Rudimentary tracker results
This commit is contained in:
Ruben van de Ven
parent
7c06913d88
commit
23162da767
8 changed files with 163 additions and 53 deletions
112
poetry.lock
generated
112
poetry.lock
generated
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@ -497,6 +497,22 @@ files = [
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{file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"},
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]
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[[package]]
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name = "deep-sort-realtime"
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version = "1.3.2"
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description = "A more realtime adaptation of Deep SORT"
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optional = false
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python-versions = "*"
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files = [
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{file = "deep-sort-realtime-1.3.2.tar.gz", hash = "sha256:32bab92f981a274fce3ff121f35894e5adab7ca00314c113c348de7bcb82d73e"},
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{file = "deep_sort_realtime-1.3.2-py3-none-any.whl", hash = "sha256:a6e144c888fdfb27245d2a060acbe0d2f3088448defbc419f7a26bce063bdd6c"},
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]
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[package.dependencies]
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numpy = "*"
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opencv-python = "*"
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scipy = "*"
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[[package]]
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name = "defusedxml"
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version = "0.7.1"
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@ -2934,38 +2950,6 @@ files = [
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{file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
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]
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[[package]]
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name = "torch"
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version = "1.12.1"
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description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
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optional = false
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python-versions = ">=3.7.0"
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files = [
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{file = "torch-1.12.1-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:9c038662db894a23e49e385df13d47b2a777ffd56d9bcd5b832593fab0a7e286"},
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{file = "torch-1.12.1-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:4e1b9c14cf13fd2ab8d769529050629a0e68a6fc5cb8e84b4a3cc1dd8c4fe541"},
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{file = "torch-1.12.1-cp310-cp310-win_amd64.whl", hash = "sha256:e9c8f4a311ac29fc7e8e955cfb7733deb5dbe1bdaabf5d4af2765695824b7e0d"},
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{file = "torch-1.12.1-cp310-none-macosx_10_9_x86_64.whl", hash = "sha256:976c3f997cea38ee91a0dd3c3a42322785414748d1761ef926b789dfa97c6134"},
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{file = "torch-1.12.1-cp310-none-macosx_11_0_arm64.whl", hash = "sha256:68104e4715a55c4bb29a85c6a8d57d820e0757da363be1ba680fa8cc5be17b52"},
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{file = "torch-1.12.1-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:743784ccea0dc8f2a3fe6a536bec8c4763bd82c1352f314937cb4008d4805de1"},
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{file = "torch-1.12.1-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:b5dbcca369800ce99ba7ae6dee3466607a66958afca3b740690d88168752abcf"},
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{file = "torch-1.12.1-cp37-cp37m-win_amd64.whl", hash = "sha256:f3b52a634e62821e747e872084ab32fbcb01b7fa7dbb7471b6218279f02a178a"},
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{file = "torch-1.12.1-cp37-none-macosx_10_9_x86_64.whl", hash = "sha256:8a34a2fbbaa07c921e1b203f59d3d6e00ed379f2b384445773bd14e328a5b6c8"},
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{file = "torch-1.12.1-cp37-none-macosx_11_0_arm64.whl", hash = "sha256:42f639501928caabb9d1d55ddd17f07cd694de146686c24489ab8c615c2871f2"},
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{file = "torch-1.12.1-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:0b44601ec56f7dd44ad8afc00846051162ef9c26a8579dda0a02194327f2d55e"},
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{file = "torch-1.12.1-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:cd26d8c5640c3a28c526d41ccdca14cf1cbca0d0f2e14e8263a7ac17194ab1d2"},
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{file = "torch-1.12.1-cp38-cp38-win_amd64.whl", hash = "sha256:42e115dab26f60c29e298559dbec88444175528b729ae994ec4c65d56fe267dd"},
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{file = "torch-1.12.1-cp38-none-macosx_10_9_x86_64.whl", hash = "sha256:a8320ba9ad87e80ca5a6a016e46ada4d1ba0c54626e135d99b2129a4541c509d"},
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{file = "torch-1.12.1-cp38-none-macosx_11_0_arm64.whl", hash = "sha256:03e31c37711db2cd201e02de5826de875529e45a55631d317aadce2f1ed45aa8"},
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{file = "torch-1.12.1-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:9b356aea223772cd754edb4d9ecf2a025909b8615a7668ac7d5130f86e7ec421"},
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{file = "torch-1.12.1-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:6cf6f54b43c0c30335428195589bd00e764a6d27f3b9ba637aaa8c11aaf93073"},
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{file = "torch-1.12.1-cp39-cp39-win_amd64.whl", hash = "sha256:f00c721f489089dc6364a01fd84906348fe02243d0af737f944fddb36003400d"},
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{file = "torch-1.12.1-cp39-none-macosx_10_9_x86_64.whl", hash = "sha256:bfec2843daa654f04fda23ba823af03e7b6f7650a873cdb726752d0e3718dada"},
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{file = "torch-1.12.1-cp39-none-macosx_11_0_arm64.whl", hash = "sha256:69fe2cae7c39ccadd65a123793d30e0db881f1c1927945519c5c17323131437e"},
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]
