class TensorflowLite::Image::PoseEstimation

Included Modules

Defined in:

tflite_image/pose_estimation.cr

Instance Method Summary

Instance methods inherited from module TensorflowLite::Image::Common

client : Client client, detection_adjustments(image : Canvas, scale_mode : Scale = @scaling_mode)
detection_adjustments(image_width : Int32, image_height : Int32, scale_mode : Scale = @scaling_mode) detection_adjustments, input_format : Format input_format, labels : Array(String) labels, resolution : Tuple(Int32, Int32) resolution, run(canvas : Canvas, scale_mode : Scale = @scaling_mode, resize_method : StumpyResize::InterpolationMethod = :bilinear) run, scaling_mode : Scale scaling_mode, scaling_mode=(scaling_mode : Scale) scaling_mode=

Constructor methods inherited from module TensorflowLite::Image::Common

new(client : Client, labels : Array(String) | Nil = nil, scaling_mode : Scale = DEFAULT_SCALE_MODE, input_format : Format | Nil = nil, output_format : Format | Nil = nil) new

Instance Method Detail

def adjust(detections : Array(Output), target_width : Int32, target_height : Int32, offset_left : Int32, offset_top : Int32) : Array(Output) #

adjust the detections so they can be applied directly to the source image (or a scaled version in the same aspect ratio)

you can run detection_adjustments just once and then apply them to detections for each invokation using this function


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def adjust(detections : Array(Output), image : Canvas, offset_left : Int32, offset_top : Int32) : Array(Output) #

adjust the detections so they can be applied directly to the source image (or a scaled version in the same aspect ratio)

you can run detection_adjustments just once and then apply them to detections for each invokation using this function


[View source]
def process(image : Canvas) : Tuple(Canvas, PoseEstimation::Output) #

attempts to classify the object, assumes the canvas has already been prepared


[View source]