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.gitignore
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.gitignore
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# Created by .ignore support plugin (hsz.mobi)
|
||||
### Python template
|
||||
# Byte-compiled / optimized / DLL files
|
||||
__pycache__/
|
||||
*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
|
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env/
|
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build/
|
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develop-eggs/
|
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dist/
|
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downloads/
|
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eggs/
|
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.eggs/
|
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lib/
|
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lib64/
|
||||
parts/
|
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sdist/
|
||||
var/
|
||||
*.egg-info/
|
||||
.installed.cfg
|
||||
*.egg
|
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|
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# PyInstaller
|
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# Usually these files are written by a python script from a template
|
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
|
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*.manifest
|
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*.spec
|
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|
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
|
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htmlcov/
|
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.tox/
|
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.coverage
|
||||
.coverage.*
|
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.cache
|
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nosetests.xml
|
||||
coverage.xml
|
||||
*,cover
|
||||
.hypothesis/
|
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|
||||
# Translations
|
||||
*.mo
|
||||
*.pot
|
||||
|
||||
# Django stuff:
|
||||
*.log
|
||||
local_settings.py
|
||||
|
||||
# Flask stuff:
|
||||
instance/
|
||||
.webassets-cache
|
||||
|
||||
# Scrapy stuff:
|
||||
.scrapy
|
||||
|
||||
# Sphinx documentation
|
||||
docs/_build/
|
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|
||||
# PyBuilder
|
||||
target/
|
||||
|
||||
# IPython Notebook
|
||||
.ipynb_checkpoints
|
||||
|
||||
# pyenv
|
||||
.python-version
|
||||
|
||||
# celery beat schedule file
|
||||
celerybeat-schedule
|
||||
|
||||
# dotenv
|
||||
.env
|
||||
|
||||
# virtualenv
|
||||
venv/
|
||||
ENV/
|
||||
|
||||
# Spyder project settings
|
||||
.spyderproject
|
||||
|
||||
# Rope project settings
|
||||
.ropeproject
|
||||
### VirtualEnv template
|
||||
# Virtualenv
|
||||
# http://iamzed.com/2009/05/07/a-primer-on-virtualenv/
|
||||
[Bb]in
|
||||
[Ii]nclude
|
||||
[Ll]ib
|
||||
[Ll]ib64
|
||||
[Ll]ocal
|
||||
[Ss]cripts
|
||||
pyvenv.cfg
|
||||
.venv
|
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pip-selfcheck.json
|
||||
|
||||
### JetBrains template
|
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# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider
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# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
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# User-specific stuff
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.idea/**/workspace.xml
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|
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.idea/**/shelf
|
||||
|
||||
# AWS User-specific
|
||||
.idea/**/aws.xml
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|
||||
# Generated files
|
||||
.idea/**/contentModel.xml
|
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|
||||
# Sensitive or high-churn files
|
||||
.idea/**/dataSources/
|
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.idea/**/dataSources.ids
|
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.idea/**/dataSources.local.xml
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|
||||
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|
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.idea/**/uiDesigner.xml
|
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.idea/**/dbnavigator.xml
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|
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# Gradle
|
||||
.idea/**/gradle.xml
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.idea/**/libraries
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# Gradle and Maven with auto-import
|
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# When using Gradle or Maven with auto-import, you should exclude module files,
|
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# since they will be recreated, and may cause churn. Uncomment if using
|
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# auto-import.
|
||||
# .idea/artifacts
|
||||
# .idea/compiler.xml
|
||||
# .idea/jarRepositories.xml
|
||||
# .idea/modules.xml
|
||||
# .idea/*.iml
|
||||
# .idea/modules
|
||||
# *.iml
|
||||
# *.ipr
|
||||
|
||||
# CMake
|
||||
cmake-build-*/
|
||||
|
||||
# Mongo Explorer plugin
|
||||
.idea/**/mongoSettings.xml
|
||||
|
||||
# File-based project format
|
||||
*.iws
|
||||
|
||||
# IntelliJ
|
||||
out/
|
||||
|
||||
# mpeltonen/sbt-idea plugin
|
||||
.idea_modules/
|
||||
|
||||
# JIRA plugin
|
||||
atlassian-ide-plugin.xml
|
||||
|
||||
# Cursive Clojure plugin
|
||||
.idea/replstate.xml
|
||||
|
||||
# SonarLint plugin
|
||||
.idea/sonarlint/
|
||||
|
||||
# Crashlytics plugin (for Android Studio and IntelliJ)
|
||||
com_crashlytics_export_strings.xml
|
||||
crashlytics.properties
|
||||
crashlytics-build.properties
|
||||
fabric.properties
|
||||
|
||||
# Editor-based Rest Client
|
||||
.idea/httpRequests
|
||||
|
||||
# Android studio 3.1+ serialized cache file
|
||||
.idea/caches/build_file_checksums.ser
|
||||
|
||||
# idea folder, uncomment if you don't need it
|
||||
.idea
|
||||
.vscode
|
||||
.vscode/
|
||||
config.py
|
||||
36
LED_color_detector/led_detector.py
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36
LED_color_detector/led_detector.py
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import cv2 as cv
|
||||
import numpy as np
|
||||
from skimage.io import imread
|
||||
import os
|
||||
|
||||
|
||||
def get_led_color(robot_image, model):
|
||||
green_color = 1
|
||||
red_color = 2
|
||||
white_color = 3
|
||||
arr_led = []
|
||||
X_led = {}
|
||||
|
||||
resized_image = cv.resize(robot_image, (128, 128), interpolation=cv.INTER_AREA)
|
||||
resized_image = resized_image / 255
|
||||
|
||||
arr_led.append(resized_image)
|
||||
X_led["values"] = np.array(arr_led)
|
||||
image_with_alpha_led = X_led["values"][:, :, :, :3]
|
||||
pred = predict(image_with_alpha_led, model)
|
||||
|
||||
if pred[0] == green_color:
|
||||
return "green"
|
||||
elif pred[0] == red_color:
|
||||
return "red"
|
||||
elif pred[0] == white_color:
|
||||
return "white"
|
||||
else:
|
||||
return "other"
|
||||
|
||||
|
||||
def predict(image, model):
|
||||
predict_x = model.predict(image)
|
||||
classes_x = np.argmax(predict_x, axis=1)
|
||||
print(f'{classes_x}\n')
|
||||
return classes_x
|
||||
48
README.md
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48
README.md
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|
||||
# Flask API
|
||||
|
||||
To create conda env just run:
|
||||
|
||||
`conda env create conda_env.yml`
|
||||
|
||||
And then select newly created env in your IDE.
|
||||
|
||||
### To run API in project folder run this command:
|
||||
|
||||
`export FLASK_APP=main.py`
|
||||
|
||||
### After that you are ready to deploy API:
|
||||
|
||||
`flask run --without-threads`
|
||||
|
||||
Supported requests:
|
||||
|
||||
- `API_address/detectRobot1?img=...`
|
||||
- response model:
|
||||
```
|
||||
{
|
||||
"0": ["color", int(width), int(x)]
|
||||
}
|
||||
```
|
||||
- when no objects were detected response is:
|
||||
```
|
||||
{
|
||||
"0": []
|
||||
}
|
||||
```
|
||||
|
||||
- `API_address/detectRat1?img=...`
|
||||
- response model:
|
||||
```
|
||||
{
|
||||
"0": [int(width), int(x)]
|
||||
}
|
||||
```
|
||||
- when no objects were detected response is:
|
||||
```
|
||||
{
|
||||
"0": []
|
||||
}
|
||||
```
|
||||
|
||||
# Przed uruchomieniem
|
||||
###### Skopiuj pliki z folderu ``dependencies`` do folderu ``/CoppeliaRobotics/CoppeliaSimEdu/lua``
|
||||
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VGG16_led_detector/.DS_Store
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VGG16_led_detector/.DS_Store
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VGG16_led_detector/keras_metadata.pb
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VGG16_led_detector/keras_metadata.pb
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VGG16_led_detector/saved_model.pb
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VGG16_led_detector/variables/variables.data-00000-of-00001
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VGG16_led_detector/variables/variables.index
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__MACOSX/(1)._model_VGG16_LED_color_bigger_scene2
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__MACOSX/(1)._model_VGG16_LED_color_bigger_scene2
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__MACOSX/._model_VGG16_LED_color_bigger_scene2
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__MACOSX/._model_VGG16_LED_color_bigger_scene2
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__MACOSX/model_VGG16_LED_color_bigger_scene2/(1)._assets
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__MACOSX/model_VGG16_LED_color_bigger_scene2/(1)._variables
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__MACOSX/model_VGG16_LED_color_bigger_scene2/._(1).DS_Store
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__MACOSX/model_VGG16_LED_color_bigger_scene2/._(1).DS_Store
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__MACOSX/model_VGG16_LED_color_bigger_scene2/._.DS_Store
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__MACOSX/model_VGG16_LED_color_bigger_scene2/._.DS_Store
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__MACOSX/model_VGG16_LED_color_bigger_scene2/._assets
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__MACOSX/model_VGG16_LED_color_bigger_scene2/._assets
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__MACOSX/model_VGG16_LED_color_bigger_scene2/._variables
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api.yml
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api.yml
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||||
name: tensorflow-sem
|
||||
channels:
|
||||
- apple
|
||||
- anaconda
|
||||
- conda-forge
|
||||
- defaults
|
||||
dependencies:
|
||||
- _ipython_minor_entry_point=8.7.0=h8cf3c4a_0
|
||||
- anyio=3.6.2=pyhd8ed1ab_0
|
||||
- appnope=0.1.3=pyhd8ed1ab_0
|
||||
- argon2-cffi=21.3.0=pyhd8ed1ab_0
|
||||
- argon2-cffi-bindings=21.2.0=py39h02fc5c5_3
|
||||
- asttokens=2.2.1=pyhd8ed1ab_0
|
||||
- attrs=22.1.0=pyh71513ae_1
|
||||
- backcall=0.2.0=pyh9f0ad1d_0
|
||||
- backports=1.0=pyhd8ed1ab_3
|
||||
- backports.functools_lru_cache=1.6.4=pyhd8ed1ab_0
|
||||
- beautifulsoup4=4.11.1=pyha770c72_0
|
||||
- bleach=5.0.1=pyhd8ed1ab_0
|
||||
- blosc=1.21.0=h98b2900_1
|
||||
- boto3=1.26.33=pyhd8ed1ab_0
|
||||
- botocore=1.29.33=pyhd8ed1ab_0
|
||||
- brotli=1.0.9=h1a8c8d9_8
|
||||
- brotli-bin=1.0.9=h1a8c8d9_8
|
||||
- brotlipy=0.7.0=py39h02fc5c5_1005
|
||||
- brunsli=0.1=hc377ac9_1
|
||||
- bzip2=1.0.8=h3422bc3_4
|
||||
- c-ares=1.18.1=h3422bc3_0
|
||||
- ca-certificates=2022.12.7=h4653dfc_0
|
||||
- cached-property=1.5.2=hd8ed1ab_1
|
||||
- cached_property=1.5.2=pyha770c72_1
|
||||
- cairo=1.16.0=he69dfd1_1008
|
||||
- certifi=2022.12.7=pyhd8ed1ab_0
|
||||
- cffi=1.15.1=py39h7e6b969_3
|
||||
- cfitsio=3.470=h7f6438f_7
|
||||
- charls=2.2.0=hc377ac9_0
|
||||
- charset-normalizer=2.1.1=pyhd8ed1ab_0
|
||||
- click=8.1.3=unix_pyhd8ed1ab_2
|
||||
- colorama=0.4.6=pyhd8ed1ab_0
|
||||
- comm=0.1.2=pyhd8ed1ab_0
|
||||
- contourpy=1.0.6=py39haaf3ac1_0
|
||||
- cryptography=38.0.4=py39haa0b8cc_0
|
||||
- cycler=0.11.0=pyhd8ed1ab_0
|
||||
- cytoolz=0.11.0=py39h1a28f6b_0
|
||||
- dask-core=2022.5.0=py39hca03da5_0
|
||||
- debugpy=1.6.4=py39h23fbdae_0
|
||||
- decorator=5.1.1=pyhd8ed1ab_0
|
||||
- defusedxml=0.7.1=pyhd8ed1ab_0
|
||||
- eigen=3.4.0=hc021e02_0
|
||||
- entrypoints=0.4=pyhd8ed1ab_0
|
||||
- executing=1.2.0=pyhd8ed1ab_0
|
||||
- flask=2.2.2=pyhd8ed1ab_0
|
||||
- flit-core=3.8.0=pyhd8ed1ab_0
|
||||
- fontconfig=2.13.94=heb65262_0
|
||||
- fonttools=4.38.0=py39h02fc5c5_1
|
||||
- freetype=2.12.1=hd633e50_1
|
||||
- fsspec=2022.3.0=py39hca03da5_0
|
||||
- gettext=0.21.1=h0186832_0
|
||||
- giflib=5.2.1=h1a28f6b_0
|
||||
- glib=2.74.1=hb5ab8b9_1
|
||||
- glib-tools=2.74.1=hb5ab8b9_1
|
||||
- graphite2=1.3.14=hc377ac9_1
|
||||
- grpcio=1.42.0=py39h95c9599_0
|
||||
- gst-plugins-base=1.14.1=hf0a386a_0
|
||||
- gstreamer=1.14.1=he09cfb7_0
|
||||
- h5py=3.6.0=nompi_py39hd982b79_100
|
||||
- harfbuzz=4.3.0=hb1b0ec1_0
|
||||
- hdf5=1.12.1=nompi_hf9525e8_104
|
||||
- icu=68.2=hbdafb3b_0
|
||||
- idna=3.4=pyhd8ed1ab_0
|
||||
- imagecodecs=2021.8.26=py39h0dccdf0_1
|
||||
- imageio=2.9.0=pyhd3eb1b0_0
|
||||
- imath=3.1.6=hb5ab8b9_1
|
||||
- importlib-metadata=5.2.0=pyha770c72_0
|
||||
- importlib_resources=5.10.1=pyhd8ed1ab_0
|
||||
- ipykernel=6.19.4=pyh736e0ef_0
|
||||
- ipython=8.7.0=pyhd1c38e8_0
|
||||
- ipython_genutils=0.2.0=py_1
|
||||
- ipywidgets=8.0.3=pyhd8ed1ab_0
|
||||
- itsdangerous=2.1.2=pyhd8ed1ab_0
|
||||
- jedi=0.18.2=pyhd8ed1ab_0
|
||||
- jinja2=3.1.2=pyhd8ed1ab_1
|
||||
- jmespath=1.0.1=pyhd8ed1ab_0
|
||||
- joblib=1.2.0=pyhd8ed1ab_0
|
||||
- jpeg=9e=he4db4b2_2
|
||||
- jsonschema=4.17.3=pyhd8ed1ab_0
|
||||
- jupyter=1.0.0=py39h2804cbe_8
|
||||
- jupyter_client=7.4.8=pyhd8ed1ab_0
|
||||
- jupyter_console=6.4.4=pyhd8ed1ab_0
|
||||
- jupyter_core=5.1.0=py39h2804cbe_0
|
||||
- jupyter_events=0.5.0=pyhd8ed1ab_0
|
||||
- jupyter_server=2.0.1=pyhd8ed1ab_0
|
||||
