fix(object_detection): add link from image_manip to nn node

see https://github.com/luxonis/depthai-experiments/tree/master/gen2-mobile-object-localizer

.Dockerignore

@@ -1,2 +1,6 @@
 venv
+dist/*
+build-docker.sh
+Dockerfile
+
 

.gitignore

@@ -3,3 +3,6 @@
 *.egg-info
 .idea
 */__pycache__/
+/dist/
+build
+__pycache__

.pylintrc

@@ -0,0 +1,618 @@
+[MAIN]
+
+# Analyse import fallback blocks. This can be used to support both Python 2 and
+# 3 compatible code, which means that the block might have code that exists
+# only in one or another interpreter, leading to false positives when analysed.
+analyse-fallback-blocks=no
+
+# Load and enable all available extensions. Use --list-extensions to see a list
+# all available extensions.
+#enable-all-extensions=
+
+# In error mode, messages with a category besides ERROR or FATAL are
+# suppressed, and no reports are done by default. Error mode is compatible with
+# disabling specific errors.
+#errors-only=
+
+# Always return a 0 (non-error) status code, even if lint errors are found.
+# This is primarily useful in continuous integration scripts.
+#exit-zero=
+
+# A comma-separated list of package or module names from where C extensions may
+# be loaded. Extensions are loading into the active Python interpreter and may
+# run arbitrary code.
+extension-pkg-allow-list=depthai,node,cv2,events.*
+
+# A comma-separated list of package or module names from where C extensions may
+# be loaded. Extensions are loading into the active Python interpreter and may
+# run arbitrary code. (This is an alternative name to extension-pkg-allow-list
+# for backward compatibility.)
+extension-pkg-whitelist=
+
+# Return non-zero exit code if any of these messages/categories are detected,
+# even if score is above --fail-under value. Syntax same as enable. Messages
+# specified are enabled, while categories only check already-enabled messages.
+fail-on=
+
+# Specify a score threshold under which the program will exit with error.
+fail-under=10
+
+# Interpret the stdin as a python script, whose filename needs to be passed as
+# the module_or_package argument.
+#from-stdin=
+
+# Files or directories to be skipped. They should be base names, not paths.
+ignore=CVS
+
+# Add files or directories matching the regular expressions patterns to the
+# ignore-list. The regex matches against paths and can be in Posix or Windows
+# format. Because '\' represents the directory delimiter on Windows systems, it
+# can't be used as an escape character.
+ignore-paths=
+
+# Files or directories matching the regular expression patterns are skipped.
+# The regex matches against base names, not paths. The default value ignores
+# Emacs file locks
+ignore-patterns=^\.#,test_.*?py
+
+# List of module names for which member attributes should not be checked
+# (useful for modules/projects where namespaces are manipulated during runtime
+# and thus existing member attributes cannot be deduced by static analysis). It
+# supports qualified module names, as well as Unix pattern matching.
+ignored-modules=
+
+# Python code to execute, usually for sys.path manipulation such as
+# pygtk.require().
+#init-hook=
+
+# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the
+# number of processors available to use, and will cap the count on Windows to
+# avoid hangs.
+jobs=1
+
+# Control the amount of potential inferred values when inferring a single
+# object. This can help the performance when dealing with large functions or
+# complex, nested conditions.
+limit-inference-results=100
+
+# List of plugins (as comma separated values of python module names) to load,
+# usually to register additional checkers.
+load-plugins=
+
+# Pickle collected data for later comparisons.
+persistent=yes
+
+# Minimum Python version to use for version dependent checks. Will default to
+# the version used to run pylint.
+py-version=3.10
+
+# Discover python modules and packages in the file system subtree.
+recursive=yes
+
+# When enabled, pylint would attempt to guess common misconfiguration and emit
+# user-friendly hints instead of false-positive error messages.
+suggestion-mode=yes
+
+# Allow loading of arbitrary C extensions. Extensions are imported into the
+# active Python interpreter and may run arbitrary code.
