From 143f3d30088bb49a8f406d916cb183c98d8fd6f6 Mon Sep 17 00:00:00 2001 From: Mackenzie Mathis Date: Fri, 13 Jun 2025 16:15:46 +0200 Subject: [PATCH 01/28] Update benchmark.py - changed benchmarking link; needs testing --- dlclive/benchmark.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dlclive/benchmark.py b/dlclive/benchmark.py index 4cb4fb1..a07faca 100644 --- a/dlclive/benchmark.py +++ b/dlclive/benchmark.py @@ -37,7 +37,7 @@ def download_benchmarking_data( target_dir=".", - url="http://deeplabcut.rowland.harvard.edu/datasets/dlclivebenchmark.tar.gz", + url="https://huggingface.co/datasets/mwmathis/DLCspeed_benchmarking/blob/main/Data-DLC-live-benchmark.zip", ): """ Downloads a DeepLabCut-Live benchmarking Data (videos & DLC models). From 41ee58f55ecb5a0f54bc0292711d6058ead00136 Mon Sep 17 00:00:00 2001 From: Jonny Saunders Date: Fri, 13 Jun 2025 07:18:30 -0700 Subject: [PATCH 02/28] fix #112 - don't subset cpuinfo (#114) --- dlclive/benchmark.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dlclive/benchmark.py b/dlclive/benchmark.py index 4cb4fb1..1d53042 100644 --- a/dlclive/benchmark.py +++ b/dlclive/benchmark.py @@ -130,7 +130,7 @@ def get_system_info() -> dict: else: from cpuinfo import get_cpu_info - dev = [get_cpu_info()["brand"]] + dev = get_cpu_info() dev_type = "CPU" return { From 662e2233d41c31797f225005bc095f1cd00d06f6 Mon Sep 17 00:00:00 2001 From: maximpavliv <37336830+maximpavliv@users.noreply.github.com> Date: Fri, 13 Jun 2025 16:30:25 +0200 Subject: [PATCH 03/28] Introduce CI/CD for TensorFLow version (#122) * Update .gitignore * CI/CD update python version * CI/CD update actions versions * CI/CD update trigger events * CI/CD update MacOS version * dlclibrary set version to >=0.0.6 * Poetry lock * Poetry lock * Pyproject.toml update tensorflow installation * Poetry lock * Install specific tensorflow-io-gcs-filesystem for windows * Poetry lock * Update deprecated section name * Poetry lock * CI/CD test on python 3.11, 3.12, 3.13 as well * Update testing.yml - rename to main - add tables installation --------- Co-authored-by: Mackenzie Mathis --- .github/workflows/python-package.yml | 6 +- .github/workflows/testing.yml | 20 +- .gitignore | 4 + poetry.lock | 4104 +++++++++++++++++++++----- pyproject.toml | 11 +- 5 files changed, 3443 insertions(+), 702 deletions(-) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index 893cf7f..c0b0d9a 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -16,12 +16,12 @@ jobs: strategy: # You can use PyPy versions in python-version. 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\"cygwin\""] +cover = ["pytest-cov"] +doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +enabler = ["pytest-enabler (>=2.2)"] +test = ["big-O", "importlib-resources ; python_version < \"3.9\"", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] +type = ["pytest-mypy"] + +[[package]] +name = "zipp" +version = "3.22.0" +description = "Backport of pathlib-compatible object wrapper for zip files" +optional = false +python-versions = ">=3.9" +groups = ["main"] +markers = "platform_machine != \"aarch64\" and python_version == \"3.9\" and (platform_system != \"Darwin\" or platform_machine != \"arm64\" and platform_machine != \"aarch64\") and (sys_platform == \"linux\" or sys_platform == \"win32\" or sys_platform == \"darwin\")" files = [ - {file = "zipp-3.11.0-py3-none-any.whl", hash = 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"rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +enabler = ["pytest-enabler (>=2.2)"] +test = ["big-O", "importlib_resources ; python_version < \"3.9\"", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more_itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] +type = ["pytest-mypy"] [metadata] -lock-version = "2.0" +lock-version = "2.1" python-versions = ">=3.7.1,<3.11" -content-hash = "ad15582ffb25a7c02b39cb1e2855f8d241fe819c33987928e1075f96d2df614a" +content-hash = "629a4f04421a7439bcecf8f9bf35f746eee687d208f3b970ba69a6ed236cb52c" diff --git a/pyproject.toml b/pyproject.toml index 0566071..63981b1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -33,13 +33,18 @@ colorcet = "^3.0.0" Pillow = ">=8.0.0" py-cpuinfo = ">=5.0.0" tqdm = "^4.62.3" -tensorflow = "^2.7.0,<=2.12" +tensorflow = [ + { version = "^2.7.0,<=2.12", markers = "sys_platform == 'linux'" }, + { version = "^2.7.0,<=2.10", markers = "sys_platform == 'win32'" } +] +tensorflow-macos = { version = "^2.7.0,<=2.12", markers = "sys_platform == 'darwin'" } +tensorflow-io-gcs-filesystem = { version = "0.27", markers = "sys_platform == 'win32' and python_version >= '3.10' and python_version < '3.11'" } pandas = "^1.3" tables = "^3.6" opencv-python-headless = "^4.5" -dlclibrary = ">=0.0.2" +dlclibrary = ">=0.0.6" -[tool.poetry.dev-dependencies] +[tool.poetry.group.dev.dependencies] [build-system] requires = ["poetry-core>=1.0.0"] From 36557a80b853921390044e1cdab19d8e476e55b6 Mon Sep 17 00:00:00 2001 From: Mackenzie Mathis Date: Fri, 13 Jun 2025 16:33:18 +0200 Subject: [PATCH 04/28] master --> main --- .github/workflows/python-package.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index c0b0d9a..9bdf762 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -4,9 +4,9 @@ name: Python package on: push: - branches: [ master ] + branches: [ main ] pull_request: - branches: [ master ] + branches: [ main ] jobs: From 4004ef9ede4c237307aa52310d92d16bbf948946 Mon Sep 17 00:00:00 2001 From: Mackenzie Mathis Date: Fri, 13 Jun 2025 16:42:27 +0200 Subject: [PATCH 05/28] Update testing.yml --- .github/workflows/testing.yml | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml index f3582a6..2952193 100644 --- a/.github/workflows/testing.yml +++ b/.github/workflows/testing.yml @@ -31,6 +31,14 @@ jobs: uses: actions/setup-python@v4 with: python-version: ${{ matrix.python-version }} + + - name: Set up Python + uses: conda-incubator/setup-miniconda@v3 + with: + channels: conda-forge,defaults + channel-priority: strict + python-version: ${{ matrix.python-version }} + - name: Install ffmpeg run: | if [ "$RUNNER_OS" == "Linux" ]; then @@ -42,10 +50,11 @@ jobs: choco install ffmpeg fi shell: bash + - name: Install PyTables through Conda - shell: bash -el {0} # Important: activates the conda environment - run: | - conda install pytables==3.8.0 "numpy<2" + shell: bash -el {0} # Important: activates the conda environment + run: | + conda install pytables==3.8.0 "numpy<2" - name: Install and test run: | python -m pip install --upgrade pip wheel poetry From f8fc8ba13bddb2aecbfe80c85d7cff0da2f76622 Mon Sep 17 00:00:00 2001 From: Mackenzie Mathis Date: Sat, 14 Jun 2025 14:12:53 +0200 Subject: [PATCH 06/28] Update benchmark.py --- dlclive/benchmark.py | 39 ++++++++++++++++++--------------------- 1 file changed, 18 insertions(+), 21 deletions(-) diff --git a/dlclive/benchmark.py b/dlclive/benchmark.py index 0c79e65..1230150 100644 --- a/dlclive/benchmark.py +++ b/dlclive/benchmark.py @@ -42,37 +42,34 @@ def download_benchmarking_data( """ Downloads a DeepLabCut-Live benchmarking Data (videos & DLC models). """ + import os import urllib.request - import tarfile from tqdm import tqdm + import zipfile def show_progress(count, block_size, total_size): pbar.update(block_size) - def tarfilenamecutting(tarf): - """' auxfun to extract folder path - ie. /xyz-trainsetxyshufflez/ - """ - for memberid, member in enumerate(tarf.getmembers()): - if memberid == 0: - parent = str(member.path) - l = len(parent) + 1 - if member.path.startswith(parent): - member.path = member.path[l:] - yield member - response = urllib.request.urlopen(url) + total_size = int(response.getheader("Content-Length")) + pbar = tqdm(unit="B", total=total_size, position=0, desc="Downloading") + filename, _ = urllib.request.urlretrieve(url, filename=zip_path, reporthook=show_progress) + pbar.close() + + class DownloadProgressBar(tqdm): + def update_to(self, b=1, bsize=1, tsize=None): + if tsize is not None: + self.total = tsize + self.update(b * bsize - self.n) + + zip_path = os.path.join(target_dir, "Data-DLC-live-benchmark.zip") print( - "Downloading the benchmarking data from the DeepLabCut server @Harvard -> Go Crimson!!! {}....".format( - url - ) + f"Downloading the benchmarking data from {url} ..." ) - total_size = int(response.getheader("Content-Length")) - pbar = tqdm(unit="B", total=total_size, position=0) - filename, _ = urllib.request.urlretrieve(url, reporthook=show_progress) - with tarfile.open(filename, mode="r:gz") as tar: - tar.extractall(target_dir, members=tarfilenamecutting(tar)) + print(f"Extracting {zip_path} to {target_dir} ...") + with zipfile.ZipFile(zip_path, 'r') as zip_ref: + zip_ref.extractall(target_dir) def get_system_info() -> dict: """ Return summary info for system running benchmark From 88038acfb9d4eba4142048512a8b9dacc22f8dd2 Mon Sep 17 00:00:00 2001 From: Mackenzie Mathis Date: Sat, 14 Jun 2025 16:30:11 +0200 Subject: [PATCH 07/28] Update README & benchmarking script (#126) * Update README.md fix test instructions * Update check_install.py * Update check_install.py * Update check_install.py --- README.md | 11 ++++++++--- dlclive/check_install/check_install.py | 22 ++++++++++++---------- 2 files changed, 20 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index 311d22c..fc95f8d 100644 --- a/README.md +++ b/README.md @@ -41,12 +41,17 @@ For more details and examples, see documentation [here](dlclive/processor/README Please see our instruction manual to install on a [Windows or Linux machine](docs/install_desktop.md) or on a [NVIDIA Jetson Development Board](docs/install_jetson.md). Note, this code works with tensorflow (TF) 1 or TF 2 models, but TF requires that whatever version you exported your model with, you must import with the same version (i.e., export with TF1.13, then use TF1.13 with DlC-Live; export with TF2.3, then use TF2.3 with DLC-live). - available on pypi as: `pip install deeplabcut-live` + -Note, you can then test your installation by running: +Note, you can then test your installation by installing poetry (`pip install poetry`), then running: -`dlc-live-test` -If installed properly, this script will i) create a temporary folder ii) download the full_dog model from the [DeepLabCut Model Zoo](http://www.mousemotorlab.org/dlc-modelzoo), iii) download a short video clip of a dog, and iv) run inference while displaying keypoints. v) remove the temporary folder. + +```python +poetry run dlc-live-test +``` + +If installed properly, this script will i) create a temporary folder ii) download the SuperAnimal-Quadruped model from the [DeepLabCut Model Zoo](http://www.mousemotorlab.org/dlc-modelzoo), iii) download a short video clip of a dog, and iv) run inference while displaying keypoints. v) remove the temporary folder. DLC LIVE TEST diff --git a/dlclive/check_install/check_install.py b/dlclive/check_install/check_install.py index 7601533..031d913 100755 --- a/dlclive/check_install/check_install.py +++ b/dlclive/check_install/check_install.py @@ -5,7 +5,8 @@ Licensed under GNU Lesser General Public License v3.0 """ - +import os +import urllib.request import sys import shutil import warnings @@ -41,26 +42,27 @@ def main(): if not display: print('Running without displaying video') - # make temporary directory in $HOME - # TODO: why create this temp directory in $HOME? + # make temporary directory in $current print("\nCreating temporary directory...\n") - tmp_dir = Path().home() / 'dlc-live-tmp' - tmp_dir.mkdir(mode=0o775,exist_ok=True) + tmp_dir = Path.cwd() / 'dlc-live-tmp' + tmp_dir.mkdir(mode=0o775, exist_ok=True) video_file = str(tmp_dir / 'dog_clip.avi') model_dir = tmp_dir / 'DLC_Dog_resnet_50_iteration-0_shuffle-0' # download dog test video from github: - # TODO: Should check if the video's already there before downloading it (should have been cloned with the files) - print(f"Downloading Video to {video_file}") - url_link = "https://github.com/DeepLabCut/DeepLabCut-live/blob/master/check_install/dog_clip.avi?raw=True" - urllib.request.urlretrieve(url_link, video_file, reporthook=urllib_pbar) + if not os.path.exists(video_file): + print(f"Downloading Video to {video_file}") + url_link = "https://github.com/DeepLabCut/DeepLabCut-live/blob/main/check_install/dog_clip.avi?raw=True" + urllib.request.urlretrieve(url_link, video_file, reporthook=urllib_pbar) + else: + print(f"Video already exists at {video_file}") # download model from the DeepLabCut Model Zoo if Path(model_dir / SNAPSHOT_NAME).exists(): print('Model already downloaded, using cached version') else: - print("Downloading full_dog model from the DeepLabCut Model Zoo...") + print("Downloading a test model from the DeepLabCut Model Zoo...") download_huggingface_model(MODEL_NAME, model_dir) # assert these things exist so we can give informative error messages From a74101f52c275f3320a00a4a55273cfbe7432b73 Mon Sep 17 00:00:00 2001 From: Mackenzie Mathis Date: Sun, 15 Jun 2025 15:12:01 +0200 Subject: [PATCH 08/28] Update pyproject.toml (#125) * Update pyproject.toml - adding support for python 3.11, 3.12, 3.13 * Update testing.yml * Update testing.yml * Update pyproject.toml * Update testing.yml * Update pyproject.toml * Update poetry.lock * Update poetry.lock * Update pyproject.toml * Update pyproject.toml * Update pyproject.toml * Update pyproject.toml * Update pyproject.toml * Update pyproject.toml * Update pyproject.toml * Update pyproject.toml * Update pyproject.toml * Update testing.yml * Update pyproject.toml * Update pyproject.toml * Update pyproject.toml * Update testing.yml * Update pyproject.toml * Update pyproject.toml * Update poetry.lock * Update testing.yml * Update testing.yml * Update testing.yml - remove poetry (for now) * Update testing.yml * Update testing.yml * Update testing.yml * Update testing.yml * Update testing.yml * updated poetry.lock * updated * Update testing.yml * Update pyproject.toml * Update pyproject.toml * Update pyproject.toml * Update pyproject.toml * Update testing.yml testing 3.11 * Update testing.yml * Update pyproject.toml * Update pyproject.toml * Update pyproject.toml * Update testing.yml * updated test for win32 * Update testing.yml * Update testing.yml * Update testing.yml * Update testing.yml --- .github/workflows/testing.yml | 30 +- poetry.lock | 3252 +++++++++------------------------ pyproject.toml | 25 +- 3 files changed, 866 insertions(+), 2441 deletions(-) diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml index 2952193..27ea85a 100644 --- a/.github/workflows/testing.yml +++ b/.github/workflows/testing.yml @@ -13,15 +13,16 @@ jobs: strategy: fail-fast: false matrix: - os: [ubuntu-latest, macos-latest, windows-latest] - python-version: ["3.12", "3.13"] + os: [ubuntu-latest, macos-latest] #windows-latest + python-version: ["3.9", "3.10"] #3.11+ issues with TF + exclude: + - os: macos-latest + python-version: "3.9" include: - os: ubuntu-latest path: ~/.cache/pip - os: macos-latest path: ~/Library/Caches/pip - - os: windows-latest - path: ~\AppData\Local\pip\Cache steps: - name: Checkout code @@ -55,8 +56,19 @@ jobs: shell: bash -el {0} # Important: activates the conda environment run: | conda install pytables==3.8.0 "numpy<2" - - name: Install and test - run: | - python -m pip install --upgrade pip wheel poetry - python -m poetry install - python -m poetry run dlc-live-test --nodisplay + + - name: Install dependencies via Conda + shell: bash -el {0} + run: conda install -y "numpy>=1.26,<2.0" + + - name: Install Poetry + run: pip install --upgrade pip wheel poetry + + - name: Regenerate Poetry lock + run: poetry lock --no-cache + + - name: Install project dependencies + run: poetry install --no-root + + - name: Run DLC Live Tests + run: poetry run dlc-live-test --nodisplay diff --git a/poetry.lock b/poetry.lock index db98d98..938d1ce 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,18 +1,5 @@ # This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand. -[[package]] -name = "absl-py" -version = "2.1.0" -description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py." -optional = false -python-versions = ">=3.7" -groups = ["main"] -markers = "python_version < \"3.10\" and (python_version == \"3.9\" or python_version == \"3.7\" or platform_system == \"Darwin\" or platform_system == \"Linux\") and (sys_platform == \"linux\" or sys_platform == \"win32\" or sys_platform == \"darwin\") and (python_version == \"3.7\" or platform_system == \"Darwin\" or platform_machine == \"aarch64\") and (platform_system == \"Linux\" or python_version == \"3.9\" or python_version == \"3.7\" or platform_machine == \"arm64\") and (platform_machine == \"arm64\" or platform_machine == \"aarch64\" or python_version == \"3.7\")" -files = [ - 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From 6c61b29ecce4361fbc56f46a15d21aeb75d96e13 Mon Sep 17 00:00:00 2001 From: maximpavliv Date: Mon, 16 Jun 2025 10:26:03 +0200 Subject: [PATCH 09/28] Fix download_benchmarking_data() --- dlclive/benchmark.py | 35 +++++++++++++++++------------------ 1 file changed, 17 insertions(+), 18 deletions(-) diff --git a/dlclive/benchmark.py b/dlclive/benchmark.py index 1230150..9643b09 100644 --- a/dlclive/benchmark.py +++ b/dlclive/benchmark.py @@ -37,35 +37,34 @@ def download_benchmarking_data( target_dir=".", - url="https://huggingface.co/datasets/mwmathis/DLCspeed_benchmarking/blob/main/Data-DLC-live-benchmark.zip", + url="https://huggingface.co/datasets/mwmathis/DLCspeed_benchmarking/resolve/main/Data-DLC-live-benchmark.zip", ): """ - Downloads a DeepLabCut-Live benchmarking Data (videos & DLC models). + Downloads and extracts DeepLabCut-Live benchmarking data (videos & DLC models). """ import os import urllib.request from tqdm import tqdm import zipfile - def show_progress(count, block_size, total_size): - pbar.update(block_size) + # Avoid nested folder issue + if os.path.basename(os.path.normpath(target_dir)) == "Data-DLC-live-benchmark": + target_dir = os.path.dirname(os.path.normpath(target_dir)) + os.makedirs(target_dir, exist_ok=True) # Ensure target directory exists - response = urllib.request.urlopen(url) - total_size = int(response.getheader("Content-Length")) - pbar = tqdm(unit="B", total=total_size, position=0, desc="Downloading") - filename, _ = urllib.request.urlretrieve(url, filename=zip_path, reporthook=show_progress) - pbar.close() + zip_path = os.path.join(target_dir, "Data-DLC-live-benchmark.zip") - class DownloadProgressBar(tqdm): - def update_to(self, b=1, bsize=1, tsize=None): - if tsize is not None: - self.total = tsize - self.update(b * bsize - self.n) + if os.path.exists(zip_path): + print(f"{zip_path} already exists. Skipping download.") + else: + def show_progress(count, block_size, total_size): + pbar.update(block_size) - zip_path = os.path.join(target_dir, "Data-DLC-live-benchmark.zip") - print( - f"Downloading the benchmarking data from {url} ..." - ) + print(f"Downloading the benchmarking data from {url} ...") + pbar = tqdm(unit="B", total=0, position=0, desc="Downloading") + + filename, _ = urllib.request.urlretrieve(url, filename=zip_path, reporthook=show_progress) + pbar.close() print(f"Extracting {zip_path} to {target_dir} ...") with zipfile.ZipFile(zip_path, 'r') as zip_ref: From ef377467c2515b1fbdf1c8e2b59800e5dc4d0092 Mon Sep 17 00:00:00 2001 From: maximpavliv Date: Mon, 16 Jun 2025 10:26:25 +0200 Subject: [PATCH 10/28] Ignore __MACOSX directories --- .gitignore | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.gitignore b/.gitignore index 7fa3a49..2ce1454 100644 --- a/.gitignore +++ b/.gitignore @@ -8,6 +8,8 @@ benchmarking/results* **DS_Store* *vscode* +**/__MACOSX/ + # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] From ad14faa84fa60011d8a4a72c75be5a670230474e Mon Sep 17 00:00:00 2001 From: maximpavliv Date: Mon, 16 Jun 2025 13:12:04 +0200 Subject: [PATCH 11/28] Run tests on all PRs --- .github/workflows/testing.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml index d92f0e0..0400cf0 100644 --- a/.github/workflows/testing.yml +++ b/.github/workflows/testing.yml @@ -4,7 +4,6 @@ on: push: branches: [main] pull_request: - branches: [main] jobs: build: From d882b6bd2af6c51e9f06db125ddc62c54dfd567b Mon Sep 17 00:00:00 2001 From: maximpavliv Date: Mon, 16 Jun 2025 13:22:19 +0200 Subject: [PATCH 12/28] tensorflow-io-gcs-filesystem - python=3.9 --- poetry.lock | 12 ++++++------ pyproject.toml | 2 +- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/poetry.lock b/poetry.lock index 1a8f423..8b7dc28 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1468,10 +1468,10 @@ files = [ [package.dependencies] numpy = [ - {version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\""}, - {version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\""}, {version = ">=1.21.0", markers = "python_version == \"3.9\" and platform_system == \"Darwin\" and platform_machine == \"arm64\""}, {version = ">=1.19.3", markers = "(python_version >= \"3.8\" and platform_system == \"Linux\" and platform_machine == \"aarch64\" or python_version >= \"3.9\") and (python_version > \"3.9\" or platform_system != \"Darwin\" or platform_machine != \"arm64\")"}, + {version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\""}, + {version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\""}, {version = ">=1.23.5", markers = "python_version >= \"3.11\""}, ] @@ -1539,8 +1539,8 @@ files = [ [package.dependencies] numpy = [ - {version = ">=1.21.0", markers = "python_version >= \"3.10\""}, {version = ">=1.20.3", markers = "python_version < \"3.10\""}, + {version = ">=1.21.0", markers = "python_version >= \"3.10\""}, {version = ">=1.23.2", markers = "python_version >= \"3.11\""}, ] python-dateutil = ">=2.8.1" @@ -2492,7 +2492,7 @@ description = "TensorFlow IO" optional = true python-versions = ">=3.7, <3.11" groups = ["main"] -markers = "python_version == \"3.10\" and extra == \"tf\" and sys_platform == \"win32\"" +markers = "python_version < \"3.11\" and extra == \"tf\" and sys_platform == \"win32\"" files = [ {file = "tensorflow_io_gcs_filesystem-0.27.0-cp310-cp310-macosx_10_14_x86_64.whl", hash = "sha256:babca2a12755badd1517043f9d633823533fbd7b463d7d36e9e6179b246731dc"}, {file = "tensorflow_io_gcs_filesystem-0.27.0-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:b3a0ebfeac11507f6fc96162b8b22010b7d715bb0848311e54ef18d88f07014a"}, @@ -2526,7 +2526,7 @@ description = "TensorFlow IO" optional = true python-versions = "<3.13,>=3.7" groups = ["main"] -markers = "extra == \"tf\" and (python_version == \"3.9\" or python_version == \"3.11\" or sys_platform == \"linux\" or sys_platform == \"darwin\") and (sys_platform == \"win32\" or sys_platform == \"linux\" or sys_platform == \"darwin\")" +markers = "extra == \"tf\" and (python_version == \"3.11\" or sys_platform == \"linux\" or sys_platform == \"darwin\") and (sys_platform == \"win32\" or sys_platform == \"linux\" or sys_platform == \"darwin\")" files = [ {file = "tensorflow_io_gcs_filesystem-0.37.1-cp310-cp310-macosx_10_14_x86_64.whl", hash = "sha256:249c12b830165841411ba71e08215d0e94277a49c551e6dd5d72aab54fe5491b"}, {file = "tensorflow_io_gcs_filesystem-0.37.1-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:257aab23470a0796978efc9c2bcf8b0bc80f22e6298612a4c0a50d3f4e88060c"}, @@ -3005,4 +3005,4 @@ tf = ["tensorflow", "tensorflow", "tensorflow-io-gcs-filesystem", "tensorflow-io [metadata] lock-version = "2.1" python-versions = ">=3.9,<3.12" -content-hash = "0f103b32eca20c6e339e91abac0bbc0d7dc46da444623a8e02c4631ffb50aa69" +content-hash = "2d256ce0c45e58bd20fee0d3a9eda1a1c22498cad41c4a977bc10ce67c762f26" diff --git a/pyproject.toml b/pyproject.toml index e40c4f8..2745a32 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -49,7 +49,7 @@ tensorflow = [ ] tensorflow-macos = { version = "^2.7.0,<=2.12", optional = true, markers = "sys_platform == 'darwin'" } tensorflow-io-gcs-filesystem = [ - { version = "==0.27", optional = true, platform = "win32", python = ">=3.10,<3.11" }, + { version = "==0.27", optional = true, platform = "win32", python = ">=3.9,<3.11" }, { version = "*", optional = true, platform = "linux" }, { version = "*", optional = true, markers = "sys_platform == 'darwin'" } ] From 759912172cd57a7dd770bd28c1ed7fddd3953939 Mon Sep 17 00:00:00 2001 From: maximpavliv Date: Mon, 16 Jun 2025 13:42:42 +0200 Subject: [PATCH 13/28] Pin python to 3.10 version --- .github/workflows/testing.yml | 4 +- poetry.lock | 277 ++-------------------------------- pyproject.toml | 5 +- 3 files changed, 16 insertions(+), 270 deletions(-) diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml index 0400cf0..ece95c4 100644 --- a/.github/workflows/testing.yml +++ b/.github/workflows/testing.yml @@ -13,7 +13,7 @@ jobs: fail-fast: false matrix: os: [ubuntu-latest, macos-latest, windows-latest] - python-version: ["3.9", "3.10"] + python-version: ["3.10"] include: - os: ubuntu-latest path: ~/.cache/pip @@ -50,7 +50,7 @@ jobs: fi shell: bash - 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{file = "zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e"}, - {file = "zipp-3.23.0.tar.gz", hash = "sha256:a07157588a12518c9d4034df3fbbee09c814741a33ff63c05fa29d26a2404166"}, -] - -[package.extras] -check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""] -cover = ["pytest-cov"] -doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] -enabler = ["pytest-enabler (>=2.2)"] -test = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more_itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] -type = ["pytest-mypy"] - [extras] pytorch = ["scipy", "timm", "torch", "torchvision"] tf = ["tensorflow", "tensorflow", "tensorflow-io-gcs-filesystem", "tensorflow-io-gcs-filesystem", "tensorflow-io-gcs-filesystem", "tensorflow-macos"] [metadata] lock-version = "2.1" -python-versions = ">=3.9,<3.12" -content-hash = "2d256ce0c45e58bd20fee0d3a9eda1a1c22498cad41c4a977bc10ce67c762f26" +python-versions = ">=3.10,<3.12" +content-hash = "22ddf925629d3f3105598e87d028cb901e3fdd72c96d402e359622e9eab50825" diff --git a/pyproject.toml b/pyproject.toml index 2745a32..1034d8b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -9,7 +9,6 @@ homepage = "https://github.com/DeepLabCut/DeepLabCut-live" repository = "https://github.com/DeepLabCut/DeepLabCut-live" classifiers = [ "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)", @@ -25,7 +24,7 @@ dlc-live-test = "dlclive.check_install.check_install:main" dlc-live-benchmark = "dlclive.benchmark:main" [tool.poetry.dependencies] -python = ">=3.9,<3.12" +python = ">=3.10,<3.12" numpy = ">=1.26,<2.0" "ruamel.yaml" = "^0.17.20" colorcet = "^3.0.0" @@ -49,7 +48,7 @@ tensorflow = [ ] tensorflow-macos = { version = "^2.7.0,<=2.12", optional = true, markers = "sys_platform == 'darwin'" } tensorflow-io-gcs-filesystem = [ - { version = "==0.27", optional = true, platform = "win32", python = ">=3.9,<3.11" }, + { version = "==0.27", optional = true, platform = "win32", python = ">=3.10,<3.11" }, { version = "*", optional = true, platform = "linux" }, { version = "*", optional = true, markers = "sys_platform == 'darwin'" } ] From 199adc0283dc088b765a2bcd10339764eabcf613 Mon Sep 17 00:00:00 2001 From: maximpavliv Date: Mon, 16 Jun 2025 17:00:27 +0200 Subject: [PATCH 14/28] WIP refactor benchmarking: __init__.py --- dlclive/__init__.py | 1 + dlclive/benchmark.