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detectNet.cpp
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/*
* Copyright (c) 2017, NVIDIA CORPORATION. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a
* copy of this software and associated documentation files (the "Software"),
* to deal in the Software without restriction, including without limitation
* the rights to use, copy, modify, merge, publish, distribute, sublicense,
* and/or sell copies of the Software, and to permit persons to whom the
* Software is furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
* THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
* DEALINGS IN THE SOFTWARE.
*/
#include "detectNet.h"
#include "imageNet.cuh"
#include "cudaFont.h"
#include "cudaMappedMemory.h"
#include "cudaOverlay.h"
#include "cudaResize.h"
#include <iostream>
#include "commandLine.h"
#include "filesystem.h"
#define OUTPUT_CVG 0
#define OUTPUT_BBOX 1
#define OUTPUT_UFF 0 // UFF has primary output containing detection results
#define OUTPUT_NUM 1 // UFF has secondary output containing one detection count
//#define DEBUG_CLUSTERING
#define CHECK_NULL_STR(x) (x != NULL) ? x : "NULL"
// constructor
detectNet::detectNet() : tensorNet() {
mCoverageThreshold = 0.5f;
mMeanPixel = 0.0f;
mCustomClasses = 0;
mClassColors[0] = NULL; // cpu ptr
mClassColors[1] = NULL; // gpu ptr
}
// constructor
detectNet::detectNet(float meanPixel) : tensorNet() {
mCoverageThreshold = DETECTNET_DEFAULT_THRESHOLD;
mMeanPixel = meanPixel;
mCustomClasses = 0;
mNumClasses = 0;
mClassColors[0] = NULL; // cpu ptr
mClassColors[1] = NULL; // gpu ptr
mDetectionSets[0] = NULL; // cpu ptr
mDetectionSets[1] = NULL; // gpu ptr
mDetectionSet = 0;
mMaxDetections = 0;
}
// destructor
detectNet::~detectNet() {
if (mDetectionSets != NULL) {
CUDA(cudaFreeHost(mDetectionSets[0]));
mDetectionSets[0] = NULL;
mDetectionSets[1] = NULL;
}
if (mClassColors != NULL) {
CUDA(cudaFreeHost(mClassColors[0]));
mClassColors[0] = NULL;
mClassColors[1] = NULL;
}
}
// Create
detectNet* detectNet::Create(const char* prototxt, const char* model,
float mean_pixel, const char* class_labels,
float threshold, const char* input_blob,
const char* coverage_blob, const char* bbox_blob,
uint32_t maxBatchSize, precisionType precision,
deviceType device, bool allowGPUFallback) {
detectNet* net = new detectNet(0.0f);
if (!net) return NULL;
printf("\n");
printf("detectNet -- loading detection network model from:\n");
printf(" -- prototxt %s\n", prototxt);
printf(" -- model %s\n", model);
printf(" -- input_blob '%s'\n", input_blob);
printf(" -- output_cvg '%s'\n", coverage_blob);
printf(" -- output_bbox '%s'\n", bbox_blob);
printf(" -- mean_pixel %f\n", mean_pixel);
printf(" -- class_labels %s\n",
(class_labels != NULL) ? class_labels : "NULL");
printf(" -- threshold %f\n", threshold);
printf(" -- batch_size %u\n\n", maxBatchSize);
//net->EnableDebug();
std::vector<std::string> output_blobs;
output_blobs.push_back(coverage_blob);
output_blobs.push_back(bbox_blob);
// load the model
if (!net->LoadNetwork(prototxt, model, NULL, input_blob, output_blobs,
maxBatchSize, precision, device, allowGPUFallback)) {
printf("detectNet -- failed to initialize.\n");
return NULL;
}
// set default class colors
if (!net->defaultColors()) return NULL;
// load class descriptions
net->loadClassDesc(class_labels);
net->defaultClassDesc();
// set the threshold & mean pixel
net->SetThreshold(threshold);
net->mMeanPixel = mean_pixel;
// get the maximum bounding boxes
printf("detectNet -- maximum bounding boxes: %u\n",
net->GetMaxBoundingBoxes());
return net;
}
// Create