- jupyter_server_terminals=0.4.3=pyhd8ed1ab_0
|
||||
- jupyterlab_pygments=0.2.2=pyhd8ed1ab_0
|
||||
- jupyterlab_widgets=3.0.4=pyhd8ed1ab_0
|
||||
- jxrlib=1.1=h1a28f6b_2
|
||||
- kiwisolver=1.4.4=py39haaf3ac1_1
|
||||
- krb5=1.19.3=hf9b2bbe_0
|
||||
- lcms2=2.12=hba8e193_0
|
||||
- lerc=3.0=hc377ac9_0
|
||||
- libaec=1.0.4=hc377ac9_1
|
||||
- libblas=3.9.0=16_osxarm64_openblas
|
||||
- libbrotlicommon=1.0.9=h1a8c8d9_8
|
||||
- libbrotlidec=1.0.9=h1a8c8d9_8
|
||||
- libbrotlienc=1.0.9=h1a8c8d9_8
|
||||
- libcblas=3.9.0=16_osxarm64_openblas
|
||||
- libcurl=7.86.0=hd538317_1
|
||||
- libcxx=14.0.6=h2692d47_0
|
||||
- libdeflate=1.8=h1a28f6b_5
|
||||
- libedit=3.1.20191231=hc8eb9b7_2
|
||||
- libev=4.33=h642e427_1
|
||||
- libffi=3.4.2=h3422bc3_5
|
||||
- libgfortran=5.0.0=11_3_0_hd922786_27
|
||||
- libgfortran5=11.3.0=hdaf2cc0_27
|
||||
- libglib=2.74.1=h4646484_1
|
||||
- libiconv=1.17=he4db4b2_0
|
||||
- liblapack=3.9.0=16_osxarm64_openblas
|
||||
- libllvm12=12.0.1=h93073aa_2
|
||||
- libnghttp2=1.47.0=h232270b_1
|
||||
- libopenblas=0.3.21=openmp_hc731615_3
|
||||
- libpng=1.6.39=h76d750c_0
|
||||
- libpq=12.9=h65cfe13_3
|
||||
- libprotobuf=3.19.4=hccf11d3_0
|
||||
- libsodium=1.0.18=h27ca646_1
|
||||
- libsqlite=3.40.0=h76d750c_0
|
||||
- libssh2=1.10.0=hb80f160_3
|
||||
- libtiff=4.2.0=h01837e1_1
|
||||
- libwebp=1.2.4=h68602c7_0
|
||||
- libwebp-base=1.2.4=h57fd34a_0
|
||||
- libxcb=1.13=h9b22ae9_1004
|
||||
- libxml2=2.9.14=h8c5e841_0
|
||||
- libxslt=1.1.35=h9833966_0
|
||||
- libzlib=1.2.13=h03a7124_4
|
||||
- libzopfli=1.0.3=hc377ac9_0
|
||||
- llvm-openmp=15.0.6=h7cfbb63_0
|
||||
- locket=1.0.0=py39hca03da5_0
|
||||
- lxml=4.9.1=py39h2fae87d_0
|
||||
- lz4-c=1.9.3=hc377ac9_0
|
||||
- markupsafe=2.1.1=py39h02fc5c5_2
|
||||
- matplotlib=3.6.2=py39hdf13c20_0
|
||||
- matplotlib-base=3.6.2=py39h35e9e80_0
|
||||
- matplotlib-inline=0.1.6=pyhd8ed1ab_0
|
||||
- mistune=2.0.4=pyhd8ed1ab_0
|
||||
- munkres=1.1.4=pyh9f0ad1d_0
|
||||
- nbclassic=0.4.8=pyhd8ed1ab_0
|
||||
- nbclient=0.7.2=pyhd8ed1ab_0
|
||||
- nbconvert=7.2.7=pyhd8ed1ab_0
|
||||
- nbconvert-core=7.2.7=pyhd8ed1ab_0
|
||||
- nbconvert-pandoc=7.2.7=pyhd8ed1ab_0
|
||||
- nbformat=5.7.1=pyhd8ed1ab_0
|
||||
- ncurses=6.3=h07bb92c_1
|
||||
- nest-asyncio=1.5.6=pyhd8ed1ab_0
|
||||
- networkx=2.7.1=pyhd3eb1b0_0
|
||||
- notebook=6.5.2=pyha770c72_1
|
||||
- notebook-shim=0.2.2=pyhd8ed1ab_0
|
||||
- nspr=4.35=hb7217d7_0
|
||||
- nss=3.78=h1483a63_0
|
||||
- numpy=1.23.2=py39h3668e8b_0
|
||||
- opencv=4.6.0=py39h8794c10_2
|
||||
- openexr=3.1.5=h264c651_1
|
||||
- openjpeg=2.3.0=h7a6adac_2
|
||||
- openssl=1.1.1s=h03a7124_1
|
||||
- packaging=22.0=pyhd8ed1ab_0
|
||||
- pandas=1.5.2=py39hde7b980_0
|
||||
- pandas-datareader=0.10.0=pyh6c4a22f_0
|
||||
- pandoc=2.19.2=hce30654_1
|
||||
- pandocfilters=1.5.0=pyhd8ed1ab_0
|
||||
- parso=0.8.3=pyhd8ed1ab_0
|
||||
- partd=1.2.0=pyhd3eb1b0_1
|
||||
- pcre2=10.40=hb34f9b4_0
|
||||
- pexpect=4.8.0=pyh1a96a4e_2
|
||||
- pickleshare=0.7.5=py_1003
|
||||
- pillow=9.0.1=py39h4d1bdd5_0
|
||||
- pip=22.3.1=pyhd8ed1ab_0
|
||||
- pixman=0.40.0=h27ca646_0
|
||||
- pkgutil-resolve-name=1.3.10=pyhd8ed1ab_0
|
||||
- platformdirs=2.6.0=pyhd8ed1ab_0
|
||||
- prometheus_client=0.15.0=pyhd8ed1ab_0
|
||||
- prompt-toolkit=3.0.36=pyha770c72_0
|
||||
- prompt_toolkit=3.0.36=hd8ed1ab_0
|
||||
- protobuf=3.19.4=py39hfb83b0d_0
|
||||
- psutil=5.9.4=py39h02fc5c5_0
|
||||
- pthread-stubs=0.4=h27ca646_1001
|
||||
- ptyprocess=0.7.0=pyhd3deb0d_0
|
||||
- pure_eval=0.2.2=pyhd8ed1ab_0
|
||||
- pycparser=2.21=pyhd8ed1ab_0
|
||||
- pygments=2.13.0=pyhd8ed1ab_0
|
||||
- pyopenssl=22.1.0=pyhd8ed1ab_0
|
||||
- pyparsing=3.0.9=pyhd8ed1ab_0
|
||||
- pyrsistent=0.19.2=py39h02fc5c5_0
|
||||
- pysocks=1.7.1=pyha2e5f31_6
|
||||
- python=3.9.15=h2d96c93_0_cpython
|
||||
- python-dateutil=2.8.2=pyhd8ed1ab_0
|
||||
- python-fastjsonschema=2.16.2=pyhd8ed1ab_0
|
||||
- python-json-logger=2.0.1=pyh9f0ad1d_0
|
||||
- python_abi=3.9=3_cp39
|
||||
- pytz=2022.7=pyhd8ed1ab_0
|
||||
- pywavelets=1.3.0=py39h1a28f6b_0
|
||||
- pyyaml=6.0=py39h02fc5c5_5
|
||||
- pyzmq=24.0.1=py39h0553236_1
|
||||
- qt-main=5.15.2=ha2d02b5_7
|
||||
- qt-webengine=5.15.9=h2903aaf_4
|
||||
- qtwebkit=5.212=h0f11f3c_4
|
||||
- readline=8.1.2=h46ed386_0
|
||||
- requests=2.28.1=pyhd8ed1ab_1
|
||||
- s3transfer=0.6.0=pyhd8ed1ab_0
|
||||
- scikit-image=0.19.2=py39h9197a36_0
|
||||
- scikit-learn=1.2.0=py39h57c6424_0
|
||||
- scipy=1.9.3=py39h18313fe_2
|
||||
- seaborn=0.11.2=pyhd3eb1b0_0
|
||||
- send2trash=1.8.0=pyhd8ed1ab_0
|
||||
- setuptools=65.6.3=pyhd8ed1ab_0
|
||||
- six=1.16.0=pyh6c4a22f_0
|
||||
- snappy=1.1.9=hc377ac9_0
|
||||
- sniffio=1.3.0=pyhd8ed1ab_0
|
||||
- soupsieve=2.3.2.post1=pyhd8ed1ab_0
|
||||
- sqlite=3.40.0=h2229b38_0
|
||||
- stack_data=0.6.2=pyhd8ed1ab_0
|
||||
- tensorflow-deps=2.10.0=0
|
||||
- terminado=0.17.1=pyhd1c38e8_0
|
||||
- threadpoolctl=3.1.0=pyh8a188c0_0
|
||||
- tifffile=2021.7.2=pyhd3eb1b0_2
|
||||
- tinycss2=1.2.1=pyhd8ed1ab_0
|
||||
- tk=8.6.12=he1e0b03_0
|
||||
- toolz=0.11.2=pyhd3eb1b0_0
|
||||
- tornado=6.2=py39h02fc5c5_1
|
||||
- tqdm=4.64.1=pyhd8ed1ab_0
|
||||
- traitlets=5.8.0=pyhd8ed1ab_0
|
||||
- typing_extensions=4.4.0=pyha770c72_0
|
||||
- tzdata=2022g=h191b570_0
|
||||
- unicodedata2=15.0.0=py39h02fc5c5_0
|
||||
- urllib3=1.26.13=pyhd8ed1ab_0
|
||||
- wcwidth=0.2.5=pyh9f0ad1d_2
|
||||
- webencodings=0.5.1=py_1
|
||||
- websocket-client=1.4.2=pyhd8ed1ab_0
|
||||
- werkzeug=2.2.2=pyhd8ed1ab_0
|
||||
- wheel=0.38.4=pyhd8ed1ab_0
|
||||
- widgetsnbextension=4.0.4=pyhd8ed1ab_0
|
||||
- xorg-libxau=1.0.9=h27ca646_0
|
||||
- xorg-libxdmcp=1.1.3=h27ca646_0
|
||||
- xz=5.2.6=h57fd34a_0
|
||||
- yaml=0.2.5=h3422bc3_2
|
||||
- zeromq=4.3.4=hbdafb3b_1
|
||||
- zfp=0.5.5=hc377ac9_6
|
||||
- zipp=3.11.0=pyhd8ed1ab_0
|
||||
- zlib=1.2.13=h03a7124_4
|
||||
- zstd=1.5.2=h8128057_4
|
||||
- pip:
|
||||
- absl-py==1.3.0
|
||||
- astunparse==1.6.3
|
||||
- bayesian-optimization==1.4.2
|
||||
- cachetools==5.2.0
|
||||
- cbor==1.0.0
|
||||
- cloudpickle==2.2.0
|
||||
- flatbuffers==22.12.6
|
||||
- gast==0.4.0
|
||||
- google-auth==2.15.0
|
||||
- google-auth-oauthlib==0.4.6
|
||||
- google-pasta==0.2.0
|
||||
- gym==0.26.2
|
||||
- gym-notices==0.0.8
|
||||
- imageai==3.0.3
|
||||
- imutils==0.5.4
|
||||
- kaggle==1.5.12
|
||||
- keras==2.11.0
|
||||
- libclang==14.0.6
|
||||
- markdown==3.4.1
|
||||
- oauthlib==3.2.2
|
||||
- opencv-python==4.7.0.68
|
||||
- opt-einsum==3.3.0
|
||||
- pyasn1==0.4.8
|
||||
- pyasn1-modules==0.2.8
|
||||
- python-slugify==7.0.0
|
||||
- requests-oauthlib==1.3.1
|
||||
- rsa==4.9
|
||||
- tensorboard==2.11.0
|
||||
- tensorboard-data-server==0.6.1
|
||||
- tensorboard-plugin-wit==1.8.1
|
||||
- tensorflow-addons==0.19.0
|
||||
- tensorflow-estimator==2.11.0
|
||||
- tensorflow-macos==2.11.0
|
||||
- tensorflow-metal==0.7.0
|
||||
- termcolor==2.1.1
|
||||
- text-unidecode==1.3
|
||||
- typeguard==2.13.3
|
||||
- wrapt==1.14.1
|
||||
254
conda_env.yml
Normal file
254
conda_env.yml
Normal file
@ -0,0 +1,254 @@
|
||||
name: tensorflow
|
||||
channels:
|
||||
- apple
|
||||
- conda-forge
|
||||
- defaults
|
||||
dependencies:
|
||||
- anyio=3.6.2=pyhd8ed1ab_0
|
||||
- appdirs=1.4.4=pyh9f0ad1d_0
|
||||
- appnope=0.1.3=pyhd8ed1ab_0
|
||||
- argon2-cffi=21.3.0=pyhd8ed1ab_0
|
||||
- argon2-cffi-bindings=21.2.0=py39h02fc5c5_3
|
||||
- asttokens=2.2.1=pyhd8ed1ab_0
|
||||
- attrs=22.2.0=pyh71513ae_0
|
||||
- backcall=0.2.0=pyh9f0ad1d_0
|
||||
- backports=1.0=pyhd8ed1ab_3
|
||||
- backports.functools_lru_cache=1.6.4=pyhd8ed1ab_0
|
||||
- beautifulsoup4=4.11.1=pyha770c72_0
|
||||
- black=22.6.0=py39hca03da5_0
|
||||
- bleach=5.0.1=pyhd8ed1ab_0
|
||||
- boto3=1.26.44=pyhd8ed1ab_0
|
||||
- botocore=1.29.44=pyhd8ed1ab_0
|
||||
- brotli=1.0.9=h1a8c8d9_8
|
||||
- brotli-bin=1.0.9=h1a8c8d9_8
|
||||
- brotlipy=0.7.0=py39h02fc5c5_1005
|
||||
- bzip2=1.0.8=h3422bc3_4
|
||||
- c-ares=1.18.1=h3422bc3_0
|
||||
- ca-certificates=2022.10.11=hca03da5_0
|
||||
- cached-property=1.5.2=hd8ed1ab_1
|
||||
- cached_property=1.5.2=pyha770c72_1
|
||||
- certifi=2022.12.7=py39hca03da5_0
|
||||
- cffi=1.15.1=py39h7e6b969_3
|
||||
- charset-normalizer=2.1.1=pyhd8ed1ab_0
|
||||
- click=8.1.3=unix_pyhd8ed1ab_2
|
||||
- colorama=0.4.6=pyhd8ed1ab_0
|
||||
- comm=0.1.2=pyhd8ed1ab_0
|
||||
- contourpy=1.0.6=py39haaf3ac1_0
|
||||
- cryptography=39.0.0=py39he2a39a8_0
|
||||
- cycler=0.11.0=pyhd8ed1ab_0
|
||||
- debugpy=1.6.5=py39h23fbdae_0
|
||||
- decorator=5.1.1=pyhd8ed1ab_0
|
||||
- defusedxml=0.7.1=pyhd8ed1ab_0
|
||||
- entrypoints=0.4=pyhd8ed1ab_0
|
||||
- executing=1.2.0=pyhd8ed1ab_0
|
||||
- flask=2.2.2=pyhd8ed1ab_0
|
||||
- flit-core=3.8.0=pyhd8ed1ab_0
|
||||
- fonttools=4.38.0=py39h02fc5c5_1
|
||||
- freetype=2.12.1=hd633e50_1
|
||||
- grpcio=1.46.3=py39h365d37b_0
|
||||
- h5py=3.6.0=nompi_py39hd982b79_100
|
||||
- hdf5=1.12.1=nompi_hd9dbc9e_104
|
||||
- icu=70.1=h6b3803e_0
|
||||
- idna=3.4=pyhd8ed1ab_0
|
||||
- importlib-metadata=6.0.0=pyha770c72_0
|
||||
- importlib_resources=5.10.2=pyhd8ed1ab_0
|
||||
- iniconfig=1.1.1=pyhd3eb1b0_0
|
||||
- ipykernel=6.19.4=pyh736e0ef_0
|
||||
- ipython=8.8.0=pyhd1c38e8_0
|
||||
- ipython_genutils=0.2.0=py_1
|
||||
- ipywidgets=8.0.4=pyhd8ed1ab_0
|
||||
- itsdangerous=2.1.2=pyhd8ed1ab_0
|
||||
- jedi=0.18.2=pyhd8ed1ab_0
|
||||
- jinja2=3.1.2=pyhd8ed1ab_1
|
||||
- jmespath=1.0.1=pyhd8ed1ab_0
|
||||
- joblib=1.2.0=pyhd8ed1ab_0
|
||||
- jpeg=9e=he4db4b2_2
|
||||
- jsonschema=4.17.3=pyhd8ed1ab_0
|
||||
- jupyter=1.0.0=py39h2804cbe_8
|
||||
- jupyter_client=7.4.8=pyhd8ed1ab_0
|
||||
- jupyter_console=6.4.4=pyhd8ed1ab_0
|
||||
- jupyter_core=5.1.2=py39h2804cbe_0
|
||||
- jupyter_events=0.5.0=pyhd8ed1ab_1
|
||||
- jupyter_server=2.0.6=pyhd8ed1ab_0
|
||||
- jupyter_server_terminals=0.4.3=pyhd8ed1ab_0
|
||||
- jupyterlab_pygments=0.2.2=pyhd8ed1ab_0
|
||||
- jupyterlab_widgets=3.0.5=pyhd8ed1ab_0
|
||||
- keras-preprocessing=1.1.2=pyhd3eb1b0_0
|
||||
- kiwisolver=1.4.4=py39haaf3ac1_1
|
||||
- krb5=1.20.1=h69eda48_0
|
||||
- lcms2=2.14=h481adae_1
|
||||
- lerc=4.0.0=h9a09cb3_0
|
||||
- libblas=3.9.0=16_osxarm64_openblas
|
||||
- libbrotlicommon=1.0.9=h1a8c8d9_8
|
||||
- libbrotlidec=1.0.9=h1a8c8d9_8
|
||||
- libbrotlienc=1.0.9=h1a8c8d9_8
|
||||
- libcblas=3.9.0=16_osxarm64_openblas
|
||||
- libcurl=7.87.0=h9049daf_0
|
||||
- libcxx=14.0.6=h2692d47_0
|
||||
- libdeflate=1.14=h1a8c8d9_0
|
||||
- libedit=3.1.20191231=hc8eb9b7_2
|
||||
- libev=4.33=h642e427_1
|
||||
- libffi=3.4.2=h3422bc3_5
|
||||
- libgfortran=5.0.0=11_3_0_hd922786_27
|
||||
- libgfortran5=11.3.0=hdaf2cc0_27
|
||||
- libiconv=1.17=he4db4b2_0
|
||||
- libjpeg-turbo=2.1.4=h1a8c8d9_0
|
||||
- liblapack=3.9.0=16_osxarm64_openblas
|
||||
- libnghttp2=1.51.0=hae82a92_0
|
||||
- libopenblas=0.3.21=openmp_hc731615_3
|
||||
- libpng=1.6.39=h76d750c_0
|
||||
- libprotobuf=3.19.4=hccf11d3_0
|
||||
- libsodium=1.0.18=h27ca646_1
|
||||
- libsqlite=3.40.0=h76d750c_0
|
||||
- libssh2=1.10.0=h7a5bd25_3
|
||||
- libtiff=4.5.0=heb92581_0
|
||||
- libwebp-base=1.2.4=h57fd34a_0
|
||||
- libxcb=1.13=h9b22ae9_1004
|
||||
- libxml2=2.10.3=h87b0503_0
|
||||
- libxslt=1.1.37=h1bd8bc4_0
|
||||
- libzlib=1.2.13=h03a7124_4
|
||||
- llvm-openmp=15.0.6=h7cfbb63_0
|
||||
- lxml=4.9.2=py39h0520ce3_0
|
||||
- markupsafe=2.1.1=py39h02fc5c5_2
|
||||
- matplotlib=3.6.2=py39hdf13c20_0
|
||||
- matplotlib-base=3.6.2=py39h35e9e80_0
|
||||
- matplotlib-inline=0.1.6=pyhd8ed1ab_0
|
||||
- mistune=2.0.4=pyhd8ed1ab_0
|
||||
- munkres=1.1.4=pyh9f0ad1d_0
|
||||
- mypy_extensions=0.4.3=py39hca03da5_1
|
||||
- nbclassic=0.4.8=pyhd8ed1ab_0
|
||||
- nbclient=0.7.2=pyhd8ed1ab_0
|
||||
- nbconvert=7.2.7=pyhd8ed1ab_0
|
||||
- nbconvert-core=7.2.7=pyhd8ed1ab_0
|
||||
- nbconvert-pandoc=7.2.7=pyhd8ed1ab_0
|
||||
- nbformat=5.7.1=pyhd8ed1ab_0
|
||||
- ncurses=6.3=h07bb92c_1
|
||||
- nest-asyncio=1.5.6=pyhd8ed1ab_0
|
||||
- notebook=6.5.2=pyha770c72_1
|
||||
- notebook-shim=0.2.2=pyhd8ed1ab_0
|
||||
- numpy=1.23.2=py39h3668e8b_0
|
||||
- openjpeg=2.5.0=hbc2ba62_2
|
||||
- openssl=3.0.7=h03a7124_1
|
||||
- packaging=22.0=pyhd8ed1ab_0
|
||||
- pandas=1.5.2=py39hde7b980_0
|
||||
- pandas-datareader=0.10.0=pyh6c4a22f_0
|
||||
- pandoc=2.19.2=hce30654_1
|
||||
- pandocfilters=1.5.0=pyhd8ed1ab_0
|
||||
- parso=0.8.3=pyhd8ed1ab_0
|
||||
- pathspec=0.9.0=py39hca03da5_0
|
||||
- pexpect=4.8.0=pyh1a96a4e_2
|
||||
- pickleshare=0.7.5=py_1003
|
||||
- pillow=9.4.0=py39h8bbe137_0
|
||||
- pip=22.3.1=pyhd8ed1ab_0
|
||||
- pkgutil-resolve-name=1.3.10=pyhd8ed1ab_0
|
||||
- platformdirs=2.6.2=pyhd8ed1ab_0
|
||||
- pluggy=1.0.0=py39hca03da5_1
|
||||
- pooch=1.6.0=pyhd8ed1ab_0
|
||||
- progressbar2=3.37.1=py39hca03da5_0
|
||||
- prometheus_client=0.15.0=pyhd8ed1ab_0
|
||||
- prompt-toolkit=3.0.36=pyha770c72_0
|
||||
- prompt_toolkit=3.0.36=hd8ed1ab_0
|
||||
- protobuf=3.19.4=py39hfb83b0d_0
|
||||
- psutil=5.9.4=py39h02fc5c5_0
|
||||
- pthread-stubs=0.4=h27ca646_1001
|
||||
- ptyprocess=0.7.0=pyhd3deb0d_0
|
||||
- pure_eval=0.2.2=pyhd8ed1ab_0
|
||||
- py=1.11.0=pyhd3eb1b0_0
|
||||
- pycparser=2.21=pyhd8ed1ab_0
|
||||
- pygments=2.14.0=pyhd8ed1ab_0
|
||||
- pyopenssl=23.0.0=pyhd8ed1ab_0
|
||||
- pyparsing=3.0.9=pyhd8ed1ab_0
|
||||
- pyrsistent=0.19.3=py39h02fc5c5_0
|
||||
- pysocks=1.7.1=pyha2e5f31_6
|
||||
- pytest=7.1.2=py39hca03da5_0
|
||||
- pytest-runner=6.0.0=py39hca03da5_0
|
||||
- python=3.9.15=hea58f1e_0_cpython
|
||||
- python-dateutil=2.8.2=pyhd8ed1ab_0
|
||||
- python-fastjsonschema=2.16.2=pyhd8ed1ab_0
|
||||
- python-json-logger=2.0.4=pyhd8ed1ab_0
|
||||
- python-utils=3.3.3=py39hca03da5_0
|
||||
- python_abi=3.9=3_cp39
|
||||
- pytz=2022.7=pyhd8ed1ab_0
|
||||
- pyyaml=6.0=py39h02fc5c5_5
|
||||
- pyzmq=24.0.1=py39h0553236_1
|
||||
- readline=8.1.2=h46ed386_0
|
||||
- requests=2.28.1=pyhd8ed1ab_1
|
||||
- s3transfer=0.6.0=pyhd8ed1ab_0
|
||||
- scikit-learn=1.2.0=py39h57c6424_0
|
||||
- scipy=1.10.0=py39h18313fe_0
|
||||
- seaborn=0.12.1=py39hca03da5_0
|
||||
- send2trash=1.8.0=pyhd8ed1ab_0
|
||||
- setuptools=65.6.3=pyhd8ed1ab_0
|
||||
- six=1.16.0=pyh6c4a22f_0
|
||||
- sniffio=1.3.0=pyhd8ed1ab_0
|
||||
- soupsieve=2.3.2.post1=pyhd8ed1ab_0
|
||||
- stack_data=0.6.2=pyhd8ed1ab_0
|
||||
- tensorflow-deps=2.10.0=0
|
||||
- terminado=0.17.1=pyhd1c38e8_0
|
||||
- threadpoolctl=3.1.0=pyh8a188c0_0
|
||||
