From dc59b8affbd1c04b539e8bba523439ab0358c7a6 Mon Sep 17 00:00:00 2001 From: asreva Date: Wed, 20 May 2026 16:15:38 +0200 Subject: [PATCH] Add S3 storage support via s3fs; make storage field required - New filesystem.py: make_fs() factory (returns s3fs.S3FileSystem or None), plus fsjoin/fsstem/fsname path helpers - config.py: storage field is now required ('local' or 's3'); load_config raises a clear ValueError when it is missing - video_loader, clip_selector, annotator: thread fs through all file I/O; local paths unchanged, S3 paths use fs.open/fs.exists/fs.pipe - annotation_script: load .env via python-dotenv at startup, create fs from config and pass to Annotator - Add .env.example with SwitchEngines endpoint and AWS checksum env vars - pyproject.toml: add s3fs and python-dotenv dependencies - Reduce default mask alpha from 40% to 15% - Update example clip names to colon-separated timestamps Co-Authored-By: Claude Sonnet 4.6 --- .env.example | 5 + .gitignore | 1 + README.md | 97 +- config/clips.example.txt | 16 +- config/config.example.yaml | 8 + pyproject.toml | 2 + requirements.txt | 972 +++++++++++++++++- .../annotation_script.py | 11 + src/river_annotation_tool/annotator.py | 151 ++- src/river_annotation_tool/clip_selector.py | 58 +- src/river_annotation_tool/config.py | 5 + src/river_annotation_tool/filesystem.py | 35 + src/river_annotation_tool/mask_canvas.py | 2 +- src/river_annotation_tool/video_loader.py | 11 +- uv.lock | 271 +++++ 15 files changed, 1539 insertions(+), 106 deletions(-) create mode 100644 .env.example create mode 100644 src/river_annotation_tool/filesystem.py diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..49be4b5 --- /dev/null +++ b/.env.example @@ -0,0 +1,5 @@ +S3_ACCESS_KEY=your-access-key-here +S3_SECRET_ACCESS_KEY=your-secret-key-here +S3_ENDPOINT_URL=https://os.zhdk.cloud.switch.ch +AWS_REQUEST_CHECKSUM_CALCULATION="when_required" +AWS_RESPONSE_CHECKSUM_VALIDATION="when_required" diff --git a/.gitignore b/.gitignore index a1f9796..2662552 100644 --- a/.gitignore +++ b/.gitignore @@ -18,6 +18,7 @@ data/** # User-specific config (copy from *.example.* files) config/config.yaml config/clips.txt +.env # Notebooks notebooks/ diff --git a/README.md b/README.md index 71db46f..34a201a 100644 --- a/README.md +++ b/README.md @@ -35,6 +35,33 @@ cp config/clips.example.txt config/clips.txt Edit `config/config.yaml` to set your `data_dir` and `out_dir`, then edit `config/clips.txt` to list the clips you want to annotate. +### S3 storage (optional) + +By default the tool reads clips from and writes annotations to the local filesystem (`storage: local`). To use an S3-compatible object store instead, set `storage: s3` in `config/config.yaml` and give `data_dir` / `out_dir` as `bucket/prefix` paths: + +```yaml +storage: s3 +data_dir: my-bucket/clips +out_dir: my-bucket/annotation_results +``` + +Copy `.env.example` to `.env` and fill in your credentials — the app loads this file automatically at startup: + +```sh +cp .env.example .env +# edit .env with your credentials +``` + +| Variable | Description | +|---|---| +| `S3_ACCESS_KEY` | Access key ID | +| `S3_SECRET_ACCESS_KEY` | Secret access key | +| `S3_ENDPOINT_URL` | Endpoint URL (defaults to `https://os.zhdk.cloud.switch.ch` if not set) | +| `AWS_REQUEST_CHECKSUM_CALCULATION` | Set to `when_required` to avoid checksum errors on SwitchEngines/Ceph | +| `AWS_RESPONSE_CHECKSUM_VALIDATION` | Set to `when_required` to avoid checksum errors on SwitchEngines/Ceph | + +The `clips_file` (the list of clip filenames to annotate) is always read from the local filesystem even when `storage: s3`. + ## Usage ```sh @@ -74,6 +101,26 @@ python -m river_annotation_tool.annotation_script --clip left_20230615T120000 All settings live in `config/config.yaml`. Copy `config/config.example.yaml` to get started. ```yaml +storage: local # required: 'local' or 's3' + +data_dir: data/clips # directory containing ZIP archives (local path or bucket/prefix for S3) +out_dir: data/annotation_results +clips_file: config/clips.txt +# optical_flow_config_file: config/optical_flow_config.yaml # optional, enables Auto Segment + +display_max: 720 # longest side in pixels for display +fps_fallback: 25 # FPS to use if the video header is missing +max_frames: 100 # max frames to extract per clip + +questions: + - section: River + items: + - key: flow + label: "Flow Regime" + options: [Turbulent, Laminar, Uncertain] + default: Laminar + # add more items or sections as needed + filenames: video_in_zip: left.mp4 # video filename inside each ZIP archive video_tmp_suffix: .mp4 # suffix for the extraction temp file @@ -87,24 +134,6 @@ filenames: gif_original_lowres: video_original_lowres.gif gif_overlay_hires: video_overlay_hires.gif gif_overlay_lowres: video_overlay_lowres.gif - -display_max: 720 # longest side in pixels for display -fps_fallback: 25 # FPS to use if the video header is missing -max_frames: 100 # max frames to extract per clip - -data_dir: data/clips # directory containing ZIP archives -out_dir: data/annotation_results -clips_file: config/clips.txt -# optical_flow_config_file: config/optical_flow_config.yaml # optional, enables Auto Segment - -questions: - - section: River - items: - - key: flow - label: "Flow Regime" - options: [Turbulent, Laminar, Uncertain] - default: Laminar - # add more items or sections as needed ``` Add, remove, or reorder questions directly in the YAML — the UI rebuilds automatically. `key` is what gets saved in `metadata.json`; `default` selects the pre-checked option (omit or set to `null` to leave unselected). @@ -192,7 +221,7 @@ Polygons are drawn as overlays and do not affect the mask until you use **Fill** | Redo | **Redo** | | Clear entire mask | **Clear** | | Toggle mask overlay | **Hide Mask / Show Mask** — button turns red when hidden; does not affect mask data | -| Mask transparency | **Mask Alpha** slider (0–100%, default 40%); click **↺** to reset | +| Mask transparency | **Mask Alpha** slider (0–100%, default 15%); click **↺** to reset | ### Starting-point shortcuts @@ -280,22 +309,24 @@ When a clip is loaded that already has a saved `mask.png` and `metadata.json`, t ## Repository structure ``` +.env.example # S3 credential template (copy to .env and fill in) config/ - config.yaml # Your local config (git-ignored, copy from example) - config.example.yaml # Example config to copy and edit - clips.txt # Your clip list (git-ignored, copy from example) - clips.example.txt # Example clip list - optical_flow_config.yaml # Optional optical flow parameters (enable via config.yaml) + config.yaml # Your local config (git-ignored, copy from example) + config.example.yaml # Example config to copy and edit + clips.txt # Your clip list (git-ignored, copy from example) + clips.example.txt # Example clip list + optical_flow_config.yaml # Optional optical flow parameters (enable via config.yaml) src/river_annotation_tool/ - annotation_script.py # Entry point — argument parsing and app launch - annotator.py # Main QMainWindow — orchestrates all components - clip_selector.py # Reads the clip list and picks the next clip - mask_canvas.py # Drawing widget — brush, undo, erase, mouse events - video_loader.py # ZIP extraction and frame resizing - compute_optical_flow.py # Optical flow river segmentation (Auto Segment button) - config.py # AppConfig dataclass and YAML loader - __init__.py # Package version -pyproject.toml # Project metadata and dependencies + annotation_script.py # Entry point — argument parsing and app launch + annotator.py # Main QMainWindow — orchestrates all components + clip_selector.py # Reads the clip list and picks the next clip + filesystem.py # Storage backend — local passthrough or S3 via s3fs + mask_canvas.py # Drawing widget — brush, undo, erase, mouse events + video_loader.py # ZIP extraction and frame resizing + compute_optical_flow.py # Optical flow river segmentation (Auto Segment button) + config.py # AppConfig dataclass and YAML loader + __init__.py # Package version +pyproject.toml # Project metadata and dependencies ``` ## Development diff --git a/config/clips.example.txt b/config/clips.example.txt index 3721792..4491270 100644 --- a/config/clips.example.txt +++ b/config/clips.example.txt @@ -1,10 +1,10 @@ # List the clip filenames (without path) to annotate, one per line. # Lines starting with # are ignored. Order is preserved. -GRAMMONT_2025-11-17T11_31_38.546953+00_00.zip -GRAMMONT_2025-11-17T12_31_39.650554+00_00.zip -GRAMMONT_2025-11-17T15_32_07.184007+00_00.zip -GRAMMONT_2025-11-17T15_32_07.184007+00_00.zip -GRAMMONT_2025-11-17T15_47_10.070449+00_00.zip -GRAMMONT_2025-11-22T10_47_02.705611+00_00.zip -GRAMMONT_2025-11-22T14_47_00.096714+00_00.zip -GRAMMONT_2025-11-22T15_32_01.015469+00_00.zip \ No newline at end of file +GRAMMONT_2025-11-17T11:31:38.546953+00:00.zip +GRAMMONT_2025-11-17T12:31:39.650554+00:00.zip +GRAMMONT_2025-11-17T15:32:07.184007+00:00.zip +GRAMMONT_2025-11-17T15:32:07.184007+00:00.zip +GRAMMONT_2025-11-17T15:47:10.070449+00:00.zip +GRAMMONT_2025-11-22T10:47:02.705611+00:00.zip +GRAMMONT_2025-11-22T14:47:00.096714+00:00.zip +GRAMMONT_2025-11-22T15:32:01.015469+00:00.zip diff --git a/config/config.example.yaml b/config/config.example.yaml index 209584f..b27316b 100644 --- a/config/config.example.yaml +++ b/config/config.example.yaml @@ -1,5 +1,13 @@ +# For local storage, set data_dir and out_dir to file-system paths: +storage: local # 'local' (default) or 's3' data_dir: data/filtered_data out_dir: data/annotation_results +# For S3 storage, set storage: s3 and use bucket/prefix paths: +# storage: s3 +# data_dir: my-bucket/clips +# out_dir: my-bucket/annotation_results +# Credentials are read from env vars (copy .env.example to .env): +# S3_ACCESS_KEY, S3_SECRET_ACCESS_KEY, S3_ENDPOINT_URL clips_file: config/clips.txt