diff --git a/poetry.lock b/poetry.lock
index 72bf1de..b26fb36 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -13,13 +13,13 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "4.7.0"
+version = "4.8.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "anyio-4.7.0-py3-none-any.whl", hash = "sha256:ea60c3723ab42ba6fff7e8ccb0488c898ec538ff4df1f1d5e642c3601d07e352"},
-    {file = "anyio-4.7.0.tar.gz", hash = "sha256:2f834749c602966b7d456a7567cafcb309f96482b5081d14ac93ccd457f9dd48"},
+    {file = "anyio-4.8.0-py3-none-any.whl", hash = "sha256:b5011f270ab5eb0abf13385f851315585cc37ef330dd88e27ec3d34d651fd47a"},
+    {file = "anyio-4.8.0.tar.gz", hash = "sha256:1d9fe889df5212298c0c0723fa20479d1b94883a2df44bd3897aa91083316f7a"},
 ]
 
 [package.dependencies]
@@ -30,7 +30,7 @@ typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
 
 [package.extras]
 doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
 trio = ["trio (>=0.26.1)"]
 
 [[package]]
@@ -151,50 +151,65 @@ typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
 
 [[package]]
 name = "attrs"
-version = "24.2.0"
+version = "25.1.0"
 description = "Classes Without Boilerplate"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "attrs-24.2.0-py3-none-any.whl", hash = "sha256:81921eb96de3191c8258c199618104dd27ac608d9366f5e35d011eae1867ede2"},
-    {file = "attrs-24.2.0.tar.gz", hash = "sha256:5cfb1b9148b5b086569baec03f20d7b6bf3bcacc9a42bebf87ffaaca362f6346"},
+    {file = "attrs-25.1.0-py3-none-any.whl", hash = "sha256:c75a69e28a550a7e93789579c22aa26b0f5b83b75dc4e08fe092980051e1090a"},
+    {file = "attrs-25.1.0.tar.gz", hash = "sha256:1c97078a80c814273a76b2a298a932eb681c87415c11dee0a6921de7f1b02c3e"},
 ]
 
 [package.extras]
 benchmark = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
 cov = ["cloudpickle", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
-dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit-uv", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
 docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
 tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
 tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
 
 [[package]]
 name = "babel"
-version = "2.16.0"
+version = "2.17.0"
 description = "Internationalization utilities"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "babel-2.16.0-py3-none-any.whl", hash = "sha256:368b5b98b37c06b7daf6696391c3240c938b37767d4584413e8438c5c435fa8b"},
-    {file = "babel-2.16.0.tar.gz", hash = "sha256:d1f3554ca26605fe173f3de0c65f750f5a42f924499bf134de6423582298e316"},
+    {file = "babel-2.17.0-py3-none-any.whl", hash = "sha256:4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2"},
+    {file = "babel-2.17.0.tar.gz", hash = "sha256:0c54cffb19f690cdcc52a3b50bcbf71e07a808d1c80d549f2459b9d2cf0afb9d"},
 ]
 
 [package.extras]
-dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
+dev = ["backports.zoneinfo", "freezegun (>=1.0,<2.0)", "jinja2 (>=3.0)", "pytest (>=6.0)", "pytest-cov", "pytz", "setuptools", "tzdata"]
+
+[[package]]
+name = "baumer-neoapi"
+version = "1.4.1"
+description = "neoapi the new camera programming experience"
+optional = false
+python-versions = ">=3.4, <=3.12"
+files = [
+    {file = "baumer_neoapi-1.4.1-cp34.cp35.cp36.cp37.cp38.cp39.cp310.cp311.cp312-none-linux_x86_64.whl", hash = "sha256:22e378f98cc4112f942db88fca9de74ba34fdc91a05512312aa57adf8e2cf84e"},
+]
+
+[package.source]
+type = "file"
+url = "../../Downloads/Baumer_neoAPI_1.4.1_lin_x86_64_python/wheel/baumer_neoapi-1.4.1-cp34.cp35.cp36.cp37.cp38.cp39.cp310.cp311.cp312-none-linux_x86_64.whl"
 
 [[package]]
 name = "beautifulsoup4"
-version = "4.12.3"
+version = "4.13.3"
 description = "Screen-scraping library"
 optional = false
-python-versions = ">=3.6.0"
+python-versions = ">=3.7.0"
 files = [
-    {file = "beautifulsoup4-4.12.3-py3-none-any.whl", hash = "sha256:b80878c9f40111313e55da8ba20bdba06d8fa3969fc68304167741bbf9e082ed"},
-    {file = "beautifulsoup4-4.12.3.tar.gz", hash = "sha256:74e3d1928edc070d21748185c46e3fb33490f22f52a3addee9aee0f4f7781051"},
+    {file = "beautifulsoup4-4.13.3-py3-none-any.whl", hash = "sha256:99045d7d3f08f91f0d656bc9b7efbae189426cd913d830294a15eefa0ea4df16"},
+    {file = "beautifulsoup4-4.13.3.tar.gz", hash = "sha256:1bd32405dacc920b42b83ba01644747ed77456a65760e285fbc47633ceddaf8b"},
 ]
 
 [package.dependencies]
 soupsieve = ">1.2"
+typing-extensions = ">=4.0.0"
 
 [package.extras]
 cchardet = ["cchardet"]
@@ -215,6 +230,7 @@ files = [
 ]
 
 [package.dependencies]
+tinycss2 = {version = ">=1.1.0,<1.5", optional = true, markers = "extra == \"css\""}
 webencodings = "*"
 
 [package.extras]
@@ -241,24 +257,24 @@ resolved_reference = "527c3b44533cfcac0a480909d1d8287f5b0cba6c"
 
 [[package]]
 name = "cachetools"
-version = "5.5.0"
+version = "5.5.1"
 description = "Extensible memoizing collections and decorators"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "cachetools-5.5.0-py3-none-any.whl", hash = "sha256:02134e8439cdc2ffb62023ce1debca2944c3f289d66bb17ead3ab3dede74b292"},
-    {file = "cachetools-5.5.0.tar.gz", hash = "sha256:2cc24fb4cbe39633fb7badd9db9ca6295d766d9c2995f245725a46715d050f2a"},
+    {file = "cachetools-5.5.1-py3-none-any.whl", hash = "sha256:b76651fdc3b24ead3c648bbdeeb940c1b04d365b38b4af66788f9ec4a81d42bb"},
+    {file = "cachetools-5.5.1.tar.gz", hash = "sha256:70f238fbba50383ef62e55c6aff6d9673175fe59f7c6782c7a0b9e38f4a9df95"},
 ]
 
 [[package]]
 name = "certifi"
-version = "2024.8.30"
+version = "2025.1.31"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8"},
-    {file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"},
+    {file = "certifi-2025.1.31-py3-none-any.whl", hash = "sha256:ca78db4565a652026a4db2bcdf68f2fb589ea80d0be70e03929ed730746b84fe"},
+    {file = "certifi-2025.1.31.tar.gz", hash = "sha256:3d5da6925056f6f18f119200434a4780a94263f10d1c21d032a6f6b2baa20651"},
 ]
 
 [[package]]
@@ -342,116 +358,103 @@ pycparser = "*"
 
 [[package]]
 name = "charset-normalizer"
-version = "3.4.0"
+version = "3.4.1"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
-python-versions = ">=3.7.0"
+python-versions = ">=3.7"
 files = [
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 [[package]]
@@ -499,37 +502,37 @@ tests = ["pytest", "pytest-cov", "pytest-xdist"]
 