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[package.dependencies]
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typing-extensions = "*"
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[[package]]
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name = "torch"
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version = "1.12.1+cu113"
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@ -2983,6 +2967,68 @@ typing-extensions = "*"
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type = "url"
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url = "https://download.pytorch.org/whl/cu113/torch-1.12.1%2Bcu113-cp310-cp310-linux_x86_64.whl"
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[[package]]
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name = "torchvision"
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version = "0.13.1"
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description = "image and video datasets and models for torch deep learning"
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optional = false
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python-versions = ">=3.7"
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files = [
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{file = "torchvision-0.13.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:19286a733c69dcbd417b86793df807bd227db5786ed787c17297741a9b0d0fc7"},
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{file = "torchvision-0.13.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:08f592ea61836ebeceb5c97f4d7a813b9d7dc651bbf7ce4401563ccfae6a21fc"},
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{file = "torchvision-0.13.1-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:ef5fe3ec1848123cd0ec74c07658192b3147dcd38e507308c790d5943e87b88c"},
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{file = "torchvision-0.13.1-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:099874088df104d54d8008f2a28539ca0117b512daed8bf3c2bbfa2b7ccb187a"},
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{file = "torchvision-0.13.1-cp310-cp310-win_amd64.whl", hash = "sha256:8e4d02e4d8a203e0c09c10dfb478214c224d080d31efc0dbf36d9c4051f7f3c6"},
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{file = "torchvision-0.13.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:5e631241bee3661de64f83616656224af2e3512eb2580da7c08e08b8c965a8ac"},
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{file = "torchvision-0.13.1-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:899eec0b9f3b99b96d6f85b9aa58c002db41c672437677b553015b9135b3be7e"},
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{file = "torchvision-0.13.1-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:83e9e2457f23110fd53b0177e1bc621518d6ea2108f570e853b768ce36b7c679"},
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{file = "torchvision-0.13.1-cp37-cp37m-win_amd64.whl", hash = "sha256:7552e80fa222252b8b217a951c85e172a710ea4cad0ae0c06fbb67addece7871"},
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{file = "torchvision-0.13.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:f230a1a40ed70d51e463ce43df243ec520902f8725de2502e485efc5eea9d864"},
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{file = "torchvision-0.13.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:e9a563894f9fa40692e24d1aa58c3ef040450017cfed3598ff9637f404f3fe3b"},
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{file = "torchvision-0.13.1-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:7cb789ceefe6dcd0dc8eeda37bfc45efb7cf34770eac9533861d51ca508eb5b3"},
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{file = "torchvision-0.13.1-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:87c137f343197769a51333076e66bfcd576301d2cd8614b06657187c71b06c4f"},
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{file = "torchvision-0.13.1-cp38-cp38-win_amd64.whl", hash = "sha256:4d8bf321c4380854ef04613935fdd415dce29d1088a7ff99e06e113f0efe9203"},
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{file = "torchvision-0.13.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:0298bae3b09ac361866088434008d82b99d6458fe8888c8df90720ef4b347d44"},
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{file = "torchvision-0.13.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:c5ed609c8bc88c575226400b2232e0309094477c82af38952e0373edef0003fd"},
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{file = "torchvision-0.13.1-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:3567fb3def829229ec217c1e38f08c5128ff7fb65854cac17ebac358ff7aa309"},
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{file = "torchvision-0.13.1-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:b167934a5943242da7b1e59318f911d2d253feeca0d13ad5d832b58eed943401"},
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{file = "torchvision-0.13.1-cp39-cp39-win_amd64.whl", hash = "sha256:0e77706cc90462653620e336bb90daf03d7bf1b88c3a9a3037df8d111823a56e"},
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]
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[package.dependencies]
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numpy = "*"
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pillow = ">=5.3.0,<8.3.dev0 || >=8.4.dev0"
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requests = "*"
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torch = "1.12.1"
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typing-extensions = "*"
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[package.extras]
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scipy = ["scipy"]
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[[package]]
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name = "torchvision"
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version = "0.13.1+cu113"
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description = "image and video datasets and models for torch deep learning"
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optional = false
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python-versions = ">=3.7"
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files = [
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{file = "torchvision-0.13.1+cu113-cp310-cp310-linux_x86_64.whl", hash = "sha256:b471090622d074d19fadacbc91ec02ddc1972d2fa4ada7f1c8a087bca3a5bcb0"},
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]
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[package.dependencies]