detectNet* detectNet::Create(const char* prototxt, const char* model,
const char* mean_binary, const char* class_labels,
float threshold, const char* input_blob,
const char* coverage_blob, const char* bbox_blob,
uint32_t maxBatchSize, precisionType precision,
deviceType device, bool allowGPUFallback) {
detectNet* net = new detectNet(0.0f);
if (!net) return NULL;
printf("\n");
printf("detectNet -- loading detection network model from:\n");
printf(" -- prototxt %s\n", prototxt);
printf(" -- model %s\n", model);
printf(" -- input_blob '%s'\n", input_blob);
printf(" -- output_cvg '%s'\n", coverage_blob);
printf(" -- output_bbox '%s'\n", bbox_blob);
printf(" -- mean_binary %s\n",
(mean_binary != NULL) ? mean_binary : "NULL");
printf(" -- class_labels %s\n",
(class_labels != NULL) ? class_labels : "NULL");
printf(" -- threshold %f\n", threshold);
printf(" -- batch_size %u\n\n", maxBatchSize);
//net->EnableDebug();
std::vector<std::string> output_blobs;
output_blobs.push_back(coverage_blob);
output_blobs.push_back(bbox_blob);
// load the model
if (!net->LoadNetwork(prototxt, model, mean_binary, input_blob, output_blobs,
maxBatchSize, precision, device, allowGPUFallback)) {
printf("detectNet -- failed to initialize.\n");
return NULL;
}
// set default class colors
if (!net->defaultColors()) return NULL;
// load class descriptions
net->loadClassDesc(class_labels);
net->defaultClassDesc();
// set the specified threshold
net->SetThreshold(threshold);
// get the maximum bounding boxes
printf("detectNet -- maximum bounding boxes: %u\n",
net->GetMaxBoundingBoxes());
return net;
}
// defaultColors
bool detectNet::defaultColors() {
const uint32_t numClasses = GetNumClasses();
if (!cudaAllocMapped((void**)&mClassColors[0], (void**)&mClassColors[1],
numClasses * sizeof(float4)))
return false;
// if there are a large number of classes (MS COCO)
// programatically generate the class color map
if (numClasses > 10) {
// https://github.com/dusty-nv/pytorch-segmentation/blob/16882772bc767511d892d134918722011d1ea771/datasets/sun_remap.py#L90
#define bitget(byteval, idx) ((byteval & (1 << idx)) != 0)
for (int i = 0; i < numClasses; i++) {
int r = 0;
int g = 0;
int b = 0;
int c = i;
for (int j = 0; j < 8; j++) {
r = r | (bitget(c, 0) << 7 - j);
g = g | (bitget(c, 1) << 7 - j);
b = b | (bitget(c, 2) << 7 - j);
c = c >> 3;
}
mClassColors[0][i * 4 + 0] = r;
mClassColors[0][i * 4 + 1] = g;
mClassColors[0][i * 4 + 2] = b;
mClassColors[0][i * 4 + 3] = DETECTNET_DEFAULT_ALPHA;
//printf(LOG_TRT "color %02i %3i %3i %3i %3i\n", i, (int)r, (int)g, (int)b, (int)alpha);
}
} else {
// blue colors, except class 1 is green
for (uint32_t n = 0; n < numClasses; n++) {
if (n != 1) {
mClassColors[0][n * 4 + 0] = 0.0f; // r
mClassColors[0][n * 4 + 1] = 200.0f; // g
mClassColors[0][n * 4 + 2] = 255.0f; // b
mClassColors[0][n * 4 + 3] = DETECTNET_DEFAULT_ALPHA; // a
} else {
mClassColors[0][n * 4 + 0] = 0.0f; // r
mClassColors[0][n * 4 + 1] = 255.0f; // g
mClassColors[0][n * 4 + 2] = 175.0f; // b
mClassColors[0][n * 4 + 3] = 75.0f; // a
}
}
}
return true;
}
// defaultClassDesc
void detectNet::defaultClassDesc() {
const uint32_t numClasses = GetNumClasses();
const int syn = 9; // length of synset prefix (in characters)
// assign defaults to classes that have no info
for (uint32_t n = mClassDesc.size(); n < numClasses; n++) {
char syn_str[10];
sprintf(syn_str, "n%08u", mCustomClasses);
char desc_str[16];
sprintf(desc_str, "class #%u", mCustomClasses);
mClassSynset.push_back(syn_str);
mClassDesc.push_back(desc_str);
mCustomClasses++;
}
}
// loadClassDesc
bool detectNet::loadClassDesc(const char* filename) {
