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segNet.cpp
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segNet.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 "segNet.h"
#include "tensorConvert.h"
#include "cudaMappedMemory.h"
#include "cudaOverlay.h"
#include "cudaResize.h"
#include "cudaFont.h"
#include "commandLine.h"
#include "filesystem.h"
#include "imageIO.h"
// constructor
segNet::segNet() : tensorNet()
{
mLastInputImg = NULL;
mLastInputWidth = 0;
mLastInputHeight = 0;
mLastInputFormat = IMAGE_UNKNOWN;
mColorsAlphaSet = NULL;
mClassColors = NULL;
mClassMap = NULL;
mNetworkType = SEGNET_CUSTOM;
}
// destructor
segNet::~segNet()
{
CUDA_FREE_HOST(mClassColors);
CUDA_FREE_HOST(mClassMap);
if( mColorsAlphaSet != NULL )
{
free(mColorsAlphaSet);
mColorsAlphaSet = NULL;
}
}
// VisualizationFlagsFromStr
uint32_t segNet::VisualizationFlagsFromStr( const char* str_user, uint32_t default_value )
{
if( !str_user )
return default_value;
// copy the input string into a temporary array,
// because strok modifies the string
const size_t str_length = strlen(str_user);
if( str_length == 0 )
return default_value;
char* str = (char*)malloc(str_length + 1);
if( !str )
return default_value;
strcpy(str, str_user);
// tokenize string by delimiters ',' and '|'
const char* delimiters = ",|";
char* token = strtok(str, delimiters);
if( !token )
{
free(str);
return default_value;
}
// look for the tokens: "overlay", "mask"
uint32_t flags = 0;
while( token != NULL )
{
//printf("%s\n", token);
if( strcasecmp(token, "overlay") == 0 )
flags |= VISUALIZE_OVERLAY;
else if( strcasecmp(token, "mask") == 0 )
flags |= VISUALIZE_MASK;
token = strtok(NULL, delimiters);
}
free(str);
return flags;
}
// FilterModeFromStr
segNet::FilterMode segNet::FilterModeFromStr( const char* str, FilterMode default_value )
{
if( !str )
return default_value;
if( strcasecmp(str, "point") == 0 )
return segNet::FILTER_POINT;
else if( strcasecmp(str, "linear") == 0 )
return segNet::FILTER_LINEAR;
return default_value;
}
// NetworkTypeFromStr
segNet::NetworkType segNet::NetworkTypeFromStr( const char* modelName )
{
if( !modelName )
return segNet::SEGNET_CUSTOM;
segNet::NetworkType type = segNet::FCN_RESNET18_VOC_320x320;
// ONNX models
if( strcasecmp(modelName, "fcn-resnet18-cityscapes-512x256") == 0 || strcasecmp(modelName, "fcn-resnet18-cityscapes") == 0 )
type = segNet::FCN_RESNET18_CITYSCAPES_512x256;
else if( strcasecmp(modelName, "fcn-resnet18-cityscapes-1024x512") == 0 )
type = segNet::FCN_RESNET18_CITYSCAPES_1024x512;
else if( strcasecmp(modelName, "fcn-resnet18-cityscapes-2048x1024") == 0 )
type = segNet::FCN_RESNET18_CITYSCAPES_2048x1024;
else if( strcasecmp(modelName, "fcn-resnet18-deepscene-576x320") == 0 || strcasecmp(modelName, "fcn-resnet18-deepscene") == 0)
type = segNet::FCN_RESNET18_DEEPSCENE_576x320;
else if( strcasecmp(modelName, "fcn-resnet18-deepscene-864x480") == 0 )
type = segNet::FCN_RESNET18_DEEPSCENE_864x480;
else if( strcasecmp(modelName, "fcn-resnet18-mhp-512x320") == 0 || strcasecmp(modelName, "fcn-resnet18-mhp") == 0 )
type = segNet::FCN_RESNET18_MHP_512x320;
else if( strcasecmp(modelName, "fcn-resnet18-mhp-640x360") == 0 )
type = segNet::FCN_RESNET18_MHP_640x360;
else if( strcasecmp(modelName, "fcn-resnet18-voc-320x320") == 0 || strcasecmp(modelName, "fcn-resnet18-pascal-voc-320x320") == 0 || strcasecmp(modelName, "fcn-resnet18-voc") == 0 || strcasecmp(modelName, "fcn-resnet18-pascal-voc") == 0 )
type = segNet::FCN_RESNET18_VOC_320x320;
else if( strcasecmp(modelName, "fcn-resnet18-voc-512x320") == 0 || strcasecmp(modelName, "fcn-resnet18-pascal-voc-512x320") == 0 )