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numpy = "*"
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pillow = ">=5.3.0,<8.3.dev0 || >=8.4.dev0"
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requests = "*"
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torch = "1.12.1"
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typing-extensions = "*"
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[package.extras]
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scipy = ["scipy"]
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[package.source]
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type = "url"
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url = "https://download.pytorch.org/whl/cu113/torchvision-0.13.1%2Bcu113-cp310-cp310-linux_x86_64.whl"
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[[package]]
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name = "tornado"
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version = "6.3.3"
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@ -3226,4 +3272,4 @@ test = ["pytest (>=6.0.0)", "setuptools (>=65)"]
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[metadata]
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lock-version = "2.0"
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python-versions = "^3.10,<3.12,"
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content-hash = "839e0f10b4ecc9e489cb1b5aface622a5e129b69650546de2e04f87f1654360d"
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content-hash = "06852d16ba90b0438b84e1c51c084c6aa6a5c7a78a6af1e17b7d59e5304ed5a7"
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@ -9,6 +9,13 @@ readme = "README.md"
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python = "^3.10,<3.12,"
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trajectron-plus-plus = { path = "../Trajectron-plus-plus/", develop = true }
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torchvision = [
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{ version="0.13.1" },
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# { url = "https://download.pytorch.org/whl/cu113/torchvision-0.13.1%2Bcu113-cp38-cp38-linux_x86_64.whl", markers = "python_version ~= '3.8' and sys_platform == 'linux'" },
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{ url = "https://download.pytorch.org/whl/cu113/torchvision-0.13.1%2Bcu113-cp310-cp310-linux_x86_64.whl", markers = "python_version ~= '3.10' and sys_platform == 'linux'" },
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]
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deep-sort-realtime = "^1.3.2"
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[build-system]
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requires = ["poetry-core"]
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@ -23,9 +23,10 @@ parser.add_argument(
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)
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# parser.add_argument('--foo')
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inference_parser = parser.add_argument_group('inference server')
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connection_parser = parser.add_argument_group('connection')
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inference_parser = parser.add_argument_group('Inference')
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connection_parser = parser.add_argument_group('Connection')
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frame_emitter_parser = parser.add_argument_group('Frame emitter')
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tracker_parser = parser.add_argument_group('Tracker')
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inference_parser.add_argument("--model_dir",
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help="directory with the model to use for inference",
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@ -164,5 +165,12 @@ frame_emitter_parser.add_argument("--video-src",
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help="source video to track from",
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type=Path,
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default='../DATASETS/VIRAT_subset_0102x/VIRAT_S_010200_00_000060_000218.mp4')
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#TODO: camera as source
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#TODO: camera
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# Tracker
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tracker_parser.add_argument("--homography",
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help="File with homography params",
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type=Path,
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default='../DATASETS/VIRAT_subset_0102x/VIRAT_0102_homography_img2world.txt')
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@ -1,9 +1,12 @@
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from argparse import Namespace
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import logging
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import pickle
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import time
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import cv2
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import zmq
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logger = logging.getLogger('trap.frame_emitter')
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class FrameEmitter:
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'''
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@ -17,6 +20,7 @@ class FrameEmitter:
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self.frame_sock = context.socket(zmq.PUB)
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self.frame_sock.bind(config.zmq_frame_addr)
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self.frame_sock.setsockopt(zmq.CONFLATE, 1) # only keep latest frame
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logger.info(f"Connection socket {config.zmq_frame_addr}")
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def emit_video(self):
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video = cv2.VideoCapture(str(self.config.video_src))
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@ -33,7 +37,9 @@ class FrameEmitter:
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ret, frame = video.read()
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assert ret is not False # not really error proof...
