WIP

fix: regenerate event struct with recent version of protobuf compiler
2022-08-02 12:27:48 +02:00 · 2022-08-02 12:27:15 +02:00
20 changed files with 324 additions and 6323 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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 # List of note tags to take in consideration, separated by a comma.
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--- a/46
+++ b/46
@ -1,47 +1,23 @@
-FROM docker.io/library/python:3.12-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]\n" > /etc/pip.conf &&\
+RUN echo -n "[global]\nextra-index-url=https://www.piwheels.org/simple\n" >> /etc/pip.conf
    echo -n "extra-index-url = https://www.piwheels.org/simple https://git.cyrilix.bzh/api/packages/robocars/pypi/simple \n" >> /etc/pip.conf
-RUN apt-get update && apt-get install -y libgl1 libglib2.0-0 procps cmake g++ gcc
+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
 #################
 FROM base as builder
 RUN apt-get install -y git && \
    pip3 install poetry && \
    poetry self add "poetry-dynamic-versioning[plugin]"
 ADD poetry.lock .
 ADD pyproject.toml .
 ADD camera camera
-ADD README.md .
+ADD setup.cfg setup.cfg
 ADD setup.py setup.py
-# Poetry expect to found a git project
+ENV PYTHON_EGG_CACHE=/tmp/cache
-ADD .git .git
+RUN python3 setup.py install
 RUN poetry build
 #################
 FROM base
 COPY camera_tunning /camera_tuning
 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/README.md
+++ b/README.md
@ -9,136 +9,3 @@ To build images, run script:
 ```bash 
 ./build-docker.sh
 ```
 ## Usage
 ```shell
 usage: cli.py [-h] [-u MQTT_USERNAME] [-p MQTT_PASSWORD] [-b MQTT_BROKER_HOST]
              [-P MQTT_BROKER_PORT] [-C MQTT_CLIENT_ID]
              [-c MQTT_TOPIC_ROBOCAR_OAK_CAMERA]
              [-o MQTT_TOPIC_ROBOCAR_OBJECTS] [-t OBJECTS_THRESHOLD]
              [-d MQTT_TOPIC_ROBOCAR_DISPARITY] [-f CAMERA_FPS]
              [--camera-tuning-exposition {default,500us,8300us}]
              [-H IMAGE_HEIGHT] [-W IMAGE_WIDTH] [--log {info,debug}]
              [--stereo-mode-lr-check] [--stereo-mode-extended-disparity]
              [--stereo-mode-subpixel]
              [--stereo-post-processing-median-filter]
              [--stereo-post-processing-median-value {MEDIAN_OFF,KERNEL_3x3,KERNEL_5x5,KERNEL_7x7}]
              [--stereo-post-processing-speckle-filter]
              [--stereo-post-processing-speckle-enable STEREO_POST_PROCESSING_SPECKLE_ENABLE]
              [--stereo-post-processing-speckle-range STEREO_POST_PROCESSING_SPECKLE_RANGE]
              [--stereo-post-processing-temporal-filter]
              [--stereo-post-processing-temporal-persistency-mode {PERSISTENCY_OFF,VALID_8_OUT_OF_8,VALID_2_IN_LAST_3,VALID_2_IN_LAST_4,VALID_2_OUT_OF_8,VALID_1_IN_LAST_2,VALID_1_IN_LAST_5,VALID_1_IN_LAST_8,PERSISTENCY_INDEFINITELY}]
              [--stereo-post-processing-temporal-alpha STEREO_POST_PROCESSING_TEMPORAL_ALPHA]
              [--stereo-post-processing-temporal-delta STEREO_POST_PROCESSING_TEMPORAL_DELTA]
              [--stereo-post-processing-spatial-filter]
              [--stereo-post-processing-spatial-enable STEREO_POST_PROCESSING_SPATIAL_ENABLE]
              [--stereo-post-processing-spatial-hole-filling-radius STEREO_POST_PROCESSING_SPATIAL_HOLE_FILLING_RADIUS]
              [--stereo-post-processing-spatial-alpha STEREO_POST_PROCESSING_SPATIAL_ALPHA]
              [--stereo-post-processing-spatial-delta STEREO_POST_PROCESSING_SPATIAL_DELTA]
              [--stereo-post-processing-spatial-num-iterations STEREO_POST_PROCESSING_SPATIAL_NUM_ITERATIONS]
              [--stereo-post-processing-threshold-filter]
              [--stereo-post-processing-threshold-min-range STEREO_POST_PROCESSING_THRESHOLD_MIN_RANGE]
              [--stereo-post-processing-threshold-max-range STEREO_POST_PROCESSING_THRESHOLD_MAX_RANGE]
              [--stereo-post-processing-decimation-filter]
              [--stereo-post-processing-decimation-decimal-factor {1,2,3,4}]
              [--stereo-post-processing-decimation-mode {PIXEL_SKIPPING,NON_ZERO_MEDIAN,NON_ZERO_MEAN}]
 options:
  -h, --help            show this help message and exit
  -u MQTT_USERNAME, --mqtt-username MQTT_USERNAME
                        MQTT user
  -p MQTT_PASSWORD, --mqtt-password MQTT_PASSWORD
                        MQTT password
  -b MQTT_BROKER_HOST, --mqtt-broker-host MQTT_BROKER_HOST
                        MQTT broker host
  -P MQTT_BROKER_PORT, --mqtt-broker-port MQTT_BROKER_PORT
                        MQTT broker port
  -C MQTT_CLIENT_ID, --mqtt-client-id MQTT_CLIENT_ID
                        MQTT client id
  -c MQTT_TOPIC_ROBOCAR_OAK_CAMERA, --mqtt-topic-robocar-oak-camera MQTT_TOPIC_ROBOCAR_OAK_CAMERA
                        MQTT topic where to publish robocar-oak-camera frames
  -o MQTT_TOPIC_ROBOCAR_OBJECTS, ---mqtt-topic-robocar-objects MQTT_TOPIC_ROBOCAR_OBJECTS
                        MQTT topic where to publish objects detection results
  -t OBJECTS_THRESHOLD, --objects-threshold OBJECTS_THRESHOLD
                        threshold to filter detected objects
  -d MQTT_TOPIC_ROBOCAR_DISPARITY, ---mqtt-topic-robocar-disparity MQTT_TOPIC_ROBOCAR_DISPARITY
                        MQTT topic where to publish disparity results
  -f CAMERA_FPS, --camera-fps CAMERA_FPS
                        set rate at which camera should produce frames
  --camera-tuning-exposition {default,500us,8300us}
                        override camera exposition configuration
  -H IMAGE_HEIGHT, --image-height IMAGE_HEIGHT
                        image height
  -W IMAGE_WIDTH, --image-width IMAGE_WIDTH
                        image width
  --log {info,debug}    Log level
  --stereo-mode-lr-check
                        remove incorrectly calculated disparity pixels due to
                        occlusions at object borders
  --stereo-mode-extended-disparity
                        allows detecting closer distance objects for the given
                        baseline. This increases the maximum disparity search
                        from 96 to 191, meaning the range is now: [0..190]
  --stereo-mode-subpixel
                        iimproves the precision and is especially useful for
                        long range measurements
  --stereo-post-processing-median-filter
                        enable post-processing median filter
  --stereo-post-processing-median-value {MEDIAN_OFF,KERNEL_3x3,KERNEL_5x5,KERNEL_7x7}
                        Median filter config
  --stereo-post-processing-speckle-filter
                        enable post-processing speckle filter
  --stereo-post-processing-speckle-enable STEREO_POST_PROCESSING_SPECKLE_ENABLE
                        enable post-processing speckle filter
  --stereo-post-processing-speckle-range STEREO_POST_PROCESSING_SPECKLE_RANGE
                        Speckle search range
  --stereo-post-processing-temporal-filter
                        enable post-processing temporal filter
  --stereo-post-processing-temporal-persistency-mode {PERSISTENCY_OFF,VALID_8_OUT_OF_8,VALID_2_IN_LAST_3,VALID_2_IN_LAST_4,VALID_2_OUT_OF_8,VALID_1_IN_LAST_2,VALID_1_IN_LAST_5,VALID_1_IN_LAST_8,PERSISTENCY_INDEFINITELY}
                        Persistency mode.