type = segNet::FCN_RESNET18_VOC_512x320;
else if( strcasecmp(modelName, "fcn-resnet18-sun-512x400") == 0 || strcasecmp(modelName, "fcn-resnet18-sun-rgbd-512x400") == 0 || strcasecmp(modelName, "fcn-resnet18-sun") == 0 || strcasecmp(modelName, "fcn-resnet18-sunrgb") == 0 )
type = segNet::FCN_RESNET18_SUNRGB_512x400;
else if( strcasecmp(modelName, "fcn-resnet18-sun-640x512") == 0 || strcasecmp(modelName, "fcn-resnet18-sun-rgbd-640x512") == 0 )
type = segNet::FCN_RESNET18_SUNRGB_640x512;
// legacy models
else if( strcasecmp(modelName, "fcn-alexnet-cityscapes-sd") == 0 || strcasecmp(modelName, "fcn-alexnet-cityscapes") == 0 )
type = segNet::FCN_ALEXNET_CITYSCAPES_SD;
else if( strcasecmp(modelName, "fcn-alexnet-cityscapes-hd") == 0 )
type = segNet::FCN_ALEXNET_CITYSCAPES_HD;
else if( strcasecmp(modelName, "fcn-alexnet-pascal-voc") == 0 )
type = segNet::FCN_ALEXNET_PASCAL_VOC;
else if( strcasecmp(modelName, "synthia-cvpr16") == 0 || strcasecmp(modelName, "fcn-alexnet-synthia-cvpr16") == 0 )
type = segNet::FCN_ALEXNET_SYNTHIA_CVPR16;
else if( strcasecmp(modelName, "synthia-summer-sd") == 0 || strcasecmp(modelName, "fcn-alexnet-synthia-summer-sd") == 0 )
type = segNet::FCN_ALEXNET_SYNTHIA_SUMMER_SD;
else if( strcasecmp(modelName, "synthia-summer-hd") == 0 || strcasecmp(modelName, "fcn-alexnet-synthia-summer-hd") == 0 )
type = segNet::FCN_ALEXNET_SYNTHIA_SUMMER_HD;
else if( strcasecmp(modelName, "aerial-fpv") == 0 || strcasecmp(modelName, "aerial-fpv-720p") == 0 || strcasecmp(modelName, "fcn-alexnet-aerial-fpv-720p") == 0 )
type = segNet::FCN_ALEXNET_AERIAL_FPV_720p;
else
type = segNet::SEGNET_CUSTOM;
return type;
}
// NetworkTypeToStr
const char* segNet::NetworkTypeToStr( segNet::NetworkType type )
{
switch(type)
{
// ONNX models
case FCN_RESNET18_CITYSCAPES_512x256: return "fcn-resnet18-cityscapes-512x256";
case FCN_RESNET18_CITYSCAPES_1024x512: return "fcn-resnet18-cityscapes-1024x512";
case FCN_RESNET18_CITYSCAPES_2048x1024: return "fcn-resnet18-cityscapes-2048x1024";
case FCN_RESNET18_DEEPSCENE_576x320: return "fcn-resnet18-deepscene-576x320";
case FCN_RESNET18_DEEPSCENE_864x480: return "fcn-resnet18-deepscene-864x480";
case FCN_RESNET18_MHP_512x320: return "fcn-resnet18-mhp-512x320";
case FCN_RESNET18_MHP_640x360: return "fcn-resnet18-mhp-640x360";
case FCN_RESNET18_VOC_320x320: return "fcn-resnet18-voc-320x320";
case FCN_RESNET18_VOC_512x320: return "fcn-resnet18-voc-512x320";
case FCN_RESNET18_SUNRGB_512x400: return "fcn-resnet18-sun-512x400";
case FCN_RESNET18_SUNRGB_640x512: return "fcn-resnet18-sun-640x512";
// legacy models
case FCN_ALEXNET_PASCAL_VOC: return "fcn-alexnet-pascal-voc";
case FCN_ALEXNET_SYNTHIA_CVPR16: return "fcn-alexnet-synthia-cvpr16";
case FCN_ALEXNET_SYNTHIA_SUMMER_HD: return "fcn-alexnet-synthia-summer-hd";
case FCN_ALEXNET_SYNTHIA_SUMMER_SD: return "fcn-alexnet-synthia-summer-sd";
case FCN_ALEXNET_CITYSCAPES_HD: return "fcn-alexnet-cityscapes-hd";
case FCN_ALEXNET_CITYSCAPES_SD: return "fcn-alexnet-cityscapes-sd";
case FCN_ALEXNET_AERIAL_FPV_720p: return "fcn-alexnet-aerial-fpv-720p";
default: return "custom segNet";
}
}
// Create
segNet* segNet::Create( NetworkType networkType, uint32_t maxBatchSize,
precisionType precision, deviceType device, bool allowGPUFallback )
{
segNet* net = NULL;
#define LOAD_ONNX(x) Create(NULL, "networks/" x "/fcn_resnet18.onnx", "networks/" x "/classes.txt", "networks/" x "/colors.txt", "input_0", "output_0", maxBatchSize, precision, device, allowGPUFallback )
// ONNX models
if( networkType == FCN_RESNET18_CITYSCAPES_512x256 )
net = LOAD_ONNX("FCN-ResNet18-Cityscapes-512x256");
else if( networkType == FCN_RESNET18_CITYSCAPES_1024x512 )