- tinycss2=1.2.1=pyhd8ed1ab_0
|
||||
- tk=8.6.12=he1e0b03_0
|
||||
- tomli=2.0.1=py39hca03da5_0
|
||||
- tornado=6.2=py39h02fc5c5_1
|
||||
- tqdm=4.64.1=pyhd8ed1ab_0
|
||||
- traitlets=5.8.0=pyhd8ed1ab_0
|
||||
- typing-extensions=4.4.0=hd8ed1ab_0
|
||||
- typing_extensions=4.4.0=pyha770c72_0
|
||||
- tzdata=2022g=h191b570_0
|
||||
- unicodedata2=15.0.0=py39h02fc5c5_0
|
||||
- urllib3=1.26.13=pyhd8ed1ab_0
|
||||
- wcwidth=0.2.5=pyh9f0ad1d_2
|
||||
- webencodings=0.5.1=py_1
|
||||
- websocket-client=1.4.2=pyhd8ed1ab_0
|
||||
- werkzeug=2.2.2=pyhd8ed1ab_0
|
||||
- wheel=0.38.4=pyhd8ed1ab_0
|
||||
- widgetsnbextension=4.0.5=pyhd8ed1ab_0
|
||||
- xorg-libxau=1.0.9=h27ca646_0
|
||||
- xorg-libxdmcp=1.1.3=h27ca646_0
|
||||
- xz=5.2.6=h57fd34a_0
|
||||
- yaml=0.2.5=h3422bc3_2
|
||||
- zeromq=4.3.4=hbdafb3b_1
|
||||
- zipp=3.11.0=pyhd8ed1ab_0
|
||||
- zlib=1.2.13=h03a7124_4
|
||||
- zstd=1.5.2=h8128057_4
|
||||
- pip:
|
||||
- absl-py==1.3.0
|
||||
- astunparse==1.6.3
|
||||
- bayesian-optimization==1.4.2
|
||||
- cachetools==5.2.0
|
||||
- cloudpickle==2.2.0
|
||||
- flatbuffers==23.1.4
|
||||
- gast==0.4.0
|
||||
- google-auth==2.15.0
|
||||
- google-auth-oauthlib==0.4.6
|
||||
- google-pasta==0.2.0
|
||||
- gym==0.26.2
|
||||
- gym-notices==0.0.8
|
||||
- imageio==2.23.0
|
||||
- kaggle==1.5.12
|
||||
- keras==2.11.0
|
||||
- keras-applications==1.0.8
|
||||
- libclang==14.0.6
|
||||
- markdown==3.4.1
|
||||
- networkx==2.8.8
|
||||
- oauthlib==3.2.2
|
||||
- opencv-python==4.7.0.68
|
||||
- opt-einsum==3.3.0
|
||||
- pyasn1==0.4.8
|
||||
- pyasn1-modules==0.2.8
|
||||
- python-slugify==7.0.0
|
||||
- pywavelets==1.4.1
|
||||
- requests-oauthlib==1.3.1
|
||||
- rsa==4.9
|
||||
- scikit-image==0.19.3
|
||||
- tensorboard==2.11.0
|
||||
- tensorboard-data-server==0.6.1
|
||||
- tensorboard-plugin-wit==1.8.1
|
||||
- tensorflow-estimator==2.11.0
|
||||
- tensorflow-macos==2.11.0
|
||||
- tensorflow-metal==0.7.0
|
||||
- termcolor==2.2.0
|
||||
- text-unidecode==1.3
|
||||
- tifffile==2022.10.10
|
||||
- wrapt==1.14.1
|
||||
- yolo3==1.0
|
||||
388
depdencies/json.lua
Normal file
388
depdencies/json.lua
Normal file
@ -0,0 +1,388 @@
|
||||
--
|
||||
-- json.lua
|
||||
--
|
||||
-- Copyright (c) 2020 rxi
|
||||
--
|
||||
-- Permission is hereby granted, free of charge, to any person obtaining a copy of
|
||||
-- this software and associated documentation files (the "Software"), to deal in
|
||||
-- the Software without restriction, including without limitation the rights to
|
||||
-- use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
|
||||
-- of the Software, and to permit persons to whom the Software is furnished to do
|
||||
-- so, subject to the following conditions:
|
||||
--
|
||||
-- The above copyright notice and this permission notice shall be included in all
|
||||
-- copies or substantial portions of the Software.
|
||||
--
|
||||
-- THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
-- IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
-- FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
-- AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
-- LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
-- OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
-- SOFTWARE.
|
||||
--
|
||||
|
||||
local json = { _version = "0.1.2" }
|
||||
|
||||
-------------------------------------------------------------------------------
|
||||
-- Encode
|
||||
-------------------------------------------------------------------------------
|
||||
|
||||
local encode
|
||||
|
||||
local escape_char_map = {
|
||||
[ "\\" ] = "\\",
|
||||
[ "\"" ] = "\"",
|
||||
[ "\b" ] = "b",
|
||||
[ "\f" ] = "f",
|
||||
[ "\n" ] = "n",
|
||||
[ "\r" ] = "r",
|
||||
[ "\t" ] = "t",
|
||||
}
|
||||
|
||||
local escape_char_map_inv = { [ "/" ] = "/" }
|
||||
for k, v in pairs(escape_char_map) do
|
||||
escape_char_map_inv[v] = k
|
||||
end
|
||||
|
||||
|
||||
local function escape_char(c)
|
||||
return "\\" .. (escape_char_map[c] or string.format("u%04x", c:byte()))
|
||||
end
|
||||
|
||||
|
||||
local function encode_nil(val)
|
||||
return "null"
|
||||
end
|
||||
|
||||
|
||||
local function encode_table(val, stack)
|
||||
local res = {}
|
||||
stack = stack or {}
|
||||
|
||||
-- Circular reference?
|
||||
if stack[val] then error("circular reference") end
|
||||
|
||||
stack[val] = true
|
||||
|
||||
if rawget(val, 1) ~= nil or next(val) == nil then
|
||||
-- Treat as array -- check keys are valid and it is not sparse
|
||||
local n = 0
|
||||
for k in pairs(val) do
|
||||
if type(k) ~= "number" then
|
||||
error("invalid table: mixed or invalid key types")
|
||||
end
|
||||
n = n + 1
|
||||
end
|
||||
if n ~= #val then
|
||||
error("invalid table: sparse array")
|
||||
end
|
||||
-- Encode
|
||||
for i, v in ipairs(val) do
|
||||
table.insert(res, encode(v, stack))
|
||||
end
|
||||
stack[val] = nil
|
||||
return "[" .. table.concat(res, ",") .. "]"
|
||||
|
||||
else
|
||||
-- Treat as an object
|
||||
for k, v in pairs(val) do
|
||||
if type(k) ~= "string" then
|
||||
error("invalid table: mixed or invalid key types")
|
||||
end
|
||||
table.insert(res, encode(k, stack) .. ":" .. encode(v, stack))
|
||||
end
|
||||
stack[val] = nil
|
||||
return "{" .. table.concat(res, ",") .. "}"
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
local function encode_string(val)
|
||||
return '"' .. val:gsub('[%z\1-\31\\"]', escape_char) .. '"'
|
||||
end
|
||||
|
||||
|
||||
local function encode_number(val)
|
||||
-- Check for NaN, -inf and inf
|
||||
if val ~= val or val <= -math.huge or val >= math.huge then
|
||||
error("unexpected number value '" .. tostring(val) .. "'")
|
||||
end
|
||||
return string.format("%.14g", val)
|
||||
end
|
||||
|
||||
|
||||
local type_func_map = {
|
||||
[ "nil" ] = encode_nil,
|
||||
[ "table" ] = encode_table,
|
||||
[ "string" ] = encode_string,
|
||||
[ "number" ] = encode_number,
|
||||
[ "boolean" ] = tostring,
|
||||
}
|
||||
|
||||
|
||||
encode = function(val, stack)
|
||||
local t = type(val)
|
||||
local f = type_func_map[t]
|
||||
if f then
|
||||
return f(val, stack)
|
||||
end
|
||||
error("unexpected type '" .. t .. "'")
|
||||
end
|
||||
|
||||
|
||||
function json.encode(val)
|
||||
return ( encode(val) )
|
||||
end
|
||||
|
||||
|
||||
-------------------------------------------------------------------------------
|
||||
-- Decode
|
||||
-------------------------------------------------------------------------------
|
||||
|
||||
local parse
|
||||
|
||||
local function create_set(...)
|
||||
local res = {}
|
||||
for i = 1, select("#", ...) do
|
||||
res[ select(i, ...) ] = true
|
||||
end
|
||||
return res
|
||||
end
|
||||
|
||||
local space_chars = create_set(" ", "\t", "\r", "\n")
|
||||
local delim_chars = create_set(" ", "\t", "\r", "\n", "]", "}", ",")
|
||||
local escape_chars = create_set("\\", "/", '"', "b", "f", "n", "r", "t", "u")
|
||||
local literals = create_set("true", "false", "null")
|
||||
|
||||
local literal_map = {
|
||||
[ "true" ] = true,
|
||||
[ "false" ] = false,
|
||||
[ "null" ] = nil,
|
||||
}
|
||||
|
||||
|
||||
local function next_char(str, idx, set, negate)
|
||||
for i = idx, #str do
|
||||
if set[str:sub(i, i)] ~= negate then
|
||||
return i
|
||||
end
|
||||
end
|
||||
return #str + 1
|
||||
end
|
||||
|
||||
|
||||
local function decode_error(str, idx, msg)
|
||||
local line_count = 1
|
||||
local col_count = 1
|
||||
for i = 1, idx - 1 do
|
||||
col_count = col_count + 1
|
||||
if str:sub(i, i) == "\n" then
|
||||
line_count = line_count + 1
|
||||
col_count = 1
|
||||
end
|
||||
end
|
||||
error( string.format("%s at line %d col %d", msg, line_count, col_count) )
|
||||
end
|
||||
|
||||
|
||||
local function codepoint_to_utf8(n)
|
||||
-- http://scripts.sil.org/cms/scripts/page.php?site_id=nrsi&id=iws-appendixa
|
||||
local f = math.floor
|
||||
if n <= 0x7f then
|
||||
return string.char(n)
|
||||
elseif n <= 0x7ff then
|
||||
return string.char(f(n / 64) + 192, n % 64 + 128)
|
||||
elseif n <= 0xffff then
|
||||
return string.char(f(n / 4096) + 224, f(n % 4096 / 64) + 128, n % 64 + 128)
|
||||
elseif n <= 0x10ffff then
|
||||
return string.char(f(n / 262144) + 240, f(n % 262144 / 4096) + 128,
|
||||
f(n % 4096 / 64) + 128, n % 64 + 128)
|
||||
end
|
||||
error( string.format("invalid unicode codepoint '%x'", n) )
|
||||
end
|
||||
|
||||
|
||||
local function parse_unicode_escape(s)
|
||||
local n1 = tonumber( s:sub(1, 4), 16 )
|
||||
local n2 = tonumber( s:sub(7, 10), 16 )
|
||||
-- Surrogate pair?
|
||||
if n2 then
|
||||
return codepoint_to_utf8((n1 - 0xd800) * 0x400 + (n2 - 0xdc00) + 0x10000)
|
||||
else
|
||||
return codepoint_to_utf8(n1)
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
local function parse_string(str, i)
|
||||
local res = ""
|
||||
local j = i + 1
|
||||
local k = j
|
||||
|
||||
while j <= #str do
|
||||
local x = str:byte(j)
|
||||
|
||||
if x < 32 then
|
||||
decode_error(str, j, "control character in string")
|
||||
|
||||
elseif x == 92 then -- `\`: Escape
|
||||
res = res .. str:sub(k, j - 1)
|
||||
j = j + 1
|
||||
local c = str:sub(j, j)
|
||||
if c == "u" then
|
||||
local hex = str:match("^[dD][89aAbB]%x%x\\u%x%x%x%x", j + 1)
|
||||
or str:match("^%x%x%x%x", j + 1)
|
||||
or decode_error(str, j - 1, "invalid unicode escape in string")
|
||||
res = res .. parse_unicode_escape(hex)
|
||||
j = j + #hex
|
||||
else
|
||||
if not escape_chars[c] then
|
||||
decode_error(str, j - 1, "invalid escape char '" .. c .. "' in string")
|
||||
end
|
||||
res = res .. escape_char_map_inv[c]
|
||||
end
|
||||
k = j + 1
|
||||
|
||||
elseif x == 34 then -- `"`: End of string
|
||||
res = res .. str:sub(k, j - 1)
|
||||
return res, j + 1
|
||||
end
|
||||
|
||||
j = j + 1
|
||||
end
|
||||
|
||||
decode_error(str, i, "expected closing quote for string")
|
||||
end
|
||||
|
||||
|
||||
local function parse_number(str, i)
|
||||
local x = next_char(str, i, delim_chars)
|
||||
local s = str:sub(i, x - 1)
|
||||
local n = tonumber(s)
|
||||
if not n then
|
||||
decode_error(str, i, "invalid number '" .. s .. "'")
|
||||
end
|
||||
return n, x
|
||||
end
|
||||
|
||||
|
||||
local function parse_literal(str, i)
|
||||
local x = next_char(str, i, delim_chars)
|
||||
local word = str:sub(i, x - 1)
|
||||
if not literals[word] then
|
||||
decode_error(str, i, "invalid literal '" .. word .. "'")
|
||||
end
|
||||
return literal_map[word], x
|
||||
end
|
||||
|
||||
|
||||
local function parse_array(str, i)
|
||||
local res = {}
|
||||
local n = 1
|
||||
i = i + 1
|
||||
while 1 do
|
||||
local x
|
||||
i = next_char(str, i, space_chars, true)
|
||||
-- Empty / end of array?
|
||||
if str:sub(i, i) == "]" then
|
||||
i = i + 1
|
||||
break
|
||||
end
|
||||
-- Read token
|
||||
x, i = parse(str, i)
|
||||
res[n] = x
|
||||
n = n + 1
|
||||
-- Next token
|
||||
i = next_char(str, i, space_chars, true)
|
||||
local chr = str:sub(i, i)
|
||||
i = i + 1
|
||||
if chr == "]" then break end
|
||||
if chr ~= "," then decode_error(str, i, "expected ']' or ','") end
|
||||
end
|
||||
return res, i
|
||||
end
|
||||
|
||||
|
||||
local function parse_object(str, i)
|
||||
local res = {}
|
||||
i = i + 1
|
||||
while 1 do
|
||||
local key, val
|
||||
i = next_char(str, i, space_chars, true)
|
||||
-- Empty / end of object?
|
||||
if str:sub(i, i) == "}" then
|
||||
i = i + 1
|
||||
break
|
||||
end
|
||||
-- Read key
|
||||
if str:sub(i, i) ~= '"' then
|
||||
decode_error(str, i, "expected string for key")
|
||||
end
|
||||
key, i = parse(str, i)
|
||||
-- Read ':' delimiter
|
||||
i = next_char(str, i, space_chars, true)
|
||||
if str:sub(i, i) ~= ":" then
|
||||
decode_error(str, i, "expected ':' after key")
|
||||
end
|
||||
i = next_char(str, i + 1, space_chars, true)
|
||||
-- Read value
|
||||
val, i = parse(str, i)
|
||||
-- Set
|
||||
res[key] = val
|
||||
-- Next token
|
||||
i = next_char(str, i, space_chars, true)
|
||||
local chr = str:sub(i, i)
|
||||
i = i + 1
|
||||
if chr == "}" then break end
|
||||
if chr ~= "," then decode_error(str, i, "expected '}' or ','") end
|
||||
end
|
||||
return res, i
|
||||
end
|
||||
|
||||
|
||||
local char_func_map = {
|
||||
[ '"' ] = parse_string,
|
||||
[ "0" ] = parse_number,
|
||||
[ "1" ] = parse_number,
|
||||
[ "2" ] = parse_number,
|
||||
[ "3" ] = parse_number,
|
||||
[ "4" ] = parse_number,
|
||||
[ "5" ] = parse_number,
|
||||
[ "6" ] = parse_number,
|
||||
[ "7" ] = parse_number,
|
||||
[ "8" ] = parse_number,
|
||||
[ "9" ] = parse_number,
|
||||
[ "-" ] = parse_number,
|
||||
[ "t" ] = parse_literal,
|
||||
[ "f" ] = parse_literal,
|
||||
[ "n" ] = parse_literal,
|
||||
[ "[" ] = parse_array,
|
||||
[ "{" ] = parse_object,
|
||||
}
|
||||
|
||||
|
||||
parse = function(str, idx)
|
||||
local chr = str:sub(idx, idx)
|
||||
local f = char_func_map[chr]
|
||||
if f then
|
||||
return f(str, idx)
|
||||
end
|
||||
decode_error(str, idx, "unexpected character '" .. chr .. "'")
|
||||
end
|
||||
|
||||
|
||||
function json.decode(str)
|
||||
if type(str) ~= "string" then
|
||||
error("expected argument of type string, got " .. type(str))
|
||||
end
|
||||
local res, idx = parse(str, next_char(str, 1, space_chars, true))
|
||||
idx = next_char(str, idx, space_chars, true)
|
||||
if idx <= #str then
|
||||
decode_error(str, idx, "trailing garbage")
|
||||
end
|
||||
return res
|
||||
end
|
||||
|
||||
|
||||
return json
|
||||
388
dependencies/json.lua
vendored
Normal file
388
dependencies/json.lua
vendored
Normal file
@ -0,0 +1,388 @@
|
||||
--
|
||||
-- json.lua
|
||||
--
|
||||
-- Copyright (c) 2020 rxi
|
||||
--
|
||||
-- Permission is hereby granted, free of charge, to any person obtaining a copy of
|
||||
-- this software and associated documentation files (the "Software"), to deal in
|
||||
-- the Software without restriction, including without limitation the rights to
|
||||
-- use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
|
||||
-- of the Software, and to permit persons to whom the Software is furnished to do
|
||||
-- so, subject to the following conditions:
|
||||
--
|
||||
-- The above copyright notice and this permission notice shall be included in all
|
||||
-- copies or substantial portions of the Software.