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 [package.extras]
@@ -803,70 +806,70 @@ tqdm = "*"
 
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 description = "HTTP/2-based RPC framework"
 optional = false
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 [package.extras]
-protobuf = ["grpcio-tools (>=1.68.1)"]
+protobuf = ["grpcio-tools (>=1.70.0)"]
 
 [[package]]
 name = "h11"
@@ -973,13 +976,13 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio
 
 [[package]]
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 [package.dependencies]
@@ -1044,13 +1047,13 @@ testing = ["Django", "attrs", "colorama", "docopt", "pytest (<9.0.0)"]
 
 [[package]]
 name = "jinja2"
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 python-versions = ">=3.7"
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 [package.dependencies]
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 [[package]]
 name = "jupyter-events"
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 [package.dependencies]
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 python-json-logger = ">=2.0.4"
 pyyaml = ">=5.3"
 referencing = "*"
@@ -1216,7 +1220,7 @@ traitlets = ">=5.3"
 
 [package.extras]
 cli = ["click", "rich"]
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 [[package]]
@@ -1235,13 +1239,13 @@ jupyter-server = ">=1.1.2"
 
 [[package]]
 name = "jupyter-server"
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+version = "2.15.0"
 description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
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 [package.dependencies]
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 jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
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 jupyter-server-terminals = ">=0.4.4"
 nbconvert = ">=6.4.4"
 nbformat = ">=5.3.0"
@@ -1290,13 +1294,13 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>
 
 [[package]]
 name = "jupyterlab"
-version = "4.3.3"
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 description = "JupyterLab computational environment"
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 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
@@ -1360,125 +1364,91 @@ test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-v
 
 [[package]]
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-version = "1.4.7"
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-python-versions = ">=3.8"
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 ]
 
 [[package]]
@@ -1702,24 +1672,27 @@ traitlets = "*"
 
 [[package]]
 name = "mistune"
-version = "3.0.2"
+version = "3.1.1"
 description = "A sane and fast Markdown parser with useful plugins and renderers"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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+    {file = "mistune-3.1.1-py3-none-any.whl", hash = "sha256:02106ac2aa4f66e769debbfa028509a275069dcffce0dfa578edd7b991ee700a"},
+    {file = "mistune-3.1.1.tar.gz", hash = "sha256:e0740d635f515119f7d1feb6f9b192ee60f0cc649f80a8f944f905706a21654c"},
 ]
 
+[package.dependencies]
+typing-extensions = {version = "*", markers = "python_version < \"3.11\""}
+
 [[package]]
 name = "nbclient"
-version = "0.10.1"
+version = "0.10.2"
 description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
 optional = false
-python-versions = ">=3.8.0"
+python-versions = ">=3.9.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -1730,23 +1703,23 @@ traitlets = ">=5.4"
 
 [package.extras]
 dev = ["pre-commit"]
-docs = ["autodoc-traits", "flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "mock", "moto", "myst-parser", "nbconvert (>=7.0.0)", "pytest (>=7.0,<8)", "pytest-asyncio", "pytest-cov (>=4.0)", "sphinx (>=1.7)", "sphinx-book-theme", "sphinxcontrib-spelling", "testpath", "xmltodict"]
-test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=7.0.0)", "pytest (>=7.0,<8)", "pytest-asyncio", "pytest-cov (>=4.0)", "testpath", "xmltodict"]
+docs = ["autodoc-traits", "flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "mock", "moto", "myst-parser", "nbconvert (>=7.1.0)", "pytest (>=7.0,<8)", "pytest-asyncio", "pytest-cov (>=4.0)", "sphinx (>=1.7)", "sphinx-book-theme", "sphinxcontrib-spelling", "testpath", "xmltodict"]
+test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=7.1.0)", "pytest (>=7.0,<8)", "pytest-asyncio", "pytest-cov (>=4.0)", "testpath", "xmltodict"]
 
 [[package]]
 name = "nbconvert"
-version = "7.16.4"
+version = "7.16.6"
 description = "Converting Jupyter Notebooks (.ipynb files) to other formats.  Output formats include asciidoc, html, latex, markdown, pdf, py, rst, script.  nbconvert can be used both as a Python library (`import nbconvert`) or as a command line tool (invoked as `jupyter nbconvert ...`)."
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "nbconvert-7.16.6-py3-none-any.whl", hash = "sha256:1375a7b67e0c2883678c48e506dc320febb57685e5ee67faa51b18a90f3a712b"},
+    {file = "nbconvert-7.16.6.tar.gz", hash = "sha256:576a7e37c6480da7b8465eefa66c17844243816ce1ccc372633c6b71c3c0f582"},
 ]
 
 [package.dependencies]
 beautifulsoup4 = "*"
-bleach = "!=5.0.0"
+bleach = {version = "!=5.0.0", extras = ["css"]}
 defusedxml = "*"
 jinja2 = ">=3.0"
 jupyter-core = ">=4.7"
@@ -1758,7 +1731,6 @@ nbformat = ">=5.7"
 packaging = "*"
 pandocfilters = ">=1.4.1"
 pygments = ">=2.4.1"
-tinycss2 = "*"
 traitlets = ">=5.1"
 
 [package.extras]
@@ -1867,18 +1839,18 @@ files = [
 
 [[package]]
 name = "notebook"
-version = "7.3.1"
+version = "7.3.2"
 description = "Jupyter Notebook - A web-based notebook environment for interactive computing"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "notebook-7.3.2.tar.gz", hash = "sha256:705e83a1785f45b383bf3ee13cb76680b92d24f56fb0c7d2136fe1d850cd3ca8"},
 ]
 
 [package.dependencies]
 jupyter-server = ">=2.4.0,<3"
-jupyterlab = ">=4.3.2,<4.4"
+jupyterlab = ">=4.3.4,<4.4"
 jupyterlab-server = ">=2.27.1,<3"
 notebook-shim = ">=0.2,<0.3"
 tornado = ">=6.2.0"
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 [[package]]
 name = "orjson"
-version = "3.10.12"
+version = "3.10.15"
 description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [[package]]
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 fpx = ["olefile"]
 mic = ["olefile"]
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 xmp = ["defusedxml"]
 
@@ -2366,13 +2338,13 @@ twisted = ["twisted"]
 
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+version = "3.0.50"
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 ]
 
 [package.dependencies]
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 [[package]]
 name = "requests"
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 [package.dependencies]
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-    {file = "ultralytics_thop-2.0.13.tar.gz", hash = "sha256:afd71619b61aa1d11858d69fff29c1c1e438a1c76bc274e0eaa040548627c384"},
+    {file = "ultralytics_thop-2.0.14-py3-none-any.whl", hash = "sha256:720b421e2459179fee21ec8f730d242a20774cd4b0a00a58d02351a39ec3881c"},
+    {file = "ultralytics_thop-2.0.14.tar.gz", hash = "sha256:38ebfdbd3cd8dafdc3d26ec3a7d4f604fbeed5e69a74e61a48060b39736c945c"},
 ]
 
 [package.dependencies]
@@ -3843,13 +3840,13 @@ dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake
 
