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@@ -11,7 +11,7 @@ jobs:
|
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name: 👋 Welcome
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runs-on: ubuntu-latest
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steps:
|
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- uses: actions/first-interaction@v1.2.0
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- uses: actions/first-interaction@v1.3.0
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with:
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||||
repo-token: ${{ secrets.GITHUB_TOKEN }}
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||||
issue-message: "Hello there, thank you for opening an Issue ! 🙏🏻 The team was notified and they will get back to you asap."
|
||||
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@@ -71,7 +71,7 @@ repos:
|
||||
- id: black
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- repo: https://github.com/astral-sh/ruff-pre-commit
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rev: v0.1.5
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rev: v0.1.6
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hooks:
|
||||
- id: ruff
|
||||
args: [--fix, --exit-non-zero-on-fix]
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@@ -21,3 +21,11 @@
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## draw_image
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:::supervision.draw.utils.draw_image
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## calculate_dynamic_font_scale
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:::supervision.draw.utils.calculate_dynamic_font_scale
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## calculate_dynamic_line_thickness
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:::supervision.draw.utils.calculate_dynamic_line_thickness
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@@ -79,6 +79,8 @@ theme:
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font:
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text: Roboto
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code: Roboto Mono
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features:
|
||||
- content.code.copy
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||||
|
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plugins:
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||||
- mkdocstrings
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||||
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Generated
+44
-44
@@ -1,4 +1,4 @@
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||||
# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
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# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
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||||
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[[package]]
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||||
name = "anyio"
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||||
@@ -571,34 +571,34 @@ test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
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||||
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[[package]]
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||||
name = "cryptography"
|
||||
version = "41.0.4"
|
||||
version = "41.0.6"
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||||
description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
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||||
{file = "cryptography-41.0.4-cp37-abi3-macosx_10_12_x86_64.whl", hash = "sha256:35c00f637cd0b9d5b6c6bd11b6c3359194a8eba9c46d4e875a3660e3b400005f"},
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||||
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||||
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|
||||
{file = "cryptography-41.0.4-pp310-pypy310_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:86defa8d248c3fa029da68ce61fe735432b047e32179883bdb1e79ed9bb8195e"},
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||||
{file = "cryptography-41.0.4-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:37480760ae08065437e6573d14be973112c9e6dcaf5f11d00147ee74f37a3829"},
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||||
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{file = "cryptography-41.0.4.tar.gz", hash = "sha256:7febc3094125fc126a7f6fb1f420d0da639f3f32cb15c8ff0dc3997c4549f51a"},