+unsafe-load-any-extension=no
+
+# In verbose mode, extra non-checker-related info will be displayed.
+#verbose=
+
+
+[REPORTS]
+
+# Python expression which should return a score less than or equal to 10. You
+# have access to the variables 'fatal', 'error', 'warning', 'refactor',
+# 'convention', and 'info' which contain the number of messages in each
+# category, as well as 'statement' which is the total number of statements
+# analyzed. This score is used by the global evaluation report (RP0004).
+evaluation=max(0, 0 if fatal else 10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10))
+
+# Template used to display messages. This is a python new-style format string
+# used to format the message information. See doc for all details.
+msg-template=
+
+# Set the output format. Available formats are text, parseable, colorized, json
+# and msvs (visual studio). You can also give a reporter class, e.g.
+# mypackage.mymodule.MyReporterClass.
+#output-format=
+
+# Tells whether to display a full report or only the messages.
+reports=no
+
+# Activate the evaluation score.
+score=yes
+
+
+[MESSAGES CONTROL]
+
+# Only show warnings with the listed confidence levels. Leave empty to show
+# all. Valid levels: HIGH, CONTROL_FLOW, INFERENCE, INFERENCE_FAILURE,
+# UNDEFINED.
+confidence=HIGH,
+           CONTROL_FLOW,
+           INFERENCE,
+           INFERENCE_FAILURE,
+           UNDEFINED
+
+# Disable the message, report, category or checker with the given id(s). You
+# can either give multiple identifiers separated by comma (,) or put this
+# option multiple times (only on the command line, not in the configuration
+# file where it should appear only once). You can also use "--disable=all" to
+# disable everything first and then re-enable specific checks. For example, if
+# you want to run only the similarities checker, you can use "--disable=all
+# --enable=similarities". If you want to run only the classes checker, but have
+# no Warning level messages displayed, use "--disable=all --enable=classes
+# --disable=W".
+disable=raw-checker-failed,
+        bad-inline-option,
+        locally-disabled,
+        file-ignored,
+        suppressed-message,
+        useless-suppression,
+        deprecated-pragma,
+        use-symbolic-message-instead
+
+# Enable the message, report, category or checker with the given id(s). You can
+# either give multiple identifier separated by comma (,) or put this option
+# multiple time (only on the command line, not in the configuration file where
+# it should appear only once). See also the "--disable" option for examples.
+enable=c-extension-no-member
+
+
+[LOGGING]
+
+# The type of string formatting that logging methods do. `old` means using %
+# formatting, `new` is for `{}` formatting.
+logging-format-style=old
+
+# Logging modules to check that the string format arguments are in logging
+# function parameter format.
+logging-modules=logging
+
+
+[SPELLING]
+
+# Limits count of emitted suggestions for spelling mistakes.
+max-spelling-suggestions=4
+
+# Spelling dictionary name. Available dictionaries: none. To make it work,
+# install the 'python-enchant' package.
+spelling-dict=
+
+# List of comma separated words that should be considered directives if they
+# appear at the beginning of a comment and should not be checked.
+spelling-ignore-comment-directives=fmt: on,fmt: off,noqa:,noqa,nosec,isort:skip,mypy:
+
+# List of comma separated words that should not be checked.
+spelling-ignore-words=
+
+# A path to a file that contains the private dictionary; one word per line.
+spelling-private-dict-file=
+
+# Tells whether to store unknown words to the private dictionary (see the
+# --spelling-private-dict-file option) instead of raising a message.
+spelling-store-unknown-words=no
+
+
+[MISCELLANEOUS]
+
+# List of note tags to take in consideration, separated by a comma.
+notes=FIXME,
+      XXX,
+      TODO
+
+# Regular expression of note tags to take in consideration.
+notes-rgx=
+
+
+[TYPECHECK]
+
+# List of decorators that produce context managers, such as
+# contextlib.contextmanager. Add to this list to register other decorators that
+# produce valid context managers.
+contextmanager-decorators=contextlib.contextmanager
+
