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robocars:master
robocars:feat/simulator
robocars:build/poetry
robocars:feat/object_detection
robocars:feat/text_recognition
robocars:feat/depth
robocars:v0.6.1
robocars:v0.6.0
robocars:v0.5.0
robocars:v0.4.0
robocars:v0.3.1
robocars:v0.3.0
robocars:v0.2.0
robocars:v0.1.0
robocars:v0.0.1
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compare: robocars:feat/depth
Branches Tags
robocars:master
robocars:feat/simulator
robocars:build/poetry
robocars:feat/object_detection
robocars:feat/text_recognition
robocars:feat/depth
robocars:v0.6.1
robocars:v0.6.0
robocars:v0.5.0
robocars:v0.4.0
robocars:v0.3.1
robocars:v0.3.0
robocars:v0.2.0
robocars:v0.1.0
robocars:v0.0.1

2 Commits
master ... feat/depth

Author SHA1 Message Date
Cyrille Nofficial
32eb2f618f WIP 2022-08-02 12:27:48 +02:00
Cyrille Nofficial
2c9c7d9078 fix: regenerate event struct with recent version of protobuf compiler 2022-08-02 12:27:15 +02:00
20 changed files with 324 additions and 6323 deletions
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4
.Dockerignore
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venv
dist/*
build-docker.sh
Dockerfile

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.gitignore vendored
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*.egg-info
.idea
*/__pycache__/
/dist/
build
__pycache__

618
.pylintrc
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[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=

46
Dockerfile
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@ -1,47 +1,23 @@
FROM docker.io/library/python:3.12-slim as base
FROM docker.io/library/python:3.9-slim
# Configure piwheels repo to use pre-compiled numpy wheels for arm
RUN echo -n "[global]\n" > /etc/pip.conf &&\
echo -n "extra-index-url = https://www.piwheels.org/simple https://git.cyrilix.bzh/api/packages/robocars/pypi/simple \n" >> /etc/pip.conf
RUN echo -n "[global]\nextra-index-url=https://www.piwheels.org/simple\n" >> /etc/pip.conf
RUN apt-get update && apt-get install -y libgl1 libglib2.0-0 procps cmake g++ gcc
RUN apt-get update && apt-get install -y libgl1 libglib2.0-0
#################
FROM base as model-builder
RUN pip3 install numpy
RUN python3 -m pip install blobconverter
ADD requirements.txt requirements.txt
RUN mkdir -p /models
RUN pip3 install -r requirements.txt
RUN blobconverter --zoo-name mobile_object_localizer_192x192 --zoo-type depthai --shaves 6 --version 2021.4 --output-dir /models || echo ""
#################
FROM base as builder
RUN apt-get install -y git && \
pip3 install poetry && \
poetry self add "poetry-dynamic-versioning[plugin]"
ADD poetry.lock .
ADD pyproject.toml .
ADD events events
ADD camera camera
ADD README.md .
ADD setup.cfg setup.cfg
ADD setup.py setup.py
# Poetry expect to found a git project
ADD .git .git
RUN poetry build
#################
FROM base
COPY camera_tunning /camera_tuning
RUN mkdir /models
COPY --from=model-builder /models/mobile_object_localizer_192x192_openvino_2021.4_6shave.blob /models/mobile_object_localizer_192x192_openvino_2021.4_6shave.blob
COPY --from=builder dist/*.whl /tmp/
RUN pip3 install /tmp/*.whl
ENV PYTHON_EGG_CACHE=/tmp/cache
RUN python3 setup.py install
WORKDIR /tmp
USER 1234

133
README.md
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@ -9,136 +9,3 @@ To build images, run script:
```bash
./build-docker.sh
```
## Usage
```shell
usage: cli.py [-h] [-u MQTT_USERNAME] [-p MQTT_PASSWORD] [-b MQTT_BROKER_HOST]
[-P MQTT_BROKER_PORT] [-C MQTT_CLIENT_ID]
[-c MQTT_TOPIC_ROBOCAR_OAK_CAMERA]
[-o MQTT_TOPIC_ROBOCAR_OBJECTS] [-t OBJECTS_THRESHOLD]
[-d MQTT_TOPIC_ROBOCAR_DISPARITY] [-f CAMERA_FPS]
[--camera-tuning-exposition {default,500us,8300us}]
[-H IMAGE_HEIGHT] [-W IMAGE_WIDTH] [--log {info,debug}]
[--stereo-mode-lr-check] [--stereo-mode-extended-disparity]
[--stereo-mode-subpixel]
[--stereo-post-processing-median-filter]
[--stereo-post-processing-median-value {MEDIAN_OFF,KERNEL_3x3,KERNEL_5x5,KERNEL_7x7}]
[--stereo-post-processing-speckle-filter]
[--stereo-post-processing-speckle-enable STEREO_POST_PROCESSING_SPECKLE_ENABLE]
[--stereo-post-processing-speckle-range STEREO_POST_PROCESSING_SPECKLE_RANGE]
[--stereo-post-processing-temporal-filter]
[--stereo-post-processing-temporal-persistency-mode {PERSISTENCY_OFF,VALID_8_OUT_OF_8,VALID_2_IN_LAST_3,VALID_2_IN_LAST_4,VALID_2_OUT_OF_8,VALID_1_IN_LAST_2,VALID_1_IN_LAST_5,VALID_1_IN_LAST_8,PERSISTENCY_INDEFINITELY}]
[--stereo-post-processing-temporal-alpha STEREO_POST_PROCESSING_TEMPORAL_ALPHA]
[--stereo-post-processing-temporal-delta STEREO_POST_PROCESSING_TEMPORAL_DELTA]
[--stereo-post-processing-spatial-filter]
[--stereo-post-processing-spatial-enable STEREO_POST_PROCESSING_SPATIAL_ENABLE]
[--stereo-post-processing-spatial-hole-filling-radius STEREO_POST_PROCESSING_SPATIAL_HOLE_FILLING_RADIUS]
[--stereo-post-processing-spatial-alpha STEREO_POST_PROCESSING_SPATIAL_ALPHA]
[--stereo-post-processing-spatial-delta STEREO_POST_PROCESSING_SPATIAL_DELTA]
[--stereo-post-processing-spatial-num-iterations STEREO_POST_PROCESSING_SPATIAL_NUM_ITERATIONS]
[--stereo-post-processing-threshold-filter]
[--stereo-post-processing-threshold-min-range STEREO_POST_PROCESSING_THRESHOLD_MIN_RANGE]
[--stereo-post-processing-threshold-max-range STEREO_POST_PROCESSING_THRESHOLD_MAX_RANGE]
[--stereo-post-processing-decimation-filter]
[--stereo-post-processing-decimation-decimal-factor {1,2,3,4}]
[--stereo-post-processing-decimation-mode {PIXEL_SKIPPING,NON_ZERO_MEDIAN,NON_ZERO_MEAN}]
options:
-h, --help show this help message and exit
-u MQTT_USERNAME, --mqtt-username MQTT_USERNAME
MQTT user
-p MQTT_PASSWORD, --mqtt-password MQTT_PASSWORD
MQTT password
-b MQTT_BROKER_HOST, --mqtt-broker-host MQTT_BROKER_HOST
MQTT broker host
-P MQTT_BROKER_PORT, --mqtt-broker-port MQTT_BROKER_PORT
MQTT broker port
-C MQTT_CLIENT_ID, --mqtt-client-id MQTT_CLIENT_ID
MQTT client id
-c MQTT_TOPIC_ROBOCAR_OAK_CAMERA, --mqtt-topic-robocar-oak-camera MQTT_TOPIC_ROBOCAR_OAK_CAMERA
MQTT topic where to publish robocar-oak-camera frames
-o MQTT_TOPIC_ROBOCAR_OBJECTS, ---mqtt-topic-robocar-objects MQTT_TOPIC_ROBOCAR_OBJECTS
MQTT topic where to publish objects detection results
-t OBJECTS_THRESHOLD, --objects-threshold OBJECTS_THRESHOLD
threshold to filter detected objects
-d MQTT_TOPIC_ROBOCAR_DISPARITY, ---mqtt-topic-robocar-disparity MQTT_TOPIC_ROBOCAR_DISPARITY
MQTT topic where to publish disparity results
-f CAMERA_FPS, --camera-fps CAMERA_FPS
set rate at which camera should produce frames
--camera-tuning-exposition {default,500us,8300us}
override camera exposition configuration
-H IMAGE_HEIGHT, --image-height IMAGE_HEIGHT
image height
-W IMAGE_WIDTH, --image-width IMAGE_WIDTH
image width
--log {info,debug} Log level
--stereo-mode-lr-check
remove incorrectly calculated disparity pixels due to
occlusions at object borders
--stereo-mode-extended-disparity
allows detecting closer distance objects for the given
baseline. This increases the maximum disparity search
from 96 to 191, meaning the range is now: [0..190]
--stereo-mode-subpixel
iimproves the precision and is especially useful for
long range measurements
--stereo-post-processing-median-filter
enable post-processing median filter
--stereo-post-processing-median-value {MEDIAN_OFF,KERNEL_3x3,KERNEL_5x5,KERNEL_7x7}
Median filter config
--stereo-post-processing-speckle-filter
enable post-processing speckle filter
--stereo-post-processing-speckle-enable STEREO_POST_PROCESSING_SPECKLE_ENABLE
enable post-processing speckle filter
--stereo-post-processing-speckle-range STEREO_POST_PROCESSING_SPECKLE_RANGE
Speckle search range
--stereo-post-processing-temporal-filter
enable post-processing temporal filter
--stereo-post-processing-temporal-persistency-mode {PERSISTENCY_OFF,VALID_8_OUT_OF_8,VALID_2_IN_LAST_3,VALID_2_IN_LAST_4,VALID_2_OUT_OF_8,VALID_1_IN_LAST_2,VALID_1_IN_LAST_5,VALID_1_IN_LAST_8,PERSISTENCY_INDEFINITELY}
Persistency mode.
--stereo-post-processing-temporal-alpha STEREO_POST_PROCESSING_TEMPORAL_ALPHA
The Alpha factor in an exponential moving average with
Alpha=1 - no filter. Alpha = 0 - infinite filter.
Determines the extent of the temporal history that
should be averaged.
--stereo-post-processing-temporal-delta STEREO_POST_PROCESSING_TEMPORAL_DELTA
Step-size boundary. Establishes the threshold used to
preserve surfaces (edges). If the disparity value
between neighboring pixels exceed the disparity
threshold set by this delta parameter, then filtering
will be temporarily disabled. Default value 0 means
auto: 3 disparity integer levels. In case of subpixel
mode it’s 3*number of subpixel levels.
--stereo-post-processing-spatial-filter
enable post-processing spatial filter
--stereo-post-processing-spatial-enable STEREO_POST_PROCESSING_SPATIAL_ENABLE
Whether to enable or disable the filter
--stereo-post-processing-spatial-hole-filling-radius STEREO_POST_PROCESSING_SPATIAL_HOLE_FILLING_RADIUS
An in-place heuristic symmetric hole-filling mode
applied horizontally during the filter passes
--stereo-post-processing-spatial-alpha STEREO_POST_PROCESSING_SPATIAL_ALPHA
The Alpha factor in an exponential moving average with
Alpha=1 - no filter. Alpha = 0 - infinite filter
--stereo-post-processing-spatial-delta STEREO_POST_PROCESSING_SPATIAL_DELTA
Step-size boundary. Establishes the threshold used to
preserve edges
--stereo-post-processing-spatial-num-iterations STEREO_POST_PROCESSING_SPATIAL_NUM_ITERATIONS
Number of iterations over the image in both horizontal
and vertical direction
--stereo-post-processing-threshold-filter
enable post-processing threshold filter
--stereo-post-processing-threshold-min-range STEREO_POST_PROCESSING_THRESHOLD_MIN_RANGE
Minimum range in depth units. Depth values under this
value are invalidated
--stereo-post-processing-threshold-max-range STEREO_POST_PROCESSING_THRESHOLD_MAX_RANGE
Maximum range in depth units. Depth values over this
value are invalidated.
--stereo-post-processing-decimation-filter
enable post-processing decimation filter
--stereo-post-processing-decimation-decimal-factor {1,2,3,4}
Decimation factor
--stereo-post-processing-decimation-mode {PIXEL_SKIPPING,NON_ZERO_MEDIAN,NON_ZERO_MEAN}
Decimation algorithm type
```

