Spaces:
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Running
on
Zero
onuralpszr
commited on
Commit
•
94d19e9
1
Parent(s):
d552355
feat: ✨ For segmentation methods are added
Browse filesSigned-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>
- .gitignore +168 -0
- app.py +70 -9
- helpers/{utils.py → file_utils.py} +0 -0
- helpers/segment_utils.py +189 -0
- requirements.txt +3 -1
.gitignore
ADDED
@@ -0,0 +1,168 @@
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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# PyBuilder
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.pybuilder/
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# UV
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# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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#uv.lock
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# poetry
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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# pdm
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# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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#pdm.lock
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# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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# in version control.
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# https://pdm.fming.dev/latest/usage/project/#working-with-version-control
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.pdm.toml
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.pdm-python
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.pdm-build/
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# PyCharm
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# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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app.py
CHANGED
@@ -6,7 +6,8 @@ import numpy as np
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from PIL import Image
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import gradio as gr
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import spaces
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from helpers.
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import os
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BOX_ANNOTATOR = sv.BoxAnnotator()
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MASK_ANNOTATOR = sv.MaskAnnotator()
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DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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VIDEO_TARGET_DIRECTORY = "tmp"
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INTRO_TEXT = """
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-
## PaliGemma 2 Detection with Supervision - Demo
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<div style="display: flex; gap: 10px;">
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<a href="https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/paligemma/README.md">
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@@ -60,6 +63,14 @@ def parse_class_names(prompt):
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classes_text = prompt[7:].strip()
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return [cls.strip() for cls in classes_text.split(';') if cls.strip()]
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@spaces.GPU
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def paligemma_detection(input_image, input_text, max_new_tokens):
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model_inputs = processor(text=input_text,
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@@ -110,10 +121,60 @@ def annotate_image(result, resolution_wh, prompt, cv_image):
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def process_image(input_image, input_text, max_new_tokens):
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cv_image = cv2.cvtColor(np.array(input_image), cv2.COLOR_RGB2BGR)
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-
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return annotated_image, result
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@@ -188,13 +249,13 @@ def process_video(input_video, input_text, max_new_tokens, progress=gr.Progress(
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with gr.Blocks() as app:
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gr.Markdown(INTRO_TEXT)
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with gr.Tab("Image Detection"):
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with gr.Row():
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with gr.Column():
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input_image = gr.Image(type="pil", label="Input Image")
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input_text = gr.Textbox(
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lines=2,
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placeholder="Enter prompt in format like this: detect person;dog;building",
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label="Enter detection prompt"
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)
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max_new_tokens = gr.Slider(minimum=20, maximum=200, value=100, step=10, label="Max New Tokens", info="Set to larger for longer generation.")
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input_video = gr.Video(label="Input Video")
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input_text = gr.Textbox(
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lines=2,
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placeholder="Enter prompt in format like this: detect person;dog;building",
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label="Enter detection prompt"
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)
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max_new_tokens = gr.Slider(minimum=20, maximum=200, value=100, step=1, label="Max New Tokens", info="Set to larger for longer generation.")
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from PIL import Image
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import gradio as gr
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import spaces
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from helpers.file_utils import create_directory, delete_directory, generate_unique_name
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from helpers.segment_utils import parse_segmentation, extract_objs
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import os
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BOX_ANNOTATOR = sv.BoxAnnotator()
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MASK_ANNOTATOR = sv.MaskAnnotator()
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DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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VIDEO_TARGET_DIRECTORY = "tmp"
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VAE_MODEL = "vae-oid.npz"
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COLORS = ['#4285f4', '#db4437', '#f4b400', '#0f9d58', '#e48ef1']
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INTRO_TEXT = """
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## PaliGemma 2 Detection/Segmentation with Supervision - Demo
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<div style="display: flex; gap: 10px;">
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<a href="https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/paligemma/README.md">
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classes_text = prompt[7:].strip()
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return [cls.strip() for cls in classes_text.split(';') if cls.strip()]
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def parse_prompt_type(prompt):
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"""Determine if the prompt is for detection or segmentation."""
