# YOLO\_BBOX

`YoloToBoundingBoxStep` is used to convert an NDArray for the predictions of a YOLO model to a list of bounding box.The NDArray is assumed to be in standard YOLO output format, after activation functions (sigmoid/softmax) have been applied. 

Input must be a float or double NDArray with shape `[minibatch, B*(5+C), H, W]` if `nchw` is `true` or `[minibatch, H, W, B*(5+C)]` if `nchw` is `false` where B is number of bounding box priors, C is number of classes, H is output/label height and W is output/label width.

Along the channel dimension for each box prior, we have the following values: 

* 0: px = predicted x location within grid cell, 0.0 to 1.0
* 1: py = predicted y location within grid cell, 0.0 to 1.0
* 2: pw = predicted width, in grid cell, for example 0.0 to H (for example, pw = 2.0 -> 2.0/W fraction of image)
* 3: ph = predicted height, in grid cell, for example 0.0 to H (for example, ph = 2.0 -> 2.0/H fraction of image)
* 4: c = object confidence - i.e., probability an object is present or not, 0.0 to 1.0
* 5 to 4+C = probability of class (given an object is present), 0.0 to 1.0, with values summing to 1.0

Note that the height/width dimensions are grid cell units - for example, with `416x416` input, 32 down sampling by the network we have `13x13` grid cells (each corresponding to 32 pixels in the input image). Thus, a center of X of 5.5 would be `xPixels = 5.5x32 = 176 pixels` from left. Widths and heights are similar: in this example, a width of 13 would be the entire image (416 pixels), and a height of 6.5 would be `6.5/13 = 0.5` of the image (208 pixels).

| Configs         | Descriptions                                                                                                                             |
| --------------- | ---------------------------------------------------------------------------------------------------------------------------------------- |
| input           | Name of the input - optional. If not set, the input is inferred (assuming a single NDArray exists in the input).                         |
| output          | Name of the input - optional. If not set, `bounding_boxes` is used                                                                       |
| nchw            | The data format - NCHW (true) or NHWC (false) as known as `channels first`for true or `channels last` for false.                         |
| threshold       | The threshold, in range `0.0` to `1.0`. Any boxes with object confidence less than this will be ignored. Default value is `0.5`.         |
| nmsThreshold    | Non-max suppression threshold to use, to filter closely overlapping objects. Default value is `0.50`.                                    |
| numClasses      | Number of classes. Not required if `classLabels` are provided.                                                                           |
| classLabels     | Optional - names of the object classes.                                                                                                  |
| keepOtherValues | If `true`: keep all other input fields in the data instance. If `false`: only return the list of bounding box. Default is set to `true`. |