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self.frame_sock.send(frame)
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# TODO: this is very dirty, need to find another way.
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# perhaps multiprocessing queue?
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self.frame_sock.send(pickle.dumps(frame))
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# defer next loop
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new_frame_time = time.time()
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@ -5,6 +5,7 @@ from trap.config import parser
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from trap.frame_emitter import run_frame_emitter
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from trap.prediction_server import InferenceServer, run_inference_server
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from trap.socket_forwarder import run_ws_forwarder
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from trap.tracker import run_tracker
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logger = logging.getLogger("trap.plumbing")
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@ -25,12 +26,13 @@ def start():
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# instantiating process with arguments
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procs = [
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# Process(target=run_ws_forwarder, args=(args,)),
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Process(target=run_frame_emitter, args=(args,)),
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Process(target=run_ws_forwarder, args=(args,), name='forwarder'),
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Process(target=run_frame_emitter, args=(args,), name='frame_emitter'),
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Process(target=run_tracker, args=(args,), name='tracker'),
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]
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if not args.bypass_prediction:
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procs.append(
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Process(target=run_inference_server, args=(args,)),
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Process(target=run_inference_server, args=(args,), name='inference'),
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)
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logger.info("start")
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@ -21,7 +21,7 @@ import matplotlib.pyplot as plt
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import zmq
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logger = logging.getLogger("trap.inference")
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logger = logging.getLogger("trap.prediction")
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# if not torch.cuda.is_available() or self.config.device == 'cpu':
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@ -117,7 +117,7 @@ class InferenceServer:
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self.prediction_socket: zmq.Socket = context.socket(zmq.PUB)
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self.prediction_socket.bind(config.zmq_prediction_addr)
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print(self.prediction_socket)
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# print(self.prediction_socket)
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def run(self):
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@ -222,7 +222,7 @@ class InferenceServer:
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data = self.trajectory_socket.recv_string()
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trajectory_data = json.loads(data)
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logger.info(f"Receive {trajectory_data}")
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logger.debug(f"Receive {trajectory_data}")
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# class FakeNode:
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# def __init__(self, node_type: NodeType):
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@ -326,7 +326,7 @@ class InferenceServer:
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futures_dict = futures_dict[ts_key]
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response = {}
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print(histories_dict)
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logger.debug(f"{histories_dict=}")
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for node in histories_dict:
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history = histories_dict[node]
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@ -1,4 +1,8 @@