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{file = "cryptography-41.0.6-cp37-abi3-macosx_10_12_universal2.whl", hash = "sha256:0f27acb55a4e77b9be8d550d762b0513ef3fc658cd3eb15110ebbcbd626db12c"},
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||||
{file = "cryptography-41.0.6-cp37-abi3-macosx_10_12_x86_64.whl", hash = "sha256:ae236bb8760c1e55b7a39b6d4d32d2279bc6c7c8500b7d5a13b6fb9fc97be35b"},
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{file = "cryptography-41.0.6-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:afda76d84b053923c27ede5edc1ed7d53e3c9f475ebaf63c68e69f1403c405a8"},
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{file = "cryptography-41.0.6-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:da46e2b5df770070412c46f87bac0849b8d685c5f2679771de277a422c7d0b86"},
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{file = "cryptography-41.0.6-cp37-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:ff369dd19e8fe0528b02e8df9f2aeb2479f89b1270d90f96a63500afe9af5cae"},
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{file = "cryptography-41.0.6-cp37-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:b648fe2a45e426aaee684ddca2632f62ec4613ef362f4d681a9a6283d10e079d"},
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{file = "cryptography-41.0.6-cp37-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:5daeb18e7886a358064a68dbcaf441c036cbdb7da52ae744e7b9207b04d3908c"},
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{file = "cryptography-41.0.6-cp37-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:068bc551698c234742c40049e46840843f3d98ad7ce265fd2bd4ec0d11306596"},
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{file = "cryptography-41.0.6-cp37-abi3-win32.whl", hash = "sha256:2132d5865eea673fe6712c2ed5fb4fa49dba10768bb4cc798345748380ee3660"},
|
||||
{file = "cryptography-41.0.6-cp37-abi3-win_amd64.whl", hash = "sha256:48783b7e2bef51224020efb61b42704207dde583d7e371ef8fc2a5fb6c0aabc7"},
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{file = "cryptography-41.0.6-pp310-pypy310_pp73-macosx_10_12_x86_64.whl", hash = "sha256:8efb2af8d4ba9dbc9c9dd8f04d19a7abb5b49eab1f3694e7b5a16a5fc2856f5c"},
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||||
{file = "cryptography-41.0.6-pp310-pypy310_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:c5a550dc7a3b50b116323e3d376241829fd326ac47bc195e04eb33a8170902a9"},
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||||
{file = "cryptography-41.0.6-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:85abd057699b98fce40b41737afb234fef05c67e116f6f3650782c10862c43da"},
|
||||
{file = "cryptography-41.0.6-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:f39812f70fc5c71a15aa3c97b2bbe213c3f2a460b79bd21c40d033bb34a9bf36"},
|
||||
{file = "cryptography-41.0.6-pp38-pypy38_pp73-macosx_10_12_x86_64.whl", hash = "sha256:742ae5e9a2310e9dade7932f9576606836ed174da3c7d26bc3d3ab4bd49b9f65"},
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||||
{file = "cryptography-41.0.6-pp38-pypy38_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:35f3f288e83c3f6f10752467c48919a7a94b7d88cc00b0668372a0d2ad4f8ead"},
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||||
{file = "cryptography-41.0.6-pp38-pypy38_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:4d03186af98b1c01a4eda396b137f29e4e3fb0173e30f885e27acec8823c1b09"},
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||||
{file = "cryptography-41.0.6-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:b27a7fd4229abef715e064269d98a7e2909ebf92eb6912a9603c7e14c181928c"},
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{file = "cryptography-41.0.6-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:398ae1fc711b5eb78e977daa3cbf47cec20f2c08c5da129b7a296055fbb22aed"},
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||||
{file = "cryptography-41.0.6-pp39-pypy39_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:7e00fb556bda398b99b0da289ce7053639d33b572847181d6483ad89835115f6"},
|
||||
{file = "cryptography-41.0.6-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:60e746b11b937911dc70d164060d28d273e31853bb359e2b2033c9e93e6f3c43"},
|
||||
{file = "cryptography-41.0.6-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:3288acccef021e3c3c10d58933f44e8602cf04dba96d9796d70d537bb2f4bbc4"},
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||||
{file = "cryptography-41.0.6.tar.gz", hash = "sha256:422e3e31d63743855e43e5a6fcc8b4acab860f560f9321b0ee6269cc7ed70cc3"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -3169,28 +3169,28 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "ruff"