+# List of members which are set dynamically and missed by pylint inference
+# system, and so shouldn't trigger E1101 when accessed. Python regular
+# expressions are accepted.
+generated-members=cv2,events.events_pb2,depthai.*,dai.*
+
+# Tells whether to warn about missing members when the owner of the attribute
+# is inferred to be None.
+ignore-none=yes
+
+# This flag controls whether pylint should warn about no-member and similar
+# checks whenever an opaque object is returned when inferring. The inference
+# can return multiple potential results while evaluating a Python object, but
+# some branches might not be evaluated, which results in partial inference. In
+# that case, it might be useful to still emit no-member and other checks for
+# the rest of the inferred objects.
+ignore-on-opaque-inference=yes
+
+# List of symbolic message names to ignore for Mixin members.
+ignored-checks-for-mixins=no-member,
+                          not-async-context-manager,
+                          not-context-manager,
+                          attribute-defined-outside-init
+
+# List of class names for which member attributes should not be checked (useful
+# for classes with dynamically set attributes). This supports the use of
+# qualified names.
+ignored-classes=optparse.Values,thread._local,_thread._local,argparse.Namespace
+
+# Show a hint with possible names when a member name was not found. The aspect
+# of finding the hint is based on edit distance.
+missing-member-hint=yes
+
+# The minimum edit distance a name should have in order to be considered a
+# similar match for a missing member name.
+missing-member-hint-distance=1
+
+# The total number of similar names that should be taken in consideration when
+
+missing-member-max-choices=1
+
+# Regex pattern to define which classes are considered mixins.
+mixin-class-rgx=.*[Mm]ixin
+
+# List of decorators that change the signature of a decorated function.
+signature-mutators=
+
+
+[CLASSES]
+
+# Warn about protected attribute access inside special methods
+check-protected-access-in-special-methods=no
+
+# List of method names used to declare (i.e. assign) instance attributes.
+defining-attr-methods=__init__,
+                      __new__,
+                      setUp,
+                      __post_init__
+
+# List of member names, which should be excluded from the protected access
+# warning.
+exclude-protected=_asdict,
+                  _fields,
+                  _replace,
+                  _source,
+                  _make
+
+# List of valid names for the first argument in a class method.
+valid-classmethod-first-arg=cls
+
+# List of valid names for the first argument in a metaclass class method.
+valid-metaclass-classmethod-first-arg=cls
+
+
+[VARIABLES]
+
+# List of additional names supposed to be defined in builtins. Remember that
+# you should avoid defining new builtins when possible.
+additional-builtins=
+
+# Tells whether unused global variables should be treated as a violation.
+allow-global-unused-variables=yes
+
+# List of names allowed to shadow builtins
+allowed-redefined-builtins=
+
+# List of strings which can identify a callback function by name. A callback
+# name must start or end with one of those strings.
+callbacks=cb_,
+          _cb
+
+# A regular expression matching the name of dummy variables (i.e. expected to
+# not be used).
+dummy-variables-rgx=_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_
+
+# Argument names that match this expression will be ignored.
+ignored-argument-names=_.*|^ignored_|^unused_
+
+# Tells whether we should check for unused import in __init__ files.
+init-import=no
+
+# List of qualified module names which can have objects that can redefine
+# builtins.
+redefining-builtins-modules=six.moves,past.builtins,future.builtins,builtins,io
+
+
+[FORMAT]
+
+# Expected format of line ending, e.g. empty (any line ending), LF or CRLF.
+expected-line-ending-format=
+
+# Regexp for a line that is allowed to be longer than the limit.
+ignore-long-lines=^\s*(# )?<?https?://\S+>?$
+
+# Number of spaces of indent required inside a hanging or continued line.
+indent-after-paren=4
+
+# String used as indentation unit. This is usually "    " (4 spaces) or "\t" (1
+# tab).
+indent-string='    '