printf("detectNet -- model has %u object classes\n", GetNumClasses());
if (!filename) return false;
// locate the file
const std::string path = locateFile(filename);
if (path.length() == 0) {
printf("detectNet -- failed to find %s\n", filename);
return false;
}
// open the file
FILE* f = fopen(path.c_str(), "r");
if (!f) {
printf("detectNet -- failed to open %s\n", path.c_str());
return false;
}
// read class descriptions
char str[512];
while (fgets(str, 512, f) != NULL) {
const int syn = 9; // length of synset prefix (in characters)
const int len = strlen(str);
if (len > syn && str[0] == 'n' && str[syn] == ' ') {
str[syn] = 0;
str[len - 1] = 0;
const std::string a = str;
const std::string b = (str + syn + 1);
//printf("a=%s b=%s\n", a.c_str(), b.c_str());
mClassSynset.push_back(a);
mClassDesc.push_back(b);
} else if (len >
0) // no 9-character synset prefix (i.e. from DIGITS snapshot)
{
char a[10];
sprintf(a, "n%08u", mCustomClasses);
//printf("a=%s b=%s (custom non-synset)\n", a, str);
mCustomClasses++;
if (str[len - 1] == '\n') str[len - 1] = 0;
mClassSynset.push_back(a);
mClassDesc.push_back(str);
}
}
fclose(f);
printf("detectNet -- loaded %zu class info entries\n", mClassSynset.size());
if (mClassSynset.size() == 0) return false;
if (IsModelType(MODEL_UFF)) mNumClasses = mClassDesc.size();
printf("detectNet -- number of object classes: %u\n", mNumClasses);
mClassPath = path;
return true;
}
// Create
detectNet* detectNet::Create(NetworkType networkType, float threshold,
uint32_t maxBatchSize, precisionType precision,
deviceType device, bool allowGPUFallback) {
#if 1
if (networkType == PEDNET_MULTI)
return Create(
"third_party/jetson-inference/data/multiped-500/deploy.prototxt",
"third_party/jetson-inference/data/multiped-500/"
"snapshot_iter_178000.caffemodel",
117.0f,
"third_party/jetson-inference/data/multiped-500/class_labels.txt",
threshold, DETECTNET_DEFAULT_INPUT, DETECTNET_DEFAULT_COVERAGE,
DETECTNET_DEFAULT_BBOX, maxBatchSize, precision, device,
allowGPUFallback);
else if (networkType == FACENET)
return Create(
"third_party/jetson-inference/data/facenet-120/deploy.prototxt",
"third_party/jetson-inference/data/facenet-120/"
"snapshot_iter_24000.caffemodel",
0.0f, "third_party/jetson-inference/data/facenet-120/class_labels.txt",
threshold, DETECTNET_DEFAULT_INPUT, DETECTNET_DEFAULT_COVERAGE,
DETECTNET_DEFAULT_BBOX, maxBatchSize, precision, device,
allowGPUFallback);
else if (networkType == PEDNET)
return Create("third_party/jetson-inference/data/ped-100/deploy.prototxt",
"third_party/jetson-inference/data/ped-100/"
"snapshot_iter_70800.caffemodel",
0.0f,
"third_party/jetson-inference/data/ped-100/class_labels.txt",
threshold, DETECTNET_DEFAULT_INPUT,
DETECTNET_DEFAULT_COVERAGE, DETECTNET_DEFAULT_BBOX,
maxBatchSize, precision, device, allowGPUFallback);
else if (networkType == COCO_AIRPLANE)
return Create(
"third_party/jetson-inference/data/DetectNet-COCO-Airplane/"
"deploy.prototxt",
"third_party/jetson-inference/data/DetectNet-COCO-Airplane/"
"snapshot_iter_22500.caffemodel",
0.0f,
"third_party/jetson-inference/data/DetectNet-COCO-Airplane/"
"class_labels.txt",
threshold, DETECTNET_DEFAULT_INPUT, DETECTNET_DEFAULT_COVERAGE,
DETECTNET_DEFAULT_BBOX, maxBatchSize, precision, device,
allowGPUFallback);
else if (networkType == COCO_BOTTLE)
return Create(
"third_party/jetson-inference/data/DetectNet-COCO-Bottle/"
"deploy.prototxt",
"third_party/jetson-inference/data/DetectNet-COCO-Bottle/"
"snapshot_iter_59700.caffemodel",
0.0f,
"third_party/jetson-inference/data/DetectNet-COCO-Bottle/"
"class_labels.txt",