4
build-docker.sh
View File

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

385
camera/cli.py
View File

@ -1,373 +1,66 @@
"""
Mqtt gateway for oak-lite device
Publish data from oak-lite device
Usage: rc-oak-camera [-u USERNAME | --mqtt-username=USERNAME] [--mqtt-password=PASSWORD] [--mqtt-broker=HOSTNAME] \
[--mqtt-topic-robocar-oak-camera="TOPIC_CAMERA"] [--mqtt-client-id=CLIENT_ID] \
[-H IMG_HEIGHT | --image-height=IMG_HEIGHT] [-W IMG_WIDTH | --image-width=IMG_width]
Options:
-h --help Show this screen.
-u USERID --mqtt-username=USERNAME MQTT user
-p PASSWORD --mqtt-password=PASSWORD MQTT password
-b HOSTNAME --mqtt-broker=HOSTNAME MQTT broker host
-C CLIENT_ID --mqtt-client-id=CLIENT_ID MQTT client id
-c TOPIC_CAMERA --mqtt-topic-robocar-oak-camera=TOPIC_CAMERA MQTT topic where to publish robocar-oak-camera frames
-H IMG_HEIGHT --image-height=IMG_HEIGHT IMG_HEIGHT image height
-W IMG_WIDTH --image-width=IMG_width IMG_WIDTH image width
"""
import argparse
import logging
import os
import signal
import types
import typing
from typing import List
import depthai as dai
from . import depthai as cam
from docopt import docopt
import paho.mqtt.client as mqtt
from camera import oak_pipeline as cam
from camera.oak_pipeline import StereoDepthPostFilter, MedianFilter, SpeckleFilter, TemporalFilter, SpatialFilter, \
ThresholdFilter, DecimationFilter
CAMERA_EXPOSITION_DEFAULT = "default"
CAMERA_EXPOSITION_8300US = "8300us"
CAMERA_EXPOSITION_500US = "500us"
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
_DEFAULT_CLIENT_ID = "robocar-depthai"
default_client_id = "robocar-depthai"
def _parse_args_cli() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("-u", "--mqtt-username",
help="MQTT user",
default=_get_env_value("MQTT_USERNAME", ""))
parser.add_argument("-p", "--mqtt-password",
help="MQTT password",
default=_get_env_value("MQTT_PASSWORD", ""))
parser.add_argument("-b", "--mqtt-broker-host",
help="MQTT broker host",
default=_get_env_value("MQTT_BROKER_HOST", "localhost"))
parser.add_argument("-P", "--mqtt-broker-port",
help="MQTT broker port",
type=int,
default=_get_env_int_value("MQTT_BROKER_PORT", 1883))
parser.add_argument("-C", "--mqtt-client-id",
help="MQTT client id",
default=_get_env_value("MQTT_CLIENT_ID", _DEFAULT_CLIENT_ID))
parser.add_argument("-c", "--mqtt-topic-robocar-oak-camera",
help="MQTT topic where to publish robocar-oak-camera frames",
default=_get_env_value("MQTT_TOPIC_CAMERA", "/oak/camera_rgb"))
parser.add_argument("-o", "---mqtt-topic-robocar-objects",
help="MQTT topic where to publish objects detection results",
default=_get_env_value("MQTT_TOPIC_OBJECTS", "/objects"))
parser.add_argument("-t", "--objects-threshold",
help="threshold to filter detected objects",
type=float,
default=_get_env_float_value("OBJECTS_THRESHOLD", 0.2))
parser.add_argument("-d", "---mqtt-topic-robocar-disparity",
help="MQTT topic where to publish disparity results",
default=_get_env_value("MQTT_TOPIC_DISPARITY", "/disparity"))
parser.add_argument("-f", "--camera-fps",
help="set rate at which camera should produce frames",
type=int,
default=30)
parser.add_argument("--camera-tuning-exposition", type=str,
default=CAMERA_EXPOSITION_DEFAULT,
help="override camera exposition configuration",
choices=[CAMERA_EXPOSITION_DEFAULT, CAMERA_EXPOSITION_500US, CAMERA_EXPOSITION_8300US])
parser.add_argument("-H", "--image-height", help="image height",
type=int,
default=_get_env_int_value("IMAGE_HEIGHT", 120))
parser.add_argument("-W", "--image-width", help="image width",
type=int,
default=_get_env_int_value("IMAGE_WIDTH", 126))
parser.add_argument("--log", help="Log level",
type=str,
default="info",
choices=["info", "debug"])
parser.add_argument("--disable-disparity", action="store_true",
help="enable disparity frame",
default=False
)
parser.add_argument("--stereo-mode-lr-check",
help="remove incorrectly calculated disparity pixels due to occlusions at object borders",
default=False, action="store_true"
)
parser.add_argument("--stereo-mode-extended-disparity",
help="allows detecting closer distance objects for the given baseline. This increases the maximum disparity search from 96 to 191, meaning the range is now: [0..190]",
default=False, action="store_true"
)
parser.add_argument("--stereo-mode-subpixel",
help="iimproves the precision and is especially useful for long range measurements",
default=False, action="store_true"
)
parser.add_argument("--stereo-post-processing-median-filter",
help="enable post-processing median filter",
default=False, action="store_true"
)
parser.add_argument("--stereo-post-processing-median-value",
help="Median filter config ",
type=str,
choices=["MEDIAN_OFF", "KERNEL_3x3", "KERNEL_5x5", "KERNEL_7x7"],
default="KERNEL_7x7",
)
parser.add_argument("--stereo-post-processing-speckle-filter",
help="enable post-processing speckle filter",
default=False, action="store_true"
)
parser.add_argument("--stereo-post-processing-speckle-enable",
help="enable post-processing speckle filter",
type=bool, default=False
)
parser.add_argument("--stereo-post-processing-speckle-range",
help="Speckle search range",
type=int, default=50
)
parser.add_argument("--stereo-post-processing-temporal-filter",
help="enable post-processing temporal filter",
default=False, action="store_true"
)
parser.add_argument("--stereo-post-processing-temporal-persistency-mode",
help="Persistency mode.",
type=str, default="VALID_2_IN_LAST_4",
choices=["PERSISTENCY_OFF", "VALID_8_OUT_OF_8", "VALID_2_IN_LAST_3", "VALID_2_IN_LAST_4",
"VALID_2_OUT_OF_8", "VALID_1_IN_LAST_2", "VALID_1_IN_LAST_5", "VALID_1_IN_LAST_8",
"PERSISTENCY_INDEFINITELY"]
)
parser.add_argument("--stereo-post-processing-temporal-alpha",
help="The Alpha factor in an exponential moving average with Alpha=1 - no filter. "
"Alpha = 0 - infinite filter. Determines the extent of the temporal history that should be "
"averaged. ",
type=float, default=0.4,
)
parser.add_argument("--stereo-post-processing-temporal-delta",
help="Step-size boundary. Establishes the threshold used to preserve surfaces (edges). "
"If the disparity value between neighboring pixels exceed the disparity threshold set by "
"this delta parameter, then filtering will be temporarily disabled. Default value 0 means "
"auto: 3 disparity integer levels. In case of subpixel mode it’s 3*number of subpixel "
"levels.",
type=int, default=0,
)
parser.add_argument("--stereo-post-processing-spatial-filter",
help="enable post-processing spatial filter",
default=False, action="store_true"
)
parser.add_argument("--stereo-post-processing-spatial-enable",
help="Whether to enable or disable the filter",
type=bool, default=False,
)
parser.add_argument("--stereo-post-processing-spatial-hole-filling-radius",
help="An in-place heuristic symmetric hole-filling mode applied horizontally during the filter passes",
type=int, default=2,
)
parser.add_argument("--stereo-post-processing-spatial-alpha",
help="The Alpha factor in an exponential moving average with Alpha=1 - no filter. Alpha = 0 - infinite filter",
type=float, default=0.5,
)
parser.add_argument("--stereo-post-processing-spatial-delta",
help="Step-size boundary. Establishes the threshold used to preserve edges",
type=int, default=0,
)
parser.add_argument("--stereo-post-processing-spatial-num-iterations",
help="Number of iterations over the image in both horizontal and vertical direction",
type=int, default=1,
)
parser.add_argument("--stereo-post-processing-threshold-filter",
help="enable post-processing threshold filter",
default=False, action="store_true"
)
parser.add_argument("--stereo-post-processing-threshold-min-range",
help="Minimum range in depth units. Depth values under this value are invalidated",
type=int, default=500,
)
parser.add_argument("--stereo-post-processing-threshold-max-range",
help="Maximum range in depth units. Depth values over this value are invalidated.",