net = LOAD_ONNX("FCN-ResNet18-Cityscapes-1024x512");
else if( networkType == FCN_RESNET18_CITYSCAPES_2048x1024 )
net = LOAD_ONNX("FCN-ResNet18-Cityscapes-2048x1024");
else if( networkType == FCN_RESNET18_DEEPSCENE_576x320 )
net = LOAD_ONNX("FCN-ResNet18-DeepScene-576x320");
else if( networkType == FCN_RESNET18_DEEPSCENE_864x480 )
net = LOAD_ONNX("FCN-ResNet18-DeepScene-864x480");
else if( networkType == FCN_RESNET18_MHP_512x320 )
net = LOAD_ONNX("FCN-ResNet18-MHP-512x320");
else if( networkType == FCN_RESNET18_MHP_640x360 )
net = LOAD_ONNX("FCN-ResNet18-MHP-640x360");
else if( networkType == FCN_RESNET18_VOC_320x320 )
net = LOAD_ONNX("FCN-ResNet18-Pascal-VOC-320x320");
else if( networkType == FCN_RESNET18_VOC_512x320 )
net = LOAD_ONNX("FCN-ResNet18-Pascal-VOC-512x320");
else if( networkType == FCN_RESNET18_SUNRGB_512x400 )
net = LOAD_ONNX("FCN-ResNet18-SUN-RGBD-512x400");
else if( networkType == FCN_RESNET18_SUNRGB_640x512 )
net = LOAD_ONNX("FCN-ResNet18-SUN-RGBD-640x512");
// legacy models
else if( networkType == FCN_ALEXNET_PASCAL_VOC )
net = Create("networks/FCN-Alexnet-Pascal-VOC/deploy.prototxt", "networks/FCN-Alexnet-Pascal-VOC/snapshot_iter_146400.caffemodel", "networks/FCN-Alexnet-Pascal-VOC/pascal-voc-classes.txt", "networks/FCN-Alexnet-Pascal-VOC/pascal-voc-colors.txt", SEGNET_DEFAULT_INPUT, SEGNET_DEFAULT_OUTPUT, maxBatchSize, precision, device, allowGPUFallback );
else if( networkType == FCN_ALEXNET_SYNTHIA_CVPR16 )
net = Create("networks/FCN-Alexnet-SYNTHIA-CVPR16/deploy.prototxt", "networks/FCN-Alexnet-SYNTHIA-CVPR16/snapshot_iter_1206700.caffemodel", "networks/FCN-Alexnet-SYNTHIA-CVPR16/synthia-cvpr16-labels.txt", "networks/FCN-Alexnet-SYNTHIA-CVPR16/synthia-cvpr16-train-colors.txt", SEGNET_DEFAULT_INPUT, SEGNET_DEFAULT_OUTPUT, maxBatchSize, precision, device, allowGPUFallback );
else if( networkType == FCN_ALEXNET_SYNTHIA_SUMMER_HD )
net = Create("networks/FCN-Alexnet-SYNTHIA-Summer-HD/deploy.prototxt", "networks/FCN-Alexnet-SYNTHIA-Summer-HD/snapshot_iter_902888.caffemodel", "networks/FCN-Alexnet-SYNTHIA-Summer-HD/synthia-seq-labels.txt", "networks/FCN-Alexnet-SYNTHIA-Summer-HD/synthia-seq-train-colors.txt", SEGNET_DEFAULT_INPUT, SEGNET_DEFAULT_OUTPUT, maxBatchSize, precision, device, allowGPUFallback );
else if( networkType == FCN_ALEXNET_SYNTHIA_SUMMER_SD )
net = Create("networks/FCN-Alexnet-SYNTHIA-Summer-SD/deploy.prototxt", "networks/FCN-Alexnet-SYNTHIA-Summer-SD/snapshot_iter_431816.caffemodel", "networks/FCN-Alexnet-SYNTHIA-Summer-SD/synthia-seq-labels.txt", "networks/FCN-Alexnet-SYNTHIA-Summer-SD/synthia-seq-train-colors.txt", SEGNET_DEFAULT_INPUT, SEGNET_DEFAULT_OUTPUT, maxBatchSize, precision, device, allowGPUFallback );
else if( networkType == FCN_ALEXNET_CITYSCAPES_HD )
net = Create("networks/FCN-Alexnet-Cityscapes-HD/deploy.prototxt", "networks/FCN-Alexnet-Cityscapes-HD/snapshot_iter_367568.caffemodel", "networks/FCN-Alexnet-Cityscapes-HD/cityscapes-labels.txt", "networks/FCN-Alexnet-Cityscapes-HD/cityscapes-deploy-colors.txt", SEGNET_DEFAULT_INPUT, SEGNET_DEFAULT_OUTPUT, maxBatchSize, precision, device, allowGPUFallback );
else if( networkType == FCN_ALEXNET_CITYSCAPES_SD )
net = Create("networks/FCN-Alexnet-Cityscapes-SD/deploy.prototxt", "networks/FCN-Alexnet-Cityscapes-SD/snapshot_iter_2756640.caffemodel", "networks/FCN-Alexnet-Cityscapes-SD/cityscapes-labels.txt", "networks/FCN-Alexnet-Cityscapes-SD/cityscapes-deploy-colors.txt", SEGNET_DEFAULT_INPUT, SEGNET_DEFAULT_OUTPUT, maxBatchSize, precision, device, allowGPUFallback );
//else if( networkType == FCN_ALEXNET_AERIAL_FPV_720p_4ch )