threshold, DETECTNET_DEFAULT_INPUT, DETECTNET_DEFAULT_COVERAGE,
DETECTNET_DEFAULT_BBOX, maxBatchSize, precision, device,
allowGPUFallback);
else if (networkType == COCO_CHAIR)
return Create(
"third_party/jetson-inference/data/DetectNet-COCO-Chair/"
"deploy.prototxt",
"third_party/jetson-inference/data/DetectNet-COCO-Chair/"
"snapshot_iter_89500.caffemodel",
0.0f,
"third_party/jetson-inference/data/DetectNet-COCO-Chair/"
"class_labels.txt",
threshold, DETECTNET_DEFAULT_INPUT, DETECTNET_DEFAULT_COVERAGE,
DETECTNET_DEFAULT_BBOX, maxBatchSize, precision, device,
allowGPUFallback);
else if (networkType == COCO_DOG)
return Create(
"third_party/jetson-inference/data/DetectNet-COCO-Dog/deploy.prototxt",
"third_party/jetson-inference/data/DetectNet-COCO-Dog/"
"snapshot_iter_38600.caffemodel",
0.0f,
"third_party/jetson-inference/data/DetectNet-COCO-Dog/class_labels.txt",
threshold, DETECTNET_DEFAULT_INPUT, DETECTNET_DEFAULT_COVERAGE,
DETECTNET_DEFAULT_BBOX, maxBatchSize, precision, device,
allowGPUFallback);
# if NV_TENSORRT_MAJOR > 4
else if (networkType == SSD_INCEPTION_V2)
return Create(
"third_party/jetson-inference/data/SSD-Inception-v2/"
"ssd_inception_v2_coco.uff",
"third_party/jetson-inference/data/SSD-Inception-v2/"
"ssd_coco_labels.txt",
threshold, "Input", Dims3(3, 300, 300), "NMS", "NMS_1", maxBatchSize,
precision, device, allowGPUFallback);
else if (networkType == SSD_MOBILENET_V1)
return Create(
"third_party/jetson-inference/data/SSD-Mobilenet-v1/"
"ssd_mobilenet_v1_coco.uff",
"third_party/jetson-inference/data/"
"ssd_coco_labels.txt",
threshold, "Input", Dims3(3, 300, 300), "Postprocessor",
"Postprocessor_1", maxBatchSize, precision, device, allowGPUFallback);
else if (networkType == SSD_MOBILENET_V2)
return Create(
"third_party/jetson-inference/data/"
"ssd_mobilenet_v2_coco.uff",
"third_party/jetson-inference/data/SSD-Mobilenet-v2/"
"ssd_coco_labels.txt",
threshold, "Input", Dims3(3, 300, 300), "NMS", "NMS_1", maxBatchSize,
precision, device, allowGPUFallback);
# endif
else
return NULL;
#else
#endif
}
// NetworkTypeFromStr
detectNet::NetworkType detectNet::NetworkTypeFromStr(const char* modelName) {
if (!modelName) return detectNet::CUSTOM;
detectNet::NetworkType type = detectNet::PEDNET;
if (strcasecmp(modelName, "multiped") == 0 ||
strcasecmp(modelName, "multiped-500") == 0)
type = detectNet::PEDNET_MULTI;
else if (strcasecmp(modelName, "pednet") == 0 ||
strcasecmp(modelName, "ped-100") == 0)
type = detectNet::PEDNET;
else if (strcasecmp(modelName, "facenet") == 0 ||
strcasecmp(modelName, "facenet-120") == 0 ||
strcasecmp(modelName, "face-120") == 0)
type = detectNet::FACENET;
else if (strcasecmp(modelName, "coco-airplane") == 0 ||
strcasecmp(modelName, "airplane") == 0)
type = detectNet::COCO_AIRPLANE;
else if (strcasecmp(modelName, "coco-bottle") == 0 ||
strcasecmp(modelName, "bottle") == 0)
type = detectNet::COCO_BOTTLE;
else if (strcasecmp(modelName, "coco-chair") == 0 ||
strcasecmp(modelName, "chair") == 0)
type = detectNet::COCO_CHAIR;
else if (strcasecmp(modelName, "coco-dog") == 0 ||
strcasecmp(modelName, "dog") == 0)
type = detectNet::COCO_DOG;
#if NV_TENSORRT_MAJOR > 4
else if (strcasecmp(modelName, "ssd-inception") == 0 ||
strcasecmp(modelName, "ssd-inception-v2") == 0 ||
strcasecmp(modelName, "coco-ssd-inception") == 0 ||
strcasecmp(modelName, "coco-ssd-inception-v2") == 0)
type = detectNet::SSD_INCEPTION_V2;
else if (strcasecmp(modelName, "ssd-mobilenet-v1") == 0 ||
strcasecmp(modelName, "coco-ssd-mobilenet-v1") == 0)
type = detectNet::SSD_MOBILENET_V1;
else if (strcasecmp(modelName, "ssd-mobilenet-v2") == 0 ||
strcasecmp(modelName, "coco-ssd-mobilenet-v2") == 0 ||