+
+# Maximum number of characters on a single line.
+max-line-length=120
+
+# Maximum number of lines in a module.
+max-module-lines=1000
+
+# Allow the body of a class to be on the same line as the declaration if body
+# contains single statement.
+single-line-class-stmt=no
+
+# Allow the body of an if to be on the same line as the test if there is no
+# else.
+single-line-if-stmt=no
+
+
+[IMPORTS]
+
+# List of modules that can be imported at any level, not just the top level
+# one.
+allow-any-import-level=
+
+# Allow wildcard imports from modules that define __all__.
+allow-wildcard-with-all=no
+
+# Deprecated modules which should not be used, separated by a comma.
+deprecated-modules=
+
+# Output a graph (.gv or any supported image format) of external dependencies
+# to the given file (report RP0402 must not be disabled).
+ext-import-graph=
+
+# Output a graph (.gv or any supported image format) of all (i.e. internal and
+# external) dependencies to the given file (report RP0402 must not be
+# disabled).
+import-graph=
+
+# Output a graph (.gv or any supported image format) of internal dependencies
+# to the given file (report RP0402 must not be disabled).
+int-import-graph=
+
+# Force import order to recognize a module as part of the standard
+# compatibility libraries.
+known-standard-library=
+
+# Force import order to recognize a module as part of a third party library.
+known-third-party=enchant
+
+# Couples of modules and preferred modules, separated by a comma.
+preferred-modules=
+
+
+[METHOD_ARGS]
+
+# List of qualified names (i.e., library.method) which require a timeout
+# parameter e.g. 'requests.api.get,requests.api.post'
+timeout-methods=requests.api.delete,requests.api.get,requests.api.head,requests.api.options,requests.api.patch,requests.api.post,requests.api.put,requests.api.request
+
+
+[EXCEPTIONS]
+
+# Exceptions that will emit a warning when caught.
+overgeneral-exceptions=BaseException,
+                       Exception
+
+
+[REFACTORING]
+
+# Maximum number of nested blocks for function / method body
+max-nested-blocks=5
+
+# Complete name of functions that never returns. When checking for
+# inconsistent-return-statements if a never returning function is called then
+# it will be considered as an explicit return statement and no message will be
+# printed.
+never-returning-functions=sys.exit,argparse.parse_error
+
+
+[SIMILARITIES]
+
+# Comments are removed from the similarity computation
+ignore-comments=yes
+
+# Docstrings are removed from the similarity computation
+ignore-docstrings=yes
+
+# Imports are removed from the similarity computation
+ignore-imports=yes
+
+# Signatures are removed from the similarity computation
+ignore-signatures=yes
+
+# Minimum lines number of a similarity.
+min-similarity-lines=4
+
+
+[DESIGN]
+
+# List of regular expressions of class ancestor names to ignore when counting
+# public methods (see R0903)
+exclude-too-few-public-methods=
+
+# List of qualified class names to ignore when counting class parents (see
+# R0901)
+ignored-parents=
+
+# Maximum number of arguments for function / method.
+max-args=5
+
+# Maximum number of attributes for a class (see R0902).
+max-attributes=7
+
+# Maximum number of boolean expressions in an if statement (see R0916).
+max-bool-expr=5
+
+# Maximum number of branch for function / method body.
+max-branches=12
+
+# Maximum number of locals for function / method body.
+max-locals=15
+
+# Maximum number of parents for a class (see R0901).
+max-parents=7
+
+# Maximum number of public methods for a class (see R0904).
+max-public-methods=20
+
+# Maximum number of return / yield for function / method body.
+max-returns=6
+
+# Maximum number of statements in function / method body.
+max-statements=50
+
+# Minimum number of public methods for a class (see R0903).
+min-public-methods=1
+
+
+[STRING]
+
+# This flag controls whether inconsistent-quotes generates a warning when the
+# character used as a quote delimiter is used inconsistently within a module.
+check-quote-consistency=no
+
+# This flag controls whether the implicit-str-concat should generate a warning
+# on implicit string concatenation in sequences defined over several lines.
+check-str-concat-over-line-jumps=no
+
+