|
||||
version = "0.1.5"
|
||||
version = "0.1.6"
|
||||
description = "An extremely fast Python linter and code formatter, written in Rust."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "ruff-0.1.5-py3-none-macosx_10_7_x86_64.whl", hash = "sha256:32d47fc69261c21a4c48916f16ca272bf2f273eb635d91c65d5cd548bf1f3d96"},
|
||||
{file = "ruff-0.1.5-py3-none-macosx_10_9_x86_64.macosx_11_0_arm64.macosx_10_9_universal2.whl", hash = "sha256:171276c1df6c07fa0597fb946139ced1c2978f4f0b8254f201281729981f3c17"},
|
||||
{file = "ruff-0.1.5-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:17ef33cd0bb7316ca65649fc748acc1406dfa4da96a3d0cde6d52f2e866c7b39"},
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||||
{file = "ruff-0.1.5-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:b2c205827b3f8c13b4a432e9585750b93fd907986fe1aec62b2a02cf4401eee6"},
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{file = "ruff-0.1.5-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:bb408e3a2ad8f6881d0f2e7ad70cddb3ed9f200eb3517a91a245bbe27101d379"},
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{file = "ruff-0.1.5-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:f20dc5e5905ddb407060ca27267c7174f532375c08076d1a953cf7bb016f5a24"},
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{file = "ruff-0.1.5-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:aafb9d2b671ed934998e881e2c0f5845a4295e84e719359c71c39a5363cccc91"},
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{file = "ruff-0.1.5-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a4894dddb476597a0ba4473d72a23151b8b3b0b5f958f2cf4d3f1c572cdb7af7"},
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{file = "ruff-0.1.5-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a00a7ec893f665ed60008c70fe9eeb58d210e6b4d83ec6654a9904871f982a2a"},
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{file = "ruff-0.1.5-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:a8c11206b47f283cbda399a654fd0178d7a389e631f19f51da15cbe631480c5b"},
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{file = "ruff-0.1.5-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:fa29e67b3284b9a79b1a85ee66e293a94ac6b7bb068b307a8a373c3d343aa8ec"},
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{file = "ruff-0.1.5-py3-none-musllinux_1_2_i686.whl", hash = "sha256:9b97fd6da44d6cceb188147b68db69a5741fbc736465b5cea3928fdac0bc1aeb"},
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||||
{file = "ruff-0.1.5-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:721f4b9d3b4161df8dc9f09aa8562e39d14e55a4dbaa451a8e55bdc9590e20f4"},
|
||||
{file = "ruff-0.1.5-py3-none-win32.whl", hash = "sha256:f80c73bba6bc69e4fdc73b3991db0b546ce641bdcd5b07210b8ad6f64c79f1ab"},
|
||||
{file = "ruff-0.1.5-py3-none-win_amd64.whl", hash = "sha256:c21fe20ee7d76206d290a76271c1af7a5096bc4c73ab9383ed2ad35f852a0087"},
|
||||
{file = "ruff-0.1.5-py3-none-win_arm64.whl", hash = "sha256:82bfcb9927e88c1ed50f49ac6c9728dab3ea451212693fe40d08d314663e412f"},
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||||
{file = "ruff-0.1.5.tar.gz", hash = "sha256:5cbec0ef2ae1748fb194f420fb03fb2c25c3258c86129af7172ff8f198f125ab"},
|
||||
{file = "ruff-0.1.6-py3-none-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:88b8cdf6abf98130991cbc9f6438f35f6e8d41a02622cc5ee130a02a0ed28703"},
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{file = "ruff-0.1.6-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:5c549ed437680b6105a1299d2cd30e4964211606eeb48a0ff7a93ef70b902248"},
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{file = "ruff-0.1.6-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1cf5f701062e294f2167e66d11b092bba7af6a057668ed618a9253e1e90cfd76"},
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||||
{file = "ruff-0.1.6-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:05991ee20d4ac4bb78385360c684e4b417edd971030ab12a4fbd075ff535050e"},
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||||
{file = "ruff-0.1.6-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:87455a0c1f739b3c069e2f4c43b66479a54dea0276dd5d4d67b091265f6fd1dc"},
|
||||
{file = "ruff-0.1.6-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:683aa5bdda5a48cb8266fcde8eea2a6af4e5700a392c56ea5fb5f0d4bfdc0240"},
|
||||
{file = "ruff-0.1.6-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:137852105586dcbf80c1717facb6781555c4e99f520c9c827bd414fac67ddfb6"},
|
||||
{file = "ruff-0.1.6-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:bd98138a98d48a1c36c394fd6b84cd943ac92a08278aa8ac8c0fdefcf7138f35"},
