholder_img = np.zeros((32, 200, 3), np.uint8)
                    transformed = np.hstack((transformed, rec_placeholder_img))
                    if cropped_stacked is None:
                        cropped_stacked = transformed
                    else:
                        cropped_stacked = np.vstack((cropped_stacked, transformed))
        if cropped_stacked is not None:
            cv2.imshow('cropped_stacked', cropped_stacked)
        if frame is not None:
            cv2.imshow('frame', frame)
        key = cv2.waitKey(1)
        if  key == ord('q'):
            break
        elif key == ord('t'):
            print("Autofocus trigger (and disable continuous)")
            ctrl = dai.CameraControl()
            ctrl.setAutoFocusMode(dai.CameraControl.AutoFocusMode.AUTO)
            ctrl.setAutoFocusTrigger()
            controlQueue.send(ctrl)
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,839 +0,0 @@
 [[package]]
 name = "astroid"
 version = "2.12.12"
 description = "An abstract syntax tree for Python with inference support."
 category = "dev"
 optional = false
 python-versions = ">=3.7.2"
 [package.dependencies]
 lazy-object-proxy = ">=1.4.0"
 wrapt = [
    {version = ">=1.11,<2", markers = "python_version < \"3.11\""},
    {version = ">=1.14,<2", markers = "python_version >= \"3.11\""},
 ]
 [[package]]
 name = "attrs"
 version = "22.1.0"
 description = "Classes Without Boilerplate"
 category = "dev"
 optional = false
 python-versions = ">=3.5"
 [package.extras]
 dev = ["cloudpickle", "coverage[toml] (>=5.0.2)", "furo", "hypothesis", "mypy (>=0.900,!=0.940)", "pre-commit", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "sphinx", "sphinx-notfound-page", "zope.interface"]
 docs = ["furo", "sphinx", "sphinx-notfound-page", "zope.interface"]
 tests = ["cloudpickle", "coverage[toml] (>=5.0.2)", "hypothesis", "mypy (>=0.900,!=0.940)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "zope.interface"]
 tests-no-zope = ["cloudpickle", "coverage[toml] (>=5.0.2)", "hypothesis", "mypy (>=0.900,!=0.940)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins"]
 [[package]]
 name = "beautifulsoup4"
 version = "4.11.1"
 description = "Screen-scraping library"
 category = "main"
 optional = false
 python-versions = ">=3.6.0"
 [package.dependencies]
 soupsieve = ">1.2"
 [package.extras]
 html5lib = ["html5lib"]
 lxml = ["lxml"]
 [[package]]
 name = "blobconverter"
 version = "1.3.0"
 description = "The tool that allows you to convert neural networks to MyriadX blob"
 category = "main"
 optional = false
 python-versions = "*"
 [package.dependencies]
 boto3 = "*"
 PyYAML = "*"
 requests = "*"
 [[package]]
 name = "boto3"
 version = "1.24.94"
 description = "The AWS SDK for Python"
 category = "main"
 optional = false
 python-versions = ">= 3.7"
 [package.dependencies]
 botocore = ">=1.27.94,<1.28.0"
 jmespath = ">=0.7.1,<2.0.0"
 s3transfer = ">=0.6.0,<0.7.0"
 [package.extras]
 crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 [[package]]
 name = "botocore"
 version = "1.27.94"
 description = "Low-level, data-driven core of boto 3."
 category = "main"
 optional = false
 python-versions = ">= 3.7"
 [package.dependencies]
 jmespath = ">=0.7.1,<2.0.0"
 python-dateutil = ">=2.1,<3.0.0"
 urllib3 = ">=1.25.4,<1.27"
 [package.extras]
 crt = ["awscrt (==0.14.0)"]
 [[package]]
 name = "certifi"
 version = "2022.9.24"
 description = "Python package for providing Mozilla's CA Bundle."
 category = "main"
 optional = false
 python-versions = ">=3.6"
 [[package]]
 name = "charset-normalizer"
 version = "2.1.1"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 category = "main"
 optional = false
 python-versions = ">=3.6.0"
 [package.extras]
 unicode-backport = ["unicodedata2"]
 [[package]]
 name = "colorama"
 version = "0.4.5"
 description = "Cross-platform colored terminal text."
 category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 [[package]]
 name = "depthai"
 version = "2.17.4.0"
 description = "DepthAI Python Library"
 category = "main"
 optional = false
 python-versions = ">=3.6"
 [[package]]
 name = "dill"
 version = "0.3.5.1"
 description = "serialize all of python"
 category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
 [package.extras]
 graph = ["objgraph (>=1.7.2)"]
 [[package]]
 name = "google"
 version = "3.0.0"
 description = "Python bindings to the Google search engine."