strcasecmp(modelName, "ssd-mobilenet") == 0)
type = detectNet::SSD_MOBILENET_V2;
#endif
else
type = detectNet::CUSTOM;
return type;
}
// Create
detectNet* detectNet::Create(int argc, char** argv) {
commandLine cmdLine(argc, argv);
const char* modelName = cmdLine.GetString("model");
if (!modelName) {
if (argc == 2)
modelName = argv[1];
else if (argc == 4)
modelName = argv[3];
else
modelName = "pednet";
}
//if( argc > 3 )
// modelName = argv[3];
detectNet::NetworkType type = detectNet::PEDNET_MULTI;
if (strcasecmp(modelName, "multiped") == 0 ||
strcasecmp(modelName, "multiped-500") == 0)
type = detectNet::PEDNET_MULTI;
else if (strcasecmp(modelName, "pednet") == 0 ||
strcasecmp(modelName, "ped-100") == 0)
type = detectNet::PEDNET;
else if (strcasecmp(modelName, "facenet") == 0 ||
strcasecmp(modelName, "facenet-120") == 0 ||
strcasecmp(modelName, "face-120") == 0)
type = detectNet::FACENET;
else if (strcasecmp(modelName, "coco-airplane") == 0 ||
strcasecmp(modelName, "airplane") == 0)
type = detectNet::COCO_AIRPLANE;
else if (strcasecmp(modelName, "coco-bottle") == 0 ||
strcasecmp(modelName, "bottle") == 0)
type = detectNet::COCO_BOTTLE;
else if (strcasecmp(modelName, "coco-chair") == 0 ||
strcasecmp(modelName, "chair") == 0)
type = detectNet::COCO_CHAIR;
else if (strcasecmp(modelName, "coco-dog") == 0 ||
strcasecmp(modelName, "dog") == 0)
type = detectNet::COCO_DOG;
else {
const char* prototxt = cmdLine.GetString("prototxt");
const char* input = cmdLine.GetString("input_blob");
const char* out_cvg = cmdLine.GetString("output_cvg");
const char* out_bbox = cmdLine.GetString("output_bbox");
const char* class_labels = cmdLine.GetString("class_labels");
if (!input) input = DETECTNET_DEFAULT_INPUT;
if (!out_cvg) out_cvg = DETECTNET_DEFAULT_COVERAGE;
if (!out_bbox) out_bbox = DETECTNET_DEFAULT_BBOX;
float meanPixel = cmdLine.GetFloat("mean_pixel");
float threshold = cmdLine.GetFloat("threshold");
if (threshold == 0.0f) threshold = 0.5f;
int maxBatchSize = cmdLine.GetInt("batch_size");
if (maxBatchSize < 1) maxBatchSize = 2;
return detectNet::Create(prototxt, modelName, meanPixel, class_labels,
threshold, input, out_cvg, out_bbox, maxBatchSize);
}
// create segnet from pretrained model
return detectNet::Create(type);
}
// Create (UFF)
detectNet* detectNet::Create(const char* model, const char* class_labels,
float threshold, const char* input,
const Dims3& inputDims, const char* output,
const char* numDetections, uint32_t maxBatchSize,
precisionType precision, deviceType device,
bool allowGPUFallback) {
detectNet* net = new detectNet(0.0f);
if (!net) return NULL;
printf("\n");
printf("detectNet -- loading detection network model from:\n");
printf(" -- model %s\n", CHECK_NULL_STR(model));
printf(" -- input_blob '%s'\n", CHECK_NULL_STR(input));
printf(" -- output_blob '%s'\n", CHECK_NULL_STR(output));
printf(" -- output_count '%s'\n", CHECK_NULL_STR(numDetections));
printf(" -- class_labels %s\n", CHECK_NULL_STR(class_labels));
printf(" -- threshold %f\n", threshold);
printf(" -- batch_size %u\n\n", maxBatchSize);
//net->EnableDebug();
// create list of output names
std::vector<std::string> output_blobs;
if (output != NULL) output_blobs.push_back(output);
if (numDetections != NULL) output_blobs.push_back(numDetections);
// load the model
if (!net->LoadNetwork(NULL, model, NULL, input, inputDims, output_blobs,
maxBatchSize, precision, device, allowGPUFallback)) {
printf("detectNet -- failed to initialize.\n");
return NULL;
}
// allocate detection sets
if (!net->allocDetections()) return NULL;
// load class descriptions
net->loadClassDesc(class_labels);