|
||||
--
|
||||
-- THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
-- IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
-- FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
-- AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
-- LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
-- OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
-- SOFTWARE.
|
||||
--
|
||||
|
||||
local json = { _version = "0.1.2" }
|
||||
|
||||
-------------------------------------------------------------------------------
|
||||
-- Encode
|
||||
-------------------------------------------------------------------------------
|
||||
|
||||
local encode
|
||||
|
||||
local escape_char_map = {
|
||||
[ "\\" ] = "\\",
|
||||
[ "\"" ] = "\"",
|
||||
[ "\b" ] = "b",
|
||||
[ "\f" ] = "f",
|
||||
[ "\n" ] = "n",
|
||||
[ "\r" ] = "r",
|
||||
[ "\t" ] = "t",
|
||||
}
|
||||
|
||||
local escape_char_map_inv = { [ "/" ] = "/" }
|
||||
for k, v in pairs(escape_char_map) do
|
||||
escape_char_map_inv[v] = k
|
||||
end
|
||||
|
||||
|
||||
local function escape_char(c)
|
||||
return "\\" .. (escape_char_map[c] or string.format("u%04x", c:byte()))
|
||||
end
|
||||
|
||||
|
||||
local function encode_nil(val)
|
||||
return "null"
|
||||
end
|
||||
|
||||
|
||||
local function encode_table(val, stack)
|
||||
local res = {}
|
||||
stack = stack or {}
|
||||
|
||||
-- Circular reference?
|
||||
if stack[val] then error("circular reference") end
|
||||
|
||||
stack[val] = true
|
||||
|
||||
if rawget(val, 1) ~= nil or next(val) == nil then
|
||||
-- Treat as array -- check keys are valid and it is not sparse
|
||||
local n = 0
|
||||
for k in pairs(val) do
|
||||
if type(k) ~= "number" then
|
||||
error("invalid table: mixed or invalid key types")
|
||||
end
|
||||
n = n + 1
|
||||
end
|
||||
if n ~= #val then
|
||||
error("invalid table: sparse array")
|
||||
end
|
||||
-- Encode
|
||||
for i, v in ipairs(val) do
|
||||
table.insert(res, encode(v, stack))
|
||||
end
|
||||
stack[val] = nil
|
||||
return "[" .. table.concat(res, ",") .. "]"
|
||||
|
||||
else
|
||||
-- Treat as an object
|
||||
for k, v in pairs(val) do
|
||||
if type(k) ~= "string" then
|
||||
error("invalid table: mixed or invalid key types")
|
||||
end
|
||||
table.insert(res, encode(k, stack) .. ":" .. encode(v, stack))
|
||||
end
|
||||
stack[val] = nil
|
||||
return "{" .. table.concat(res, ",") .. "}"
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
local function encode_string(val)
|
||||
return '"' .. val:gsub('[%z\1-\31\\"]', escape_char) .. '"'
|
||||
end
|
||||
|
||||
|
||||
local function encode_number(val)
|
||||
-- Check for NaN, -inf and inf
|
||||
if val ~= val or val <= -math.huge or val >= math.huge then
|
||||
error("unexpected number value '" .. tostring(val) .. "'")
|
||||
end
|
||||
return string.format("%.14g", val)
|
||||
end
|
||||
|
||||
|
||||
local type_func_map = {
|
||||
[ "nil" ] = encode_nil,
|
||||
[ "table" ] = encode_table,
|
||||
[ "string" ] = encode_string,
|
||||
[ "number" ] = encode_number,
|
||||
[ "boolean" ] = tostring,
|
||||
}
|
||||
|
||||
|
||||
encode = function(val, stack)
|
||||
local t = type(val)
|
||||
local f = type_func_map[t]
|
||||
if f then
|
||||
return f(val, stack)
|
||||
end
|
||||
error("unexpected type '" .. t .. "'")
|
||||
end
|
||||
|
||||
|
||||
function json.encode(val)
|
||||
return ( encode(val) )
|
||||
end
|
||||
|
||||
|
||||
-------------------------------------------------------------------------------
|
||||
-- Decode
|
||||
-------------------------------------------------------------------------------
|
||||
|
||||
local parse
|
||||
|
||||
local function create_set(...)
|
||||
local res = {}
|
||||
for i = 1, select("#", ...) do
|
||||
res[ select(i, ...) ] = true
|
||||
end
|
||||
return res
|
||||
end
|
||||
|
||||
local space_chars = create_set(" ", "\t", "\r", "\n")
|
||||
local delim_chars = create_set(" ", "\t", "\r", "\n", "]", "}", ",")
|
||||
local escape_chars = create_set("\\", "/", '"', "b", "f", "n", "r", "t", "u")
|
||||
local literals = create_set("true", "false", "null")
|
||||
|
||||
local literal_map = {
|
||||
[ "true" ] = true,
|
||||
[ "false" ] = false,
|
||||
[ "null" ] = nil,
|
||||
}
|
||||
|
||||
|
||||
local function next_char(str, idx, set, negate)
|
||||
for i = idx, #str do
|
||||
if set[str:sub(i, i)] ~= negate then
|
||||
return i
|
||||
end
|
||||
end
|
||||
return #str + 1
|
||||
end
|
||||
|
||||
|
||||
local function decode_error(str, idx, msg)
|
||||
local line_count = 1
|
||||
local col_count = 1
|
||||
for i = 1, idx - 1 do
|
||||
col_count = col_count + 1
|
||||
if str:sub(i, i) == "\n" then
|
||||
line_count = line_count + 1
|
||||
col_count = 1
|
||||
end
|
||||
end
|
||||
error( string.format("%s at line %d col %d", msg, line_count, col_count) )
|
||||
end
|
||||
|
||||
|
||||
local function codepoint_to_utf8(n)
|
||||
-- http://scripts.sil.org/cms/scripts/page.php?site_id=nrsi&id=iws-appendixa
|
||||
local f = math.floor
|
||||
if n <= 0x7f then
|
||||
return string.char(n)
|
||||
elseif n <= 0x7ff then
|
||||
return string.char(f(n / 64) + 192, n % 64 + 128)
|
||||
elseif n <= 0xffff then
|
||||
return string.char(f(n / 4096) + 224, f(n % 4096 / 64) + 128, n % 64 + 128)
|
||||
elseif n <= 0x10ffff then
|
||||
return string.char(f(n / 262144) + 240, f(n % 262144 / 4096) + 128,
|
||||
f(n % 4096 / 64) + 128, n % 64 + 128)
|
||||
end
|
||||
error( string.format("invalid unicode codepoint '%x'", n) )
|
||||
end
|
||||
|
||||
|
||||
local function parse_unicode_escape(s)
|
||||
local n1 = tonumber( s:sub(1, 4), 16 )
|
||||
local n2 = tonumber( s:sub(7, 10), 16 )
|
||||
-- Surrogate pair?
|
||||
if n2 then
|
||||
return codepoint_to_utf8((n1 - 0xd800) * 0x400 + (n2 - 0xdc00) + 0x10000)
|
||||
else
|
||||
return codepoint_to_utf8(n1)
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
local function parse_string(str, i)
|
||||
local res = ""
|
||||
local j = i + 1
|
||||
local k = j
|
||||
|
||||
while j <= #str do
|
||||
local x = str:byte(j)
|
||||
|
||||
if x < 32 then
|
||||
decode_error(str, j, "control character in string")
|
||||
|
||||
elseif x == 92 then -- `\`: Escape
|
||||
res = res .. str:sub(k, j - 1)
|
||||
j = j + 1
|
||||
local c = str:sub(j, j)
|
||||
if c == "u" then
|
||||
local hex = str:match("^[dD][89aAbB]%x%x\\u%x%x%x%x", j + 1)
|
||||
or str:match("^%x%x%x%x", j + 1)
|
||||
or decode_error(str, j - 1, "invalid unicode escape in string")
|
||||
res = res .. parse_unicode_escape(hex)
|
||||
j = j + #hex
|
||||
else
|
||||
if not escape_chars[c] then
|
||||
decode_error(str, j - 1, "invalid escape char '" .. c .. "' in string")
|
||||
end
|
||||
res = res .. escape_char_map_inv[c]
|
||||
end
|
||||
k = j + 1
|
||||
|
||||
elseif x == 34 then -- `"`: End of string
|
||||
res = res .. str:sub(k, j - 1)
|
||||
return res, j + 1
|
||||
end
|
||||
|
||||
j = j + 1
|
||||
end
|
||||
|
||||
decode_error(str, i, "expected closing quote for string")
|
||||
end
|
||||
|
||||
|
||||
local function parse_number(str, i)
|
||||
local x = next_char(str, i, delim_chars)
|
||||
local s = str:sub(i, x - 1)
|
||||
local n = tonumber(s)
|
||||
if not n then
|
||||
decode_error(str, i, "invalid number '" .. s .. "'")
|
||||
end
|
||||
return n, x
|
||||
end
|
||||
|
||||
|
||||
local function parse_literal(str, i)
|
||||
local x = next_char(str, i, delim_chars)
|
||||
local word = str:sub(i, x - 1)
|
||||
if not literals[word] then
|
||||
decode_error(str, i, "invalid literal '" .. word .. "'")
|
||||
end
|
||||
return literal_map[word], x
|
||||
end
|
||||
|
||||
|
||||
local function parse_array(str, i)
|
||||
local res = {}
|
||||
local n = 1
|
||||
i = i + 1
|
||||
while 1 do
|
||||
local x
|
||||
i = next_char(str, i, space_chars, true)
|
||||
-- Empty / end of array?
|
||||
if str:sub(i, i) == "]" then
|
||||
i = i + 1
|
||||
break
|
||||
end
|
||||
-- Read token
|
||||
x, i = parse(str, i)
|
||||
res[n] = x
|
||||
n = n + 1
|
||||
-- Next token
|
||||
i = next_char(str, i, space_chars, true)
|
||||
local chr = str:sub(i, i)
|
||||
i = i + 1
|
||||
if chr == "]" then break end
|
||||
if chr ~= "," then decode_error(str, i, "expected ']' or ','") end
|
||||
end
|
||||
return res, i
|
||||
end
|
||||
|
||||
|
||||
local function parse_object(str, i)
|
||||
local res = {}
|
||||
i = i + 1
|
||||
while 1 do
|
||||
local key, val
|
||||
i = next_char(str, i, space_chars, true)
|
||||
-- Empty / end of object?
|
||||
if str:sub(i, i) == "}" then
|
||||
i = i + 1
|
||||
break
|
||||
end
|
||||
-- Read key
|
||||
if str:sub(i, i) ~= '"' then
|
||||
decode_error(str, i, "expected string for key")
|
||||
end
|
||||
key, i = parse(str, i)
|
||||
-- Read ':' delimiter
|
||||
i = next_char(str, i, space_chars, true)
|
||||
if str:sub(i, i) ~= ":" then
|
||||
decode_error(str, i, "expected ':' after key")
|
||||
end
|
||||
i = next_char(str, i + 1, space_chars, true)
|
||||
-- Read value
|
||||
val, i = parse(str, i)
|
||||
-- Set
|
||||
res[key] = val
|
||||
-- Next token
|
||||
i = next_char(str, i, space_chars, true)
|
||||
local chr = str:sub(i, i)
|
||||
i = i + 1
|
||||
if chr == "}" then break end
|
||||
if chr ~= "," then decode_error(str, i, "expected '}' or ','") end
|
||||
end
|
||||
return res, i
|
||||
end
|
||||
|
||||
|
||||
local char_func_map = {
|
||||
[ '"' ] = parse_string,
|
||||
[ "0" ] = parse_number,
|
||||
[ "1" ] = parse_number,
|
||||
[ "2" ] = parse_number,
|
||||
[ "3" ] = parse_number,
|
||||
[ "4" ] = parse_number,
|
||||
[ "5" ] = parse_number,
|
||||
[ "6" ] = parse_number,
|
||||
[ "7" ] = parse_number,
|
||||
[ "8" ] = parse_number,
|
||||
[ "9" ] = parse_number,
|
||||
[ "-" ] = parse_number,
|
||||
[ "t" ] = parse_literal,
|
||||
[ "f" ] = parse_literal,
|
||||
[ "n" ] = parse_literal,
|
||||
[ "[" ] = parse_array,
|
||||
[ "{" ] = parse_object,
|
||||
}
|
||||
|
||||
|
||||
parse = function(str, idx)
|
||||
local chr = str:sub(idx, idx)
|
||||
local f = char_func_map[chr]
|
||||
if f then
|
||||
return f(str, idx)
|
||||
end
|
||||
decode_error(str, idx, "unexpected character '" .. chr .. "'")
|
||||
end
|
||||
|
||||
|
||||
function json.decode(str)
|
||||
if type(str) ~= "string" then
|
||||
error("expected argument of type string, got " .. type(str))
|
||||
end
|
||||
local res, idx = parse(str, next_char(str, 1, space_chars, true))
|
||||
idx = next_char(str, idx, space_chars, true)
|
||||
if idx <= #str then
|
||||
decode_error(str, idx, "trailing garbage")
|
||||
end
|
||||
return res
|
||||
end
|
||||
|
||||
|
||||
return json
|
||||
54
image_detector/rat_detector.py
Normal file
54
image_detector/rat_detector.py
Normal file
@ -0,0 +1,54 @@
|
||||
from PIL import Image
|
||||
import cv2 as cv
|
||||
import re
|
||||
|
||||
|
||||
def detect_img(yolo, img_path):
|
||||
detected_rats = []
|
||||
|
||||
try:
|
||||
image = Image.open(img_path)
|
||||
except:
|
||||
print('Image open Error! Try again!')
|
||||
return None
|
||||
else:
|
||||
r_image, pred = yolo.detect_image(image)
|
||||
processed_image = cv.imread(img_path)
|
||||
# r_image.show()
|
||||
# r_image.save(img_path)
|
||||
|
||||
## FIXME : better list mapping
|
||||
for prediction in pred:
|
||||
is_rat_detected = re.search("rat", prediction[0])
|
||||
if is_rat_detected:
|
||||
x1 = prediction[1][0]
|
||||
x2 = prediction[2][0]
|
||||
y1 = prediction[1][1]
|
||||
y2 = prediction[2][1]
|
||||
w = abs(x1 - x2)
|
||||
h = abs(y1 - y2)
|
||||
# print(pred)
|
||||
# print(f'x1: {x1}, x2: {x2}, y1: {y1}, y2: {y2}, w: {w}, h: {h}')
|
||||
|
||||
rat_img = processed_image[y1:y1 + h, x1:x1 + w]
|
||||
rat_img = cv.resize(rat_img, (128, 128), interpolation=cv.INTER_AREA)
|
||||
rat_pos = w, x1
|
||||
detected_rats.append((rat_img, rat_pos))
|
||||
|
||||
return detected_rats
|
||||
|
||||
|
||||
def get_turn_value(cords):
|
||||
img_width = 1920
|
||||
w, x = cords
|
||||
center_of_object = (x + w) / 2
|
||||
object_position = center_of_object / img_width
|
||||
return round(object_position * 100, 2)
|
||||
|
||||
|
||||
def detect_rat(model, img_path):
|
||||
detected_rats = detect_img(model, img_path)
|
||||
return detected_rats
|
||||
# rat_position = detected_rats[0][1]
|
||||
# turn_val = get_turn_value(rat_position)
|
||||
# return turn_val
|
||||
59
image_detector/robot_detector.py
Normal file
59
image_detector/robot_detector.py
Normal file
@ -0,0 +1,59 @@
|
||||
from PIL import Image
|
||||
import cv2 as cv
|
||||
import re
|
||||
|
||||
|
||||
def detect_img(yolo, img_path):
|
||||
detected_robots = []
|
||||
|
||||
try:
|
||||
image = Image.open(img_path)
|
||||
except:
|
||||
print('Image open Error! Try again!')
|
||||
return None
|
||||
else:
|
||||
r_image, pred = yolo.detect_image(image)
|
||||
processed_image = cv.imread(img_path)
|
||||
# r_image.show()
|
||||
# r_image.save(img_path)
|
||||
|
||||
if not pred:
|
||||
return None
|
||||
## FIXME : better list mapping
|
||||
for prediction in pred:
|
||||
is_robot_detected = re.search("robot", prediction[0])
|
||||
if is_robot_detected:
|
||||
x1 = prediction[1][0]
|
||||
x2 = prediction[2][0]
|
||||
y1 = prediction[1][1]
|
||||
y2 = prediction[2][1]
|
||||
w = abs(x1 - x2)
|
||||
h = abs(y1 - y2)
|
||||
# print(pred)
|
||||
# print(f'x1: {x1}, x2: {x2}, y1: {y1}, y2: {y2}, w: {w}, h: {h}')
|
||||
|
||||
robot_img = processed_image[y1:y1 + h, x1:x1 + w]
|
||||
robot_img = cv.resize(robot_img, (128, 128), interpolation=cv.INTER_AREA)
|
||||
robot_pos = w, x1
|
||||
detected_robots.append((robot_img, robot_pos))
|
||||
|
||||
return detected_robots
|
||||
|
||||
|
||||
def get_turn_value(cords):
|
||||
img_width = 1920
|
||||
w, x = cords
|
||||
center_of_object = (x + w) / 2
|
||||
object_position = center_of_object / img_width
|
||||
return round(object_position * 100, 2)
|
||||
|
||||
|
||||
|
||||
def detect_robot(model, img_path):
|
||||
detected_robots = detect_img(model, img_path)
|
||||
return detected_robots
|
||||
# if not detected_robots:
|
||||
# return None
|
||||
# robot_position = detected_robots[0][1]
|
||||
# turn_val = get_turn_value(robot_position)
|
||||
# return turn_val
|
||||
BIN
image_detector/yolo3/font/FiraMono-Medium.otf
Normal file
BIN
image_detector/yolo3/font/FiraMono-Medium.otf
Normal file
Binary file not shown.