type=int, default=15000,
)
parser.add_argument("--stereo-post-processing-decimation-filter",
help="enable post-processing decimation filter",
default=False, action="store_true"
)
parser.add_argument("--stereo-post-processing-decimation-decimal-factor",
help="Decimation factor",
type=int, default=1, choices=[1, 2, 3, 4]
)
parser.add_argument("--stereo-post-processing-decimation-mode",
help="Decimation algorithm type",
type=str, default="PIXEL_SKIPPING",
choices=["PIXEL_SKIPPING", "NON_ZERO_MEDIAN", "NON_ZERO_MEAN"]
)
args = parser.parse_args()
return args
def _init_mqtt_client(broker_host: str, broker_port: int, user: str, password: str, client_id: str) -> mqtt.Client:
def init_mqtt_client(broker_host: str, user: str, password: str, client_id: str) -> mqtt.Client:
logger.info("Start part.py-robocar-oak-camera")
client = mqtt.Client(client_id=client_id, clean_session=True, userdata=None, protocol=mqtt.MQTTv311)
client.username_pw_set(user, password)
logger.info("Connect to mqtt broker %s", broker_host)
client.connect(host=broker_host, port=broker_port, keepalive=60)
logger.info("Connect to mqtt broker "+ broker_host)
client.connect(host=broker_host, port=1883, keepalive=60)
logger.info("Connected to mqtt broker")
return client
def execute_from_command_line() -> None:
"""
Cli entrypoint
:return:
"""
args = _parse_args_cli()
if args.log == "info":
def execute_from_command_line():
logging.basicConfig(level=logging.INFO)
elif args.log == "debug":
logging.basicConfig(level=logging.DEBUG)
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,
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),
)
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)
if args.disable_disparity == False:
depth_source = cam.DepthSource(pipeline=pipeline,
extended_disparity=args.stereo_mode_extended_disparity,
subpixel=args.stereo_mode_subpixel,
lr_check=args.stereo_mode_lr_check,
stereo_filters=stereo_filters),
disparity_processor = cam.DisparityProcessor(mqtt_client=client, disparity_topic=args.mqtt_topic_robocar_disparity)
else:
disparity_processor = None
depth_source = None
frame_topic = get_default_value(args["--mqtt-topic-robocar-oak-camera"], "MQTT_TOPIC_CAMERA", "/oak/camera_rgb")
pipeline = dai.Pipeline()
if args.camera_tuning_exposition == CAMERA_EXPOSITION_500US:
pipeline.setCameraTuningBlobPath('/camera_tuning/tuning_exp_limit_500us.bin')
elif args.camera_tuning_exposition == CAMERA_EXPOSITION_8300US:
pipeline.setCameraTuningBlobPath('/camera_tuning/tuning_exp_limit_8300us.bin')
stereo_filters = _get_stereo_filters(args)
pipeline_controller = cam.PipelineController(pipeline=pipeline,
frame_processor=frame_processor,
object_processor=object_processor,
object_node=cam.ObjectDetectionNN(pipeline=pipeline),
camera=cam.CameraSource(pipeline=pipeline,
img_width=args.image_width,
img_height=args.image_height,
fps=args.camera_fps,
),
depth_source=depth_source,
disparity_processor=disparity_processor)
def sigterm_handler(signum: int, frame: typing.Optional[
types.FrameType]) -> None: # pylint: disable=unused-argument # need to implement handler signature
logger.info("exit on SIGTERM")
pipeline_controller.stop()
signal.signal(signal.SIGTERM, sigterm_handler)
pipeline_controller.run()
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()
def _get_env_value(env_var: str, default_value: str) -> str:
def get_default_value(value, env_var: str, default_value) -> str:
if value:
return value
if env_var in os.environ:
return os.environ[env_var]
return default_value
def _get_env_int_value(env_var: str, default_value: int) -> int:
value = _get_env_value(env_var, str(default_value))
return int(value)
def _get_env_float_value(env_var: str, default_value: float) -> float:
value = _get_env_value(env_var, str(default_value))
return float(value)
def _get_stereo_filters(args: argparse.Namespace) -> List[StereoDepthPostFilter]:
filters = []
if args.stereo_post_processing_median_filter:
if args.stereo_post_processing_median_value == "MEDIAN_OFF":
value = dai.MedianFilter.MEDIAN_OFF
elif args.stereo_post_processing_median_value == "KERNEL_3x3":
value = dai.MedianFilter.KERNEL_3x3
elif args.stereo_post_processing_median_value == "KERNEL_5x5":
value = dai.MedianFilter.KERNEL_5x5
elif args.stereo_post_processing_median_value == "KERNEL_7x7":
value = dai.MedianFilter.KERNEL_7x7
else:
value = dai.MedianFilter.KERNEL_7x7
filters.append(MedianFilter(value=value))
if args.stereo_post_processing_speckle_filter:
filters.append(SpeckleFilter(enable=args.stereo_post_processing_speckle_enable,
speckle_range=args.stereo_post_processing_speckle_range))
if args.stereo_post_processing_temporal_filter:
if args.stereo_post_processing_temporal_persistency-mode == "PERSISTENCY_OFF":
mode=dai.RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode.PERSISTENCY_OFF
elif args.stereo_post_processing_temporal_persistency-mode == "VALID_8_OUT_OF_8":
mode=dai.RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode.VALID_8_OUT_OF_8
elif args.stereo_post_processing_temporal_persistency-mode == "VALID_2_IN_LAST_3":
mode=dai.RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode.VALID_2_IN_LAST_3
elif args.stereo_post_processing_temporal_persistency-mode == "VALID_2_IN_LAST_4":
mode=dai.RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode.VALID_2_IN_LAST_4
elif args.stereo_post_processing_temporal_persistency-mode == "VALID_2_OUT_OF_8":
mode=dai.RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode.VALID_2_OUT_OF_8
elif args.stereo_post_processing_temporal_persistency-mode == "VALID_1_IN_LAST_2":
mode=dai.RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode.VALID_1_IN_LAST_2
elif args.stereo_post_processing_temporal_persistency-mode == "VALID_1_IN_LAST_5":
mode=dai.RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode.VALID_1_IN_LAST_5
elif args.stereo_post_processing_temporal_persistency-mode == "VALID_1_IN_LAST_8":
mode=dai.RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode.VALID_1_IN_LAST_8
elif args.stereo_post_processing_temporal_persistency-mode == "PERSISTENCY_INDEFINITELY":
mode=dai.RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode.PERSISTENCY_INDEFINITELY
else:
mode=dai.RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode.VALID_2_IN_LAST_4
filters.append(TemporalFilter(
enable=args.stereo_post_processing_temporal_enable,
persistencyMode=mode,
alpha=args.stereo_post_processing_temporal_alpha,
delta=args.stereo_post_processing_temporal_delta
))
if args.stereo_post_processing_spatial_filter:
filters.append(SpatialFilter(enable=args.stereo_post_processing_spatial_enable,
hole_filling_radius=args.stereo_post_processing_spatial_hole_filling_radius,
alpha=args.stereo_post_processing_spatial_alpha,
delta=args.stereo_post_processing_spatial_delta,
num_iterations=args.stereo_post_processing_spatial_num_iterations,
))
if args.stereo_post_processing_threshold_filter:
filters.append(ThresholdFilter(
min_range=args.stereo_post_processing_threshold_min_range,
max_range=args.stereo_post_processing_threshold_max_range,
))
if args.stereo_post_processing_decimation_filter:
if args.stereo_post_processing_decimation_mode == "PIXEL_SKIPPING":
mode=dai.RawStereoDepthConfig.PostProcessing.DecimationFilter.DecimationMode.PIXEL_SKIPPING
if args.stereo_post_processing_decimation_mode == "NON_ZERO_MEDIAN":
mode=dai.RawStereoDepthConfig.PostProcessing.DecimationFilter.DecimationMode.NON_ZERO_MEDIAN
if args.stereo_post_processing_decimation_mode == "NON_ZERO_MEAN":
mode=dai.RawStereoDepthConfig.PostProcessing.DecimationFilter.DecimationMode.NON_ZERO_MEAN
else:
mode=dai.RawStereoDepthConfig.PostProcessing.DecimationFilter.DecimationMode.PIXEL_SKIPPING
filters.append(DecimationFilter(
decimation_factor=args.stereo_post_processing_decimation_decimal_factor,
mode=mode
))
return filters
if __name__ == '__main__':
execute_from_command_line()

