Detections

advanced filtering

The advanced filtering capabilities of the Detections class offer users a versatile and efficient way to narrow down and refine object detections. This section outlines various filtering methods, including filtering by specific class or a set of classes, confidence, object area, bounding box area, relative area, box dimensions, and designated zones. Each method is demonstrated with concise code examples to provide users with a clear understanding of how to implement the filters in their applications.

by specific class

Allows you to select detections that belong only to one selected class.

AfterBefore

import supervision as sv

detections = sv.Detections(...)
detections = detections[detections.class_id == 0]

import supervision as sv

detections = sv.Detections(...)
detections = detections[detections.class_id == 0]

by set of classes

Allows you to select detections that belong only to selected set of classes.

AfterBefore

import numpy as np
import supervision as sv

selected_classes = [0, 2, 3]
detections = sv.Detections(...)
detections = detections[np.isin(detections.class_id, selected_classes)]

import numpy as np
import supervision as sv

class_id = [0, 2, 3]
detections = sv.Detections(...)
detections = detections[np.isin(detections.class_id, class_id)]

by confidence

Allows you to select detections with specific confidence value, for example higher than selected threshold.

AfterBefore

import supervision as sv

detections = sv.Detections(...)
detections = detections[detections.confidence > 0.5]

import supervision as sv

detections = sv.Detections(...)
detections = detections[detections.confidence > 0.5]

by area

Allows you to select detections based on their size. We define the area as the number of pixels occupied by the detection in the image. In the example below, we have sifted out the detections that are too small.

AfterBefore

import supervision as sv

detections = sv.Detections(...)
detections = detections[detections.area > 1000]

import supervision as sv

detections = sv.Detections(...)
detections = detections[detections.area > 1000]

by relative area

Allows you to select detections based on their size in relation to the size of whole image. Sometimes the concept of detection size changes depending on the image. Detection occupying 10000 square px can be large on a 1280x720 image but small on a 3840x2160 image. In such cases, we can filter out detections based on the percentage of the image area occupied by them. In the example below, we remove too large detections.

AfterBefore

import supervision as sv

image = ...
height, width, channels = image.shape
image_area = height * width

detections = sv.Detections(...)
detections = detections[(detections.area / image_area) < 0.8]

import supervision as sv

image = ...
height, width, channels = image.shape
image_area = height * width

detections = sv.Detections(...)
detections = detections[(detections.area / image_area) < 0.8]

by box dimensions

Allows you to select detections based on their dimensions. The size of the bounding box, as well as its coordinates, can be criteria for rejecting detection. Implementing such filtering requires a bit of custom code but is relatively simple and fast.

AfterBefore

import supervision as sv

detections = sv.Detections(...)
w = detections.xyxy[:, 2] - detections.xyxy[:, 0]
h = detections.xyxy[:, 3] - detections.xyxy[:, 1]
detections = detections[(w > 200) & (h > 200)]

import supervision as sv

detections = sv.Detections(...)
w = detections.xyxy[:, 2] - detections.xyxy[:, 0]
h = detections.xyxy[:, 3] - detections.xyxy[:, 1]
detections = detections[(w > 200) & (h > 200)]

by PolygonZone

Allows you to use Detections in combination with PolygonZone to weed out bounding boxes that are in and out of the zone. In the example below you can see how to filter out all detections located in the lower part of the image.

AfterBefore

import supervision as sv

zone = sv.PolygonZone(...)
detections = sv.Detections(...)
mask = zone.trigger(detections=detections)
detections = detections[mask]

import supervision as sv

zone = sv.PolygonZone(...)
detections = sv.Detections(...)
mask = zone.trigger(detections=detections)
detections = detections[mask]

by mixed conditions

Detections' greatest strength, however, is that you can build arbitrarily complex logical conditions by simply combining separate conditions using & or |.

AfterBefore

import supervision as sv

zone = sv.PolygonZone(...)
detections = sv.Detections(...)
mask = zone.trigger(detections=detections)
detections = detections[(detections.confidence > 0.7) & mask]

import supervision as sv

zone = sv.PolygonZone(...)
detections = sv.Detections(...)
mask = zone.trigger(detections=detections)
detections = detections[mask]