static int etherflow_(Api_send_tensor_byte_lua)(lua_State *L) {
// get params
THByteTensor *tensor = luaT_toudata(L, 1, luaT_checktypename2id(L, "torch.ByteTensor"));
int size = THByteTensor_nElement(tensor);
unsigned char *data = THByteTensor_data(tensor);
etherflow_send_ByteTensor_C(data, size);
return 0;
}
static int libjpeg_(Main_load)(lua_State *L)
{
/* This struct contains the JPEG decompression parameters and pointers to
* working space (which is allocated as needed by the JPEG library).
*/
struct jpeg_decompress_struct cinfo;
/* We use our private extension JPEG error handler.
* Note that this struct must live as long as the main JPEG parameter
* struct, to avoid dangling-pointer problems.
*/
struct my_error_mgr jerr;
/* More stuff */
FILE * infile; /* source file (if loading from file) */
unsigned char * inmem; /* source memory (if loading from memory) */
unsigned long inmem_size; /* source memory size (bytes) */
JSAMPARRAY buffer; /* Output row buffer */
/* int row_stride; /1* physical row width in output buffer *1/ */
int i, k;
THTensor *tensor = NULL;
const int load_from_file = luaL_checkint(L, 1);
if (load_from_file == 1) {
const char *filename = luaL_checkstring(L, 2);
/* In this example we want to open the input file before doing anything else,
* so that the setjmp() error recovery below can assume the file is open.
* VERY IMPORTANT: use "b" option to fopen() if you are on a machine that
* requires it in order to read binary files.
*/
if ((infile = fopen(filename, "rb")) == NULL)
{
luaL_error(L, "cannot open file <%s> for reading", filename);
}
} else {
/* We're loading from a ByteTensor */
THByteTensor *src = luaT_checkudata(L, 2, "torch.ByteTensor");
inmem = THByteTensor_data(src);
inmem_size = src->size[0];
infile = NULL;
}
/* Step 1: allocate and initialize JPEG decompression object */
/* We set up the normal JPEG error routines, then override error_exit. */
cinfo.err = jpeg_std_error(&jerr.pub);
jerr.pub.error_exit = libjpeg_(Main_error);
/* Establish the setjmp return context for my_error_exit to use. */
if (setjmp(jerr.setjmp_buffer)) {
/* If we get here, the JPEG code has signaled an error.
* We need to clean up the JPEG object, close the input file, and return.
*/
jpeg_destroy_decompress(&cinfo);
if (infile) {
fclose(infile);
}
return 0;
}
/* Now we can initialize the JPEG decompression object. */
jpeg_create_decompress(&cinfo);
/* Step 2: specify data source (eg, a file) */
if (load_from_file == 1) {
jpeg_stdio_src(&cinfo, infile);
} else {
jpeg_mem_src(&cinfo, inmem, inmem_size);
}
/* Step 3: read file parameters with jpeg_read_header() */
(void) jpeg_read_header(&cinfo, TRUE);
/* We can ignore the return value from jpeg_read_header since
* (a) suspension is not possible with the stdio data source, and
* (b) we passed TRUE to reject a tables-only JPEG file as an error.
* See libjpeg.doc for more info.
*/
/* Step 4: set parameters for decompression */
/* In this example, we don't need to change any of the defaults set by
* jpeg_read_header(), so we do nothing here.
*/
/* Step 5: Start decompressor */
(void) jpeg_start_decompress(&cinfo);
/* We can ignore the return value since suspension is not possible
* with the stdio data source.
*/
/* We may need to do some setup of our own at this point before reading
* the data. After jpeg_start_decompress() we have the correct scaled
* output image dimensions available, as well as the output colormap
* if we asked for color quantization.
* In this example, we need to make an output work buffer of the right size.
*/
/* Make a one-row-high sample array that will go away when done with image */
tensor = THTensor_(newWithSize3d)(cinfo.output_components, cinfo.output_height, cinfo.output_width);
buffer = (*cinfo.mem->alloc_sarray)
((j_common_ptr) &cinfo, JPOOL_IMAGE, cinfo.output_width * cinfo.output_components, 1);
/* Step 6: while (scan lines remain to be read) */
/* jpeg_read_scanlines(...); */
/* Here we use the library's state variable cinfo.output_scanline as the
* loop counter, so that we don't have to keep track ourselves.