// net = Create("FCN-Alexnet-Aerial-FPV-4ch-720p/deploy.prototxt", "FCN-Alexnet-Aerial-FPV-4ch-720p/snapshot_iter_1777146.caffemodel", "FCN-Alexnet-Aerial-FPV-4ch-720p/fpv-labels.txt", "FCN-Alexnet-Aerial-FPV-4ch-720p/fpv-deploy-colors.txt", "data", "score_fr_4classes", SEGNET_DEFAULT_INPUT, SEGNET_DEFAULT_OUTPUT, maxBatchSize );
else if( networkType == FCN_ALEXNET_AERIAL_FPV_720p )
net = Create("networks/FCN-Alexnet-Aerial-FPV-720p/fcn_alexnet.deploy.prototxt", "networks/FCN-Alexnet-Aerial-FPV-720p/snapshot_iter_10280.caffemodel", "networks/FCN-Alexnet-Aerial-FPV-720p/fpv-labels.txt", "networks/FCN-Alexnet-Aerial-FPV-720p/fpv-deploy-colors.txt", SEGNET_DEFAULT_INPUT, SEGNET_DEFAULT_OUTPUT, maxBatchSize, precision, device, allowGPUFallback );
else
return NULL;
if( net != NULL )
net->mNetworkType = networkType;
return net;
}
// Create
segNet* segNet::Create( int argc, char** argv )
{
return Create(commandLine(argc, argv));
}
// Create
segNet* segNet::Create( const commandLine& cmdLine )
{
segNet* net = NULL;
// obtain the model name
const char* modelName = cmdLine.GetString("model");
if( !modelName )
modelName = cmdLine.GetString("network", "fcn-resnet18-voc-320x320");
// parse the model type
const segNet::NetworkType type = NetworkTypeFromStr(modelName);
if( type == SEGNET_CUSTOM )
{
const char* prototxt = cmdLine.GetString("prototxt");
const char* labels = cmdLine.GetString("labels");
const char* colors = cmdLine.GetString("colors");
const char* input = cmdLine.GetString("input_blob");
const char* output = cmdLine.GetString("output_blob");
if( !input ) input = SEGNET_DEFAULT_INPUT;
if( !output ) output = SEGNET_DEFAULT_OUTPUT;
int maxBatchSize = cmdLine.GetInt("batch_size");
if( maxBatchSize < 1 )
maxBatchSize = DEFAULT_MAX_BATCH_SIZE;
net = segNet::Create(prototxt, modelName, labels, colors, input, output, maxBatchSize);
}
else
{
// create segnet from pretrained model
net = segNet::Create(type);
}
if( !net )
return NULL;
// save the legend if desired
const char* legend = cmdLine.GetString("legend");
if( legend != NULL )
net->saveClassLegend(legend);
// enable layer profiling if desired
if( cmdLine.GetFlag("profile") )
net->EnableLayerProfiler();
// set overlay alpha value
net->SetOverlayAlpha(cmdLine.GetFloat("alpha", SEGNET_DEFAULT_ALPHA));
return net;
}
// Create
segNet* segNet::Create( const char* prototxt, const char* model, const char* labels_path, const char* colors_path,
const char* input_blob, const char* output_blob, uint32_t maxBatchSize,
precisionType precision, deviceType device, bool allowGPUFallback )
{
// create segmentation model
segNet* net = new segNet();
if( !net )
return NULL;
LogInfo("\n");
LogInfo("segNet -- loading segmentation network model from:\n");
LogInfo(" -- prototxt: %s\n", prototxt);
LogInfo(" -- model: %s\n", model);
LogInfo(" -- labels: %s\n", labels_path);
LogInfo(" -- colors: %s\n", colors_path);
LogInfo(" -- input_blob '%s'\n", input_blob);
LogInfo(" -- output_blob '%s'\n", output_blob);
LogInfo(" -- batch_size %u\n\n", maxBatchSize);
//net->EnableProfiler();
//net->EnableDebug();
//net->DisableFP16(); // debug;
// load network
std::vector<std::string> output_blobs;
output_blobs.push_back(output_blob);
if( !net->LoadNetwork(prototxt, model, NULL, input_blob, output_blobs, maxBatchSize,
precision, device, allowGPUFallback) )
{
LogError(LOG_TRT "segNet -- failed to load.\n");
return NULL;
}
// initialize array of class colors
const uint32_t numClasses = net->GetNumClasses();
if( !cudaAllocMapped((void**)&net->mClassColors, numClasses * sizeof(float4)) )
return NULL;
for( uint32_t n=0; n < numClasses; n++ )
{
net->mClassColors[n*4+0] = 255.0f; // r