137
camera/depthai.py Normal file
View File

@ -0,0 +1,137 @@
import datetime
import logging
import paho.mqtt.client as mqtt
import events.events_pb2
import depthai as dai
import cv2
import numpy as np
logger = logging.getLogger(__name__)
# Closer-in minimum depth, disparity range is doubled (from 95 to 190):
extended_disparity = False
# Better accuracy for longer distance, fractional disparity 32-levels:
subpixel = True
# Better handling for occlusions:
lr_check = True
class FramePublisher:
def __init__(self, mqtt_client: mqtt.Client, frame_topic: str, img_width: int, img_height: int):
self._mqtt_client = mqtt_client
self._frame_topic = frame_topic
self._img_width = img_width
self._img_height = img_height
self._depth = None
self._pipeline = self._configure_pipeline()
def _configure_pipeline(self) -> dai.Pipeline:
logger.info("configure pipeline")
pipeline = dai.Pipeline()
cam_rgb = pipeline.create(dai.node.ColorCamera)
xout_rgb = pipeline.create(dai.node.XLinkOut)
xout_rgb.setStreamName("rgb")
monoLeft = pipeline.create(dai.node.MonoCamera)
monoRight = pipeline.create(dai.node.MonoCamera)
depth = pipeline.create(dai.node.StereoDepth)
xout = pipeline.create(dai.node.XLinkOut)
self._depth = depth
xout.setStreamName("disparity")
# Properties
monoLeft.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
monoLeft.setBoardSocket(dai.CameraBoardSocket.LEFT)
monoRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
monoRight.setBoardSocket(dai.CameraBoardSocket.RIGHT)
# Create a node that will produce the depth map (using disparity output as it's easier to visualize depth this way)
depth.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY)
# Options: MEDIAN_OFF, KERNEL_3x3, KERNEL_5x5, KERNEL_7x7 (default)
depth.initialConfig.setMedianFilter(dai.MedianFilter.KERNEL_7x7)
depth.setLeftRightCheck(lr_check)
depth.setExtendedDisparity(extended_disparity)
depth.setSubpixel(subpixel)
config = depth.initialConfig.get()
config.postProcessing.speckleFilter.enable = True
config.postProcessing.speckleFilter.speckleRange = 50
config.postProcessing.temporalFilter.enable = False
config.postProcessing.spatialFilter.enable = False
config.postProcessing.spatialFilter.holeFillingRadius = 2
config.postProcessing.spatialFilter.numIterations = 1
#config.postProcessing.thresholdFilter.minRange = 400
#config.postProcessing.thresholdFilter.maxRange = 15000
config.postProcessing.decimationFilter.decimationFactor = 2
depth.initialConfig.set(config)
# Linking
monoLeft.out.link(depth.left)
monoRight.out.link(depth.right)
depth.disparity.link(xout.input)
# Properties
cam_rgb.setBoardSocket(dai.CameraBoardSocket.RGB)
cam_rgb.setPreviewSize(width=self._img_width, height=self._img_height)
cam_rgb.setInterleaved(False)
cam_rgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.RGB)
cam_rgb.setFps(30)
# Linking
cam_rgb.preview.link(xout_rgb.input)
logger.info("pipeline configured")
return pipeline
def run(self):
# Connect to device and start pipeline
with dai.Device(self._pipeline) as device:
logger.info('MxId: %s', device.getDeviceInfo().getMxId())
logger.info('USB speed: %s', device.getUsbSpeed())
logger.info('Connected cameras: %s', device.getConnectedCameras())
logger.info("output queues found: %s", device.getOutputQueueNames())
device.startPipeline()
# Queues
queue_size = 4
q_rgb = device.getOutputQueue("rgb", maxSize=queue_size, blocking=False)
# Output queue will be used to get the disparity frames from the outputs defined above
q_disparity = device.getOutputQueue(name="disparity", maxSize=4, blocking=False)
while True:
try:
logger.debug("wait for new frame")
inRgb = q_rgb.get() # blocking call, will wait until a new data has arrived
inDisparity = q_disparity.get()
# im_resize = inRgb.getCvFrame()
im_resize = inDisparity.getCvFrame()
# Normalization for better visualization
im_resize = (im_resize * (255 / self._depth.initialConfig.getMaxDisparity())).astype(np.uint8)
# Available color maps: https://docs.opencv.org/3.4/d3/d50/group__imgproc__colormap.html
# im_resize = cv2.applyColorMap(im_resize, cv2.COLORMAP_JET)
is_success, im_buf_arr = cv2.imencode(".jpg", im_resize)
byte_im = im_buf_arr.tobytes()
now = datetime.datetime.now()
frame_msg = events.events_pb2.FrameMessage()
frame_msg.id.name = "robocar-oak-camera-oak"
frame_msg.id.id = str(int(now.timestamp() * 1000))
frame_msg.id.created_at.FromDatetime(now)
frame_msg.frame = byte_im
logger.debug("publish frame event to %s", self._frame_topic)
self._mqtt_client.publish(topic=self._frame_topic,
payload=frame_msg.SerializeToString(),
qos=0,
retain=False)
except Exception as e:
logger.exception("unexpected error: %s", str(e))