+[BASIC]
+
+# Naming style matching correct argument names.
+argument-naming-style=snake_case
+
+# Regular expression matching correct argument names. Overrides argument-
+# naming-style. If left empty, argument names will be checked with the set
+# naming style.
+#argument-rgx=
+
+# Naming style matching correct attribute names.
+attr-naming-style=snake_case
+
+# Regular expression matching correct attribute names. Overrides attr-naming-
+# style. If left empty, attribute names will be checked with the set naming
+# style.
+#attr-rgx=
+
+# Bad variable names which should always be refused, separated by a comma.
+bad-names=foo,
+          bar,
+          baz,
+          toto,
+          tutu,
+          tata
+
+# Bad variable names regexes, separated by a comma. If names match any regex,
+# they will always be refused
+bad-names-rgxs=
+
+# Naming style matching correct class attribute names.
+class-attribute-naming-style=any
+
+# Regular expression matching correct class attribute names. Overrides class-
+# attribute-naming-style. If left empty, class attribute names will be checked
+# with the set naming style.
+#class-attribute-rgx=
+
+# Naming style matching correct class constant names.
+class-const-naming-style=UPPER_CASE
+
+# Regular expression matching correct class constant names. Overrides class-
+# 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
+
+# Regular expression matching correct class names. Overrides class-naming-
+# style. If left empty, class names will be checked with the set naming style.
+#class-rgx=
+
+# Naming style matching correct constant names.
+const-naming-style=UPPER_CASE
+
+# Regular expression matching correct constant names. Overrides const-naming-
+# style. If left empty, constant names will be checked with the set naming
+# style.
+#const-rgx=
+
+# Minimum line length for functions/classes that require docstrings, shorter
+# ones are exempt.
+docstring-min-length=-1
+
+# Naming style matching correct function names.
+function-naming-style=snake_case
+
+# Regular expression matching correct function names. Overrides function-
+# naming-style. If left empty, function names will be checked with the set
+# naming style.
+#function-rgx=
+
+# Good variable names which should always be accepted, separated by a comma.
+good-names=i,
+           j,
+           k,
+           ex,
+           Run,
+           _
+
+# Good variable names regexes, separated by a comma. If names match any regex,
+# they will always be accepted
+good-names-rgxs=
+
+# Include a hint for the correct naming format with invalid-name.
+include-naming-hint=no
+
+# Naming style matching correct inline iteration names.
+inlinevar-naming-style=any
+
+# Regular expression matching correct inline iteration names. Overrides
+# inlinevar-naming-style. If left empty, inline iteration names will be checked
+# with the set naming style.
+#inlinevar-rgx=
+
+# Naming style matching correct method names.
+method-naming-style=snake_case
+
+# Regular expression matching correct method names. Overrides method-naming-
+# 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
+
+# Regular expression matching correct module names. Overrides module-naming-
+# style. If left empty, module names will be checked with the set naming style.
+#module-rgx=
+
+# Colon-delimited sets of names that determine each other's naming style when
+# the name regexes allow several styles.
+name-group=
+
+# Regular expression which should only match function or class names that do
+# not require a docstring.
+no-docstring-rgx=^_
+
+# List of decorators that produce properties, such as abc.abstractproperty. Add
+# to this list to register other decorators that produce valid properties.
+# These decorators are taken in consideration only for invalid-name.
+property-classes=abc.abstractproperty
+
+# Regular expression matching correct type variable names. If left empty, type
+# variable names will be checked with the set naming style.
+#typevar-rgx=
+
+# Naming style matching correct variable names.
+variable-naming-style=snake_case
+
+# Regular expression matching correct variable names. Overrides variable-
+# naming-style. If left empty, variable names will be checked with the set
+# naming style.
+#variable-rgx=