  --stereo-post-processing-temporal-alpha STEREO_POST_PROCESSING_TEMPORAL_ALPHA
                        The Alpha factor in an exponential moving average with
                        Alpha=1 - no filter. Alpha = 0 - infinite filter.
                        Determines the extent of the temporal history that
                        should be averaged.
  --stereo-post-processing-temporal-delta STEREO_POST_PROCESSING_TEMPORAL_DELTA
                        Step-size boundary. Establishes the threshold used to
                        preserve surfaces (edges). If the disparity value
                        between neighboring pixels exceed the disparity
                        threshold set by this delta parameter, then filtering
                        will be temporarily disabled. Default value 0 means
                        auto: 3 disparity integer levels. In case of subpixel
                        mode it’s 3*number of subpixel levels.
  --stereo-post-processing-spatial-filter
                        enable post-processing spatial filter
  --stereo-post-processing-spatial-enable STEREO_POST_PROCESSING_SPATIAL_ENABLE
                        Whether to enable or disable the filter
  --stereo-post-processing-spatial-hole-filling-radius STEREO_POST_PROCESSING_SPATIAL_HOLE_FILLING_RADIUS
                        An in-place heuristic symmetric hole-filling mode
                        applied horizontally during the filter passes
  --stereo-post-processing-spatial-alpha STEREO_POST_PROCESSING_SPATIAL_ALPHA
                        The Alpha factor in an exponential moving average with
                        Alpha=1 - no filter. Alpha = 0 - infinite filter
  --stereo-post-processing-spatial-delta STEREO_POST_PROCESSING_SPATIAL_DELTA
                        Step-size boundary. Establishes the threshold used to
                        preserve edges
  --stereo-post-processing-spatial-num-iterations STEREO_POST_PROCESSING_SPATIAL_NUM_ITERATIONS
                        Number of iterations over the image in both horizontal
                        and vertical direction
  --stereo-post-processing-threshold-filter
                        enable post-processing threshold filter
  --stereo-post-processing-threshold-min-range STEREO_POST_PROCESSING_THRESHOLD_MIN_RANGE
                        Minimum range in depth units. Depth values under this
                        value are invalidated
  --stereo-post-processing-threshold-max-range STEREO_POST_PROCESSING_THRESHOLD_MAX_RANGE
                        Maximum range in depth units. Depth values over this
                        value are invalidated.
  --stereo-post-processing-decimation-filter
                        enable post-processing decimation filter
  --stereo-post-processing-decimation-decimal-factor {1,2,3,4}
                        Decimation factor
  --stereo-post-processing-decimation-mode {PIXEL_SKIPPING,NON_ZERO_MEDIAN,NON_ZERO_MEAN}
                        Decimation algorithm type
 ```
--- 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 "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
@ -1,373 +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
+from . import depthai as cam
-import types
+from docopt import docopt
 import typing
 from typing import List
 import depthai as dai
 import paho.mqtt.client as mqtt
 from camera import oak_pipeline as cam
 from camera.oak_pipeline import StereoDepthPostFilter, MedianFilter, SpeckleFilter, TemporalFilter, SpatialFilter, \
    ThresholdFilter, DecimationFilter
 CAMERA_EXPOSITION_DEFAULT = "default"
 CAMERA_EXPOSITION_8300US = "8300us"
 CAMERA_EXPOSITION_500US = "500us"
 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("-d", "---mqtt-topic-robocar-disparity",
                        help="MQTT topic where to publish disparity results",
                        default=_get_env_value("MQTT_TOPIC_DISPARITY", "/disparity"))
    parser.add_argument("-f", "--camera-fps",
                        help="set rate at which camera should produce frames",
                        type=int,
                        default=30)
    parser.add_argument("--camera-tuning-exposition", type=str,
                        default=CAMERA_EXPOSITION_DEFAULT,
                        help="override camera exposition configuration",
                        choices=[CAMERA_EXPOSITION_DEFAULT, CAMERA_EXPOSITION_500US, CAMERA_EXPOSITION_8300US])
    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))
    parser.add_argument("--log", help="Log level",
                        type=str,
                        default="info",
                        choices=["info", "debug"])
    parser.add_argument("--disable-disparity", action="store_true",
                    help="enable disparity frame",
                    default=False
                    )
    parser.add_argument("--stereo-mode-lr-check",
                        help="remove incorrectly calculated disparity pixels due to occlusions at object borders",
                        default=False, action="store_true"
                        )
    parser.add_argument("--stereo-mode-extended-disparity",
                        help="allows detecting closer distance objects for the given baseline. This increases the maximum disparity search from 96 to 191, meaning the range is now: [0..190]",
                        default=False, action="store_true"
                        )
    parser.add_argument("--stereo-mode-subpixel",
                        help="iimproves the precision and is especially useful for long range measurements",
                        default=False, action="store_true"
                        )
    parser.add_argument("--stereo-post-processing-median-filter",
                        help="enable post-processing median filter",
                        default=False, action="store_true"
                        )
    parser.add_argument("--stereo-post-processing-median-value",
                        help="Median filter config ",
                        type=str,
                        choices=["MEDIAN_OFF", "KERNEL_3x3", "KERNEL_5x5", "KERNEL_7x7"],
                        default="KERNEL_7x7",
                        )
    parser.add_argument("--stereo-post-processing-speckle-filter",
                        help="enable post-processing speckle filter",
                        default=False, action="store_true"
                        )
    parser.add_argument("--stereo-post-processing-speckle-enable",
                        help="enable post-processing speckle filter",
                        type=bool, default=False
                        )
    parser.add_argument("--stereo-post-processing-speckle-range",
                        help="Speckle search range",
                        type=int, default=50
                        )
    parser.add_argument("--stereo-post-processing-temporal-filter",
                        help="enable post-processing temporal filter",
                        default=False, action="store_true"
                        )
    parser.add_argument("--stereo-post-processing-temporal-persistency-mode",
                        help="Persistency mode.",
                        type=str, default="VALID_2_IN_LAST_4",
                        choices=["PERSISTENCY_OFF", "VALID_8_OUT_OF_8", "VALID_2_IN_LAST_3", "VALID_2_IN_LAST_4",
                                 "VALID_2_OUT_OF_8", "VALID_1_IN_LAST_2", "VALID_1_IN_LAST_5", "VALID_1_IN_LAST_8",
                                 "PERSISTENCY_INDEFINITELY"]
                        )
    parser.add_argument("--stereo-post-processing-temporal-alpha",
                        help="The Alpha factor in an exponential moving average with Alpha=1 - no filter. "
                             "Alpha = 0 - infinite filter. Determines the extent of the temporal history that should be "
                             "averaged. ",
                        type=float, default=0.4,
                        )
    parser.add_argument("--stereo-post-processing-temporal-delta",
                        help="Step-size boundary. Establishes the threshold used to preserve surfaces (edges). "
                             "If the disparity value between neighboring pixels exceed the disparity threshold set by "
                             "this delta parameter, then filtering will be temporarily disabled. Default value 0 means "
                             "auto: 3 disparity integer levels. In case of subpixel mode it’s 3*number of subpixel "
                             "levels.",
                        type=int, default=0,
                        )