 [[package]]
 name = "urllib3"
-version = "2.2.3"
+version = "2.3.0"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "urllib3-2.2.3-py3-none-any.whl", hash = "sha256:ca899ca043dcb1bafa3e262d73aa25c465bfb49e0bd9dd5d59f1d0acba2f8fac"},
-    {file = "urllib3-2.2.3.tar.gz", hash = "sha256:e7d814a81dad81e6caf2ec9fdedb284ecc9c73076b62654547cc64ccdcae26e9"},
+    {file = "urllib3-2.3.0-py3-none-any.whl", hash = "sha256:1cee9ad369867bfdbbb48b7dd50374c0967a0bb7710050facf0dd6911440e3df"},
+    {file = "urllib3-2.3.0.tar.gz", hash = "sha256:f8c5449b3cf0861679ce7e0503c7b44b5ec981bec0d1d3795a07f1ba96f0204d"},
 ]
 
 [package.extras]
@@ -3927,4 +3924,4 @@ watchdog = ["watchdog (>=2.3)"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10,<3.12,"
-content-hash = "9cc6f3a29659b174253167ef5d9a4dbc1b99399265f372453200b7f86c833322"
+content-hash = "8a4b9895c94c13642a75d7b4a00f36e271674325009d24758df44bf9a023f103"
diff --git a/pyproject.toml b/pyproject.toml
index 9d30319..751a7b2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -44,6 +44,8 @@ bytetracker =  { git = "https://github.com/rubenvandeven/bytetrack-pip" }
 jsonlines = "^4.0.0"
 tensorboardx = "^2.6.2.2"
 shapely = "^1"
+baumer-neoapi = {path = "../../Downloads/Baumer_neoAPI_1.4.1_lin_x86_64_python/wheel/baumer_neoapi-1.4.1-cp34.cp35.cp36.cp37.cp38.cp39.cp310.cp311.cp312-none-linux_x86_64.whl"}
+qrcode = "^8.0"
 
