MLECO-2873: Object detection usecase follow-up
Change-Id: Ic14e93a50fb7b3f3cfd9497bac1280794cc0fc15
Signed-off-by: Isabella Gottardi <isabella.gottardi@arm.com>
diff --git a/source/use_case/object_detection/src/UseCaseHandler.cc b/source/use_case/object_detection/src/UseCaseHandler.cc
index 45df4f8..ce3ef06 100644
--- a/source/use_case/object_detection/src/UseCaseHandler.cc
+++ b/source/use_case/object_detection/src/UseCaseHandler.cc
@@ -18,19 +18,23 @@
#include "InputFiles.hpp"
#include "YoloFastestModel.hpp"
#include "UseCaseCommonUtils.hpp"
-#include "DetectionUseCaseUtils.hpp"
#include "DetectorPostProcessing.hpp"
#include "hal.h"
#include <inttypes.h>
-
-/* used for presentation, original images are read-only"*/
-static uint8_t g_image_buffer[INPUT_IMAGE_WIDTH*INPUT_IMAGE_HEIGHT*FORMAT_MULTIPLY_FACTOR] IFM_BUF_ATTRIBUTE = {};
-
namespace arm {
namespace app {
+ /**
+ * @brief Presents inference results along using the data presentation
+ * object.
+ * @param[in] platform Reference to the hal platform object.
+ * @param[in] results Vector of detection results to be displayed.
+ * @return true if successful, false otherwise.
+ **/
+ static bool PresentInferenceResult(hal_platform& platform,
+ const std::vector<arm::app::object_detection::DetectionResult>& results);
/* Object detection classification handler. */
bool ObjectDetectionHandler(ApplicationContext& ctx, uint32_t imgIndex, bool runAll)
@@ -48,7 +52,7 @@
platform.data_psn->clear(COLOR_BLACK);
auto& model = ctx.Get<Model&>("model");
-
+
/* If the request has a valid size, set the image index. */
if (imgIndex < NUMBER_OF_FILES) {
if (!SetAppCtxIfmIdx(ctx, imgIndex, "imgIndex")) {
@@ -76,9 +80,10 @@
const uint32_t nCols = inputShape->data[arm::app::YoloFastestModel::ms_inputColsIdx];
const uint32_t nRows = inputShape->data[arm::app::YoloFastestModel::ms_inputRowsIdx];
- const uint32_t nPresentationChannels = FORMAT_MULTIPLY_FACTOR;
+ const uint32_t nPresentationChannels = channelsImageDisplayed;
- std::vector<DetectionResult> results;
+ /* Get pre/post-processing objects. */
+ auto& postp = ctx.Get<object_detection::DetectorPostprocessing&>("postprocess");
do {
/* Strings for presentation/logging. */
@@ -86,19 +91,23 @@
const uint8_t* curr_image = get_img_array(ctx.Get<uint32_t>("imgIndex"));
- /* Copy over the data and convert to gryscale */
-#if DISPLAY_RGB_IMAGE
- memcpy(g_image_buffer,curr_image, INPUT_IMAGE_WIDTH*INPUT_IMAGE_HEIGHT*FORMAT_MULTIPLY_FACTOR);
-#else
- RgbToGrayscale(curr_image,g_image_buffer,INPUT_IMAGE_WIDTH,INPUT_IMAGE_HEIGHT);
-#endif /*DISPLAY_RGB_IMAGE*/
-
- RgbToGrayscale(curr_image,inputTensor->data.uint8,INPUT_IMAGE_WIDTH,INPUT_IMAGE_HEIGHT);
+ /* Copy over the data and convert to grayscale */
+ auto* dstPtr = static_cast<uint8_t*>(inputTensor->data.uint8);
+ const size_t copySz = inputTensor->bytes < IMAGE_DATA_SIZE ?
+ inputTensor->bytes : IMAGE_DATA_SIZE;
+ /* Copy of the image used for presentation, original images are read-only */
+ std::vector<uint8_t> g_image_buffer(nCols*nRows*channelsImageDisplayed);
+ if (nPresentationChannels == 3) {
+ memcpy(g_image_buffer.data(),curr_image, nCols * nRows * channelsImageDisplayed);
+ } else {
+ image::RgbToGrayscale(curr_image, g_image_buffer.data(), nCols * nRows);
+ }
+ image::RgbToGrayscale(curr_image, dstPtr, copySz);
/* Display this image on the LCD. */
platform.data_psn->present_data_image(
- g_image_buffer,
+ g_image_buffer.data(),
nCols, nRows, nPresentationChannels,
dataPsnImgStartX, dataPsnImgStartY, dataPsnImgDownscaleFactor);
@@ -125,27 +134,27 @@
dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
/* Detector post-processing*/
- TfLiteTensor* output_arr[2] = {nullptr,nullptr};
- output_arr[0] = model.GetOutputTensor(0);
- output_arr[1] = model.GetOutputTensor(1);
- RunPostProcessing(g_image_buffer,output_arr,results);
+ std::vector<object_detection::DetectionResult> results;
+ TfLiteTensor* modelOutput0 = model.GetOutputTensor(0);
+ TfLiteTensor* modelOutput1 = model.GetOutputTensor(1);
+ postp.RunPostProcessing(
+ g_image_buffer.data(),
+ nRows,
+ nCols,
+ modelOutput0,
+ modelOutput1,
+ results);
platform.data_psn->present_data_image(
- g_image_buffer,
+ g_image_buffer.data(),
nCols, nRows, nPresentationChannels,
dataPsnImgStartX, dataPsnImgStartY, dataPsnImgDownscaleFactor);
- /*Detector post-processing*/
-
-
- /* Add results to context for access outside handler. */
- ctx.Set<std::vector<DetectionResult>>("results", results);
-
#if VERIFY_TEST_OUTPUT
arm::app::DumpTensor(outputTensor);
#endif /* VERIFY_TEST_OUTPUT */
- if (!image::PresentInferenceResult(platform, results)) {
+ if (!PresentInferenceResult(platform, results)) {
return false;
}
@@ -158,5 +167,24 @@
return true;
}
+
+ static bool PresentInferenceResult(hal_platform& platform,
+ const std::vector<arm::app::object_detection::DetectionResult>& results)
+ {
+ platform.data_psn->set_text_color(COLOR_GREEN);
+
+ /* If profiling is enabled, and the time is valid. */
+ info("Final results:\n");
+ info("Total number of inferences: 1\n");
+
+ for (uint32_t i = 0; i < results.size(); ++i) {
+ info("%" PRIu32 ") (%f) -> %s {x=%d,y=%d,w=%d,h=%d}\n", i,
+ results[i].m_normalisedVal, "Detection box:",
+ results[i].m_x0, results[i].m_y0, results[i].m_w, results[i].m_h );
+ }
+
+ return true;
+ }
+
} /* namespace app */
} /* namespace arm */