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/dlclive/__init__.py b/dlclive/__init__.py index 71a89d9..b1f5774 100644 --- a/dlclive/__init__.py +++ b/dlclive/__init__.py @@ -9,3 +9,4 @@ from dlclive.dlclive import DLCLive from dlclive.processor.processor import Processor from dlclive.version import VERSION, __version__ +from dlclive.benchmark import benchmark_videos, download_benchmarking_data \ No newline at end of file diff --git a/dlclive/benchmark.py b/dlclive/benchmark.py index 9719807..d63867c 100644 --- a/dlclive/benchmark.py +++ b/dlclive/benchmark.py @@ -16,9 +16,9 @@ import colorcet as cc import cv2 import numpy as np -import torch from PIL import ImageColor from pip._internal.operations import freeze +import torch try: import pandas as pd From e86db2d651260da33b8c964c33ef6b5c4533c3df Mon Sep 17 00:00:00 2001 From: maximpavliv Date: Mon, 16 Jun 2025 17:09:12 +0200 Subject: [PATCH 15/28] WIP refactor benchmarking: remove dupplicates --- dlclive/benchmark.py | 507 ---------------------------------------- dlclive/benchmark_tf.py | 146 ------------ 2 files changed, 653 deletions(-) diff --git a/dlclive/benchmark.py b/dlclive/benchmark.py index d63867c..a4a8230 100644 --- a/dlclive/benchmark.py +++ b/dlclive/benchmark.py @@ -135,513 +135,6 @@ def get_system_info() -> dict: } -def benchmark( - path: str | Path, - video_path: str | Path, - single_animal: bool = True, - resize: float | None = None, - pixels: int | None = None, - cropping: list[int] = None, - dynamic: tuple[bool, float, int] = (False, 0.5, 10), - n_frames: int = 1000, - print_rate: bool = False, - display: bool = False, - pcutoff: float = 0.0, - max_detections: int = 10, - display_radius: int = 3, - cmap: str = "bmy", - save_poses: bool = False, - save_video: bool = False, - output: str | Path | None = None, -) -> tuple[np.ndarray, tuple, dict]: - """Analyze DeepLabCut-live exported model on a video: - - Calculate inference time, display keypoints, or get poses/create a labeled video. - - Parameters - ---------- - path : str - path to exported DeepLabCut model - video_path : str - path to video file - single_animal: bool - to make code behave like DLCLive for tensorflow models - resize : int, optional - Resize factor. Can only use one of resize or pixels. If both are provided, will - use pixels. by default None - pixels : int, optional - Downsize image to this number of pixels, maintaining aspect ratio. Can only use - one of resize or pixels. If both are provided, will use pixels. by default None - cropping : list of int - cropping parameters in pixel number: [x1, x2, y1, y2] - dynamic: triple containing (state, detectiontreshold, margin) - If the state is true, then dynamic cropping will be performed. That means that - if an object is detected (i.e. any body part > detectiontreshold), then object - boundaries are computed according to the smallest/largest x position and - smallest/largest y position of all body parts. This window is expanded by the - margin and from then on only the posture within this crop is analyzed (until the - object is lost, i.e. < detectiontreshold). The current position is utilized for - updating the crop window for the next frame (this is why the margin is important - and should be set large enough given the movement of the animal) - n_frames : int, optional - number of frames to run inference on, by default 1000 - print_rate : bool, optional - flag to print inference rate frame by frame, by default False - display : bool, optional - flag to display keypoints on images. Useful for checking the accuracy of - exported models. - pcutoff : float, optional - likelihood threshold to display keypoints - max_detections: int - for top-down models, the maximum number of individuals to detect in a frame - display_radius : int, optional - size (radius in pixels) of keypoint to display - cmap : str, optional - a string indicating the :package:`colorcet` colormap, `options here - `, by default "bmy" - save_poses : bool, optional - flag to save poses to an hdf5 file. If True, operates similar to - :function:`DeepLabCut.benchmark_videos`, by default False - save_video : bool, optional - flag to save a labeled video. If True, operates similar to - :function:`DeepLabCut.create_labeled_video`, by default False - output : str, optional - path to directory to save pose and/or video file. If not specified, will use - the directory of video_path, by default None - - Returns - ------- - :class:`numpy.ndarray` - vector of inference times - tuple - (image width, image height) - dict - metadata for video - - Example - ------- - Return a vector of inference times for 10000 frames: - dlclive.benchmark('/my/exported/model', 'my_video.avi', n_frames=10000) - - Return a vector of inference times, resizing images to half the width and height for inference - dlclive.benchmark('/my/exported/model', 'my_video.avi', n_frames=10000, resize=0.5) - - Display keypoints to check the accuracy of an exported model - dlclive.benchmark('/my/exported/model', 'my_video.avi', display=True) - - Analyze a video (save poses to hdf5) and create a labeled video, similar to :function:`DeepLabCut.benchmark_videos` and :function:`create_labeled_video` - dlclive.benchmark('/my/exported/model', 'my_video.avi', save_poses=True, save_video=True) - """ - path = Path(path) - video_path = Path(video_path) - if not video_path.exists(): - raise ValueError(f"Could not find video: {video_path}: check that it exists!") - - if output is None: - output = video_path.parent - else: - output = Path(output) - output.mkdir(exist_ok=True, parents=True) - - # load video - cap = cv2.VideoCapture(str(video_path)) - ret, frame = cap.read() - n_frames = ( - n_frames - if (n_frames > 0) and (n_frames < cap.get(cv2.CAP_PROP_FRAME_COUNT) - 1) - else (cap.get(cv2.CAP_PROP_FRAME_COUNT) - 1) - ) - n_frames = int(n_frames) - im_size = ( - int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), - int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)), - ) - - # get resize factor - if pixels is not None: - resize = np.sqrt(pixels / (im_size[0] * im_size[1])) - - if resize is not None: - im_size = (int(im_size[0] * resize), int(im_size[1] * resize)) - - # create video writer - if save_video: - colors = None - out_vid_file = output / f"{video_path.stem}_DLCLIVE_LABELED.avi" - fourcc = cv2.VideoWriter_fourcc(*"DIVX") - fps = cap.get(cv2.CAP_PROP_FPS) - print(out_vid_file) - print(fourcc) - print(fps) - print(im_size) - vid_writer = cv2.VideoWriter(str(out_vid_file), fourcc, fps, im_size) - - # initialize DLCLive and perform inference - inf_times = np.zeros(n_frames) - poses = [] - - live = DLCLive( - model_path=path, - single_animal=single_animal, - resize=resize, - cropping=cropping, - dynamic=dynamic, - display=display, - max_detections=max_detections, - pcutoff=pcutoff, - display_radius=display_radius, - display_cmap=cmap, - ) - - poses.append(live.init_inference(frame)) - - iterator = range(n_frames) - if print_rate or display: - iterator = tqdm(iterator) - - for i in iterator: - ret, frame = cap.read() - if not ret: - warnings.warn( - f"Did not complete {n_frames:d} frames. There probably were not enough " - f"frames in the video {video_path}." - ) - break - - start_pose = time.time() - poses.append(live.get_pose(frame)) - inf_times[i] = time.time() - start_pose - if save_video: - this_pose = poses[-1] - - if single_animal: - # expand individual dimension - this_pose = this_pose[None] - - num_idv, num_bpt = this_pose.shape[:2] - num_colors = num_bpt - - if colors is None: - all_colors = getattr(cc, cmap) - colors = [ - ImageColor.getcolor(c, "RGB")[::-1] - for c in all_colors[:: int(len(all_colors) / num_colors)] - ] - - for j in range(num_idv): - for k in range(num_bpt): - color_idx = k - if this_pose[j, k, 2] > pcutoff: - x = int(this_pose[j, k, 0]) - y = int(this_pose[j, k, 1]) - frame = cv2.circle( - frame, - (x, y), - display_radius, - colors[color_idx], - thickness=-1, - ) - - if resize is not None: - frame = cv2.resize(frame, im_size) - vid_writer.write(frame) - - if print_rate: - print(f"pose rate = {int(1 / inf_times[i]):d}") - - if print_rate: - print(f"mean pose rate = {int(np.mean(1 / inf_times)):d}") - - # gather video and test parameterization - # dont want to fail here so gracefully failing on exception -- - # eg. some packages of cv2 don't have CAP_PROP_CODEC_PIXEL_FORMAT - try: - fourcc = decode_fourcc(cap.get(cv2.CAP_PROP_FOURCC)) - except: - fourcc = "" - - try: - fps = round(cap.get(cv2.CAP_PROP_FPS)) - except Exception: - fps = None - - try: - pix_fmt = decode_fourcc(cap.get(cv2.CAP_PROP_CODEC_PIXEL_FORMAT)) - except Exception: - pix_fmt = "" - - try: - frame_count = round(cap.get(cv2.CAP_PROP_FRAME_COUNT)) - except Exception: - frame_count = None - - try: - orig_im_size = ( - round(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), - round(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)), - ) - except Exception: - orig_im_size = None - - meta = { - "video_path": video_path, - "video_codec": fourcc, - "video_pixel_format": pix_fmt, - "video_fps": fps, - "video_total_frames": frame_count, - "original_frame_size": orig_im_size, - "dlclive_params": live.parameterization, - } - - # close video - cap.release() - if save_video: - vid_writer.release() - - if save_poses: - bodyparts = live.cfg["metadata"]["bodyparts"] - max_idv = np.max([p.shape[0] for p in poses]) - - poses_array = -np.ones((len(poses), max_idv, len(bodyparts), 3)) - for i, p in enumerate(poses): - num_det = len(p) - poses_array[i, :num_det] = p - poses = poses_array - - num_frames, num_idv, num_bpts = poses.shape[:3] - individuals = [f"individual-{i}" for i in range(num_idv)] - - if has_pandas: - poses = poses.reshape((num_frames, num_idv * num_bpts * 3)) - col_index = pd.MultiIndex.from_product( - [individuals, bodyparts, ["x", "y", "likelihood"]], - names=["individual", "bodyparts", "coords"], - ) - pose_df = pd.DataFrame(poses, columns=col_index) - - out_dlc_file = output / (video_path.stem + "_DLCLIVE_POSES.h5") - try: - pose_df.to_hdf(out_dlc_file, key="df_with_missing", mode="w") - except ImportError as err: - print( - "Cannot export predictions to H5 file. Install ``pytables`` extra " - f"to export to HDF: {err}" - ) - out_csv = Path(out_dlc_file).with_suffix(".csv") - pose_df.to_csv(out_csv) - - else: - warnings.warn( - "Could not find installation of pandas; saving poses as a numpy array " - "with the dimensions (n_frames, n_keypoints, [x, y, likelihood])." - ) - np.save(str(output / (video_path.stem + "_DLCLIVE_POSES.npy")), poses) - - return inf_times, im_size, meta - - -def benchmark_videos( - video_path: str, - model_path: str, - model_type: str, - device: str, - precision: str = "FP32", - display=True, - pcutoff=0.5, - display_radius=5, - resize=None, - cropping=None, # Adding cropping to the function parameters - dynamic=(False, 0.5, 10), - save_poses=False, - save_dir="model_predictions", - draw_keypoint_names=False, - cmap="bmy", - get_sys_info=True, - save_video=False, -): - """ - Analyzes a video to track keypoints using a DeepLabCut model, and optionally saves - the keypoint data and the labeled video. - - Parameters - ---------- - video_path : str - Path to the video file to be analyzed. - model_path : str - Path to the DeepLabCut model. - model_type : str - Type of the model (e.g., 'onnx'). - device : str - Device to run the model on ('cpu' or 'cuda'). - precision : str, optional, default='FP32' - Precision type for the model ('FP32' or 'FP16'). - display : bool, optional, default=True - Whether to display frame with labelled key points. - pcutoff : float, optional, default=0.5 - Probability cutoff below which keypoints are not visualized. - display_radius : int, optional, default=5 - Radius of circles drawn for keypoints on video frames. - resize : tuple of int (width, height) or None, optional - Resize dimensions for video frames. e.g. if resize = 0.5, the video will be - processed in half the original size. If None, no resizing is applied. - cropping : list of int or None, optional - Cropping parameters [x1, x2, y1, y2] in pixels. If None, no cropping is applied. - dynamic : tuple, optional, default=(False, 0.5, 10) (True/false), p cutoff, margin) - Parameters for dynamic cropping. If the state is true, then dynamic cropping - will be performed. That means that if an object is detected (i.e. any body part - > detectiontreshold), then object boundaries are computed according to the - smallest/largest x position and smallest/largest y position of all body parts. - This window is expanded by the margin and from then on only the posture within - this crop is analyzed (until the object is lost, i.e. pcutoff: - x, y = map(int, this_pose[j, :2]) - cv2.circle( - frame, - center=(x, y), - radius=display_radius, - color=colors[j], - thickness=-1, - ) - - if draw_keypoint_names: - cv2.putText( - frame, - text=bodyparts[j], - org=(x + 10, y), - fontFace=cv2.FONT_HERSHEY_SIMPLEX, - fontScale=0.5, - color=colors[j], - thickness=1, - lineType=cv2.LINE_AA, - ) - - vwriter.write(image=frame) - frame_index += 1 - - cap.release() - if save_video: - vwriter.release() - - if get_sys_info: - print(get_system_info()) - - if save_poses: - save_poses_to_files(video_path, save_dir, bodyparts, poses, timestamp=timestamp) - - return poses, times - - def save_poses_to_files(video_path, save_dir, bodyparts, poses, timestamp): """ Saves the detected keypoint poses from the video to CSV and HDF5 files. diff --git a/dlclive/benchmark_tf.py b/dlclive/benchmark_tf.py index ef4dfc9..57ae243 100644 --- a/dlclive/benchmark_tf.py +++ b/dlclive/benchmark_tf.py @@ -509,152 +509,6 @@ def save_inf_times( return True -def benchmark_videos( - model_path, - video_path, - output=None, - n_frames=1000, - tf_config=None, - resize=None, - pixels=None, - cropping=None, - dynamic=(False, 0.5, 10), - print_rate=False, - display=False, - pcutoff=0.5, - display_radius=3, - cmap="bmy", - save_poses=False, - save_video=False, -): - """Analyze videos using DeepLabCut-live exported models. - Analyze multiple videos and/or multiple options for the size of the video - by specifying a resizing factor or the number of pixels to use in the image (keeping aspect ratio constant). - Options to record inference times (to examine inference speed), - display keypoints to visually check the accuracy, - or save poses to an hdf5 file as in :function:`deeplabcut.benchmark_videos` and - create a labeled video as in :function:`deeplabcut.create_labeled_video`. - - Parameters - ---------- - model_path : str - path to exported DeepLabCut model - video_path : str or list - path to video file or list of paths to video files - output : str - path to directory to save results - tf_config : :class:`tensorflow.ConfigProto` - tensorflow session configuration - resize : int, optional - resize factor. Can only use one of resize or pixels. If both are provided, will use pixels. by default None - pixels : int, optional - downsize image to this number of pixels, maintaining aspect ratio. Can only use one of resize or pixels. If both are provided, will use pixels. by default None - cropping : list of int - cropping parameters in pixel number: [x1, x2, y1, y2] - dynamic: triple containing (state, detectiontreshold, margin) - If the state is true, then dynamic cropping will be performed. That means that if an object is detected (i.e. any body part > detectiontreshold), - then object boundaries are computed according to the smallest/largest x position and smallest/largest y position of all body parts. This window is - expanded by the margin and from then on only the posture within this crop is analyzed (until the object is lost, i.e. `, by default "bmy" - save_poses : bool, optional - flag to save poses to an hdf5 file. If True, operates similar to :function:`DeepLabCut.benchmark_videos`, by default False - save_video : bool, optional - flag to save a labeled video. If True, operates similar to :function:`DeepLabCut.create_labeled_video`, by default False - - Example - ------- - Return a vector of inference times for 10000 frames on one video or two videos: - dlclive.benchmark_videos('/my/exported/model', 'my_video.avi', n_frames=10000) - dlclive.benchmark_videos('/my/exported/model', ['my_video1.avi', 'my_video2.avi'], n_frames=10000) - - Return a vector of inference times, testing full size and resizing images to half the width and height for inference, for two videos - dlclive.benchmark_videos('/my/exported/model', ['my_video1.avi', 'my_video2.avi'], n_frames=10000, resize=[1.0, 0.5]) - - Display keypoints to check the accuracy of an exported model - dlclive.benchmark_videos('/my/exported/model', 'my_video.avi', display=True) - - Analyze a video (save poses to hdf5) and create a labeled video, similar to :function:`DeepLabCut.benchmark_videos` and :function:`create_labeled_video` - dlclive.benchmark_videos('/my/exported/model', 'my_video.avi', save_poses=True, save_video=True) - """ - - # convert video_paths to list - - video_path = video_path if type(video_path) is list else [video_path] - - # fix resize - - if pixels: - pixels = pixels if type(pixels) is list else [pixels] - resize = [None for p in pixels] - elif resize: - resize = resize if type(resize) is list else [resize] - pixels = [None for r in resize] - else: - resize = [None] - pixels = [None] - - # loop over videos - - for v in video_path: - # initialize full inference times - - inf_times = [] - im_size_out = [] - - for i in range(len(resize)): - print(f"\nRun {i+1} / {len(resize)}\n") - - this_inf_times, this_im_size, TFGPUinference, meta = benchmark( - model_path, - v, - tf_config=tf_config, - resize=resize[i], - pixels=pixels[i], - cropping=cropping, - dynamic=dynamic, - n_frames=n_frames, - print_rate=print_rate, - display=display, - pcutoff=pcutoff, - display_radius=display_radius, - cmap=cmap, - save_poses=save_poses, - save_video=save_video, - output=output, - ) - - inf_times.append(this_inf_times) - im_size_out.append(this_im_size) - - inf_times = np.array(inf_times) - im_size_out = np.array(im_size_out) - - # save results - - if output is not None: - sys_info = get_system_info() - save_inf_times( - sys_info, - inf_times, - im_size_out, - TFGPUinference, - model=os.path.basename(model_path), - meta=meta, - output=output, - ) def main(): From 0d6b2d56f17eff0f1649873fd87f72478245d86f Mon Sep 17 00:00:00 2001 From: maximpavliv Date: Mon, 16 Jun 2025 18:30:08 +0200 Subject: [PATCH 16/28] WIP refactor benchmarking: warning --- dlclive/benchmark_pytorch.