Dockerfile

@@ -1,23 +1,45 @@
-FROM docker.io/library/python:3.9-slim
+FROM docker.io/library/python:3.10-slim as base
 
 # 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 echo -n "[global]\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
 
-RUN pip3 install numpy
+#################
+FROM base as model-builder
 
-ADD requirements.txt requirements.txt
+RUN python3 -m pip install blobconverter
 
-RUN pip3 install -r requirements.txt
+RUN mkdir -p /models
 
-ADD events events
+RUN blobconverter --zoo-name mobile_object_localizer_192x192 --zoo-type depthai --shaves 6 --version 2021.4 --output-dir /models || echo ""
+
+#################
+FROM base as builder
+
+RUN apt-get install -y git && \
+    pip3 install poetry==1.2.0 && \
+    poetry self add "poetry-dynamic-versioning[plugin]"
+
+ADD poetry.lock .
+ADD pyproject.toml .
 ADD camera camera
-ADD setup.cfg setup.cfg
-ADD setup.py setup.py
+ADD README.md .
 
-ENV PYTHON_EGG_CACHE=/tmp/cache
-RUN python3 setup.py install
+# Poetry expect to found a git project
+ADD .git .git
+
+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

build-docker.sh

@@ -2,11 +2,11 @@
 
 IMAGE_NAME=robocar-oak-camera
 TAG=$(git describe)
-FULL_IMAGE_NAME=docker.io/cyrilix/${IMAGE_NAME}:${TAG}
+FULL_IMAGE_NAME=git.cyrilix.bzh/robocars/${IMAGE_NAME}:${TAG}
 PLATFORM="linux/amd64,linux/arm64"
 #PLATFORM="linux/amd64,linux/arm64,linux/arm/v7"
 
 podman build . --platform "${PLATFORM}" --manifest "${IMAGE_NAME}:${TAG}"
-podman manifest push --format v2s2 "localhost/${IMAGE_NAME}:${TAG}" "docker://${FULL_IMAGE_NAME}"
+podman manifest push --all "localhost/${IMAGE_NAME}:${TAG}" "docker://${FULL_IMAGE_NAME}"
 
 printf "\nImage %s published" "docker://${FULL_IMAGE_NAME}"

camera/cli.py

@@ -1,66 +1,128 @@
 """
-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
+Mqtt gateway for oak-lite device
 """
+import argparse
 import logging
 import os
-from . import depthai as cam
-from docopt import docopt
+import signal
+import typing, types
+
+import depthai as dai
 import paho.mqtt.client as mqtt
 
+from . import depthai as cam  # pylint: disable=reimported
+
 logger = logging.getLogger(__name__)
-logging.basicConfig(level=logging.INFO)
 
-default_client_id = "robocar-depthai"
+_DEFAULT_CLIENT_ID = "robocar-depthai"
 
 
-def init_mqtt_client(broker_host: str, user: str, password: str, client_id: str) -> mqtt.Client:
+def _parse_args_cli() -> argparse.Namespace:
+    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))
+    parser.add_argument("--log", help="Log level",
+                        type=str,
+                        default="info",
+                        choices=["info", "debug"])
+    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 "+ broker_host)
-    client.connect(host=broker_host, port=1883, keepalive=60)
+    logger.info("Connect to mqtt broker %s", broker_host)
+    client.connect(host=broker_host, port=broker_port, keepalive=60)
     logger.info("Connected to mqtt broker")
     return client
 
 
-def execute_from_command_line():
+def execute_from_command_line() -> None:
+    """
+    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)
 
-    args = docopt(__doc__)
-
-    client = init_mqtt_client(broker_host=get_default_value(args["--mqtt-broker"], "MQTT_BROKER", "localhost"),
-                              user=get_default_value(args["--mqtt-username"], "MQTT_USERNAME", ""),
-                              password=get_default_value(args["--mqtt-password"], "MQTT_PASSWORD", ""),
-                              client_id=get_default_value(args["--mqtt-client-id"], "MQTT_CLIENT_ID",
-                                                          default_client_id),
+    client = _init_mqtt_client(broker_host=args.mqtt_broker_host,
+                               broker_port=args.mqtt_broker_port,
+                               user=args.mqtt_username,
+                               password=args.mqtt_password,
+                               client_id=args.mqtt_client_id,
                                )
-    frame_topic = get_default_value(args["--mqtt-topic-robocar-oak-camera"], "MQTT_TOPIC_CAMERA", "/oak/camera_rgb")
+    frame_processor = cam.FrameProcessor(mqtt_client=client, frame_topic=args.mqtt_topic_robocar_oak_camera)
+    object_processor = cam.ObjectProcessor(mqtt_client=client,
+                                           objects_topic=args.mqtt_topic_robocar_objects,
+                                           objects_threshold=args.objects_threshold)
 
-    frame_processor = cam.FramePublisher(mqtt_client=client,
-                                         frame_topic=frame_topic,
-                                         img_width=int(get_default_value(args["--image-width"], "IMAGE_WIDTH", 160)),
-                                         img_height=int(get_default_value(args["--image-height"], "IMAGE_HEIGHT", 120)))
-    frame_processor.run()
+    pipeline = dai.Pipeline()
+    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_width,
+                                                                         ))
+
+    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_default_value(value, env_var: str, default_value) -> str:
-    if value:
-        return value
+def _get_env_value(env_var: str, default_value: str) -> str:
     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)

camera/depthai.py

@@ -1,301 +1,380 @@
+"""
+Camera event loop
+"""
+import abc
 import datetime
 import logging
-import paho.mqtt.client as mqtt
+import pathlib
+import typing
+from dataclasses import dataclass
 