optical_flow_config_file: config/optical_flow_config.yaml diff --git a/pyproject.toml b/pyproject.toml index 162c719..f80a502 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -19,7 +19,9 @@ dependencies = [ "matplotlib-inline>=0.2.1", "pillow>=12.2.0", "pyside6>=6.11.0", + "python-dotenv>=1.0", "pyyaml>=6.0", + "s3fs>=2024.0", ] dynamic = ["version"] diff --git a/requirements.txt b/requirements.txt index de6f95a..de3dc9b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1 +1,971 @@ -# This file will be autogenerated from pyproject.toml +# This file was autogenerated by uv via the following command: +# uv export --frozen --output-file=requirements.txt +-e . +aiobotocore==3.7.0 \ + --hash=sha256:680bde7c64679a821a9312641b759d9497f790ba8b2e88c6959e6273ee765b8e \ + --hash=sha256:c64d871ed5491a6571948dd48eabd185b46c6c23b64e3afd0c059fc7593ada30 + # via s3fs +aiohappyeyeballs==2.6.1 \ + --hash=sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558 \ + --hash=sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8 + # via aiohttp +aiohttp==3.13.5 \ + --hash=sha256:023ecba036ddd840b0b19bf195bfae970083fd7024ce1ac22e9bba90464620e9 \ + --hash=sha256:02e048037a6501a5ec1f6fc9736135aec6eb8a004ce48838cb951c515f32c80b \ + --hash=sha256:110e448e02c729bcebb18c60b9214a87ba33bac4a9fa5e9a5f139938b56c6cb1 \ + --hash=sha256:15c933ad7920b7d9a20de151efcd05a6e38302cbf0e10c9b2acb9a42210a2416 \ + --hash=sha256:31cebae8b26f8a615d2b546fee45d5ffb76852ae6450e2a03f42c9102260d6fe \ + --hash=sha256:327cc432fdf1356fb4fbc6fe833ad4e9f6aacb71a8acaa5f1855e4b25910e4a9 \ + --hash=sha256:33add2463dde55c4f2d9635c6ab33ce154e5ecf322bd26d09af95c5f81cfa286 \ + --hash=sha256:55b3bdd3292283295774ab585160c4004f4f2f203946997f49aac032c84649e9 \ + --hash=sha256:7c35b0bf0b48a70b4cb4fc5d7bed9b932532728e124874355de1a0af8ec4bc88 \ + --hash=sha256:888e78eb5ca55a615d285c3c09a7a91b42e9dd6fc699b166ebd5dee87c9ccf14 \ + --hash=sha256:8bd3ec6376e68a41f9f95f5ed170e2fcf22d4eb27a1f8cb361d0508f6e0557f3 \ + --hash=sha256:9d98cc980ecc96be6eb4c1994ce35d28d8b1f5e5208a23b421187d1209dbb7d1 \ + --hash=sha256:a60eaa2d440cd4707696b52e40ed3e2b0f73f65be07fd0ef23b6b539c9c0b0b4 \ + --hash=sha256:ab2899f9fa2f9f741896ebb6fa07c4c883bfa5c7f2ddd8cf2aafa86fa981b2d2 \ + --hash=sha256:b18f31b80d5a33661e08c89e202edabf1986e9b49c42b4504371daeaa11b47c1 \ + --hash=sha256:b38765950832f7d728297689ad78f5f2cf79ff82487131c4d26fe6ceecdc5f8e \ + --hash=sha256:c2b2355dc094e5f7d45a7bb262fe7207aa0460b37a0d87027dcf21b5d890e7d5 \ + --hash=sha256:df23d57718f24badef8656c49743e11a89fd6f5358fa8a7b96e728fda2abf7d3 + # via + # aiobotocore + # s3fs +aioitertools==0.13.0 \ + --hash=sha256:0be0292b856f08dfac90e31f4739432f4cb6d7520ab9eb73e143f4f2fa5259be \ + --hash=sha256:620bd241acc0bbb9ec819f1ab215866871b4bbd1f73836a55f799200ee86950c + # via aiobotocore +aiosignal==1.4.0 \ + --hash=sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e \ + --hash=sha256:f47eecd9468083c2029cc99945502cb7708b082c232f9aca65da147157b251c7 + # via aiohttp +anyio==4.13.0 \ + --hash=sha256:08b310f9e24a9594186fd75b4f73f4a4152069e3853f1ed8bfbf58369f4ad708 \ + --hash=sha256:334b70e641fd2221c1505b3890c69882fe4a2df910cba14d97019b90b24439dc + # via + # httpx + # jupyter-server +appnope==0.1.4 ; sys_platform == 'darwin' \ + --hash=sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee \ + --hash=sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c + # via ipykernel +argon2-cffi==25.1.0 \ + --hash=sha256:694ae5cc8a42f4c4e2bf2ca0e64e51e23a040c6a517a85074683d3959e1346c1 \ + --hash=sha256:fdc8b074db390fccb6eb4a3604ae7231f219aa669a2652e0f20e16ba513d5741 + # via jupyter-server +argon2-cffi-bindings==25.1.0 \ + --hash=sha256:1db89609c06afa1a214a69a462ea741cf735b29a57530478c06eb81dd403de99 \ + --hash=sha256:1e021e87faa76ae0d413b619fe2b65ab9a037f24c60a1e6cc43457ae20de6dc6 \ + --hash=sha256:2630b6240b495dfab90aebe159ff784d08ea999aa4b0d17efa734055a07d2f44 \ + --hash=sha256:473bcb5f82924b1becbb637b63303ec8d10e84c8d241119419897a26116515d2 \ + --hash=sha256:7aef0c91e2c0fbca6fc68e7555aa60ef7008a739cbe045541e438373bc54d2b0 \ + --hash=sha256:a98cd7d17e9f7ce244c0803cad3c23a7d379c301ba618a5fa76a67d116618b98 \ + --hash=sha256:aecba1723ae35330a008418a91ea6cfcedf6d31e5fbaa056a166462ff066d500 \ + --hash=sha256:b0fdbcf513833809c882823f98dc2f931cf659d9a1429616ac3adebb49f5db94 \ + --hash=sha256:b957f3e6ea4d55d820e40ff76f450952807013d361a65d7f28acc0acbf29229d \ + --hash=sha256:c87b72589133f0346a1cb8d5ecca4b933e3c9b64656c9d175270a000e73b288d \ + --hash=sha256:d3e924cfc503018a714f94a49a149fdc0b644eaead5d1f089330399134fa028a + # via argon2-cffi +arrow==1.4.0 \ + --hash=sha256:749f0769958ebdc79c173ff0b0670d59051a535fa26e8eba02953dc19eb43205 \ + --hash=sha256:ed0cc050e98001b8779e84d461b0098c4ac597e88704a655582b21d116e526d7 + # via isoduration +asttokens==3.0.1 \ + --hash=sha256:15a3ebc0f43c2d0a50eeafea25e19046c68398e487b9f1f5b517f7c0f40f976a \ + --hash=sha256:71a4ee5de0bde6a31d64f6b13f2293ac190344478f081c3d1bccfcf5eacb0cb7 + # via stack-data +async-lru==2.3.0 \ + --hash=sha256:89bdb258a0140d7313cf8f4031d816a042202faa61d0ab310a0a538baa1c24b6 \ + --hash=sha256:eea27b01841909316f2cc739807acea1c623df2be8c5cfad7583286397bb8315 + # via jupyterlab +attrs==26.1.0 \ + --hash=sha256:c647aa4a12dfbad9333ca4e71fe62ddc36f4e63b2d260a37a8b83d2f043ac309 \ + --hash=sha256:d03ceb89cb322a8fd706d4fb91940737b6642aa36998fe130a9bc96c985eff32 + # via + # aiohttp + # jsonschema + # referencing +babel==2.18.0 \ + --hash=sha256:b80b99a14bd085fcacfa15c9165f651fbb3406e66cc603abf11c5750937c992d \ + --hash=sha256:e2b422b277c2b9a9630c1d7903c2a00d0830c409c59ac8cae9081c92f1aeba35 + # via jupyterlab-server +beautifulsoup4==4.14.3 \ + --hash=sha256:0918bfe44902e6ad8d57732ba310582e98da931428d231a5ecb9e7c703a735bb \ + --hash=sha256:6292b1c5186d356bba669ef9f7f051757099565ad9ada5dd630bd9de5fa7fb86 + # via nbconvert +bleach==6.3.0 \ + --hash=sha256:6f3b91b1c0a02bb9a78b5a454c92506aa0fdf197e1d5e114d2e00c6f64306d22 \ + --hash=sha256:fe10ec77c93ddf3d13a73b035abaac7a9f5e436513864ccdad516693213c65d6 + # via nbconvert +botocore==1.43.0 \ + --hash=sha256:cc5b15eaec3c6eac05d8012cb5ef17ebe891beb88a16ca13c374bfaece1241e6 \ + --hash=sha256:e933b31a2d644253e1d029d7d39e99ba41b87e29300534f189744cc438cdf928 + # via aiobotocore +certifi==2026.4.22 \ + --hash=sha256:3cb2210c8f88ba2318d29b0388d1023c8492ff72ecdde4ebdaddbb13a31b1c4a \ + --hash=sha256:8d455352a37b71bf76a79caa83a3d6c25afee4a385d632127b6afb3963f1c580 + # via + # httpcore + # httpx + # requests +cffi==2.0.0 \ + --hash=sha256:1e3a615586f05fc4065a8b22b8152f0c1b00cdbc60596d187c2a74f9e3036e4e \ + --hash=sha256:21d1152871b019407d8ac3985f6775c079416c282e431a4da6afe7aefd2bccbe \ + --hash=sha256:2c8f814d84194c9ea681642fd164267891702542f028a15fc97d4674b6206187 \ + --hash=sha256:3925dd22fa2b7699ed2617149842d2e6adde22b262fcbfada50e3d195e4b3a94 \ + --hash=sha256:3e17ed538242334bf70832644a32a7aae3d83b57567f9fd60a26257e992b79ba \ + --hash=sha256:44d1b5909021139fe36001ae048dbdde8214afa20200eda0f64c068cac5d5529 \ + --hash=sha256:4671d9dd5ec934cb9a73e7ee9676f9362aba54f7f34910956b84d727b0d73fb6 \ + --hash=sha256:6d02d6655b0e54f54c4ef0b94eb6be0607b70853c45ce98bd278dc7de718be5d \ + --hash=sha256:81afed14892743bbe14dacb9e36d9e0e504cd204e0b165062c488942b9718037 \ + --hash=sha256:8eca2a813c1cb7ad4fb74d368c2ffbbb4789d377ee5bb8df98373c2cc0dee76c \ + --hash=sha256:b21e08af67b8a103c71a250401c78d5e0893beff75e28c53c98f4de42f774062 \ + --hash=sha256:da68248800ad6320861f129cd9c1bf96ca849a2771a59e0344e88681905916f5 \ + --hash=sha256:da902562c3e9c550df360bfa53c035b2f241fed6d9aef119048073680ace4a18 + # via + # argon2-cffi-bindings + # pyzmq +cfgv==3.5.0 \ + --hash=sha256:a8dc6b26ad22ff227d2634a65cb388215ce6cc96bbcc5cfde7641ae87e8dacc0 \ + --hash=sha256:d5b1034354820651caa73ede66a6294d6e95c1b00acc5e9b098e917404669132 + # via pre-commit +charset-normalizer==3.4.7 \ + --hash=sha256:0f7eb884681e3938906ed0434f20c63046eacd0111c4ba96f27b76084cd679f5 \ + --hash=sha256:203104ed3e428044fd943bc4bf45fa73c0730391f9621e37fe39ecf477b128cb \ + --hash=sha256:2257141f39fe65a3fdf38aeccae4b953e5f3b3324f4ff0daf9f15b8518666a2c \ + --hash=sha256:298930cec56029e05497a76988377cbd7457ba864beeea92ad7e844fe74cd1f1 \ + --hash=sha256:3dce51d0f5e7951f8bb4900c257dad282f49190fdbebecd4ba99bcc41fef404d \ + --hash=sha256:4dc1e73c36828f982bfe79fadf5919923f8a6f4df2860804db9a98c48824ce8d \ + --hash=sha256:5649fd1c7bade02f320a462fdefd0b4bd3ce036065836d4f42e0de958038e116 \ + --hash=sha256:56be790f86bfb2c98fb742ce566dfb4816e5a83384616ab59c49e0604d49c51d \ + --hash=sha256:5ed6ab538499c8644b8a3e18debabcd7ce684f3fa91cf867521a7a0279cab2d6 \ + --hash=sha256:6178f72c5508bfc5fd446a5905e698c6212932f25bcdd4b47a757a50605a90e2 \ + --hash=sha256:708838739abf24b2ceb208d0e22403dd018faeef86ddac04319a62ae884c4f15 \ + --hash=sha256:ae89db9e5f98a11a4bf50407d4363e7b09b31e55bc117b4f7d80aab97ba009e5 \ + --hash=sha256:aed52fea0513bac0ccde438c188c8a471c4e0f457c2dd20cdbf6ea7a450046c7 \ + --hash=sha256:bb6d88045545b26da47aa879dd4a89a71d1dce0f0e549b1abcb31dfe4a8eac49 \ + --hash=sha256:e1421b502d83040e6d7fb2fb18dff63957f720da3d77b2fbd3187ceb63755d7b \ + --hash=sha256:eca9705049ad3c7345d574e3510665cb2cf844c2f2dcfe675332677f081cbd46 \ + --hash=sha256:edac0f1ab77644605be2cbba52e6b7f630731fc42b34cb0f634be1a6eface56a \ + --hash=sha256:fea24543955a6a729c45a73fe90e08c743f0b3334bbf3201e6c4bc1b0c7fa464 + # via requests +colorama==0.4.6 ; sys_platform == 'win32' \ + --hash=sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44 \ + --hash=sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6 + # via ipython +comm==0.2.3 \ + --hash=sha256:2dc8048c10962d55d7ad693be1e7045d891b7ce8d999c97963a5e3e99c055971 \ + --hash=sha256:c615d91d75f7f04f095b30d1c1711babd43bdc6419c1be9886a85f2f4e489417 + # via ipykernel +contourpy==1.3.3 \ + --hash=sha256:023b44101dfe49d7d53932be418477dba359649246075c996866106da069af69 \ + --hash=sha256:07ce5ed73ecdc4a03ffe3e1b3e3c1166db35ae7584be76f65dbbe28a7791b0cc \ + --hash=sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880 \ + --hash=sha256:451e71b5a7d597379ef572de31eeb909a87246974d960049a9848c3bc6c41bf7 \ + --hash=sha256:459c1f020cd59fcfe6650180678a9993932d80d44ccde1fa1868977438f0b411 \ + --hash=sha256:4d00e655fcef08aba35ec9610536bfe90267d7ab5ba944f7032549c55a146da1 \ + --hash=sha256:556dba8fb6f5d8742f2923fe9457dbdd51e1049c4a43fd3986a0b14a1d815fc6 \ + --hash=sha256:626d60935cf668e70a5ce6ff184fd713e9683fb458898e4249b63be9e28286ea \ + --hash=sha256:8153b8bfc11e1e4d75bcb0bff1db232f9e10b274e0929de9d608027e0d34ff8b \ + --hash=sha256:92d9abc807cf7d0e047b95ca5d957cf4792fcd04e920ca70d48add15c1a90ea7 \ + --hash=sha256:b08a32ea2f8e42cf1d4be3169a98dd4be32bafe4f22b6c4cb4ba810fa9e5d2cb \ + --hash=sha256:b2e8faa0ed68cb29af51edd8e24798bb661eac3bd9f65420c1887b6ca89987c8 + # via matplotlib +cycler==0.12.1 \ + --hash=sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30 \ + --hash=sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c + # via matplotlib +debugpy==1.8.20 \ + --hash=sha256:4057ac68f892064e5f98209ab582abfee3b543fb55d2e87610ddc133a954d390 \ + --hash=sha256:4ae3135e2089905a916909ef31922b2d733d756f66d87345b3e5e52b7a55f13d \ + --hash=sha256:55bc8701714969f1ab89a6d5f2f3d40c36f91b2cbe2f65d98bf8196f6a6a2c33 \ + --hash=sha256:5be9bed9ae3be00665a06acaa48f8329d2b9632f15fd09f6a9a8c8d9907e54d7 \ + --hash=sha256:88f47850a4284b88bd2bfee1f26132147d5d504e4e86c22485dfa44b97e19b4b \ + --hash=sha256:a1a8f851e7cf171330679ef6997e9c579ef6dd33c9098458bd9986a0f4ca52e3 + # via ipykernel +decorator==5.2.1 \ + --hash=sha256:65f266143752f734b0a7cc83c46f4618af75b8c5911b00ccb61d0ac9b6da0360 \ + --hash=sha256:d316bb415a2d9e2d2b3abcc4084c6502fc09240e292cd76a76afc106a1c8e04a + # via ipython +defusedxml==0.7.1 \ + --hash=sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69 \ + --hash=sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61 + # via nbconvert +distlib==0.4.0 \ + --hash=sha256:9659f7d87e46584a30b5780e43ac7a2143098441670ff0a49d5f9034c54a6c16 \ + --hash=sha256:feec40075be03a04501a973d81f633735b4b69f98b05450592310c0f401a4e0d + # via virtualenv +executing==2.2.1 \ + --hash=sha256:3632cc370565f6648cc328b32435bd120a1e4ebb20c77e3fdde9a13cd1e533c4 \ + --hash=sha256:760643d3452b4d777d295bb167ccc74c64a81df23fb5e08eff250c425a4b2017 + # via stack-data +fastjsonschema==2.21.2 \ + --hash=sha256:1c797122d0a86c5cace2e54bf4e819c36223b552017172f32c5c024a6b77e463 \ + --hash=sha256:b1eb43748041c880796cd077f1a07c3d94e93ae84bba5ed36800a33554ae05de + # via nbformat +filelock==3.29.0 \ + --hash=sha256:69974355e960702e789734cb4871f884ea6fe50bd8404051a3530bc07809cf90 \ + --hash=sha256:96f5f6344709aa1572bbf631c640e4ebeeb519e08da902c39a001882f30ac258 + # via + # python-discovery + # virtualenv +fonttools==4.62.1 \ + --hash=sha256:0aa72c43a601cfa9273bb1ae0518f1acadc01ee181a6fc60cd758d7fdadffc04 \ + --hash=sha256:12859ff0b47dd20f110804c3e0d0970f7b832f561630cd879969011541a464a9 \ + --hash=sha256:149f7d84afca659d1a97e39a4778794a2f83bf344c5ee5134e09995086cc2392 \ + --hash=sha256:19177c8d96c7c36359266e571c5173bcee9157b59cfc8cb0153c5673dc5a3a7d \ + --hash=sha256:7487782e2113861f4ddcc07c3436450659e3caa5e470b27dc2177cade2d8e7fd \ + --hash=sha256:90365821debbd7db678809c7491ca4acd1e0779b9624cdc6ddaf1f31992bf974 \ + --hash=sha256:9c125ffa00c3d9003cdaaf7f2c79e6e535628093e14b5de1dccb08859b680936 \ + --hash=sha256:9e7863e10b3de72376280b515d35b14f5eeed639d1aa7824f4cf06779ec65e42 \ + --hash=sha256:a24decd24d60744ee8b4679d38e88b8303d86772053afc29b19d23bb8207803c \ + --hash=sha256:e54c75fd6041f1122476776880f7c3c3295ffa31962dc6ebe2543c00dca58b5d + # via matplotlib +fqdn==1.5.1 \ + --hash=sha256:105ed3677e767fb5ca086a0c1f4bb66ebc3c100be518f0e0d755d9eae164d89f \ + --hash=sha256:3a179af3761e4df6eb2e026ff9e1a3033d3587bf980a0b1b2e1e5d08d7358014 + # via jsonschema +frozenlist==1.8.0 \ + --hash=sha256:0c18a16eab41e82c295618a77502e17b195883241c563b00f0aa5106fc4eaa0d \ + --hash=sha256:229bf37d2e4acdaf808fd3f06e854a4a7a3661e871b10dc1f8f1896a3b05f18b \ + --hash=sha256:294e487f9ec720bd8ffcebc99d575f7eff3568a08a253d1ee1a0378754b74143 \ + --hash=sha256:34187385b08f866104f0c0617404c8eb08165ab1272e884abc89c112e9c00746 \ + --hash=sha256:3462dd9475af2025c31cc61be6652dfa25cbfb56cbbf52f4ccfe029f38decaf8 \ + --hash=sha256:3ede829ed8d842f6cd48fc7081d7a41001a56f1f38603f9d49bf3020d59a31ad \ + --hash=sha256:405e8fe955c2280ce66428b3ca55e12b3c4e9c336fb2103a4937e891c69a4a29 \ + --hash=sha256:433403ae80709741ce34038da08511d4a77062aa924baf411ef73d1146e74faf \ + --hash=sha256:494a5952b1c597ba44e0e78113a7266e656b9794eec897b19ead706bd7074383 \ + --hash=sha256:74c51543498289c0c43656701be6b077f4b265868fa7f8a8859c197006efb608 \ + --hash=sha256:776f352e8329135506a1d6bf16ac3f87bc25b28e765949282dcc627af36123aa \ + --hash=sha256:78f7b9e5d6f2fdb88cdde9440dc147259b62b9d3b019924def9f6478be254ac1 \ + --hash=sha256:908bd3f6439f2fef9e85031b59fd4f1297af54415fb60e4254a95f75b3cab3f3 \ + --hash=sha256:96f423a119f4777a4a056b66ce11527366a8bb92f54e541ade21f2374433f6d4 \ + --hash=sha256:c4c800524c9cd9bac5166cd6f55285957fcfc907db323e193f2afcd4d9abd69b \ + --hash=sha256:d6a5df73acd3399d893dafc71663ad22534b5aa4f94e8a2fabfe856c3c1b6a52 \ + --hash=sha256:f833670942247a14eafbb675458b4e61c82e002a148f49e68257b79296e865c4 \ + --hash=sha256:fe3c58d2f5db5fbd18c2987cba06d51b0529f52bc3a6cdc33d3f4eab725104bd + # via + # aiohttp + # aiosignal +fsspec==2026.4.0 \ + --hash=sha256:11ef7bb35dab8a394fde6e608221d5cf3e8499401c249bebaeaad760a1a8dec2 \ + --hash=sha256:301d8ac70ae90ef3ad05dcf94d6c3754a097f9b5fe4667d2787aa359ec7df7e4 + # via s3fs +h11==0.16.0 \ + --hash=sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1 \ + --hash=sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86 + # via httpcore +httpcore==1.0.9 \ + --hash=sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55 \ + --hash=sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8 + # via httpx +httpx==0.28.1 \ + --hash=sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc \ + --hash=sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad + # via jupyterlab +identify==2.6.19 \ + --hash=sha256:20e6a87f786f768c092a721ad107fc9df0eb89347be9396cadf3f4abbd1fb78a \ + --hash=sha256:6be5020c38fcb07da56c53733538a3081ea5aa70d36a156f83044bfbf9173842 + # via pre-commit +idna==3.13 \ + --hash=sha256:585ea8fe5d69b9181ec1afba340451fba6ba764af97026f92a91d4eef164a242 \ + --hash=sha256:892ea0cde124a99ce773decba204c5552b69c3c67ffd5f232eb7696135bc8bb3 + # via + # anyio + # httpx + # jsonschema + # requests + # yarl +ipykernel==7.2.0 \ + --hash=sha256:18ed160b6dee2cbb16e5f3575858bc19d8f1fe6046a9a680c708494ce31d909e \ + --hash=sha256:3bbd4420d2b3cc105cbdf3756bfc04500b1e52f090a90716851f3916c62e1661 + # via jupyterlab +ipython==9.13.0 \ + --hash=sha256:57f9d4639e20818d328d287c7b549af3d05f12486ea8f2e7f73e52a36ec4d201 \ + --hash=sha256:7e834b6afc99f020e3f05966ced34792f40267d64cb1ea9043886dab0dde5967 + # via ipykernel +ipython-pygments-lexers==1.1.1 \ + --hash=sha256:09c0138009e56b6854f9535736f4171d855c8c08a563a0dcd8022f78355c7e81 \ + --hash=sha256:a9462224a505ade19a605f71f8fa63c2048833ce50abc86768a0d81d876dc81c + # via ipython +isoduration==20.11.0 \ + --hash=sha256:ac2f9015137935279eac671f94f89eb00584f940f5dc49462a0c4ee692ba1bd9 \ + --hash=sha256:b2904c2a4228c3d44f409c8ae8e2370eb21a26f7ac2ec5446df141dde3452042 + # via jsonschema +jedi==0.19.2 \ + --hash=sha256:4770dc3de41bde3966b02eb84fbcf557fb33cce26ad23da12c742fb50ecb11f0 \ + --hash=sha256:a8ef22bde8490f57fe5c7681a3c83cb58874daf72b4784de3cce5b6ef6edb5b9 + # via ipython +jinja2==3.1.6 \ + --hash=sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d \ + --hash=sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67 + # via + # jupyter-server + # jupyterlab + # jupyterlab-server + # nbconvert +jmespath==1.1.0 \ + --hash=sha256:472c87d80f36026ae83c6ddd0f1d05d4e510134ed462851fd5f754c8c3cbb88d \ + --hash=sha256:a5663118de4908c91729bea0acadca56526eb2698e83de10cd116ae0f4e97c64 + # via + # aiobotocore + # botocore +json5==0.14.0 \ + --hash=sha256:56cf861bab076b1178eb8c92e1311d273a9b9acea2ccc82c276abf839ebaef3a \ + --hash=sha256:b3f492fad9f6cdbced8b7d40b28b9b1c9701c5f561bef0d33b81c2ff433fefcb + # via jupyterlab-server +jsonpointer==3.1.1 \ + --hash=sha256:0b801c7db33a904024f6004d526dcc53bbb8a4a0f4e32bfd10beadf60adf1900 \ + --hash=sha256:8ff8b95779d071ba472cf5bc913028df06031797532f08a7d5b602d8b2a488ca + # via jsonschema +jsonschema==4.26.0 \ + --hash=sha256:0c26707e2efad8aa1bfc5b7ce170f3fccc2e4918ff85989ba9ffa9facb2be326 \ + --hash=sha256:d489f15263b8d200f8387e64b4c3a75f06629559fb73deb8fdfb525f2dab50ce + # via + # jupyter-events + # jupyterlab-server + # nbformat +jsonschema-specifications==2025.9.1 \ + --hash=sha256:98802fee3a11ee76ecaca44429fda8a41bff98b00a0f2838151b113f210cc6fe \ + --hash=sha256:b540987f239e745613c7a9176f3edb72b832a4ac465cf02712288397832b5e8d + # via jsonschema +jupyter-client==8.8.0 \ + --hash=sha256:d556811419a4f2d96c869af34e854e3f059b7cc2d6d01a9cd9c85c267691be3e \ + --hash=sha256:f93a5b99c5e23a507b773d3a1136bd6e16c67883ccdbd9a829b0bbdb98cd7d7a + # via + # ipykernel + # jupyter-server + # nbclient +jupyter-core==5.9.1 \ + --hash=sha256:4d09aaff303b9566c3ce657f580bd089ff5c91f5f89cf7d8846c3cdf465b5508 \ + --hash=sha256:ebf87fdc6073d142e114c72c9e29a9d7ca03fad818c5d300ce2adc1fb0743407 + # via + # ipykernel + # jupyter-client + # jupyter-server + # jupyterlab + # nbclient + # nbconvert + # nbformat +jupyter-events==0.12.1 \ + --hash=sha256:c366585253f537a627da52fa7ca7410c5b5301fe893f511e7b077c2d93ec8bcf \ + --hash=sha256:faff25f77218335752f35f23c5fe6e4a392a7bd99a5939ccb9b8fbf594636cf3 + # via jupyter-server +jupyter-lsp==2.3.1 \ + --hash=sha256:71b954d834e85ff3096400554f2eefaf7fe37053036f9a782b0f7c5e42dadb81 \ + --hash=sha256:fdf8a4aa7d85813976d6e29e95e6a2c8f752701f926f2715305249a3829805a6 + # via jupyterlab +jupyter-server==2.17.0 \ + --hash=sha256:c38ea898566964c888b4772ae1ed58eca84592e88251d2cfc4d171f81f7e99d5 \ + --hash=sha256:e8cb9c7db4251f51ed307e329b81b72ccf2056ff82d50524debde1ee1870e13f + # via + # jupyter-lsp + # jupyterlab + # jupyterlab-server + # notebook + # notebook-shim +jupyter-server-terminals==0.5.4 \ + --hash=sha256:55be353fc74a80bc7f3b20e6be50a55a61cd525626f578dcb66a5708e2007d14 \ + --hash=sha256:bbda128ed41d0be9020349f9f1f2a4ab9952a73ed5f5ac9f1419794761fb87f5 + # via jupyter-server +jupyterlab==4.5.7 \ + --hash=sha256:55a9822c4754da305f41e113452c68383e214dcf96de760146af89ce5d5117b0 \ + --hash=sha256:fba4cb0e2c44a52859669d8c98b45de029d5e515f8407bf8534d2a8fc5f0964d + # via notebook +jupyterlab-pygments==0.3.0 \ + --hash=sha256:721aca4d9029252b11cfa9d185e5b5af4d54772bb8072f9b7036f4170054d35d \ + --hash=sha256:841a89020971da1d8693f1a99997aefc5dc424bb1b251fd6322462a1b8842780 + # via nbconvert +jupyterlab-server==2.28.0 \ + --hash=sha256:35baa81898b15f93573e2deca50d11ac0ae407ebb688299d3a5213265033712c \ + --hash=sha256:e4355b148fdcf34d312bbbc80f22467d6d20460e8b8736bf235577dd18506968 + # via + # jupyterlab + # notebook +kiwisolver==1.5.0 \ + --hash=sha256:1465387ac63576c3e125e5337a6892b9e99e0627d52317f3ca79e6930d889d15 \ + --hash=sha256:1d9daea4ea6b9be74fe2f01f7fbade8d6ffab263e781274cffca0dba9be9eec9 \ + --hash=sha256:1f1489f769582498610e015a8ef2d36f28f505ab3096d0e16b4858a9ec214f57 \ + --hash=sha256:4e9750bc21b886308024f8a54ccb9a2cc38ac9fa813bf4348434e3d54f337ff9 \ + --hash=sha256:530a3fd64c87cffa844d4b6b9768774763d9caa299e9b75d8eca6a4423b31314 \ + --hash=sha256:5ae8e62c147495b01a0f4765c878e9bfdf843412446a247e28df59936e99e797 \ + --hash=sha256:6ab8ba9152203feec73758dad83af9a0bbe05001eb4639e547207c40cfb52083 \ + --hash=sha256:72ec46b7eba5b395e0a7b63025490d3214c11013f4aacb4f5e8d6c3041829588 \ + --hash=sha256:7c60d3c9b06fb23bd9c6139281ccbdc384297579ae037f08ae90c69f6845c0b1 \ + --hash=sha256:b0f172dc8ffaccb8522d7c5d899de00133f2f1ca7b0a49b7da98e901de87bf2d \ + --hash=sha256:b2af221f268f5af85e776a73d62b0845fc8baf8ef0abfae79d29c77d0e776aaf \ + --hash=sha256:bb5136fb5352d3f422df33f0c879a1b0c204004324150cc3b5e3c4f310c9049f \ + --hash=sha256:c31c13da98624f957b0fb1b5bae5383b2333c2c3f6793d9825dd5ce79b525cb7 \ + --hash=sha256:cdee07c4d7f6d72008d3f73b9bf027f4e11550224c7c50d8df1ae4a37c1402a6 \ + --hash=sha256:d4193f3d9dc3f6f79aaed0e5637f45d98850ebf01f7ca20e69457f3e8946b66a \ + --hash=sha256:e315e5ec90d88e140f57696ff85b484ff68bb311e36f2c414aa4286293e6dee0 \ + --hash=sha256:ed3a984b31da7481b103f68776f7128a89ef26ed40f4dc41a2223cda7fb24819 \ + --hash=sha256:f18c2d9782259a6dc132fdc7a63c168cbc74b35284b6d75c673958982a378384 \ + --hash=sha256:f6764a4ccab3078db14a632420930f6186058750df066b8ea2a7106df91d3203 \ + --hash=sha256:f7c7553b13f69c1b29a5bde08ddc6d9d0c8bfb84f9ed01c30db25944aeb852a7 + # via matplotlib +lark==1.3.1 \ + --hash=sha256:b426a7a6d6d53189d318f2b6236ab5d6429eaf09259f1ca33eb716eed10d2905 \ + --hash=sha256:c629b661023a014c37da873b4ff58a817398d12635d3bbb2c5a03be7fe5d1e12 + # via rfc3987-syntax +markupsafe==3.0.3 \ + --hash=sha256:1872df69a4de6aead3491198eaf13810b565bdbeec3ae2dc8780f14458ec73ce \ + --hash=sha256:26a5784ded40c9e318cfc2bdb30fe164bdb8665ded9cd64d500a34fb42067b1c \ + --hash=sha256:35add3b638a5d900e807944a078b51922212fb3dedb01633a8defc4b01a3c85f \ + --hash=sha256:3a7e8ae81ae39e62a41ec302f972ba6ae23a5c5396c8e60113e9066ef893da0d \ + --hash=sha256:722695808f4b6457b320fdc131280796bdceb04ab50fe1795cd540799ebe1698 \ + --hash=sha256:77f0643abe7495da77fb436f50f8dab76dbc6e5fd25d39589a0f1fe6548bfa2b \ + --hash=sha256:83891d0e9fb81a825d9a6d61e3f07550ca70a076484292a70fde82c4b807286f \ + --hash=sha256:94c6f0bb423f739146aec64595853541634bde58b2135f27f61c1ffd1cd4d16a \ + --hash=sha256:be8813b57049a7dc738189df53d69395eba14fb99345e0a5994914a3864c8a4b \ + --hash=sha256:d53197da72cc091b024dd97249dfc7794d6a56530370992a5e1a08983ad9230e \ + --hash=sha256:d6dd0be5b5b189d31db7cda48b91d7e0a9795f31430b7f271219ab30f1d3ac9d \ + --hash=sha256:d88b440e37a16e651bda4c7c2b930eb586fd15ca7406cb39e211fcff3bf3017d + # via + # jinja2 + # nbconvert +matplotlib==3.10.9 \ + --hash=sha256:10cc5ce06d10231c36f40e875f3c7e8050362a4ee8f0ee5d29a6b3277d57bb42 \ + --hash=sha256:41cb28c2bd769aa3e98322c6ab09854cbcc52ab69d2759d681bba3e327b2b320 \ + --hash=sha256:6c63ebcd8b4b169eb2f5c200552ae6b8be8999a005b6b507ed76fb8d7d674fe2 \ + --hash=sha256:ae20801130378b82d647ff5047c07316295b68dc054ca6b3c13519d0ea624285 \ + --hash=sha256:d091f9d758b34aaaaa6331d13574bf01891d903b3dec59bfff458ef7551de5d6 \ + --hash=sha256:d75d11c949914165976c621b2324f9ef162af7ebf4b057ddf95dd1dba7e5edcf \ + --hash=sha256:f0c3c28d9fbcc1fe7a03be236d73430cf6409c41fb2383a7ac52fe932b072cb1 \ + --hash=sha256:fd66508e8c6877d98e586654b608a0456db8d7e8a546eb1e2600efd957302358 + # via river-annotation-tool +matplotlib-inline==0.2.1 \ + --hash=sha256:d56ce5156ba6085e00a9d54fead6ed29a9c47e215cd1bba2e976ef39f5710a76 \ + --hash=sha256:e1ee949c340d771fc39e241ea75683deb94762c8fa5f2927ec57c83c4dffa9fe + # via + # ipykernel + # ipython + # river-annotation-tool +mistune==3.2.0 \ + --hash=sha256:708487c8a8cdd99c9d90eb3ed4c3ed961246ff78ac82f03418f5183ab70e398a \ + --hash=sha256:febdc629a3c78616b94393c6580551e0e34cc289987ec6c35ed3f4be42d0eee1 + # via nbconvert +multidict==6.7.1 \ + --hash=sha256:28ca5ce2fd9716631133d0e9a9b9a745ad7f60bac2bccafb56aa380fc0b6c511 \ + --hash=sha256:3758692429e4e32f1ba0df23219cd0b4fc0a52f476726fff9337d1a57676a582 \ + --hash=sha256:398c1478926eca669f2fd6a5856b6de9c0acf23a2cb59a14c0ba5844fa38077e \ + --hash=sha256:3d51ff4785d58d3f6c91bdbffcb5e1f7ddfda557727043aa20d20ec4f65e324a \ + --hash=sha256:3fccb473e87eaa1382689053e4a4618e7ba7b9b9b8d6adf2027ee474597128cd \ + --hash=sha256:55d97cc6dae627efa6a6e548885712d4864b81110ac76fa4e534c03819fa4a56 \ + --hash=sha256:9c90fed18bffc0189ba814749fdcc102b536e83a9f738a9003e569acd540a733 \ + --hash=sha256:a088b62bd733e2ad12c50dad01b7d0166c30287c166e137433d3b410add807a6 \ + --hash=sha256:a90f75c956e32891a4eda3639ce6dd86e87105271f43d43442a3aedf3cddf172 \ + --hash=sha256:b0fa96985700739c4c7853a43c0b3e169360d6855780021bfc6d0f1ce7c123e7 \ + --hash=sha256:ba0a9fb644d0c1a2194cf7ffb043bd852cea63a57f66fbd33959f7dae18517bf \ + --hash=sha256:bfde23ef6ed9db7eaee6c37dcec08524cb43903c60b285b172b6c094711b3961 \ + --hash=sha256:c102791b1c4f3ab36ce4101154549105a53dc828f016356b3e3bcae2e3a039d3 \ + --hash=sha256:c3a32d23520ee37bf327d1e1a656fec76a2edd5c038bf43eddfa0572ec49c60b \ + --hash=sha256:cb2a55f408c3043e42b40cc8eecd575afa27b7e0b956dfb190de0f8499a57a53 \ + --hash=sha256:da62917e6076f512daccfbbde27f46fed1c98fee202f0559adec8ee0de67f71a \ + --hash=sha256:eb0ce7b2a32d09892b3dd6cc44877a0d02a33241fafca5f25c8b6b62374f8b75 \ + --hash=sha256:ec6652a1bee61c53a3e5776b6049172c53b6aaba34f18c9ad04f82712bac623d \ + --hash=sha256:fc5907494fccf3e7d3f94f95c91d6336b092b5fc83811720fae5e2765890dfba \ + --hash=sha256:fcee94dfbd638784645b066074b338bc9cc155d4b4bffa4adce1615c5a426c19 + # via + # aiobotocore + # aiohttp + # yarl +nbclient==0.10.4 \ + --hash=sha256:1e54091b16e6da39e297b0ece3e10f6f29f4ac4e8ee515d29f8a7099bd6553c9 \ + --hash=sha256:9162df5a7373d70d606527300a95a975a47c137776cd942e52d9c7e29ff83440 + # via nbconvert +nbconvert==7.17.1 \ + --hash=sha256:34d0d0a7e73ce3cbab6c5aae8f4f468797280b01fd8bd2ca746da8569eddd7d2 \ + --hash=sha256:aa85c087b435e7bf1ffd03319f658e285f2b89eccab33bc1ba7025495ab3e7c8 + # via jupyter-server +nbformat==5.10.4 \ + --hash=sha256:322168b14f937a5d11362988ecac2a4952d3d8e3a2cbeb2319584631226d5b3a \ + --hash=sha256:3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b + # via + # jupyter-server + # nbclient + # nbconvert +nest-asyncio==1.6.0 \ + --hash=sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe \ + --hash=sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c + # via ipykernel +nodeenv==1.10.0 \ + --hash=sha256:5bb13e3eed2923615535339b3c620e76779af4cb4c6a90deccc9e36b274d3827 \ + --hash=sha256:996c191ad80897d076bdfba80a41994c2b47c68e224c542b48feba42ba00f8bb + # via pre-commit +notebook==7.5.6 \ + --hash=sha256:4dde3f8fb55fa8fb7946d58c6e869ce9baf46d00fc070664f62604569d0faca0 \ + --hash=sha256:621174aade80108f0020b0f00738000b215f75fa3cd90771ad7aa0f24536a4e1 +notebook-shim==0.2.4 \ + --hash=sha256:411a5be4e9dc882a074ccbcae671eda64cceb068767e9a3419096986560e1cef \ + --hash=sha256:b4b2cfa1b65d98307ca24361f5b30fe785b53c3fd07b7a47e89acb5e6ac638cb + # via + # jupyterlab + # notebook +numpy==2.2.6 \ + --hash=sha256:37c0ca431f82cd5fa716eca9506aefcabc247fb27ba69c5062a6d3ade8cf8f49 \ + --hash=sha256:41c5a21f4a04fa86436124d388f6ed60a9343a6f767fced1a8a71c3fbca038ff \ + --hash=sha256:4eeaae00d789f66c7a25ac5f34b71a7035bb474e679f410e5e1a94deb24cf2d4 \ + --hash=sha256:71594f7c51a18e728451bb50cc60a3ce4e6538822731b2933209a1f3614e9282 \ + --hash=sha256:894b3a42502226a1cac872f840030665f33326fc3dac8e57c607905773cdcde3 \ + --hash=sha256:c1f9540be57940698ed329904db803cf7a402f3fc200bfe599334c9bd84a40b2 \ + --hash=sha256:de749064336d37e340f640b05f24e9e3dd678c57318c7289d222a8a2f543e90c \ + --hash=sha256:e29554e2bef54a90aa5cc07da6ce955accb83f21ab5de01a62c8478897b264fd \ + --hash=sha256:f2618db89be1b4e05f7a1a847a9c1c0abd63e63a1607d892dd54668dd92faf87 \ + --hash=sha256:fd83c01228a688733f1ded5201c678f0c53ecc1006ffbc404db9f7a899ac6249 \ + --hash=sha256:fe27749d33bb772c80dcd84ae7e8df2adc920ae8297400dabec45f0dedb3f6de + # via + # contourpy + # matplotlib + # opencv-contrib-python-headless + # pandas +opencv-contrib-python-headless==4.12.0.88 \ + --hash=sha256:3d8a7b23a5faba4ad34e13f51668c56be791e57ab02d68d9016200fed3c12c77 \ + --hash=sha256:85b520e527052a85a682f09cdc12e5f156f56d8c277261b4b65b48431abae96f \ + --hash=sha256:902888b4e1b4826c721840d9107e91d32f146a2c3bc8cb728f0088bf44204e4b \ + --hash=sha256:a17ebb914f309afe72447c33b9187ff02f23f1483faa5c0ffde7aadc88711e2a \ + --hash=sha256:b183e2322468c9d3bd9cac4ba44b272d828ec22842395bcfa51df31765224c0a \ + --hash=sha256:c57e32812fea2a542bb220088fb3ce8a210fe114c9454d1c9e8cd162e1a1fde8 \ + --hash=sha256:d60a12b915c55a50468c013fcd839e941b49ccc1f37b914b62543382c36bf81d + # via river-annotation-tool +packaging==26.2 \ + --hash=sha256:5fc45236b9446107ff2415ce77c807cee2862cb6fac22b8a73826d0693b0980e \ + --hash=sha256:ff452ff5a3e828ce110190feff1178bb1f2ea2281fa2075aadb987c2fb221661 + # via + # ipykernel + # jupyter-events + # jupyter-server + # jupyterlab + # jupyterlab-server + # matplotlib + # nbconvert +pandas==3.0.2 \ + --hash=sha256:08504503f7101300107ecdc8df73658e4347586db5cfdadabc1592e9d7e7a0fd \ + --hash=sha256:232a70ebb568c0c4d2db4584f338c1577d81e3af63292208d615907b698a0f18 \ + --hash=sha256:32cc41f310ebd4a296d93515fcac312216adfedb1894e879303987b8f1e2b97d \ + --hash=sha256:970762605cff1ca0d3f71ed4f3a769ea8f85fc8e6348f6e110b8fea7e6eb5a14 \ + --hash=sha256:a4785e1d6547d8427c5208b748ae2efb64659a21bd82bf440d4262d02bfa02a4 \ + --hash=sha256:aff4e6f4d722e0652707d7bcb190c445fe58428500c6d16005b02401764b1b3d \ + --hash=sha256:ef8b27695c3d3dc78403c9a7d5e59a62d5464a7e1123b4e0042763f7104dc74f \ + --hash=sha256:f4753e73e34c8d83221ba58f232433fca2748be8b18dbca02d242ed153945043 \ + --hash=sha256:f8d68083e49e16b84734eb1a4dcae4259a75c90fb6e2251ab9a00b61120c06ab + # via river-annotation-tool +pandocfilters==1.5.1 \ + --hash=sha256:002b4a555ee4ebc03f8b66307e287fa492e4a77b4ea14d3f934328297bb4939e \ + --hash=sha256:93be382804a9cdb0a7267585f157e5d1731bbe5545a85b268d6f5fe6232de2bc + # via nbconvert +parso==0.8.6 \ + --hash=sha256:2b9a0332696df97d454fa67b81618fd69c35a7b90327cbe6ba5c92d2c68a7bfd \ + --hash=sha256:2c549f800b70a5c4952197248825584cb00f033b29c692671d3bf08bf380baff + # via jedi +pexpect==4.9.0 ; sys_platform != 'emscripten' and sys_platform != 'win32' \ + --hash=sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523 \ + --hash=sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f + # via ipython +pillow==12.2.0 \ + --hash=sha256:01afa7cf67f74f09523699b4e88c73fb55c13346d212a59a2db1f86b0a63e8c5 \ + --hash=sha256:03e7e372d5240cc23e9f07deca4d775c0817bffc641b01e9c3af208dbd300987 \ + --hash=sha256:2d192a155bbcec180f8564f693e6fd9bccff5a7af9b32e2e4bf8c9c69dbad6b5 \ + --hash=sha256:58f62cc0f00fd29e64b29f4fd923ffdb3859c9f9e6105bfc37ba1d08994e8940 \ + --hash=sha256:62f5409336adb0663b7caa0da5c7d9e7bdbaae9ce761d34669420c2a801b2780 \ + --hash=sha256:7371b48c4fa448d20d2714c9a1f775a81155050d383333e0a6c15b1123dda005 \ + --hash=sha256:7f84204dee22a783350679a0333981df803dac21a0190d706a50475e361c93f5 \ + --hash=sha256:a830b1a40919539d07806aa58e1b114df53ddd43213d9c8b75847eee6c0182b5 \ + --hash=sha256:af73337013e0b3b46f175e79492d96845b16126ddf79c438d7ea7ff27783a414 \ + --hash=sha256:b86024e52a1b269467a802258c25521e6d742349d760728092e1bc2d135b4d76 \ + --hash=sha256:f3f40b3c5a968281fd507d519e444c35f0ff171237f4fdde090dd60699458421 \ + --hash=sha256:fc3d34d4a8fbec3e88a79b92e5465e0f9b842b628675850d860b8bd300b159f5 + # via + # matplotlib + # river-annotation-tool +platformdirs==4.9.6 \ + --hash=sha256:3bfa75b0ad0db84096ae777218481852c0ebc6c727b3168c1b9e0118e458cf0a \ + --hash=sha256:e61adb1d5e5cb3441b4b7710bea7e4c12250ca49439228cc1021c00dcfac0917 + # via + # jupyter-core + # python-discovery + # virtualenv +pre-commit==4.6.0 \ + --hash=sha256:718d2208cef53fdc38206e40524a6d4d9576d103eb16f0fec11c875e7716e9d9 \ + --hash=sha256:e2cf246f7299edcabcf15f9b0571fdce06058527f0a06535068a86d38089f29b +prometheus-client==0.25.0 \ + --hash=sha256:5e373b75c31afb3c86f1a52fa1ad470c9aace18082d39ec0d2f918d11cc9ba28 \ + --hash=sha256:d5aec89e349a6ec230805d0df882f3807f74fd6c1a2fa86864e3c2279059fed1 + # via jupyter-server +prompt-toolkit==3.0.52 \ + --hash=sha256:28cde192929c8e7321de85de1ddbe736f1375148b02f2e17edd840042b1be855 \ + --hash=sha256:9aac639a3bbd33284347de5ad8d68ecc044b91a762dc39b7c21095fcd6a19955 + # via ipython +propcache==0.5.2 \ + --hash=sha256:01c4fc7480cd0598bb4b57022df55b9ca296da7fc5a8760bd8451a7e63a7d427 \ + --hash=sha256:178b4a2cdaac1818e2bf1c5a99b94383fa73ea5382e032a48dec07dc5668dc42 \ + --hash=sha256:1d1ad32d9d4355e2be65574fd0bfd3677e7066b009cd5b9b2dee8aa6a6393b33 \ + --hash=sha256:2800a4a8ead6b28cccd1ec54b59346f0def7922ee1c7598e8499c733cfbb7c84 \ + --hash=sha256:45f11346f884bc47444f6e6647131055844134c3175b629f84952e2b5cd62b64 \ + --hash=sha256:5671d09a36b06d0fd4a3da0fccbcae360e9b1570924171a15e9e0997f0249fba \ + --hash=sha256:5dbc581d2814337da56222fab8dc5f161cd798a434e49bac27930aaef798e144 \ + --hash=sha256:6f328175a2cde1f0ff2c4ed8ce968b9dcfb55f3a7153f39e2957ed994da13476 \ + --hash=sha256:80168e2ebe4d3ec6599d10ad8f520304ae1cad9b6c5a95372aef1b66b7bfb53a \ + --hash=sha256:806719138ecd720339a12410fb9614ac9b2b2d3a5fdf8235d56981c36f4039ba \ + --hash=sha256:857187f381f88c8e2fa2fe56ab94879d011b883d5a2ee5a1b60a8cd2a06846d9 \ + --hash=sha256:8c7972d8f193740d9175f0998ab38717e6cd322d5935c5b0fef8c0d323fd9031 \ + --hash=sha256:8e778ebd44ef4f66ed60a0416b06b489687db264a9c0b3620362f26489492913 \ + --hash=sha256:be1ddfcbb376e3de5d2e2db1d58d6d67463e6b4f9f040c000de8e300295465fe \ + --hash=sha256:c0cb9ed24c8964e172768d455a38254c2dd8a552905729ce006cad3d3dda59b1 \ + --hash=sha256:c80f4ba3e8f00189165999a742ee526ebeccedf6c3f7beb0c7df821e9772435a \ + --hash=sha256:d9ee8826a7d47863a08ac44e1a5f611a462eefc3a194b492da242128bec75b42 \ + --hash=sha256:db2b80ea58eab4f86b2beec3cc8b39e8ff9276ac20e96b7cce43c8ae84cd6b5a \ + --hash=sha256:e5cbfac9f61484f7e9f3597775500cd3ebe8274e9b050c38f9525c77c97520bf + # via + # aiohttp + # yarl +psutil==7.2.2 \ + --hash=sha256:0746f5f8d406af344fd547f1c8daa5f5c33dbc293bb8d6a16d80b4bb88f59372 \ + --hash=sha256:076a2d2f923fd4821644f5ba89f059523da90dc9014e85f8e45a5774ca5bc6f9 \ + --hash=sha256:1a7b04c10f32cc88ab39cbf606e117fd74721c831c98a27dc04578deb0c16979 \ + --hash=sha256:8c233660f575a5a89e6d4cb65d9f938126312bca76d8fe087b947b3a1aaac9ee \ + --hash=sha256:b0726cecd84f9474419d67252add4ac0cd9811b04d61123054b9fb6f57df6e9e \ + --hash=sha256:b58fabe35e80b264a4e3bb23e6b96f9e45a3df7fb7eed419ac0e5947c61e47cc \ + --hash=sha256:eb7e81434c8d223ec4a219b5fc1c47d0417b12be7ea866e24fb5ad6e84b3d988 \ + --hash=sha256:ed0cace939114f62738d808fdcecd4c869222507e266e574799e9c0faa17d486 \ + --hash=sha256:fd04ef36b4a6d599bbdb225dd1d3f51e00105f6d48a28f006da7f9822f2606d8 + # via + # ipykernel + # ipython +ptyprocess==0.7.0 ; os_name != 'nt' or (sys_platform != 'emscripten' and sys_platform != 'win32') \ + --hash=sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35 \ + --hash=sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220 + # via + # pexpect + # terminado +pure-eval==0.2.3 \ + --hash=sha256:1db8e35b67b3d218d818ae653e27f06c3aa420901fa7b081ca98cbedc874e0d0 \ + --hash=sha256:5f4e983f40564c576c7c8635ae88db5956bb2229d7e9237d03b3c0b0190eaf42 + # via stack-data +pycparser==3.0 ; implementation_name != 'PyPy' \ + --hash=sha256:600f49d217304a5902ac3c37e1281c9fe94e4d0489de643a9504c5cdfdfc6b29 \ + --hash=sha256:b727414169a36b7d524c1c3e31839a521725078d7b2ff038656844266160a992 + # via cffi +pygments==2.20.0 \ + --hash=sha256:6757cd03768053ff99f3039c1a36d6c0aa0b263438fcab17520b30a303a82b5f \ + --hash=sha256:81a9e26dd42fd28a23a2d169d86d7ac03b46e2f8b59ed4698fb4785f946d0176 + # via + # ipython + # ipython-pygments-lexers + # nbconvert +pyparsing==3.3.2 \ + --hash=sha256:850ba148bd908d7e2411587e247a1e4f0327839c40e2e5e6d05a007ecc69911d \ + --hash=sha256:c777f4d763f140633dcb6d8a3eda953bf7a214dc4eff598413c070bcdc117cbc + # via matplotlib +pyside6==6.11.0 \ + --hash=sha256:1f2735dc4f2bd4ec452ae50502c8a22128bba0aced35358a2bbc58384b820c6f \ + --hash=sha256:267b344c73580ac938ca63c611881fb42a3922ebfe043e271005f4f06c372c4e \ + --hash=sha256:9092cb002ca43c64006afb2e0d0f6f51aef17aa737c33a45e502326a081ddcbc \ + --hash=sha256:b15f39acc2b8f46251a630acad0d97f9a0a0461f2baffcd66d7adfada8eb641e \ + --hash=sha256:c642e2d25704ca746fd37f56feacf25c5aecc4cd40bef23d18eec81f87d9dc00 + # via river-annotation-tool +pyside6-addons==6.11.0 \ + --hash=sha256:413e6121c24f5ffdce376298059eddecff74aa6d638e94e0f6015b33d29b889e \ + --hash=sha256:8ffb40222456078930816ebcac2f2511716d2acbc11716dd5acc5c365179a753 \ + --hash=sha256:aaaee83385977a0fe134b2f4fbfb92b45a880d5b656e4d90a708eef10b1b6de8 \ + --hash=sha256:ac6fe3d4ef4497dde3efc5e896b0acd53ff6c93be4bf485f045690f919419f35 \ + --hash=sha256:d5eaa4643302e3a0fa94c5766234bee4073d7d5ab9c2b7fd222692a176faf182 + # via pyside6 +pyside6-essentials==6.11.0 \ + --hash=sha256:3b3362882ad9389357a80504e600180006a957731fec05786fced7b038461fdf \ + --hash=sha256:4854cb0a1b061e7a576d8fb7bb7cf9f49540d558b1acb7df0742a7afefe61e4e \ + --hash=sha256:81ca603dbf21bc39f89bb42db215c25ebe0c879a1a4c387625c321d2730ec187 \ + --hash=sha256:85d6ca87ef35fa6565d385ede72ae48420dd3f63113929d10fc800f6b0360e01 \ + --hash=sha256:dc20e7afd5fc6fe51297db91cef997ce60844be578f7a49fc61b7ab9657a8849 + # via + # pyside6 + # pyside6-addons +python-dateutil==2.9.0.post0 \ + --hash=sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3 \ + --hash=sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 + # via + # aiobotocore + # arrow + # botocore + # jupyter-client + # matplotlib + # pandas +python-discovery==1.2.2 \ + --hash=sha256:876e9c57139eb757cb5878cbdd9ae5379e5d96266c99ef731119e04fffe533bb \ + --hash=sha256:e1ae95d9af875e78f15e19aed0c6137ab1bb49c200f21f5061786490c9585c7a + # via virtualenv +python-dotenv==1.2.2 \ + --hash=sha256:1d8214789a24de455a8b8bd8ae6fe3c6b69a5e3d64aa8a8e5d68e694bbcb285a \ + --hash=sha256:2c371a91fbd7ba082c2c1dc1f8bf89ca22564a087c2c287cd9b662adde799cf3 + # via river-annotation-tool +python-json-logger==4.1.0 \ + --hash=sha256:132994765cf75bf44554be9aa49b06ef2345d23661a96720262716438141b6b2 \ + --hash=sha256:b396b9e3ed782b09ff9d6e4f1683d46c83ad0d35d2e407c09a9ebbf038f88195 + # via jupyter-events +pywinpty==3.0.3 ; os_name == 'nt' \ + --hash=sha256:15e79d870e18b678fb8a5a6105fd38496b55697c66e6fc0378236026bc4d59e9 \ + --hash=sha256:523441dc34d231fb361b4b00f8c99d3f16de02f5005fd544a0183112bcc22412 \ + --hash=sha256:c9081df0e49ffa86d15db4a6ba61530630e48707f987df42c9d3313537e81fc0 + # via + # jupyter-server + # jupyter-server-terminals + # terminado +pyyaml==6.0.3 \ + --hash=sha256:41715c910c881bc081f1e8872880d3c650acf13dfa8214bad49ed4cede7c34ea \ + --hash=sha256:5fcd34e47f6e0b794d17de1b4ff496c00986e1c83f7ab2fb8fcfe9616ff7477b \ + --hash=sha256:5fdec68f91a0c6739b380c83b951e2c72ac0197ace422360e6d5a959d8d97b2c \ + --hash=sha256:64386e5e707d03a7e172c0701abfb7e10f0fb753ee1d773128192742712a98fd \ + --hash=sha256:7f047e29dcae44602496db43be01ad42fc6f1cc0d8cd6c83d342306c32270196 \ + --hash=sha256:8dc52c23056b9ddd46818a57b78404882310fb473d63f17b07d5c40421e47f8e \ + --hash=sha256:9149cad251584d5fb4981be1ecde53a1ca46c891a79788c0df828d2f166bda28 \ + --hash=sha256:96b533f0e99f6579b3d4d4995707cf36df9100d67e0c8303a0c55b27b5f99bc5 \ + --hash=sha256:ba1cc08a7ccde2d2ec775841541641e4548226580ab850948cbfda66a1befcdc \ + --hash=sha256:d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f \ + --hash=sha256:fc09d0aa354569bc501d4e787133afc08552722d3ab34836a80547331bb5d4a0 + # via + # jupyter-events + # pre-commit + # river-annotation-tool +pyzmq==27.1.0 \ + --hash=sha256:0de3028d69d4cdc475bfe47a6128eb38d8bc0e8f4d69646adfbcd840facbac28 \ + --hash=sha256:1c179799b118e554b66da67d88ed66cd37a169f1f23b5d9f0a231b4e8d44a113 \ + --hash=sha256:250e5436a4ba13885494412b3da5d518cd0d3a278a1ae640e113c073a5f88edd \ + --hash=sha256:3837439b7f99e60312f0c926a6ad437b067356dc2bc2ec96eb395fd0fe804233 \ + --hash=sha256:43ad9a73e3da1fab5b0e7e13402f0b2fb934ae1c876c51d0afff0e7c052eca31 \ + --hash=sha256:452631b640340c928fa343801b0d07eb0c3789a5ffa843f6e1a9cee0ba4eb4fc \ + --hash=sha256:75a2f36223f0d535a0c919e23615fc85a1e23b71f40c7eb43d7b1dedb4d8f15f \ + --hash=sha256:9ce490cf1d2ca2ad84733aa1d69ce6855372cb5ce9223802450c9b2a7cba0ccf \ + --hash=sha256:ac0765e3d44455adb6ddbf4417dcce460fc40a05978c08efdf2948072f6db540 \ + --hash=sha256:cf44a7763aea9298c0aa7dbf859f87ed7012de8bda0f3977b6fb1d96745df856 \ + --hash=sha256:f30f395a9e6fbca195400ce833c731e7b64c3919aa481af4d88c3759e0cb7496 + # via + # ipykernel + # jupyter-client + # jupyter-server +referencing==0.37.0 \ + --hash=sha256:381329a9f99628c9069361716891d34ad94af76e461dcb0335825aecc7692231 \ + --hash=sha256:44aefc3142c5b842538163acb373e24cce6632bd54bdb01b21ad5863489f50d8 + # via + # jsonschema + # jsonschema-specifications + # jupyter-events +requests==2.33.1 \ + --hash=sha256:18817f8c57c6263968bc123d237e3b8b08ac046f5456bd1e307ee8f4250d3517 \ + --hash=sha256:4e6d1ef462f3626a1f0a0a9c42dd93c63bad33f9f1c1937509b8c5c8718ab56a + # via jupyterlab-server +rfc3339-validator==0.1.4 \ + --hash=sha256:138a2abdf93304ad60530167e51d2dfb9549521a836871b88d7f4695d0022f6b \ + --hash=sha256:24f6ec1eda14ef823da9e36ec7113124b39c04d50a4d3d3a3c2859577e7791fa + # via + # jsonschema + # jupyter-events +rfc3986-validator==0.1.1 \ + --hash=sha256:2f235c432ef459970b4306369336b9d5dbdda31b510ca1e327636e01f528bfa9 \ + --hash=sha256:3d44bde7921b3b9ec3ae4e3adca370438eccebc676456449b145d533b240d055 + # via + # jsonschema + # jupyter-events +rfc3987-syntax==1.1.0 \ + --hash=sha256:6c3d97604e4c5ce9f714898e05401a0445a641cfa276432b0a648c80856f6a3f \ + --hash=sha256:717a62cbf33cffdd16dfa3a497d81ce48a660ea691b1ddd7be710c22f00b4a0d + # via jsonschema +rpds-py==0.30.0 \ + --hash=sha256:1ab5b83dbcf55acc8b08fc62b796ef672c457b17dbd7820a11d6c52c06839bdf \ + --hash=sha256:2e6ecb5a5bcacf59c3f912155044479af1d0b6681280048b338b28e364aca1f6 \ + --hash=sha256:47f236970bccb2233267d89173d3ad2703cd36a0e2a6e92d0560d333871a3d23 \ + --hash=sha256:5ba103fb455be00f3b1c2076c9d4264bfcb037c976167a6047ed82f23153f02e \ + --hash=sha256:669b1805bd639dd2989b281be2cfd951c6121b65e729d9b843e9639ef1fd555e \ + --hash=sha256:6abc8880d9d036ecaafe709079969f56e876fcf107f7a8e9920ba6d5a3878d05 \ + --hash=sha256:73c67f2db7bc334e518d097c6d1e6fed021bbc9b7d678d6cc433478365d1d5f5 \ + --hash=sha256:7cee9c752c0364588353e627da8a7e808a66873672bcb5f52890c33fd965b394 \ + --hash=sha256:a090322ca841abd453d43456ac34db46e8b05fd9b3b4ac0c78bcde8b089f959b \ + --hash=sha256:a1010ed9524c73b94d15919ca4d41d8780980e1765babf85f9a2f90d247153dd \ + --hash=sha256:a161f20d9a43006833cd7068375a94d035714d73a172b681d8881820600abfad \ + --hash=sha256:a8fa71a2e078c527c3e9dc9fc5a98c9db40bcc8a92b4e8858e36d329f8684b51 \ + --hash=sha256:ca28829ae5f5d569bb62a79512c842a03a12576375d5ece7d2cadf8abe96ec28 \ + --hash=sha256:d948b135c4693daff7bc2dcfc4ec57237a29bd37e60c2fabf5aff2bbacf3e2f1 \ + --hash=sha256:dd8ff7cf90014af0c0f787eea34794ebf6415242ee1d6fa91eaba725cc441e84 \ + --hash=sha256:f8d1736cfb49381ba528cd5baa46f82fdc65c06e843dab24dd70b63d09121b3f + # via + # jsonschema + # referencing +ruff==0.15.0 \ + --hash=sha256:238a717ef803e501b6d51e0bdd0d2c6e8513fe9eec14002445134d3907cd46c3 \ + --hash=sha256:5298d518e493061f2eabd4abd067c7e4fb89e2f63291c94332e35631c07c3662 \ + --hash=sha256:650bd9c56ae03102c51a5e4b554d74d825ff3abe4db22b90fd32d816c2e90621 \ + --hash=sha256:6bdea47cdbea30d40f8f8d7d69c0854ba7c15420ec75a26f463290949d7f7e9a \ + --hash=sha256:6f6e80850a01eb13b3e42ee0ebdf6e4497151b48c35051aab51c101266d187a3 \ + --hash=sha256:6f811f97b0f092b35320d1556f3353bf238763420ade5d9e62ebd2b73f2ff179 \ + --hash=sha256:761ec0a66680fab6454236635a39abaf14198818c8cdf691e036f4bc0f406b2d \ + --hash=sha256:77e515f6b15f828b94dc17d2b4ace334c9ddb7d9468c54b2f9ed2b9c1593ef16 \ + --hash=sha256:940f11c2604d317e797b289f4f9f3fa5555ffe4fb574b55ed006c3d9b6f0eb78 \ + --hash=sha256:9a121a96db1d75fa3eb39c4539e607f628920dd72ff1f7c5ee4f1b768ac62d6e \ + --hash=sha256:a6664b7eac559e3048223a2da77769c2f92b43a6dfd4720cef42654299a599c9 \ + --hash=sha256:aac4ebaa612a82b23d45964586f24ae9bc23ca101919f5590bdb368d74ad5455 \ + --hash=sha256:afb6e603d6375ff0d6b0cee563fa21ab570fd15e65c852cb24922cef25050cf1 \ + --hash=sha256:bcbca3d40558789126da91d7ef9a7c87772ee107033db7191edefa34e2c7f1b4 \ + --hash=sha256:c480d632cc0ca3f0727acac8b7d053542d9e114a462a145d0b00e7cd658c515a \ + --hash=sha256:d747e3319b2bce179c7c1eaad3d884dc0a199b5f4d5187620530adf9105268ce \ + --hash=sha256:dcd4be7cc75cfbbca24a98d04d0b9b36a270d0833241f776b788d59f4142b14d \ + --hash=sha256:dd5e4d3301dc01de614da3cdffc33d4b1b96fb89e45721f1598e5532ccf78b18 +s3fs==2026.4.0 \ + --hash=sha256:5bdce0abb00b0435ee150807a45fea727451dbc22de4cbc116464f8504ab9d37 \ + --hash=sha256:de0d2a1f33cdf03831fd2382d278c6e4e31fe57c3bf2f703c61f8aec6b703e2a + # via river-annotation-tool +send2trash==2.1.0 \ + --hash=sha256:0da2f112e6d6bb22de6aa6daa7e144831a4febf2a87261451c4ad849fe9a873c \ + --hash=sha256:1c72b39f09457db3c05ce1d19158c2cbef4c32b8bedd02c155e49282b7ea7459 + # via jupyter-server +setuptools==82.0.1 \ + --hash=sha256:7d872682c5d01cfde07da7bccc7b65469d3dca203318515ada1de5eda35efbf9 \ + --hash=sha256:a59e362652f08dcd477c78bb6e7bd9d80a7995bc73ce773050228a348ce2e5bb + # via jupyterlab +shiboken6==6.11.0 \ + --hash=sha256:3bd76cf56105ab2d62ecaff630366f11264f69b88d488f10f048da9a065781f4 \ + --hash=sha256:483ff78a73c7b3189ca924abc694318084f078bcfeaffa68e32024ff2d025ee1 \ + --hash=sha256:a10dc7718104ea2dc15d5b0b96909b77162ce1c76fcc6968e6df692b947a00e9 \ + --hash=sha256:ad54e64f8192ddbdff0c54ac82b89edcd62ed623f502ea21c960541d19514053 \ + --hash=sha256:d88e8a1eb705f2b9ad21db08a61ae1dc0c773e5cd86a069de0754c4cf1f9b43b + # via + # pyside6 + # pyside6-addons + # pyside6-essentials +six==1.17.0 \ + --hash=sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274 \ + --hash=sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81 + # via + # python-dateutil + # rfc3339-validator +soupsieve==2.8.3 \ + --hash=sha256:3267f1eeea4251fb42728b6dfb746edc9acaffc4a45b27e19450b676586e8349 \ + --hash=sha256:ed64f2ba4eebeab06cc4962affce381647455978ffc1e36bb79a545b91f45a95 + # via beautifulsoup4 +stack-data==0.6.3 \ + --hash=sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9 \ + --hash=sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695 + # via ipython +terminado==0.18.1 \ + --hash=sha256:a4468e1b37bb318f8a86514f65814e1afc977cf29b3992a4500d9dd305dcceb0 \ + --hash=sha256:de09f2c4b85de4765f7714688fff57d3e75bad1f909b589fde880460c753fd2e + # via + # jupyter-server + # jupyter-server-terminals +tinycss2==1.4.0 \ + --hash=sha256:10c0972f6fc0fbee87c3edb76549357415e94548c1ae10ebccdea16fb404a9b7 \ + --hash=sha256:3a49cf47b7675da0b15d0c6e1df8df4ebd96e9394bb905a5775adb0d884c5289 + # via bleach +tornado==6.5.5 \ + --hash=sha256:192b8f3ea91bd7f1f50c06955416ed76c6b72f96779b962f07f911b91e8d30e9 \ + --hash=sha256:2c9a876e094109333f888539ddb2de4361743e5d21eece20688e3e351e4990a6 \ + --hash=sha256:36abed1754faeb80fbd6e64db2758091e1320f6bba74a4cf8c09cd18ccce8aca \ + --hash=sha256:3f54aa540bdbfee7b9eb268ead60e7d199de5021facd276819c193c0fb28ea4e \ + --hash=sha256:435319e9e340276428bbdb4e7fa732c2d399386d1de5686cb331ec8eee754f07 \ + --hash=sha256:487dc9cc380e29f58c7ab88f9e27cdeef04b2140862e5076a66fb6bb68bb1bfa \ + --hash=sha256:6443a794ba961a9f619b1ae926a2e900ac20c34483eea67be4ed8f1e58d3ef7b \ + --hash=sha256:65a7f1d46d4bb41df1ac99f5fcb685fb25c7e61613742d5108b010975a9a6521 \ + --hash=sha256:dd3eafaaeec1c7f2f8fdcd5f964e8907ad788fe8a5a32c4426fbbdda621223b7 \ + --hash=sha256:e74c92e8e65086b338fd56333fb9a68b9f6f2fe7ad532645a290a464bcf46be5 + # via + # ipykernel + # jupyter-client + # jupyter-server + # jupyterlab + # notebook + # terminado +traitlets==5.14.3 \ + --hash=sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7 \ + --hash=sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f + # via + # ipykernel + # ipython + # jupyter-client + # jupyter-core + # jupyter-events + # jupyter-server + # jupyterlab + # matplotlib-inline + # nbclient + # nbconvert + # nbformat +typing-extensions==4.15.0 \ + --hash=sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466 \ + --hash=sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548 + # via + # aiosignal + # anyio + # beautifulsoup4 + # referencing +tzdata==2026.2 \ + --hash=sha256:9173fde7d80d9018e02a662e168e5a2d04f87c41ea174b139fbef642eda62d10 \ + --hash=sha256:bbe9af844f658da81a5f95019480da3a89415801f6cc966806612cc7169bffe7 + # via + # arrow + # pandas +uri-template==1.3.0 \ + --hash=sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7 \ + --hash=sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363 + # via jsonschema +urllib3==2.6.3 \ + --hash=sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed \ + --hash=sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4 + # via + # botocore + # requests +virtualenv==21.3.0 \ + --hash=sha256:4d28ee41f6d9ec8f1f00cd472b9ffbcedda1b3d3b9a575b5c94a2d004fd51bd7 \ + --hash=sha256:733750db978ec95c2d8eb4feadaa57091002bce404cb39ba69899cf7bd28944e + # via pre-commit +wcwidth==0.6.0 \ + --hash=sha256:1a3a1e510b553315f8e146c54764f4fb6264ffad731b3d78088cdb1478ffbdad \ + --hash=sha256:cdc4e4262d6ef9a1a57e018384cbeb1208d8abbc64176027e2c2455c81313159 + # via prompt-toolkit +webcolors==25.10.0 \ + --hash=sha256:032c727334856fc0b968f63daa252a1ac93d33db2f5267756623c210e57a4f1d \ + --hash=sha256:62abae86504f66d0f6364c2a8520de4a0c47b80c03fc3a5f1815fedbef7c19bf + # via jsonschema +webencodings==0.5.1 \ + --hash=sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78 \ + --hash=sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923 + # via + # bleach + # tinycss2 +websocket-client==1.9.0 \ + --hash=sha256:9e813624b6eb619999a97dc7958469217c3176312b3a16a4bd1bc7e08a46ec98 \ + --hash=sha256:af248a825037ef591efbf6ed20cc5faa03d3b47b9e5a2230a529eeee1c1fc3ef + # via jupyter-server +wrapt==2.1.2 \ + --hash=sha256:1c51c738d7d9faa0b3601708e7e2eda9bf779e1b601dce6c77411f2a1b324a63 \ + --hash=sha256:3996a67eecc2c68fd47b4e3c564405a5777367adfd9b8abb58387b63ee83b21e \ + --hash=sha256:6433ea84e1cfacf32021d2a4ee909554ade7fd392caa6f7c13f1f4bf7b8e8748 \ + --hash=sha256:6f2c5390460de57fa9582bc8a1b7a6c86e1a41dfad74c5225fc07044c15cc8d1 \ + --hash=sha256:79847b83eb38e70d93dc392c7c5b587efe65b3e7afcc167aa8abd5d60e8761c8 \ + --hash=sha256:7dfa9f2cf65d027b951d05c662cc99ee3bd01f6e4691ed39848a7a5fffc902b2 \ + --hash=sha256:b8fd6fa2b2c4e7621808f8c62e8317f4aae56e59721ad933bac5239d913cf0e8 \ + --hash=sha256:c20b757c268d30d6215916a5fa8461048d023865d888e437fab451139cad6c8e \ + --hash=sha256:c8e46ae8e4032792eb2f677dbd0d557170a8e5524d22acc55199f43efedd39bf \ + --hash=sha256:e3d3b35eedcf5f7d022291ecd7533321c4775f7b9cd0050a31a68499ba45757c \ + --hash=sha256:eba8155747eb2cae4a0b913d9ebd12a1db4d860fc4c829d7578c7b989bd3f2f0 \ + --hash=sha256:f8fba1bae256186a83d1875b2b1f4e2d1242e8fac0f58ec0d7e41b26967b965c \ + --hash=sha256:ff2aad9c4cda28a8f0653fc2d487596458c2a3f475e56ba02909e950a9efa6a9 + # via aiobotocore +yarl==1.24.2 \ + --hash=sha256:044a09d8401fcf8681977faef6d286b8ade1e2d2e9dceda175d1cfa5ca496f30 \ + --hash=sha256:2783d9226db8797636cd6896e4de81feed252d1db72265686c9558d97a4d94b9 \ + --hash=sha256:3065657c80a2321225e804048597ad55658a7e76b32d6f5ee4074d04c50401db \ + --hash=sha256:3b075301a2836a0e297b1b658cb6d6135df535d62efefdd60366bd589c2c82f2 \ + --hash=sha256:47a55d6cf6db2f401017a9e96e5288844e5051911fb4e0c8311a3980f5e59a7d \ + --hash=sha256:507cc19f0b45454e2d6dcd62ff7d062b9f77a2812404e62dbdaec05b50faa035 \ + --hash=sha256:5ec8356b8a6afcf81fc7aeeef13b1ff7a49dec00f313394bbb9e83830d32ccd7 \ + --hash=sha256:7dafe10c12ddd4d120d528c4b5599c953bd7b12845347d507b95451195bb6cad \ + --hash=sha256:7e7ebcdef69dec6c6451e616f32b622a6d4a2e92b445c992f7c8e5274a6bbc4c \ + --hash=sha256:8ae44649b00947634ab0dab2a374a638f52923a6e67083f2c156cd5cbd1a881d \ + --hash=sha256:990de4f680b1c217e77ff0d6aa0029f9eb79889c11fb3e9a3942c7eba29c1996 \ + --hash=sha256:9ac374123c6fd7abf64d1fec93962b0bd4ee2c19751755a762a72dd96c0378f8 \ + --hash=sha256:abb8ec0323b80161e3802da3150ef660b41d0e9be2048b76a363d93eee992c2b \ + --hash=sha256:b975866c184564c827e0877380f0dae57dcca7e52782128381b72feff6dfceb8 \ + --hash=sha256:c4c17bad5a530912d2111825d3f05e89bab2dd376aaa8cbc77e449e6db63e576 \ + --hash=sha256:cb84b80d88e19ede158619b80813968713d8d008b0e2497a576e6a0557d50712 \ + --hash=sha256:e30dd55825dc554ec5b66a94953b8eda8745926514c5089dfcacecb9c99b5bd1 \ + --hash=sha256:e7977781f83638a4c73e0f88425563d70173e0dfd90ac006a45c65036293ee3c \ + --hash=sha256:f5f0cbb112838a4a293985b6ed73948a547dadcc1ba6d2089938e7abdedceef8 + # via aiohttp diff --git a/src/river_annotation_tool/annotation_script.py b/src/river_annotation_tool/annotation_script.py index 0ac4864..68c0cf4 100644 --- a/src/river_annotation_tool/annotation_script.py +++ b/src/river_annotation_tool/annotation_script.py @@ -11,6 +11,7 @@ from PySide6.QtWidgets import QApplication, QMessageBox from .annotator import Annotator from .config import load_config +from .filesystem import make_fs def parse_args(): @@ -40,6 +41,13 @@ def parse_args(): if __name__ == "__main__": + try: + from dotenv import load_dotenv + + load_dotenv() + except ImportError: + pass + args = parse_args() cfg = load_config(Path(args.config)) @@ -50,6 +58,8 @@ if __name__ == "__main__": if args.clips: cfg.clips_file = args.clips + fs = make_fs(cfg.storage) + app = QApplication([]) try: win = Annotator( @@ -57,6 +67,7 @@ if __name__ == "__main__": clip=args.clip, extras=args.extras, skip_annotated=not args.no_skip, + fs=fs, ) except RuntimeError as e: QMessageBox.information(None, "No clips", str(e)) diff --git a/src/river_annotation_tool/annotator.py b/src/river_annotation_tool/annotator.py index 8c01bfd..d678e8f 100644 --- a/src/river_annotation_tool/annotator.py +++ b/src/river_annotation_tool/annotator.py @@ -1,3 +1,4 @@ +import io import json from pathlib import Path @@ -22,6 +23,7 @@ from PySide6.QtWidgets import ( from .clip_selector import ClipSelector from .compute_optical_flow import compute_optical_flow_mask from .config import AppConfig, load_optical_flow_config +from .filesystem import fsjoin, fsname, fsstem from .mask_canvas import MaskCanvas from .video_loader import load_frames @@ -33,11 +35,13 @@ class Annotator(QMainWindow): clip: str = None, extras: bool = False, skip_annotated: bool = True, + fs=None, ): super().__init__() self.cfg = config - self.out_dir = Path(config.out_dir) + self.fs = fs + self.out_dir = config.out_dir self.extras = extras self.of_cfg = ( load_optical_flow_config(Path(config.optical_flow_config_file)) @@ -46,15 +50,16 @@ class Annotator(QMainWindow): ) self.selector = ClipSelector( - data_dir=Path(config.data_dir), + data_dir=config.data_dir, out_dir=self.out_dir, clips_file=Path(config.clips_file), mask_filename=config.filenames.mask, zip_extension=config.filenames.zip_extension, skip_annotated=skip_annotated, + fs=fs, ) - self.history: list[Path] = [] + self.history: list[str] = [] self.history_pos: int = -1 self.setWindowTitle("River Annotator") @@ -63,8 +68,53 @@ class Annotator(QMainWindow): self._init_ui() self._init_timer() + # ── filesystem helpers ───────────────────────────────────────── + def _out_path(self, *parts: str) -> str: + return fsjoin(self.out_dir, fsstem(self.filename), *parts) + + def _fs_exists(self, path: str) -> bool: + if self.fs is None: + return Path(path).exists() + return self.fs.exists(path) + + def _fs_makedirs(self, path: str): + if self.fs is None: + Path(path).mkdir(parents=True, exist_ok=True) + else: + self.fs.makedirs(path, exist_ok=True) + + def _pil_open(self, path: str) -> Image.Image: + if self.fs is None: + return Image.open(path) + with self.fs.open(path, "rb") as f: + return Image.open(io.BytesIO(f.read())) + + def _pil_save(self, img: Image.Image, path: str): + if self.fs is None: + img.save(path) + else: + ext = str(path).rsplit(".", 1)[-1].lower() + fmt = "JPEG" if ext in ("jpg", "jpeg") else ext.upper() + buf = io.BytesIO() + img.save(buf, format=fmt) + self.fs.pipe(path, buf.getvalue()) + + def _json_read(self, path: str): + if self.fs is None: + with open(path) as f: + return json.load(f) + with self.fs.open(path, "r") as f: + return json.load(f) + + def _json_write(self, data, path: str): + if self.fs is None: + with open(path, "w") as f: + json.dump(data, f, indent=2) + else: + self.fs.pipe(path, json.dumps(data, indent=2).encode()) + # ── clip loading ─────────────────────────────────────────────── - def _load_clip(self, specific: str = None, path: Path = None): + def _load_clip(self, specific: str = None, path: str = None): if path is not None: self.filename = path else: @@ -76,6 +126,7 @@ class Annotator(QMainWindow): self.cfg.fps_fallback, self.cfg.filenames.video_in_zip, self.cfg.filenames.video_tmp_suffix, + fs=self.fs, ) self._pending_answers = self._read_saved_answers() @@ -85,10 +136,10 @@ class Annotator(QMainWindow): self.history_pos = len(self.history) - 1 def _read_saved_mask(self): - mask_path = self.out_dir / self.filename.stem / self.cfg.filenames.mask - if not mask_path.exists(): + mask_path = self._out_path(self.cfg.filenames.mask) + if not self._fs_exists(mask_path): return None - mask_full = np.array(Image.open(mask_path).convert("L")) + mask_full = np.array(self._pil_open(mask_path).convert("L")) return cv2.resize( (mask_full > 127).astype(np.uint8), (self.dw, self.dh), @@ -96,16 +147,15 @@ class Annotator(QMainWindow): ) def _read_saved_answers(self): - meta_path = self.out_dir / self.filename.stem / self.cfg.filenames.metadata - if not meta_path.exists(): + meta_path = self._out_path(self.cfg.filenames.metadata) + if not self._fs_exists(meta_path): return None - with open(meta_path) as f: - return json.load(f) + return self._json_read(meta_path) # ── UI setup ─────────────────────────────────────────────────── def _init_ui(self): self.mc = MaskCanvas(self.frames, self.dh, self.dw) - self.mc.set_title(self.filename.name) + self.mc.set_title(fsname(self.filename)) self.mc.reset(self._read_saved_mask()) self.q_widgets = {} @@ -279,22 +329,34 @@ class Annotator(QMainWindow): overlay[m] = (1 - alpha) * overlay[m] + alpha * green[m] return overlay.astype(np.uint8) - def _save_gif(self, frames, out_path, scale=1.0): + def _save_gif(self, frames, out_path: str, scale=1.0): h, w = frames[0].shape[:2] nh, nw = max(1, int(h * scale)), max(1, int(w * scale)) pil_frames = [Image.fromarray(cv2.resize(f, (nw, nh))) for f in frames] - pil_frames[0].save( - out_path, - save_all=True, - append_images=pil_frames[1:], - duration=int(1000 / self.fps), - loop=0, - ) + if self.fs is None: + pil_frames[0].save( + out_path, + save_all=True, + append_images=pil_frames[1:], + duration=int(1000 / self.fps), + loop=0, + ) + else: + buf = io.BytesIO() + pil_frames[0].save( + buf, + format="GIF", + save_all=True, + append_images=pil_frames[1:], + duration=int(1000 / self.fps), + loop=0, + ) + self.fs.pipe(out_path, buf.getvalue()) # ── actions ──────────────────────────────────────────────────── def save(self): - out = self.out_dir / self.filename.stem - out.mkdir(parents=True, exist_ok=True) + out = fsjoin(self.out_dir, fsstem(self.filename)) + self._fs_makedirs(out) mask_full = cv2.resize( self.mc.mask.astype(np.uint8), @@ -302,25 +364,28 @@ class Annotator(QMainWindow): interpolation=cv2.INTER_NEAREST, ) fn = self.cfg.filenames - Image.fromarray(mask_full * 255).save(out / fn.mask) - - with open(out / fn.metadata, "w") as f: - json.dump(self.get_answers(), f, indent=2) + self._pil_save(Image.fromarray(mask_full * 255), fsjoin(out, fn.mask)) + self._json_write(self.get_answers(), fsjoin(out, fn.metadata)) mid = len(self.frames) // 2 frame = self.frames[mid] - Image.fromarray(frame).save(out / fn.frame) - Image.fromarray(self._make_overlay(frame)).save(out / fn.overlay) + self._pil_save(Image.fromarray(frame), fsjoin(out, fn.frame)) + self._pil_save( + Image.fromarray(self._make_overlay(frame)), fsjoin(out, fn.overlay) + ) if self.extras: - Image.fromarray((self.mc.mask * 255).astype(np.uint8)).save( - out / fn.mask_vis + self._pil_save( + Image.fromarray((self.mc.mask * 255).astype(np.uint8)), + fsjoin(out, fn.mask_vis), ) overlay_frames = [self._make_overlay(f) for f in self.frames] - self._save_gif(self.frames, out / fn.gif_original_hires, scale=1.0) - self._save_gif(self.frames, out / fn.gif_original_lowres, scale=0.5) - self._save_gif(overlay_frames, out / fn.gif_overlay_hires, scale=1.0) - self._save_gif(overlay_frames, out / fn.gif_overlay_lowres, scale=0.5) + self._save_gif(self.frames, fsjoin(out, fn.gif_original_hires), scale=1.0) + self._save_gif(self.frames, fsjoin(out, fn.gif_original_lowres), scale=0.5) + self._save_gif(overlay_frames, fsjoin(out, fn.gif_overlay_hires), scale=1.0) + self._save_gif( + overlay_frames, fsjoin(out, fn.gif_overlay_lowres), scale=0.5 + ) print("Saved:", out) @@ -331,7 +396,7 @@ class Annotator(QMainWindow): self.dh, self.dw, mask=self._read_saved_mask(), - title=self.filename.name, + title=fsname(self.filename), ) if self._pending_answers: self._set_answers(self._pending_answers) @@ -366,12 +431,12 @@ class Annotator(QMainWindow): self._switch_ui_to_clip() def next_clip(self): - mask_path = self.out_dir / self.filename.stem / self.cfg.filenames.mask - if mask_path.exists(): + mask_path = self._out_path(self.cfg.filenames.mask) + if self._fs_exists(mask_path): msg = QMessageBox(self) msg.setWindowTitle("Existing annotation found") msg.setText( - f"'{self.filename.stem}' already has a saved annotation.\n" + f"'{fsstem(self.filename)}' already has a saved annotation.\n" "Replace it with your current work, or keep the existing save?" ) btn_replace = msg.addButton( @@ -408,13 +473,15 @@ class Annotator(QMainWindow): ) return prev_clip = self.selector.clips[idx - 1] - mask_path = self.out_dir / prev_clip.stem / self.cfg.filenames.mask - if not mask_path.exists(): + mask_path = fsjoin(self.out_dir, fsstem(prev_clip), self.cfg.filenames.mask) + if not self._fs_exists(mask_path): QMessageBox.information( - self, "No mask found", f"No saved mask found for '{prev_clip.stem}'." + self, + "No mask found", + f"No saved mask found for '{fsstem(prev_clip)}'.", ) return - mask_full = np.array(Image.open(mask_path).convert("L")) + mask_full = np.array(self._pil_open(mask_path).convert("L")) mask = cv2.resize( (mask_full > 127).astype(np.uint8), (self.dw, self.dh), diff --git a/src/river_annotation_tool/clip_selector.py b/src/river_annotation_tool/clip_selector.py index 02b34ae..a73b71f 100644 --- a/src/river_annotation_tool/clip_selector.py +++ b/src/river_annotation_tool/clip_selector.py @@ -1,50 +1,72 @@ from pathlib import Path +from .filesystem import fsjoin, fsstem + class ClipSelector: def __init__( self, - data_dir: Path, - out_dir: Path, + data_dir, + out_dir, clips_file: Path, mask_filename: str = "mask.png", zip_extension: str = ".zip", skip_annotated: bool = True, + fs=None, ): - self.data_dir = data_dir - self.out_dir = out_dir + self.data_dir = str(data_dir) + self.out_dir = str(out_dir) self.mask_filename = mask_filename self.zip_extension = zip_extension self.skip_annotated = skip_annotated + self.fs = fs self.clips = self._load_clips(clips_file) self.index = 0 - def _load_clips(self, clips_file: Path) -> list[Path]: + def _load_clips(self, clips_file: Path) -> list: lines = clips_file.read_text().splitlines() return [ - self.data_dir / name.strip() + fsjoin(self.data_dir, name.strip()) for name in lines if name.strip() and not name.strip().startswith("#") ] - def is_annotated(self, path: Path) -> bool: - return (self.out_dir / path.stem / self.mask_filename).exists() + def is_annotated(self, path) -> bool: + mask_path = fsjoin(self.out_dir, fsstem(path), self.mask_filename) + if self.fs is None: + return Path(mask_path).exists() + return self.fs.exists(mask_path) - def next(self, specific: str = None) -> Path: + def next(self, specific: str = None) -> str: if specific: return self._resolve_specific(specific) return self._pick_next() - def _resolve_specific(self, specific: str) -> Path: - matches = list(self.data_dir.glob(f"{specific}{self.zip_extension}")) - if not matches: - p = self.data_dir / specific - matches = [p] if p.exists() else [] - if not matches: - raise FileNotFoundError(f"Clip '{specific}' not found in {self.data_dir}") - return matches[0] + def _resolve_specific(self, specific: str) -> str: + if self.fs is None: + data_dir = Path(self.data_dir) + matches = list(data_dir.glob(f"{specific}{self.zip_extension}")) + if not matches: + p = data_dir / specific + matches = [p] if p.exists() else [] + if not matches: + raise FileNotFoundError( + f"Clip '{specific}' not found in {self.data_dir}" + ) + return str(matches[0]) + else: + pattern = fsjoin(self.data_dir, f"{specific}{self.zip_extension}") + matches = self.fs.glob(pattern) + if not matches: + p = fsjoin(self.data_dir, specific) + matches = [p] if self.fs.exists(p) else [] + if not matches: + raise FileNotFoundError( + f"Clip '{specific}' not found in {self.data_dir}" + ) + return matches[0] - def _pick_next(self) -> Path: + def _pick_next(self) -> str: while self.index < len(self.clips): clip = self.clips[self.index] self.index += 1 diff --git a/src/river_annotation_tool/config.py b/src/river_annotation_tool/config.py index 80a3a64..230bd9e 100644 --- a/src/river_annotation_tool/config.py +++ b/src/river_annotation_tool/config.py @@ -22,6 +22,7 @@ class FilenameConfig: @dataclass class AppConfig: + storage: str # required: 'local' or 's3' display_max: int = 480 fps_fallback: int = 25 max_frames: int = 100 @@ -71,6 +72,10 @@ def load_optical_flow_config(path: Path) -> OpticalFlowConfig: def load_config(path: Path) -> AppConfig: with open(path) as f: data = yaml.safe_load(f) + if "storage" not in data: + raise ValueError( + f"{path}: missing required field 'storage'. Set it to 'local' or 's3'." + ) fn_data = data.pop("filenames", {}) cfg = AppConfig(**data) cfg.filenames = FilenameConfig(**fn_data) diff --git a/src/river_annotation_tool/filesystem.py b/src/river_annotation_tool/filesystem.py new file mode 100644 index 0000000..b63ba16 --- /dev/null +++ b/src/river_annotation_tool/filesystem.py @@ -0,0 +1,35 @@ +import os + + +_DEFAULT_ENDPOINT = "https://os.zhdk.cloud.switch.ch" + + +def make_fs(storage: str): + """Return an S3FileSystem for storage='s3', or None for local.""" + if storage != "s3": + return None + import s3fs + + return s3fs.S3FileSystem( + key=os.environ["S3_ACCESS_KEY"], + secret=os.environ["S3_SECRET_ACCESS_KEY"], + client_kwargs={ + "endpoint_url": os.environ.get("S3_ENDPOINT_URL", _DEFAULT_ENDPOINT) + }, + ) + + +def fsjoin(base, *parts: str) -> str: + """Join path segments with forward slashes (works for both local and S3).""" + return "/".join([str(base).rstrip("/"), *[str(p).strip("/") for p in parts if p]]) + + +def fsstem(path) -> str: + """Filename stem (no extension) for local Path or S3 string.""" + name = str(path).replace("\\", "/").split("/")[-1] + return name.rsplit(".", 1)[0] if "." in name else name + + +def fsname(path) -> str: + """Filename component (with extension) for local Path or S3 string.""" + return str(path).replace("\\", "/").split("/")[-1] diff --git a/src/river_annotation_tool/mask_canvas.py b/src/river_annotation_tool/mask_canvas.py index 6055f62..43d4f3c 100644 --- a/src/river_annotation_tool/mask_canvas.py +++ b/src/river_annotation_tool/mask_canvas.py @@ -11,7 +11,7 @@ class MaskCanvas: """Matplotlib canvas with brush/polygon mask drawing, undo/redo, and erase.""" _BRUSH_DEFAULT = 5 - _ALPHA_DEFAULT = 40 + _ALPHA_DEFAULT = 15 _BRIGHTNESS_DEFAULT = 0 _CONTRAST_DEFAULT = 0 _GAMMA_DEFAULT = 100 diff --git a/src/river_annotation_tool/video_loader.py b/src/river_annotation_tool/video_loader.py index 59a07dc..8c866cb 100644 --- a/src/river_annotation_tool/video_loader.py +++ 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