Drawing results

The input ImageTensor (or ImageTensorU8) can render inference results directly onto the image: bounding boxes, segmentations, barcodes, and optional labels. The in-memory output is uint8 RGB in [batch, height, width, 3] (BHWC) layout.

Overlay behavior

• Labels are multi-line chips placed above each box when possible (below the box if there is no room above), so the object stays visible.
• Nested OD → classification: the label leads with the top classification score(s); the detector class line is omitted when classifications are present. OCR text children are shown as "…".
• Color: for OD → classification, both the box outline and the label use the top nested classification class color (not the detector class, and not split colors). Otherwise the geometry's own class color is used.
• Box outline is expanded slightly outward and supports a configurable line width so thick strokes do not cover the object.

Python

import denkflow

pipeline = denkflow.Pipeline.from_denkflow("path/to/model.denkflow", pat="YOUR-PAT")
simplified = denkflow.SimplifiedPipeline(pipeline)

image_tensor = denkflow.ImageTensor.from_file("path/to/image.jpg")
results = simplified.run(image_tensor, confidence_threshold=0.5)

annotated = image_tensor.to_images_with_annotations(
    results,
    annotation_label_font_size=16.0,
    top_classifications=1,
    bounding_box_line_width=2,
    save_path="annotated.png",  # optional; batch > 1 is saved as a grid
)

Parameters:

• segmentation_fill_alpha: None for outline-only segmentations; a value in (0, 1] fills at that opacity.
• annotation_label_font_size: None to skip labels; otherwise a size relative to a 1080px-tall reference image.
• top_classifications: how many nested classification scores (highest first) to show on each label (0 omits them; default 1).
• bounding_box_line_width: outline thickness in pixels (default 1).
• draw_orientation_lines: draws orientation lines on rotated boxes.
• save_path: optional path to write the annotated image(s). Format is taken from the extension. With a batch larger than one, images are arranged into a near-square grid and saved as a single file.

ImageTensorU8 exposes the same to_images_with_annotations(...) method.

C / C++

// run_from_tensor / run_from_file consume the tensor; load a second tensor for annotation.
DenkflowImageTensor* annotation_tensor = NULL;
denkflow_image_tensor_from_file(&annotation_tensor, image_path);

/* The result is a DenkflowImage: RGB pixels in BHWC layout. */
DenkflowImage* annotated = NULL;
denkflow_image_tensor_to_images_with_inference_results(
	annotation_tensor,
	inference_results,
	false,          /* draw_orientation_lines */
	0.0f,           /* segmentation_fill_alpha: 0 = outline segmentations */
	16.0f,          /* annotation_label_font_size: 0 = no labels */
	1,              /* top_classifications */
	2,              /* bounding_box_line_width (0 is treated as 1) */
	"annotated.png",/* save_path: NULL to skip writing a file */
	&annotated);

/* Access the pixels via annotated->buffer, annotated->batch,
   annotated->height, annotated->width, annotated->channels. */

denkflow_image_free(&annotated);
denkflow_image_tensor_free(&annotation_tensor);

Parameters (same semantics as Python):

• segmentation_fill_alpha: 0.0 disables fill (outline only); (0, 1] fills at that opacity.
• annotation_label_font_size: 0.0 skips labels; otherwise relative to a 1080px-tall reference image.
• top_classifications: nested classification scores on each label (0 omits them).
• bounding_box_line_width: outline thickness in pixels (0 → 1).
• save_path: optional filesystem path (NULL to skip). Batch size > 1 is saved as one near-square grid. Format comes from the file extension.

For ImageTensorU8 inputs, use denkflow_image_tensor_u8_to_images_with_inference_results with the same argument list.

run_from_file and run_from_tensor consume the image, so a separate tensor is loaded for annotation. The returned DenkflowImage holds the RGB pixels in [batch, height, width, channels] (BHWC) order and is released with denkflow_image_free.