    parser.add_argument("--stereo-post-processing-spatial-filter",
                        help="enable post-processing spatial filter",
                        default=False, action="store_true"
                        )
    parser.add_argument("--stereo-post-processing-spatial-enable",
                        help="Whether to enable or disable the filter",
                        type=bool, default=False,
                        )
    parser.add_argument("--stereo-post-processing-spatial-hole-filling-radius",
                        help="An in-place heuristic symmetric hole-filling mode applied horizontally during the filter passes",
                        type=int, default=2,
                        )
    parser.add_argument("--stereo-post-processing-spatial-alpha",
                        help="The Alpha factor in an exponential moving average with Alpha=1 - no filter. Alpha = 0 - infinite filter",
                        type=float, default=0.5,
                        )
    parser.add_argument("--stereo-post-processing-spatial-delta",
                        help="Step-size boundary. Establishes the threshold used to preserve edges",
                        type=int, default=0,
                        )
    parser.add_argument("--stereo-post-processing-spatial-num-iterations",
                        help="Number of iterations over the image in both horizontal and vertical direction",
                        type=int, default=1,
                        )
    parser.add_argument("--stereo-post-processing-threshold-filter",
                        help="enable post-processing threshold filter",
                        default=False, action="store_true"
                        )
    parser.add_argument("--stereo-post-processing-threshold-min-range",
                        help="Minimum range in depth units. Depth values under this value are invalidated",
                        type=int, default=500,
                        )
    parser.add_argument("--stereo-post-processing-threshold-max-range",
                        help="Maximum range in depth units. Depth values over this value are invalidated.",
                        type=int, default=15000,
                        )
    parser.add_argument("--stereo-post-processing-decimation-filter",
                        help="enable post-processing decimation filter",
                        default=False, action="store_true"
                        )
    parser.add_argument("--stereo-post-processing-decimation-decimal-factor",
                        help="Decimation factor",
                        type=int, default=1, choices=[1, 2, 3, 4]
                        )
    parser.add_argument("--stereo-post-processing-decimation-mode",
                        help="Decimation algorithm type",
                        type=str, default="PIXEL_SKIPPING",
                        choices=["PIXEL_SKIPPING", "NON_ZERO_MEDIAN", "NON_ZERO_MEAN"]
                        )
    args = parser.parse_args()
    return args
 def _init_mqtt_client(broker_host: str, broker_port: int, 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:
    """
    args = _parse_args_cli()
    if args.log == "info":
    logging.basicConfig(level=logging.INFO)
    elif args.log == "debug":
        logging.basicConfig(level=logging.DEBUG)
-    client = _init_mqtt_client(broker_host=args.mqtt_broker_host,
+    args = docopt(__doc__)
-                               broker_port=args.mqtt_broker_port,
+
-                               user=args.mqtt_username,
+    client = init_mqtt_client(broker_host=get_default_value(args["--mqtt-broker"], "MQTT_BROKER", "localhost"),
-                               password=args.mqtt_password,
+                              user=get_default_value(args["--mqtt-username"], "MQTT_USERNAME", ""),
-                               client_id=args.mqtt_client_id,
+                              password=get_default_value(args["--mqtt-password"], "MQTT_PASSWORD", ""),
                              client_id=get_default_value(args["--mqtt-client-id"], "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)
    if args.disable_disparity == False:
        depth_source = cam.DepthSource(pipeline=pipeline,
                                       extended_disparity=args.stereo_mode_extended_disparity,
                                       subpixel=args.stereo_mode_subpixel,
                                       lr_check=args.stereo_mode_lr_check,
                                       stereo_filters=stereo_filters),
        disparity_processor = cam.DisparityProcessor(mqtt_client=client, disparity_topic=args.mqtt_topic_robocar_disparity)
    else:
        disparity_processor = None
        depth_source = None
-    pipeline = dai.Pipeline()
+    frame_processor = cam.FramePublisher(mqtt_client=client,
-    if args.camera_tuning_exposition == CAMERA_EXPOSITION_500US:
+                                         frame_topic=frame_topic,
-        pipeline.setCameraTuningBlobPath('/camera_tuning/tuning_exp_limit_500us.bin')
+                                         img_width=int(get_default_value(args["--image-width"], "IMAGE_WIDTH", 160)),
-    elif args.camera_tuning_exposition == CAMERA_EXPOSITION_8300US:
+                                         img_height=int(get_default_value(args["--image-height"], "IMAGE_HEIGHT", 120)))
-        pipeline.setCameraTuningBlobPath('/camera_tuning/tuning_exp_limit_8300us.bin')
+    frame_processor.run()
    stereo_filters = _get_stereo_filters(args)
    pipeline_controller = cam.PipelineController(pipeline=pipeline,
                                                 frame_processor=frame_processor,
                                                 object_processor=object_processor,
                                                 object_node=cam.ObjectDetectionNN(pipeline=pipeline),
                                                 camera=cam.CameraSource(pipeline=pipeline,
                                                                         img_width=args.image_width,
                                                                         img_height=args.image_height,
                                                                         fps=args.camera_fps,
                                                                         ),
                                                 depth_source=depth_source,
                                                 disparity_processor=disparity_processor)
    def sigterm_handler(signum: int, frame: typing.Optional[
        types.FrameType]) -> None:  # pylint: disable=unused-argument  # need to implement handler signature
        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)
 def _get_stereo_filters(args: argparse.Namespace) -> List[StereoDepthPostFilter]:
    filters = []
    if args.stereo_post_processing_median_filter:
        if args.stereo_post_processing_median_value == "MEDIAN_OFF":
            value = dai.MedianFilter.MEDIAN_OFF
        elif args.stereo_post_processing_median_value == "KERNEL_3x3":
            value = dai.MedianFilter.KERNEL_3x3
        elif args.stereo_post_processing_median_value == "KERNEL_5x5":
            value = dai.MedianFilter.KERNEL_5x5
        elif args.stereo_post_processing_median_value == "KERNEL_7x7":
            value = dai.MedianFilter.KERNEL_7x7
        else:
            value = dai.MedianFilter.KERNEL_7x7
        filters.append(MedianFilter(value=value))
    if args.stereo_post_processing_speckle_filter:
        filters.append(SpeckleFilter(enable=args.stereo_post_processing_speckle_enable,
                                     speckle_range=args.stereo_post_processing_speckle_range))
    if args.stereo_post_processing_temporal_filter:
        if args.stereo_post_processing_temporal_persistency-mode == "PERSISTENCY_OFF":
            mode=dai.RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode.PERSISTENCY_OFF
        elif args.stereo_post_processing_temporal_persistency-mode == "VALID_8_OUT_OF_8":
            mode=dai.RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode.VALID_8_OUT_OF_8
        elif args.stereo_post_processing_temporal_persistency-mode == "VALID_2_IN_LAST_3":
            mode=dai.RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode.VALID_2_IN_LAST_3
        elif args.stereo_post_processing_temporal_persistency-mode == "VALID_2_IN_LAST_4":
            mode=dai.RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode.VALID_2_IN_LAST_4
        elif args.stereo_post_processing_temporal_persistency-mode == "VALID_2_OUT_OF_8":