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camera/oak_pipeline.py
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@ -1,666 +0,0 @@
"""
Camera event loop
"""
import abc
import datetime
import logging
import pathlib
import time
from dataclasses import dataclass
from typing import List, Any
import cv2
import depthai as dai
import events.events_pb2 as evt
import numpy as np
import numpy.typing as npt
import paho.mqtt.client as mqtt
from depthai import Device
logger = logging.getLogger(__name__)
_NN_PATH = "/models/mobile_object_localizer_192x192_openvino_2021.4_6shave.blob"
_NN_WIDTH = 192
_NN_HEIGHT = 192
_PREVIEW_WIDTH = 640
_PREVIEW_HEIGHT = 480
_CAMERA_BASELINE_IN_MM = 75
class ObjectProcessor:
"""
Processor for Object detection
"""
def __init__(self, mqtt_client: mqtt.Client, objects_topic: str, objects_threshold: float):
self._mqtt_client = mqtt_client
self._objects_topic = objects_topic
self._objects_threshold = objects_threshold
def process(self, in_nn: dai.NNData, frame_ref: evt.FrameRef) -> None:
"""
Parse and publish result of NeuralNetwork result
:param in_nn: NeuralNetwork result read from device
:param frame_ref: Id of the frame where objects are been detected
:return:
"""
detection_boxes = np.array(in_nn.getLayerFp16("ExpandDims")).reshape((100, 4))
detection_scores = np.array(in_nn.getLayerFp16("ExpandDims_2")).reshape((100,))
# keep boxes bigger than threshold
mask = detection_scores >= self._objects_threshold
boxes = detection_boxes[mask]
scores = detection_scores[mask]
if boxes.shape[0] > 0:
self._publish_objects(boxes, frame_ref, scores)
def _publish_objects(self, boxes: npt.NDArray[np.float64], frame_ref: evt.FrameRef, scores: npt.NDArray[np.float64]) -> None:
objects_msg = evt.ObjectsMessage()
objs = []
for i in range(boxes.shape[0]):
logger.debug("new object detected: %s", str(boxes[i]))
objs.append(_bbox_to_object(boxes[i], scores[i].astype(float)))
objects_msg.objects.extend(objs)
objects_msg.frame_ref.name = frame_ref.name
objects_msg.frame_ref.id = frame_ref.id
objects_msg.frame_ref.created_at.FromDatetime(frame_ref.created_at.ToDatetime())
logger.debug("publish object event to %s", self._objects_topic)
self._mqtt_client.publish(topic=self._objects_topic,
payload=objects_msg.SerializeToString(),
qos=0,
retain=False)
class FrameProcessError(Exception):
"""
Error base for invalid frame processing
Attributes:
message -- explanation of the error
"""
def __init__(self, message: str):
"""
:param message: explanation of the error
"""
self.message = message
class FrameProcessor:
"""
Processor for camera frames
"""
def __init__(self, mqtt_client: mqtt.Client, frame_topic: str):
self._mqtt_client = mqtt_client
self._frame_topic = frame_topic
def process(self, img: dai.ImgFrame) -> Any:
"""
Publish camera frames
:param img: image read from camera
:return:
id frame reference
:raise:
FrameProcessError if frame can't be processed
"""
im_resize = img.getCvFrame()
is_success, im_buf_arr = cv2.imencode(".jpg", im_resize)
if not is_success:
raise FrameProcessError("unable to process to encode frame to jpg")
byte_im = im_buf_arr.tobytes()
now = datetime.datetime.now()
frame_msg = evt.FrameMessage()
frame_msg.id.name = "robocar-oak-camera-oak"
frame_msg.id.id = str(int(now.timestamp() * 1000))
frame_msg.id.created_at.FromDatetime(now)
frame_msg.frame = byte_im
logger.debug("publish frame event to %s", self._frame_topic)
self._mqtt_client.publish(topic=self._frame_topic,
payload=frame_msg.SerializeToString(),
qos=0,
retain=False)
return frame_msg.id
class DisparityProcessor:
"""
Processor for camera frames
"""
def __init__(self, mqtt_client: mqtt.Client, disparity_topic: str):
self._mqtt_client = mqtt_client
self._disparity_topic = disparity_topic
def process(self, img: dai.ImgFrame, frame_ref: evt.FrameRef, focal_length_in_pixels: float,
baseline_mm: float = _CAMERA_BASELINE_IN_MM) -> None:
im_frame = img.getCvFrame()
is_success, im_buf_arr = cv2.imencode(".jpg", im_frame)
if not is_success:
raise FrameProcessError("unable to process to encode frame to jpg")
byte_im = im_buf_arr.tobytes()
disparity_msg = evt.DisparityMessage()
disparity_msg.disparity = byte_im
disparity_msg.frame_ref.name = frame_ref.name
disparity_msg.frame_ref.id = frame_ref.id
disparity_msg.frame_ref.created_at.FromDatetime(frame_ref.created_at.ToDatetime())
disparity_msg.focal_length_in_pixels = focal_length_in_pixels
disparity_msg.baseline_in_mm = baseline_mm
self._mqtt_client.publish(topic=self._disparity_topic,
payload=disparity_msg.SerializeToString(),
qos=0,
retain=False)
class Source(abc.ABC):
"""Base class for image source"""
@abc.abstractmethod
def get_stream_name(self) -> str:
"""
Queue/stream name to use to get data
:return: steam name
"""
@abc.abstractmethod
def link(self, input_node: dai.Node.Input) -> None:
"""
Link this source to the input node
:param: input_node: input node to link
"""
class ObjectDetectionNN:
"""
Node to detect objects into image
Read image as input and apply resize transformation before to run NN on it
Result is available with 'get_stream_name()' stream
"""
def __init__(self, pipeline: dai.Pipeline):
# Define a neural network that will make predictions based on the source frames
detection_nn = pipeline.createNeuralNetwork()
detection_nn.setBlobPath(pathlib.Path(_NN_PATH))
detection_nn.setNumPoolFrames(4)
detection_nn.input.setBlocking(False)
detection_nn.setNumInferenceThreads(2)
self._detection_nn = detection_nn
self._xout = self._configure_xout_nn(pipeline)
self._detection_nn.out.link(self._xout.input)
self._manip_image = self._configure_manip(pipeline)
self._manip_image.out.link(self._detection_nn.input)
@staticmethod
def _configure_manip(pipeline: dai.Pipeline) -> dai.node.ImageManip:
# Resize image
manip = pipeline.createImageManip()
manip.initialConfig.setResize(_NN_WIDTH, _NN_HEIGHT)
manip.initialConfig.setFrameType(dai.ImgFrame.Type.RGB888p)
manip.initialConfig.setKeepAspectRatio(False)
return manip
@staticmethod
def _configure_xout_nn(pipeline: dai.Pipeline) -> dai.node.XLinkOut:
xout_nn = pipeline.createXLinkOut()
xout_nn.setStreamName("nn")
xout_nn.input.setBlocking(False)
return xout_nn
def get_stream_name(self) -> str:
"""
Queue/stream name to use to get data
:return: stream name
"""
return self._xout.getStreamName()
def get_input(self) -> dai.Node.Input:
"""
Get input node to use to link with source node
:return: input to link with source output, see Source.link()
"""
return self._manip_image.inputImage
class CameraSource(Source):
"""Image source based on camera preview"""
def __init__(self, pipeline: dai.Pipeline, img_width: int, img_height: int, fps: int):
self._cam_rgb = pipeline.createColorCamera()
self._xout_rgb = pipeline.createXLinkOut()
self._xout_rgb.setStreamName("rgb")
# Properties
self._cam_rgb.setBoardSocket(dai.CameraBoardSocket.RGB)
self._cam_rgb.setPreviewSize(width=_PREVIEW_WIDTH, height=_PREVIEW_HEIGHT)
self._cam_rgb.setInterleaved(False)
self._cam_rgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.RGB)
self._cam_rgb.setFps(fps)
self._resize_manip = self._configure_manip(pipeline=pipeline, img_width=img_width, img_height=img_height)
# link camera preview to output
self._cam_rgb.preview.link(self._resize_manip.inputImage)
self._resize_manip.out.link(self._xout_rgb.input)
def link(self, input_node: dai.Node.Input) -> None:
self._cam_rgb.preview.link(input_node)
def get_stream_name(self) -> str:
return self._xout_rgb.getStreamName()
@staticmethod
def _configure_manip(pipeline: dai.Pipeline, img_width: int, img_height: int) -> dai.node.ImageManip:
# Resize image
manip = pipeline.createImageManip()
manip.initialConfig.setResize(img_width, img_height)
manip.initialConfig.setFrameType(dai.ImgFrame.Type.RGB888p)
manip.initialConfig.setKeepAspectRatio(False)
return manip
class StereoDepthPostFilter(abc.ABC):
@abc.abstractmethod
def apply(self, config: dai.RawStereoDepthConfig) -> None:
pass
class MedianFilter(StereoDepthPostFilter):
"""
This is a non-edge preserving Median filter, which can be used to reduce noise and smoothen the depth map.
Median filter is implemented in hardware, so it’s the fastest filter.
"""
def __init__(self, value: dai.MedianFilter = dai.MedianFilter.KERNEL_7x7) -> None:
self._value = value
def apply(self, config: dai.RawStereoDepthConfig) -> None:
config.postProcessing.median.value = self._value
class SpeckleFilter(StereoDepthPostFilter):
"""
Speckle Filter is used to reduce the speckle noise. Speckle noise is a region with huge variance between
neighboring disparity/depth pixels, and speckle filter tries to filter this region.
"""
def __init__(self, enable: bool = True, speckle_range: int = 50) -> None:
"""
:param enable: Whether to enable or disable the filter.
:param speckle_range: Speckle search range.
"""
self._enable = enable
self._speckle_range = speckle_range
def apply(self, config: dai.RawStereoDepthConfig) -> None:
config.postProcessing.speckleFilter.enable = self._enable
config.postProcessing.speckleFilter.speckleRange = self._speckle_range
class TemporalFilter(StereoDepthPostFilter):
"""
Temporal Filter is intended to improve the depth data persistency by manipulating per-pixel values based on
previous frames. The filter performs a single pass on the data, adjusting the depth values while also updating the
tracking history. In cases where the pixel data is missing or invalid, the filter uses a user-defined persistency
mode to decide whether the missing value should be rectified with stored data. Note that due to its reliance on
historic data the filter may introduce visible blurring/smearing artifacts, and therefore is best-suited for
static scenes.
"""
def __init__(self,
enable: bool = True,
persistencyMode: dai.RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode=dai.RawStereoDepthConfig.PostProcessing.TemporalFilter.PersistencyMode.VALID_2_IN_LAST_4,
alpha: float = 0.4,
delta: int = 0):
"""
:param enable: Whether to enable or disable the filter.
:param persistencyMode: Persistency mode. If the current disparity/depth value is invalid, it will be replaced
by an older value, based on persistency mode.
:param alpha: The Alpha factor in an exponential moving average with Alpha=1 - no filter.
Alpha = 0 - infinite filter. Determines the extent of the temporal history that should be averaged.
:param delta: Step-size boundary. Establishes the threshold used to preserve surfaces (edges).
If the disparity value between neighboring pixels exceed the disparity threshold set by this delta parameter,
then filtering will be temporarily disabled. Default value 0 means auto: 3 disparity integer levels.
In case of subpixel mode it’s 3*number of subpixel levels.
"""
self._enable = enable
self._persistencyMode = persistencyMode
self._alpha = alpha
self._delta = delta
def apply(self, config: dai.RawStereoDepthConfig) -> None:
config.postProcessing.temporalFilter.enable = self._enable
config.postProcessing.temporalFilter.persistencyMode = self._persistencyMode
config.postProcessing.temporalFilter.alpha = self._alpha
config.postProcessing.temporalFilter.delta = self._delta
class SpatialFilter(StereoDepthPostFilter):
"""
Spatial Edge-Preserving Filter will fill invalid depth pixels with valid neighboring depth pixels. It performs a