 [build-system]
 requires = ["poetry-core"]
diff --git a/trap/base.py b/trap/base.py
new file mode 100644
index 0000000..710d863
--- /dev/null
+++ b/trap/base.py
@@ -0,0 +1,404 @@
+from __future__ import annotations
+
+from ast import List
+from enum import IntFlag
+from itertools import cycle
+import json
+import logging
+from pathlib import Path
+import time
+from typing import Iterable, Optional
+import cv2
+from dataclasses import dataclass, field
+
+import numpy as np
+from deep_sort_realtime.deep_sort.track import Track as DeepsortTrack
+from deep_sort_realtime.deep_sort.track import TrackState as DeepsortTrackState
+from bytetracker.byte_tracker import STrack as ByteTrackTrack
+from bytetracker.basetrack import TrackState as ByteTrackTrackState
+import pandas as pd
+
+from trap.utils import get_bins, inv_lerp, lerp
+from trajectron.environment import Environment, Node, Scene
+from urllib.parse import urlparse
+
+logger = logging.getLogger('trap.base')
+
+class UrlOrPath():
+    def __init__(self, string):
+        self.url = urlparse(str(string))
+
+    def __str__(self) -> str:
+        return self.url.geturl()
+
+    def is_url(self) -> bool:
+        return len(self.url.netloc) > 0
+
+    def path(self) -> Path:
+        if self.is_url():
+            return Path(self.url.path)
+        return Path(self.url.geturl()) # can include scheme, such as C:/
+    
+class Space(IntFlag):
+    Image = 1 # As detected in the image
+    Undistorted = 2 # After applying lense undistortiion
+    World = 4 # After lens undistort and homography
+    Render = 8 # View space of renderer
+    
+
+@dataclass
+class Position:
+    x: float
+    y: float
+    conf: float
+    state: DetectionState
+    frame_nr: int
+    det_class: str
+
+    
+
+class DetectionState(IntFlag):
+    Tentative = 1 # state before n_init (see DeepsortTrack)
+    Confirmed = 2 # after tentative
+    Lost = 4 # lost when DeepsortTrack.time_since_update > 0 but not Deleted
+    Interpolated = 8 # A position estimated through interpolation of adjecent detections
+
+    @classmethod
+    def from_deepsort_track(cls, track: DeepsortTrack):
+        if track.state == DeepsortTrackState.Tentative:
+            return cls.Tentative
+        if track.state == DeepsortTrackState.Confirmed:
+            if track.time_since_update > 0:
+                return cls.Lost
+            return cls.Confirmed
+        raise RuntimeError("Should not run into Deleted entries here")
+
+    @classmethod
+    def from_bytetrack_track(cls, track: ByteTrackTrack):
+        if track.state == ByteTrackTrackState.New:
+            return cls.Tentative
+        if track.state == ByteTrackTrackState.Lost:
+            return cls.Lost
+            # if track.time_since_update > 0:
+        if track.state == ByteTrackTrackState.Tracked:
+            return cls.Confirmed
+        raise RuntimeError("Should not run into Deleted entries here")
+
+
+
+def H_from_path(path: Path):
+    if path.suffix == '.json':
+        with path.open('r') as fp:
+            H = np.array(json.load(fp))
+    else:
+        H = np.loadtxt(path, delimiter=',')
+    return H
+
+@dataclass
+class Camera:
+    mtx: cv2.Mat
+    dist: cv2.Mat
+    w: float
+    h: float
+    H: cv2.Mat # homography
+
+    newcameramtx: cv2.Mat = field(init=False)
+    roi: cv2.typing.Rect = field(init=False)
+    fps: float
+
+    def __post_init__(self):
+        self.newcameramtx, self.roi = cv2.getOptimalNewCameraMatrix(self.mtx, self.dist, (self.w,self.h), 1, (self.w,self.h))
+
+    @classmethod
+    def from_calibfile(cls, calibration_path, H, fps):
+        with calibration_path.open('r') as fp:
+            data = json.load(fp)
+            # print(data)
+            # print(data['camera_matrix'])
+        # camera = {
+        #     'camera_matrix': np.array(data['camera_matrix']), 
+        #     'dist_coeff': np.array(data['dist_coeff']),
+        # }
+        return cls(
+            np.array(data['camera_matrix']), 
+            np.array(data['dist_coeff']), 
+            data['dim']['width'], 
+            data['dim']['height'], 
+            H, fps)
+    
+    @classmethod
+    def from_paths(cls, calibration_path, h_path, fps):
+        H = H_from_path(h_path)
+        return cls.from_calibfile(calibration_path, H, fps)
+    
+    # def __init__(self, mtx, dist, w, h, H):
+    #     self.mtx = mtx
+    #     self.dist = dist
+    #     self.w = w
+    #     self.h = h
+    #     self.newcameramtx, self.roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w,h), 1, (w,h))
+    #     self.H = H # homography
+
+
+@dataclass
+class Detection:
+    track_id: str # deepsort track id association
+    l: int # left - image space
+    t: int # top - image space
+    w: int # width - image space
+    h: int # height - image space
+    conf: float # object detector probablity
+    state: DetectionState
+    frame_nr: int
+    det_class: str
+
+    def get_foot_coords(self) -> list[float, float]:
+        return [self.l + 0.5 * self.w, self.t+self.h]
+
+    @classmethod
+    def from_deepsort(cls, dstrack: DeepsortTrack, frame_nr: int):
+        return cls(dstrack.track_id, *dstrack.to_ltwh(), dstrack.det_conf, DetectionState.from_deepsort_track(dstrack), frame_nr, dstrack.det_class)
+    
+
+    @classmethod
+    def from_bytetrack(cls, bstrack: ByteTrackTrack, frame_nr: int):
+        return cls(bstrack.track_id, *bstrack.tlwh, bstrack.score, DetectionState.from_bytetrack_track(bstrack), frame_nr, bstrack.cls)
+    
+    def get_scaled(self, scale: float = 1):
+        if scale == 1:
+            return self
+        
+        return Detection(
+            self.track_id,
+            self.l*scale,
+            self.t*scale,
+            self.w*scale,
+            self.h*scale,
+            self.conf,
+            self.state,
+            self.frame_nr,
+            self.det_class)
+    
+    def to_ltwh(self):
+        return (int(self.l), int(self.t), int(self.w), int(self.h))
+    
+    def to_ltrb(self):
+        return (int(self.l), int(self.t), int(self.l+self.w), int(self.t+self.h))
+
+
+@dataclass
+class Track:
+    """A bit of an haphazardous wrapper around the 'real' tracker to provide 
+    a history, with which the predictor can work, as we then can deduce velocity
+    and acceleration.
+    """
+    track_id: str = None
+    history: List[Detection] = field(default_factory=list)
+    predictor_history: Optional[list] = None # in image space
+    predictions: Optional[list] = None
+    fps: int = 12 # TODO)) convert this to camera? That way, incorporates H and dist, alternatively, each track is as a whole attached to a space
+    source: Optional[int] = None # to keep track of processed tracks
+
+    def get_projected_history(self, H: Optional[cv2.Mat] = None, camera: Optional[Camera]= None) -> np.array:
+        foot_coordinates = [d.get_foot_coords() for d in self.history]
+        # TODO)) Undistort points before perspective transform
+        if len(foot_coordinates):
+            if camera:
+                coords = cv2.undistortPoints(np.array([foot_coordinates]).astype('float32'), camera.mtx, camera.dist, None, camera.newcameramtx)
+                coords = cv2.perspectiveTransform(np.array(coords),camera.H)
+                return coords.reshape((coords.shape[0],2))
+            else:
+                coords = cv2.perspectiveTransform(np.array([foot_coordinates]),H)
+            return coords[0]
+        return np.array([])
+    
+    def get_projected_history_as_dict(self, H, camera: Optional[Camera]= None) -> dict:
+        coords = self.get_projected_history(H, camera)
+        return [{"x":c[0], "y":c[1]} for c in coords]
+
+    def get_with_interpolated_history(self) -> Track:
+        # new_history = [Detection(d.track_id, l, t, w, h, d.conf, d.state, d.frame_nr, d.det_class) for l, t, w, h, d in zip(ls,ts,ws,hs, track.history)]
+        # new_track = Track(track.track_id, new_history, track.predictor_history, track.predictions)
+        new_history = []
+        for j in range(len(self.history)):
+            a = self.history[j]
+            new_history.append(Detection(a.track_id, a.l, a.t, a.w, a.h, a.conf, a.state, a.frame_nr, a.det_class))
+
+            if j+1 >= len(self.history):
+                break
+
+            b = self.history[j+1]
+            gap = b.frame_nr - a.frame_nr
+            if gap < 1:
+                logger.error(f"WARNING, gap between frames {a.frame_nr} -> {b.frame_nr} is negative?")
+            if gap > 1:
+                for g in range(1, gap):
+                    l = lerp(a.l, b.l, g/gap)
+                    t = lerp(a.t, b.t, g/gap)
+                    w = lerp(a.w, b.w, g/gap)
+                    h = lerp(a.h, b.h, g/gap)
+                    conf = 0
+                    state = DetectionState.Lost
+                    frame_nr = a.frame_nr + g
+                    new_history.append(Detection(a.track_id, l, t, w, h, conf, state, frame_nr, a.det_class))
+
+        return Track(
+            self.track_id,
+            new_history,
+            self.predictor_history,
+            self.predictions,
+            self.fps)
+
+    def is_complete(self):
+        diffs = [(b.frame_nr - a.frame_nr) for a,b in zip(self.history[:-1], self.history[1:])]
+        return any([d != 1 for d in diffs])
+
+    
+    def get_sampled(self, step_size = 1, offset=0):
+        if not self.is_complete():
+            t = self.get_with_interpolated_history()
+        else:
+            t = self
+        
+        return Track(
+            t.track_id,
+            t.history[offset::step_size],
+            t.predictor_history,
+            t.predictions,
+            t.fps/step_size)
+    
+    def get_binned(self, bin_size, camera: Camera, bin_start=True):
+        """
+        For an experiment: what if we predict using only concrete positions, by mapping 
+        dx,dy to a grid. Thus prediction can be for 8 moves, or rather headings
+        see ~/notes/attachments example svg
+        """
+
+        history = self.get_projected_history_as_dict(H=None, camera=camera)
+
+        def round_to_grid_precision(x):
+            factor = 1/bin_size
+            return round(x * factor) / factor
+
+        new_history: List[dict] = []
+        for i, (det0, det1) in enumerate(zip(history[:-1], history[1:])):
+            if i == 0:
+                new_history.append({
+                    'x': round_to_grid_precision(det0['x']),
+                    'y': round_to_grid_precision(det0['y'])
+                    } if bin_start else det0)
+                continue
+            if abs(det1['x'] - new_history[-1]['x']) < bin_size and abs(det1['y'] - new_history[-1]['y']) < bin_size:
+                continue
+            
+            # det1 falls outside of the box [-bin_size:+bin_size] around last detection
+            
+            # 1. Interpolate exact point between det0 and det1 that this happens
+            if abs(det1['x'] - new_history[-1]['x']) >= bin_size:
+                if det1['x'] - new_history[-1]['x'] >= bin_size:
+                    # det1 left of last
+                    x = new_history[-1]['x'] + bin_size
+                    f = inv_lerp(det0['x'], det1['x'], x)
+                elif new_history[-1]['x'] - det1['x'] >= bin_size:
+                    # det1 left of last
+                    x = new_history[-1]['x'] - bin_size
+                    f = inv_lerp(det0['x'], det1['x'], x)
+                y = lerp(det0['y'], det1['y'], f)
+            if abs(det1['y'] - new_history[-1]['y']) >= bin_size:
+                if det1['y'] - new_history[-1]['y'] >= bin_size:
+                    # det1 left of last
+                    y = new_history[-1]['y'] + bin_size
+                    f = inv_lerp(det0['y'], det1['y'], y)
+                elif new_history[-1]['y'] - det1['y'] >= bin_size:
+                    # det1 left of last
+                    y = new_history[-1]['y'] - bin_size
+                    f = inv_lerp(det0['y'], det1['y'], y)
+                x = lerp(det0['x'], det1['x'], f)
+                
+
+            # 2. Find closest point on rectangle (rectangle's four corners, or 4 midpoints)
+            points = get_bins(bin_size)
+            points = [[new_history[-1]['x']+p[0], new_history[-1]['y'] + p[1]] for p in points]
+
+            distances = [np.linalg.norm([p[0] - x, p[1]-y]) for p in points]
+            closest = np.argmin(distances)
+
+            point = points[closest]
+
+            new_history.append({'x': point[0], 'y':point[1]})
+            # todo Offsets to points:[ history for in points]
+        return new_history
+
+
+    def to_trajectron_node(self, camera: Camera, env: Environment) -> Node:
+        positions = self.get_projected_history(None, camera)
+        velocity = np.gradient(positions, 1/self.fps, axis=0)
+        acceleration = np.gradient(velocity, 1/self.fps, axis=0)
+
+        new_first_idx = self.history[0].frame_nr
+
+        data_columns = pd.MultiIndex.from_product([['position', 'velocity', 'acceleration'], ['x', 'y']])
+        
+
+        # vx = derivative_of(x, scene.dt)
+        # vy = derivative_of(y, scene.dt)
+        # ax = derivative_of(vx, scene.dt)
+        # ay = derivative_of(vy, scene.dt)
+
+        data_dict = {
+            ('position', 'x'): positions[:,0],
+            ('position', 'y'): positions[:,1],
+            ('velocity', 'x'): velocity[:,0],
+            ('velocity', 'y'): velocity[:,1],
+            ('acceleration', 'x'): acceleration[:,0],
+            ('acceleration', 'y'): acceleration[:,1]
+            }
+
+        node_data = pd.DataFrame(data_dict, columns=data_columns)
+        
+        return Node(node_type=env.NodeType.PEDESTRIAN, node_id=self.track_id, data=node_data, first_timestep=new_first_idx)
+
+@dataclass
+class Frame:
+    index: int
+    img: np.array
+    time: float= field(default_factory=lambda: time.time())
+    tracks: Optional[dict[str, Track]] = None
+    H: Optional[np.array] = None
+    camera: Optional[Camera] = None
+    maps: Optional[List[cv2.Mat]] = None
+
+    def aslist(self) -> List[dict]:
+        return { t.track_id:
+            {
+                'id': t.track_id,
+                'history': t.get_projected_history(self.H).tolist(),
+                'det_conf': t.history[-1].conf,
+                #     'det_conf': trajectory_data[node.id]['det_conf'],
+                #     'bbox': trajectory_data[node.id]['bbox'],
+                #     'history': history.tolist(),
+                'predictions': t.predictions
+            } for t in self.tracks.values()
+        }
+
+    def without_img(self):
+        return Frame(self.index, None, self.time, self.tracks, self.H, self.camera, self.maps)
+
+def video_src_from_config(config) -> Iterable[UrlOrPath]:
+    if config.video_loop:
+        video_srcs: Iterable[UrlOrPath] = cycle(config.video_src)
+    else:
+        video_srcs: Iterable[UrlOrPath] = config.video_src
+    return video_srcs
+
+
+@dataclass
+class Trajectory:
+    # TODO)) Replace history and predictions in Track with Trajectory
+    space: Space
+    fps: int = 12
+    points: List[Detection] = field(default_factory=list)
+
+    def __iter__(self):
+        for d in self.points:
+            yield d
\ No newline at end of file
diff --git a/trap/frame_emitter.py b/trap/frame_emitter.py
index 4c722d4..f214b53 100644
--- a/trap/frame_emitter.py
+++ b/trap/frame_emitter.py
@@ -25,8 +25,10 @@ from bytetracker.basetrack import TrackState as ByteTrackTrackState
 from trajectron.environment import Environment, Node, Scene
 from urllib.parse import urlparse
 