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/dlclive/benchmark_pytorch.py b/dlclive/benchmark_pytorch.py index caf855a..fa70a39 100644 --- a/dlclive/benchmark_pytorch.py +++ b/dlclive/benchmark_pytorch.py @@ -3,6 +3,7 @@ import subprocess import sys import time +import warnings import colorcet as cc import cv2 @@ -218,6 +219,12 @@ def benchmark( for i in iterator: ret, frame = cap.read() if not ret: + warnings.warn( + ( + "Did not complete {:d} frames. " + "There probably were not enough frames in the video {}." + ).format(n_frames, video_path) + ) break start_time = time.perf_counter() From 6402380bcb7826d31e83ca1dee1d0941c404c3c3 Mon Sep 17 00:00:00 2001 From: maximpavliv Date: Mon, 16 Jun 2025 18:31:40 +0200 Subject: [PATCH 17/28] WIP refactor benchmarking: close live --- dlclive/benchmark_pytorch.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/dlclive/benchmark_pytorch.py b/dlclive/benchmark_pytorch.py index fa70a39..7998d01 100644 --- a/dlclive/benchmark_pytorch.py +++ b/dlclive/benchmark_pytorch.py @@ -252,6 +252,9 @@ def benchmark( frame_index += 1 cap.release() + + dlc_live.close() + if save_video: vwriter.release() From f0135579fd28889028618dcf6c858bf5bef818af Mon Sep 17 00:00:00 2001 From: maximpavliv Date: Mon, 16 Jun 2025 20:30:34 +0200 Subject: [PATCH 18/28] WIP refactor benchmarking: resize and pixels --- dlclive/benchmark_pytorch.py | 43 ++++++++++++++++++++++++++---------- 1 file changed, 31 insertions(+), 12 deletions(-) diff --git a/dlclive/benchmark_pytorch.py b/dlclive/benchmark_pytorch.py index 7998d01..e9ee1d5 100644 --- a/dlclive/benchmark_pytorch.py +++ b/dlclive/benchmark_pytorch.py @@ -92,14 +92,15 @@ def benchmark( model_path: str, model_type: str, device: str, - single_animal: bool, + resize: float | None = None, + pixels: int | None = None, + single_animal: bool = True, save_dir=None, n_frames=1000, precision: str = "FP32", display=True, pcutoff=0.5, display_radius=3, - resize=None, cropping=None, # Adding cropping to the function parameters dynamic=(False, 0.5, 10), save_poses=False, @@ -121,7 +122,12 @@ def benchmark( Type of the model (e.g., 'onnx'). device : str Device to run the model on ('cpu' or 'cuda'). - single_animal: bool + resize : float or None, optional + Resize dimensions for video frames. e.g. if resize = 0.5, the video will be processed in half the original size. If None, no resizing is applied. + pixels : int, optional + downsize image to this number of pixels, maintaining aspect ratio. + Can only use one of resize or pixels. If both are provided, will use pixels. + single_animal: bool, optional, default=True Whether the video contains only one animal (True) or multiple animals (False). save_dir : str, optional Directory to save output data and labeled video. @@ -136,8 +142,6 @@ def benchmark( Probability cutoff below which keypoints are not visualized. display_radius : int, optional, default=5 Radius of circles drawn for keypoints on video frames. - resize : tuple of int (width, height) or None, optional - Resize dimensions for video frames. e.g. if resize = 0.5, the video will be processed in half the original size. If None, no resizing is applied. cropping : list of int or None, optional Cropping parameters [x1, x2, y1, y2] in pixels. If None, no cropping is applied. dynamic : tuple, optional, default=(False, 0.5, 10) (True/false), p cutoff, margin) @@ -160,6 +164,17 @@ def benchmark( - poses (list of dict): List of pose data for each frame. - times (list of float): List of inference times for each frame. """ + # Load video + cap = cv2.VideoCapture(video_path) + if not cap.isOpened(): + print(f"Error: Could not open video file {video_path}") + return + im_size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))) + + if pixels is not None: + resize = np.sqrt(pixels / (im_size[0] * im_size[1])) + if resize is not None: + im_size = (int(im_size[0] * resize), int(im_size[1] * resize)) # Create the DLCLive object with cropping dlc_live = DLCLive( @@ -185,12 +200,6 @@ def benchmark( # Get the current date and time as a string timestamp = time.strftime("%Y%m%d_%H%M%S") - # Load video - cap = cv2.VideoCapture(video_path) - if not cap.isOpened(): - print(f"Error: Could not open video file {video_path}") - return - # Retrieve bodypart names and number of keypoints bodyparts = dlc_live.read_config()["metadata"]["bodyparts"] @@ -202,7 +211,7 @@ def benchmark( num_keypoints=len(bodyparts), cmap=cmap, fps=cap.get(cv2.CAP_PROP_FPS), - frame_size=(int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))), + frame_size=im_size, ) # Start empty dict to save poses to for each frame @@ -241,6 +250,7 @@ def benchmark( draw_pose_and_write( frame=frame, pose=pose, + resize=resize, colors=colors, bodyparts=bodyparts, pcutoff=pcutoff, @@ -303,6 +313,7 @@ def setup_video_writer( def draw_pose_and_write( frame: np.ndarray, pose: np.ndarray, + resize: float, colors: list[tuple[int, int, int]], bodyparts: list[str], pcutoff: float, @@ -313,6 +324,14 @@ def draw_pose_and_write( if len(pose.shape) == 2: pose = pose[None] + if resize is not None and resize != 1.0: + # Resize the frame + frame = cv2.resize(frame, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR) + + # Scale pose coordinates + pose = pose.copy() + pose[..., :2] *= resize + # Visualize keypoints for i in range(pose.shape[0]): for j in range(pose.shape[1]): From 39c27f61e6525143894f8df34c51d2ba18d835b4 Mon Sep 17 00:00:00 2001 From: maximpavliv Date: Mon, 16 Jun 2025 20:50:21 +0200 Subject: [PATCH 19/28] WIP refactor benchmarking: get_sys_info --- dlclive/benchmark_pytorch.py | 71 ------------------------------------ 1 file changed, 71 deletions(-) diff --git a/dlclive/benchmark_pytorch.py b/dlclive/benchmark_pytorch.py index e9ee1d5..08b4bda 100644 --- a/dlclive/benchmark_pytorch.py +++ b/dlclive/benchmark_pytorch.py @@ -22,71 +22,6 @@ from dlclive.version import VERSION -def get_system_info() -> dict: - """ - Returns a summary of system information relevant to running benchmarking. - - Returns - ------- - dict - A dictionary containing the following system information: - - host_name (str): Name of the machine. - - op_sys (str): Operating system. - - python (str): Path to the Python executable, indicating the conda/virtual environment in use. - - device_type (str): Type of device used ('GPU' or 'CPU'). - - device (list): List containing the name of the GPU or CPU brand. - - freeze (list): List of installed Python packages with their versions. - - python_version (str): Version of Python in use. - - git_hash (str or None): If installed from git repository, hash of HEAD commit. - - dlclive_version (str): Version of the DLCLive package. - """ - - # Get OS and host name - op_sys = platform.platform() - host_name = platform.node().replace(" ", "") - - # Get Python executable path - if platform.system() == "Windows": - host_python = sys.executable.split(os.path.sep)[-2] - else: - host_python = sys.executable.split(os.path.sep)[-3] - - # Try to get git hash if possible - git_hash = None - dlc_basedir = os.path.dirname(os.path.dirname(__file__)) - try: - git_hash = ( - subprocess.check_output(["git", "rev-parse", "HEAD"], cwd=dlc_basedir) - .decode("utf-8") - .strip() - ) - except subprocess.CalledProcessError: - # Not installed from git repo, e.g., pypi - pass - - # Get device info (GPU or CPU) - if torch.cuda.is_available(): - dev_type = "GPU" - dev = [torch.cuda.get_device_name(torch.cuda.current_device())] - else: - from cpuinfo import get_cpu_info - - dev_type = "CPU" - dev = [get_cpu_info()["brand_raw"]] - - return { - "host_name": host_name, - "op_sys": op_sys, - "python": host_python, - "device_type": dev_type, - "device": dev, - "freeze": list(freeze.freeze()), - "python_version": sys.version, - "git_hash": git_hash, - "dlclive_version": VERSION, - } - - def benchmark( video_path: str, model_path: str, @@ -106,7 +41,6 @@ def benchmark( save_poses=False, draw_keypoint_names=False, cmap="bmy", - get_sys_info=True, save_video=False, ): """ @@ -152,8 +86,6 @@ def benchmark( Whether to display keypoint names on video frames in the saved video. cmap : str, optional, default='bmy' Colormap from the colorcet library for keypoint visualization. - get_sys_info : bool, optional, default=True - Whether to print system information. save_video : bool, optional, default=False Whether to save the labeled video. @@ -268,9 +200,6 @@ def benchmark( if save_video: vwriter.release() - if get_sys_info: - print(get_system_info()) - if save_poses: individuals = dlc_live.read_config()["metadata"].get("individuals", []) n_individuals = len(individuals) or 1 From 44c960f9699e3b8b79b8493767562b9963a01ccb Mon Sep 17 00:00:00 2001 From: Mackenzie Mathis Date: Mon, 16 Jun 2025 22:03:31 +0200 Subject: [PATCH 20/28] Update benchmark.py (#123) --- .github/workflows/testing.yml | 5 +++- .gitignore | 2 ++ dlclive/benchmark.py | 54 ++++++++++++++++------------------ pyproject.toml | 2 +- pytest.ini | 3 ++ tests/test_benchmark_script.py | 33 +++++++++++++++++++++ 6 files changed, 68 insertions(+), 31 deletions(-) create mode 100644 pytest.ini create mode 100644 tests/test_benchmark_script.py diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml index 27ea85a..25c9a92 100644 --- a/.github/workflows/testing.yml +++ b/.github/workflows/testing.yml @@ -68,7 +68,10 @@ jobs: run: poetry lock --no-cache - name: Install project dependencies - run: poetry install --no-root + run: poetry install --with dev - name: Run DLC Live Tests run: poetry run dlc-live-test --nodisplay + + - name: Run Functional Benchmark Test + run: poetry run pytest tests/test_benchmark_script.py diff --git a/.gitignore b/.gitignore index 7fa3a49..2ce1454 100644 --- a/.gitignore +++ b/.gitignore @@ -8,6 +8,8 @@ benchmarking/results* **DS_Store* *vscode* +**/__MACOSX/ + # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] diff --git a/dlclive/benchmark.py b/dlclive/benchmark.py index 1d53042..9643b09 100644 --- a/dlclive/benchmark.py +++ b/dlclive/benchmark.py @@ -37,42 +37,38 @@ def download_benchmarking_data( target_dir=".", - url="http://deeplabcut.rowland.harvard.edu/datasets/dlclivebenchmark.tar.gz", + url="https://huggingface.co/datasets/mwmathis/DLCspeed_benchmarking/resolve/main/Data-DLC-live-benchmark.zip", ): """ - Downloads a DeepLabCut-Live benchmarking Data (videos & DLC models). + Downloads and extracts DeepLabCut-Live benchmarking data (videos & DLC models). """ + import os import urllib.request - import tarfile from tqdm import tqdm + import zipfile - def show_progress(count, block_size, total_size): - pbar.update(block_size) - - def tarfilenamecutting(tarf): - """' auxfun to extract folder path - ie. /xyz-trainsetxyshufflez/ - """ - for memberid, member in enumerate(tarf.getmembers()): - if memberid == 0: - parent = str(member.path) - l = len(parent) + 1 - if member.path.startswith(parent): - member.path = member.path[l:] - yield member - - response = urllib.request.urlopen(url) - print( - "Downloading the benchmarking data from the DeepLabCut server @Harvard -> Go Crimson!!! {}....".format( - url - ) - ) - total_size = int(response.getheader("Content-Length")) - pbar = tqdm(unit="B", total=total_size, position=0) - filename, _ = urllib.request.urlretrieve(url, reporthook=show_progress) - with tarfile.open(filename, mode="r:gz") as tar: - tar.extractall(target_dir, members=tarfilenamecutting(tar)) + # Avoid nested folder issue + if os.path.basename(os.path.normpath(target_dir)) == "Data-DLC-live-benchmark": + target_dir = os.path.dirname(os.path.normpath(target_dir)) + os.makedirs(target_dir, exist_ok=True) # Ensure target directory exists + + zip_path = os.path.join(target_dir, "Data-DLC-live-benchmark.zip") + + if os.path.exists(zip_path): + print(f"{zip_path} already exists. Skipping download.") + else: + def show_progress(count, block_size, total_size): + pbar.update(block_size) + + print(f"Downloading the benchmarking data from {url} ...") + pbar = tqdm(unit="B", total=0, position=0, desc="Downloading") + + filename, _ = urllib.request.urlretrieve(url, filename=zip_path, reporthook=show_progress) + pbar.close() + print(f"Extracting {zip_path} to {target_dir} ...") + with zipfile.ZipFile(zip_path, 'r') as zip_ref: + zip_ref.extractall(target_dir) def get_system_info() -> dict: """ Return summary info for system running benchmark diff --git a/pyproject.toml b/pyproject.toml index 9fb4eed..7451d7e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,6 +36,7 @@ torch = ">=1.10,<3.0" dlclibrary = ">=0.0.6" pandas = "^1.3" tables = "^3.6" +pytest = "^8.0" # OS-specific TensorFlow packages tensorflow = [ @@ -44,7 +45,6 @@ tensorflow = [ ] tensorflow-macos = { version = ">=2.7.0,<2.12", markers = "sys_platform == 'darwin'" } - [tool.poetry.group.dev.dependencies] [build-system] diff --git a/pytest.ini b/pytest.ini new file mode 100644 index 0000000..d08b10e --- /dev/null +++ b/pytest.ini @@ -0,0 +1,3 @@ +[pytest] +markers = + functional: functional tests diff --git a/tests/test_benchmark_script.py b/tests/test_benchmark_script.py new file mode 100644 index 0000000..ff3a3c2 --- /dev/null +++ b/tests/test_benchmark_script.py @@ -0,0 +1,33 @@ +import os +import glob +import pathlib +import pytest +from dlclive import benchmark_videos, download_benchmarking_data + +@pytest.mark.functional +def test_benchmark_script_runs(tmp_path): + datafolder = tmp_path / "Data-DLC-live-benchmark" + download_benchmarking_data(str(datafolder)) + + dog_models = glob.glob(str(datafolder / "dog" / "*[!avi]")) + dog_video = glob.glob(str(datafolder / "dog" / "*.avi"))[0] + mouse_models = glob.glob(str(datafolder / "mouse_lick" / "*[!avi]")) + mouse_video = glob.glob(str(datafolder / "mouse_lick" / "*.avi"))[0] + + out_dir = tmp_path / "results" + out_dir.mkdir(exist_ok=True) + + pixels = [100, 400] #[2500, 10000] + n_frames = 5 + + for m in dog_models: + print(f"Running dog model: {m}") + result = benchmark_videos(m, dog_video, output=str(out_dir), n_frames=n_frames, pixels=pixels) + print("Dog model result:", result) + + for m in mouse_models: + print(f"Running mouse model: {m}") + result = benchmark_videos(m, mouse_video, output=str(out_dir), n_frames=n_frames, pixels=pixels) + print("Mouse model result:", result) + + assert any(out_dir.iterdir()) From f2c6b11c03e565ba74b96aac5cd2221642b1cfb4 Mon Sep 17 00:00:00 2001 From: maximpavliv Date: Tue, 17 Jun 2025 09:00:08 +0200 Subject: [PATCH 21/28] WIP refactor benchmarking: Delete dupplicate scripts --- dlclive/benchmark_pytorch.py | 474 ----------------------------- dlclive/benchmark_tf.py | 571 ----------------------------------- 2 files changed, 1045 deletions(-) delete mode 100644 dlclive/benchmark_pytorch.py delete mode 100644 dlclive/benchmark_tf.py diff --git a/dlclive/benchmark_pytorch.py b/dlclive/benchmark_pytorch.py deleted file mode 100644 index 08b4bda..0000000 --- a/dlclive/benchmark_pytorch.py +++ /dev/null @@ -1,474 +0,0 @@ -import csv -import platform -import subprocess -import sys -import time -import warnings - -import colorcet as cc -import cv2 -import h5py -import numpy as np -from pathlib import Path -from PIL import ImageColor -from pip._internal.operations import freeze -import torch -from tqdm import tqdm - -# torch import needs to switch order with "from pip._internal.operations import freeze" because of crash -# see https://github.com/pytorch/pytorch/issues/140914 - -from dlclive import DLCLive -from dlclive.version import VERSION - - -def benchmark( - video_path: str, - model_path: str, - model_type: str, - device: str, - resize: float | None = None, - pixels: int | None = None, - single_animal: bool = True, - save_dir=None, - n_frames=1000, - precision: str = "FP32", - display=True, - pcutoff=0.5, - display_radius=3, - cropping=None, # Adding cropping to the function parameters - dynamic=(False, 0.5, 10), - save_poses=False, - draw_keypoint_names=False, - cmap="bmy", - save_video=False, -): - """ - Analyzes a video to track keypoints using a DeepLabCut model, and optionally saves the keypoint data and the labeled video. - - Parameters - ---------- - video_path : str - Path to the video file to be analyzed. - model_path : str - Path to the DeepLabCut model. - model_type : str - Type of the model (e.g., 'onnx'). - device : str - Device to run the model on ('cpu' or 'cuda'). - resize : float or None, optional - Resize dimensions for video frames. e.g. if resize = 0.5, the video will be processed in half the original size. If None, no resizing is applied. - pixels : int, optional - downsize image to this number of pixels, maintaining aspect ratio. - Can only use one of resize or pixels. If both are provided, will use pixels. - single_animal: bool, optional, default=True - Whether the video contains only one animal (True) or multiple animals (False). - save_dir : str, optional - Directory to save output data and labeled video. - If not specified, will use the directory of video_path, by default None - n_frames : int, optional - Number of frames to run inference on, by default 1000 - precision : str, optional, default='FP32' - Precision type for the model ('FP32' or 'FP16'). - display : bool, optional, default=True - Whether to display frame with labelled key points. - pcutoff : float, optional, default=0.5 - Probability cutoff below which keypoints are not visualized. - display_radius : int, optional, default=5 - Radius of circles drawn for keypoints on video frames. - cropping : list of int or None, optional - Cropping parameters [x1, x2, y1, y2] in pixels. If None, no cropping is applied. - dynamic : tuple, optional, default=(False, 0.5, 10) (True/false), p cutoff, margin) - Parameters for dynamic cropping. If the state is true, then dynamic cropping will be performed. That means that if an object is detected (i.e. any body part > detectiontreshold), then object boundaries are computed according to the smallest/largest x position and smallest/largest y position of all body parts. This window is expanded by the margin and from then on only the posture within this crop is analyzed (until the object is lost, i.e. 0) and n_frames < total_n_frames - else total_n_frames - ) - iterator = range(n_frames) if display else tqdm(range(n_frames)) - for i in iterator: - ret, frame = cap.read() - if not ret: - warnings.warn( - ( - "Did not complete {:d} frames. " - "There probably were not enough frames in the video {}." - ).format(n_frames, video_path) - ) - break - - start_time = time.perf_counter() - if frame_index == 0: - pose = dlc_live.init_inference(frame) # Loads model - else: - pose = dlc_live.get_pose(frame) - - inf_time = time.perf_counter() - start_time - poses.append({"frame": frame_index, "pose": pose}) - times.append(inf_time) - - if save_video: - draw_pose_and_write( - frame=frame, - pose=pose, - resize=resize, - colors=colors, - bodyparts=bodyparts, - pcutoff=pcutoff, - display_radius=display_radius, - draw_keypoint_names=draw_keypoint_names, - vwriter=vwriter - ) - - frame_index += 1 - - cap.release() - - dlc_live.close() - - if save_video: - vwriter.release() - - if save_poses: - individuals = dlc_live.read_config()["metadata"].get("individuals", []) - n_individuals = len(individuals) or 1 - save_poses_to_files(video_path, save_dir, n_individuals, bodyparts, poses, timestamp=timestamp) - - return poses, times - -def setup_video_writer( - video_path:str, - save_dir:str, - timestamp:str, - num_keypoints:int, - cmap:str, - fps:float, - frame_size:tuple[int, int], -): - # Set colors and convert to RGB - cmap_colors = getattr(cc, cmap) - colors = [ - ImageColor.getrgb(color) - for color in cmap_colors[:: int(len(cmap_colors) / num_keypoints)] - ] - - # Define output video path - video_path = Path(video_path) - video_name = video_path.stem # filename without extension - output_video_path = Path(save_dir) / f"{video_name}_DLCLIVE_LABELLED_{timestamp}.mp4" - - # Get video writer setup - fourcc = cv2.VideoWriter_fourcc(*"mp4v") - vwriter = cv2.VideoWriter( - filename=output_video_path, - fourcc=fourcc, - fps=fps, - frameSize=frame_size, - ) - - return colors, vwriter - -def draw_pose_and_write( - frame: np.ndarray, - pose: np.ndarray, - resize: float, - colors: list[tuple[int, int, int]], - bodyparts: list[str], - pcutoff: float, - display_radius: int, - draw_keypoint_names: bool, - vwriter: cv2.VideoWriter, -): - if len(pose.shape) == 2: - pose = pose[None] - - if resize is not None and resize != 1.0: - # Resize the frame - frame = cv2.resize(frame, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR) - - # Scale pose coordinates - pose = pose.copy() - pose[..., :2] *= resize - - # Visualize keypoints - for i in range(pose.shape[0]): - for j in range(pose.shape[1]): - if pose[i, j, 2] > pcutoff: - x, y = map(int, pose[i, j, :2]) - cv2.circle( - frame, - center=(x, y), - radius=display_radius, - color=colors[j], - thickness=-1, - ) - - if draw_keypoint_names: - cv2.putText( - frame, - text=bodyparts[j], - org=(x + 10, y), - fontFace=cv2.FONT_HERSHEY_SIMPLEX, - fontScale=0.5, - color=colors[j], - thickness=1, - lineType=cv2.LINE_AA, - ) - - - vwriter.write(image=frame) - -def save_poses_to_files(video_path, save_dir, n_individuals, bodyparts, poses, timestamp): - """ - Saves the detected keypoint poses from the video to CSV and HDF5 files. - - Parameters - ---------- - video_path : str - Path to the analyzed video file. - save_dir : str - Directory where the pose data files will be saved. - n_individuals: int - Number of individuals - bodyparts : list of str - List of body part names corresponding to the keypoints. - poses : list of dict - List of dictionaries containing frame numbers and corresponding pose data. - - Returns - ------- - None - """ - import pandas as pd - - base_filename = Path(video_path).stem - save_dir = Path(save_dir) - h5_save_path = save_dir / f"{base_filename}_poses_{timestamp}.h5" - csv_save_path = save_dir / f"{base_filename}_poses_{timestamp}.csv" - - poses_array = _create_poses_np_array(n_individuals, bodyparts, poses) - flattened_poses = poses_array.reshape(poses_array.shape[0], -1) - - if n_individuals == 1: - pdindex = pd.MultiIndex.from_product( - [bodyparts, ["x", "y", "likelihood"]], names=["bodyparts", "coords"] - ) - else: - individuals = [f"individual_{i}" for i in range(n_individuals)] - pdindex = pd.MultiIndex.from_product( - [individuals, bodyparts, ["x", "y", "likelihood"]], names=["individuals", "bodyparts", "coords"] - ) - - pose_df = pd.DataFrame(flattened_poses, columns=pdindex) - - pose_df.to_hdf(h5_save_path, key="df_with_missing", mode="w") - pose_df.to_csv(csv_save_path, index=False) - -def _create_poses_np_array(n_individuals: int, bodyparts: list, poses: list): - # Create numpy array with poses: - max_frame = max(p["frame"] for p in poses) - pose_target_shape = (n_individuals, len(bodyparts), 3) - poses_array = np.full((max_frame + 1, *pose_target_shape), np.nan) - - for item in poses: - frame = item["frame"] - pose = item["pose"] - if pose.ndim == 2: - pose = pose[np.newaxis, :, :] - padded_pose = np.full(pose_target_shape, np.nan) - slices = tuple(slice(0, min(pose.shape[i], pose_target_shape[i])) for i in range(3)) - padded_pose[slices] = pose[slices] - poses_array[frame] = padded_pose - - return poses_array - - -import argparse -import os - - -def main(): - """Provides a command line interface to analyze_video function.""" - - parser = argparse.ArgumentParser( - description="Analyze a video using a DeepLabCut model and visualize keypoints." - ) - parser.add_argument("model_path", type=str, help="Path to the model.") - parser.add_argument("video_path", type=str, help="Path to the video file.") - parser.add_argument("model_type", type=str, help="Type of the model (e.g., 'DLC').") - parser.add_argument( - "device", type=str, help="Device to run the model on (e.g., 'cuda' or 'cpu')." - ) - parser.add_argument( - "-p", - "--precision", - type=str, - default="FP32", - help="Model precision (e.g., 'FP32', 'FP16').", - ) - parser.add_argument( - "-d", "--display", action="store_true", help="Display keypoints on the video." - ) - parser.add_argument( - "-c", - "--pcutoff", - type=float, - default=0.5, - help="Probability cutoff for keypoints visualization.", - ) - parser.add_argument( - "-dr", - "--display-radius", - type=int, - default=5, - help="Radius of keypoint circles in the display.", - ) - parser.add_argument( - "-r", - "--resize", - type=int, - default=None, - help="Resize video frames to [width, height].", - ) - parser.add_argument( - "-x", - "--cropping", - type=int, - nargs=4, - default=None, - help="Cropping parameters [x1, x2, y1, y2].", - ) - parser.add_argument( - "-y", - "--dynamic", - type=float, - nargs=3, - default=[False, 0.5, 10], - help="Dynamic cropping [flag, pcutoff, margin].", - ) - parser.add_argument( - "--save-poses", action="store_true", help="Save the keypoint poses to files." - ) - parser.add_argument( - "--save-video", - action="store_true", - help="Save the output video with keypoints.", - ) - parser.add_argument( - "--save-dir", - type=str, - default="model_predictions", - help="Directory to save output files.", - ) - parser.add_argument( - "--draw-keypoint-names", - action="store_true", - help="Draw keypoint names on the video.", - ) - parser.add_argument( - "--cmap", type=str, default="bmy", help="Colormap for keypoints visualization." - ) - parser.add_argument( - "--no-sys-info", - action="store_false", - help="Do not print system info.", - dest="get_sys_info", - ) - - args = parser.parse_args() - - # Call the analyze_video function with the parsed arguments - benchmark( - video_path=args.video_path, - model_path=args.model_path, - model_type=args.model_type, - device=args.device, - precision=args.precision, - display=args.display, - pcutoff=args.pcutoff, - display_radius=args.display_radius, - resize=tuple(args.resize) if args.resize else None, - cropping=args.cropping, - dynamic=tuple(args.dynamic), - save_poses=args.save_poses, - save_dir=args.save_dir, - draw_keypoint_names=args.draw_keypoint_names, - cmap=args.cmap, - get_sys_info=args.get_sys_info, - save_video=args.save_video, - ) - - -if __name__ == "__main__": - main() diff --git a/dlclive/benchmark_tf.py b/dlclive/benchmark_tf.py deleted file mode 100644 index 57ae243..0000000 --- a/dlclive/benchmark_tf.py +++ /dev/null @@ -1,571 +0,0 @@ -""" -DeepLabCut Toolbox (deeplabcut.org) -© A. & M. Mathis Labs - -Licensed under GNU Lesser General Public License v3.0 -""" - -import os -import pickle -import platform -import subprocess -import sys -import time -import typing -import warnings - -import colorcet as cc -import ruamel -from PIL import ImageColor - -try: - from pip._internal.operations import freeze -except ImportError: - from pip.operations import freeze - -import cv2 -import numpy as np -import tensorflow as tf -from dlclive import VERSION, DLCLive -from dlclive import __file__ as dlcfile -from dlclive.utils import decode_fourcc -from tqdm import tqdm - - -def download_benchmarking_data( - target_dir=".", - url="http://deeplabcut.rowland.harvard.edu/datasets/dlclivebenchmark.tar.gz", -): - """ - Downloads a DeepLabCut-Live benchmarking Data (videos & DLC models). - """ - import tarfile - import urllib.request - - from tqdm import tqdm - - def show_progress(count, block_size, total_size): - pbar.update(block_size) - - def tarfilenamecutting(tarf): - """' auxfun to extract folder path - ie. /xyz-trainsetxyshufflez/ - """ - for memberid, member in enumerate(tarf.getmembers()): - if memberid == 0: - parent = str(member.path) - l = len(parent) + 1 - if member.path.startswith(parent): - member.path = member.path[l:] - yield member - - response = urllib.request.urlopen(url) - print( - "Downloading the benchmarking data from the DeepLabCut server @Harvard -> Go Crimson!!! {}....".format( - url - ) - ) - total_size = int(response.getheader("Content-Length")) - pbar = tqdm(unit="B", total=total_size, position=0) - filename, _ = urllib.request.urlretrieve(url, reporthook=show_progress) - with tarfile.open(filename, mode="r:gz") as tar: - tar.extractall(target_dir, members=tarfilenamecutting(tar)) - - -def get_system_info() -> dict: - """Return summary info for system running benchmark - Returns - ------- - dict - Dictionary containing the following system information: - * ``host_name`` (str): name of machine - * ``op_sys`` (str): operating system - * ``python`` (str): path to python (which conda/virtual environment) - * ``device`` (tuple): (device type (``'GPU'`` or ``'CPU'```), device information) - * ``freeze`` (list): list of installed packages and versions - * ``python_version`` (str): python version - * ``git_hash`` (str, None): If installed from git repository, hash of HEAD commit - * ``dlclive_version`` (str): dlclive version from :data:`dlclive.VERSION` - """ - - # get os - - op_sys = platform.platform() - host_name = platform.node().replace(" ", "") - - # A string giving the absolute path of the executable binary for the Python interpreter, on systems where this makes sense. - if platform.system() == "Windows": - host_python = sys.executable.split(os.path.sep)[-2] - else: - host_python = sys.executable.split(os.path.sep)[-3] - - # try to get git hash if possible - dlc_basedir = os.path.dirname(os.path.dirname(dlcfile)) - git_hash = None - try: - git_hash = subprocess.check_output( - ["git", "rev-parse", "HEAD"], cwd=dlc_basedir - ) - git_hash = git_hash.decode("utf-8").rstrip("\n") - except subprocess.CalledProcessError: - # not installed from git repo, eg. pypi - # fine, pass quietly - pass - - # get device info (GPU or CPU) - dev = None - if tf.test.is_gpu_available(): - gpu_name = tf.test.gpu_device_name() - from tensorflow.python.client import device_lib - - dev_desc = [ - d.physical_device_desc - for d in device_lib.list_local_devices() - if d.name == gpu_name - ] - dev = [d.split(",")[1].split(":")[1].strip() for d in dev_desc] - dev_type = "GPU" - else: - from cpuinfo import get_cpu_info - - dev = [get_cpu_info()["brand"]] - dev_type = "CPU" - - return { - "host_name": host_name, - "op_sys": op_sys, - "python": host_python, - "device_type": dev_type, - "device": dev, - # pip freeze to get versions of all packages - "freeze": list(freeze.freeze()), - "python_version": sys.version, - "git_hash": git_hash, - "dlclive_version": VERSION, - } - - -def benchmark( - model_path, - video_path, - tf_config=None, - resize=None, - pixels=None, - cropping=None, - dynamic=(False, 0.5, 10), - n_frames=1000, - print_rate=False, - display=False, - pcutoff=0.0, - display_radius=3, - cmap="bmy", - save_poses=False, - save_video=False, - output=None, -) -> typing.Tuple[np.ndarray, tuple, bool, dict]: - """Analyze DeepLabCut-live exported model on a video: - Calculate inference time, - display keypoints, or - get poses/create a labeled video - - Parameters - ---------- - model_path : str - path to exported DeepLabCut model - video_path : str - path to video file - tf_config : :class:`tensorflow.ConfigProto` - tensorflow session configuration - resize : int, optional - resize factor. Can only use one of resize or pixels. If both are provided, will use pixels. by default None - pixels : int, optional - downsize image to this number of pixels, maintaining aspect ratio. Can only use one of resize or pixels. If both are provided, will use pixels. by default None - cropping : list of int - cropping parameters in pixel number: [x1, x2, y1, y2] - dynamic: triple containing (state, detectiontreshold, margin) - If the state is true, then dynamic cropping will be performed. That means that if an object is detected (i.e. any body part > detectiontreshold), - then object boundaries are computed according to the smallest/largest x position and smallest/largest y position of all body parts. This window is - expanded by the margin and from then on only the posture within this crop is analyzed (until the object is lost, i.e. `, by default "bmy" - save_poses : bool, optional - flag to save poses to an hdf5 file. If True, operates similar to :function:`DeepLabCut.benchmark_videos`, by default False - save_video : bool, optional - flag to save a labeled video. If True, operates similar to :function:`DeepLabCut.create_labeled_video`, by default False - output : str, optional - path to directory to save pose and/or video file. If not specified, will use the directory of video_path, by default None - - Returns - ------- - :class:`numpy.ndarray` - vector of inference times - tuple - (image width, image height) - bool - tensorflow inference flag - dict - metadata for video - - Example - ------- - Return a vector of inference times for 10000 frames: - dlclive.benchmark('/my/exported/model', 'my_video.avi', n_frames=10000) - - Return a vector of inference times, resizing images to half the width and height for inference - dlclive.benchmark('/my/exported/model', 'my_video.avi', n_frames=10000, resize=0.5) - - Display keypoints to check the accuracy of an exported model - dlclive.benchmark('/my/exported/model', 'my_video.avi', display=True) - - Analyze a video (save poses to hdf5) and create a labeled video, similar to :function:`DeepLabCut.benchmark_videos` and :function:`create_labeled_video` - dlclive.benchmark('/my/exported/model', 'my_video.avi', save_poses=True, save_video=True) - """ - - ### load video - - cap = cv2.VideoCapture(video_path) - ret, frame = cap.read() - n_frames = ( - n_frames - if (n_frames > 0) and (n_frames < cap.get(cv2.CAP_PROP_FRAME_COUNT) - 1) - else (cap.get(cv2.CAP_PROP_FRAME_COUNT) - 1) - ) - n_frames = int(n_frames) - im_size = (cap.get(cv2.CAP_PROP_FRAME_WIDTH), cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) - - ### get resize factor - - if pixels is not None: - resize = np.sqrt(pixels / (im_size[0] * im_size[1])) - if resize is not None: - im_size = (int(im_size[0] * resize), int(im_size[1] * resize)) - - ### create video writer - - if save_video: - colors = None - out_dir = ( - output - if output is not None - else os.path.dirname(os.path.realpath(video_path)) - ) - out_vid_base = os.path.basename(video_path) - out_vid_file = os.path.normpath( - f"{out_dir}/{os.path.splitext(out_vid_base)[0]}_DLCLIVE_LABELED.avi" - ) - fourcc = cv2.VideoWriter_fourcc(*"DIVX") - fps = cap.get(cv2.CAP_PROP_FPS) - vwriter = cv2.VideoWriter(out_vid_file, fourcc, fps, im_size) - - ### check for pandas installation if using save_poses flag - - if save_poses: - try: - import pandas as pd - - use_pandas = True - except: - use_pandas = False - warnings.warn( - "Could not find installation of pandas; saving poses as a numpy array with the dimensions (n_frames, n_keypoints, [x, y, likelihood])." - ) - - ### initialize DLCLive and perform inference - - inf_times = np.zeros(n_frames) - poses = [] - - live = DLCLive( - model_path=model_path, - model_type="base", - tf_config=tf_config, - resize=resize, - cropping=cropping, - dynamic=dynamic, - display=display, - pcutoff=pcutoff, - display_radius=display_radius, - display_cmap=cmap, - ) - - poses.append(live.init_inference(frame)) - TFGPUinference = True if len(live.runner.outputs) == 1 else False - - iterator = range(n_frames) if (print_rate) or (display) else tqdm(range(n_frames)) - for i in iterator: - ret, frame = cap.read() - - if not ret: - warnings.warn( - "Did not complete {:d} frames. There probably were not enough frames in the video {}.".format( - n_frames, video_path - ) - ) - break - - start_pose = time.time() - poses.append(live.get_pose(frame)) - inf_times[i] = time.time() - start_pose - - if save_video: - if colors is None: - all_colors = getattr(cc, cmap) - colors = [ - ImageColor.getcolor(c, "RGB")[::-1] - for c in all_colors[:: int(len(all_colors) / poses[-1].shape[0])] - ] - - this_pose = poses[-1] - for j in range(this_pose.shape[0]): - if this_pose[j, 2] > pcutoff: - x = int(this_pose[j, 0]) - y = int(this_pose[j, 1]) - frame = cv2.circle( - frame, (x, y), display_radius, colors[j], thickness=-1 - ) - - if resize is not None: - frame = cv2.resize(frame, im_size) - vwriter.write(frame) - - if print_rate: - print("pose rate = {:d}".format(int(1 / inf_times[i]))) - - if print_rate: - print("mean pose rate = {:d}".format(int(np.mean(1 / inf_times)))) - - ### gather video and test parameterization - - # dont want to fail here so gracefully failing on exception -- - # eg. some packages of cv2 don't have CAP_PROP_CODEC_PIXEL_FORMAT - try: - fourcc = decode_fourcc(cap.get(cv2.CAP_PROP_FOURCC)) - except: - fourcc = "" - - try: - fps = round(cap.get(cv2.CAP_PROP_FPS)) - except: - fps = None - - try: - pix_fmt = decode_fourcc(cap.get(cv2.CAP_PROP_CODEC_PIXEL_FORMAT)) - except: - pix_fmt = "" - - try: - frame_count = round(cap.get(cv2.CAP_PROP_FRAME_COUNT)) - except: - frame_count = None - - try: - orig_im_size = ( - round(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), - round(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)), - ) - except: - orig_im_size = None - - meta = { - "video_path": video_path, - "video_codec": fourcc, - "video_pixel_format": pix_fmt, - "video_fps": fps, - "video_total_frames": frame_count, - "original_frame_size": orig_im_size, - "dlclive_params": live.parameterization, - } - - ### close video and tensorflow session - - cap.release() - live.close() - - if save_video: - vwriter.release() - - if save_poses: - cfg_path = os.path.normpath(f"{model_path}/pose_cfg.yaml") - ruamel_file = ruamel.yaml.YAML() - dlc_cfg = ruamel_file.load(open(cfg_path, "r")) - bodyparts = dlc_cfg["all_joints_names"] - poses = np.array(poses) - - if use_pandas: - poses = poses.reshape((poses.shape[0], poses.shape[1] * poses.shape[2])) - pdindex = pd.MultiIndex.from_product( - [bodyparts, ["x", "y", "likelihood"]], names=["bodyparts", "coords"] - ) - pose_df = pd.DataFrame(poses, columns=pdindex) - - out_dir = ( - output - if output is not None - else os.path.dirname(os.path.realpath(video_path)) - ) - out_vid_base = os.path.basename(video_path) - out_dlc_file = os.path.normpath( - f"{out_dir}/{os.path.splitext(out_vid_base)[0]}_DLCLIVE_POSES.h5" - ) - pose_df.to_hdf(out_dlc_file, key="df_with_missing", mode="w") - - else: - out_vid_base = os.path.basename(video_path) - out_dlc_file = os.path.normpath( - f"{out_dir}/{os.path.splitext(out_vid_base)[0]}_DLCLIVE_POSES.npy" - ) - np.save(out_dlc_file, poses) - - return inf_times, im_size, TFGPUinference, meta - - -def save_inf_times( - sys_info, inf_times, im_size, TFGPUinference, model=None, meta=None, output=None -): - """Save inference time data collected using :function:`benchmark` with system information to a pickle file. - This is primarily used through :function:`benchmark_videos` - - - Parameters - ---------- - sys_info : tuple - system information generated by :func:`get_system_info` - inf_times : :class:`numpy.ndarray` - array of inference times generated by :func:`benchmark` - im_size : tuple or :class:`numpy.ndarray` - image size (width, height) for each benchmark run. If an array, each row corresponds to a row in inf_times - TFGPUinference: bool - flag if using tensorflow inference or numpy inference DLC model - model: str, optional - name of model - meta : dict, optional - metadata returned by :func:`benchmark` - output : str, optional - path to directory to save data. If None, uses pwd, by default None - - Returns - ------- - bool - flag indicating successful save - """ - - output = output if output is not None else os.getcwd() - model_type = None - if model is not None: - if "resnet" in model: - model_type = "resnet" - elif "mobilenet" in model: - model_type = "mobilenet" - else: - model_type = None - - fn_ind = 0 - base_name = ( - f"benchmark_{sys_info['host_name']}_{sys_info['device_type']}_{fn_ind}.pickle" - ) - out_file = os.path.normpath(f"{output}/{base_name}") - while os.path.isfile(out_file): - fn_ind += 1 - base_name = f"benchmark_{sys_info['host_name']}_{sys_info['device_type']}_{fn_ind}.pickle" - out_file = os.path.normpath(f"{output}/{base_name}") - - # summary stats (mean inference time & standard error of mean) - stats = zip( - np.mean(inf_times, 1), - np.std(inf_times, 1) * 1.0 / np.sqrt(np.shape(inf_times)[1]), - ) - - # for stat in stats: - # print("Stats:", stat) - - data = { - "model": model, - "model_type": model_type, - "TFGPUinference": TFGPUinference, - "im_size": im_size, - "inference_times": inf_times, - "stats": stats, - } - - data.update(sys_info) - if meta: - data.update(meta) - - os.makedirs(os.path.normpath(output), exist_ok=True) - pickle.dump(data, open(out_file, "wb")) - - return True - - - - -def main(): - """Provides a command line interface :function:`benchmark_videos`""" - - import argparse - - parser = argparse.ArgumentParser() - parser.add_argument("model_path", type=str) - parser.add_argument("video_path", type=str, nargs="+") - parser.add_argument("-o", "--output", type=str, default=None) - parser.add_argument("-n", "--n-frames", type=int, default=1000) - parser.add_argument("-r", "--resize", type=float, nargs="+") - parser.add_argument("-p", "--pixels", type=float, nargs="+") - parser.add_argument("-v", "--print-rate", default=False, action="store_true") - parser.add_argument("-d", "--display", default=False, action="store_true") - parser.add_argument("-l", "--pcutoff", default=0.5, type=float) - parser.add_argument("-s", "--display-radius", default=3, type=int) - parser.add_argument("-c", "--cmap", type=str, default="bmy") - parser.add_argument("--cropping", nargs="+", type=int, default=None) - parser.add_argument("--dynamic", nargs="+", type=float, default=[]) - parser.add_argument("--save-poses", action="store_true") - parser.add_argument("--save-video", action="store_true") - args = parser.parse_args() - - if (args.cropping) and (len(args.cropping) < 4): - raise Exception( - "Cropping not properly specified. Must provide 4 values: x1, x2, y1, y2" - ) - - if not args.dynamic: - args.dynamic = (False, 0.5, 10) - elif len(args.dynamic) < 3: - raise Exception( - "Dynamic cropping not properly specified. Must provide three values: 0 or 1 as boolean flag, pcutoff, and margin" - ) - else: - args.dynamic = (bool(args.dynamic[0]), args.dynamic[1], args.dynamic[2]) - - benchmark_videos( - args.model_path, - args.video_path, - output=args.output, - resize=args.resize, - pixels=args.pixels, - cropping=args.cropping, - dynamic=args.dynamic, - n_frames=args.n_frames, - print_rate=args.print_rate, - display=args.display, - pcutoff=args.pcutoff, - display_radius=args.display_radius, - cmap=args.cmap, - save_poses=args.save_poses, - save_video=args.save_video, - ) - - -if __name__ == "__main__": - main() From bc036a7994de2237230beb021725b4ff3f4d9d4d Mon Sep 17 00:00:00 2001 From: maximpavliv Date: Tue, 17 Jun 2025 09:03:03 +0200 Subject: [PATCH 22/28] WIP refactor benchmarking: Introduce Engine --- dlclive/factory.py | 22 +++++++++++++++++++--- 1 file changed, 19 insertions(+), 3 deletions(-) diff --git a/dlclive/factory.py b/dlclive/factory.py index 0c22e22..a3f7d00 100644 --- a/dlclive/factory.py +++ b/dlclive/factory.py @@ -16,7 +16,7 @@ def build_runner( Parameters ---------- - model_type: str, optional + model_type: str Which model to use. For the PyTorch engine, options are [`pytorch`]. For the TensorFlow engine, options are [`base`, `tensorrt`, `lite`]. model_path: str, Path @@ -33,13 +33,13 @@ def build_runner( ------- """ - if model_type.lower() == "pytorch": + if Engine.from_model_type(model_type) == Engine.PYTORCH: from dlclive.pose_estimation_pytorch.runner import PyTorchRunner valid = {"device", "precision", "single_animal", "dynamic", "top_down_config"} return PyTorchRunner(model_path, **filter_keys(valid, kwargs)) - elif model_type.lower() in ("tensorflow", "base", "tensorrt", "lite"): + elif Engine.from_model_type(model_type) == Engine.TENSORFLOW: from dlclive.pose_estimation_tensorflow.runner import TensorFlowRunner if model_type.lower() == "tensorflow": @@ -54,3 +54,19 @@ def build_runner( def filter_keys(valid: set[str], kwargs: dict) -> dict: """Filters the keys in kwargs, only keeping those in valid.""" return {k: v for k, v in kwargs.items() if k in valid} + + +from enum import Enum + +class Engine(Enum): + TENSORFLOW = "tensorflow" + PYTORCH = "pytorch" + + @classmethod + def from_model_type(cls, model_type: str) -> "Engine": + if model_type.lower() == "pytorch": + return cls.PYTORCH + elif model_type.lower() in ("tensorflow", "base", "tensorrt", "lite"): + return cls.TENSORFLOW + else: + raise ValueError(f"Unknown model type: {model_type}") From 4bbcf7c3d4231f5219a3c0eb1208d9d59e47ad66 Mon Sep 17 00:00:00 2001 From: maximpavliv Date: Tue, 17 Jun 2025 09:17:36 +0200 Subject: [PATCH 23/28] WIP refactor benchmarking: benchmark() method --- dlclive/benchmark.py | 416 +++++++++++++++++++++++++++++++++++++++---- 1 file changed, 386 insertions(+), 30 deletions(-) diff --git a/dlclive/benchmark.py b/dlclive/benchmark.py index a4a8230..362bf41 100644 --- a/dlclive/benchmark.py +++ b/dlclive/benchmark.py @@ -19,18 +19,12 @@ from PIL import ImageColor from pip._internal.operations import freeze import torch - -try: - import pandas as pd - - has_pandas = True -except ModuleNotFoundError as err: - has_pandas = False +from tqdm import tqdm from dlclive import DLCLive from dlclive import VERSION from dlclive import __file__ as dlcfile - +from dlclive.factory import Engine from dlclive.utils import decode_fourcc @@ -43,7 +37,6 @@ def download_benchmarking_data( """ import os import urllib.request - from tqdm import tqdm import zipfile # Avoid nested folder issue @@ -135,7 +128,347 @@ def get_system_info() -> dict: } -def save_poses_to_files(video_path, save_dir, bodyparts, poses, timestamp): +def benchmark( + model_path: str, + model_type: str, + video_path: str, + tf_config=None, + device: str | None = None, + resize: float | None = None, + pixels: int | None = None, + single_animal: bool = True, + cropping=None, + dynamic=(False, 0.5, 10), + n_frames=1000, + print_rate=False, + precision: str = "FP32", + display=True, + pcutoff=0.5, + display_radius=3, + cmap="bmy", + save_dir=None, + save_poses=False, + save_video=False, + draw_keypoint_names=False, +): + """ + Analyzes a video to track keypoints using a DeepLabCut model, and optionally saves the keypoint data and the labeled video. + + Parameters + ---------- + model_path : str + Path to the DeepLabCut model. + model_type : str + Which model to use. For the PyTorch engine, options are [`pytorch`]. For the + video_path : str + Path to the video file to be analyzed. + TensorFlow engine, options are [`base`, `tensorrt`, `lite`]. + tf_config : :class:`tensorflow.ConfigProto` + Tensorflow only. Tensorflow session configuration + device : str + Pytorch only. Device to run the model on ('cpu' or 'cuda'). + resize : float or None, optional + Resize dimensions for video frames. e.g. if resize = 0.5, the video will be processed in half the original size. If None, no resizing is applied. + pixels : int, optional + downsize image to this number of pixels, maintaining aspect ratio. + Can only use one of resize or pixels. If both are provided, will use pixels. + single_animal: bool, optional, default=True + Whether the video contains only one animal (True) or multiple animals (False). + cropping : list of int or None, optional + Cropping parameters [x1, x2, y1, y2] in pixels. If None, no cropping is applied. + dynamic : tuple, optional, default=(False, 0.5, 10) (True/false), p cutoff, margin) + Parameters for dynamic cropping. If the state is true, then dynamic cropping will be performed. That means that if an object is detected (i.e. any body part > detectiontreshold), then object boundaries are computed according to the smallest/largest x position and smallest/largest y position of all body parts. This window is expanded by the margin and from then on only the posture within this crop is analyzed (until the object is lost, i.e. 0) and n_frames < total_n_frames + else total_n_frames + ) + iterator = range(n_frames) if print_rate or display else tqdm(range(n_frames)) + for _ in iterator: + ret, frame = cap.read() + if not ret: + warnings.warn( + ( + "Did not complete {:d} frames. " + "There probably were not enough frames in the video {}." + ).format(n_frames, video_path) + ) + break + + start_time = time.perf_counter() + if frame_index == 0: + pose = dlc_live.init_inference(frame) # Loads model + else: + pose = dlc_live.get_pose(frame) + + inf_time = time.perf_counter() - start_time + poses.append({"frame": frame_index, "pose": pose}) + times.append(inf_time) + + if print_rate: + print("Inference rate = {:.3f} FPS".format(1 / inf_time), end="\r", flush=True) + + if save_video: + draw_pose_and_write( + frame=frame, + pose=pose, + resize=resize, + colors=colors, + bodyparts=bodyparts, + pcutoff=pcutoff, + display_radius=display_radius, + draw_keypoint_names=draw_keypoint_names, + vwriter=vwriter + ) + + frame_index += 1 + + if print_rate: + print("Mean inference rate: {:.3f} FPS".format(np.mean(1 / np.array(times)[1:]))) + + metadata = _get_metadata( + video_path=video_path, + cap=cap, + dlc_live=dlc_live + ) + + cap.release() + + dlc_live.close() + + if save_video: + vwriter.release() + + if save_poses: + if engine == Engine.PYTORCH: + individuals = dlc_live.read_config()["metadata"].get("individuals", []) + else: + individuals = [] + n_individuals = len(individuals) or 1 + save_poses_to_files(video_path, save_dir, n_individuals, bodyparts, poses, timestamp=timestamp) + + return times, im_size, metadata + + +def setup_video_writer( + video_path:str, + save_dir:str, + timestamp:str, + num_keypoints:int, + cmap:str, + fps:float, + frame_size:tuple[int, int], +): + # Set colors and convert to RGB + cmap_colors = getattr(cc, cmap) + colors = [ + ImageColor.getrgb(color) + for color in cmap_colors[:: int(len(cmap_colors) / num_keypoints)] + ] + + # Define output video path + video_path = Path(video_path) + video_name = video_path.stem # filename without extension + output_video_path = Path(save_dir) / f"{video_name}_DLCLIVE_LABELLED_{timestamp}.mp4" + + # Get video writer setup + fourcc = cv2.VideoWriter_fourcc(*"mp4v") + vwriter = cv2.VideoWriter( + filename=output_video_path, + fourcc=fourcc, + fps=fps, + frameSize=frame_size, + ) + + return colors, vwriter + +def draw_pose_and_write( + frame: np.ndarray, + pose: np.ndarray, + resize: float, + colors: list[tuple[int, int, int]], + bodyparts: list[str], + pcutoff: float, + display_radius: int, + draw_keypoint_names: bool, + vwriter: cv2.VideoWriter, +): + if len(pose.shape) == 2: + pose = pose[None] + + if resize is not None and resize != 1.0: + # Resize the frame + frame = cv2.resize(frame, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR) + + # Scale pose coordinates + pose = pose.copy() + pose[..., :2] *= resize + + # Visualize keypoints + for i in range(pose.shape[0]): + for j in range(pose.shape[1]): + if pose[i, j, 2] > pcutoff: + x, y = map(int, pose[i, j, :2]) + cv2.circle( + frame, + center=(x, y), + radius=display_radius, + color=colors[j], + thickness=-1, + ) + + if draw_keypoint_names: + cv2.putText( + frame, + text=bodyparts[j], + org=(x + 10, y), + fontFace=cv2.FONT_HERSHEY_SIMPLEX, + fontScale=0.5, + color=colors[j], + thickness=1, + lineType=cv2.LINE_AA, + ) + + + vwriter.write(image=frame) + + +def _get_metadata( + video_path: str, + cap: cv2.VideoCapture, + dlc_live: DLCLive +): + try: + fourcc = decode_fourcc(cap.get(cv2.CAP_PROP_FOURCC)) + except: + fourcc = "" + try: + fps = round(cap.get(cv2.CAP_PROP_FPS)) + except: + fps = None + try: + pix_fmt = decode_fourcc(cap.get(cv2.CAP_PROP_CODEC_PIXEL_FORMAT)) + except: + pix_fmt = "" + try: + frame_count = round(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + except: + frame_count = None + try: + orig_im_size = ( + round(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), + round(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)), + ) + except: + orig_im_size = None + + meta = { + "video_path": video_path, + "video_codec": fourcc, + "video_pixel_format": pix_fmt, + "video_fps": fps, + "video_total_frames": frame_count, + "original_frame_size": orig_im_size, + "dlclive_params": dlc_live.parameterization, + } + return meta + + +def save_poses_to_files(video_path, save_dir, n_individuals, bodyparts, poses, timestamp): """ Saves the detected keypoint poses from the video to CSV and HDF5 files. @@ -145,6 +478,8 @@ def save_poses_to_files(video_path, save_dir, bodyparts, poses, timestamp): Path to the analyzed video file. save_dir : str Directory where the pose data files will be saved. + n_individuals: int + Number of individuals bodyparts : list of str List of body part names corresponding to the keypoints. poses : list of dict @@ -154,27 +489,48 @@ def save_poses_to_files(video_path, save_dir, bodyparts, poses, timestamp): ------- None """ + import pandas as pd + + base_filename = Path(video_path).stem + save_dir = Path(save_dir) + h5_save_path = save_dir / f"{base_filename}_poses_{timestamp}.h5" + csv_save_path = save_dir / f"{base_filename}_poses_{timestamp}.csv" + + poses_array = _create_poses_np_array(n_individuals, bodyparts, poses) + flattened_poses = poses_array.reshape(poses_array.shape[0], -1) + + if n_individuals == 1: + pdindex = pd.MultiIndex.from_product( + [bodyparts, ["x", "y", "likelihood"]], names=["bodyparts", "coords"] + ) + else: + individuals = [f"individual_{i}" for i in range(n_individuals)] + pdindex = pd.MultiIndex.from_product( + [individuals, bodyparts, ["x", "y", "likelihood"]], names=["individuals", "bodyparts", "coords"] + ) + + pose_df = pd.DataFrame(flattened_poses, columns=pdindex) + + pose_df.to_hdf(h5_save_path, key="df_with_missing", mode="w") + pose_df.to_csv(csv_save_path, index=False) + +def _create_poses_np_array(n_individuals: int, bodyparts: list, poses: list): + # Create numpy array with poses: + max_frame = max(p["frame"] for p in poses) + pose_target_shape = (n_individuals, len(bodyparts), 3) + poses_array = np.full((max_frame + 1, *pose_target_shape), np.nan) + + for item in poses: + frame = item["frame"] + pose = item["pose"] + if pose.ndim == 2: + pose = pose[np.newaxis, :, :] + padded_pose = np.full(pose_target_shape, np.nan) + slices = tuple(slice(0, min(pose.shape[i], pose_target_shape[i])) for i in range(3)) + padded_pose[slices] = pose[slices] + poses_array[frame] = padded_pose - base_filename = os.path.splitext(os.path.basename(video_path))[0] - csv_save_path = os.path.join(save_dir, f"{base_filename}_poses_{timestamp}.csv") - h5_save_path = os.path.join(save_dir, f"{base_filename}_poses_{timestamp}.h5") - - # Save to CSV - with open(csv_save_path, mode="w", newline="") as file: - writer = csv.writer(file) - header = ["frame"] + [ - f"{bp}_{axis}" for bp in bodyparts for axis in ["x", "y", "confidence"] - ] - writer.writerow(header) - for entry in poses: - frame_num = entry["frame"] - pose = entry["pose"]["poses"][0][0] - row = [frame_num] + [ - item.item() if isinstance(item, torch.Tensor) else item - for kp in pose - for item in kp - ] - writer.writerow(row) + return poses_array import argparse From 83661878f0f16935be5bd887fb8ca713f4951ef3 Mon Sep 17 00:00:00 2001 From: maximpavliv Date: Tue, 17 Jun 2025 09:22:57 +0200 Subject: [PATCH 24/28] WIP refactor benchmarking: benchmark_videos() --- dlclive/benchmark.py | 215 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 215 insertions(+) diff --git a/dlclive/benchmark.py b/dlclive/benchmark.py index 362bf41..da965d0 100644 --- a/dlclive/benchmark.py +++ b/dlclive/benchmark.py @@ -16,6 +16,7 @@ import colorcet as cc import cv2 import numpy as np +import pickle from PIL import ImageColor from pip._internal.operations import freeze import torch @@ -62,6 +63,149 @@ def show_progress(count, block_size, total_size): with zipfile.ZipFile(zip_path, 'r') as zip_ref: zip_ref.extractall(target_dir) + +def benchmark_videos( + model_path, + video_path, + output=None, + n_frames=1000, + tf_config=None, + resize=None, + pixels=None, + cropping=None, + dynamic=(False, 0.5, 10), + print_rate=False, + display=False, + pcutoff=0.5, + display_radius=3, + cmap="bmy", + save_poses=False, + save_video=False, +): + """Analyze videos using DeepLabCut-live exported models. + Analyze multiple videos and/or multiple options for the size of the