series of 1D horizontal and vertical passes or iterations, to enhance the smoothness of the reconstructed data.
"""
def __init__(self,
enable: bool = True,
hole_filling_radius: int = 2,
alpha: float = 0.5,
delta: int = 0,
num_iterations: int = 1):
"""
:param enable: Whether to enable or disable the filter.
:param hole_filling_radius: An in-place heuristic symmetric hole-filling mode applied horizontally during
the filter passes. Intended to rectify minor artefacts with minimal performance impact. Search radius for
hole filling.
:param alpha: The Alpha factor in an exponential moving average with Alpha=1 - no filter.
Alpha = 0 - infinite filter. Determines the amount of smoothing.
:param delta: Step-size boundary. Establishes the threshold used to preserve “edges”. If the disparity value
between neighboring pixels exceed the disparity threshold set by this delta parameter, then filtering will be
temporarily disabled. Default value 0 means auto: 3 disparity integer levels. In case of subpixel mode it’s
3*number of subpixel levels.
:param num_iterations: Number of iterations over the image in both horizontal and vertical direction.
"""
self._enable = enable
self._hole_filling_radius = hole_filling_radius
self._alpha = alpha
self._delta = delta
self._num_iterations = num_iterations
def apply(self, config: dai.RawStereoDepthConfig) -> None:
config.postProcessing.spatialFilter.enable = self._enable
config.postProcessing.spatialFilter.holeFillingRadius = self._hole_filling_radius
config.postProcessing.spatialFilter.alpha = self._alpha
config.postProcessing.spatialFilter.delta = self._delta
config.postProcessing.spatialFilter.numIterations = self._num_iterations
class ThresholdFilter(StereoDepthPostFilter):
"""
Threshold Filter filters out all disparity/depth pixels outside the configured min/max threshold values.
"""
def __init__(self, min_range: int = 400, max_range: int = 15000):
"""
:param min_range: Minimum range in depth units. Depth values under this value are invalidated.
:param max_range: Maximum range in depth units. Depth values over this value are invalidated.
"""
self._min_range = min_range
self._max_range = max_range
def apply(self, config: dai.RawStereoDepthConfig) -> None:
config.postProcessing.thresholdFilter.minRange = self._min_range
config.postProcessing.thresholdFilter.maxRange = self._max_range
class DecimationFilter(StereoDepthPostFilter):
"""
Decimation Filter will sub-samples the depth map, which means it reduces the depth scene complexity and allows
other filters to run faster. Setting decimationFactor to 2 will downscale 1280x800 depth map to 640x400.
"""
def __init__(self,
decimation_factor: int = 1,
decimation_mode: dai.RawStereoDepthConfig.PostProcessing.DecimationFilter.DecimationMode = dai.RawStereoDepthConfig.PostProcessing.DecimationFilter.DecimationMode.PIXEL_SKIPPING
):
"""
:param decimation_factor: Decimation factor. Valid values are 1,2,3,4. Disparity/depth map x/y resolution will
be decimated with this value.
:param decimation_mode: Decimation algorithm type.
"""
self._decimation_factor = decimation_factor
self._mode = decimation_mode
def apply(self, config: dai.RawStereoDepthConfig) -> None:
config.postProcessing.decimationFilter.decimationFactor = self._decimation_factor
config.postProcessing.decimationFilter.decimationMode = self._mode
class DepthSource(Source):
def __init__(self, pipeline: dai.Pipeline,
extended_disparity: bool = False,
subpixel: bool = False,
lr_check: bool = True,
stereo_filters: List[StereoDepthPostFilter] = []
) -> None:
"""
# Closer-in minimum depth, disparity range is doubled (from 95 to 190):
extended_disparity = False
# Better accuracy for longer distance, fractional disparity 32-levels:
subpixel = False
# Better handling for occlusions:
lr_check = True
"""
self._monoLeft = pipeline.create(dai.node.MonoCamera)
self._monoRight = pipeline.create(dai.node.MonoCamera)
self._depth = pipeline.create(dai.node.StereoDepth)
self._xout_disparity = pipeline.create(dai.node.XLinkOut)
self._xout_disparity.setStreamName("disparity")
# Properties
self._monoLeft.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
self._monoLeft.setCamera("left")
self._monoLeft.out.link(self._depth.left)
self._monoRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
self._monoRight.setCamera("right")
self._monoRight.out.link(self._depth.right)
# Create a node that will produce the depth map
# (using disparity output as it's easier to visualize depth this way)
self._depth.setDefaultProfilePreset(dai.node.StereoDepth.PresetMode.HIGH_DENSITY)
# Options: MEDIAN_OFF, KERNEL_3x3, KERNEL_5x5, KERNEL_7x7 (default)
self._depth.initialConfig.setMedianFilter(dai.MedianFilter.KERNEL_7x7)
self._depth.setLeftRightCheck(lr_check)
self._depth.setExtendedDisparity(extended_disparity)
self._depth.setSubpixel(subpixel)
self._depth.disparity.link(self._xout_disparity.input)
if len(stereo_filters) > 0:
# Configure post-processing filters
config = self._depth.initialConfig.get()
for filter in stereo_filters:
filter.apply(config)
self._depth.initialConfig.set(config)
def get_stream_name(self) -> str:
return self._xout_disparity.getStreamName()
def link(self, input_node: dai.Node.Input) -> None:
self._depth.disparity.link(input_node)
@dataclass
class MqttConfig:
"""MQTT configuration"""
host: str
topic: str
port: int = 1883
qos: int = 0
class MqttSource(Source):
"""Image source based onto mqtt stream"""
def __init__(self, device: Device, pipeline: dai.Pipeline, mqtt_config: MqttConfig):
self._mqtt_config = mqtt_config
self._client = mqtt.Client()
self._client.user_data_set(mqtt_config)
self._client.on_connect = self._on_connect
self._client.on_message = self._on_message
self._img_in = pipeline.createXLinkIn()
self._img_in.setStreamName("img_input")
self._img_out = pipeline.createXLinkOut()
self._img_out.setStreamName("img_output")
self._img_in.out.link(self._img_out.input)
self._img_in_queue = device.getInputQueue(self._img_in.getStreamName())
def run(self) -> None:
""" Connect and start mqtt loop """
self._client.connect(host=self._mqtt_config.host, port=self._mqtt_config.port)
self._client.loop_start()
def stop(self) -> None:
"""Stop and disconnect mqtt loop"""
self._client.loop_stop()
self._client.disconnect()
@staticmethod
# pylint: disable=unused-argument
def _on_connect(client: mqtt.Client, userdata: MqttConfig, flags: Any,
result_connection: Any) -> None:
# if we lose the connection and reconnect then subscriptions will be renewed.
client.subscribe(topic=userdata.topic, qos=userdata.qos)
# pylint: disable=unused-argument
def _on_message(self, _: mqtt.Client, user_data: MqttConfig, msg: mqtt.MQTTMessage) -> None:
frame_msg = evt.FrameMessage()
frame_msg.ParseFromString(msg.payload)
frame = np.asarray(frame_msg.frame, dtype="uint8")
frame = cv2.imdecode(frame, cv2.IMREAD_COLOR)
nn_data = dai.NNData()
nn_data.setLayer("data", _to_planar(frame, (300, 300)))
self._img_in_queue.send(nn_data)
def get_stream_name(self) -> str:
return self._img_out.getStreamName()
def link(self, input_node: dai.Node.Input) -> None:
self._img_in.out.link(input_node)
def _to_planar(arr: npt.NDArray[np.uint8], shape: tuple[int, int]) -> list[int]:
return [val for channel in cv2.resize(arr, shape).transpose(2, 0, 1) for y_col in channel for val in y_col]
class PipelineController:
"""
Pipeline controller that drive camera device
"""
def __init__(self, frame_processor: FrameProcessor,
object_processor: ObjectProcessor, disparity_processor: DisparityProcessor,
camera: Source, depth_source: Source, object_node: ObjectDetectionNN,
pipeline: dai.Pipeline):
self._frame_processor = frame_processor
self._object_processor = object_processor
self._disparity_processor = disparity_processor
self._camera = camera
self._depth_source = depth_source
self._object_node = object_node
self._stop = False
self._pipeline = pipeline
self._configure_pipeline()
self._focal_length_in_pixels: float | None = None
def _configure_pipeline(self) -> None:
logger.info("configure pipeline")
self._pipeline.setOpenVINOVersion(version=dai.OpenVINO.VERSION_2021_4)
# Link preview to manip and manip to nn
self._camera.link(self._object_node.get_input())
logger.info("pipeline configured")
def run(self) -> None:
"""
Start event loop
:return:
"""
# Connect to device and start pipeline
with Device(pipeline=self._pipeline) as dev:
logger.info('MxId: %s', dev.getDeviceInfo().getMxId())
logger.info('USB speed: %s', dev.getUsbSpeed())
logger.info('Connected cameras: %s', str(dev.getConnectedCameras()))
logger.info("output queues found: %s", str(''.join(dev.getOutputQueueNames()))) # type: ignore
calib_data = dev.readCalibration()
intrinsics = calib_data.getCameraIntrinsics(dai.CameraBoardSocket.CAM_C)
self._focal_length_in_pixels = intrinsics[0][0]
logger.info('Right mono camera focal length in pixels: %s', self._focal_length_in_pixels)
dev.startPipeline()
# Queues
queue_size = 4
q_rgb = dev.getOutputQueue(name=self._camera.get_stream_name(), maxSize=queue_size, # type: ignore
blocking=False)
q_nn = dev.getOutputQueue(name=self._object_node.get_stream_name(), maxSize=queue_size, # type: ignore
blocking=False)
if self._disparity_processor is not None:
q_disparity = dev.getOutputQueue(name=self._depth_source.get_stream_name(), maxSize=queue_size, # type: ignore
blocking=False)
else:
q_disparity = None
start_time = time.time()
counter = 0
fps = 0
display_time = time.time()
self._stop = False
while True:
if self._stop:
logger.info("stop loop event")
return
try:
self._loop_on_camera_events(q_nn, q_rgb, q_disparity)
# pylint: disable=broad-except # bad frame or event must not stop loop
except Exception as ex:
logger.exception("unexpected error: %s", str(ex))
counter += 1
if (time.time() - start_time) > 1:
fps = counter / (time.time() - start_time)
counter = 0
start_time = time.time()
if (time.time() - display_time) >= 10:
display_time = time.time()
logger.info("fps: %s", fps)
def _loop_on_camera_events(self, q_nn: dai.DataOutputQueue, q_rgb: dai.DataOutputQueue, q_disparity: dai.DataOutputQueue) -> None:
logger.debug("wait for new frame")
# Wait for frame
in_rgb: dai.ImgFrame = q_rgb.get() # type: ignore # blocking call, will wait until a new data has arrived
try:
logger.debug("process frame")
frame_ref = self._frame_processor.process(in_rgb)
except FrameProcessError as ex:
logger.error("unable to process frame: %s", str(ex))
return
logger.debug("frame processed")
logger.debug("wait for nn response")
# Read NN result
in_nn: dai.NNData = q_nn.get() # type: ignore
logger.debug("process objects")
self._object_processor.process(in_nn, frame_ref)
logger.debug("objects processed")
logger.debug("process disparity")
if self._disparity_processor is not None:
in_disparity: dai.ImgFrame = q_disparity.get() # type: ignore
self._disparity_processor.process(in_disparity, frame_ref=frame_ref,
focal_length_in_pixels=self._focal_length_in_pixels)
logger.debug("disparity processed")
def stop(self) -> None:
"""
Stop event loop, if loop is not running, do nothing
:return:
"""
self._stop = True
def _bbox_to_object(bbox: npt.NDArray[np.float64], score: float) -> evt.Object:
obj = evt.Object()
obj.type = evt.TypeObject.ANY
obj.top = bbox[0].astype(float)
obj.right = bbox[3].astype(float)
obj.bottom = bbox[2].astype(float)
obj.left = bbox[1].astype(float)
obj.confidence = score
return obj