net->mClassColors[n*4+1] = 0.0f; // g
net->mClassColors[n*4+2] = 0.0f; // b
net->mClassColors[n*4+3] = 255.0f; // a
}
net->mColorsAlphaSet = (bool*)malloc(numClasses * sizeof(bool));
if( !net->mColorsAlphaSet )
{
printf(LOG_TRT "segNet -- failed to allocate class colors alpha flag array\n");
return NULL;
}
memset(net->mColorsAlphaSet, 0, numClasses * sizeof(bool));
// initialize array of classified argmax
const int s_w = DIMS_W(net->mOutputs[0].dims);
const int s_h = DIMS_H(net->mOutputs[0].dims);
const int s_c = DIMS_C(net->mOutputs[0].dims);
LogVerbose(LOG_TRT "segNet outputs -- s_w %i s_h %i s_c %i\n", s_w, s_h, s_c);
if( !cudaAllocMapped((void**)&net->mClassMap, s_w * s_h * sizeof(uint8_t)) )
return NULL;
// load class info
net->loadClassLabels(labels_path);
net->loadClassColors(colors_path);
return net;
}
// loadClassColors
bool segNet::loadClassColors( const char* filename )
{
if( !filename )
return false;
// locate the file
const std::string path = locateFile(filename);
if( path.length() == 0 )
{
LogError(LOG_TRT "segNet -- failed to find %s\n", filename);
return false;
}
// open the file
FILE* f = fopen(path.c_str(), "r");
if( !f )
{
LogError(LOG_TRT "segNet -- failed to open %s\n", path.c_str());
return false;
}
// read class colors
char str[512];
int idx = 0;
while( fgets(str, 512, f) != NULL )
{
const int len = strlen(str);
if( len > 0 )
{
if( str[len-1] == '\n' )
str[len-1] = 0;
int r = 255;
int g = 255;
int b = 255;
int a = 255;
sscanf(str, "%i %i %i %i", &r, &g, &b, &a);
LogVerbose(LOG_TRT "segNet -- class %02i color %i %i %i %i\n", idx, r, g, b, a);
SetClassColor(idx, r, g, b, a);
idx++;
}
}
fclose(f);
LogVerbose(LOG_TRT "segNet -- loaded %i class colors\n", idx);
if( idx == 0 )
return false;
return true;
}
// loadClassLabels
bool segNet::loadClassLabels( const char* filename )
{
if( !filename )
return false;
// locate the file
const std::string path = locateFile(filename);
if( path.length() == 0 )
{
LogError(LOG_TRT "segNet -- failed to find %s\n", filename);
return false;
}
// open the file
FILE* f = fopen(path.c_str(), "r");
if( !f )
{
LogError(LOG_TRT "segNet -- failed to open %s\n", path.c_str());
return false;
}
// read class labels
char str[512];
while( fgets(str, 512, f) != NULL )
{
const int len = strlen(str);
if( len > 0 )
{
if( str[len-1] == '\n' )
str[len-1] = 0;
LogVerbose(LOG_TRT "segNet -- class %02zu label '%s'\n", mClassLabels.size(), str);
mClassLabels.push_back(str);
}
}
fclose(f);
LogVerbose(LOG_TRT "segNet -- loaded %zu class labels\n", mClassLabels.size());
if( mClassLabels.size() == 0 )
return false;
mClassPath = path;
return true;
}
// saveClassLegend
bool segNet::saveClassLegend( const char* filename )
{
const int2 colorSize = make_int2(50,25);
const int2 xyPadding = make_int2(10,5);
const float4 bgColor = make_float4(255,255,255,255);
const float4 fgColor = make_float4(0,0,0,255);
// validate arguments
if( !filename )
return false;
const uint32_t numClasses = GetNumClasses();
if( numClasses == 0 )
return false;
// load the font
cudaFont* font = cudaFont::Create(16);
if( !font )
return false;
// determine the max size of class labels
int2 maxTextExtents = make_int2(0,0);
for( uint32_t n=0; n < numClasses; n++ )
{
char str[256];
sprintf(str, "%2d %s", n, GetClassDesc(n));
const int4 textExtents = font->TextExtents(str);
if( textExtents.z > maxTextExtents.x )
maxTextExtents.x = textExtents.z;
if( textExtents.w > maxTextExtents.y )
maxTextExtents.y = textExtents.w;
}
//if( colorSize.y > maxTextExtents.y )
// maxTextExtents.y = colorSize.y;
// allocate image to store the legend
const int imgWidth = maxTextExtents.x + colorSize.x + xyPadding.x * 3;