            mode=dai.RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode.VALID_2_OUT_OF_8
        elif args.stereo_post_processing_temporal_persistency-mode == "VALID_1_IN_LAST_2":
            mode=dai.RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode.VALID_1_IN_LAST_2
        elif args.stereo_post_processing_temporal_persistency-mode == "VALID_1_IN_LAST_5":
            mode=dai.RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode.VALID_1_IN_LAST_5
        elif args.stereo_post_processing_temporal_persistency-mode == "VALID_1_IN_LAST_8":
            mode=dai.RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode.VALID_1_IN_LAST_8
        elif args.stereo_post_processing_temporal_persistency-mode == "PERSISTENCY_INDEFINITELY":
            mode=dai.RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode.PERSISTENCY_INDEFINITELY
        else:
            mode=dai.RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode.VALID_2_IN_LAST_4
        filters.append(TemporalFilter(
            enable=args.stereo_post_processing_temporal_enable,
            persistencyMode=mode,
            alpha=args.stereo_post_processing_temporal_alpha,
            delta=args.stereo_post_processing_temporal_delta
        ))
        if args.stereo_post_processing_spatial_filter:
            filters.append(SpatialFilter(enable=args.stereo_post_processing_spatial_enable,
                                         hole_filling_radius=args.stereo_post_processing_spatial_hole_filling_radius,
                                         alpha=args.stereo_post_processing_spatial_alpha,
                                         delta=args.stereo_post_processing_spatial_delta,
                                         num_iterations=args.stereo_post_processing_spatial_num_iterations,
                                         ))
        if args.stereo_post_processing_threshold_filter:
            filters.append(ThresholdFilter(
                min_range=args.stereo_post_processing_threshold_min_range,
                max_range=args.stereo_post_processing_threshold_max_range,
            ))
        if args.stereo_post_processing_decimation_filter:
            if args.stereo_post_processing_decimation_mode == "PIXEL_SKIPPING":
                mode=dai.RawStereoDepthConfig.PostProcessing.DecimationFilter.DecimationMode.PIXEL_SKIPPING
            if args.stereo_post_processing_decimation_mode == "NON_ZERO_MEDIAN":
                mode=dai.RawStereoDepthConfig.PostProcessing.DecimationFilter.DecimationMode.NON_ZERO_MEDIAN
            if args.stereo_post_processing_decimation_mode == "NON_ZERO_MEAN":
                mode=dai.RawStereoDepthConfig.PostProcessing.DecimationFilter.DecimationMode.NON_ZERO_MEAN
            else:
                mode=dai.RawStereoDepthConfig.PostProcessing.DecimationFilter.DecimationMode.PIXEL_SKIPPING
            filters.append(DecimationFilter(
                decimation_factor=args.stereo_post_processing_decimation_decimal_factor,
                mode=mode
            ))
    return filters
 if __name__ == '__main__':
    execute_from_command_line()
--- a/camera/depthai.py
+++ b/camera/depthai.py
@ -0,0 +1,137 @@
 import datetime
 import logging
 import paho.mqtt.client as mqtt
 import events.events_pb2
 import depthai as dai
 import cv2
 import numpy as np
 logger = logging.getLogger(__name__)
 # Closer-in minimum depth, disparity range is doubled (from 95 to 190):
 extended_disparity = False
 # Better accuracy for longer distance, fractional disparity 32-levels:
 subpixel = True
 # Better handling for occlusions:
 lr_check = True
 class FramePublisher:
    def __init__(self, mqtt_client: mqtt.Client, frame_topic: str, img_width: int, img_height: int):
        self._mqtt_client = mqtt_client
        self._frame_topic = frame_topic
        self._img_width = img_width
        self._img_height = img_height
        self._depth = None
        self._pipeline = self._configure_pipeline()
    def _configure_pipeline(self) -> dai.Pipeline:
        logger.info("configure pipeline")
        pipeline = dai.Pipeline()
        cam_rgb = pipeline.create(dai.node.ColorCamera)
        xout_rgb = pipeline.create(dai.node.XLinkOut)
        xout_rgb.setStreamName("rgb")
        monoLeft = pipeline.create(dai.node.MonoCamera)
        monoRight = pipeline.create(dai.node.MonoCamera)
        depth = pipeline.create(dai.node.StereoDepth)
        xout = pipeline.create(dai.node.XLinkOut)
        self._depth = depth
        xout.setStreamName("disparity")
        # Properties
        monoLeft.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
        monoLeft.setBoardSocket(dai.CameraBoardSocket.LEFT)
        monoRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
        monoRight.setBoardSocket(dai.CameraBoardSocket.RIGHT)
        # Create a node that will produce the depth map (using disparity output as it's easier to visualize depth this way)
        depth.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY)
        # Options: MEDIAN_OFF, KERNEL_3x3, KERNEL_5x5, KERNEL_7x7 (default)
        depth.initialConfig.setMedianFilter(dai.MedianFilter.KERNEL_7x7)
        depth.setLeftRightCheck(lr_check)
        depth.setExtendedDisparity(extended_disparity)
        depth.setSubpixel(subpixel)
        config = depth.initialConfig.get()
        config.postProcessing.speckleFilter.enable = True
        config.postProcessing.speckleFilter.speckleRange = 50
        config.postProcessing.temporalFilter.enable = False
        config.postProcessing.spatialFilter.enable = False
        config.postProcessing.spatialFilter.holeFillingRadius = 2
        config.postProcessing.spatialFilter.numIterations = 1
        #config.postProcessing.thresholdFilter.minRange = 400
        #config.postProcessing.thresholdFilter.maxRange = 15000
        config.postProcessing.decimationFilter.decimationFactor = 2
        depth.initialConfig.set(config)
        # Linking
        monoLeft.out.link(depth.left)
        monoRight.out.link(depth.right)
        depth.disparity.link(xout.input)
        # Properties
        cam_rgb.setBoardSocket(dai.CameraBoardSocket.RGB)
        cam_rgb.setPreviewSize(width=self._img_width, height=self._img_height)
        cam_rgb.setInterleaved(False)
        cam_rgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.RGB)
        cam_rgb.setFps(30)
        # Linking
        cam_rgb.preview.link(xout_rgb.input)
        logger.info("pipeline configured")
        return pipeline
    def run(self):
        # 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("rgb", maxSize=queue_size, blocking=False)
            # Output queue will be used to get the disparity frames from the outputs defined above
            q_disparity = device.getOutputQueue(name="disparity", maxSize=4, blocking=False)
            while True:
                try:
                    logger.debug("wait for new frame")
                    inRgb = q_rgb.get()  # blocking call, will wait until a new data has arrived
                    inDisparity = q_disparity.get()
                    # im_resize = inRgb.getCvFrame()
                    im_resize = inDisparity.getCvFrame()
                    # Normalization for better visualization
                    im_resize = (im_resize * (255 / self._depth.initialConfig.getMaxDisparity())).astype(np.uint8)
                    # Available color maps: https://docs.opencv.org/3.4/d3/d50/group__imgproc__colormap.html
                    # im_resize = cv2.applyColorMap(im_resize, cv2.COLORMAP_JET)
                    is_success, im_buf_arr = cv2.imencode(".jpg", im_resize)
                    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)
                except Exception as e:
                    logger.exception("unexpected error: %s", str(e))
--- a/camera/oak_pipeline.py
+++ b/camera/oak_pipeline.py
@ -1,666 +0,0 @@
 """
 Camera event loop
 """
 import abc
 import datetime
 import logging
 import pathlib
 import time
 from dataclasses import dataclass
 from typing import List, Any
 import cv2
 import depthai as dai
 import events.events_pb2 as evt
 import numpy as np
 import numpy.typing as npt
 import paho.mqtt.client as mqtt
 from depthai import Device
 logger = logging.getLogger(__name__)
 _NN_PATH = "/models/mobile_object_localizer_192x192_openvino_2021.4_6shave.blob"
 _NN_WIDTH = 192
 _NN_HEIGHT = 192
 _PREVIEW_WIDTH = 640
 _PREVIEW_HEIGHT = 480
 _CAMERA_BASELINE_IN_MM = 75
 class ObjectProcessor:
    """
    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: evt.FrameRef) -> 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: npt.NDArray[np.float64], frame_ref: evt.FrameRef, scores: npt.NDArray[np.float64]) -> None:
        objects_msg = evt.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):
    """
    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) -> Any:
        """
        Publish camera frames
        :param img: image read from camera
        :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 = evt.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 DisparityProcessor:
    """
       Processor for camera frames
       """
    def __init__(self, mqtt_client: mqtt.Client, disparity_topic: str):
        self._mqtt_client = mqtt_client
        self._disparity_topic = disparity_topic
    def process(self, img: dai.ImgFrame, frame_ref: evt.FrameRef, focal_length_in_pixels: float,
                baseline_mm: float = _CAMERA_BASELINE_IN_MM) -> None:
        im_frame = img.getCvFrame()
        is_success, im_buf_arr = cv2.imencode(".jpg", im_frame)
        if not is_success:
            raise FrameProcessError("unable to process to encode frame to jpg")
        byte_im = im_buf_arr.tobytes()
        disparity_msg = evt.DisparityMessage()
        disparity_msg.disparity = byte_im
        disparity_msg.frame_ref.name = frame_ref.name
        disparity_msg.frame_ref.id = frame_ref.id
        disparity_msg.frame_ref.created_at.FromDatetime(frame_ref.created_at.ToDatetime())
        disparity_msg.focal_length_in_pixels = focal_length_in_pixels
        disparity_msg.baseline_in_mm = baseline_mm
        self._mqtt_client.publish(topic=self._disparity_topic,
                                  payload=disparity_msg.SerializeToString(),
                                  qos=0,
                                  retain=False)
 class Source(abc.ABC):
    """Base class for image source"""
    @abc.abstractmethod
    def get_stream_name(self) -> str:
        """
        Queue/stream name to use to get data
        :return: steam name
        """
    @abc.abstractmethod
    def link(self, input_node: dai.Node.Input) -> None:
        """
        Link this source to the input node
        :param: input_node:  input node to link
        """
 class ObjectDetectionNN:
    """
    Node to detect objects into image
    Read image as input and apply resize transformation before to run NN on it
    Result is available with 'get_stream_name()' stream
    """
    def __init__(self, pipeline: dai.Pipeline):
        # Define a neural network that will make predictions based on the source frames
        detection_nn = pipeline.createNeuralNetwork()
        detection_nn.setBlobPath(pathlib.Path(_NN_PATH))
        detection_nn.setNumPoolFrames(4)
        detection_nn.input.setBlocking(False)
        detection_nn.setNumInferenceThreads(2)
        self._detection_nn = detection_nn
        self._xout = self._configure_xout_nn(pipeline)
        self._detection_nn.out.link(self._xout.input)
        self._manip_image = self._configure_manip(pipeline)
        self._manip_image.out.link(self._detection_nn.input)
    @staticmethod
    def _configure_manip(pipeline: dai.Pipeline) -> dai.node.ImageManip:
        # Resize image
        manip = pipeline.createImageManip()
        manip.initialConfig.setResize(_NN_WIDTH, _NN_HEIGHT)
        manip.initialConfig.setFrameType(dai.ImgFrame.Type.RGB888p)
        manip.initialConfig.setKeepAspectRatio(False)
        return manip
    @staticmethod
    def _configure_xout_nn(pipeline: dai.Pipeline) -> dai.node.XLinkOut:
        xout_nn = pipeline.createXLinkOut()
        xout_nn.setStreamName("nn")
        xout_nn.input.setBlocking(False)
        return xout_nn
    def get_stream_name(self) -> str:
        """
        Queue/stream name to use to get data
        :return: stream name
        """
        return self._xout.getStreamName()
    def get_input(self) -> dai.Node.Input:
        """
        Get input node to use to link with source node
        :return: input to link with source output, see Source.link()
        """
        return self._manip_image.inputImage
 class CameraSource(Source):
    """Image source based on camera preview"""
    def __init__(self, pipeline: dai.Pipeline, img_width: int, img_height: int, fps: int):
        self._cam_rgb = pipeline.createColorCamera()
        self._xout_rgb = pipeline.createXLinkOut()
        self._xout_rgb.setStreamName("rgb")
        # Properties
        self._cam_rgb.setBoardSocket(dai.CameraBoardSocket.RGB)
        self._cam_rgb.setPreviewSize(width=_PREVIEW_WIDTH, height=_PREVIEW_HEIGHT)
        self._cam_rgb.setInterleaved(False)
        self._cam_rgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.RGB)
        self._cam_rgb.setFps(fps)
        self._resize_manip = self._configure_manip(pipeline=pipeline, img_width=img_width, img_height=img_height)
        # link camera preview to output
        self._cam_rgb.preview.link(self._resize_manip.inputImage)
        self._resize_manip.out.link(self._xout_rgb.input)
    def link(self, input_node: dai.Node.Input) -> None:
        self._cam_rgb.preview.link(input_node)
    def get_stream_name(self) -> str:
        return self._xout_rgb.getStreamName()
    @staticmethod
    def _configure_manip(pipeline: dai.Pipeline, img_width: int, img_height: int) -> dai.node.ImageManip:
        # Resize image
        manip = pipeline.createImageManip()
        manip.initialConfig.setResize(img_width, img_height)
        manip.initialConfig.setFrameType(dai.ImgFrame.Type.RGB888p)
        manip.initialConfig.setKeepAspectRatio(False)
        return manip
 class StereoDepthPostFilter(abc.ABC):
    @abc.abstractmethod
    def apply(self, config: dai.RawStereoDepthConfig) -> None:
        pass
 class MedianFilter(StereoDepthPostFilter):
    """
    This is a non-edge preserving Median filter, which can be used to reduce noise and smoothen the depth map.
    Median filter is implemented in hardware, so it’s the fastest filter.
    """
    def __init__(self, value: dai.MedianFilter = dai.MedianFilter.KERNEL_7x7) -> None:
        self._value = value
    def apply(self, config: dai.RawStereoDepthConfig) -> None:
        config.postProcessing.median.value = self._value
 class SpeckleFilter(StereoDepthPostFilter):
    """
    Speckle Filter is used to reduce the speckle noise. Speckle noise is a region with huge variance between
    neighboring disparity/depth pixels, and speckle filter tries to filter this region.
    """
    def __init__(self, enable: bool = True, speckle_range: int = 50) -> None:
        """
        :param enable: Whether to enable or disable the filter.
        :param speckle_range: Speckle search range.
        """
        self._enable = enable
        self._speckle_range = speckle_range
    def apply(self, config: dai.RawStereoDepthConfig) -> None:
        config.postProcessing.speckleFilter.enable = self._enable
        config.postProcessing.speckleFilter.speckleRange = self._speckle_range
 class TemporalFilter(StereoDepthPostFilter):
    """
    Temporal Filter is intended to improve the depth data persistency by manipulating per-pixel values based on
    previous frames. The filter performs a single pass on the data, adjusting the depth values while also updating the
    tracking history. In cases where the pixel data is missing or invalid, the filter uses a user-defined persistency
    mode to decide whether the missing value should be rectified with stored data. Note that due to its reliance on
    historic data the filter may introduce visible blurring/smearing artifacts, and therefore is best-suited for
    static scenes.
    """
    def __init__(self,
                 enable: bool = True,
                 persistencyMode: dai.RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode=dai.RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode.VALID_2_IN_LAST_4,
                 alpha: float = 0.4,
                 delta: int = 0):
        """
        :param enable: Whether to enable or disable the filter.
        :param persistencyMode: Persistency mode. If the current disparity/depth value is invalid, it will be replaced
        by an older value, based on persistency mode.
        :param alpha: The Alpha factor in an exponential moving average with Alpha=1 - no filter.
        Alpha = 0 - infinite filter. Determines the extent of the temporal history that should be averaged.
        :param delta: Step-size boundary. Establishes the threshold used to preserve surfaces (edges).
        If the disparity value between neighboring pixels exceed the disparity threshold set by this delta parameter,
        then filtering will be temporarily disabled. Default value 0 means auto: 3 disparity integer levels.