+from trap.base import *
 from trap.utils import get_bins
 from trap.utils import inv_lerp, lerp
+from trap.video_sources import get_video_source
 
 logger = logging.getLogger('trap.frame_emitter')
 
@@ -56,380 +58,8 @@ class DataclassJSONEncoder(json.JSONEncoder):
             return super().default(o)
 
 
-class UrlOrPath():
-    def __init__(self, string):
-        self.url = urlparse(str(string))
-
-    def __str__(self) -> str:
-        return self.url.geturl()
-
-    def is_url(self) -> bool:
-        return len(self.url.netloc) > 0
-
-    def path(self) -> Path:
-        if self.is_url():
-            return Path(self.url.path)
-        return Path(self.url.geturl()) # can include scheme, such as C:/
-    
-class Space(IntFlag):
-    Image = 1 # As detected in the image
-    Undistorted = 2 # After applying lense undistortiion
-    World = 4 # After lens undistort and homography
-    Render = 8 # View space of renderer
-    
-class DetectionState(IntFlag):
-    Tentative = 1 # state before n_init (see DeepsortTrack)
-    Confirmed = 2 # after tentative
-    Lost = 4 # lost when DeepsortTrack.time_since_update > 0 but not Deleted
-    Interpolated = 8 # A position estimated through interpolation of adjecent detections
-
-    @classmethod
-    def from_deepsort_track(cls, track: DeepsortTrack):
-        if track.state == DeepsortTrackState.Tentative:
-            return cls.Tentative
-        if track.state == DeepsortTrackState.Confirmed:
-            if track.time_since_update > 0:
-                return cls.Lost
-            return cls.Confirmed
-        raise RuntimeError("Should not run into Deleted entries here")
-
-    @classmethod
-    def from_bytetrack_track(cls, track: ByteTrackTrack):
-        if track.state == ByteTrackTrackState.New:
-            return cls.Tentative
-        if track.state == ByteTrackTrackState.Lost:
-            return cls.Lost
-            # if track.time_since_update > 0:
-        if track.state == ByteTrackTrackState.Tracked:
-            return cls.Confirmed
-        raise RuntimeError("Should not run into Deleted entries here")
-
-def H_from_path(path: Path):
-    if path.suffix == '.json':
-        with path.open('r') as fp:
-            H = np.array(json.load(fp))
-    else:
-        H = np.loadtxt(path, delimiter=',')
-    return H
-
-@dataclass
-class Camera:
-    mtx: cv2.Mat
-    dist: cv2.Mat
-    w: float
-    h: float
-    H: cv2.Mat # homography
-
-    newcameramtx: cv2.Mat = field(init=False)
-    roi: cv2.typing.Rect = field(init=False)
-    fps: float
-
-    def __post_init__(self):
-        self.newcameramtx, self.roi = cv2.getOptimalNewCameraMatrix(self.mtx, self.dist, (self.w,self.h), 1, (self.w,self.h))
-
-    @classmethod
-    def from_calibfile(cls, calibration_path, H, fps):
-        with calibration_path.open('r') as fp:
-            data = json.load(fp)
-            # print(data)
-            # print(data['camera_matrix'])
-        # camera = {
-        #     'camera_matrix': np.array(data['camera_matrix']), 
-        #     'dist_coeff': np.array(data['dist_coeff']),
-        # }
-        return cls(
-            np.array(data['camera_matrix']), 
-            np.array(data['dist_coeff']), 
-            data['dim']['width'], 
-            data['dim']['height'], 
-            H, fps)
-    
-    @classmethod
-    def from_paths(cls, calibration_path, h_path, fps):
-        H = H_from_path(h_path)
-        return cls.from_calibfile(calibration_path, H, fps)
-    
-    # def __init__(self, mtx, dist, w, h, H):
-    #     self.mtx = mtx
-    #     self.dist = dist
-    #     self.w = w
-    #     self.h = h
-    #     self.newcameramtx, self.roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w,h), 1, (w,h))
-    #     self.H = H # homography
-
-@dataclass
-class Position:
-    x: float
-    y: float
-    conf: float
-    state: DetectionState
-    frame_nr: int
-    det_class: str
-
-@dataclass
-class Detection:
-    track_id: str # deepsort track id association
-    l: int # left - image space
-    t: int # top - image space
-    w: int # width - image space
-    h: int # height - image space
-    conf: float # object detector probablity
-    state: DetectionState
-    frame_nr: int
-    det_class: str
-
-    def get_foot_coords(self) -> list[float, float]:
-        return [self.l + 0.5 * self.w, self.t+self.h]
-
-    @classmethod
-    def from_deepsort(cls, dstrack: DeepsortTrack, frame_nr: int):
-        return cls(dstrack.track_id, *dstrack.to_ltwh(), dstrack.det_conf, DetectionState.from_deepsort_track(dstrack), frame_nr, dstrack.det_class)
-    
-
-    @classmethod
-    def from_bytetrack(cls, bstrack: ByteTrackTrack, frame_nr: int):
-        return cls(bstrack.track_id, *bstrack.tlwh, bstrack.score, DetectionState.from_bytetrack_track(bstrack), frame_nr, bstrack.cls)
-    
-    def get_scaled(self, scale: float = 1):
-        if scale == 1:
-            return self
-        
-        return Detection(
-            self.track_id,
-            self.l*scale,
-            self.t*scale,
-            self.w*scale,
-            self.h*scale,
-            self.conf,
-            self.state,
-            self.frame_nr,
-            self.det_class)
-    
-    def to_ltwh(self):
-        return (int(self.l), int(self.t), int(self.w), int(self.h))
-    
-    def to_ltrb(self):
-        return (int(self.l), int(self.t), int(self.l+self.w), int(self.t+self.h))
-
-@dataclass
-class Trajectory:
-    # TODO)) Replace history and predictions in Track with Trajectory
-    space: Space
-    fps: int = 12
-    points: List[Detection] = field(default_factory=list)
-
-    def __iter__(self):
-        for d in self.points:
-            yield d
-    
-    
-@dataclass
-class Track:
-    """A bit of an haphazardous wrapper around the 'real' tracker to provide 
-    a history, with which the predictor can work, as we then can deduce velocity
-    and acceleration.
-    """
-    track_id: str = None
-    history: List[Detection] = field(default_factory=list)
-    predictor_history: Optional[list] = None # in image space
-    predictions: Optional[list] = None
-    fps: int = 12 # TODO)) convert this to camera? That way, incorporates H and dist, alternatively, each track is as a whole attached to a space
-    source: Optional[int] = None # to keep track of processed tracks
-
-    def get_projected_history(self, H: Optional[cv2.Mat] = None, camera: Optional[Camera]= None) -> np.array:
-        foot_coordinates = [d.get_foot_coords() for d in self.history]
-        # TODO)) Undistort points before perspective transform
-        if len(foot_coordinates):
-            if camera:
-                coords = cv2.undistortPoints(np.array([foot_coordinates]).astype('float32'), camera.mtx, camera.dist, None, camera.newcameramtx)
-                coords = cv2.perspectiveTransform(np.array(coords),camera.H)
-                return coords.reshape((coords.shape[0],2))
-            else:
-                coords = cv2.perspectiveTransform(np.array([foot_coordinates]),H)
-            return coords[0]
-        return np.array([])
-    
-    def get_projected_history_as_dict(self, H, camera: Optional[Camera]= None) -> dict:
-        coords = self.get_projected_history(H, camera)
-        return [{"x":c[0], "y":c[1]} for c in coords]
-
-    def get_with_interpolated_history(self) -> Track:
-        # new_history = [Detection(d.track_id, l, t, w, h, d.conf, d.state, d.frame_nr, d.det_class) for l, t, w, h, d in zip(ls,ts,ws,hs, track.history)]
-        # new_track = Track(track.track_id, new_history, track.predictor_history, track.predictions)
-        new_history = []
-        for j in range(len(self.history)):
-            a = self.history[j]
-            new_history.append(Detection(a.track_id, a.l, a.t, a.w, a.h, a.conf, a.state, a.frame_nr, a.det_class))
-
-            if j+1 >= len(self.history):
-                break
-
-            b = self.history[j+1]
-            gap = b.frame_nr - a.frame_nr
-            if gap < 1:
-                logger.error(f"WARNING, gap between frames {a.frame_nr} -> {b.frame_nr} is negative?")
-            if gap > 1:
-                for g in range(1, gap):
-                    l = lerp(a.l, b.l, g/gap)
-                    t = lerp(a.t, b.t, g/gap)
-                    w = lerp(a.w, b.w, g/gap)
-                    h = lerp(a.h, b.h, g/gap)
-                    conf = 0
-                    state = DetectionState.Lost
-                    frame_nr = a.frame_nr + g
-                    new_history.append(Detection(a.track_id, l, t, w, h, conf, state, frame_nr, a.det_class))
-
-        return Track(
-            self.track_id,
-            new_history,
-            self.predictor_history,
-            self.predictions,
-            self.fps)
-
-    def is_complete(self):
-        diffs = [(b.frame_nr - a.frame_nr) for a,b in zip(self.history[:-1], self.history[1:])]
-        return any([d != 1 for d in diffs])
 