45
image_detector/yolo3/font/SIL Open Font License.txt
Normal file
45
image_detector/yolo3/font/SIL Open Font License.txt
Normal file
@ -0,0 +1,45 @@
|
||||
Copyright (c) 2014, Mozilla Foundation https://mozilla.org/ with Reserved Font Name Fira Mono.
|
||||
|
||||
Copyright (c) 2014, Telefonica S.A.
|
||||
|
||||
This Font Software is licensed under the SIL Open Font License, Version 1.1.
|
||||
This license is copied below, and is also available with a FAQ at: http://scripts.sil.org/OFL
|
||||
|
||||
-----------------------------------------------------------
|
||||
SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007
|
||||
-----------------------------------------------------------
|
||||
|
||||
PREAMBLE
|
||||
The goals of the Open Font License (OFL) are to stimulate worldwide development of collaborative font projects, to support the font creation efforts of academic and linguistic communities, and to provide a free and open framework in which fonts may be shared and improved in partnership with others.
|
||||
|
||||
The OFL allows the licensed fonts to be used, studied, modified and redistributed freely as long as they are not sold by themselves. The fonts, including any derivative works, can be bundled, embedded, redistributed and/or sold with any software provided that any reserved names are not used by derivative works. The fonts and derivatives, however, cannot be released under any other type of license. The requirement for fonts to remain under this license does not apply to any document created using the fonts or their derivatives.
|
||||
|
||||
DEFINITIONS
|
||||
"Font Software" refers to the set of files released by the Copyright Holder(s) under this license and clearly marked as such. This may include source files, build scripts and documentation.
|
||||
|
||||
"Reserved Font Name" refers to any names specified as such after the copyright statement(s).
|
||||
|
||||
"Original Version" refers to the collection of Font Software components as distributed by the Copyright Holder(s).
|
||||
|
||||
"Modified Version" refers to any derivative made by adding to, deleting, or substituting -- in part or in whole -- any of the components of the Original Version, by changing formats or by porting the Font Software to a new environment.
|
||||
|
||||
"Author" refers to any designer, engineer, programmer, technical writer or other person who contributed to the Font Software.
|
||||
|
||||
PERMISSION & CONDITIONS
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy of the Font Software, to use, study, copy, merge, embed, modify, redistribute, and sell modified and unmodified copies of the Font Software, subject to the following conditions:
|
||||
|
||||
1) Neither the Font Software nor any of its individual components, in Original or Modified Versions, may be sold by itself.
|
||||
|
||||
2) Original or Modified Versions of the Font Software may be bundled, redistributed and/or sold with any software, provided that each copy contains the above copyright notice and this license. These can be included either as stand-alone text files, human-readable headers or in the appropriate machine-readable metadata fields within text or binary files as long as those fields can be easily viewed by the user.
|
||||
|
||||
3) No Modified Version of the Font Software may use the Reserved Font Name(s) unless explicit written permission is granted by the corresponding Copyright Holder. This restriction only applies to the primary font name as presented to the users.
|
||||
|
||||
4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font Software shall not be used to promote, endorse or advertise any Modified Version, except to acknowledge the contribution(s) of the Copyright Holder(s) and the Author(s) or with their explicit written permission.
|
||||
|
||||
5) The Font Software, modified or unmodified, in part or in whole, must be distributed entirely under this license, and must not be distributed under any other license. The requirement for fonts to remain under this license does not apply to any document created using the Font Software.
|
||||
|
||||
TERMINATION
|
||||
This license becomes null and void if any of the above conditions are not met.
|
||||
|
||||
DISCLAIMER
|
||||
THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM OTHER DEALINGS IN THE FONT SOFTWARE.
|
||||
412
image_detector/yolo3/model.py
Normal file
412
image_detector/yolo3/model.py
Normal file
@ -0,0 +1,412 @@
|
||||
"""YOLO_v3 Model Defined in Keras."""
|
||||
|
||||
from functools import wraps
|
||||
|
||||
import numpy as np
|
||||
import tensorflow as tf
|
||||
from keras import backend as K
|
||||
from keras.layers import Conv2D, Add, ZeroPadding2D, UpSampling2D, Concatenate, MaxPooling2D
|
||||
from keras.layers import LeakyReLU
|
||||
from keras.layers import BatchNormalization
|
||||
from keras.models import Model
|
||||
from keras.regularizers import l2
|
||||
|
||||
from image_detector.yolo3.utils import compose
|
||||
|
||||
|
||||
@wraps(Conv2D)
|
||||
def DarknetConv2D(*args, **kwargs):
|
||||
"""Wrapper to set Darknet parameters for Convolution2D."""
|
||||
darknet_conv_kwargs = {'kernel_regularizer': l2(5e-4)}
|
||||
darknet_conv_kwargs['padding'] = 'valid' if kwargs.get('strides')==(2,2) else 'same'
|
||||
darknet_conv_kwargs.update(kwargs)
|
||||
return Conv2D(*args, **darknet_conv_kwargs)
|
||||
|
||||
def DarknetConv2D_BN_Leaky(*args, **kwargs):
|
||||
"""Darknet Convolution2D followed by BatchNormalization and LeakyReLU."""
|
||||
no_bias_kwargs = {'use_bias': False}
|
||||
no_bias_kwargs.update(kwargs)
|
||||
return compose(
|
||||
DarknetConv2D(*args, **no_bias_kwargs),
|
||||
BatchNormalization(),
|
||||
LeakyReLU(alpha=0.1))
|
||||
|
||||
def resblock_body(x, num_filters, num_blocks):
|
||||
'''A series of resblocks starting with a downsampling Convolution2D'''
|
||||
# Darknet uses left and top padding instead of 'same' mode
|
||||
x = ZeroPadding2D(((1,0),(1,0)))(x)
|
||||
x = DarknetConv2D_BN_Leaky(num_filters, (3,3), strides=(2,2))(x)
|
||||
for i in range(num_blocks):
|
||||
y = compose(
|
||||
DarknetConv2D_BN_Leaky(num_filters//2, (1,1)),
|
||||
DarknetConv2D_BN_Leaky(num_filters, (3,3)))(x)
|
||||
x = Add()([x,y])
|
||||
return x
|
||||
|
||||
def darknet_body(x):
|
||||
'''Darknent body having 52 Convolution2D layers'''
|
||||
x = DarknetConv2D_BN_Leaky(32, (3,3))(x)
|
||||
x = resblock_body(x, 64, 1)
|
||||
x = resblock_body(x, 128, 2)
|
||||
x = resblock_body(x, 256, 8)
|
||||
x = resblock_body(x, 512, 8)
|
||||
x = resblock_body(x, 1024, 4)
|
||||
return x
|
||||
|
||||
def make_last_layers(x, num_filters, out_filters):
|
||||
'''6 Conv2D_BN_Leaky layers followed by a Conv2D_linear layer'''
|
||||
x = compose(
|
||||
DarknetConv2D_BN_Leaky(num_filters, (1,1)),
|
||||
DarknetConv2D_BN_Leaky(num_filters*2, (3,3)),
|
||||
DarknetConv2D_BN_Leaky(num_filters, (1,1)),
|
||||
DarknetConv2D_BN_Leaky(num_filters*2, (3,3)),
|
||||
DarknetConv2D_BN_Leaky(num_filters, (1,1)))(x)
|
||||
y = compose(
|
||||
DarknetConv2D_BN_Leaky(num_filters*2, (3,3)),
|
||||
DarknetConv2D(out_filters, (1,1)))(x)
|
||||
return x, y
|
||||
|
||||
|
||||
def yolo_body(inputs, num_anchors, num_classes):
|
||||
"""Create YOLO_V3 model CNN body in Keras."""
|
||||
darknet = Model(inputs, darknet_body(inputs))
|
||||
x, y1 = make_last_layers(darknet.output, 512, num_anchors*(num_classes+5))
|
||||
|
||||
x = compose(
|
||||
DarknetConv2D_BN_Leaky(256, (1,1)),
|
||||
UpSampling2D(2))(x)
|
||||
x = Concatenate()([x,darknet.layers[152].output])
|
||||
x, y2 = make_last_layers(x, 256, num_anchors*(num_classes+5))
|
||||
|
||||
x = compose(
|
||||
DarknetConv2D_BN_Leaky(128, (1,1)),
|
||||
UpSampling2D(2))(x)
|
||||
x = Concatenate()([x,darknet.layers[92].output])
|
||||
x, y3 = make_last_layers(x, 128, num_anchors*(num_classes+5))
|
||||
|
||||
return Model(inputs, [y1,y2,y3])
|
||||
|
||||
def tiny_yolo_body(inputs, num_anchors, num_classes):
|
||||
'''Create Tiny YOLO_v3 model CNN body in keras.'''
|
||||
x1 = compose(
|
||||
DarknetConv2D_BN_Leaky(16, (3,3)),
|
||||
MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
|
||||
DarknetConv2D_BN_Leaky(32, (3,3)),
|
||||
MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
|
||||
DarknetConv2D_BN_Leaky(64, (3,3)),
|
||||
MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
|
||||
DarknetConv2D_BN_Leaky(128, (3,3)),
|
||||
MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
|
||||
DarknetConv2D_BN_Leaky(256, (3,3)))(inputs)
|
||||
x2 = compose(
|
||||
MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
|
||||
DarknetConv2D_BN_Leaky(512, (3,3)),
|
||||
MaxPooling2D(pool_size=(2,2), strides=(1,1), padding='same'),
|
||||
DarknetConv2D_BN_Leaky(1024, (3,3)),
|
||||
DarknetConv2D_BN_Leaky(256, (1,1)))(x1)
|
||||
y1 = compose(
|
||||
DarknetConv2D_BN_Leaky(512, (3,3)),
|
||||
DarknetConv2D(num_anchors*(num_classes+5), (1,1)))(x2)
|
||||
|
||||
x2 = compose(
|
||||
DarknetConv2D_BN_Leaky(128, (1,1)),
|
||||
UpSampling2D(2))(x2)
|
||||
y2 = compose(
|
||||
Concatenate(),
|
||||
DarknetConv2D_BN_Leaky(256, (3,3)),
|
||||
DarknetConv2D(num_anchors*(num_classes+5), (1,1)))([x2,x1])
|
||||
|
||||
return Model(inputs, [y1,y2])
|
||||
|
||||
|
||||
def yolo_head(feats, anchors, num_classes, input_shape, calc_loss=False):
|
||||
"""Convert final layer features to bounding box parameters."""
|
||||
num_anchors = len(anchors)
|
||||
# Reshape to batch, height, width, num_anchors, box_params.
|
||||
anchors_tensor = K.reshape(K.constant(anchors), [1, 1, 1, num_anchors, 2])
|
||||
|
||||
grid_shape = K.shape(feats)[1:3] # height, width
|
||||
grid_y = K.tile(K.reshape(K.arange(0, stop=grid_shape[0]), [-1, 1, 1, 1]),
|
||||
[1, grid_shape[1], 1, 1])
|
||||
grid_x = K.tile(K.reshape(K.arange(0, stop=grid_shape[1]), [1, -1, 1, 1]),
|
||||
[grid_shape[0], 1, 1, 1])
|
||||
grid = K.concatenate([grid_x, grid_y])
|
||||
grid = K.cast(grid, K.dtype(feats))
|
||||
|
||||
feats = K.reshape(
|
||||
feats, [-1, grid_shape[0], grid_shape[1], num_anchors, num_classes + 5])
|
||||
|
||||
# Adjust preditions to each spatial grid point and anchor size.
|
||||
box_xy = (K.sigmoid(feats[..., :2]) + grid) / K.cast(grid_shape[::-1], K.dtype(feats))
|
||||
box_wh = K.exp(feats[..., 2:4]) * anchors_tensor / K.cast(input_shape[::-1], K.dtype(feats))
|
||||
box_confidence = K.sigmoid(feats[..., 4:5])
|
||||
box_class_probs = K.sigmoid(feats[..., 5:])
|
||||
|
||||
if calc_loss == True:
|
||||
return grid, feats, box_xy, box_wh
|
||||
return box_xy, box_wh, box_confidence, box_class_probs
|
||||
|
||||
|
||||
def yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape):
|
||||
'''Get corrected boxes'''
|
||||
box_yx = box_xy[..., ::-1]
|
||||
box_hw = box_wh[..., ::-1]
|
||||
input_shape = K.cast(input_shape, K.dtype(box_yx))
|
||||
image_shape = K.cast(image_shape, K.dtype(box_yx))
|
||||
new_shape = K.round(image_shape * K.min(input_shape/image_shape))
|
||||
offset = (input_shape-new_shape)/2./input_shape
|
||||
scale = input_shape/new_shape
|
||||
box_yx = (box_yx - offset) * scale
|
||||
box_hw *= scale
|
||||
|
||||
box_mins = box_yx - (box_hw / 2.)
|
||||
box_maxes = box_yx + (box_hw / 2.)
|
||||
boxes = K.concatenate([
|
||||
box_mins[..., 0:1], # y_min
|
||||
box_mins[..., 1:2], # x_min
|
||||
box_maxes[..., 0:1], # y_max
|
||||
box_maxes[..., 1:2] # x_max
|
||||
])
|
||||
|
||||
# Scale boxes back to original image shape.
|
||||
boxes *= K.concatenate([image_shape, image_shape])
|
||||
return boxes
|
||||
|
||||
|
||||
def yolo_boxes_and_scores(feats, anchors, num_classes, input_shape, image_shape):
|
||||
'''Process Conv layer output'''
|
||||
box_xy, box_wh, box_confidence, box_class_probs = yolo_head(feats,
|
||||
anchors, num_classes, input_shape)
|
||||
boxes = yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape)
|
||||
boxes = K.reshape(boxes, [-1, 4])
|
||||
box_scores = box_confidence * box_class_probs
|
||||
box_scores = K.reshape(box_scores, [-1, num_classes])
|
||||
return boxes, box_scores
|
||||
|
||||
|
||||
def yolo_eval(yolo_outputs,
|
||||
anchors,
|
||||
num_classes,
|
||||
image_shape,
|
||||
max_boxes=20,
|
||||
score_threshold=.6,
|
||||
iou_threshold=.5):
|
||||
"""Evaluate YOLO model on given input and return filtered boxes."""
|
||||
num_layers = len(yolo_outputs)
|
||||
anchor_mask = [[6,7,8], [3,4,5], [0,1,2]] if num_layers==3 else [[3,4,5], [1,2,3]] # default setting
|
||||
input_shape = K.shape(yolo_outputs[0])[1:3] * 32
|
||||
boxes = []
|
||||
box_scores = []
|
||||
for l in range(num_layers):
|
||||
_boxes, _box_scores = yolo_boxes_and_scores(yolo_outputs[l],
|
||||
anchors[anchor_mask[l]], num_classes, input_shape, image_shape)
|
||||
boxes.append(_boxes)
|
||||
box_scores.append(_box_scores)
|
||||
boxes = K.concatenate(boxes, axis=0)
|
||||
box_scores = K.concatenate(box_scores, axis=0)
|
||||
|
||||
mask = box_scores >= score_threshold
|
||||
max_boxes_tensor = K.constant(max_boxes, dtype='int32')
|
||||
boxes_ = []
|
||||
scores_ = []
|
||||
classes_ = []
|
||||
for c in range(num_classes):
|
||||
# TODO: use keras backend instead of tf.
|
||||
class_boxes = tf.boolean_mask(boxes, mask[:, c])
|
||||
class_box_scores = tf.boolean_mask(box_scores[:, c], mask[:, c])
|
||||
nms_index = tf.image.non_max_suppression(
|
||||
class_boxes, class_box_scores, max_boxes_tensor, iou_threshold=iou_threshold)
|
||||
class_boxes = K.gather(class_boxes, nms_index)
|
||||
class_box_scores = K.gather(class_box_scores, nms_index)
|
||||
classes = K.ones_like(class_box_scores, 'int32') * c
|
||||
boxes_.append(class_boxes)
|
||||
scores_.append(class_box_scores)
|
||||
classes_.append(classes)
|
||||
boxes_ = K.concatenate(boxes_, axis=0)
|
||||
scores_ = K.concatenate(scores_, axis=0)
|
||||
classes_ = K.concatenate(classes_, axis=0)
|
||||
|
||||
return boxes_, scores_, classes_
|
||||
|
||||
|
||||
def preprocess_true_boxes(true_boxes, input_shape, anchors, num_classes):
|
||||
'''Preprocess true boxes to training input format
|
||||
|
||||
Parameters
|
||||
----------
|
||||
true_boxes: array, shape=(m, T, 5)
|
||||
Absolute x_min, y_min, x_max, y_max, class_id relative to input_shape.
|
||||
input_shape: array-like, hw, multiples of 32
|
||||
anchors: array, shape=(N, 2), wh
|
||||
num_classes: integer
|
||||
|
||||
Returns
|
||||
-------
|
||||
y_true: list of array, shape like yolo_outputs, xywh are reletive value
|
||||
|
||||
'''
|
||||
assert (true_boxes[..., 4]<num_classes).all(), 'class id must be less than num_classes'
|
||||
num_layers = len(anchors)//3 # default setting
|
||||
anchor_mask = [[6,7,8], [3,4,5], [0,1,2]] if num_layers==3 else [[3,4,5], [1,2,3]]
|
||||
|
||||
true_boxes = np.array(true_boxes, dtype='float32')
|
||||
input_shape = np.array(input_shape, dtype='int32')
|
||||
boxes_xy = (true_boxes[..., 0:2] + true_boxes[..., 2:4]) // 2
|
||||
boxes_wh = true_boxes[..., 2:4] - true_boxes[..., 0:2]
|
||||
true_boxes[..., 0:2] = boxes_xy/input_shape[::-1]
|
||||
true_boxes[..., 2:4] = boxes_wh/input_shape[::-1]
|
||||
|
||||
m = true_boxes.shape[0]
|
||||
grid_shapes = [input_shape//{0:32, 1:16, 2:8}[l] for l in range(num_layers)]
|
||||
y_true = [np.zeros((m,grid_shapes[l][0],grid_shapes[l][1],len(anchor_mask[l]),5+num_classes),
|
||||
dtype='float32') for l in range(num_layers)]
|
||||
|
||||
# Expand dim to apply broadcasting.
|
||||
anchors = np.expand_dims(anchors, 0)
|
||||
anchor_maxes = anchors / 2.
|
||||
anchor_mins = -anchor_maxes
|
||||
valid_mask = boxes_wh[..., 0]>0
|
||||
|
||||
for b in range(m):
|
||||
# Discard zero rows.
|
||||
wh = boxes_wh[b, valid_mask[b]]
|
||||
if len(wh)==0: continue
|
||||
# Expand dim to apply broadcasting.
|
||||
wh = np.expand_dims(wh, -2)
|
||||
box_maxes = wh / 2.
|
||||
box_mins = -box_maxes
|
||||
|
||||
intersect_mins = np.maximum(box_mins, anchor_mins)
|
||||
intersect_maxes = np.minimum(box_maxes, anchor_maxes)
|
||||
intersect_wh = np.maximum(intersect_maxes - intersect_mins, 0.)