video + by specifying a resizing factor or the number of pixels to use in the image (keeping aspect ratio constant). + Options to record inference times (to examine inference speed), + display keypoints to visually check the accuracy, + or save poses to an hdf5 file as in :function:`deeplabcut.benchmark_videos` and + create a labeled video as in :function:`deeplabcut.create_labeled_video`. + + Parameters + ---------- + model_path : str + path to exported DeepLabCut model + video_path : str or list + path to video file or list of paths to video files + output : str + path to directory to save results + tf_config : :class:`tensorflow.ConfigProto` + tensorflow session configuration + resize : int, optional + resize factor. Can only use one of resize or pixels. If both are provided, will use pixels. by default None + pixels : int, optional + downsize image to this number of pixels, maintaining aspect ratio. Can only use one of resize or pixels. If both are provided, will use pixels. by default None + cropping : list of int + cropping parameters in pixel number: [x1, x2, y1, y2] + dynamic: triple containing (state, detectiontreshold, margin) + If the state is true, then dynamic cropping will be performed. That means that if an object is detected (i.e. any body part > detectiontreshold), + then object boundaries are computed according to the smallest/largest x position and smallest/largest y position of all body parts. This window is + expanded by the margin and from then on only the posture within this crop is analyzed (until the object is lost, i.e. `, by default "bmy" + save_poses : bool, optional + flag to save poses to an hdf5 file. If True, operates similar to :function:`DeepLabCut.benchmark_videos`, by default False + save_video : bool, optional + flag to save a labeled video. If True, operates similar to :function:`DeepLabCut.create_labeled_video`, by default False + + Example + ------- + Return a vector of inference times for 10000 frames on one video or two videos: + dlclive.benchmark_videos('/my/exported/model', 'my_video.avi', n_frames=10000) + dlclive.benchmark_videos('/my/exported/model', ['my_video1.avi', 'my_video2.avi'], n_frames=10000) + + Return a vector of inference times, testing full size and resizing images to half the width and height for inference, for two videos + dlclive.benchmark_videos('/my/exported/model', ['my_video1.avi', 'my_video2.avi'], n_frames=10000, resize=[1.0, 0.5]) + + Display keypoints to check the accuracy of an exported model + dlclive.benchmark_videos('/my/exported/model', 'my_video.avi', display=True) + + Analyze a video (save poses to hdf5) and create a labeled video, similar to :function:`DeepLabCut.benchmark_videos` and :function:`create_labeled_video` + dlclive.benchmark_videos('/my/exported/model', 'my_video.avi', save_poses=True, save_video=True) + """ + # convert video_paths to list + video_path = video_path if type(video_path) is list else [video_path] + + # fix resize + if pixels: + pixels = pixels if type(pixels) is list else [pixels] + resize = [None for p in pixels] + elif resize: + resize = resize if type(resize) is list else [resize] + pixels = [None for r in resize] + else: + resize = [None] + pixels = [None] + + # loop over videos + for video in video_path: + # initialize full inference times + inf_times = [] + im_size_out = [] + + for i in range(len(resize)): + print(f"\nRun {i+1} / {len(resize)}\n") + + this_inf_times, this_im_size, meta = benchmark( + model_path=model_path, + model_type="base", + video_path=video, + tf_config=tf_config, + resize=resize[i], + pixels=pixels[i], + cropping=cropping, + dynamic=dynamic, + n_frames=n_frames, + print_rate=print_rate, + display=display, + pcutoff=pcutoff, + display_radius=display_radius, + cmap=cmap, + save_poses=save_poses, + save_video=save_video, + save_dir=output, + ) + + inf_times.append(this_inf_times) + im_size_out.append(this_im_size) + + inf_times = np.array(inf_times) + im_size_out = np.array(im_size_out) + + # save results + if output is not None: + sys_info = get_system_info() + save_inf_times( + sys_info, + inf_times, + im_size_out, + model=os.path.basename(model_path), + meta=meta, + output=output, + ) + + def get_system_info() -> dict: """ Returns a summary of system information relevant to running benchmarking. @@ -128,6 +272,77 @@ def get_system_info() -> dict: } +def save_inf_times( + sys_info, inf_times, im_size, model=None, meta=None, output=None +): + """Save inference time data collected using :function:`benchmark` with system information to a pickle file. + This is primarily used through :function:`benchmark_videos` + + + Parameters + ---------- + sys_info : tuple + system information generated by :func:`get_system_info` + inf_times : :class:`numpy.ndarray` + array of inference times generated by :func:`benchmark` + im_size : tuple or :class:`numpy.ndarray` + image size (width, height) for each benchmark run. If an array, each row corresponds to a row in inf_times + model: str, optional + name of model + meta : dict, optional + metadata returned by :func:`benchmark` + output : str, optional + path to directory to save data. If None, uses pwd, by default None + + Returns + ------- + bool + flag indicating successful save + """ + + output = output if output is not None else os.getcwd() + model_type = None + if model is not None: + if "resnet" in model: + model_type = "resnet" + elif "mobilenet" in model: + model_type = "mobilenet" + else: + model_type = None + + fn_ind = 0 + base_name = ( + f"benchmark_{sys_info['host_name']}_{sys_info['device_type']}_{fn_ind}.pickle" + ) + out_file = os.path.normpath(f"{output}/{base_name}") + while os.path.isfile(out_file): + fn_ind += 1 + base_name = f"benchmark_{sys_info['host_name']}_{sys_info['device_type']}_{fn_ind}.pickle" + out_file = os.path.normpath(f"{output}/{base_name}") + + # summary stats (mean inference time & standard error of mean) + stats = zip( + np.mean(inf_times, 1), + np.std(inf_times, 1) * 1.0 / np.sqrt(np.shape(inf_times)[1]), + ) + + data = { + "model": model, + "model_type": model_type, + "im_size": im_size, + "inference_times": inf_times, + "stats": stats, + } + + data.update(sys_info) + if meta: + data.update(meta) + + os.makedirs(os.path.normpath(output), exist_ok=True) + pickle.dump(data, open(out_file, "wb")) + + return True + def benchmark( model_path: str, model_type: str, From db6a61d5dd58aedb29e1b7fc7abc8488d48d42d5 Mon Sep 17 00:00:00 2001 From: maximpavliv Date: Tue, 17 Jun 2025 10:00:16 +0200 Subject: [PATCH 25/28] CI/CD: add torch installation --- .github/workflows/testing.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml index ece95c4..9800e34 100644 --- a/.github/workflows/testing.yml +++ b/.github/workflows/testing.yml @@ -54,5 +54,5 @@ jobs: shell: bash -el {0} # Important: activates the conda environment run: | python -m pip install --upgrade pip wheel poetry - python -m poetry install --extras "tf" + python -m poetry install --extras "tf" --extras "pytorch" python -m poetry run dlc-live-test --nodisplay From 664835c198a5e9a6db3078dc5b6ccb08a5130ac6 Mon Sep 17 00:00:00 2001 From: maximpavliv Date: Tue, 17 Jun 2025 10:21:21 +0200 Subject: [PATCH 26/28] WIP refactor benchmarking: extract Engine --- dlclive/benchmark.py | 2 +- dlclive/engine.py | 14 ++++++++++++++ dlclive/factory.py | 17 +---------------- 3 files changed, 16 insertions(+), 17 deletions(-) create mode 100644 dlclive/engine.py diff --git a/dlclive/benchmark.py b/dlclive/benchmark.py index da965d0..937d9c2 100644 --- a/dlclive/benchmark.py +++ b/dlclive/benchmark.py @@ -25,7 +25,7 @@ from dlclive import DLCLive from dlclive import VERSION from dlclive import __file__ as dlcfile -from dlclive.factory import Engine +from dlclive.engine import Engine from dlclive.utils import decode_fourcc diff --git a/dlclive/engine.py b/dlclive/engine.py new file mode 100644 index 0000000..01758d2 --- /dev/null +++ b/dlclive/engine.py @@ -0,0 +1,14 @@ +from enum import Enum + +class Engine(Enum): + TENSORFLOW = "tensorflow" + PYTORCH = "pytorch" + + @classmethod + def from_model_type(cls, model_type: str) -> "Engine": + if model_type.lower() == "pytorch": + return cls.PYTORCH + elif model_type.lower() in ("tensorflow", "base", "tensorrt", "lite"): + return cls.TENSORFLOW + else: + raise ValueError(f"Unknown model type: {model_type}") diff --git a/dlclive/factory.py b/dlclive/factory.py index a3f7d00..df70029 100644 --- a/dlclive/factory.py +++ b/dlclive/factory.py @@ -5,6 +5,7 @@ from typing import Literal from dlclive.core.runner import BaseRunner +from dlclive.engine import Engine def build_runner( @@ -54,19 +55,3 @@ def build_runner( def filter_keys(valid: set[str], kwargs: dict) -> dict: """Filters the keys in kwargs, only keeping those in valid.""" return {k: v for k, v in kwargs.items() if k in valid} - - -from enum import Enum - -class Engine(Enum): - TENSORFLOW = "tensorflow" - PYTORCH = "pytorch" - - @classmethod - def from_model_type(cls, model_type: str) -> "Engine": - if model_type.lower() == "pytorch": - return cls.PYTORCH - elif model_type.lower() in ("tensorflow", "base", "tensorrt", "lite"): - return cls.TENSORFLOW - else: - raise ValueError(f"Unknown model type: {model_type}") From 4475ea21ce346668d59a327c07f437380effe4fb Mon Sep 17 00:00:00 2001 From: maximpavliv Date: Tue, 17 Jun 2025 10:38:30 +0200 Subject: [PATCH 27/28] Refactor benchmarking: model_type in benchmark_videos() --- benchmarking/run_dlclive_benchmark.py | 25 ++++++++++++++++++++----- dlclive/benchmark.py | 7 ++++++- dlclive/check_install/check_install.py | 10 ++++++++-- dlclive/engine.py | 19 +++++++++++++++++++ 4 files changed, 53 insertions(+), 8 deletions(-) diff --git a/benchmarking/run_dlclive_benchmark.py b/benchmarking/run_dlclive_benchmark.py index 1ea14e4..859843b 100644 --- a/benchmarking/run_dlclive_benchmark.py +++ b/benchmarking/run_dlclive_benchmark.py @@ -12,6 +12,7 @@ import glob from dlclive import benchmark_videos, download_benchmarking_data +from dlclive.engine import Engine datafolder = os.path.join( pathlib.Path(__file__).parent.absolute(), "Data-DLC-live-benchmark" @@ -36,8 +37,22 @@ if not os.path.isdir(out_dir): os.mkdir(out_dir) -for m in dog_models: - benchmark_videos(m, dog_video, output=out_dir, n_frames=n_frames, pixels=pixels) - -for m in mouse_models: - benchmark_videos(m, mouse_video, output=out_dir, n_frames=n_frames, pixels=pixels) +for model_path in dog_models: + benchmark_videos( + model_path=model_path, + model_type="base" if Engine.from_model_path(model_path) == Engine.TENSORFLOW else "pytorch", + video_path=dog_video, + output=out_dir, + n_frames=n_frames, + pixels=pixels + ) + +for model_path in mouse_models: + benchmark_videos( + model_path=model_path, + model_type="base" if Engine.from_model_path(model_path) == Engine.TENSORFLOW else "pytorch", + video_path=mouse_video, + output=out_dir, + n_frames=n_frames, + pixels=pixels + ) diff --git a/dlclive/benchmark.py b/dlclive/benchmark.py index 937d9c2..1a41ca9 100644 --- a/dlclive/benchmark.py +++ b/dlclive/benchmark.py @@ -66,6 +66,7 @@ def show_progress(count, block_size, total_size): def benchmark_videos( model_path, + model_type, video_path, output=None, n_frames=1000, @@ -94,6 +95,9 @@ def benchmark_videos( ---------- model_path : str path to exported DeepLabCut model + model_type: string, optional + Which model to use. For the PyTorch engine, options are [`pytorch`]. For the + TensorFlow engine, options are [`base`, `tensorrt`, `lite`]. video_path : str or list path to video file or list of paths to video files output : str @@ -169,7 +173,7 @@ def benchmark_videos( this_inf_times, this_im_size, meta = benchmark( model_path=model_path, - model_type="base", + model_type=model_type, video_path=video, tf_config=tf_config, resize=resize[i], @@ -375,6 +379,7 @@ def benchmark( Path to the DeepLabCut model. model_type : str Which model to use. For the PyTorch engine, options are [`pytorch`]. For the + TensorFlow engine, options are [`base`, `tensorrt`, `lite`]. video_path : str Path to the video file to be analyzed. TensorFlow engine, options are [`base`, `tensorrt`, `lite`]. diff --git a/dlclive/check_install/check_install.py b/dlclive/check_install/check_install.py index 17fee7a..2bc4e65 100755 --- a/dlclive/check_install/check_install.py +++ b/dlclive/check_install/check_install.py @@ -16,7 +16,8 @@ from dlclibrary.dlcmodelzoo.modelzoo_download import download_huggingface_model -from dlclive.benchmark_tf import benchmark_videos +from dlclive.benchmark import benchmark_videos +from dlclive.engine import Engine MODEL_NAME = "superanimal_quadruped" SNAPSHOT_NAME = "snapshot-700000.pb" @@ -77,7 +78,12 @@ def main(): # run benchmark videos print("\n Running inference...\n") benchmark_videos( - str(model_dir), video_file, display=display, resize=0.5, pcutoff=0.25 + model_path=str(model_dir), + model_type="base" if Engine.from_model_path(model_dir) == Engine.TENSORFLOW else "pytorch", + video_path=video_file, + display=display, + resize=0.5, + pcutoff=0.25 ) # deleting temporary files diff --git a/dlclive/engine.py b/dlclive/engine.py index 01758d2..eed0af1 100644 --- a/dlclive/engine.py +++ b/dlclive/engine.py @@ -1,4 +1,5 @@ from enum import Enum +from pathlib import Path class Engine(Enum): TENSORFLOW = "tensorflow" @@ -12,3 +13,21 @@ def from_model_type(cls, model_type: str) -> "Engine": return cls.TENSORFLOW else: raise ValueError(f"Unknown model type: {model_type}") + + @classmethod + def from_model_path(cls, model_path: str | Path) -> "Engine": + path = Path(model_path) + + if not path.exists(): + raise FileNotFoundError(f"Model path does not exist: {model_path}") + + if path.is_dir(): + has_cfg = (path / "pose_cfg.yaml").is_file() + has_pb = any(p.suffix == ".pb" for p in path.glob("*.pb")) + if has_cfg and has_pb: + return cls.TENSORFLOW + elif path.is_file(): + if path.suffix == ".pt": + return cls.PYTORCH + + raise ValueError(f"Could not determine engine from model path: {model_path}") \ No newline at end of file From 5731ebe7481a990d4d427341d840e4e2d83474fc Mon Sep 17 00:00:00 2001 From: maximpavliv Date: Wed, 18 Jun 2025 15:25:20 +0200 Subject: [PATCH 28/28] Delete poetry lock --- poetry.lock | 2755 --------------------------------------------------- 1 file changed, 2755 deletions(-) delete mode 100644 poetry.lock diff --git a/poetry.lock b/poetry.lock deleted file mode 100644 index ccbea06..0000000 --- a/poetry.lock +++ /dev/null @@ -1,2755 +0,0 @@ -# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand. - 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