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@ -1,201 +0,0 @@
import datetime
import typing
import unittest.mock
import depthai as dai
import events.events_pb2
import numpy as np
import numpy.typing as npt
import paho.mqtt.client as mqtt
import pytest
import pytest_mock
from camera.oak_pipeline import DisparityProcessor, ObjectProcessor, FrameProcessor, FrameProcessError
Object = dict[str, float]
@pytest.fixture
def mqtt_client(mocker: pytest_mock.MockerFixture) -> mqtt.Client:
return mocker.MagicMock() # type: ignore
class TestObjectProcessor:
@pytest.fixture
def frame_ref(self) -> events.events_pb2.FrameRef:
now = datetime.datetime.now()
frame_msg = events.events_pb2.FrameMessage()
frame_msg.id.name = "robocar-oak-camera-oak"
frame_msg.id.id = str(int(now.timestamp() * 1000))
frame_msg.id.created_at.FromDatetime(now)
return frame_msg.id
@pytest.fixture
def object1(self) -> Object:
return {
"left": 0.3,
"right": 0.7,
"top": 0.1,
"bottom": 0.6,
"score": 0.8,
}
@pytest.fixture
def raw_objects_empty(self, mocker: pytest_mock.MockerFixture) -> dai.NNData:
raw_objects = mocker.MagicMock()
def mock_return(name: str) -> typing.List[typing.Union[int, typing.List[int]]]:
if name == "ExpandDims":
return [[0] * 4] * 100
elif name == "ExpandDims_2":
return [0] * 100
else:
raise ValueError(f"{name} is not a valid arg")
m = mocker.patch(target='depthai.NNData.getLayerFp16', autospec=True)
m.getLayerFp16 = mock_return
return m
@pytest.fixture
def raw_objects_one(self, mocker: pytest_mock.MockerFixture, object1: Object) -> dai.NNData:
def mock_return(name: str) -> typing.Union[npt.NDArray[np.int64], typing.List[float]]:
if name == "ExpandDims": # Detection boxes
boxes: list[list[float]] = [[0.] * 4] * 100
boxes[0] = [object1["top"], object1["left"], object1["bottom"], object1["right"]]
return np.array(boxes)
elif name == "ExpandDims_2": # Detection scores
scores: list[float] = [0.] * 100
scores[0] = object1["score"]
return scores
else:
raise ValueError(f"{name} is not a valid arg")
m = mocker.patch(target='depthai.NNData.getLayerFp16', autospec=True)
m.getLayerFp16 = mock_return
return m
@pytest.fixture
def object_processor(self, mqtt_client: mqtt.Client) -> ObjectProcessor:
return ObjectProcessor(mqtt_client, "topic/object", 0.2)
def test_process_without_object(self, object_processor: ObjectProcessor, mqtt_client: mqtt.Client,
raw_objects_empty: dai.NNData, frame_ref: events.events_pb2.FrameRef) -> None:
object_processor.process(raw_objects_empty, frame_ref)
publish_mock: unittest.mock.MagicMock = mqtt_client.publish # type: ignore
publish_mock.assert_not_called()
def test_process_with_object_with_low_score(self, object_processor: ObjectProcessor,
mqtt_client: mqtt.Client, raw_objects_one: dai.NNData,
frame_ref: events.events_pb2.FrameRef) -> None:
object_processor._objects_threshold = 0.9
object_processor.process(raw_objects_one, frame_ref)
publish_mock: unittest.mock.MagicMock = mqtt_client.publish # type: ignore
publish_mock.assert_not_called()
def test_process_with_one_object(self,
object_processor: ObjectProcessor, mqtt_client: mqtt.Client,
raw_objects_one: dai.NNData, frame_ref: events.events_pb2.FrameRef,
object1: Object) -> None:
object_processor.process(raw_objects_one, frame_ref)
left = object1["left"]
right = object1["right"]
top = object1["top"]
bottom = object1["bottom"]
score = object1["score"]
pub_mock: unittest.mock.MagicMock = mqtt_client.publish # type: ignore
pub_mock.assert_called_once_with(payload=unittest.mock.ANY, qos=0, retain=False, topic="topic/object")
payload = pub_mock.call_args.kwargs['payload']
objects_msg = events.events_pb2.ObjectsMessage()
objects_msg.ParseFromString(payload)
assert len(objects_msg.objects) == 1
assert left - 0.0001 < objects_msg.objects[0].left < left + 0.0001
assert right - 0.0001 < objects_msg.objects[0].right < right + 0.0001
assert top - 0.0001 < objects_msg.objects[0].top < top + 0.0001
assert bottom - 0.0001 < objects_msg.objects[0].bottom < bottom + 0.0001
assert score - 0.0001 < objects_msg.objects[0].confidence < score + 0.0001
assert objects_msg.frame_ref == frame_ref
class TestFrameProcessor:
@pytest.fixture
def frame_processor(self, mqtt_client: mqtt.Client) -> FrameProcessor:
return FrameProcessor(mqtt_client, "topic/frame")
def test_process(self, frame_processor: FrameProcessor, mocker: pytest_mock.MockerFixture,
mqtt_client: mqtt.Client) -> None:
img: dai.ImgFrame = mocker.MagicMock()
mocker.patch(target="cv2.imencode").return_value = (True, np.array(b"img content"))
frame_ref = frame_processor.process(img)
pub_mock: unittest.mock.MagicMock = mqtt_client.publish # type: ignore
pub_mock.assert_called_once_with(payload=unittest.mock.ANY, qos=0, retain=False, topic="topic/frame")
payload = pub_mock.call_args.kwargs['payload']
frame_msg = events.events_pb2.FrameMessage()
frame_msg.ParseFromString(payload)
assert frame_msg.id == frame_ref
assert frame_msg.frame == b"img content"
assert frame_msg.id.name == "robocar-oak-camera-oak"
assert len(frame_msg.id.id) is 13
now = datetime.datetime.now()
assert now - datetime.timedelta(
milliseconds=10) < frame_msg.id.created_at.ToDatetime() < now + datetime.timedelta(milliseconds=10)
def test_process_error(self, frame_processor: FrameProcessor, mocker: pytest_mock.MockerFixture,
mqtt_client: mqtt.Client) -> None:
img: dai.ImgFrame = mocker.MagicMock()
mocker.patch(target="cv2.imencode").return_value = (False, None)
with pytest.raises(FrameProcessError) as ex:
_ = frame_processor.process(img)
exception_raised = ex.value
assert exception_raised.message == "unable to process to encode frame to jpg"
class TestDisparityProcessor:
@pytest.fixture
def frame_ref(self) -> events.events_pb2.FrameRef:
now = datetime.datetime.now()
frame_msg = events.events_pb2.FrameMessage()
frame_msg.id.name = "robocar-oak-camera-oak"
frame_msg.id.id = str(int(now.timestamp() * 1000))
frame_msg.id.created_at.FromDatetime(now)
return frame_msg.id
@pytest.fixture
def disparity_processor(self, mqtt_client: mqtt.Client) -> DisparityProcessor:
return DisparityProcessor(mqtt_client, "topic/disparity")
def test_process(self, disparity_processor: DisparityProcessor, mocker: pytest_mock.MockerFixture,
frame_ref: events.events_pb2.FrameRef,
mqtt_client: mqtt.Client) -> None:
img: dai.ImgFrame = mocker.MagicMock()
mocker.patch(target="cv2.imencode").return_value = (True, np.array(b"img content"))
disparity_processor.process(img, frame_ref, 42)
pub_mock: unittest.mock.MagicMock = mqtt_client.publish # type: ignore
pub_mock.assert_called_once_with(payload=unittest.mock.ANY, qos=0, retain=False, topic="topic/disparity")
payload = pub_mock.call_args.kwargs['payload']
disparity_msg = events.events_pb2.DisparityMessage()
disparity_msg.ParseFromString(payload)
assert disparity_msg.frame_ref == frame_ref
assert disparity_msg.disparity == b"img content"
assert disparity_msg.focal_length_in_pixels == 42
assert disparity_msg.baseline_in_mm == 75
def test_process_error(self, disparity_processor: DisparityProcessor, mocker: pytest_mock.MockerFixture,
frame_ref: events.events_pb2.FrameRef,
mqtt_client: mqtt.Client) -> None:
img: dai.ImgFrame = mocker.MagicMock()
mocker.patch(target="cv2.imencode").return_value = (False, None)
with pytest.raises(FrameProcessError) as ex:
disparity_processor.process(img, frame_ref, 42)
exception_raised = ex.value
assert exception_raised.message == "unable to process to encode frame to jpg"