|
||||
intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]
|
||||
box_area = wh[..., 0] * wh[..., 1]
|
||||
anchor_area = anchors[..., 0] * anchors[..., 1]
|
||||
iou = intersect_area / (box_area + anchor_area - intersect_area)
|
||||
|
||||
# Find best anchor for each true box
|
||||
best_anchor = np.argmax(iou, axis=-1)
|
||||
|
||||
for t, n in enumerate(best_anchor):
|
||||
for l in range(num_layers):
|
||||
if n in anchor_mask[l]:
|
||||
i = np.floor(true_boxes[b,t,0]*grid_shapes[l][1]).astype('int32')
|
||||
j = np.floor(true_boxes[b,t,1]*grid_shapes[l][0]).astype('int32')
|
||||
k = anchor_mask[l].index(n)
|
||||
c = true_boxes[b,t, 4].astype('int32')
|
||||
y_true[l][b, j, i, k, 0:4] = true_boxes[b,t, 0:4]
|
||||
y_true[l][b, j, i, k, 4] = 1
|
||||
y_true[l][b, j, i, k, 5+c] = 1
|
||||
|
||||
return y_true
|
||||
|
||||
|
||||
def box_iou(b1, b2):
|
||||
'''Return iou tensor
|
||||
|
||||
Parameters
|
||||
----------
|
||||
b1: tensor, shape=(i1,...,iN, 4), xywh
|
||||
b2: tensor, shape=(j, 4), xywh
|
||||
|
||||
Returns
|
||||
-------
|
||||
iou: tensor, shape=(i1,...,iN, j)
|
||||
|
||||
'''
|
||||
|
||||
# Expand dim to apply broadcasting.
|
||||
b1 = K.expand_dims(b1, -2)
|
||||
b1_xy = b1[..., :2]
|
||||
b1_wh = b1[..., 2:4]
|
||||
b1_wh_half = b1_wh/2.
|
||||
b1_mins = b1_xy - b1_wh_half
|
||||
b1_maxes = b1_xy + b1_wh_half
|
||||
|
||||
# Expand dim to apply broadcasting.
|
||||
b2 = K.expand_dims(b2, 0)
|
||||
b2_xy = b2[..., :2]
|
||||
b2_wh = b2[..., 2:4]
|
||||
b2_wh_half = b2_wh/2.
|
||||
b2_mins = b2_xy - b2_wh_half
|
||||
b2_maxes = b2_xy + b2_wh_half
|
||||
|
||||
intersect_mins = K.maximum(b1_mins, b2_mins)
|
||||
intersect_maxes = K.minimum(b1_maxes, b2_maxes)
|
||||
intersect_wh = K.maximum(intersect_maxes - intersect_mins, 0.)
|
||||
intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]
|
||||
b1_area = b1_wh[..., 0] * b1_wh[..., 1]
|
||||
b2_area = b2_wh[..., 0] * b2_wh[..., 1]
|
||||
iou = intersect_area / (b1_area + b2_area - intersect_area)
|
||||
|
||||
return iou
|
||||
|
||||
|
||||
def yolo_loss(args, anchors, num_classes, ignore_thresh=.5, print_loss=False):
|
||||
'''Return yolo_loss tensor
|
||||
|
||||
Parameters
|
||||
----------
|
||||
yolo_outputs: list of tensor, the output of yolo_body or tiny_yolo_body
|
||||
y_true: list of array, the output of preprocess_true_boxes
|
||||
anchors: array, shape=(N, 2), wh
|
||||
num_classes: integer
|
||||
ignore_thresh: float, the iou threshold whether to ignore object confidence loss
|
||||
|
||||
Returns
|
||||
-------
|
||||
loss: tensor, shape=(1,)
|
||||
|
||||
'''
|
||||
num_layers = len(anchors)//3 # default setting
|
||||
yolo_outputs = args[:num_layers]
|
||||
y_true = args[num_layers:]
|
||||
anchor_mask = [[6,7,8], [3,4,5], [0,1,2]] if num_layers==3 else [[3,4,5], [1,2,3]]
|
||||
input_shape = K.cast(K.shape(yolo_outputs[0])[1:3] * 32, K.dtype(y_true[0]))
|
||||
grid_shapes = [K.cast(K.shape(yolo_outputs[l])[1:3], K.dtype(y_true[0])) for l in range(num_layers)]
|
||||
loss = 0
|
||||
m = K.shape(yolo_outputs[0])[0] # batch size, tensor
|
||||
mf = K.cast(m, K.dtype(yolo_outputs[0]))
|
||||
|
||||
for l in range(num_layers):
|
||||
object_mask = y_true[l][..., 4:5]
|
||||
true_class_probs = y_true[l][..., 5:]
|
||||
|
||||
grid, raw_pred, pred_xy, pred_wh = yolo_head(yolo_outputs[l],
|
||||
anchors[anchor_mask[l]], num_classes, input_shape, calc_loss=True)
|
||||
pred_box = K.concatenate([pred_xy, pred_wh])
|
||||
|
||||
# Darknet raw box to calculate loss.
|
||||
raw_true_xy = y_true[l][..., :2]*grid_shapes[l][::-1] - grid
|
||||
raw_true_wh = K.log(y_true[l][..., 2:4] / anchors[anchor_mask[l]] * input_shape[::-1])
|
||||
raw_true_wh = K.switch(object_mask, raw_true_wh, K.zeros_like(raw_true_wh)) # avoid log(0)=-inf
|
||||
box_loss_scale = 2 - y_true[l][...,2:3]*y_true[l][...,3:4]
|
||||
|
||||
# Find ignore mask, iterate over each of batch.
|
||||
ignore_mask = tf.TensorArray(K.dtype(y_true[0]), size=1, dynamic_size=True)
|
||||
object_mask_bool = K.cast(object_mask, 'bool')
|
||||
def loop_body(b, ignore_mask):
|
||||
true_box = tf.boolean_mask(y_true[l][b,...,0:4], object_mask_bool[b,...,0])
|
||||
iou = box_iou(pred_box[b], true_box)
|
||||
best_iou = K.max(iou, axis=-1)
|
||||
ignore_mask = ignore_mask.write(b, K.cast(best_iou<ignore_thresh, K.dtype(true_box)))
|
||||
return b+1, ignore_mask
|
||||
_, ignore_mask = tf.while_loop(lambda b,*args: b<m, loop_body, [0, ignore_mask])
|
||||
ignore_mask = ignore_mask.stack()
|
||||
ignore_mask = K.expand_dims(ignore_mask, -1)
|
||||
|
||||
# K.binary_crossentropy is helpful to avoid exp overflow.
|
||||
xy_loss = object_mask * box_loss_scale * K.binary_crossentropy(raw_true_xy, raw_pred[...,0:2], from_logits=True)
|
||||
wh_loss = object_mask * box_loss_scale * 0.5 * K.square(raw_true_wh-raw_pred[...,2:4])
|
||||
confidence_loss = object_mask * K.binary_crossentropy(object_mask, raw_pred[...,4:5], from_logits=True)+ \
|
||||
(1-object_mask) * K.binary_crossentropy(object_mask, raw_pred[...,4:5], from_logits=True) * ignore_mask
|
||||
class_loss = object_mask * K.binary_crossentropy(true_class_probs, raw_pred[...,5:], from_logits=True)
|
||||
|
||||
xy_loss = K.sum(xy_loss) / mf
|
||||
wh_loss = K.sum(wh_loss) / mf
|
||||
confidence_loss = K.sum(confidence_loss) / mf
|
||||
class_loss = K.sum(class_loss) / mf
|
||||
loss += xy_loss + wh_loss + confidence_loss + class_loss
|
||||
if print_loss:
|
||||
loss = tf.Print(loss, [loss, xy_loss, wh_loss, confidence_loss, class_loss, K.sum(ignore_mask)], message='loss: ')
|
||||
return loss
|
||||
1
image_detector/yolo3/model_data/_anchors.txt
Normal file
1
image_detector/yolo3/model_data/_anchors.txt
Normal file
@ -0,0 +1 @@
|
||||
16,26, 23,34, 29,45, 43,62, 62,102, 102,67, 134,130, 211,218, 455,592
|
||||
3
image_detector/yolo3/model_data/_classes.txt
Normal file
3
image_detector/yolo3/model_data/_classes.txt
Normal file
@ -0,0 +1,3 @@
|
||||
grains
|
||||
rat
|
||||
robot
|
||||
BIN
image_detector/yolo3/model_data/model_weights.h5
Normal file
BIN
image_detector/yolo3/model_data/model_weights.h5
Normal file
Binary file not shown.
122
image_detector/yolo3/utils.py
Normal file
122
image_detector/yolo3/utils.py
Normal file
@ -0,0 +1,122 @@
|
||||
"""Miscellaneous utility functions."""
|
||||
|
||||
from functools import reduce
|
||||
|
||||
from PIL import Image
|
||||
import numpy as np
|
||||
from matplotlib.colors import rgb_to_hsv, hsv_to_rgb
|
||||
|
||||
def compose(*funcs):
|
||||
"""Compose arbitrarily many functions, evaluated left to right.
|
||||
|
||||
Reference: https://mathieularose.com/function-composition-in-python/
|
||||
"""
|
||||
# return lambda x: reduce(lambda v, f: f(v), funcs, x)
|
||||
if funcs:
|
||||
return reduce(lambda f, g: lambda *a, **kw: g(f(*a, **kw)), funcs)
|
||||
else:
|
||||
raise ValueError('Composition of empty sequence not supported.')
|
||||
|
||||
def letterbox_image(image, size):
|
||||
'''resize image with unchanged aspect ratio using padding'''
|
||||
iw, ih = image.size
|
||||
w, h = size
|
||||
scale = min(w/iw, h/ih)
|
||||
nw = int(iw*scale)
|
||||
nh = int(ih*scale)
|
||||
|
||||
image = image.resize((nw,nh), Image.BICUBIC)
|
||||
new_image = Image.new('RGB', size, (128,128,128))
|
||||
new_image.paste(image, ((w-nw)//2, (h-nh)//2))
|
||||
return new_image
|
||||
|
||||
def rand(a=0, b=1):
|
||||
return np.random.rand()*(b-a) + a
|
||||
|
||||
def get_random_data(annotation_line, input_shape, random=True, max_boxes=20, jitter=.3, hue=.1, sat=1.5, val=1.5, proc_img=True):
|
||||
'''random preprocessing for real-time data augmentation'''
|
||||
line = annotation_line.split()
|
||||
path = r"/keras-yolo3/train/"
|
||||
image = Image.open(path + line[0])
|
||||
iw, ih = image.size
|
||||
h, w = input_shape
|
||||
box = np.array([np.array(list(map(int,box.split(',')))) for box in line[1:]])
|
||||
|
||||
if not random:
|
||||
# resize image
|
||||
scale = min(w/iw, h/ih)
|
||||
nw = int(iw*scale)
|
||||
nh = int(ih*scale)
|
||||
dx = (w-nw)//2
|
||||
dy = (h-nh)//2
|
||||
image_data=0
|
||||
if proc_img:
|
||||
image = image.resize((nw,nh), Image.BICUBIC)
|
||||
new_image = Image.new('RGB', (w,h), (128,128,128))
|
||||
new_image.paste(image, (dx, dy))
|
||||
image_data = np.array(new_image)/255.
|
||||
|
||||
# correct boxes
|
||||
box_data = np.zeros((max_boxes,5))
|
||||
if len(box)>0:
|
||||
np.random.shuffle(box)
|
||||
if len(box)>max_boxes: box = box[:max_boxes]
|
||||
box[:, [0,2]] = box[:, [0,2]]*scale + dx
|
||||
box[:, [1,3]] = box[:, [1,3]]*scale + dy
|
||||
box_data[:len(box)] = box
|
||||
|
||||
return image_data, box_data
|
||||
|
||||
# resize image
|
||||
new_ar = w/h * rand(1-jitter,1+jitter)/rand(1-jitter,1+jitter)
|
||||
scale = rand(.25, 2)
|
||||
if new_ar < 1:
|
||||
nh = int(scale*h)
|
||||
nw = int(nh*new_ar)
|
||||
else:
|
||||
nw = int(scale*w)
|
||||
nh = int(nw/new_ar)
|
||||
image = image.resize((nw,nh), Image.BICUBIC)
|
||||
|
||||
# place image
|
||||
dx = int(rand(0, w-nw))
|
||||
dy = int(rand(0, h-nh))
|
||||
new_image = Image.new('RGB', (w,h), (128,128,128))
|
||||
new_image.paste(image, (dx, dy))
|
||||
image = new_image
|
||||
|
||||
# flip image or not
|
||||
flip = rand()<.5
|
||||
if flip: image = image.transpose(Image.FLIP_LEFT_RIGHT)
|
||||
|
||||
# distort image
|
||||
hue = rand(-hue, hue)
|
||||
sat = rand(1, sat) if rand()<.5 else 1/rand(1, sat)
|
||||
val = rand(1, val) if rand()<.5 else 1/rand(1, val)
|
||||
x = rgb_to_hsv(np.array(image)/255.)
|
||||
x[..., 0] += hue
|
||||
x[..., 0][x[..., 0]>1] -= 1
|
||||
x[..., 0][x[..., 0]<0] += 1
|
||||
x[..., 1] *= sat
|
||||
x[..., 2] *= val
|
||||
x[x>1] = 1
|
||||
x[x<0] = 0
|
||||
image_data = hsv_to_rgb(x) # numpy array, 0 to 1
|
||||
|
||||
# correct boxes
|
||||
box_data = np.zeros((max_boxes,5))
|
||||
if len(box)>0:
|
||||
np.random.shuffle(box)
|
||||
box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx
|
||||
box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy
|
||||
if flip: box[:, [0,2]] = w - box[:, [2,0]]
|
||||
box[:, 0:2][box[:, 0:2]<0] = 0
|
||||
box[:, 2][box[:, 2]>w] = w
|
||||
box[:, 3][box[:, 3]>h] = h
|
||||
box_w = box[:, 2] - box[:, 0]
|
||||
box_h = box[:, 3] - box[:, 1]
|
||||
box = box[np.logical_and(box_w>1, box_h>1)] # discard invalid box
|
||||
if len(box)>max_boxes: box = box[:max_boxes]
|
||||
box_data[:len(box)] = box
|
||||
|
||||
return image_data, box_data
|
||||
221
image_detector/yolo3/yolo.py
Normal file
221
image_detector/yolo3/yolo.py
Normal file
@ -0,0 +1,221 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Class definition of YOLO_v3 style detection model on image and video
|
||||
"""
|
||||
|
||||
import colorsys
|
||||
from timeit import default_timer as timer
|
||||
import numpy as np
|
||||
from keras import backend as K
|
||||
from keras.models import load_model
|
||||
from keras.layers import Input
|
||||
from PIL import Image, ImageFont, ImageDraw
|
||||
import os
|
||||
import tensorflow.compat.v1.keras.backend as K
|
||||
import tensorflow as tf
|
||||
|
||||
from image_detector.yolo3.utils import letterbox_image
|
||||
from image_detector.yolo3.model import yolo_eval, yolo_body, tiny_yolo_body
|
||||
# from utils import letterbox_image
|
||||
# from model import yolo_eval, yolo_body, tiny_yolo_body
|
||||
|
||||
tf.compat.v1.disable_eager_execution()
|
||||
|
||||
|
||||
class YOLO(object):
|
||||
_defaults = {
|
||||
"model_path": 'image_detector/yolo3/model_data/model_weights.h5',
|
||||
"anchors_path": 'image_detector/yolo3/model_data/_anchors.txt',
|
||||
"classes_path": 'image_detector/yolo3/model_data/_classes.txt',
|
||||
"score": 0.3,
|
||||
"iou": 0.45,
|
||||
"model_image_size": (128, 128),
|
||||
"gpu_num": 1,
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def get_defaults(cls, n):
|
||||
if n in cls._defaults:
|
||||
return cls._defaults[n]
|
||||
else:
|
||||
return "Unrecognized attribute name '" + n + "'"
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
self.__dict__.update(self._defaults) # set up default values
|
||||
self.class_names = self._get_class()
|
||||
self.anchors = self._get_anchors()
|
||||
self.sess = K.get_session()
|
||||
self.boxes, self.scores, self.classes = self.generate()
|
||||
|
||||
def _get_class(self):
|
||||
classes_path = os.path.expanduser(self.classes_path)
|
||||
with open(classes_path) as f:
|
||||
class_names = f.readlines()
|
||||
class_names = [c.strip() for c in class_names]
|
||||
return class_names
|
||||
|
||||
def _get_anchors(self):
|
||||
anchors_path = os.path.expanduser(self.anchors_path)
|
||||
with open(anchors_path) as f:
|
||||
anchors = f.readline()
|
||||
anchors = [float(x) for x in anchors.split(',')]
|
||||
return np.array(anchors).reshape(-1, 2)
|
||||
|
||||
def generate(self):
|
||||
model_path = os.path.expanduser(self.model_path)
|
||||
assert model_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'
|
||||
|
||||
# Load model, or construct model and load weights.
|
||||
num_anchors = len(self.anchors)
|
||||
num_classes = len(self.class_names)
|
||||
is_tiny_version = num_anchors == 6 # default setting
|
||||
try:
|
||||
self.yolo_model = load_model(model_path, compile=False)
|
||||
except:
|
||||
self.yolo_model = tiny_yolo_body(Input(shape=(None, None, 3)), num_anchors // 2, num_classes) \
|
||||
if is_tiny_version else yolo_body(Input(shape=(None, None, 3)), num_anchors // 3, num_classes)
|
||||
self.yolo_model.load_weights(self.model_path) # make sure model, anchors and classes match
|
||||
else:
|
||||
assert self.yolo_model.layers[-1].output_shape[-1] == \
|
||||
num_anchors / len(self.yolo_model.output) * (num_classes + 5), \
|
||||
'Mismatch between model and given anchor and class sizes'
|
||||
|
||||
print('{} model, anchors, and classes loaded.'.format(model_path))
|
||||
|
||||
# Generate colors for drawing bounding boxes.
|
||||
hsv_tuples = [(x / len(self.class_names), 1., 1.)
|
||||
for x in range(len(self.class_names))]
|
||||
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
|
||||
self.colors = list(
|
||||
map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
|
||||
self.colors))
|
||||
np.random.seed(10101) # Fixed seed for consistent colors across runs.
|
||||
np.random.shuffle(self.colors) # Shuffle colors to decorrelate adjacent classes.
|
||||
np.random.seed(None) # Reset seed to default.
|
||||
|
||||
# Generate output tensor targets for filtered bounding boxes.
|
||||
self.input_image_shape = K.placeholder(shape=(2,))
|
||||
if self.gpu_num >= 2:
|
||||
self.yolo_model = tf.multi_gpu_model(self.yolo_model, gpus=self.gpu_num)
|
||||
boxes, scores, classes = yolo_eval(self.yolo_model.output, self.anchors,
|
||||
len(self.class_names), self.input_image_shape,
|
||||
score_threshold=self.score, iou_threshold=self.iou)
|
||||
return boxes, scores, classes
|
||||
|
||||
def detect_image(self, image):
|
||||
start = timer()
|
||||
pred = []
|
||||
|
||||
if self.model_image_size != (None, None):
|
||||
assert self.model_image_size[0] % 32 == 0, 'Multiples of 32 required'
|
||||
assert self.model_image_size[1] % 32 == 0, 'Multiples of 32 required'
|
||||
boxed_image = letterbox_image(image, tuple(reversed(self.model_image_size)))
|
||||
else:
|
||||
new_image_size = (image.width - (image.width % 32),
|
||||
image.height - (image.height % 32))
|
||||
boxed_image = letterbox_image(image, new_image_size)
|
||||
image_data = np.array(boxed_image, dtype='float32')
|
||||
|
||||
print(image_data.shape)
|
||||
image_data /= 255.
