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Why flip dx1, dy1, dx2, dy2 in generateBoundingBox when y.shape[0]==1? Please help me~ #22

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KLH1472 opened this issue Oct 7, 2018 · 1 comment · Fixed by #133
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@KLH1472
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KLH1472 commented Oct 7, 2018

    def __generate_bounding_box(imap, reg, scale, t):

        # use heatmap to generate bounding boxes
        stride = 2
        cellsize = 12

        imap = np.transpose(imap)
        dx1 = np.transpose(reg[:, :, 0])
        dy1 = np.transpose(reg[:, :, 1])
        dx2 = np.transpose(reg[:, :, 2])
        dy2 = np.transpose(reg[:, :, 3])

        y, x = np.where(imap >= t)

        if y.shape[0] == 1:
            dx1 = np.flipud(dx1)
            dy1 = np.flipud(dy1)
            dx2 = np.flipud(dx2)
            dy2 = np.flipud(dy2)

        score = imap[(y, x)]
        reg = np.transpose(np.vstack([dx1[(y, x)], dy1[(y, x)], dx2[(y, x)], dy2[(y, x)]]))

        if reg.size == 0:
            reg = np.empty(shape=(0, 3))

        bb = np.transpose(np.vstack([y, x]))

        q1 = np.fix((stride * bb + 1)/scale)
        q2 = np.fix((stride * bb + cellsize)/scale)
        boundingbox = np.hstack([q1, q2, np.expand_dims(score, 1), reg])

        return boundingbox, reg
@oliviawindsir
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I am also wondering for the same thing. @KLH1472 did you manage to find out the answer to this?

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 ipazc closed this as completed in #133 Oct 8, 2024
@ipazc ipazc closed this as completed in b6eba4b Oct 8, 2024
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