const int imgHeight = (colorSize.y + xyPadding.y) * numClasses + xyPadding.y * 2;
float4* img = NULL;
if( !cudaAllocMapped((void**)&img, imgWidth * imgHeight * 4 * sizeof(float)) )
return false;
// fill the legend's background color
#define FILL_RECT(color, x1, y1, x2, y2) \
for( int y=y1; y < y2; y++ ) \
for( int x=x1; x < x2; x++ ) \
img[y*imgWidth+x] = color;
FILL_RECT(bgColor, 0, 0, imgWidth, imgHeight);
// render each class entry
int yPosition = xyPadding.y * 2;
for( uint32_t n=0; n < numClasses; n++ )
{
char str[256];
sprintf(str, "%2d %s", n, GetClassDesc(n));
// render the class text
font->OverlayText(img, imgWidth, imgHeight, str, xyPadding.x, yPosition);
CUDA(cudaDeviceSynchronize());
// fill the class color
float* classColor = GetClassColor(n);
const float4 color = make_float4(classColor[0], classColor[1], classColor[2], 255);
const int colorX = maxTextExtents.x + xyPadding.x * 2;
const int colorY = yPosition - ((colorSize.y - maxTextExtents.y) / 2);
FILL_RECT(color, colorX, colorY, colorX + colorSize.x, colorY + colorSize.y);
// advance the position
yPosition += colorSize.y + xyPadding.y;
}
// save the image
const bool result = saveImageRGBA(filename, img, imgWidth, imgHeight);
CUDA(cudaFreeHost(img));
delete font;
return result;
}
// SetClassColor
void segNet::SetClassColor( uint32_t classIndex, float r, float g, float b, float a )
{
if( classIndex >= GetNumClasses() || !mClassColors )
return;
const uint32_t i = classIndex * 4;
mClassColors[i+0] = r;
mClassColors[i+1] = g;
mClassColors[i+2] = b;
mClassColors[i+3] = a;
mColorsAlphaSet[classIndex] = (a == 255) ? false : true;
}
// SetOverlayAlpha
void segNet::SetOverlayAlpha( float alpha, bool explicit_exempt )
{
const uint32_t numClasses = GetNumClasses();
for( uint32_t n=0; n < numClasses; n++ )
{
if( !explicit_exempt || !mColorsAlphaSet[n] /*mClassColors[n*4+3] == 255*/ )
mClassColors[n*4+3] = alpha;
}
}
// FindClassID
int segNet::FindClassID( const char* label_name )
{
if( !label_name )
return -1;
const uint32_t numLabels = mClassLabels.size();
for( uint32_t n=0; n < numLabels; n++ )
{
if( strcasecmp(label_name, mClassLabels[n].c_str()) == 0 )
return n;
}
return -1;
}
// Process
bool segNet::Process( float* rgba, uint32_t width, uint32_t height, const char* ignore_class )
{
return Process(rgba, width, height, IMAGE_RGBA32F, ignore_class);
}
// Process
bool segNet::Process( void* image, uint32_t width, uint32_t height, imageFormat format, const char* ignore_class )
{
if( !image || width == 0 || height == 0 )
{
LogError(LOG_TRT "segNet::Process( 0x%p, %u, %u ) -> invalid parameters\n", image, width, height);
return false;
}
if( !imageFormatIsRGB(format) )
{
LogError(LOG_TRT "segNet -- unsupported image format (%s)\n", imageFormatToStr(format));
LogError(LOG_TRT " supported formats are:\n");
LogError(LOG_TRT " * rgb8\n");
LogError(LOG_TRT " * rgba8\n");
LogError(LOG_TRT " * rgb32f\n");
LogError(LOG_TRT " * rgba32f\n");
return cudaErrorInvalidValue;
}
PROFILER_BEGIN(PROFILER_PREPROCESS);
if( IsModelType(MODEL_ONNX) )
{
// downsample, convert to band-sequential RGB, and apply pixel normalization, mean pixel subtraction and standard deviation
if( CUDA_FAILED(cudaTensorNormMeanRGB(image, format, width, height,
mInputs[0].CUDA, GetInputWidth(), GetInputHeight(),
make_float2(0.0f, 1.0f),
make_float3(0.485f, 0.456f, 0.406f),
make_float3(0.229f, 0.224f, 0.225f),
GetStream())) )
{
LogError(LOG_TRT "segNet::Process() -- cudaTensorNormMeanRGB() failed\n");
return false;
}
}
else
{
// downsample and convert to band-sequential BGR
if( CUDA_FAILED(cudaTensorMeanBGR(image, format, width, height,
