Robotplus Challenge 2022-10/11
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.. image:: ../images/fig_robotplus_challenge_diagram.png
   :width: 100 %

1. Output format
"""""""""""""""""

.. list-table::
   :widths: 10 90
   :header-rows: 0

   * - **Field**
     - **Description**
   * - grips
     - all  grasp candidates. Nx7 with N number of grasps. Each line [x,y,z,w,h,angle,score] is a grasp candidate
   * - target_grip
     - target grasp selected among all detectors' results
   * - best_ind
     - index of target grasp
   * - best_n_ind
     - indexes of top n grasp candidates.
   * - im
     - display grip on the input image

2. Create and Load Detector
"""""""""""""""""
* Using the default Kpick's detector ::

    from kpick.detectron2_detector import get_detectron_obj
    from detector_mask import ChallengeMaskDetector
    detector = get_detectron_obj(ChallengeMaskDetector)(cfg_path=cfg_path)

.. note:: Please refer **configs/grip_mask.cfg**

3. Modify and Load Detector
"""""""""""""""""
* Extending the default Kpick's detector ::

    from kpick.detectron2_detector import get_detectron_obj
    from detector_mask import ChallengeMaskDetector
    class AppDetector(ChallengeMaskDetector):
        def new_function(self):
            print('new function')
    detector = get_detectron_obj(ChallengeMaskDetector)(cfg_path=cfg_path)

4. Demo on single RGB-D image
"""""""""""""""""
::

    import cv2
    from ketisdk.vision.utils.rgbd_utils_v2 import RGBD

    # load image
    rgb = cv2.imread('data/test_images/01_rgb.png')[:, :, ::-1]
    depth = cv2.imread('data/test_images/01_depth.png', cv2.IMREAD_UNCHANGED)
    rgbd = RGBD(rgb=rgb, depth=depth, depth_min=600, depth_max=800)

    # set crop roi
    rgbd.set_workspace(pts=[(320, 166), (870, 171), (870, 561), (321, 559)])

    # load detector
    detector = get_detectron_obj(ChallengeMaskDetector)(cfg_path='configs/grip_mask.cfg')
    detector.args.flag.show_steps = True

    # manually tuning params (optional)
    detector.args.net.score_thresh = 0.8
    detector.args.net.grip_width_range = (20,120)
    detector.args.net.grip_plate = 20

    ret = detector.predict_show_single(rgbd=rgbd)

    # show
    cv2.imshow('grip', ret['im'][:, :, ::-1])
    cv2.waitKey()

.. image:: ../images/fig_robotplus_challenge_result.png
   :width: 70 %
   :align: center
All lines and rectangles are grasp candidates. Rectangles are top n grasp poses and the thick one is the target grasp.

5. Parameters tuning
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.. list-table::
   :widths: 10 90
   :header-rows: 0

   * - **Parameter**
     - **Description**
   * - net.score_thresh
     - score threshold of object detection
   * - net.grip_width_range
     - tuple(w_min, w_max). Min and max values of gripper's width
   * - net.grip_plate
     - gripper plate's width.