 category = "main"
 optional = false
 python-versions = "*"
 [package.dependencies]
 beautifulsoup4 = "*"
 [[package]]
 name = "idna"
 version = "3.4"
 description = "Internationalized Domain Names in Applications (IDNA)"
 category = "main"
 optional = false
 python-versions = ">=3.5"
 [[package]]
 name = "iniconfig"
 version = "1.1.1"
 description = "iniconfig: brain-dead simple config-ini parsing"
 category = "dev"
 optional = false
 python-versions = "*"
 [[package]]
 name = "isort"
 version = "5.10.1"
 description = "A Python utility / library to sort Python imports."
 category = "dev"
 optional = false
 python-versions = ">=3.6.1,<4.0"
 [package.extras]
 colors = ["colorama (>=0.4.3,<0.5.0)"]
 pipfile-deprecated-finder = ["pipreqs", "requirementslib"]
 plugins = ["setuptools"]
 requirements-deprecated-finder = ["pip-api", "pipreqs"]
 [[package]]
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--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,43 +0,0 @@
 [tool.poetry]
 name = "robocar-oak-camera"
 version = "0.0.0"
 description = "Mqtt gateway for oak-lite device"
 authors = ["Cyrille Nofficial <cynoffic@cyrilix.fr>"]
 readme = "README.md"
 packages = [
    { include = "camera" },
    { include = "events" },
 ]
 [tool.poetry.dependencies]
 python = "^3.10"
 paho-mqtt = "^1.6.1"
 depthai = "^2.17.4.0"
 protobuf3 = "^0.2.1"
 google = "^3.0.0"
 opencv-python = "^4.6.0.66"
 blobconverter = "^1.3.0"
 protobuf = "^4.21.8"
 [tool.poetry.group.test.dependencies]
 pytest = "^7.1.3"
 pytest-mock = "^3.10.0"
 [tool.poetry.group.dev.dependencies]
 pylint = "^2.15.4"
 [build-system]
 requires = ["poetry-core>=1.0.0", "poetry-dynamic-versioning"]
 build-backend = "poetry_dynamic_versioning.backend"
 [tool.poetry.scripts]
 rc-oak-camera = 'camera.cli:execute_from_command_line'
 [tool.poetry-dynamic-versioning]
 enable = true
 style = 'semver'
 vcs = 'git'
 dirty = true
 bump = true
--- a/requirements.txt
+++ b/requirements.txt
@@ -0,0 +1,9 @@
 paho-mqtt~=1.6.1
 docopt~=0.6.2
 depthai==2.14.1.0
 opencv-python~=4.5.5.62
 google~=3.0.0
 google-api-core~=2.4.0
 setuptools==60.5.0
 protobuf3
 blobconverter>=1.2.9
--- a/setup.cfg
+++ b/setup.cfg
@@ -0,0 +1,5 @@
 [metadata]
 description-file = README.md
 [aliases]
 test = pytest
--- a/setup.py
+++ b/setup.py
@@ -0,0 +1,68 @@
 import os
 from setuptools import setup, find_packages
 # include the non python files
 def package_files(directory, strip_leading):
    paths = []
    for (path, directories, filenames) in os.walk(directory):
        for filename in filenames:
            package_file = os.path.join(path, filename)
            paths.append(package_file[len(strip_leading):])
    return paths
 tests_require = ['pytest',
                 ]
 setup(name='robocar-oak-camera',
      version='0.1',
      description='Mqtt gateway for oak-lite device.',
      url='https://github.com/cyrilix/robocar-oak-camera',
      license='Apache2',
      entry_points={
          'console_scripts': [
              'rc-oak-camera=camera.cli:execute_from_command_line',
          ],
      },
      setup_requires=['pytest-runner'],
      install_requires=['depthai',
                        'docopt',
                        'paho-mqtt',
                        'protobuf3',
                        'google',
                        'numpy',
                        'opencv-python',
                        'blobconverter',
                        ],
      tests_require=tests_require,
      extras_require={
          'tests': tests_require
      },
      include_package_data=True,
      classifiers=[
          # How mature is this project? Common values are
          #   3 - Alpha
          #   4 - Beta
          #   5 - Production/Stable
          'Development Status :: 3 - Alpha',
          # Indicate who your project is intended for
          'Intended Audience :: Developers',
          'Topic :: Scientific/Engineering :: Artificial Intelligence',
          # Pick your license as you wish (should match "license" above)
          'License :: OSI Approved :: Apache 2 License',
          # Specify the Python versions you support here. In particular, ensure
          # that you indicate whether you support Python 2, Python 3 or both.
          'Programming Language :: Python :: 3.7',
      ],
      keywords='selfdriving cars drive',
      packages=find_packages(exclude=(['tests', 'env'])),
      )
Author	SHA1	Message	Date
Cyrille Nofficial	0467ab780c	WIP	2022-08-10 12:28:41 +02:00
Cyrille Nofficial	2c9c7d9078	fix: regenerate event struct with recent version of protobuf compiler	2022-08-02 12:27:15 +02:00