|
||||
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
|
||||
|
||||
out_boxes, out_scores, out_classes = self.sess.run(
|
||||
[self.boxes, self.scores, self.classes],
|
||||
feed_dict={
|
||||
self.yolo_model.input: image_data,
|
||||
self.input_image_shape: [image.size[1], image.size[0]],
|
||||
K.learning_phase(): 0
|
||||
})
|
||||
|
||||
print('Found {} boxes for {}'.format(len(out_boxes), 'img'))
|
||||
|
||||
font = ImageFont.truetype(font='image_detector/yolo3/font/FiraMono-Medium.otf',
|
||||
size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
|
||||
thickness = (image.size[0] + image.size[1]) // 300
|
||||
|
||||
for i, c in reversed(list(enumerate(out_classes))):
|
||||
predicted_class = self.class_names[c]
|
||||
box = out_boxes[i]
|
||||
score = out_scores[i]
|
||||
|
||||
# score > 0.7
|
||||
if score > 0.7:
|
||||
label = '{} {:.2f}'.format(predicted_class, score)
|
||||
draw = ImageDraw.Draw(image)
|
||||
label_size = draw.textsize(label, font)
|
||||
|
||||
top, left, bottom, right = box
|
||||
top = max(0, np.floor(top + 0.5).astype('int32'))
|
||||
left = max(0, np.floor(left + 0.5).astype('int32'))
|
||||
bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32')) + 30
|
||||
right = min(image.size[0], np.floor(right + 0.5).astype('int32')) + 30
|
||||
# print('=' * 10)
|
||||
# print(label, (left, top), (right, bottom))
|
||||
pred.append([label, (left, top), (right, bottom)])
|
||||
# print('=' * 10)
|
||||
|
||||
if top - label_size[1] >= 0:
|
||||
text_origin = np.array([left, top - label_size[1]])
|
||||
else:
|
||||
text_origin = np.array([left, top + 1])
|
||||
|
||||
# My kingdom for a good redistributable image drawing library.
|
||||
for i in range(thickness):
|
||||
draw.rectangle(
|
||||
[left + i, top + i, right - i, bottom - i],
|
||||
outline=self.colors[c])
|
||||
draw.rectangle(
|
||||
[tuple(text_origin), tuple(text_origin + label_size)],
|
||||
fill=self.colors[c])
|
||||
draw.text(text_origin, label, fill=(0, 0, 0), font=font)
|
||||
del draw
|
||||
|
||||
end = timer()
|
||||
print(end - start)
|
||||
return image, pred
|
||||
|
||||
def close_session(self):
|
||||
self.sess.close()
|
||||
|
||||
|
||||
def detect_video(yolo, video_path, output_path=""):
|
||||
import cv2
|
||||
vid = cv2.VideoCapture(video_path)
|
||||
if not vid.isOpened():
|
||||
raise IOError("Couldn't open webcam or video")
|
||||
video_FourCC = int(vid.get(cv2.CAP_PROP_FOURCC))
|
||||
video_fps = vid.get(cv2.CAP_PROP_FPS)
|
||||
video_size = (int(vid.get(cv2.CAP_PROP_FRAME_WIDTH)),
|
||||
int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT)))
|
||||
isOutput = True if output_path != "" else False
|
||||
if isOutput:
|
||||
print("!!! TYPE:", type(output_path), type(video_FourCC), type(video_fps), type(video_size))
|
||||
out = cv2.VideoWriter(output_path, video_FourCC, video_fps, video_size)
|
||||
accum_time = 0
|
||||
curr_fps = 0
|
||||
fps = "FPS: ??"
|
||||
prev_time = timer()
|
||||
while True:
|
||||
return_value, frame = vid.read()
|
||||
image = Image.fromarray(frame)
|
||||
image = yolo.detect_image(image)
|
||||
result = np.asarray(image)
|
||||
curr_time = timer()
|
||||
exec_time = curr_time - prev_time
|
||||
prev_time = curr_time
|
||||
accum_time = accum_time + exec_time
|
||||
curr_fps = curr_fps + 1
|
||||
if accum_time > 1:
|
||||
accum_time = accum_time - 1
|
||||
fps = "FPS: " + str(curr_fps)
|
||||
curr_fps = 0
|
||||
cv2.putText(result, text=fps, org=(3, 15), fontFace=cv2.FONT_HERSHEY_SIMPLEX,
|
||||
fontScale=0.50, color=(255, 0, 0), thickness=2)
|
||||
cv2.namedWindow("result", cv2.WINDOW_NORMAL)
|
||||
cv2.imshow("result", result)
|
||||
if isOutput:
|
||||
out.write(result)
|
||||
if cv2.waitKey(1) & 0xFF == ord('q'):
|
||||
break
|
||||
yolo.close_session()
|
||||
146
main.py
Normal file
146
main.py
Normal file
@ -0,0 +1,146 @@
|
||||
# python -m pip install flask
|
||||
# export FLASK_APP=main.py
|
||||
# flask run --without-threads
|
||||
|
||||
from flask import Flask, request
|
||||
from tensorflow import keras
|
||||
from image_detector.yolo3.yolo import YOLO
|
||||
from image_detector.robot_detector import detect_robot
|
||||
from LED_color_detector.led_detector import get_led_color
|
||||
from image_detector.rat_detector import detect_rat
|
||||
from config import img_base_path
|
||||
|
||||
app = Flask(__name__)
|
||||
|
||||
led_model = keras.models.load_model('./model_VGG16_LED_color_bigger_scene2/')
|
||||
|
||||
""" Automatic call while FLASK init """
|
||||
yolo_model = YOLO()
|
||||
|
||||
|
||||
# def deinit_yolo(yolo):
|
||||
# yolo.close_session()
|
||||
|
||||
"""API_address/detectRobot1?img="""
|
||||
@app.get("/detectRobot1")
|
||||
def detectRobot1():
|
||||
robot_led_colors = []
|
||||
response_model = {}
|
||||
|
||||
# build path
|
||||
image_path = img_base_path + "robot1/" + request.args['img']
|
||||
detected_objects = detect_robot(model=yolo_model, img_path=image_path)
|
||||
|
||||
if not detected_objects:
|
||||
return {
|
||||
0: ["None"],
|
||||
}, 200
|
||||
|
||||
for robot in detected_objects:
|
||||
color = get_led_color(robot_image=robot[0], model=led_model)
|
||||
robot_led_colors.append(color)
|
||||
|
||||
""" for emphatic robot """
|
||||
### led color and robot positions will be sended to empathy model
|
||||
|
||||
""" for egoistic robot """
|
||||
robot_id = 0
|
||||
for robot in range(len(detected_objects)):
|
||||
response_model[int(robot_id)] = [
|
||||
robot_led_colors[robot],
|
||||
int(detected_objects[robot][1][0]),
|
||||
int(detected_objects[robot][1][1])
|
||||
]
|
||||
robot_id += 1
|
||||
return response_model, 200
|
||||
|
||||
|
||||
|
||||
"""API_address/detectRobot2?img="""
|
||||
@app.get("/detectRobot2")
|
||||
def detectRobot2():
|
||||
robot_led_colors = []
|
||||
response_model = {}
|
||||
|
||||
# build path
|
||||
image_path = img_base_path + "robot2/" + request.args['img']
|
||||
detected_objects = detect_robot(model=yolo_model, img_path=image_path)
|
||||
|
||||
if not detected_objects:
|
||||
return {
|
||||
0: ["None"],
|
||||
}, 200
|
||||
|
||||
for robot in detected_objects:
|
||||
color = get_led_color(robot_image=robot[0], model=led_model)
|
||||
robot_led_colors.append(color)
|
||||
|
||||
""" for emphatic robot """
|
||||
### led color and robot positions will be sended to empathy model
|
||||
|
||||
""" for egoistic robot """
|
||||
robot_id = 0
|
||||
for robot in range(len(detected_objects)):
|
||||
response_model[int(robot_id)] = [
|
||||
robot_led_colors[robot],
|
||||
int(detected_objects[robot][1][0]),
|
||||
int(detected_objects[robot][1][1])
|
||||
]
|
||||
robot_id += 1
|
||||
return response_model, 200
|
||||
|
||||
|
||||
"""API_address/detectRat1?img="""
|
||||
@app.get("/detectRat1")
|
||||
def detectRat1():
|
||||
response_model = {}
|
||||
|
||||
# build path
|
||||
image_path = img_base_path + "robot1/" + request.args['img']
|
||||
detected_objects = detect_rat(model=yolo_model, img_path=image_path)
|
||||
|
||||
if not detected_objects:
|
||||
return {
|
||||
0: ["None"],
|
||||
}, 200
|
||||
|
||||
""" for emphatic robot """
|
||||
### led color and robot positions will be sended to empathy model
|
||||
|
||||
""" for egoistic robot """
|
||||
rat_id = 0
|
||||
for rat in range(len(detected_objects)):
|
||||
response_model[int(rat_id)] = [
|
||||
int(detected_objects[rat][1][0]),
|
||||
int(detected_objects[rat][1][1])
|
||||
]
|
||||
rat_id += 1
|
||||
return response_model, 200
|
||||
|
||||
|
||||
"""API_address/detectRat2?img="""
|
||||
@app.get("/detectRat2")
|
||||
def detectRat2():
|
||||
response_model = {}
|
||||
|
||||
# build path
|
||||
image_path = img_base_path + "robot2/" + request.args['img']
|
||||
detected_objects = detect_rat(model=yolo_model, img_path=image_path)
|
||||
|
||||
if not detected_objects:
|
||||
return {
|
||||
0: ["None"],
|
||||
}, 200
|
||||
|
||||
""" for emphatic robot """
|
||||
### led color and robot positions will be sended to empathy model
|
||||
|
||||
""" for egoistic robot """
|
||||
rat_id = 0
|
||||
for rat in range(len(detected_objects)):
|
||||
response_model[int(rat_id)] = [
|
||||
int(detected_objects[rat][1][0]),
|
||||
int(detected_objects[rat][1][1])
|
||||
]
|
||||
rat_id += 1
|
||||
return response_model, 200
|
||||
BIN
model_VGG16_LED_color_bigger_scene2/.DS_Store
vendored
Normal file
BIN
model_VGG16_LED_color_bigger_scene2/.DS_Store
vendored
Normal file
Binary file not shown.
53
model_VGG16_LED_color_bigger_scene2/keras_metadata.pb
Normal file
53
model_VGG16_LED_color_bigger_scene2/keras_metadata.pb
Normal file
File diff suppressed because one or more lines are too long
BIN
model_VGG16_LED_color_bigger_scene2/saved_model.pb
Normal file
BIN
model_VGG16_LED_color_bigger_scene2/saved_model.pb
Normal file
Binary file not shown.
Binary file not shown.
BIN
model_VGG16_LED_color_bigger_scene2/variables/variables.index
Normal file
BIN
model_VGG16_LED_color_bigger_scene2/variables/variables.index
Normal file
Binary file not shown.
BIN
model_VGG16_LED_color_bigger_scene2_v2.zip
Normal file
BIN
model_VGG16_LED_color_bigger_scene2_v2.zip
Normal file
Binary file not shown.
460
new_egoistic.lua
Normal file
460
new_egoistic.lua
Normal file
@ -0,0 +1,460 @@
|
||||
-- Problem: Program si? wiesza przy zapisywaniu zdj?? i wysy?aniu requesta do API
|
||||
http = require('socket.http')
|
||||
json = require('json')
|
||||
|
||||
COLOR_WHITE = {255, 255, 255}
|
||||
COLOR_RED = {255, 0, 0}
|
||||
COLOR_GREEN = {0, 255, 0}
|
||||
COLOR_BLUE = {0, 0, 255}
|
||||
|
||||
OBSTACLE_AVOID_DISTANCE = 3
|
||||
MOVING_VELOCITY = 20
|
||||
TURNING_VELOCITY = 15
|
||||
PROXIMITY_STOP_DISTANCE = 1
|
||||
CONFIDENCE = 0
|
||||
IMAGE_COUNTER = 0
|
||||
CAMERA_FIELD_OF_VIEW = 60
|
||||
PHOTO_WIDTH = 1920
|
||||
BATERY_VALUE = 10
|
||||
|
||||
function sysCall_init()
|
||||
|
||||
os.execute("rm -r robot")
|
||||
os.execute("mkdir robot")
|
||||
|
||||
ModelHandle = sim.getObject(".")
|
||||
ModelName = sim.getObjectAlias(ModelHandle)
|
||||
|
||||
RatHandles = {} -- List of rats on the scene
|
||||
local i = 0
|
||||
while true do
|
||||
local rat_handle = sim.getObject("/Rat*", {index=i, noError=true})
|
||||
if rat_handle < 0 then
|
||||
break
|
||||
end
|
||||
table.insert(RatHandles, rat_handle)
|
||||
i = i + 1
|
||||
end
|
||||
|
||||
DuckHandles = {} -- List of ducks on the scene
|
||||
local i = 0
|
||||
while true do
|
||||
local duck_handle = sim.getObject("/Duck*", {index=i, noError=true})
|
||||
if duck_handle < 0 then
|
||||
break
|
||||
end
|
||||
table.insert(DuckHandles, duck_handle)
|
||||
i = i + 1
|
||||
end
|
||||
|
||||
RightWheel = sim.getObject("./RightWheel")
|
||||
LeftWheel = sim.getObject("./LeftWheel")
|
||||
Camera = sim.getObject("./Camera")
|
||||
VisionSensor = sim.getObject("./Vision_sensor")
|
||||
SonarRight = sim.getObject("./Proximity_sensor_right")
|
||||
SonarLeft = sim.getObject("./Proximity_sensor_left")
|
||||
|
||||
Diodes = { -- LED_strip has to be ungrouped
|
||||
D2 = sim.getObject("./diode_2"),
|
||||
D3 = sim.getObject("./diode_3"),
|
||||
D4 = sim.getObject("./diode_4"),
|
||||
D5 = sim.getObject("./diode_5"),
|
||||
D6 = sim.getObject("./diode_6"),
|
||||
D7 = sim.getObject("./diode_7")
|
||||
}
|
||||
|
||||
sim.setShapeColor(sim.getObject("./diode_1"), nil, 0, COLOR_WHITE)
|
||||
sim.setShapeColor(sim.getObject("./diode_8"), nil, 0, COLOR_WHITE)
|
||||
|
||||
sim.setObjectInt32Param(RightWheel,2000, 1) -- Enable velocity control
|
||||
sim.setObjectInt32Param(LeftWheel,2000, 1)
|
||||
sim.setObjectInt32Param(Camera,2000, 1)
|
||||
|
||||
Set_wheel_velocity(5, 5)
|
||||
sim.setJointTargetVelocity(Camera, 0)
|
||||
|
||||
sim.setJointTargetForce(LeftWheel, 5)
|
||||
sim.setJointTargetForce(RightWheel, 5)
|
||||
|
||||
Last_Color_Change_Time = sim.getSimulationTime()
|
||||
Last_Camera_Rotation_Time = sim.getSimulationTime()
|
||||
Last_Photo_Time = sim.getSimulationTime()
|
||||
Last_Turn_Time=sim.getSimulationTime()
|
||||
Turning = false
|
||||
Seeing_Another_Rat = 0
|
||||
Close_To_Rat = false
|
||||
CameraAngle = Get_angle(VisionSensor)
|
||||
ModelAngle = Get_angle(ModelHandle)
|
||||
Rat_Degrees_Offset_From_Photo_Center = 0
|
||||
|
||||
DEBUG_Sensor_Time = sim.getSimulationTime()
|
||||
|
||||
--sim.addBanner("Bateria = 100", 2, sim_banner_fullyfacingcamera, nil, ModelHandle, nil, nil)
|
||||
|
||||
Camera_Rotate_Counter = 0
|
||||
--IMAGE_COUNTER = 0
|
||||
end
|
||||
|
||||
|
||||
|
||||
function sysCall_actuation()
|
||||
-------------------- photo test --------------------
|
||||
if (sim.getSimulationTime() - Last_Photo_Time) > 0.5 then
|
||||
if BATERY_VALUE > 0 then
|
||||
BATERY_VALUE = BATERY_VALUE - 1
|
||||
UpdateBatery(BATERY_VALUE)
|
||||
end
|
||||
if not Close_To_Rat then
|
||||
Take_photo()
|
||||
end
|
||||
Last_Photo_Time = sim.getSimulationTime()
|
||||
end
|
||||
|
||||
-------------------- camera test -------------------
|
||||
if (sim.getSimulationTime() - Last_Camera_Rotation_Time) > 1.7 then
|
||||
if Camera_Rotate_Counter == 0 then
|
||||
sim.setJointTargetVelocity(Camera, 2)
|
||||
Camera_Rotate_Counter = 1
|
||||
else
|
||||
sim.setJointTargetVelocity(Camera, -2)
|
||||
Camera_Rotate_Counter = 0
|
||||
end
|
||||
Last_Camera_Rotation_Time = sim.getSimulationTime()
|
||||
end
|
||||
|
||||
-------------------- moving test -------------------
|
||||
if Turning == true and Angle_difference(Get_angle(ModelHandle), Desired_angle) < 10 then
|
||||
Finish_turning()
|
||||
Stop_if_close(RatHandles)
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
|
||||
|
||||
function sysCall_sensing()
|
||||
local _, _, _, detected_handle_left, _ = sim.readProximitySensor(SonarLeft)
|
||||
local _, _, _, detected_handle_right, _ = sim.readProximitySensor(SonarRight)
|
||||
if Has_Value(RatHandles, detected_handle_left) or Has_Value(RatHandles, detected_handle_right) then
|
||||
if detected_handle_left > 0 and Has_Value(RatHandles, detected_handle_left) then
|
||||
Handle = detected_handle_left
|
||||
else
|
||||
Handle = detected_handle_right
|
||||
end
|
||||
Close_To_Rat = true
|
||||
Change_strip_color(COLOR_RED)
|
||||
SensorMovement(Handle)
|
||||
elseif BATERY_VALUE > 0 then
|
||||
Close_To_Rat = false
|
||||
VisionMovement()
|
||||
else
|
||||
Set_wheel_velocity(0, 0)
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
|
||||
function sysCall_cleanup()
|
||||
-- do some clean-up here
|
||||
end
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
------------------------------ non-sysCall functions below ------------------------------
|
||||
function VisionMovement()
|
||||
if Seeing_Another_Rat == 1 then
|
||||
local adjusted_angle = Calculate_desired_angle(CameraAngle, Rat_Degrees_Offset_From_Photo_Center)
|
||||
local angle_diff = Angle_difference(ModelAngle, adjusted_angle)
|
||||
if angle_diff > 5 then
|
||||
if math.abs(ModelAngle - adjusted_angle) < 180 then
|
||||
if ModelAngle < adjusted_angle then
|
||||
print(string.format("[%s] Rat detected on the left", ModelName))
|
||||
else
|
||||
print(string.format("[%s] Rat detected on the right", ModelName))
|
||||
angle_diff = -angle_diff
|
||||
end
|
||||
else
|
||||
if ModelAngle > adjusted_angle then
|
||||
print(string.format("[%s] Rat detected on the left", ModelName))
|
||||
else
|
||||
print(string.format("[%s] Rat detected on the right", ModelName))
|
||||
angle_diff = -angle_diff
|
||||
end
|
||||
end
|
||||
DEGREES_TO_TURN = angle_diff