*/
while (cinfo.output_scanline < cinfo.output_height) {
/* jpeg_read_scanlines expects an array of pointers to scanlines.
* Here the array is only one element long, but you could ask for
* more than one scanline at a time if that's more convenient.
*/
(void) jpeg_read_scanlines(&cinfo, buffer, 1);
for(k = 0; k < cinfo.output_components; k++)
{
for(i = 0; i < cinfo.output_width; i++)
THTensor_(set3d)(tensor, k, cinfo.output_scanline-1, i,
(real)buffer[0][cinfo.output_components*i+k]);
}
}
/* Step 7: Finish decompression */
(void) jpeg_finish_decompress(&cinfo);
/* We can ignore the return value since suspension is not possible
* with the stdio data source.
*/
/* Step 8: Release JPEG decompression object */
/* This is an important step since it will release a good deal of memory. */
jpeg_destroy_decompress(&cinfo);
/* After finish_decompress, we can close the input file.
* Here we postpone it until after no more JPEG errors are possible,
* so as to simplify the setjmp error logic above. (Actually, I don't
* think that jpeg_destroy can do an error exit, but why assume anything...)
*/
if (infile) {
fclose(infile);
}
/* At this point you may want to check to see whether any corrupt-data
* warnings occurred (test whether jerr.pub.num_warnings is nonzero).
*/
/* And we're done! */
luaT_pushudata(L, tensor, torch_Tensor);
return 1;
}
/*
* save function
*
*/
int libjpeg_(Main_save)(lua_State *L) {
unsigned char *inmem = NULL; /* destination memory (if saving to memory) */
unsigned long inmem_size = 0; /* destination memory size (bytes) */
/* get args */
const char *filename = luaL_checkstring(L, 1);
THTensor *tensor = luaT_checkudata(L, 2, torch_Tensor);
THTensor *tensorc = THTensor_(newContiguous)(tensor);
real *tensor_data = THTensor_(data)(tensorc);
const int save_to_file = luaL_checkint(L, 3);
THByteTensor* tensor_dest = NULL;
if (save_to_file == 0) {
tensor_dest = luaT_checkudata(L, 5, "torch.ByteTensor");
}
int quality = luaL_checkint(L, 4);
if (quality < 0 || quality > 100) {
luaL_error(L, "quality should be between 0 and 100");
}
/* jpeg struct */
struct jpeg_compress_struct cinfo;
struct jpeg_error_mgr jerr;
/* pointer to raw image */
unsigned char *raw_image = NULL;
/* dimensions of the image we want to write */
int width=0, height=0, bytes_per_pixel=0;
int color_space=0;
if (tensorc->nDimension == 3) {
bytes_per_pixel = tensorc->size[0];
height = tensorc->size[1];
width = tensorc->size[2];
if (bytes_per_pixel == 3) {
color_space = JCS_RGB;
} else if (bytes_per_pixel == 1) {
color_space = JCS_GRAYSCALE;
} else {
luaL_error(L, "tensor should have 1 or 3 channels (gray or RGB)");
}
} else if (tensorc->nDimension == 2) {
bytes_per_pixel = 1;
height = tensorc->size[0];
width = tensorc->size[1];
color_space = JCS_GRAYSCALE;
} else {
luaL_error(L, "supports only 1 or 3 dimension tensors");
}
/* alloc raw image data */
raw_image = (unsigned char *)malloc((sizeof (unsigned char))*width*height*bytes_per_pixel);
/* convert tensor to raw bytes */
int x,y,k;
for (k=0; k<bytes_per_pixel; k++) {
for (y=0; y<height; y++) {
for (x=0; x<width; x++) {
raw_image[(y*width+x)*bytes_per_pixel+k] = *tensor_data++;
}
}
}
/* this is a pointer to one row of image data */
JSAMPROW row_pointer[1];
FILE *outfile = NULL;
if (save_to_file == 1) {
outfile = fopen( filename, "wb" );
if ( !outfile ) {
luaL_error(L, "Error opening output jpeg file %s\n!", filename );
}
}
cinfo.err = jpeg_std_error( &jerr );
jpeg_create_compress(&cinfo);
/* specify data source (eg, a file) */
if (save_to_file == 1) {
jpeg_stdio_dest(&cinfo, outfile);
} else {
jpeg_mem_dest(&cinfo, &inmem, &inmem_size);
}
/* Setting the parameters of the output file here */
cinfo.image_width = width;