-import events.events_pb2
-
-import depthai as dai
 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__)
 
-
-def to_tensor_result(packet):
-    return {
-        name: np.array(packet.getLayerFp16(name))
-        for name in [tensor.name for tensor in packet.getRaw().tensors]
-    }
+_NN_PATH = "/models/mobile_object_localizer_192x192_openvino_2021.4_6shave.blob"
+_NN_WIDTH = 192
+_NN_HEIGHT = 192
 
 
-def to_planar(frame):
-    return frame.transpose(2, 0, 1).flatten()
+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 FramePublisher:
-    def __init__(self, mqtt_client: mqtt.Client, frame_topic: str, img_width: int, img_height: int):
+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
-        self._img_width = img_width
-        self._img_height = img_height
-        self._pipeline = self._configure_pipeline()
-
-    def _configure_pipeline(self) -> dai.Pipeline:
-        logger.info("configure pipeline")
-        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)
-
-
-
-
-
-
-
-        cam_rgb = pipeline.create(dai.node.ColorCamera)
-        xout_rgb = pipeline.create(dai.node.XLinkOut)
-
-        xout_rgb.setStreamName("rgb")
-
-        # 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):
-
-        with dai.Device(self._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()
-
-            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)
-
-
-
-        # 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)
-
-            while True:
-                try:
-                    logger.debug("wait for new frame")
-                    inRgb = q_rgb.get()  # blocking call, will wait until a new data has arrived
-
-                    im_resize = inRgb.getCvFrame()
 
+    def process(self, img: dai.ImgFrame) -> typing.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 = events.events_pb2.FrameMessage()
+        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
 
-                except Exception as e:
-                    logger.exception("unexpected error: %s", str(e))
+
+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):
+        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=img_width, height=img_height)
+        self._cam_rgb.setInterleaved(False)
+        self._cam_rgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.RGB)
+        self._cam_rgb.setFps(30)
+
+        # link camera preview to output
+        self._cam_rgb.preview.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()
+
+
+@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: typing.Any,
+                    result_connection: typing.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, camera: Source, object_node: ObjectDetectionNN,
+                 pipeline: dai.Pipeline):
+        self._frame_processor = frame_processor
+        self._object_processor = object_processor
+        self._camera = camera
+        self._object_node = object_node
+        self._stop = False
+        self._pipeline = pipeline
+        self._configure_pipeline()
+
+    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
+
+            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)
+
+            self._stop = False
+            while True:
+                if self._stop:
+                    logger.info("stop loop event")
+                    return
+                try:
+                    self._loop_on_camera_events(q_nn, q_rgb)
+                # pylint: disable=broad-except # bad frame or event must not stop loop
+                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) -> 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")
+
+    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

camera/east.py

@@ -1,232 +0,0 @@
-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

camera/tests/test_depthai.py

@@ -0,0 +1,156 @@
+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
+
+import camera.depthai
+
+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) -> 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: 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: camera.depthai.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: camera.depthai.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) -> 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) -> 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: camera.depthai.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(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"

camera/text.py

@@ -1,61 +0,0 @@
-
-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

east.py

@@ -1,229 +0,0 @@
-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

events/events_pb2.py

@@ -1,53 +0,0 @@
-# -*- 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)

main.py

@@ -1,274 +0,0 @@
-#!/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)

pyproject.toml

@@ -0,0 +1,58 @@
+[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 = "^3.10"
+paho-mqtt = "^1.6.1"
+depthai = "^2.19.0"
+protobuf3 = "^0.2.1"
+google = "^3.0.0"
+blobconverter = "^1.3.0"
+protobuf = "^4.21.8"
+opencv-python-headless = "^4.6.0.66"
+robocar-protobuf = {version = "^1.1.2", 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"
+default = false
+secondary = false
+
+[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'

requirements.txt

@@ -1,9 +0,0 @@
-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

setup.cfg

@@ -1,5 +0,0 @@
-[metadata]
-description-file = README.md
-
-[aliases]
-test = pytest

setup.py

@@ -1,68 +0,0 @@
-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'])),
-      )