net->defaultClassDesc();
// set default class colors
if (!net->defaultColors()) return NULL;
// set the specified threshold
net->SetThreshold(threshold);
return net;
}
// Detect
int detectNet::Detect(float* input, uint32_t width, uint32_t height,
Detection** detections, uint32_t overlay) {
Detection* det = mDetectionSets[0] + mDetectionSet * GetMaxDetections();
if (detections != NULL) *detections = det;
mDetectionSet++;
if (mDetectionSet >= mNumDetectionSets) mDetectionSet = 0;
return Detect(input, width, height, det, overlay);
}
// Detect
int detectNet::Detect(float* rgba, uint32_t width, uint32_t height,
Detection* detections, uint32_t overlay) {
if (!rgba || width == 0 || height == 0 || !detections) {
printf(LOG_TRT "detectNet::Detect( 0x%p, %u, %u ) -> invalid parameters\n",
rgba, width, height);
return -1;
}
PROFILER_BEGIN(PROFILER_PREPROCESS);
if (IsModelType(MODEL_UFF)) {
if (CUDA_FAILED(cudaPreImageNetNormBGR(
(float4*)rgba, width, height, mInputCUDA, mWidth, mHeight,
make_float2(-1.0f, 1.0f), GetStream()))) {
printf(LOG_TRT "detectNet::Detect() -- cudaPreImageNetNorm() failed\n");
return -1;
}
} else if (IsModelType(MODEL_ONNX)) {
// downsample, convert to band-sequential RGB, and apply pixel normalization, mean pixel subtraction and standard deviation
if (CUDA_FAILED(cudaPreImageNetNormMeanRGB(
(float4*)rgba, width, height, mInputCUDA, mWidth, mHeight,
make_float2(0.0f, 1.0f), make_float3(0.485f, 0.456f, 0.406f),
make_float3(0.229f, 0.224f, 0.225f), GetStream()))) {
printf(LOG_TRT
"imageNet::PreProcess() -- cudaPreImageNetNormMeanRGB() failed\n");
return false;
}
} else {
if (mMeanPixel != 0.0f) {
if (CUDA_FAILED(cudaPreImageNetMeanBGR(
(float4*)rgba, width, height, mInputCUDA, mWidth, mHeight,
make_float3(mMeanPixel, mMeanPixel, mMeanPixel), GetStream()))) {
printf(LOG_TRT "detectNet::Detect() -- cudaPreImageNetMean() failed\n");
return -1;
}
} else {
if (CUDA_FAILED(cudaPreImageNetBGR((float4*)rgba, width, height,
mInputCUDA, mWidth, mHeight,
GetStream()))) {
printf(LOG_TRT "detectNet::Detect() -- cudaPreImageNet() failed\n");
return -1;
}
}
}
PROFILER_END(PROFILER_PREPROCESS);
PROFILER_BEGIN(PROFILER_NETWORK);
// process with TensorRT
void* inferenceBuffers[] = {mInputCUDA, mOutputs[0].CUDA, mOutputs[1].CUDA};
if (!mContext->execute(1, inferenceBuffers)) {
printf(LOG_TRT
"detectNet::Detect() -- failed to execute TensorRT context\n");
return -1;
}
PROFILER_END(PROFILER_NETWORK);
PROFILER_BEGIN(PROFILER_POSTPROCESS);
// post-processing / clustering
int numDetections = 0;
if (IsModelType(MODEL_UFF)) {
const int rawDetections = *(int*)mOutputs[OUTPUT_NUM].CPU;
const int rawParameters = DIMS_W(mOutputs[OUTPUT_UFF].dims);
#ifdef DEBUG_CLUSTERING
printf(LOG_TRT "detectNet::Detect() -- %i unfiltered detections\n",
rawDetections);
#endif
// filter the raw detections by thresholding the confidence
for (int n = 0; n < rawDetections; n++) {
float* object_data = mOutputs[OUTPUT_UFF].CPU + n * rawParameters;
if (object_data[2] < mCoverageThreshold) continue;
detections[numDetections].Instance =
numDetections; //(uint32_t)object_data[0];
detections[numDetections].ClassID = (uint32_t)object_data[1];
detections[numDetections].Confidence = object_data[2];
detections[numDetections].Left = object_data[3] * width;
detections[numDetections].Top = object_data[4] * height;
detections[numDetections].Right = object_data[5] * width;
detections[numDetections].Bottom = object_data[6] * height;
if (detections[numDetections].ClassID >= mNumClasses) {
printf(
LOG_TRT
"detectNet::Detect() -- detected object has invalid classID (%u)\n",