|
||||
{file = "ruff-0.1.6-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3a0cd909d25f227ac5c36d4e7e681577275fb74ba3b11d288aff7ec47e3ae745"},
|
||||
{file = "ruff-0.1.6-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:e8fd1c62a47aa88a02707b5dd20c5ff20d035d634aa74826b42a1da77861b5ff"},
|
||||
{file = "ruff-0.1.6-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:fd89b45d374935829134a082617954120d7a1470a9f0ec0e7f3ead983edc48cc"},
|
||||
{file = "ruff-0.1.6-py3-none-musllinux_1_2_i686.whl", hash = "sha256:491262006e92f825b145cd1e52948073c56560243b55fb3b4ecb142f6f0e9543"},
|
||||
{file = "ruff-0.1.6-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:ea284789861b8b5ca9d5443591a92a397ac183d4351882ab52f6296b4fdd5462"},
|
||||
{file = "ruff-0.1.6-py3-none-win32.whl", hash = "sha256:1610e14750826dfc207ccbcdd7331b6bd285607d4181df9c1c6ae26646d6848a"},
|
||||
{file = "ruff-0.1.6-py3-none-win_amd64.whl", hash = "sha256:4558b3e178145491e9bc3b2ee3c4b42f19d19384eaa5c59d10acf6e8f8b57e33"},
|
||||
{file = "ruff-0.1.6-py3-none-win_arm64.whl", hash = "sha256:03910e81df0d8db0e30050725a5802441c2022ea3ae4fe0609b76081731accbc"},
|
||||
{file = "ruff-0.1.6.tar.gz", hash = "sha256:1b09f29b16c6ead5ea6b097ef2764b42372aebe363722f1605ecbcd2b9207184"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@@ -3668,4 +3668,4 @@ desktop = ["opencv-python"]
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.8,<3.12.0"
|
||||
content-hash = "f2d0f1827c652b0ae160a093050bca163b13f2d166968893a0c5a02d27bfb856"
|
||||
content-hash = "abbd8c3b0bbed2349aa2ba8f4a822ab44d1092ea8b60118dc83fe11737e4ca12"
|
||||
|
||||
+2
-2
@@ -1,6 +1,6 @@
|
||||
[tool.poetry]
|
||||
name = "supervision"
|
||||
version = "0.17.0rc3"
|
||||
version = "0.17.0rc5"
|
||||
description = "A set of easy-to-use utils that will come in handy in any Computer Vision project"
|
||||
authors = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
|
||||
maintainers = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
|
||||
@@ -57,7 +57,7 @@ pytest = "^7.2.2"
|
||||
wheel = ">=0.40,<0.42"
|
||||
notebook = ">=6.5.3,<8.0.0"
|
||||
build = ">=0.10,<1.1"
|
||||
ruff = ">=0.0.280,<0.1.6"
|
||||
ruff = ">=0.0.280,<0.1.7"
|
||||
isort = "^5.12.0"
|
||||
black = "^23.7.0"
|
||||
mypy = "^1.4.1"
|
||||
|
||||
@@ -45,6 +45,8 @@ from supervision.detection.utils import (
|
||||
)
|
||||
from supervision.draw.color import Color, ColorPalette
|
||||
from supervision.draw.utils import (
|
||||
calculate_dynamic_font_scale,
|
||||
calculate_dynamic_line_thickness,
|
||||
draw_filled_rectangle,
|
||||
draw_image,
|
||||
draw_line,
|
||||
|
||||
@@ -30,7 +30,7 @@ class BoundingBoxAnnotator(BaseAnnotator):
|
||||
annotating detections.
|
||||
thickness (int): Thickness of the bounding box lines.
|
||||
color_lookup (str): Strategy for mapping colors to annotations.
|
||||
Options are `INDEX`, `CLASS`, `TRACE`.
|
||||
Options are `INDEX`, `CLASS`, `TRACK`.
|
||||
"""
|
||||
self.color: Union[Color, ColorPalette] = color
|
||||
self.thickness: int = thickness
|
||||
@@ -112,7 +112,7 @@ class MaskAnnotator(BaseAnnotator):
|
||||
annotating detections.
|
||||
opacity (float): Opacity of the overlay mask. Must be between `0` and `1`.
|
||||
color_lookup (str): Strategy for mapping colors to annotations.
|
||||
Options are `INDEX`, `CLASS`, `TRACE`.
|
||||
Options are `INDEX`, `CLASS`, `TRACK`.
|
||||
"""
|
||||
self.color: Union[Color, ColorPalette] = color
|
||||
self.opacity = opacity
|
||||
@@ -195,7 +195,7 @@ class PolygonAnnotator(BaseAnnotator):
|
||||
annotating detections.
|
||||
thickness (int): Thickness of the polygon lines.
|
||||
color_lookup (str): Strategy for mapping colors to annotations.
|
||||
Options are `INDEX`, `CLASS`, `TRACE`.
|
||||
Options are `INDEX`, `CLASS`, `TRACK`.
|
||||
"""
|
||||
self.color: Union[Color, ColorPalette] = color
|
||||
self.thickness: int = thickness
|
||||
@@ -277,7 +277,7 @@ class BoxMaskAnnotator(BaseAnnotator):
|
||||
annotating detections.
|
||||
opacity (float): Opacity of the overlay mask. Must be between `0` and `1`.
|
||||
color_lookup (str): Strategy for mapping colors to annotations.
|
||||
Options are `INDEX`, `CLASS`, `TRACE`.
|
||||
Options are `INDEX`, `CLASS`, `TRACK`.
|
||||
"""
|
||||
self.color: Union[Color, ColorPalette] = color
|
||||
self.color_lookup: ColorLookup = color_lookup
|
||||
@@ -366,7 +366,7 @@ class HaloAnnotator(BaseAnnotator):
|
||||
kernel_size (int): The size of the average pooling kernel used for creating
|
||||
the halo.
