Basic Classification

This example loads a complete classification pipeline file (.denkflow) exported from the Hub.

Python

import denkflow

pat = "YOUR-PAT"

input_topic = "camera/image"
output_topic = "classification_node/output"

denkflow_path = "path/to/model/file.denkflow"
image_path = "path/to/an/image.jpg"

pipeline = denkflow.Pipeline.from_denkflow(
    denkflow_path,
    pat=pat
)

pipeline.initialize()

image_tensor = denkflow.ImageTensor.from_file(image_path)

pipeline.publish_image_tensor(input_topic, image_tensor)

# The tensor type for classification results is the scalar tensor
results_receiver = pipeline.subscribe_scalar_tensor(output_topic)

# Run the pipeline. This operation will block until the pipeline has finished running.
pipeline.run()

tensor = results_receiver.receive()

results_per_image = tensor.to_objects()

print("Classification Results:")
for image_results in results_per_image:
    for class_label_result in image_results:
        print(f"{class_label_result.class_label}: {class_label_result.value:.2f}")

C

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "denkflow.h"

void handle_error(enum DenkflowResult error_code, const char* function_name) {
	printf("%s: %d", function_name, (int)error_code);
	if (error_code != DenkflowResult_Ok) {
		char* error_buffer = malloc(ERROR_BUFFER_SIZE);
		get_last_error(error_buffer);
		printf(" (%s)\n", error_buffer);
		free(error_buffer);
		exit(EXIT_FAILURE);
	}
	printf("\n");
}

int main() {
	Pipeline*              pipeline             = NULL;
	InitializedPipeline*   initialized_pipeline = NULL;
	ImageTensor*           image_tensor         = NULL;
	Receiver_ScalarTensor* receiver             = NULL;
	ScalarTensor*          tensor               = NULL;
	ScalarResults*         results              = NULL;
	HubLicenseSource*      hub_license_source   = NULL;

	enum DenkflowResult r;

	const uint64_t timeout_ms = 3000;

	const char* pat           = "YOUR-PAT";

	const char* input_topic   = "camera/image";
	const char* output_topic  = "classification_node/output";

	const char* denkflow_path = "path/to/model/file.denkflow";
	const char* image_path    = "path/to/an/image.jpg";

	r = hub_license_source_from_pat(&hub_license_source, pat, NULL_BYTE, NULL_BYTE);

	handle_error(r, "hub_license_source_from_pat");

	r = pipeline_from_denkflow(&pipeline, denkflow_path, (void**)&hub_license_source);
	handle_error(r, "pipeline_from_denkflow");

	r = initialize_pipeline(&initialized_pipeline, &pipeline);
	handle_error(r, "initialize_pipeline");

	r = initialized_pipeline_subscribe_scalar_tensor(&receiver, initialized_pipeline, output_topic);
	handle_error(r, "initialized_pipeline_subscribe_scalar_tensor");

	r = image_tensor_from_file(&image_tensor, image_path);
	handle_error(r, "image_tensor_from_file");

	r = initialized_pipeline_publish_image_tensor(initialized_pipeline, input_topic, &image_tensor);
	handle_error(r, "initialized_pipeline_publish_image_tensor");

	r = initialized_pipeline_run(initialized_pipeline, timeout_ms);
	handle_error(r, "initialized_pipeline_run");

	r = receiver_receive_scalar_tensor(&tensor, receiver);
	handle_error(r, "receiver_receive_scalar_tensor");

	r = scalar_tensor_to_objects(&results, tensor);
	handle_error(r, "scalar_tensor_to_objects");

	if (r == DenkflowResult_Ok) {
		for (int b = 0; b < results->scalar_batch_elements_length; b++) {
			printf("Image %d\n", b);
			for (int c = 0; c < results->scalar_batch_elements[b].scalars_length; c++) {
				printf("  %s: %f\n",
					results->scalar_batch_elements[b].scalars[c].class_label.name,
					results->scalar_batch_elements[b].scalars[c].value
				);
			}
		}
	}

	r = free_object((void**)&initialized_pipeline);
	handle_error(r, "free_object");
	r = free_object((void**)&receiver);
	handle_error(r, "free_object");
	r = free_object((void**)&tensor);
	handle_error(r, "free_object");
	r = free_object((void**)&results);
	handle_error(r, "free_object");

	return 0;
}