RasterObjectDetector

变压器接受带有光栅几何输入功能。与非光栅几何图形或无效的光栅几何图形的所有功能都将被拒绝。输入光栅的几何形状可以是图像或其他类型的数据，但必须具有1至4个频带之间，或者所有的8位或16位带中允许的解释，。输入栅格在该过程中被消耗。接受光栅的解释包括GRAY8，Gray16，GrayAlpha16，GrayAlpha32，RGB24，RGB48，RGBA32和RGBA64。

在输入栅格geome每个检测到的对象try, a feature will be produced containing a rectangular geometry that represents a bounding box of the detected object. If the matched portion of the input raster is desired, consider using aClippertransformer after the RasterObjectDetector and routing the detected boxes into the input Clipper port and the input raster into the Clippee port.

Output detected features are tagged with an attribute named_detected_object_typeby default whose value contains the name of the detection model that produced that particular detected feature (for example, ‘LBP - Frontal Face’). The detection attribute name can be changed using theDetected Attribute Nameparameter.

Thefme_basename属性也可以是在确定输出中检测到的几何形状源栅格是有用的。

Non-raster features or features with invalid raster geometries are output through this port.

被拒绝的功能处理：can be set to either terminate the translation or continue running when it encounters a rejected feature. This setting is available both as a defaultFME选项and as aworkspace parameter.

The parameter determines a group of features to be detected:

• Face
• 身体
• Animal
• Object
• Custom

对于每个检测型，多个检测模型可以使用被选择Detection Modelparameter.

指定是否检测到的对象的输出功能，应该保留输入栅格功能属性。默认值是保留输入属性。

This parameter determines the name of the attribute that will be used to tag each detected object’s feature with the detection model’s name that produced the object. By default this attribute will be named_detected_object_type.

This parameter allows you to supply a custom object detection model. You can read further about training your own model in the officialOpenCV Documentation.

检测模型的原始检测窗口常常是小的，因此输入栅格在试图检测较大物体缩小。所述比例因子确定图像多少百分比按比例缩小，范围从1％到300％，包括端值。物体检测是在光栅各规模进行，但不是在尺度之间。换句话说，如果所述比例因子为100％，检测将发生在原始栅格，然后按比例缩小X2，X4等光栅

The table above specifies some of the values used to scale down the raster. Default is 3%.

如果缩放系数小，会有找到对象的几率较高。然而，由于对象正在寻找在更精细的规模，变压器可能需要更长的时间来处理光栅。具有较高的粒度还配备了更多的噪声或假阳性检测等变压器参数的潜在可能有助于减少这些。相反也是较大的比例因子实现。

When a detection (kernel) window is being moved across the raster, an object might be detected multiple times in the same area. These similar area detections are called neighbors.邻居的最小数量specifies how many neighbors each candidate detected object requires before it as accepted as a valid detected object. The default is 2 neighbors.

When the邻居的最小数量是0时，所有检测到的对象将被保留。因此，信任每场比赛将是低的。

When the邻居的最小数量is greater than 0, the algorithm will retain a detected object only if it has at least the specified number of neighbors, thus increasing confidence in each object that is output.

The parameter affects minimum and maximum detection size parameters.

• 百分: Object size parameters will be treated as percentages relative to the size of the source raster. This is the default.
• Pixels: The object size parameters will be treated as exact pixel values.

Minimum and maximum object detection sizes specify the size limits for detected objects; ones that are smaller or larger than the specified sizes, respectively, will be ignored. If no values are provided the detection will happen at all scales defined by theScale Factor Percentparameter. If minimum and maximum sizes are specified and are same, the detection happens only at the specified size.

Specifying the minimum size can greatly improve detection performance. Maximum size is often unnecessary but can also affect performance. By default, maximum size is unset and minimum width and height is 4%.