|
||||
color_lookup (str): Strategy for mapping colors to annotations.
|
||||
Options are `INDEX`, `CLASS`, `TRACE`.
|
||||
Options are `INDEX`, `CLASS`, `TRACK`.
|
||||
"""
|
||||
self.color: Union[Color, ColorPalette] = color
|
||||
self.opacity = opacity
|
||||
@@ -459,7 +459,7 @@ class EllipseAnnotator(BaseAnnotator):
|
||||
start_angle (int): Starting angle of the ellipse.
|
||||
end_angle (int): Ending angle of the ellipse.
|
||||
color_lookup (str): Strategy for mapping colors to annotations.
|
||||
Options are `INDEX`, `CLASS`, `TRACE`.
|
||||
Options are `INDEX`, `CLASS`, `TRACK`.
|
||||
"""
|
||||
self.color: Union[Color, ColorPalette] = color
|
||||
self.thickness: int = thickness
|
||||
@@ -547,7 +547,7 @@ class BoxCornerAnnotator(BaseAnnotator):
|
||||
thickness (int): Thickness of the corner lines.
|
||||
corner_length (int): Length of each corner line.
|
||||
color_lookup (str): Strategy for mapping colors to annotations.
|
||||
Options are `INDEX`, `CLASS`, `TRACE`.
|
||||
Options are `INDEX`, `CLASS`, `TRACK`.
|
||||
"""
|
||||
self.color: Union[Color, ColorPalette] = color
|
||||
self.thickness: int = thickness
|
||||
@@ -631,7 +631,7 @@ class CircleAnnotator(BaseAnnotator):
|
||||
annotating detections.
|
||||
thickness (int): Thickness of the circle line.
|
||||
color_lookup (str): Strategy for mapping colors to annotations.
|
||||
Options are `INDEX`, `CLASS`, `TRACE`.
|
||||
Options are `INDEX`, `CLASS`, `TRACK`.
|
||||
"""
|
||||
|
||||
self.color: Union[Color, ColorPalette] = color
|
||||
@@ -717,7 +717,7 @@ class DotAnnotator(BaseAnnotator):
|
||||
radius (int): Radius of the drawn dots.
|
||||
position (Position): The anchor position for placing the dot.
|
||||
color_lookup (ColorLookup): Strategy for mapping colors to annotations.
|
||||
Options are `INDEX`, `CLASS`, `TRACE`.
|
||||
Options are `INDEX`, `CLASS`, `TRACK`.
|
||||
"""
|
||||
self.color: Union[Color, ColorPalette] = color
|
||||
self.radius: int = radius
|
||||
@@ -800,7 +800,7 @@ class LabelAnnotator:
|
||||
text_position (Position): Position of the text relative to the detection.
|
||||
Possible values are defined in the `Position` enum.
|
||||
color_lookup (str): Strategy for mapping colors to annotations.
|
||||
Options are `INDEX`, `CLASS`, `TRACE`.
|
||||
Options are `INDEX`, `CLASS`, `TRACK`.
|
||||
"""
|
||||
self.color: Union[Color, ColorPalette] = color
|
||||
self.text_color: Color = text_color
|
||||
@@ -1027,7 +1027,7 @@ class TraceAnnotator:
|
||||
points. Defaults to `30`.
|
||||
thickness (int): The thickness of the trace lines. Defaults to `2`.
|
||||
color_lookup (str): Strategy for mapping colors to annotations.
|
||||
Options are `INDEX`, `CLASS`, `TRACE`.
|
||||
Options are `INDEX`, `CLASS`, `TRACK`.
|
||||
"""
|
||||
self.color: Union[Color, ColorPalette] = color
|
||||
self.position = position
|
||||
|
||||
@@ -39,15 +39,58 @@ class Classifications:
|
||||
_validate_class_ids(self.class_id, n)
|
||||
_validate_confidence(self.confidence, n)
|
||||
|
||||
def __len__(self) -> int:
|
||||
"""
|
||||
Returns the number of classifications.
|
||||
"""
|
||||
return len(self.class_id)
|
||||
|
||||
@classmethod
|
||||
def from_clip(cls, clip_results) -> Classifications:
|
||||
"""
|
||||
Creates a Classifications instance from a
|
||||
[clip](https://github.com/openai/clip) inference result.