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from argparse import Namespace
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import json
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import logging
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import pickle
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import time
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import numpy as np
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import torch
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import zmq
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|
@ -6,19 +10,28 @@ import cv2
|
|||
|
||||
from torchvision.models.detection import retinanet_resnet50_fpn_v2, RetinaNet_ResNet50_FPN_V2_Weights
|
||||
from deep_sort_realtime.deepsort_tracker import DeepSort
|
||||
from deep_sort_realtime.deep_sort.track import Track
|
||||
|
||||
Detection = [int, int, int, int, float, int]
|
||||
Detections = [Detection]
|
||||
|
||||
logger = logging.getLogger("trap.tracker")
|
||||
|
||||
class Tracker:
|
||||
def __init__(self, config: Namespace):
|
||||
self.config = config
|
||||
|
||||
context = zmq.Context()
|
||||
self.frame_sock = context.socket(zmq.SUB)
|
||||
self.frame_sock.bind(config.zmq_frame_addr)
|
||||
self.frame_sock.connect(config.zmq_frame_addr)
|
||||
self.frame_sock.setsockopt(zmq.SUBSCRIBE, b'')
|
||||
self.frame_sock.setsockopt(zmq.CONFLATE, 1) # only keep latest frame
|
||||
|
||||
# TODO: config device
|
||||
self.trajectory_socket = context.socket(zmq.PUB)
|
||||
self.trajectory_socket.bind(config.zmq_trajectory_addr)
|
||||
self.trajectory_socket.setsockopt(zmq.CONFLATE, 1) # only keep latest frame
|
||||
|
||||
# # TODO: config device
|
||||
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
weights = RetinaNet_ResNet50_FPN_V2_Weights.DEFAULT
|
||||
|
|
@ -29,17 +42,44 @@ class Tracker:
|
|||
# Get the transforms for the model's weights
|
||||
self.preprocess = weights.transforms().to(self.device)
|
||||
|
||||
|
||||
# homography = list(source.glob('*img2world.txt'))[0]
|
||||
|
||||
self.H = np.loadtxt(self.config.homography, delimiter=',')
|
||||
|
||||
self.mot_tracker = DeepSort(max_age=5)
|
||||
logger.debug("Set up tracker")
|
||||
|
||||
|
||||
def track(self):
|
||||
while True:
|
||||
frame = self.frame_sock.recv()
|
||||
frame = pickle.loads(self.frame_sock.recv())
|
||||
detections = self.detect_persons(frame)
|
||||
tracks = self.mot_tracker.update_tracks(detections, frame=frame)
|
||||
tracks: [Track] = self.mot_tracker.update_tracks(detections, frame=frame)
|
||||
|
||||
TEMP_boxes = [t.to_ltwh() for t in tracks]
|
||||
TEMP_coords = np.array([[[det[0] + 0.5 * det[2], det[1]+det[3]]] for det in TEMP_boxes])
|
||||
if len(TEMP_coords):
|
||||
TEMP_proj_coords = cv2.perspectiveTransform(TEMP_coords,self.H)
|
||||
else:
|
||||
TEMP_proj_coords = []
|
||||
|
||||
# print(TEMP_proj_coords)
|
||||
trajectories = {}
|
||||
for i, coords in enumerate(TEMP_proj_coords):
|
||||
tid = tracks[i].track_id
|
||||
trajectories[tid] = {
|
||||
"id": tid,
|
||||
"history": [{"x":c[0], "y":c[1]} for c in coords] # already doubles nested, fine for test
|
||||
}
|
||||
logger.debug(f"{trajectories}")
|
||||
self.trajectory_socket.send_string(json.dumps(trajectories))
|
||||
# provide a {ID: {id: ID, history: [[x,y],[x,y],...]}}
|
||||
# TODO: provide a track object that actually keeps history (unlike tracker)
|
||||
|
||||
#TODO calculate fps (also for other loops to see asynchonity)
|
||||
# fpsfilter=fpsfilter*.9+(1/dt)*.1 #trust value in order to stabilize fps display
|
||||
|
||||
|
||||
def detect_persons(self, frame) -> Detections:
|
||||
t = torch.from_numpy(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
|
||||
|
|
@ -70,7 +110,7 @@ class Tracker:
|
|||
return detections
|
||||
|
||||
@classmethod
|
||||
def detect_persons_deepsort_wrapper(detections):
|
||||
def detect_persons_deepsort_wrapper(cls, detections):
|
||||
"""make detect_persons() compatible with
|
||||
deep_sort_realtime tracker by going from ltrb to ltwh and
|
||||
different nesting
|
||||
|
|
|
|||
|
|
@ -30,7 +30,7 @@
|
|||
|
||||
<script>
|
||||
// map the field to coordinates of our dummy tracker
|
||||
const field_range = { x: [-10, 10], y: [-10, 10] }
|
||||
const field_range = { x: [-30, 10], y: [-10, 10] }
|
||||
|
||||
// Create WebSocket connection.
|
||||
const trajectory_socket = new WebSocket(`ws://${window.location.hostname}:{{ ws_port }}/ws/trajectory`);
|
||||
|
|
@ -111,8 +111,9 @@
|
|||
}
|
||||
}
|
||||
}
|
||||
console.log(tracker)
|
||||
trajectory_socket.send(JSON.stringify(tracker))
|
||||
|
||||
// TODO: make variable in template
|
||||
//trajectory_socket.send(JSON.stringify(tracker))
|
||||
|
||||
setTimeout(appendAndSendPositions, 200)
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in a new issue