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if prompt.lower().startswith('detect '):
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return 'detection', prompt[7:].strip()
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elif prompt.lower().startswith('segment '):
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return 'segmentation', prompt[8:].strip()
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return None, prompt
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@spaces.GPU
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def paligemma_detection(input_image, input_text, max_new_tokens):
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model_inputs = processor(text=input_text,
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def process_image(input_image, input_text, max_new_tokens):
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cv_image = cv2.cvtColor(np.array(input_image), cv2.COLOR_RGB2BGR)
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prompt_type, cleaned_prompt = parse_prompt_type(input_text)
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if prompt_type == 'detection':
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# Existing detection logic
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result = paligemma_detection(input_image, input_text, max_new_tokens)
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class_names = [cls.strip() for cls in cleaned_prompt.split(';') if cls.strip()]
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detections = sv.Detections.from_lmm(
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sv.LMM.PALIGEMMA,
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result,
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resolution_wh=(input_image.width, input_image.height),
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classes=class_names
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)
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annotated_image = BOX_ANNOTATOR.annotate(scene=cv_image.copy(), detections=detections)
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annotated_image = LABEL_ANNOTATOR.annotate(scene=annotated_image, detections=detections)
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annotated_image = MASK_ANNOTATOR.annotate(scene=annotated_image, detections=detections)
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elif prompt_type == 'segmentation':
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# New segmentation logic
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result = paligemma_detection(input_image, input_text, max_new_tokens)
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objs = extract_objs(result.lstrip("\n"), input_image.width, input_image.height, unique_labels=True)
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# Create masks and annotations
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annotated_image = cv_image.copy()
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for obj in objs:
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if 'mask' in obj and obj['mask'] is not None:
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mask = obj['mask']
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# Convert mask to uint8 for visualization
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mask_vis = (mask * 255).astype(np.uint8)
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# Create colored mask
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colored_mask = np.zeros_like(cv_image)
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color_idx = hash(obj['name']) % len(COLORS)
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color = tuple(int(COLORS[color_idx].lstrip('#')[i:i+2], 16) for i in (0, 2, 4))
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colored_mask[mask > 0] = color
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# Blend mask with image
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alpha = 0.5
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annotated_image = cv2.addWeighted(annotated_image, 1, colored_mask, alpha, 0)
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# Add label
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if 'xyxy' in obj:
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x1, y1, x2, y2 = obj['xyxy']
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cv2.putText(annotated_image, obj['name'], (x1, y1-10),
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cv2.FONT_HERSHEY_SIMPLEX, 0.9, color, 2)
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else:
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gr.Warning("Invalid prompt format. Please use 'detect' or 'segment' followed by class names")
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return input_image, "Invalid prompt format"
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# Convert back to RGB for display
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annotated_image = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
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annotated_image = Image.fromarray(annotated_image)
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return annotated_image, result
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with gr.Blocks() as app:
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gr.Markdown(INTRO_TEXT)
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with gr.Tab("Image Detection/Segmentation"):
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with gr.Row():
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with gr.Column():
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input_image = gr.Image(type="pil", label="Input Image")
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input_text = gr.Textbox(
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lines=2,
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placeholder="Enter prompt in format like this: detect person;dog;building or segment person;dog;building",
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label="Enter detection prompt"
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)
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max_new_tokens = gr.Slider(minimum=20, maximum=200, value=100, step=10, label="Max New Tokens", info="Set to larger for longer generation.")
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input_video = gr.Video(label="Input Video")
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input_text = gr.Textbox(
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lines=2,
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placeholder="Enter prompt in format like this: detect person;dog;building or segment person;dog;building",
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label="Enter detection prompt"
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)
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max_new_tokens = gr.Slider(minimum=20, maximum=200, value=100, step=1, label="Max New Tokens", info="Set to larger for longer generation.")
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helpers/{utils.py → file_utils.py}
RENAMED
File without changes
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helpers/segment_utils.py
ADDED
@@ -0,0 +1,189 @@
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1 |
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import flax.linen as nn
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2 |
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import jax
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import jax.numpy as jnp
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import re
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import numpy as np
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import functools
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from PIL import Image
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### Postprocessing Utils for Segmentation Tokens
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### Segmentation tokens are passed to another VAE which decodes them to a mask
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_MODEL_PATH = 'vae-oid.npz'
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_SEGMENT_DETECT_RE = re.compile(
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r'(.*?)' +
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r'<loc(\d{4})>' * 4 + r'\s*' +
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'(?:%s)?' % (r'<seg(\d{3})>' * 16) +
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r'\s*([^;<>]+)? ?(?:; )?',
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)
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def parse_segmentation(input_image, input_text, inference_output):
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out = infer(input_image, input_text, max_new_tokens=100)
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objs = extract_objs(out.lstrip("\n"), input_image.size[0], input_image.size[1], unique_labels=True)
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labels = set(obj.get('name') for obj in objs if obj.get('name'))
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color_map = {l: COLORS[i % len(COLORS)] for i, l in enumerate(labels)}
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highlighted_text = [(obj['content'], obj.get('name')) for obj in objs]
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annotated_img = (
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input_image,
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[
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(
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obj['mask'] if obj.get('mask') is not None else obj['xyxy'],
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obj['name'] or '',
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)
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for obj in objs
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if 'mask' in obj or 'xyxy' in obj
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],
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)
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has_annotations = bool(annotated_img[1])
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return annotated_img
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def _get_params(checkpoint):
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"""Converts PyTorch checkpoint to Flax params."""