|
||||
|
||||
Args:
|
||||
clip_results (np.ndarray): The inference result from clip model.
|
||||
|
||||
Returns:
|
||||
Classifications: A new Classifications object.
|
||||
|
||||
Example:
|
||||
```python
|
||||
>>> from PIL import Image
|
||||
>>> import clip
|
||||
>>> import supervision as sv
|
||||
|
||||
>>> model, preprocess = clip.load('ViT-B/32')
|
||||
|
||||
>>> image = cv2.imread(SOURCE_IMAGE_PATH)
|
||||
>>> image = preprocess(image).unsqueeze(0)
|
||||
|
||||
>>> text = clip.tokenize(["a diagram", "a dog", "a cat"])
|
||||
>>> output, _ = model(image, text)
|
||||
>>> classifications = sv.Classifications.from_clip(output)
|
||||
```
|
||||
"""
|
||||
|
||||
confidence = clip_results.softmax(dim=-1).cpu().detach().numpy()[0]
|
||||
|
||||
if len(confidence) == 0:
|
||||
return cls(class_id=np.array([]), confidence=np.array([]))
|
||||
|
||||
class_ids = np.arange(len(confidence))
|
||||
return cls(class_id=class_ids, confidence=confidence)
|
||||
|
||||
@classmethod
|
||||
def from_ultralytics(cls, ultralytics_results) -> Classifications:
|
||||
"""
|
||||
Creates a Classifications instance from a
|
||||
(https://github.com/ultralytics/ultralytics) inference result.
|
||||
[ultralytics](https://github.com/ultralytics/ultralytics) inference result.
|
||||
|
||||
Args:
|
||||
ultralytics_results (ultralytics.engine.results.Results):
|
||||
The output Results instance from ultralytics model
|
||||
The inference result from ultralytics model.
|
||||
|
||||
Returns:
|
||||
Classifications: A new Classifications object.
|
||||
@@ -60,15 +103,57 @@ class Classifications:
|
||||
|
||||
>>> image = cv2.imread(SOURCE_IMAGE_PATH)
|
||||
>>> model = YOLO('yolov8n-cls.pt')
|
||||
>>> model = YOLO('yolov8s-cls.pt')
|
||||
|
||||
>>> result = model(image)[0]
|
||||
>>> classifications = sv.Classifications.from_ultralytics(result)
|
||||
>>> output = model(image)[0]
|
||||
>>> classifications = sv.Classifications.from_ultralytics(output)
|
||||
```
|
||||
"""
|
||||
confidence = ultralytics_results.probs.data.cpu().numpy()
|
||||
return cls(class_id=np.arange(confidence.shape[0]), confidence=confidence)
|
||||
|
||||
@classmethod
|
||||
def from_timm(cls, timm_results) -> Classifications:
|
||||
"""
|
||||
Creates a Classifications instance from a
|
||||
[timm](https://huggingface.co/docs/hub/timm) inference result.
|
||||
|
||||
Args:
|
||||
timm_results: The inference result from timm model.
|
||||
|
||||
Returns:
|
||||
Classifications: A new Classifications object.
|
||||
|
||||
Example:
|
||||
```python
|
||||
>>> from PIL import Image
|
||||
>>> import timm
|
||||
>>> from timm.data import resolve_data_config, create_transform
|
||||
>>> import supervision as sv
|
||||
|
||||
>>> model = timm.create_model(
|
||||
... model_name='hf-hub:nateraw/resnet50-oxford-iiit-pet',
|
||||
... pretrained=True
|
||||
... ).eval()
|
||||
|
||||
>>> config = resolve_data_config({}, model=model)
|
||||
>>> transform = create_transform(**config)
|
||||
|
||||
>>> image = Image.open(SOURCE_IMAGE_PATH).convert('RGB')
|
||||
>>> x = transform(image).unsqueeze(0)
|
||||
|
||||
>>> output = model(x)
|
||||
|
||||
>>> classifications = sv.Classifications.from_timm(output)
|
||||
```
|
||||
"""
|
||||
confidence = timm_results.cpu().detach().numpy()[0]
|
||||
|
||||
if len(confidence) == 0:
|
||||
return cls(class_id=np.array([]), confidence=np.array([]))
|
||||
|
||||
class_id = np.arange(len(confidence))
|
||||
return cls(class_id=class_id, confidence=confidence)
|
||||
|
||||
def get_top_k(self, k: int) -> Tuple[np.ndarray, np.ndarray]:
|
||||
"""
|
||||
Retrieve the top k class IDs and confidences,
|
||||
|
||||
@@ -324,7 +324,7 @@ class Detections:
|
||||
|
||||
>>> inferencer = DetInferencer(model_name, checkpoint, device)
|
||||
>>> mmdet_result = inferencer(SOURCE_IMAGE_PATH, out_dir='./output',
|
||||
... return_datasample=True)["predictions"][0]
|
||||
... return_datasamples=True)["predictions"][0]
|
||||
>>> detections = sv.Detections.from_mmdet(mmdet_result)
|
||||
```
|
||||
"""
|
||||
|
||||
@@ -71,6 +71,21 @@ class Color:
|
||||
r, g, b = (int(color_hex[i : i + 2], 16) for i in range(0, 6, 2))
|
||||
return cls(r, g, b)
|
||||
|
||||
def as_hex(self) -> str:
|
||||
"""
|
||||
Converts the Color instance to a hex string.