     
-    def get_sampled(self, step_size = 1, offset=0):
-        if not self.is_complete():
-            t = self.get_with_interpolated_history()
-        else:
-            t = self
-        
-        return Track(
-            t.track_id,
-            t.history[offset::step_size],
-            t.predictor_history,
-            t.predictions,
-            t.fps/step_size)
-    
-    def get_binned(self, bin_size, camera: Camera, bin_start=True):
-        """
-        For an experiment: what if we predict using only concrete positions, by mapping 
-        dx,dy to a grid. Thus prediction can be for 8 moves, or rather headings
-        see ~/notes/attachments example svg
-        """
-
-        history = self.get_projected_history_as_dict(H=None, camera=camera)
-
-        def round_to_grid_precision(x):
-            factor = 1/bin_size
-            return round(x * factor) / factor
-
-        new_history: List[dict] = []
-        for i, (det0, det1) in enumerate(zip(history[:-1], history[1:])):
-            if i == 0:
-                new_history.append({
-                    'x': round_to_grid_precision(det0['x']),
-                    'y': round_to_grid_precision(det0['y'])
-                    } if bin_start else det0)
-                continue
-            if abs(det1['x'] - new_history[-1]['x']) < bin_size and abs(det1['y'] - new_history[-1]['y']) < bin_size:
-                continue
-            
-            # det1 falls outside of the box [-bin_size:+bin_size] around last detection
-            
-            # 1. Interpolate exact point between det0 and det1 that this happens
-            if abs(det1['x'] - new_history[-1]['x']) >= bin_size:
-                if det1['x'] - new_history[-1]['x'] >= bin_size:
-                    # det1 left of last
-                    x = new_history[-1]['x'] + bin_size
-                    f = inv_lerp(det0['x'], det1['x'], x)
-                elif new_history[-1]['x'] - det1['x'] >= bin_size:
-                    # det1 left of last
-                    x = new_history[-1]['x'] - bin_size
-                    f = inv_lerp(det0['x'], det1['x'], x)
-                y = lerp(det0['y'], det1['y'], f)
-            if abs(det1['y'] - new_history[-1]['y']) >= bin_size:
-                if det1['y'] - new_history[-1]['y'] >= bin_size:
-                    # det1 left of last
-                    y = new_history[-1]['y'] + bin_size
-                    f = inv_lerp(det0['y'], det1['y'], y)
-                elif new_history[-1]['y'] - det1['y'] >= bin_size:
-                    # det1 left of last
-                    y = new_history[-1]['y'] - bin_size
-                    f = inv_lerp(det0['y'], det1['y'], y)
-                x = lerp(det0['x'], det1['x'], f)
-                
-
-            # 2. Find closest point on rectangle (rectangle's four corners, or 4 midpoints)
-            points = get_bins(bin_size)
-            points = [[new_history[-1]['x']+p[0], new_history[-1]['y'] + p[1]] for p in points]
-
-            distances = [np.linalg.norm([p[0] - x, p[1]-y]) for p in points]
-            closest = np.argmin(distances)
-
-            point = points[closest]
-
-            new_history.append({'x': point[0], 'y':point[1]})
-            # todo Offsets to points:[ history for in points]
-        return new_history
-
-
-    def to_trajectron_node(self, camera: Camera, env: Environment) -> Node:
-        positions = self.get_projected_history(None, camera)
-        velocity = np.gradient(positions, 1/self.fps, axis=0)
-        acceleration = np.gradient(velocity, 1/self.fps, axis=0)
-
-        new_first_idx = self.history[0].frame_nr
-
-        data_columns = pd.MultiIndex.from_product([['position', 'velocity', 'acceleration'], ['x', 'y']])
-        
-
-        # vx = derivative_of(x, scene.dt)
-        # vy = derivative_of(y, scene.dt)
-        # ax = derivative_of(vx, scene.dt)
-        # ay = derivative_of(vy, scene.dt)
-
-        data_dict = {
-            ('position', 'x'): positions[:,0],
-            ('position', 'y'): positions[:,1],
-            ('velocity', 'x'): velocity[:,0],
-            ('velocity', 'y'): velocity[:,1],
-            ('acceleration', 'x'): acceleration[:,0],
-            ('acceleration', 'y'): acceleration[:,1]
-            }
-
-        node_data = pd.DataFrame(data_dict, columns=data_columns)
-        
-        return Node(node_type=env.NodeType.PEDESTRIAN, node_id=self.track_id, data=node_data, first_timestep=new_first_idx)
-        
-
-    
-
-@dataclass
-class Frame:
-    index: int
-    img: np.array
-    time: float= field(default_factory=lambda: time.time())
-    tracks: Optional[dict[str, Track]] = None
-    H: Optional[np.array] = None
-    camera: Optional[Camera] = None
-    maps: Optional[List[cv2.Mat]] = None
-
-    def aslist(self) -> [dict]:
-        return { t.track_id:
-            {
-                'id': t.track_id,
-                'history': t.get_projected_history(self.H).tolist(),
-                'det_conf': t.history[-1].conf,
-                #     'det_conf': trajectory_data[node.id]['det_conf'],
-                #     'bbox': trajectory_data[node.id]['bbox'],
-                #     'history': history.tolist(),
-                'predictions': t.predictions
-            } for t in self.tracks.values()
-        }
-
-    def without_img(self):
-        return Frame(self.index, None, self.time, self.tracks, self.H, self.camera, self.maps)
-
-def video_src_from_config(config) -> UrlOrPath:
-    if config.video_loop:
-        video_srcs: Iterable[UrlOrPath] = cycle(config.video_src)
-    else:
-        video_srcs: Iterable[UrlOrPath] = config.video_src
-    return video_srcs
 
 class FrameEmitter:
     '''
@@ -458,92 +88,19 @@ class FrameEmitter:
 
     def emit_video(self, timer_counter):
         i = 0
-        delay_generation = False
-        for video_path in self.video_srcs:
-            logger.info(f"Play from '{str(video_path)}'")
-            if str(video_path).isdigit():
-                # numeric input is a CV camera
-                video = cv2.VideoCapture(int(str(video_path)))
-                # TODO: make config variables
-                video.set(cv2.CAP_PROP_FRAME_WIDTH, int(self.config.camera.w))
-                video.set(cv2.CAP_PROP_FRAME_HEIGHT, int(self.config.camera.h))
-                print("exposure!", video.get(cv2.CAP_PROP_AUTO_EXPOSURE))
-                video.set(cv2.CAP_PROP_FPS, 5)
-                fps=5
-            elif video_path.url.scheme == 'rtsp':
-                gst = f"rtspsrc location={video_path} latency=0 buffer-mode=auto ! decodebin ! videoconvert ! appsink max-buffers=0 drop=true"
-                logger.info(f"Capture gstreamer (gst-launch-1.0): {gst}")
-                video = cv2.VideoCapture(gst, cv2.CAP_GSTREAMER)
-                fps=12
-            else:
-                # os.environ["OPENCV_FFMPEG_CAPTURE_OPTIONS"] = "fflags;nobuffer|flags;low_delay|avioflags;direct|rtsp_transport;udp"
-                video = cv2.VideoCapture(str(video_path))
-                delay_generation = True 
-                fps = video.get(cv2.CAP_PROP_FPS)
-            target_frame_duration = 1./fps
-            logger.info(f"Emit frames at {fps} fps")
 
-            if self.config.video_offset:
-                logger.info(f"Start at frame {self.config.video_offset}")
-                video.set(cv2.CAP_PROP_POS_FRAMES, self.config.video_offset)
-                i = self.config.video_offset
+        source = get_video_source(self.video_srcs, self.config.camera)
+        for i, img in enumerate(source):
 
+            with timer_counter.get_lock():
+                timer_counter.value += 1
 
-            # if '-' in video_path.path().stem:
-            #     path_stem = video_path.stem[:video_path.stem.rfind('-')]
-            # else:
-            #     path_stem = video_path.stem
-            # path_stem += "-homography"
-            # homography_path = video_path.with_stem(path_stem).with_suffix('.txt')
-            # logger.info(f'check homography file {homography_path}')
-            # if homography_path.exists():
-            #     logger.info(f'Found custom homography file! Using {homography_path}')
-            #     video_H = np.loadtxt(homography_path, delimiter=',')
-            # else:
-            #     video_H = None
-            video_H = self.config.camera.H
+            frame = Frame(i, img=img, H=self.config.camera.H, camera=self.config.camera)
 
-            prev_time = time.time()
-            
-            
-            while self.is_running.is_set():
-                with timer_counter.get_lock():
-                    timer_counter.value += 1
-
-                ret, img = video.read()
-
-                # seek to 0 if video has finished. Infinite loop
-                if not ret:
-                    # now loading multiple files        
-                    break
-                    # video.set(cv2.CAP_PROP_POS_FRAMES, 0)
-                    # ret, img = video.read()
-                    # assert ret is not False # not really error proof...
-
-                
-                if "DATASETS/hof/" in str(video_path):
-                    # hack to mask out area
-                    cv2.rectangle(img, (0,0), (800,200), (0,0,0), -1)
-
-                frame = Frame(index=i, img=img, H=self.config.H, camera=self.config.camera)
-                # TODO: this is very dirty, need to find another way.
-                # perhaps multiprocessing Array?
-                self.frame_noimg_sock.send(pickle.dumps(frame.without_img()))
-                self.frame_sock.send(pickle.dumps(frame))
-
-                # only delay consuming the next frame when using a file.
-                # Otherwise, go ASAP
-                if delay_generation:
-                    # defer next loop
-                    now = time.time()
-                    time_diff = (now - prev_time)
-                    if time_diff < target_frame_duration:
-                        time.sleep(target_frame_duration - time_diff)
-                        now += target_frame_duration - time_diff
-                    
-                    prev_time = now
-                
-                i += 1
+            # TODO: this is very dirty, need to find another way.
+            # perhaps multiprocessing Array?
+            self.frame_noimg_sock.send(pickle.dumps(frame.without_img()))
+            self.frame_sock.send(pickle.dumps(frame))
 