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events/events_pb2.py Normal file
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# -*- 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)

631
poetry.lock generated
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@ -1,631 +0,0 @@
# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
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[metadata]
lock-version = "2.0"
python-versions = "^3.12"
content-hash = "c40d76f962d6502e0946e1cbacedab80dd035d070de7e71e3ae68dc346b69b22"

56
pyproject.toml
View File

@ -1,56 +0,0 @@
[tool.poetry]
name = "robocar-oak-camera"
version = "0.0.0"
description = "Mqtt gateway for oak-lite device"
authors = ["Cyrille Nofficial <cynoffic@cyrilix.fr>"]
readme = "README.md"
packages = [
{ include = "camera" },
]
[tool.poetry.dependencies]
python = "^3.12"
paho-mqtt = "^1.6"
depthai = "^2"
protobuf3 = "^0.2.1"
google = "^3.0.0"
protobuf = "^4.21"
opencv-python-headless = "^4.6.0"
robocar-protobuf = {version = "^1.6", source = "robocar"}
[tool.poetry.group.test.dependencies]
pytest = "^7.1.3"
pytest-mock = "^3.10.0"
[tool.poetry.group.dev.dependencies]
pylint = "^2.15.4"
mypy = "^0.982"
types-paho-mqtt = "^1.6.0.1"
types-protobuf = "^3.20.4.2"
[[tool.poetry.source]]
name = "robocar"
url = "https://git.cyrilix.bzh/api/packages/robocars/pypi/simple"
priority = "explicit"
[build-system]
requires = ["poetry-core>=1.0.0", "poetry-dynamic-versioning"]
build-backend = "poetry_dynamic_versioning.backend"
[tool.poetry.scripts]
rc-oak-camera = 'camera.cli:execute_from_command_line'
[tool.poetry-dynamic-versioning]
enable = true
style = 'semver'
vcs = 'git'
dirty = true
bump = true
[tool.mypy]
strict = true
warn_unused_configs = true
plugins = 'numpy.typing.mypy_plugin'

8
requirements.txt Normal file
View File

@ -0,0 +1,8 @@
paho-mqtt~=1.6.1
docopt~=0.6.2
depthai==2.14.1.0
opencv-python~=4.5.5.62
google~=3.0.0
google-api-core~=2.4.0
setuptools==60.5.0
protobuf3

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

68
setup.py Normal file
View File

@ -0,0 +1,68 @@
import os
from setuptools import setup, find_packages
# include the non python files
def package_files(directory, strip_leading):
paths = []
for (path, directories, filenames) in os.walk(directory):
for filename in filenames:
package_file = os.path.join(path, filename)
paths.append(package_file[len(strip_leading):])
return paths
tests_require = ['pytest',
]
setup(name='robocar-oak-camera',
version='0.1',
description='Mqtt gateway for oak-lite device.',
url='https://github.com/cyrilix/robocar-oak-camera',
license='Apache2',
entry_points={
'console_scripts': [
'rc-oak-camera=camera.cli:execute_from_command_line',
],
},
setup_requires=['pytest-runner'],
install_requires=['depthai',
'docopt',
'paho-mqtt',
'protobuf3',
'google',
'numpy',
'opencv-python',
'blobconverter',
],
tests_require=tests_require,
extras_require={
'tests': tests_require
},
include_package_data=True,
classifiers=[
# How mature is this project? Common values are
# 3 - Alpha
# 4 - Beta
# 5 - Production/Stable
'Development Status :: 3 - Alpha',
# Indicate who your project is intended for
'Intended Audience :: Developers',
'Topic :: Scientific/Engineering :: Artificial Intelligence',
# Pick your license as you wish (should match "license" above)
'License :: OSI Approved :: Apache 2 License',
# Specify the Python versions you support here. In particular, ensure
# that you indicate whether you support Python 2, Python 3 or both.
'Programming Language :: Python :: 3.7',
],
keywords='selfdriving cars drive',
packages=find_packages(exclude=(['tests', 'env'])),
)