cinfo.image_height = height;
cinfo.input_components = bytes_per_pixel;
cinfo.in_color_space = color_space;
/* default compression parameters, we shouldn't be worried about these */
jpeg_set_defaults( &cinfo );
jpeg_set_quality(&cinfo, quality, (boolean)0);
/* Now do the compression .. */
jpeg_start_compress( &cinfo, TRUE );
/* like reading a file, this time write one row at a time */
while( cinfo.next_scanline < cinfo.image_height ) {
row_pointer[0] = &raw_image[ cinfo.next_scanline * cinfo.image_width * cinfo.input_components];
jpeg_write_scanlines( &cinfo, row_pointer, 1 );
}
/* similar to read file, clean up after we're done compressing */
jpeg_finish_compress( &cinfo );
jpeg_destroy_compress( &cinfo );
if (outfile != NULL) {
fclose( outfile );
}
if (save_to_file == 0) {
THByteTensor_resize1d(tensor_dest, inmem_size); /* will fail if it's not a Byte Tensor */
unsigned char* tensor_dest_data = THByteTensor_data(tensor_dest);
memcpy(tensor_dest_data, inmem, inmem_size);
free(inmem);
}
/* some cleanup */
free(raw_image);
THTensor_(free)(tensorc);
/* success code is 1! */
return 1;
}
static int torchzfp_(Main_compress)(lua_State *L) {
THTensor* in = reinterpret_cast<THTensor*>(
luaT_checkudata(L, 1, torch_Tensor));
real* in_data = THTensor_(data)(in);
const uint32_t dim = in->nDimension;
if (dim == 0) {
luaL_error(L, "Input tensor must not be empty");
}
THByteTensor* out = reinterpret_cast<THByteTensor*>(
luaT_checkudata(L, 2, "torch.ByteTensor"));
const double accuracy = static_cast<double>(lua_tonumber(L, 3));
// Hacky code to figure out what type 'real' is at runtime. This really should
// be template specialization (so it's compiled in at runtime).
real dummy;
static_cast<void>(dummy); // Silence compiler warnings.
zfp_type type;
if (typeid(dummy) == typeid(float)) {
type = zfp_type_float;
} else if (typeid(dummy) == typeid(double)) {
type = zfp_type_double;
} else {
luaL_error(L, "Input type must be double or float.");
}
// Allocate meta data for the array.
zfp_field* field;
uint32_t dim_zfp;
if (dim == 1) {
field = zfp_field_1d(in_data, type, in->size[0]);
dim_zfp = 1;
} else if (dim == 2) {
field = zfp_field_2d(in_data, type, in->size[1], in->size[0]);
dim_zfp = 2;
} else if (dim == 3) {
field = zfp_field_3d(in_data, type, in->size[2], in->size[1], in->size[0]);
dim_zfp = 3;
} else {
// ZFP only allows up to 3D tensors, so we'll have to treat the input
// tensor as a concatenated 3D tensor. This will affect compression ratios
// but there's not much we can do about this.
uint32_t sizez = 1;
for (uint32_t i = 0; i < dim - 2; i++) {
sizez *= in->size[i];
}
uint32_t sizey = in->size[dim - 2];
uint32_t sizex = in->size[dim - 1];
field = zfp_field_3d(in_data, type, sizex, sizey, sizez);
dim_zfp = 4;
}
// Allocate meta data for the compressed stream.
zfp_stream* zfp = zfp_stream_open(NULL);
// Set stream compression mode and parameters.
zfp_stream_set_accuracy(zfp, accuracy, type);
// Allocate buffer for compressed data.
size_t bufsize = zfp_stream_maximum_size(zfp, field);
std::unique_ptr<uint8_t[]> buffer(new uint8_t[bufsize]);
// Associate bit stream with allocated buffer.
bitstream* stream = stream_open(buffer.get(), bufsize);
zfp_stream_set_bit_stream(zfp, stream);
zfp_stream_rewind(zfp);
// Compress entire array.
const size_t zfpsize = zfp_compress(zfp, field);
// Clean up.
zfp_field_free(field);
zfp_stream_close(zfp);
stream_close(stream);
if (!zfpsize) {
luaL_error(L, "ZFP compression failed!");
}
// Copy the compressed array into the return tensor. NOTE: Torch does not
// support in-place resize with shrink. If you resize smaller you ALWAYS
// keep around the memory, so unfortuantely this copy is necessary (i.e.