|
||||
|
||||
Returns:
|
||||
str: The hexadecimal color string.
|
||||
|
||||
Example:
|
||||
```
|
||||
>>> Color(r=255, g=0, b=255).as_hex()
|
||||
'#ff00ff'
|
||||
```
|
||||
"""
|
||||
return f"#{self.r:02x}{self.g:02x}{self.b:02x}"
|
||||
|
||||
def as_rgb(self) -> Tuple[int, int, int]:
|
||||
"""
|
||||
Returns the color as an RGB tuple.
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import os
|
||||
from typing import Optional, Union
|
||||
from typing import Optional, Tuple, Union
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
@@ -231,3 +231,41 @@ def draw_image(
|
||||
scene[rect.y : rect.y + rect.height, rect.x : rect.x + rect.width] = blended_roi
|
||||
|
||||
return scene
|
||||
|
||||
|
||||
def calculate_dynamic_font_scale(resolution_wh: Tuple[int, int]) -> float:
|
||||
"""
|
||||
Calculate a dynamic font scale based on the resolution of an image.
|
||||
|
||||
Parameters:
|
||||
resolution_wh (Tuple[int, int]): A tuple representing the width and height
|
||||
of the image.
|
||||
|
||||
Returns:
|
||||
float: The calculated font scale factor.
|
||||
"""
|
||||
return min(resolution_wh) * 1e-3
|
||||
|
||||
|
||||
def calculate_dynamic_line_thickness(resolution_wh: Tuple[int, int]) -> int:
|
||||
"""
|
||||
Calculate a dynamic line thickness based on the resolution of an image.
|
||||
|
||||
Parameters:
|
||||
resolution_wh (Tuple[int, int]): A tuple representing the width and height
|
||||
of the image.
|
||||
|
||||
Returns:
|
||||
int: The calculated line thickness in pixels.
|
||||
"""
|
||||
min_dimension = min(resolution_wh)
|
||||
if min_dimension < 480:
|
||||
return 2
|
||||
if min_dimension < 720:
|
||||
return 2
|
||||
if min_dimension < 1080:
|
||||
return 2
|
||||
if min_dimension < 2160:
|
||||
return 4
|
||||
else:
|
||||
return 4
|
||||
|
||||
@@ -30,3 +30,22 @@ def test_color_from_hex(
|
||||
with exception:
|
||||
result = Color.from_hex(color_hex=color_hex)
|
||||
assert result == expected_result
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"color, expected_result, exception",
|
||||
[
|
||||
(Color.white(), "#ffffff", DoesNotRaise()),
|
||||
(Color.black(), "#000000", DoesNotRaise()),
|
||||
(Color.red(), "#ff0000", DoesNotRaise()),
|
||||
(Color.green(), "#00ff00", DoesNotRaise()),
|
||||
(Color.blue(), "#0000ff", DoesNotRaise()),
|
||||
(Color(r=128, g=128, b=0), "#808000", DoesNotRaise()),
|
||||
],
|
||||
)
|
||||
def test_color_as_hex(
|
||||
color: Color, expected_result: Optional[str], exception: Exception
|
||||
) -> None:
|
||||
with exception:
|
||||
result = color.as_hex()
|
||||
assert result == expected_result
|
||||
|
||||
Reference in New Issue
Block a user