        In case of subpixel mode it’s 3*number of subpixel levels.
        """
        self._enable = enable
        self._persistencyMode = persistencyMode
        self._alpha = alpha
        self._delta = delta
    def apply(self, config: dai.RawStereoDepthConfig) -> None:
        config.postProcessing.temporalFilter.enable = self._enable
        config.postProcessing.temporalFilter.persistencyMode = self._persistencyMode
        config.postProcessing.temporalFilter.alpha = self._alpha
        config.postProcessing.temporalFilter.delta = self._delta
 class SpatialFilter(StereoDepthPostFilter):
    """
    Spatial Edge-Preserving Filter will fill invalid depth pixels with valid neighboring depth pixels. It performs a
    series of 1D horizontal and vertical passes or iterations, to enhance the smoothness of the reconstructed data.
    """
    def __init__(self,
                 enable: bool = True,
                 hole_filling_radius: int = 2,
                 alpha: float = 0.5,
                 delta: int = 0,
                 num_iterations: int = 1):
        """
        :param enable: Whether to enable or disable the filter.
        :param hole_filling_radius: An in-place heuristic symmetric hole-filling mode applied horizontally during
        the filter passes. Intended to rectify minor artefacts with minimal performance impact. Search radius for
        hole filling.
        :param alpha: The Alpha factor in an exponential moving average with Alpha=1 - no filter.
        Alpha = 0 - infinite filter. Determines the amount of smoothing.
        :param delta: Step-size boundary. Establishes the threshold used to preserve “edges”. If the disparity value
        between neighboring pixels exceed the disparity threshold set by this delta parameter, then filtering will be
        temporarily disabled. Default value 0 means auto: 3 disparity integer levels. In case of subpixel mode it’s
        3*number of subpixel levels.
        :param num_iterations: Number of iterations over the image in both horizontal and vertical direction.
        """
        self._enable = enable
        self._hole_filling_radius = hole_filling_radius
        self._alpha = alpha
        self._delta = delta
        self._num_iterations = num_iterations
    def apply(self, config: dai.RawStereoDepthConfig) -> None:
        config.postProcessing.spatialFilter.enable = self._enable
        config.postProcessing.spatialFilter.holeFillingRadius = self._hole_filling_radius
        config.postProcessing.spatialFilter.alpha = self._alpha
        config.postProcessing.spatialFilter.delta = self._delta
        config.postProcessing.spatialFilter.numIterations = self._num_iterations
 class ThresholdFilter(StereoDepthPostFilter):
    """
    Threshold Filter filters out all disparity/depth pixels outside the configured min/max threshold values.
    """
    def __init__(self, min_range: int = 400, max_range: int = 15000):
        """
        :param min_range: Minimum range in depth units. Depth values under this value are invalidated.
        :param max_range: Maximum range in depth units. Depth values over this value are invalidated.
        """
        self._min_range = min_range
        self._max_range = max_range
    def apply(self, config: dai.RawStereoDepthConfig) -> None:
        config.postProcessing.thresholdFilter.minRange = self._min_range
        config.postProcessing.thresholdFilter.maxRange = self._max_range
 class DecimationFilter(StereoDepthPostFilter):
    """
    Decimation Filter will sub-samples the depth map, which means it reduces the depth scene complexity and allows
    other filters to run faster. Setting decimationFactor to 2 will downscale 1280x800 depth map to 640x400.
    """
    def __init__(self,
                 decimation_factor: int = 1,
                 decimation_mode: dai.RawStereoDepthConfig.PostProcessing.DecimationFilter.DecimationMode = dai.RawStereoDepthConfig.PostProcessing.DecimationFilter.DecimationMode.PIXEL_SKIPPING
                 ):
        """
        :param decimation_factor: Decimation factor. Valid values are 1,2,3,4. Disparity/depth map x/y resolution will
        be decimated with this value.
        :param decimation_mode: Decimation algorithm type.
        """
        self._decimation_factor = decimation_factor
        self._mode = decimation_mode
    def apply(self, config: dai.RawStereoDepthConfig) -> None:
        config.postProcessing.decimationFilter.decimationFactor = self._decimation_factor
        config.postProcessing.decimationFilter.decimationMode = self._mode
 class DepthSource(Source):
    def __init__(self, pipeline: dai.Pipeline,
                 extended_disparity: bool = False,
                 subpixel: bool = False,
                 lr_check: bool = True,
                 stereo_filters: List[StereoDepthPostFilter] = []
                 ) -> None:
        """
        # Closer-in minimum depth, disparity range is doubled (from 95 to 190):
        extended_disparity = False
        # Better accuracy for longer distance, fractional disparity 32-levels:
        subpixel = False
        # Better handling for occlusions:
        lr_check = True
        """
        self._monoLeft = pipeline.create(dai.node.MonoCamera)
        self._monoRight = pipeline.create(dai.node.MonoCamera)
        self._depth = pipeline.create(dai.node.StereoDepth)
        self._xout_disparity = pipeline.create(dai.node.XLinkOut)
        self._xout_disparity.setStreamName("disparity")
        # Properties
        self._monoLeft.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
        self._monoLeft.setCamera("left")
        self._monoLeft.out.link(self._depth.left)
        self._monoRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
        self._monoRight.setCamera("right")
        self._monoRight.out.link(self._depth.right)
        # Create a node that will produce the depth map
        # (using disparity output as it's easier to visualize depth this way)
        self._depth.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY)
        # Options: MEDIAN_OFF, KERNEL_3x3, KERNEL_5x5, KERNEL_7x7 (default)
        self._depth.initialConfig.setMedianFilter(dai.MedianFilter.KERNEL_7x7)
        self._depth.setLeftRightCheck(lr_check)
        self._depth.setExtendedDisparity(extended_disparity)
        self._depth.setSubpixel(subpixel)
        self._depth.disparity.link(self._xout_disparity.input)
        if len(stereo_filters) > 0:
            # Configure post-processing filters
            config = self._depth.initialConfig.get()
            for filter in stereo_filters:
                filter.apply(config)
            self._depth.initialConfig.set(config)
    def get_stream_name(self) -> str:
        return self._xout_disparity.getStreamName()
    def link(self, input_node: dai.Node.Input) -> None:
        self._depth.disparity.link(input_node)
@dataclass
 class MqttConfig:
    """MQTT configuration"""
    host: str
    topic: str
    port: int = 1883
    qos: int = 0
 class MqttSource(Source):
    """Image source based onto mqtt stream"""
    def __init__(self, device: Device, pipeline: dai.Pipeline, mqtt_config: MqttConfig):
        self._mqtt_config = mqtt_config
        self._client = mqtt.Client()
        self._client.user_data_set(mqtt_config)
        self._client.on_connect = self._on_connect
        self._client.on_message = self._on_message
        self._img_in = pipeline.createXLinkIn()
        self._img_in.setStreamName("img_input")
        self._img_out = pipeline.createXLinkOut()
        self._img_out.setStreamName("img_output")
        self._img_in.out.link(self._img_out.input)
        self._img_in_queue = device.getInputQueue(self._img_in.getStreamName())
    def run(self) -> None:
        """ Connect and start mqtt loop """
        self._client.connect(host=self._mqtt_config.host, port=self._mqtt_config.port)
        self._client.loop_start()
    def stop(self) -> None:
        """Stop and disconnect mqtt loop"""
        self._client.loop_stop()
        self._client.disconnect()
    @staticmethod
    # pylint: disable=unused-argument
    def _on_connect(client: mqtt.Client, userdata: MqttConfig, flags: Any,
                    result_connection: Any) -> None:
        # if we lose the connection and reconnect then subscriptions will be renewed.
        client.subscribe(topic=userdata.topic, qos=userdata.qos)
    # pylint: disable=unused-argument
    def _on_message(self, _: mqtt.Client, user_data: MqttConfig, msg: mqtt.MQTTMessage) -> None:
        frame_msg = evt.FrameMessage()
        frame_msg.ParseFromString(msg.payload)
        frame = np.asarray(frame_msg.frame, dtype="uint8")
        frame = cv2.imdecode(frame, cv2.IMREAD_COLOR)
        nn_data = dai.NNData()
        nn_data.setLayer("data", _to_planar(frame, (300, 300)))
        self._img_in_queue.send(nn_data)
    def get_stream_name(self) -> str:
        return self._img_out.getStreamName()
    def link(self, input_node: dai.Node.Input) -> None:
        self._img_in.out.link(input_node)
 def _to_planar(arr: npt.NDArray[np.uint8], shape: tuple[int, int]) -> list[int]:
    return [val for channel in cv2.resize(arr, shape).transpose(2, 0, 1) for y_col in channel for val in y_col]
 class PipelineController:
    """
    Pipeline controller that drive camera device
    """
    def __init__(self, frame_processor: FrameProcessor,
                 object_processor: ObjectProcessor, disparity_processor: DisparityProcessor,
                 camera: Source, depth_source: Source, object_node: ObjectDetectionNN,
                 pipeline: dai.Pipeline):
        self._frame_processor = frame_processor
        self._object_processor = object_processor
        self._disparity_processor = disparity_processor
        self._camera = camera
        self._depth_source = depth_source
        self._object_node = object_node
        self._stop = False
        self._pipeline = pipeline
        self._configure_pipeline()
        self._focal_length_in_pixels: float | None = None
    def _configure_pipeline(self) -> None:
        logger.info("configure pipeline")
        self._pipeline.setOpenVINOVersion(version=dai.OpenVINO.VERSION_2021_4)
        # Link preview to manip and manip to nn
        self._camera.link(self._object_node.get_input())
        logger.info("pipeline configured")
    def run(self) -> None:
        """
        Start event loop
        :return:
        """
        # Connect to device and start pipeline
        with Device(pipeline=self._pipeline) as dev:
            logger.info('MxId: %s', dev.getDeviceInfo().getMxId())
            logger.info('USB speed: %s', dev.getUsbSpeed())
            logger.info('Connected cameras: %s', str(dev.getConnectedCameras()))
            logger.info("output queues found: %s", str(''.join(dev.getOutputQueueNames())))  # type: ignore
            calib_data = dev.readCalibration()
            intrinsics = calib_data.getCameraIntrinsics(dai.CameraBoardSocket.CAM_C)
            self._focal_length_in_pixels = intrinsics[0][0]
            logger.info('Right mono camera focal length in pixels: %s', self._focal_length_in_pixels)
            dev.startPipeline()
            # Queues
            queue_size = 4
            q_rgb = dev.getOutputQueue(name=self._camera.get_stream_name(), maxSize=queue_size,  # type: ignore
                                       blocking=False)
            q_nn = dev.getOutputQueue(name=self._object_node.get_stream_name(), maxSize=queue_size,  # type: ignore
                                      blocking=False)
            if self._disparity_processor is not None:
                q_disparity = dev.getOutputQueue(name=self._depth_source.get_stream_name(), maxSize=queue_size,  # type: ignore
                                                 blocking=False)
            else:
                q_disparity = None
            start_time = time.time()
            counter = 0
            fps = 0
            display_time = time.time()
            self._stop = False
            while True:
                if self._stop:
                    logger.info("stop loop event")
                    return
                try:
                    self._loop_on_camera_events(q_nn, q_rgb, q_disparity)
                # pylint: disable=broad-except # bad frame or event must not stop loop
                except Exception as ex:
                    logger.exception("unexpected error: %s", str(ex))
                counter += 1
                if (time.time() - start_time) > 1:
                    fps = counter / (time.time() - start_time)
                    counter = 0
                    start_time = time.time()
                if (time.time() - display_time) >= 10:
                    display_time = time.time()
                    logger.info("fps: %s", fps)
    def _loop_on_camera_events(self, q_nn: dai.DataOutputQueue, q_rgb: dai.DataOutputQueue, q_disparity: dai.DataOutputQueue) -> None:
        logger.debug("wait for new frame")
        # Wait for frame
        in_rgb: dai.ImgFrame = q_rgb.get()  # type: ignore # blocking call, will wait until a new data has arrived
        try:
            logger.debug("process frame")
            frame_ref = self._frame_processor.process(in_rgb)
        except FrameProcessError as ex:
            logger.error("unable to process frame: %s", str(ex))
            return
        logger.debug("frame processed")
        logger.debug("wait for nn response")
        # Read NN result
        in_nn: dai.NNData = q_nn.get()  # type: ignore
        logger.debug("process objects")
        self._object_processor.process(in_nn, frame_ref)
        logger.debug("objects processed")
        logger.debug("process disparity")
        if self._disparity_processor is not None:
            in_disparity: dai.ImgFrame = q_disparity.get()  # type: ignore
            self._disparity_processor.process(in_disparity, frame_ref=frame_ref,
                                              focal_length_in_pixels=self._focal_length_in_pixels)
        logger.debug("disparity processed")
    def stop(self) -> None:
        """
        Stop event loop, if loop is not running, do nothing
        :return:
        """
        self._stop = True
 def _bbox_to_object(bbox: npt.NDArray[np.float64], score: float) -> evt.Object:
    obj = evt.Object()
    obj.type = evt.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/tests/test_depthai.py
+++ b/camera/tests/test_depthai.py
@ -1,201 +0,0 @@
 import datetime
 import typing
 import unittest.mock
 import depthai as dai
 import events.events_pb2
 import numpy as np
 import numpy.typing as npt
 import paho.mqtt.client as mqtt
 import pytest
 import pytest_mock
 from camera.oak_pipeline import DisparityProcessor, ObjectProcessor, FrameProcessor, FrameProcessError
 Object = dict[str, float]
@pytest.fixture
 def mqtt_client(mocker: pytest_mock.MockerFixture) -> mqtt.Client:
    return mocker.MagicMock()  # type: ignore
 class TestObjectProcessor:
    @pytest.fixture
    def frame_ref(self) -> events.events_pb2.FrameRef:
        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: str) -> typing.List[typing.Union[int, typing.List[int]]]:
            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: str) -> typing.Union[npt.NDArray[np.int64], typing.List[float]]:
            if name == "ExpandDims":  # Detection boxes
                boxes: list[list[float]] = [[0.] * 4] * 100
                boxes[0] = [object1["top"], object1["left"], object1["bottom"], object1["right"]]
                return np.array(boxes)
            elif name == "ExpandDims_2":  # Detection scores
                scores: list[float] = [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) -> ObjectProcessor:
        return ObjectProcessor(mqtt_client, "topic/object", 0.2)
    def test_process_without_object(self, object_processor: ObjectProcessor, mqtt_client: mqtt.Client,
                                    raw_objects_empty: dai.NNData, frame_ref: events.events_pb2.FrameRef) -> None:
        object_processor.process(raw_objects_empty, frame_ref)
        publish_mock: unittest.mock.MagicMock = mqtt_client.publish  # type: ignore
        publish_mock.assert_not_called()
    def test_process_with_object_with_low_score(self, object_processor: ObjectProcessor,