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def transp(kernel):
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return np.transpose(kernel, (2, 3, 1, 0))
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def conv(name):
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return {
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'bias': checkpoint[name + '.bias'],
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'kernel': transp(checkpoint[name + '.weight']),
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}
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def resblock(name):
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return {
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'Conv_0': conv(name + '.0'),
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'Conv_1': conv(name + '.2'),
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'Conv_2': conv(name + '.4'),
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}
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return {
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'_embeddings': checkpoint['_vq_vae._embedding'],
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'Conv_0': conv('decoder.0'),
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'ResBlock_0': resblock('decoder.2.net'),
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'ResBlock_1': resblock('decoder.3.net'),
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'ConvTranspose_0': conv('decoder.4'),
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'ConvTranspose_1': conv('decoder.6'),
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'ConvTranspose_2': conv('decoder.8'),
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'ConvTranspose_3': conv('decoder.10'),
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'Conv_1': conv('decoder.12'),
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}
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def _quantized_values_from_codebook_indices(codebook_indices, embeddings):
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batch_size, num_tokens = codebook_indices.shape
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assert num_tokens == 16, codebook_indices.shape
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unused_num_embeddings, embedding_dim = embeddings.shape
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encodings = jnp.take(embeddings, codebook_indices.reshape((-1)), axis=0)
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encodings = encodings.reshape((batch_size, 4, 4, embedding_dim))
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return encodings
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@functools.cache
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def _get_reconstruct_masks():
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"""Reconstructs masks from codebook indices.
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Returns:
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A function that expects indices shaped `[B, 16]` of dtype int32, each
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ranging from 0 to 127 (inclusive), and that returns a decoded masks sized
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`[B, 64, 64, 1]`, of dtype float32, in range [-1, 1].
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"""
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class ResBlock(nn.Module):
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features: int
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@nn.compact
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def __call__(self, x):
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original_x = x
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x = nn.Conv(features=self.features, kernel_size=(3, 3), padding=1)(x)
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x = nn.relu(x)
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x = nn.Conv(features=self.features, kernel_size=(3, 3), padding=1)(x)
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x = nn.relu(x)
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x = nn.Conv(features=self.features, kernel_size=(1, 1), padding=0)(x)
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return x + original_x
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class Decoder(nn.Module):
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"""Upscales quantized vectors to mask."""
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@nn.compact
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def __call__(self, x):
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num_res_blocks = 2
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dim = 128
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num_upsample_layers = 4
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x = nn.Conv(features=dim, kernel_size=(1, 1), padding=0)(x)
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x = nn.relu(x)
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for _ in range(num_res_blocks):
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x = ResBlock(features=dim)(x)
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for _ in range(num_upsample_layers):
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x = nn.ConvTranspose(
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features=dim,
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kernel_size=(4, 4),
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strides=(2, 2),
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padding=2,
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transpose_kernel=True,
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)(x)
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x = nn.relu(x)
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dim //= 2
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x = nn.Conv(features=1, kernel_size=(1, 1), padding=0)(x)
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return x
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def reconstruct_masks(codebook_indices):
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quantized = _quantized_values_from_codebook_indices(
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codebook_indices, params['_embeddings']
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)
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return Decoder().apply({'params': params}, quantized)
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with open(_MODEL_PATH, 'rb') as f:
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params = _get_params(dict(np.load(f)))
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return jax.jit(reconstruct_masks, backend='cpu')
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def extract_objs(text, width, height, unique_labels=False):
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"""Returns objs for a string with "<loc>" and "<seg>" tokens."""
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objs = []
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seen = set()
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while text:
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m = _SEGMENT_DETECT_RE.match(text)
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if not m:
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break
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print("m", m)
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gs = list(m.groups())
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before = gs.pop(0)
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name = gs.pop()
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y1, x1, y2, x2 = [int(x) / 1024 for x in gs[:4]]
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y1, x1, y2, x2 = map(round, (y1*height, x1*width, y2*height, x2*width))
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seg_indices = gs[4:20]
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162 |
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if seg_indices[0] is None:
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mask = None
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else:
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165 |
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seg_indices = np.array([int(x) for x in seg_indices], dtype=np.int32)
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166 |
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m64, = _get_reconstruct_masks()(seg_indices[None])[..., 0]
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167 |
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m64 = np.clip(np.array(m64) * 0.5 + 0.5, 0, 1)
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168 |
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m64 = Image.fromarray((m64 * 255).astype('uint8'))
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169 |
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mask = np.zeros([height, width])
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170 |
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if y2 > y1 and x2 > x1:
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171 |
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mask[y1:y2, x1:x2] = np.array(m64.resize([x2 - x1, y2 - y1])) / 255.0
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172 |
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173 |
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content = m.group()
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174 |
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if before:
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175 |
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objs.append(dict(content=before))
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176 |
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content = content[len(before):]
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177 |
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while unique_labels and name in seen:
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178 |
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name = (name or '') + "'"
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179 |
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seen.add(name)
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180 |
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objs.append(dict(
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181 |
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content=content, xyxy=(x1, y1, x2, y2), mask=mask, name=name))
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182 |
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text = text[len(before) + len(content):]
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183 |
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184 |
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if text:
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objs.append(dict(content=text))
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return objs
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#########
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requirements.txt
CHANGED
@@ -3,4 +3,6 @@ transformers==4.47.0
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3 |
requests
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4 |
tqdm
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5 |
spaces
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6 |
-
torch
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3 |
requests
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4 |
tqdm
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5 |
spaces
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6 |
+
torch
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7 |
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jax
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8 |
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flax
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