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/*
* SPDX-FileCopyrightText: Copyright 2022 Arm Limited and/or its affiliates
* <open-source-office@arm.com> SPDX-License-Identifier: Apache-2.0
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "UseCaseHandler.hpp"
#include "DetectorPostProcessing.hpp"
#include "DetectorPreProcessing.hpp"
#include "InputFiles.hpp"
#include "UseCaseCommonUtils.hpp"
#include "YoloFastestModel.hpp"
#include "hal.h"
#include "log_macros.h"
#include <cinttypes>
namespace arm {
namespace app {
/**
* @brief Presents inference results along using the data presentation
* object.
* @param[in] results Vector of detection results to be displayed.
* @return true if successful, false otherwise.
**/
static bool
PresentInferenceResult(const std::vector<object_detection::DetectionResult>& results);
/**
* @brief Draw boxes directly on the LCD for all detected objects.
* @param[in] results Vector of detection results to be displayed.
* @param[in] imageStartX X coordinate where the image starts on the LCD.
* @param[in] imageStartY Y coordinate where the image starts on the LCD.
* @param[in] imgDownscaleFactor How much image has been downscaled on LCD.
**/
static void DrawDetectionBoxes(const std::vector<object_detection::DetectionResult>& results,
uint32_t imgStartX,
uint32_t imgStartY,
uint32_t imgDownscaleFactor);
/* Object detection inference handler. */
bool ObjectDetectionHandler(ApplicationContext& ctx, uint32_t imgIndex, bool runAll)
{
auto& profiler = ctx.Get<Profiler&>("profiler");
constexpr uint32_t dataPsnImgDownscaleFactor = 1;
constexpr uint32_t dataPsnImgStartX = 10;
constexpr uint32_t dataPsnImgStartY = 35;
constexpr uint32_t dataPsnTxtInfStartX = 20;
constexpr uint32_t dataPsnTxtInfStartY = 28;
hal_lcd_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")) {
return false;
}
}
if (!model.IsInited()) {
printf_err("Model is not initialised! Terminating processing.\n");
return false;
}
auto initialImgIdx = ctx.Get<uint32_t>("imgIndex");
TfLiteTensor* inputTensor = model.GetInputTensor(0);
TfLiteTensor* outputTensor0 = model.GetOutputTensor(0);
TfLiteTensor* outputTensor1 = model.GetOutputTensor(1);
if (!inputTensor->dims) {
printf_err("Invalid input tensor dims\n");
return false;
} else if (inputTensor->dims->size < 3) {
printf_err("Input tensor dimension should be >= 3\n");
return false;
}
TfLiteIntArray* inputShape = model.GetInputShape(0);
const int inputImgCols = inputShape->data[YoloFastestModel::ms_inputColsIdx];
const int inputImgRows = inputShape->data[YoloFastestModel::ms_inputRowsIdx];
/* Set up pre and post-processing. */
DetectorPreProcess preProcess = DetectorPreProcess(inputTensor, true, model.IsDataSigned());
std::vector<object_detection::DetectionResult> results;
const object_detection::PostProcessParams postProcessParams{
inputImgRows,
inputImgCols,
object_detection::originalImageSize,
object_detection::anchor1,
object_detection::anchor2};
DetectorPostProcess postProcess =
DetectorPostProcess(outputTensor0, outputTensor1, results, postProcessParams);
do {
/* Ensure there are no results leftover from previous inference when running all. */
results.clear();
/* Strings for presentation/logging. */
std::string str_inf{"Running inference... "};
const uint8_t* currImage = GetImgArray(ctx.Get<uint32_t>("imgIndex"));
auto dstPtr = static_cast<uint8_t*>(inputTensor->data.uint8);
const size_t copySz =
inputTensor->bytes < IMAGE_DATA_SIZE ? inputTensor->bytes : IMAGE_DATA_SIZE;
/* Run the pre-processing, inference and post-processing. */
if (!preProcess.DoPreProcess(currImage, copySz)) {
printf_err("Pre-processing failed.");
return false;
}
/* Display image on the LCD. */
hal_lcd_display_image(
(arm::app::object_detection::channelsImageDisplayed == 3) ? currImage : dstPtr,
inputImgCols,
inputImgRows,
arm::app::object_detection::channelsImageDisplayed,
dataPsnImgStartX,
dataPsnImgStartY,
dataPsnImgDownscaleFactor);
/* Display message on the LCD - inference running. */
hal_lcd_display_text(
str_inf.c_str(), str_inf.size(), dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
/* Run inference over this image. */
info("Running inference on image %" PRIu32 " => %s\n",
ctx.Get<uint32_t>("imgIndex"),
GetFilename(ctx.Get<uint32_t>("imgIndex")));
if (!RunInference(model, profiler)) {
printf_err("Inference failed.");
return false;
}
if (!postProcess.DoPostProcess()) {
printf_err("Post-processing failed.");
return false;
}
/* Erase. */
str_inf = std::string(str_inf.size(), ' ');
hal_lcd_display_text(
str_inf.c_str(), str_inf.size(), dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
/* Draw boxes. */
DrawDetectionBoxes(
results, dataPsnImgStartX, dataPsnImgStartY, dataPsnImgDownscaleFactor);
#if VERIFY_TEST_OUTPUT
DumpTensor(modelOutput0);
DumpTensor(modelOutput1);
#endif /* VERIFY_TEST_OUTPUT */
if (!PresentInferenceResult(results)) {
return false;
}
profiler.PrintProfilingResult();
IncrementAppCtxIfmIdx(ctx, "imgIndex");
} while (runAll && ctx.Get<uint32_t>("imgIndex") != initialImgIdx);
return true;
}
static bool
PresentInferenceResult(const std::vector<object_detection::DetectionResult>& results)
{
hal_lcd_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;
}
static void DrawDetectionBoxes(const std::vector<object_detection::DetectionResult>& results,
uint32_t imgStartX,
uint32_t imgStartY,
uint32_t imgDownscaleFactor)
{
uint32_t lineThickness = 1;
for (const auto& result : results) {
/* Top line. */
hal_lcd_display_box(imgStartX + result.m_x0 / imgDownscaleFactor,
imgStartY + result.m_y0 / imgDownscaleFactor,
result.m_w / imgDownscaleFactor,
lineThickness,
COLOR_GREEN);
/* Bot line. */
hal_lcd_display_box(imgStartX + result.m_x0 / imgDownscaleFactor,
imgStartY + (result.m_y0 + result.m_h) / imgDownscaleFactor -
lineThickness,
result.m_w / imgDownscaleFactor,
lineThickness,
COLOR_GREEN);
/* Left line. */
hal_lcd_display_box(imgStartX + result.m_x0 / imgDownscaleFactor,
imgStartY + result.m_y0 / imgDownscaleFactor,
lineThickness,
result.m_h / imgDownscaleFactor,
COLOR_GREEN);
/* Right line. */
hal_lcd_display_box(imgStartX + (result.m_x0 + result.m_w) / imgDownscaleFactor -
lineThickness,
imgStartY + result.m_y0 / imgDownscaleFactor,
lineThickness,
result.m_h / imgDownscaleFactor,
COLOR_GREEN);
}
}
} /* namespace app */
} /* namespace arm */
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