// we will always need to perform the compression in a temporary buffer
// first).
THByteTensor_resize1d(out, zfpsize);
unsigned char* out_data = THByteTensor_data(out);
memcpy(out_data, buffer.get(), zfpsize);
return 0; // Recall: number of lua return items.
}
static int torchzfp_(Main_decompress)(lua_State *L) {
THTensor* out = reinterpret_cast<THTensor*>(
luaT_checkudata(L, 1, torch_Tensor));
real* out_data = THTensor_(data)(out);
const uint32_t dim = out->nDimension;
THByteTensor* in = reinterpret_cast<THByteTensor*>(
luaT_checkudata(L, 2, "torch.ByteTensor"));
unsigned char* in_data = THByteTensor_data(in);
const double accuracy = static_cast<double>(lua_tonumber(L, 3));
// Hacky code to figure out what type 'real' is at runtime. This really should
// be template specialization (so it's compiled in at runtime).
real dummy;
static_cast<void>(dummy); // Silence compiler warnings.
zfp_type type;
if (typeid(dummy) == typeid(float)) {
type = zfp_type_float;
} else if (typeid(dummy) == typeid(double)) {
type = zfp_type_double;
} else {
luaL_error(L, "Output type must be double or float.");
}
// Allocate meta data for the array.
zfp_field* field;
uint32_t dim_zfp;
if (dim == 1) {
field = zfp_field_1d(out_data, type, out->size[0]);
dim_zfp = 1;
} else if (dim == 2) {
field = zfp_field_2d(out_data, type, out->size[1], out->size[0]);
dim_zfp = 2;
} else if (dim == 3) {
field = zfp_field_3d(out_data, type, out->size[2], out->size[1],
out->size[0]);
dim_zfp = 3;
} else {
// ZFP only allows up to 3D tensors, so we'll have to treat the input
// tensor as a concatenated 3D tensor. This will affect compression ratios
// but there's not much we can do about this.
uint32_t sizez = 1;
for (uint32_t i = 0; i < dim - 2; i++) {
sizez *= out->size[i];
}
uint32_t sizey = out->size[dim - 2];
uint32_t sizex = out->size[dim - 1];
field = zfp_field_3d(out_data, type, sizex, sizey, sizez);
dim_zfp = 3;
}
// Allocate meta data for the compressed stream.
zfp_stream* zfp = zfp_stream_open(NULL);
// Set stream compression mode and parameters.
zfp_stream_set_accuracy(zfp, accuracy, type);
// Get buffer for compressed data.
void* buffer = reinterpret_cast<void*>(in_data);
// Associate bit stream with allocated buffer.
const uint32_t bufsize = in->size[0];
bitstream* stream = stream_open(buffer, bufsize);
zfp_stream_set_bit_stream(zfp, stream);
zfp_stream_rewind(zfp);
// Compress entire array.
const int ret = zfp_decompress(zfp, field);
// Clean up.
zfp_field_free(field);
zfp_stream_close(zfp);
stream_close(stream);
if (!ret) {
luaL_error(L, "ZFP decompression failed!");
}
return 0; // Recall: number of lua return items.