detections[numDetections].ClassID);
detections[numDetections].ClassID = 0;
}
if (strcmp(GetClassDesc(detections[(uint32_t)numDetections].ClassID),
"void") == 0)
continue;
numDetections += clusterDetections(detections, numDetections);
}
// sort the detections by confidence value
sortDetections(detections, numDetections);
} else if (IsModelType(MODEL_ONNX)) {
float* coord = mOutputs[0].CPU;
coord[0] = ((coord[0] + 1.0f) * 0.5f) * float(width);
coord[1] = ((coord[1] + 1.0f) * 0.5f) * float(height);
coord[2] = ((coord[2] + 1.0f) * 0.5f) * float(width);
coord[3] = ((coord[3] + 1.0f) * 0.5f) * float(height);
printf(
LOG_TRT
"detectNet::Detect() -- ONNX -- coord (%f, %f) (%f, %f) image %ux%u\n",
coord[0], coord[1], coord[2], coord[3], width, height);
detections[numDetections].Instance = numDetections;
detections[numDetections].ClassID = 0;
detections[numDetections].Confidence = 1;
detections[numDetections].Left = coord[0];
detections[numDetections].Top = coord[1];
detections[numDetections].Right = coord[2];
detections[numDetections].Bottom = coord[3];
numDetections++;
} else {
// cluster detections
numDetections = clusterDetections(detections, width, height);
}
PROFILER_END(PROFILER_POSTPROCESS);
// render the overlay
if (overlay != 0 && numDetections > 0) {
if (!Overlay(rgba, rgba, width, height, detections, numDetections, overlay))
printf(LOG_TRT "detectNet::Detect() -- failed to render overlay\n");
}
// wait for GPU to complete work
CUDA(cudaDeviceSynchronize());
// return the number of detections
return numDetections;
}
// clusterDetections (UFF)
int detectNet::clusterDetections(Detection* detections, int n,
float threshold) {
if (n == 0) return 1;
// test each detection to see if it intersects
for (int m = 0; m < n; m++) {
if (detections[n].Intersects(
detections[m],
threshold)) // TODO NMS or different threshold for same classes?
{
// if the intersecting detections have different classes, pick the one with highest confidence
// otherwise if they have the same object class, expand the detection bounding box
if (detections[n].ClassID != detections[m].ClassID) {
if (detections[n].Confidence > detections[m].Confidence) {
detections[m] = detections[n];
detections[m].Instance = m;
detections[m].ClassID = detections[n].ClassID;
detections[m].Confidence = detections[n].Confidence;
}
} else {
detections[m].Expand(detections[n]);
detections[m].Confidence =
fmaxf(detections[n].Confidence, detections[m].Confidence);
}
return 0; // merged detection
}
}
return 1; // new detection
}
// sortDetections (UFF)
void detectNet::sortDetections(Detection* detections, int numDetections) {
if (numDetections < 2) return;
// order by area (descending) or confidence (ascending)
for (int i = 0; i < numDetections - 1; i++) {
for (int j = 0; j < numDetections - i - 1; j++) {
if (detections[j].Area() <
detections[j + 1]
.Area()) //if( detections[j].Confidence > detections[j+1].Confidence )
{
const Detection det = detections[j];
detections[j] = detections[j + 1];
detections[j + 1] = det;
}
}
}
// renumber the instance ID's
for (int i = 0; i < numDetections; i++) detections[i].Instance = i;
}
// from detectNet.cu
cudaError_t cudaDetectionOverlay(float4* input, float4* output, uint32_t width,
uint32_t height,
detectNet::Detection* detections,
int numDetections, float4* colors);
// Overlay
bool detectNet::Overlay(float* input, float* output, uint32_t width,
uint32_t height, Detection* detections,
uint32_t numDetections, uint32_t flags) {
PROFILER_BEGIN(PROFILER_VISUALIZE);
if (flags == 0) {
printf(LOG_TRT
"detectNet -- Overlay() was called with OVERLAY_NONE, returning "
"false\n");
return false;