|
||||
Start_turning(angle_diff)
|
||||
ModelAngle = 0
|
||||
CameraAngle = 0
|
||||
Rat_Degrees_Offset_From_Photo_Center = 0
|
||||
end
|
||||
if not Turning then
|
||||
Go_Forward()
|
||||
end
|
||||
elseif not Turning then
|
||||
Random_Walk()
|
||||
end
|
||||
if not Turning then
|
||||
Stop_if_close(RatHandles)
|
||||
end
|
||||
end
|
||||
|
||||
function SensorMovement(handle)
|
||||
Follow(handle)
|
||||
end
|
||||
|
||||
|
||||
function RatCallPredictionAPI(filename)
|
||||
local response = http.request(string.format("http://127.0.0.1:5000/detectRat1?img=%s", filename))
|
||||
return json.decode(response)
|
||||
end
|
||||
|
||||
function RobotCallPredictionAPI(filename)
|
||||
local response = http.request(string.format("http://127.0.0.1:5000/detectRobot1?img=%s", filename))
|
||||
return json.decode(response)
|
||||
end
|
||||
|
||||
function UpdateBatery(batery_value)
|
||||
sim.addBanner(batery_value, 0.4, sim_banner_fullyfacingcamera, {1.0, 0, 0, 0, 0, 0}, ModelHandle, nil, {1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0})
|
||||
end
|
||||
|
||||
function Has_Value (tab, val)
|
||||
for index, value in ipairs(tab) do
|
||||
if value == val then
|
||||
return true
|
||||
end
|
||||
end
|
||||
return false
|
||||
end
|
||||
|
||||
|
||||
function Change_strip_color(rgb_vals)
|
||||
for _, v in pairs(Diodes) do
|
||||
sim.setShapeColor(v, nil, 0, rgb_vals)
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
function Set_wheel_velocity(left_vel, right_vel)
|
||||
sim.setJointTargetVelocity(LeftWheel, left_vel)
|
||||
sim.setJointTargetVelocity(RightWheel, right_vel)
|
||||
end
|
||||
|
||||
|
||||
function Take_photo()
|
||||
CameraAngle = Get_angle(VisionSensor)
|
||||
ModelAngle = Get_angle(ModelHandle)
|
||||
local img = sim.getVisionSensorImg(VisionSensor)
|
||||
local filename = "img"..IMAGE_COUNTER..".png"
|
||||
local path = "robot1/"..filename
|
||||
sim.saveImage(img, {1920,1080}, 0, path, -1)
|
||||
print(string.format("[%s] Photo taken 1", ModelName))
|
||||
local res = RatCallPredictionAPI(filename)
|
||||
local res_robot = RobotCallPredictionAPI(filename)
|
||||
print(CONFIDENCE)
|
||||
if BATERY_VALUE > 0 then
|
||||
if table.concat(res['0']) == 'None' then
|
||||
--print(CONFIDENCE)
|
||||
if CONFIDENCE > 0 then
|
||||
CONFIDENCE = CONFIDENCE - 1
|
||||
end
|
||||
Seeing_Another_Rat = 0
|
||||
if not Close_To_Rat and CONFIDENCE == 0 then
|
||||
Change_strip_color(COLOR_GREEN)
|
||||
print(string.format("[%s] No rats detected!", ModelName))
|
||||
else
|
||||
print(string.format("[%s] Chase the rat!", ModelName))
|
||||
end
|
||||
|
||||
else
|
||||
local frame_width = res['0'][1]
|
||||
local rat_pos = res['0'][2] + frame_width / 2
|
||||
Rat_Degrees_Offset_From_Photo_Center = (rat_pos - (PHOTO_WIDTH / 2)) / (PHOTO_WIDTH/CAMERA_FIELD_OF_VIEW)
|
||||
if table.concat(res_robot['0']) == 'None' then
|
||||
Seeing_Another_Rat = 1
|
||||
Change_strip_color(COLOR_RED)
|
||||
CONFIDENCE = 3
|
||||
print(string.format("[%s] Another rat detected on camera", ModelName))
|
||||
elseif res_robot['0'][1] == 'red' then
|
||||
Seeing_Another_Rat = 0
|
||||
print(string.format("[%s] I'm looking for another rat. This robot doesn't need my help", ModelName))
|
||||
if CONFIDENCE == 0 then
|
||||
Change_strip_color(COLOR_GREEN)
|
||||
end
|
||||
|
||||
elseif res_robot['0'][1] == 'white' then
|
||||
Seeing_Another_Rat = 1
|
||||
print(string.format("[%s] Robot need my help. I'm on my way!", ModelName))
|
||||
Change_strip_color(COLOR_RED)
|
||||
CONFIDENCE = 3
|
||||
end
|
||||
end
|
||||
IMAGE_COUNTER = IMAGE_COUNTER + 1
|
||||
else
|
||||
Change_strip_color(COLOR_WHITE)
|
||||
print(string.format("[%s] I have to charge my battery", ModelName))
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
|
||||
function Orientation_to_angle(orientation)
|
||||
-- returns a value between [0, 360]
|
||||
local angle = math.deg(orientation[2]) + 90
|
||||
if orientation[1] < 0 then
|
||||
angle = 360 - angle
|
||||
end
|
||||
return angle
|
||||
end
|
||||
|
||||
|
||||
function Get_angle(handle)
|
||||
local orientation = sim.getObjectOrientation(handle, sim.handle_world)
|
||||
return Orientation_to_angle(orientation)
|
||||
end
|
||||
|
||||
|
||||
function Calculate_desired_angle(old_angle, angle_delta)
|
||||
local desired = old_angle + angle_delta
|
||||
if desired > 360 then
|
||||
return desired % 360
|
||||
elseif
|
||||
desired < 0 then
|
||||
return desired + 360
|
||||
else
|
||||
return desired
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
function Angle_difference(actual_angle, desired_angle)
|
||||
-- returns a value between [0, 180]
|
||||
local diff = math.abs(desired_angle - actual_angle)
|
||||
return diff < 180 and diff or 360 - diff
|
||||
end
|
||||
|
||||
|
||||
function Turn_Left()
|
||||
Set_wheel_velocity(0, TURNING_VELOCITY)
|
||||
end
|
||||
|
||||
|
||||
function Turn_Right()
|
||||
Set_wheel_velocity(TURNING_VELOCITY,0)
|
||||
end
|
||||
|
||||
|
||||
function Go_Forward()
|
||||
Set_wheel_velocity(MOVING_VELOCITY, MOVING_VELOCITY)
|
||||
end
|
||||
|
||||
|
||||
function Stop_Moving()
|
||||
Set_wheel_velocity(0, 0)
|
||||
end
|
||||
|
||||
|
||||
function Start_turning(degrees)
|
||||
FULL_CIRCLE_PERIOD = 2.8 -- at 2.5 speed
|
||||
print(string.format("[%s] Starting to turn %.2f degrees", ModelName, degrees))
|
||||
Turning = true
|
||||
Desired_angle = Calculate_desired_angle(Get_angle(ModelHandle), degrees)
|
||||
if degrees > 0 then
|
||||
Turn_Left()
|
||||
else
|
||||
Turn_Right()
|
||||
end
|
||||
Last_Turn_Time = sim.getSimulationTime()
|
||||
end
|
||||
|
||||
|
||||
function Finish_turning()
|
||||
Go_Forward()
|
||||
Last_Turn_Time = sim.getSimulationTime()
|
||||
Turning = false
|
||||
After_turning = Get_angle(ModelHandle)
|
||||
print(string.format("[%s] Finished turning; Angle deviation from desired: %.2f (Desired: %.2f vs Actual: %.2f)",
|
||||
ModelName,
|
||||
Angle_difference(After_turning, Desired_angle),
|
||||
Desired_angle,
|
||||
After_turning))
|
||||
end
|
||||
|
||||
|
||||
function Random_Walk()
|
||||
local _, obstacle_left_distance, _, _, _ = sim.readProximitySensor(SonarLeft)
|
||||
if obstacle_left_distance == 0 then
|
||||
obstacle_left_distance = math.huge
|
||||
end
|
||||
|
||||
local _, obstacle_right_distance, _, _, _ = sim.readProximitySensor(SonarRight)
|
||||
if obstacle_right_distance == 0 then
|
||||
obstacle_right_distance = math.huge
|
||||
end
|
||||
|
||||
if obstacle_right_distance < obstacle_left_distance and obstacle_right_distance < OBSTACLE_AVOID_DISTANCE then
|
||||
Turn_Left()
|
||||
elseif obstacle_left_distance < obstacle_right_distance and obstacle_left_distance < OBSTACLE_AVOID_DISTANCE then
|
||||
Turn_Right()
|
||||
else
|
||||
Go_Forward()
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
function Follow(object_handle) -- Use with flat sensors with no blind zone in the middle
|
||||
local left_detected, left_distance, _, _, _ = sim.checkProximitySensor(SonarLeft, object_handle)
|
||||
local right_detected, right_distance, _, _, _ = sim.checkProximitySensor(SonarRight, object_handle)
|
||||
|
||||
if (sim.getSimulationTime() - Last_Photo_Time) > 0.5 then
|
||||
if not Close_To_Rat then
|
||||
local img = sim.getVisionSensorImg(VisionSensor)
|
||||
local filename = "img"..IMAGE_COUNTER..".png"
|
||||
local path = "robot1/"..filename
|
||||
sim.saveImage(img, {1920,1080}, 0, path, -1)
|
||||
end
|
||||
Last_Photo_Time = sim.getSimulationTime()
|
||||
IMAGE_COUNTER = IMAGE_COUNTER + 1
|
||||
end
|
||||
|
||||
if left_detected == 1 and right_detected == 1 then
|
||||
Go_Forward()
|
||||
elseif left_detected == 1 then
|
||||
Turn_Left()
|
||||
elseif right_detected == 1 then
|
||||
Turn_Right()
|
||||
else
|
||||
Go_Forward()
|
||||
end
|
||||
|
||||
if (left_distance > 0 and left_distance < PROXIMITY_STOP_DISTANCE) or (right_distance > 0 and right_distance < PROXIMITY_STOP_DISTANCE) then
|
||||
Stop_Moving()
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
function Stop_if_close(handle_table)
|
||||
-- Przy szczurach jest ok przy robotach z jakiegos powodu nie wykrywa jesli patrzy na kolo zamiast chassisa (kolo pewnie ma inny objecthandle?)
|
||||
local left_detected, left_distance, _, left_detected_object_handle, _ = sim.readProximitySensor(SonarLeft)
|
||||
local right_detected, right_distance, _, right_detected_object_handle, _ = sim.readProximitySensor(SonarRight)
|
||||
if Has_Value(handle_table, left_detected_object_handle) or Has_Value(handle_table, right_detected_object_handle) then
|
||||
if (left_distance > 0 and left_distance < PROXIMITY_STOP_DISTANCE) or (right_distance > 0 and right_distance < PROXIMITY_STOP_DISTANCE) then
|
||||
Stop_Moving()
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
------------------------------ DEBUG functions below ------------------------------
|
||||
function DEBUG_What_Do_I_See(sensor_handle)
|
||||
local detected, distance, detected_point, detected_object_handle, detected_surface_normal_vector = sim.readProximitySensor(sensor_handle)
|
||||
if detected == 1 then
|
||||
print(string.format("[%s] Object detected by %s; Handle: %d, Name: %s, Distance: %.2f",
|
||||
ModelName,
|
||||
sim.getObjectAlias(sensor_handle),
|
||||
detected_object_handle,
|
||||
sim.getObjectAlias(detected_object_handle, 1),
|
||||
distance))
|
||||
end
|
||||
end
|
||||
174
rat.lua
Normal file
174
rat.lua
Normal file
@ -0,0 +1,174 @@
|
||||
|
||||
WALL_AVOID_DISTANCE = 0.5
|
||||
MOVING_VELOCITY = -20
|
||||
TURNING_VELOCITY = 2.5
|
||||
ROBOT_PROXIMITY_STOP_DISTANCE = 1.2
|
||||
|
||||
|
||||
|
||||
function sysCall_init()
|
||||
|
||||
grains1 = sim.getObject("../Bump[2]/shape")
|
||||
WallHandle = sim.getObject("../ExternalWall/Cuboid")
|
||||
|
||||
rightWheel= sim.getObject("./RightWheelRat")
|
||||
leftWheel= sim.getObject("./LeftWheelRat")
|
||||
sim.setObjectInt32Param(rightWheel,2000,1)
|
||||
sim.setObjectInt32Param(leftWheel,2000,1)
|
||||
|
||||
Set_wheel_velocity(-20, -20)
|
||||
|
||||
sim.setJointTargetForce(leftWheel, 5)
|
||||
sim.setJointTargetForce(rightWheel, 5)
|
||||
|
||||
last_time=sim.getSimulationTime()
|
||||
turn_time=sim.getSimulationTime()
|
||||
rotate = false
|
||||
|
||||
sonarRight = sim.getObject("./Proximity_sensor_right_rat");
|
||||
sonarLeft = sim.getObject("./Proximity_sensor_left_rat");
|
||||
sonarCenter = sim.getObject("./Proximity_sensor_center_rat");
|
||||
max_dist = 3.0
|
||||
speed = 10
|
||||
|
||||
|
||||
RobotHandles = {} -- List of our robots on the scene
|
||||
local i = 0
|
||||
while true do
|
||||
local robot_handle = sim.getObject("/Bump*/shape", {index=i, noError=true})
|
||||
if robot_handle < 0 then
|
||||
break
|
||||
end
|
||||
table.insert(RobotHandles, robot_handle)
|
||||
i = i + 1
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
|
||||
function sysCall_actuation()
|
||||
end
|
||||
|
||||
function sysCall_sensing()
|
||||
-- put your sensing code here
|
||||
|
||||
local _, _, _, detected_handle_left, _ = sim.readProximitySensor(sonarLeft)
|
||||
local _, _, _, detected_handle_right, _ = sim.readProximitySensor(sonarRight)
|
||||
if Has_Value(RobotHandles, detected_handle_left) then -- For demonstration purposes this robot will follow any robot of the same type
|
||||
Follow(detected_handle_left)
|
||||
elseif Has_Value(RobotHandles, detected_handle_right) then
|
||||
Follow(detected_handle_right)
|
||||
else
|
||||
Random_Walk()
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
|
||||
function sysCall_cleanup()
|
||||
-- do some clean-up here
|
||||
end
|
||||
|
||||
function Has_Value (tab, val)
|
||||
for index, value in ipairs(tab) do
|
||||
if value == val then
|
||||
return true
|
||||
end
|
||||
end
|
||||
return false
|
||||
end
|
||||
|
||||
function Set_wheel_velocity(left_vel, right_vel)
|
||||
sim.setJointTargetVelocity(leftWheel, left_vel)
|
||||
sim.setJointTargetVelocity(rightWheel, right_vel)
|
||||
end
|
||||
|
||||
function Turn_Left()
|
||||
Set_wheel_velocity(TURNING_VELOCITY, -TURNING_VELOCITY)
|
||||
end
|
||||
|
||||
|
||||
function Turn_Right()
|
||||
Set_wheel_velocity(-TURNING_VELOCITY, TURNING_VELOCITY)
|
||||
end
|
||||
|
||||
|
||||
function Go_Forward()
|
||||
Set_wheel_velocity(MOVING_VELOCITY, MOVING_VELOCITY)
|
||||
end
|
||||
|
||||
|
||||
function Stop_Moving()
|
||||
Set_wheel_velocity(0, 0)
|
||||
end
|
||||
|
||||
function Random_Walk()
|
||||
-- put your sensing code here
|
||||
distSonarRight = getDistance(sonarRight, max_dist)
|
||||
distSonarLeft = getDistance(sonarLeft, max_dist)
|
||||
distSonarCenter = getDistance(sonarCenter, max_dist)
|
||||
|
||||
if distSonarRight < max_dist and distSonarRight < distSonarLeft and rotate == false then
|
||||
Set_wheel_velocity(0, -40)
|
||||
elseif distSonarRight < max_dist and distSonarLeft < max_dist and distSonarCenter < max_dist and distSonarRight < distSonarLeft and rotate == false then
|
||||
Set_wheel_velocity(0, -40)
|
||||
elseif distSonarRight < max_dist and distSonarCenter < max_dist and distSonarRight < distSonarLeft and rotate == false then
|
||||
Set_wheel_velocity(0, -40)
|
||||
elseif distSonarLeft < max_dist and distSonarCenter < max_dist and distSonarLeft < distSonarRight and rotate == false then
|
||||
Set_wheel_velocity(-40, 0)
|
||||
elseif distSonarLeft < max_dist and distSonarLeft < distSonarRight and rotate == false then
|
||||
Set_wheel_velocity(-40, 0)
|
||||
elseif distSonarCenter < max_dist and rotate == false then
|
||||
Set_wheel_velocity(0, -40)
|
||||
turn_time = sim.getSimulationTime()
|
||||
rotate = true
|
||||
elseif rotate == false then
|
||||
Set_wheel_velocity(-20, -20)
|
||||
end
|
||||
|
||||
if rotate == true and (sim.getSimulationTime() - turn_time) > 0.5 then
|
||||
Set_wheel_velocity(-20, -20)
|
||||
rotate = false
|
||||
end
|
||||
end
|
||||
|
||||
function Follow(object_handle) -- Use with flat sensors with no blind zone in the middle
|
||||
local left_detected, left_distance, _, _, _ = sim.checkProximitySensor(sonarLeft, object_handle)
|
||||
local right_detected, right_distance, _, _, _ = sim.checkProximitySensor(sonarRight, object_handle)
|
||||
|
||||
if left_detected == 1 and right_detected == 1 then
|
||||
Go_Forward()
|
||||
elseif left_detected == 1 then
|
||||
Turn_Left()
|
||||
elseif right_detected == 1 then
|
||||
Turn_Right()
|
||||
else
|
||||
Go_Forward()
|
||||
end
|
||||
|
||||
if (left_distance > 0 and left_distance < ROBOT_PROXIMITY_STOP_DISTANCE) or (right_distance > 0 and right_distance < ROBOT_PROXIMITY_STOP_DISTANCE) then
|
||||
Stop_Moving()
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
function getDistance(sensor, max_dist)
|
||||
local detected, distance
|
||||
detected, distance = sim.readProximitySensor(sensor)
|
||||
if (detected < 1) then
|
||||
distance = max_dist
|
||||
end
|
||||
return distance
|
||||
end
|
||||
|
||||
function DEBUG_What_Do_I_See(sensor_handle)
|
||||
local detected, distance, detected_point, detected_object_handle, detected_surface_normal_vector = sim.readProximitySensor(sensor_handle)
|
||||
if detected == 1 then
|
||||
print(string.format("[%s] Object detected by %s; Handle: %d, Name: %s, Distance: %.2f",
|
||||
ModelName,
|
||||
sim.getObjectAlias(sensor_handle),
|
||||
detected_object_handle,
|
||||
sim.getObjectAlias(detected_object_handle, 1),
|
||||
distance))
|
||||
end
|
||||
end
|
||||
Loading…
Reference in New Issue
Block a user