mInputs[0].CUDA, GetInputWidth(), GetInputHeight(),
make_float3(0,0,0), GetStream())) )
{
LogError(LOG_TRT "segNet::Process() -- cudaTensorMeanBGR() failed\n");
return false;
}
}
PROFILER_END(PROFILER_PREPROCESS);
PROFILER_BEGIN(PROFILER_NETWORK);
// process with TensorRT
if( !ProcessNetwork() )
return false;
PROFILER_END(PROFILER_NETWORK);
PROFILER_BEGIN(PROFILER_POSTPROCESS);
// generate argmax classification map
if( !classify(ignore_class) )
return false;
PROFILER_END(PROFILER_POSTPROCESS);
// cache pointer to last image processed
mLastInputImg = image;
mLastInputWidth = width;
mLastInputHeight = height;
mLastInputFormat = format;
return true;
}
// argmax classification
bool segNet::classify( const char* ignore_class )
{
// retrieve scores
float* scores = mOutputs[0].CPU;
const int s_w = DIMS_W(mOutputs[0].dims);
const int s_h = DIMS_H(mOutputs[0].dims);
const int s_c = DIMS_C(mOutputs[0].dims);
//const float s_x = float(width) / float(s_w); // TODO bug: this should use mWidth/mHeight dimensions, in case user dimensions are different
//const float s_y = float(height) / float(s_h);
const float s_x = float(s_w) / float(GetInputWidth());
const float s_y = float(s_h) / float(GetInputHeight());
// if desired, find the ID of the class to ignore (typically void)
const int ignoreID = FindClassID(ignore_class);
//printf(LOG_TRT "segNet::Process -- s_w %i s_h %i s_c %i s_x %f s_y %f\n", s_w, s_h, s_c, s_x, s_y);
//printf(LOG_TRT "segNet::Process -- ignoring class '%s' id=%i\n", ignore_class, ignoreID);
// find the argmax-classified class of each tile
uint8_t* classMap = mClassMap;
for( uint32_t y=0; y < s_h; y++ )
{
for( uint32_t x=0; x < s_w; x++ )
{
float p_max = -100000.0f;
int c_max = -1;
for( int c=0; c < s_c; c++ )
{
// skip ignoreID
if( c == ignoreID )
continue;
// check if this class score is higher
const float p = scores[c * s_w * s_h + y * s_w + x];
if( c_max < 0 || p > p_max )
{
p_max = p;
c_max = c;
}
}
classMap[y * s_w + x] = c_max;
//printf("(%u, %u) -> class %i\n", x, y, (uint32_t)c_max);
}
}
return true;
}
// Mask (binary)
bool segNet::Mask( uint8_t* output, uint32_t out_width, uint32_t out_height )
{
if( !output || out_width == 0 || out_height == 0 )
{
LogError(LOG_TRT "segNet::Mask( 0x%p, %u, %u ) -> invalid parameters\n", output, out_width, out_height);
return false;
}
PROFILER_BEGIN(PROFILER_VISUALIZE);
// retrieve classification map
uint8_t* classMap = mClassMap;
const int s_w = DIMS_W(mOutputs[0].dims);
const int s_h = DIMS_H(mOutputs[0].dims);
if( out_width == s_w && out_height == s_h )
{
memcpy(output, classMap, s_w * s_h * sizeof(uint8_t));
}
else
{
const float s_x = float(s_w) / float(out_width);
const float s_y = float(s_h) / float(out_height);
// overlay pixels onto original
for( uint32_t y=0; y < out_height; y++ )
{
for( uint32_t x=0; x < out_width; x++ )
{
const int cx = float(x) * s_x;
const int cy = float(y) * s_y;
// get the class ID of this cell
const uint8_t classIdx = classMap[cy * s_w + cx];
// output the pixel
output[y * out_width + x] = classIdx;
}
}
}
PROFILER_END(PROFILER_VISUALIZE);
return true;
}
// Mask (colorized)
bool segNet::Mask( float* output, uint32_t width, uint32_t height, FilterMode filter )
{
return Mask(output, width, height, IMAGE_RGBA32F, filter);
}
// Mask (colorized)
bool segNet::Mask( void* output, uint32_t width, uint32_t height, imageFormat format, FilterMode filter )
{
if( !output || width == 0 || height == 0 )
{
LogError(LOG_TRT "segNet::Mask( 0x%p, %u, %u ) -> invalid parameters\n", output, width, height);
return false;
}
// filter in point or linear
if( filter == FILTER_POINT )