}
// bounding box overlay
if (flags & OVERLAY_BOX) {
if (CUDA_FAILED(cudaDetectionOverlay((float4*)input, (float4*)output, width,
height, detections, numDetections,
(float4*)mClassColors[1])))
return false;
}
// class label overlay
if ((flags & OVERLAY_LABEL) || (flags & OVERLAY_CONFIDENCE)) {
static cudaFont* font = NULL;
// make sure the font object is created
if (!font) {
font = cudaFont::Create(adaptFontSize(width));
if (!font) {
printf(LOG_TRT
"detectNet -- Overlay() was called with OVERLAY_FONT, but "
"failed to create cudaFont()\n");
return false;
}
}
// draw each object's description
std::vector<std::pair<std::string, int2>> labels;
for (uint32_t n = 0; n < numDetections; n++) {
const char* className = GetClassDesc(detections[n].ClassID);
const float confidence = detections[n].Confidence * 100.0f;
const int2 position =
make_int2(detections[n].Left + 5, detections[n].Top + 3);
if (flags & OVERLAY_CONFIDENCE) {
char str[256];
if ((flags & OVERLAY_LABEL) && (flags & OVERLAY_CONFIDENCE))
sprintf(str, "%s %.1f%%", className, confidence);
else
sprintf(str, "%.1f%%", confidence);
labels.push_back(std::pair<std::string, int2>(str, position));
} else {
// overlay label only
labels.push_back(std::pair<std::string, int2>(className, position));
}
}
font->OverlayText((float4*)input, width, height, labels,
make_float4(255, 255, 255, 255));
}
PROFILER_END(PROFILER_VISUALIZE);
return true;
}
// allocDetections
bool detectNet::allocDetections() {
// determine max detections
if (IsModelType(MODEL_UFF)) // TODO: fixme
{
printf("W = %u H = %u C = %u\n", DIMS_W(mOutputs[OUTPUT_UFF].dims),
DIMS_H(mOutputs[OUTPUT_UFF].dims),
DIMS_C(mOutputs[OUTPUT_UFF].dims));
mMaxDetections =
DIMS_H(mOutputs[OUTPUT_UFF].dims) * DIMS_C(mOutputs[OUTPUT_UFF].dims);
} else if (IsModelType(MODEL_ONNX)) {
mMaxDetections = 1;
mNumClasses = 1;
printf("detectNet -- using ONNX model\n");
} else {
mNumClasses = DIMS_C(mOutputs[OUTPUT_CVG].dims);
mMaxDetections =
DIMS_W(mOutputs[OUTPUT_CVG].dims) *
DIMS_H(
mOutputs[OUTPUT_CVG].dims) /** DIMS_C(mOutputs[OUTPUT_CVG].dims)*/
* mNumClasses;
printf("detectNet -- number object classes: %u\n", mNumClasses);
}
printf("detectNet -- maximum bounding boxes: %u\n", mMaxDetections);
// allocate array to store detection results
const size_t det_size =
sizeof(Detection) * mNumDetectionSets * mMaxDetections;
if (!cudaAllocMapped((void**)&mDetectionSets[0], (void**)&mDetectionSets[1],
det_size))
return false;
memset(mDetectionSets[0], 0, det_size);
return true;
}
cudaError_t cudaPreImageNet(float4* input, size_t inputWidth,
size_t inputHeight, float* output,
size_t outputWidth, size_t outputHeight,
cudaStream_t stream);
cudaError_t cudaPreImageNetMean(float4* input, size_t inputWidth,
size_t inputHeight, float* output,
size_t outputWidth, size_t outputHeight,
const float3& mean_value, cudaStream_t stream);
struct float6 {
float x;
float y;
float z;
float w;
float v;
float u;
};
static inline float6 make_float6(float x, float y, float z, float w, float v,
float u) {
float6 f;
f.x = x;
f.y = y;
f.z = z;
f.w = w;
f.v = v;
f.u = u;
return f;
}
inline static bool rectOverlap(const float6& r1, const float6& r2) {
return !(r2.x > r1.z || r2.z < r1.x || r2.y > r1.w || r2.w < r1.y);
}
static void mergeRect(std::vector<float6>& rects, const float6& rect) {
const uint32_t num_rects = rects.size();
bool intersects = false;
for (uint32_t r = 0; r < num_rects; r++) {
if (rectOverlap(rects[r], rect)) {
intersects = true;
#ifdef DEBUG_CLUSTERING
printf("found overlap\n");
#endif
if (rect.x < rects[r].x) rects[r].x = rect.x;
if (rect.y < rects[r].y) rects[r].y = rect.y;