                                                mqtt_client: mqtt.Client, raw_objects_one: dai.NNData,
                                                frame_ref: events.events_pb2.FrameRef) -> None:
        object_processor._objects_threshold = 0.9
        object_processor.process(raw_objects_one, frame_ref)
        publish_mock: unittest.mock.MagicMock = mqtt_client.publish  # type: ignore
        publish_mock.assert_not_called()
    def test_process_with_one_object(self,
                                     object_processor: ObjectProcessor, mqtt_client: mqtt.Client,
                                     raw_objects_one: dai.NNData, frame_ref: events.events_pb2.FrameRef,
                                     object1: Object) -> None:
        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  # type: ignore
        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) -> FrameProcessor:
        return FrameProcessor(mqtt_client, "topic/frame")
    def test_process(self, frame_processor: FrameProcessor, mocker: pytest_mock.MockerFixture,
                     mqtt_client: mqtt.Client) -> None:
        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  # type: ignore
        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: FrameProcessor, mocker: pytest_mock.MockerFixture,
                           mqtt_client: mqtt.Client) -> None:
        img: dai.ImgFrame = mocker.MagicMock()
        mocker.patch(target="cv2.imencode").return_value = (False, None)
        with pytest.raises(FrameProcessError) as ex:
            _ = frame_processor.process(img)
        exception_raised = ex.value
        assert exception_raised.message == "unable to process to encode frame to jpg"
 class TestDisparityProcessor:
    @pytest.fixture
    def frame_ref(self) -> events.events_pb2.FrameRef:
        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 disparity_processor(self, mqtt_client: mqtt.Client) -> DisparityProcessor:
        return DisparityProcessor(mqtt_client, "topic/disparity")
    def test_process(self, disparity_processor: DisparityProcessor, mocker: pytest_mock.MockerFixture,
                     frame_ref: events.events_pb2.FrameRef,
                     mqtt_client: mqtt.Client) -> None:
        img: dai.ImgFrame = mocker.MagicMock()
        mocker.patch(target="cv2.imencode").return_value = (True, np.array(b"img content"))
        disparity_processor.process(img, frame_ref, 42)
        pub_mock: unittest.mock.MagicMock = mqtt_client.publish  # type: ignore
        pub_mock.assert_called_once_with(payload=unittest.mock.ANY, qos=0, retain=False, topic="topic/disparity")
        payload = pub_mock.call_args.kwargs['payload']
        disparity_msg = events.events_pb2.DisparityMessage()
        disparity_msg.ParseFromString(payload)
        assert disparity_msg.frame_ref == frame_ref
        assert disparity_msg.disparity == b"img content"
        assert disparity_msg.focal_length_in_pixels == 42
        assert disparity_msg.baseline_in_mm == 75
    def test_process_error(self, disparity_processor: DisparityProcessor, mocker: pytest_mock.MockerFixture,
                           frame_ref: events.events_pb2.FrameRef,
                           mqtt_client: mqtt.Client) -> None:
        img: dai.ImgFrame = mocker.MagicMock()
        mocker.patch(target="cv2.imencode").return_value = (False, None)
        with pytest.raises(FrameProcessError) as ex:
            disparity_processor.process(img, frame_ref, 42)
        exception_raised = ex.value
        assert exception_raised.message == "unable to process to encode frame to jpg"
--- a/camera_tunning/tuning_exp_limit_500us.bin
+++ b/camera_tunning/tuning_exp_limit_500us.bin
--- a/camera_tunning/tuning_exp_limit_8300us.bin
+++ b/camera_tunning/tuning_exp_limit_8300us.bin
--- a/cv2-stubs/init.pyi
+++ b/cv2-stubs/init.pyi
--- a/camera/tests/init.py
+++ b/camera/tests/init.py
--- a/events/events_pb2.py
+++ b/events/events_pb2.py
@ -0,0 +1,53 @@
 # -*- coding: utf-8 -*-
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
 # source: events/events.proto
 """Generated protocol buffer code."""
 from google.protobuf.internal import builder as _builder
 from google.protobuf import descriptor as _descriptor
 from google.protobuf import descriptor_pool as _descriptor_pool
 from google.protobuf import symbol_database as _symbol_database
 # @@protoc_insertion_point(imports)
 _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(\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())
 if _descriptor._USE_C_DESCRIPTORS == False:
  DESCRIPTOR._options = None
  DESCRIPTOR._serialized_options = b'Z\010./events'
  _DRIVEMODE._serialized_start=1196
  _DRIVEMODE._serialized_end=1241
  _TYPEOBJECT._serialized_start=1243
  _TYPEOBJECT._serialized_end=1293
  _FRAMEREF._serialized_start=72
  _FRAMEREF._serialized_end=156
  _FRAMEMESSAGE._serialized_start=158
  _FRAMEMESSAGE._serialized_end=225
  _STEERINGMESSAGE._serialized_start=227
  _STEERINGMESSAGE._serialized_end=327
  _THROTTLEMESSAGE._serialized_start=329
  _THROTTLEMESSAGE._serialized_end=429
  _DRIVEMODEMESSAGE._serialized_start=431
  _DRIVEMODEMESSAGE._serialized_end=496
  _OBJECTSMESSAGE._serialized_start=498
  _OBJECTSMESSAGE._serialized_end=600
  _OBJECT._serialized_start=603
  _OBJECT._serialized_end=731
  _SWITCHRECORDMESSAGE._serialized_start=733
  _SWITCHRECORDMESSAGE._serialized_end=771
  _ROADMESSAGE._serialized_start=774
  _ROADMESSAGE._serialized_end=914
  _POINT._serialized_start=916
  _POINT._serialized_end=945
  _ELLIPSE._serialized_start=947
  _ELLIPSE._serialized_end=1061
  _RECORDMESSAGE._serialized_start=1064
  _RECORDMESSAGE._serialized_end=1194
 # @@protoc_insertion_point(module_scope)
--- a/poetry.lock
+++ b/poetry.lock
@ -1,631 +0,0 @@
 # This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 [[package]]
 name = "astroid"
 version = "2.15.8"
 description = "An abstract syntax tree for Python with inference support."
 optional = false
 python-versions = ">=3.7.2"
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 wrapt = {version = ">=1.14,<2", markers = "python_version >= \"3.11\""}
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 [package.dependencies]
 soupsieve = ">1.2"
 [package.extras]
 cchardet = ["cchardet"]
 chardet = ["chardet"]
 charset-normalizer = ["charset-normalizer"]
 html5lib = ["html5lib"]
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 [[package]]
 name = "colorama"
 version = "0.4.6"
 description = "Cross-platform colored terminal text."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
 files = [
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 [[package]]
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 lock-version = "2.0"
 python-versions = "^3.12"
 content-hash = "c40d76f962d6502e0946e1cbacedab80dd035d070de7e71e3ae68dc346b69b22"
--- a/pyproject.toml
+++ b/pyproject.toml
@ -1,56 +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" },
 ]
 [tool.poetry.dependencies]
 python = "^3.12"
 paho-mqtt = "^1.6"
 depthai = "^2"
 protobuf3 = "^0.2.1"
 google = "^3.0.0"
 protobuf = "^4.21"
 opencv-python-headless = "^4.6.0"
 robocar-protobuf = {version = "^1.6", source = "robocar"}
 [tool.poetry.group.test.dependencies]
 pytest = "^7.1.3"
 pytest-mock = "^3.10.0"
 [tool.poetry.group.dev.dependencies]
 pylint = "^2.15.4"
 mypy = "^0.982"
 types-paho-mqtt = "^1.6.0.1"
 types-protobuf = "^3.20.4.2"
 [[tool.poetry.source]]
 name = "robocar"
 url = "https://git.cyrilix.bzh/api/packages/robocars/pypi/simple"
 priority = "explicit"
 [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
 [tool.mypy]
 strict = true
 warn_unused_configs = true
 plugins = 'numpy.typing.mypy_plugin'
--- a/requirements.txt
+++ b/requirements.txt
@ -0,0 +1,8 @@
 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
--- 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	32eb2f618f	WIP	2022-08-02 12:27:48 +02:00
Cyrille Nofficial	2c9c7d9078	fix: regenerate event struct with recent version of protobuf compiler	2022-08-02 12:27:15 +02:00