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Prevent mtcnn logging #128

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freedom9393 opened this issue Mar 31, 2024 · 8 comments
Closed

Prevent mtcnn logging #128

freedom9393 opened this issue Mar 31, 2024 · 8 comments

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@freedom9393
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When I test mtcnn with huge number of images, it shows long logs:

1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 11ms/step
3/3 [==============================] - 0s 1ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 10ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 12ms/step
1/1 [==============================] - 0s 11ms/step
1/1 [==============================] - 0s 10ms/step
1/1 [==============================] - 0s 10ms/step
1/1 [==============================] - 0s 10ms/step

and I think this process consumes more time. So, is there any way to prevent showing logs and testing faster?

@ladyshen
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ladyshen commented Mar 31, 2024 via email

@saleweaver
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Yes please!

@ladyshen
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ladyshen commented Jul 20, 2024 via email

@saleweaver
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saleweaver commented Jul 20, 2024

@freedom9393 the only thing that worked for me is to surpress everything those methods output...

@contextmanager
def suppress_stdout_stderr():
    with open(os.devnull, "w") as devnull:
        old_stdout = sys.stdout
        old_stderr = sys.stderr
        sys.stdout = devnull
        sys.stderr = devnull
        try:
            yield
        finally:
            sys.stdout = old_stdout
            sys.stderr = old_stderr

And use like

with suppress_stdout_stderr():
    results = detector.detect_faces(rgb_frame)

@wpritom
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wpritom commented Aug 28, 2024

is it originating from the mtcnn library or any other supporting library? I tried to dig in but didn't find it in the mtcnn library.

@ladyshen
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ladyshen commented Aug 28, 2024 via email

@saleweaver
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is it originating from the mtcnn library or any other supporting library? I tried to dig in but didn't find it in the mtcnn library.

I think it's from an underlying library. I found no output whatsoever from this one.

@wpritom
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wpritom commented Aug 28, 2024

is it originating from the mtcnn library or any other supporting library? I tried to dig in but didn't find it in the mtcnn library.

I think it's from an underlying library. I found no output whatsoever from this one.

Yes. The issue can be solved manually here. However, verbose flag should be added in the MTCNN library too.

#121

ipazc pushed a commit that referenced this issue Oct 7, 2024
…tch processing support

- Completely refactored the MTCNN implementation following best coding practices.
- Optimized code by removing unnecessary transpositions, resulting in faster computation. Fixes #22.
- Transposed convolutional layer weights to eliminate the need for additional transpositions during preprocessing and postprocessing, improving overall efficiency.
- Converted preprocessing and postprocessing functions into matrix operations to accelerate computation. Fixes #14, #110.
- Added batch processing support to enhance performance for multiple input images. Fixes #9, #71.
- Migrated network architecture to TensorFlow >= 2.12 for improved compatibility and performance. Fixes #80, #82, #90, #91, #93, #98, #104, #112, #114, #115, #116.
- Extensively documented the project with detailed explanations of thresholds and parameters. Fixes #12, #41, #52, #57, #99, #122, #117.
- Added support for selecting computation backends (CPU, GPU, etc.) with the `device` parameter. Fixes #23.
- Added new parameters to control the result format (support for x1, y1, x2, y2 instead of x1, y1, width, height) and the ability to return tensors instead of dictionaries. Fixes #72.
- Configured PyLint support to ensure code quality and style adherence.
- Organized functions into specific modules (`mtcnn.utils.*` and `mtcnn.stages.*`) for better modularity.
- Created Jupyter notebooks for visualization and ablation studies of each stage, allowing detailed exploration of layers, weights, and intermediate results. Fixes #88, #102.
- Added a comprehensive training guide for the model. Fixes #35, #39.
- Updated README with information on the new version, including the complete Read the Docs documentation that describes the process, theoretical background, and usage examples. Fixes #53, #73.
- Configured GitHub Actions for continuous integration and delivery (CI/CD).
- Fixed memory leak by switching to a more efficient TensorFlow method (`model(tensor)` instead of `model.predict(tensor)`). Fixes #87, #109, #121, #125, #128.
- Made TensorFlow an optional dependency to prevent conflicts with user-installed versions. Fixes #95.
- Added comprehensive unit tests for increased reliability and coverage.
@ipazc ipazc mentioned this issue Oct 8, 2024
ipazc pushed a commit that referenced this issue Oct 8, 2024
…tch processing support

- Completely refactored the MTCNN implementation following best coding practices.
- Optimized code by removing unnecessary transpositions, resulting in faster computation. Fixes #22.
- Transposed convolutional layer weights to eliminate the need for additional transpositions during preprocessing and postprocessing, improving overall efficiency.
- Converted preprocessing and postprocessing functions into matrix operations to accelerate computation. Fixes #14, #110.
- Added batch processing support to enhance performance for multiple input images. Fixes #9, #71.
- Migrated network architecture to TensorFlow >= 2.12 for improved compatibility and performance. Fixes #80, #82, #90, #91, #93, #98, #104, #112, #114, #115, #116.
- Extensively documented the project with detailed explanations of thresholds and parameters. Fixes #12, #41, #52, #57, #99, #122, #117.
- Added support for selecting computation backends (CPU, GPU, etc.) with the `device` parameter. Fixes #23.
- Added new parameters to control the result format (support for x1, y1, x2, y2 instead of x1, y1, width, height) and the ability to return tensors instead of dictionaries. Fixes #72.
- Configured PyLint support to ensure code quality and style adherence.
- Organized functions into specific modules (`mtcnn.utils.*` and `mtcnn.stages.*`) for better modularity.
- Created Jupyter notebooks for visualization and ablation studies of each stage, allowing detailed exploration of layers, weights, and intermediate results. Fixes #88, #102.
- Added a comprehensive training guide for the model. Fixes #35, #39.
- Updated README with information on the new version, including the complete Read the Docs documentation that describes the process, theoretical background, and usage examples. Fixes #53, #73.
- Configured GitHub Actions for continuous integration and delivery (CI/CD).
- Fixed memory leak by switching to a more efficient TensorFlow method (`model(tensor)` instead of `model.predict(tensor)`). Fixes #87, #109, #121, #125, #128.
- Made TensorFlow an optional dependency to prevent conflicts with user-installed versions. Fixes #95.
- Added comprehensive unit tests for increased reliability and coverage.
@ipazc ipazc closed this as completed Oct 8, 2024
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