             if not self.is_running.is_set():
                 # if not running, also break out of infinite generator loop
diff --git a/trap/video_sources.py b/trap/video_sources.py
new file mode 100644
index 0000000..0fc8dac
--- /dev/null
+++ b/trap/video_sources.py
@@ -0,0 +1,170 @@
+import logging
+import math
+from pathlib import Path
+import time
+from typing import Any, Generator, Iterable, List, Optional
+import neoapi
+import cv2
+import numpy as np
+
+from trap.frame_emitter import Camera, Frame, UrlOrPath
+
+logger = logging.getLogger('video_source')
+
+class VideoSource:
+    """Video Frame generator
+    """
+    def recv(self) -> Generator[Optional[cv2.typing.MatLike], Any, None]:
+       raise RuntimeError("Not implemented")
+
+    def __iter__(self):
+        for i in self.recv():
+            yield i
+    
+
+class GigE(VideoSource):
+    def __init__(self, identifier=None):
+
+        self.camera = neoapi.Cam()
+        # self.camera.Connect('-B127')
+        self.camera.Connect(identifier)   
+        # Default buffer mode, streaming, always returns latest frame
+        self.camera.SetImageBufferCount(10)
+        # neoAPI docs: Setting the neoapi.Cam.SetImageBufferCycleCount()to one ensures that all buffers but one are given back to the neoAPI to be re-cycled and never given to the user by the neoapi.Cam.GetImage() method.
+        self.camera.SetImageBufferCycleCount(1) 
+        # if self.camera.IsConnected(): 
+            # self.camera.f.PixelFormat.Set(neoapi.PixelFormat_RGB8)
+            # self.camera.f.BinningHorizontal.Set(2)
+            # self.camera.f.BinningVertical.Set(2)
+        self.pixfmt = self.camera.f.PixelFormat.Get()
+    
+    def recv(self):
+        while True:
+            if not self.camera.IsConnected():
+                return
+            
+            i = self.camera.GetImage(0)    
+            if i.IsEmpty():
+                time.sleep(.01)
+                continue
+
+            imgarray = i.GetNPArray()
+            if self.pixfmt == neoapi.PixelFormat_BayerRG12:
+                img = cv2.cvtColor(imgarray, cv2.COLOR_BayerRG2RGB)
+            elif self.pixfmt == neoapi.PixelFormat_BayerRG8:
+                img = cv2.cvtColor(imgarray, cv2.COLOR_BayerRG2RGB)
+            else:
+                img = cv2.cvtColor(imgarray, cv2.COLOR_BGR2RGB)
+            
+            if img.dtype == np.uint16:
+                img = cv2.convertScaleAbs(img, alpha=(255.0/65535.0))
+            yield img
+    
+
+class SingleCvVideoSource(VideoSource):
+    def recv(self):
+        while True:
+            ret, img = self.video.read()
+            self.frame_idx+=1
+
+            # seek to 0 if video has finished. Infinite loop
+            if not ret:
+                # now loading multiple files        
+                break
+
+            # frame = Frame(index=self.n, img=img, H=self.camera.H, camera=self.camera)
+            yield img
+
+class RtspSource(SingleCvVideoSource):
+    def __init__(self, video_url: str | Path, camera: Camera = None):
+        gst = f"rtspsrc location={video_url} latency=0 buffer-mode=auto ! decodebin ! videoconvert ! appsink max-buffers=0 drop=true"
+        logger.info(f"Capture gstreamer (gst-launch-1.0): {gst}")
+        self.video = cv2.VideoCapture(gst, cv2.CAP_GSTREAMER)
+        self.frame_idx = 0
+        
+
+class FilelistSource(SingleCvVideoSource):
+    def __init__(self, video_sources: Iterable[UrlOrPath], camera: Camera = None, delay = True):
+        # store current position
+        self.video_sources = video_sources
+        self.camera = camera
+        self.video_path = None
+        self.video_nr = None
+        self.frame_count = None
+        self.frame_idx = None
+        self.n = 0
+        self.delay_generation = delay
+
+    def recv(self):
+        prev_time = time.time()
+
+        for video_nr, video_path in enumerate(self.video_sources):
+            self.video_path = video_path
+            self.video_nr = video_nr
+            logger.info(f"Play from '{str(video_path)}'")
+            video = cv2.VideoCapture(str(video_path))
+            fps = video.get(cv2.CAP_PROP_FPS)
+            target_frame_duration = 1./fps
+            self.frame_count = video.get(cv2.CAP_PROP_FRAME_COUNT)
+            if self.frame_count < 0:
+                self.frame_count = math.inf
+            self.frame_idx = 0
+            # TODO)) Video offset
+            # if self.config.video_offset:
+            #     logger.info(f"Start at frame {self.config.video_offset}")
+            #     video.set(cv2.CAP_PROP_POS_FRAMES, self.config.video_offset)
+            #     self.frame_idx = self.config.video_offset
+            
+            while True:
+                ret, img = video.read()
+                self.frame_idx+=1
+                self.n+=1
+
+                # seek to 0 if video has finished. Infinite loop
+                if not ret:
+                    # now loading multiple files        
+                    break
+
+                if "DATASETS/hof/" in str(video_path):
+                    # hack to mask out area
+                    cv2.rectangle(img, (0,0), (800,200), (0,0,0), -1)
+
+
+                # frame = Frame(index=self.n, img=img, H=self.camera.H, camera=self.camera)
+                yield img
+
+                if self.delay_generation:
+                    # defer next loop
+                    now = time.time()
+                    time_diff = (now - prev_time)
+                    if time_diff < target_frame_duration:
+                        time.sleep(target_frame_duration - time_diff)
+                        now += target_frame_duration - time_diff
+                    
+                    prev_time = now
+
+class CameraSource(SingleCvVideoSource):
+    def __init__(self, identifier: int, camera: Camera):
+        self.video = cv2.VideoCapture(identifier)
+        self.camera = camera
+
+        # TODO: make config variables
+        self.video.set(cv2.CAP_PROP_FRAME_WIDTH, int(self.camera.w))
+        self.video.set(cv2.CAP_PROP_FRAME_HEIGHT, int(self.camera.h))
+        # print("exposure!", video.get(cv2.CAP_PROP_AUTO_EXPOSURE))
+        self.video.set(cv2.CAP_PROP_FPS, self.camera.fps)
+        self.frame_idx = 0
+ 
+def get_video_source(video_sources: List[UrlOrPath], camera: Camera):
+    if str(video_sources[0]).isdigit():
+        # numeric input is a CV camera
+        return CameraSource(int(str(video_sources[0])), camera)
+    elif video_sources[0].url.scheme == 'rtsp':
+        video_sources[0].url.hostname
+        return RtspSource(video_sources[0])
+    elif video_sources[0].url.scheme == 'gige':
+        return GigE(video_sources[0].url.hostname)
+    else:
+        return FilelistSource(video_sources)
+        # os.environ["OPENCV_FFMPEG_CAPTURE_OPTIONS"] = "fflags;nobuffer|flags;low_delay|avioflags;direct|rtsp_transport;udp"
+