}
static void load_array_to_lua(lua_State *L, cnpy::NpyArray& arr){
int ndims = arr.shape.size();
//based on code from mattorch with stride fix
int k;
THLongStorage *size = THLongStorage_newWithSize(ndims);
THLongStorage *stride = THLongStorage_newWithSize(ndims);
for (k=0; k<ndims; k++) {
THLongStorage_set(size, k, arr.shape[k]);
if (k > 0)
THLongStorage_set(stride, ndims-k-1, arr.shape[ndims-k]*THLongStorage_get(stride,ndims-k));
else
THLongStorage_set(stride, ndims-k-1, 1);
}
void * tensorDataPtr = NULL;
size_t numBytes = 0;
if ( arr.arrayType == 'f' ){ // float32/64
if ( arr.word_size == 4 ){ //float32
THFloatTensor *tensor = THFloatTensor_newWithSize(size, stride);
tensorDataPtr = (void *)(THFloatTensor_data(tensor));
numBytes = THFloatTensor_nElement(tensor) * arr.word_size;
luaT_pushudata(L, tensor, luaT_checktypename2id(L, "torch.FloatTensor"));
}else if ( arr.word_size == 8){ //float 64
THDoubleTensor *tensor = THDoubleTensor_newWithSize(size, stride);
tensorDataPtr = (void *)(THDoubleTensor_data(tensor));
numBytes = THDoubleTensor_nElement(tensor) * arr.word_size;
luaT_pushudata(L, tensor, luaT_checktypename2id(L, "torch.DoubleTensor"));
}
}else if ( arr.arrayType == 'i' || arr.arrayType == 'u' ){ // does torch have unsigned types .. need to look
if ( arr.word_size == 1 ){ //int8
THByteTensor *tensor = THByteTensor_newWithSize(size, stride);
tensorDataPtr = (void *)(THByteTensor_data(tensor));
numBytes = THByteTensor_nElement(tensor) * arr.word_size;
luaT_pushudata(L, tensor, luaT_checktypename2id(L, "torch.ByteTensor"));
}else if ( arr.word_size == 2 ){ //int16
THShortTensor *tensor = THShortTensor_newWithSize(size, stride);
tensorDataPtr = (void *)(THShortTensor_data(tensor));
numBytes = THShortTensor_nElement(tensor) * arr.word_size;
luaT_pushudata(L, tensor, luaT_checktypename2id(L, "torch.ShortTensor"));
}else if ( arr.word_size == 4 ){ //int32
THIntTensor *tensor = THIntTensor_newWithSize(size, stride);
tensorDataPtr = (void *)(THIntTensor_data(tensor));
numBytes = THIntTensor_nElement(tensor) * arr.word_size;
luaT_pushudata(L, tensor, luaT_checktypename2id(L, "torch.IntTensor"));
}else if ( arr.word_size == 8){ //long 64
THLongTensor *tensor = THLongTensor_newWithSize(size, stride);
tensorDataPtr = (void *)(THLongTensor_data(tensor));
numBytes = THLongTensor_nElement(tensor) * arr.word_size;
luaT_pushudata(L, tensor, luaT_checktypename2id(L, "torch.LongTensor"));
}
}else{
printf("array type unsupported");
throw std::runtime_error("unsupported data type");
}
// now copy the data
assert(tensorDataPtr);
memcpy(tensorDataPtr, (void *)(arr.data<void>()), numBytes);
}
void THNN_ByteSpatialMaxPooling_updateOutput(
THNNState *state,
THByteTensor *input,
THByteTensor *output,
THByteTensor *indices,
int kW,
int kH,
int dW,
int dH,
int padW,
int padH,
bool ceil_mode)
{
int dimw = 2;
int dimh = 1;
long nbatch = 1;
long nslices;
long iheight;
long iwidth;
long oheight;
long owidth;
uint8_t *input_data;
uint8_t *output_data;
uint8_t *indices_data;
THArgCheck(input->nDimension == 3 || input->nDimension == 4 , 2, "3D or 4D (batch mode) tensor expected");
if (input->nDimension == 4)
{
nbatch = input->size[0];
dimw++;
dimh++;
}
THArgCheck(input->size[dimw] >= kW - padW && input->size[dimh] >= kH - padH, 2, "input image smaller than kernel size");
THArgCheck(kW/2 >= padW && kH/2 >= padH, 2, "pad should be smaller than half of kernel size");
/* sizes */
nslices = input->size[dimh-1];
iheight = input->size[dimh];
iwidth = input->size[dimw];
if (ceil_mode)
{
oheight = (long)(ceil((float)(iheight - kH + 2*padH) / dH)) + 1;
owidth = (long)(ceil((float)(iwidth - kW + 2*padW) / dW)) + 1;
}
else
{
oheight = (long)(floor((float)(iheight - kH + 2*padH) / dH)) + 1;
