Interpreting results

run returns a list of ImageInferenceResult, one per input image. Each holds a flat list of InferenceResult entries under results.

InferenceResult

Each InferenceResult carries the output of one network stage. The populated field depends on network_type:

Field	Populated for
bounding_box	object detection, instance segmentation
segmentation	segmentation, instance segmentation
scalar	classification, anomaly detection
text	optical character recognition
barcode	barcode reading
sub_results	nested results from downstream stages

Additional fields: network_type (the producing stage), index (position within its sibling list), and batch_index (the source image). Coordinates on bounding_box, segmentation and barcoce are absolute pixel values.

Object definition

Python

class InferenceResult:
    network_type: str
    batch_index: int
    index: int
    sub_results: list[InferenceResult]
    scalar: Scalar | None
    bounding_box: BoundingBox | None
    segmentation: Segmentation | None
    text: str | None
    barcode: Barcode | None

C / C++

typedef struct DenkflowInferenceResult {
    DenkflowNetworkType network_type;
    size_t index;
    struct DenkflowInferenceResult *sub_results;
    size_t sub_results_length;
    DenkflowBoundingBox *bounding_box;
    DenkflowSegmentation *segmentation;
    DenkflowScalar *scalar;
    char *text;
    DenkflowBarcode *barcode;
} DenkflowInferenceResult;

Walking the results

Python

for image_result in results:
    for r in image_result.results:
        if r.bounding_box:
            print(r.bounding_box.class_label.name, r.bounding_box.confidence)
        if r.text is not None:
            print(r.text)
        for sub in r.sub_results:
            ...  # nested stage output

C / C++

for (size_t b = 0; b < inference_results->image_results_length; ++b) {
	DenkflowImageInferenceResult* image_result = &inference_results->image_results[b];
	for (size_t i = 0; i < image_result->results_length; ++i) {
		DenkflowInferenceResult* r = &image_result->results[i];
		if (r->bounding_box != NULL) {
			printf("%s: %f\n", r->bounding_box->class_label.name, r->bounding_box->confidence);
		}
	}
}

In C, the result tree is released once with denkflow_inference_results_free.

Nested pipelines (for example detection followed by per-object classification) expose the downstream output under sub_results.