return overlayPoint(NULL, 0, 0, IMAGE_UNKNOWN, output, width, height, format, true);
else if( filter == FILTER_LINEAR )
return overlayLinear(NULL, 0, 0, IMAGE_UNKNOWN, output, width, height, format, true);
return false;
}
// Overlay
bool segNet::Overlay( float* output, uint32_t width, uint32_t height, FilterMode filter )
{
return Overlay(output, width, height, IMAGE_RGBA32F, filter);
}
// Overlay
bool segNet::Overlay( void* output, uint32_t width, uint32_t height, imageFormat format, segNet::FilterMode filter )
{
if( !output || width == 0 || height == 0 )
{
LogError(LOG_TRT "segNet::Overlay( 0x%p, %u, %u ) -> invalid parameters\n", output, width, height);
return false;
}
if( !mLastInputImg )
{
LogError(LOG_TRT "segNet -- Process() must be called before Overlay()\n");
return false;
}
// filter in point or linear
if( filter == FILTER_POINT )
return overlayPoint(mLastInputImg, mLastInputWidth, mLastInputHeight, mLastInputFormat, output, width, height, format, false);
else if( filter == FILTER_LINEAR )
return overlayLinear(mLastInputImg, mLastInputWidth, mLastInputHeight, mLastInputFormat, output, width, height, format, false);
return false;
}
#define OVERLAY_CUDA
// declaration from segNet.cu
cudaError_t cudaSegOverlay( void* input, uint32_t in_width, uint32_t in_height,
void* output, uint32_t out_width, uint32_t out_height, imageFormat format,
float4* class_colors, uint8_t* scores, const int2& scores_dim,
bool filter_linear, bool mask_only, cudaStream_t stream );
// overlayLinear
bool segNet::overlayPoint( void* input, uint32_t in_width, uint32_t in_height, imageFormat in_format, void* output, uint32_t out_width, uint32_t out_height, imageFormat out_format, bool mask_only )
{
if( input != NULL && in_format != out_format )
{
LogError(LOG_TRT "segNet -- input image format (%s) and overlay/mask image format (%s) don't match\n", imageFormatToStr(in_format), imageFormatToStr(out_format));
return false;
}
PROFILER_BEGIN(PROFILER_VISUALIZE);
#ifdef OVERLAY_CUDA
// generate overlay on the GPU
if( CUDA_FAILED(cudaSegOverlay(input, in_width, in_height, output, out_width, out_height, out_format,
(float4*)mClassColors, mClassMap, make_int2(DIMS_W(mOutputs[0].dims), DIMS_H(mOutputs[0].dims)),
false, mask_only, GetStream())) )
{
LogError(LOG_TRT "segNet -- failed to process %ux%u overlay/mask with CUDA\n", out_width, out_height);
return false;
}
#else
// retrieve classification map
uint8_t* classMap = mClassMap;
const int s_w = DIMS_W(mOutputs[0].dims);
const int s_h = DIMS_H(mOutputs[0].dims);
const float s_x = float(s_w) / float(out_width);
const float s_y = float(s_h) / float(out_height);
// overlay pixels onto original
for( uint32_t y=0; y < out_height; y++ )
{
for( uint32_t x=0; x < out_width; x++ )
{
const int cx = float(x) * s_x;
const int cy = float(y) * s_y;
// get the class ID of this cell
const uint8_t classIdx = classMap[cy * s_w + cx];
// find the color of this class
float* c_color = GetClassColor(classIdx);
// output the pixel
float* px_out = output + (((y * out_width * 4) + x * 4));
if( mask_only )
{
// only draw the segmentation mask
px_out[0] = c_color[0];
px_out[1] = c_color[1];
px_out[2] = c_color[2];
px_out[3] = 255.0f;
}
else
{
// alpha blend with input image
const uint32_t x_in = float(x) / float(out_width) * float(in_width);
const uint32_t y_in = float(y) / float(out_height) * float(in_height);
float* px_in = input + (((y_in * in_width * 4) + x_in * 4));
const float alph = c_color[3] / 255.0f;
const float inva = 1.0f - alph;
px_out[0] = alph * c_color[0] + inva * px_in[0];
px_out[1] = alph * c_color[1] + inva * px_in[1];
px_out[2] = alph * c_color[2] + inva * px_in[2];
px_out[3] = 255.0f;
}