owidth = (long)(floor((float)(iwidth - kW + 2*padW) / dW)) + 1;
}
if (padW || padH)
{
// ensure that the last pooling starts inside the image
if ((oheight - 1)*dH >= iheight + padH)
--oheight;
if ((owidth - 1)*dW >= iwidth + padW)
--owidth;
}
/* get contiguous input */
input = THByteTensor_newContiguous(input);
/* resize output */
if (input->nDimension == 3)
{
THByteTensor_resize3d(output, nslices, oheight, owidth);
/* indices will contain the locations for each output point */
THByteTensor_resize3d(indices, nslices, oheight, owidth);
input_data = THByteTensor_data(input);
output_data = THByteTensor_data(output);
indices_data = THByteTensor_data(indices);
THNN_ByteSpatialMaxPooling_updateOutput_frame(input_data, output_data,
indices_data,
nslices,
iwidth, iheight,
owidth, oheight,
kW, kH, dW, dH,
padW, padH);
}
else
{
long p;
THByteTensor_resize4d(output, nbatch, nslices, oheight, owidth);
/* indices will contain the locations for each output point */
THByteTensor_resize4d(indices, nbatch, nslices, oheight, owidth);
input_data = THByteTensor_data(input);
output_data = THByteTensor_data(output);
indices_data = THByteTensor_data(indices);
#pragma omp parallel for private(p)
for (p = 0; p < nbatch; p++)
{
THNN_ByteSpatialMaxPooling_updateOutput_frame(input_data+p*nslices*iwidth*iheight, output_data+p*nslices*owidth*oheight,
indices_data+p*nslices*owidth*oheight,
nslices,
iwidth, iheight,
owidth, oheight,
kW, kH, dW, dH,
padW, padH);
}
}
/* cleanup */
THByteTensor_free(input);
}
static int libpng_(Main_load)(lua_State *L)
{
png_byte header[8]; // 8 is the maximum size that can be checked
int width, height, bit_depth;
png_byte color_type;
png_structp png_ptr;
png_infop info_ptr;
png_bytep * row_pointers;
size_t fread_ret;
FILE* fp;
libpng_inmem_buffer inmem = {0}; /* source memory (if loading from memory) */
libpng_errmsg errmsg;
const int load_from_file = luaL_checkint(L, 1);
if (load_from_file == 1){
const char *file_name = luaL_checkstring(L, 2);
/* open file and test for it being a png */
fp = fopen(file_name, "rb");
if (!fp)
luaL_error(L, "[read_png_file] File %s could not be opened for reading", file_name);
fread_ret = fread(header, 1, 8, fp);
if (fread_ret != 8)
luaL_error(L, "[read_png_file] File %s error reading header", file_name);
if (png_sig_cmp(header, 0, 8))
luaL_error(L, "[read_png_file] File %s is not recognized as a PNG file", file_name);
} else {
/* We're loading from a ByteTensor */
THByteTensor *src = luaT_checkudata(L, 2, "torch.ByteTensor");
inmem.buffer = THByteTensor_data(src);
inmem.length = src->size[0];
inmem.offset = 8;
fp = NULL;
if (png_sig_cmp(inmem.buffer, 0, 8))
luaL_error(L, "[read_png_byte_tensor] ByteTensor is not recognized as a PNG file");
}
/* initialize stuff */
png_ptr = png_create_read_struct(PNG_LIBPNG_VER_STRING, NULL, NULL, NULL);
if (!png_ptr)
luaL_error(L, "[read_png] png_create_read_struct failed");
png_set_error_fn(png_ptr, &errmsg, libpng_error_fn, NULL);
info_ptr = png_create_info_struct(png_ptr);
if (!info_ptr)
luaL_error(L, "[read_png] png_create_info_struct failed");
if (setjmp(png_jmpbuf(png_ptr)))
luaL_error(L, "[read_png] Error during init_io: %s", errmsg.str);
if (load_from_file == 1){
png_init_io(png_ptr, fp);
} else {
/* set the read callback */
png_set_read_fn(png_ptr,(png_voidp)&inmem, libpng_userReadData);
}
png_set_sig_bytes(png_ptr, 8);
png_read_info(png_ptr, info_ptr);
width = png_get_image_width(png_ptr, info_ptr);
height = png_get_image_height(png_ptr, info_ptr);
color_type = png_get_color_type(png_ptr, info_ptr);
bit_depth = png_get_bit_depth(png_ptr, info_ptr);
/* get depth */
int depth = 0;
if (color_type == PNG_COLOR_TYPE_RGBA)
depth = 4;
else if (color_type == PNG_COLOR_TYPE_RGB)
depth = 3;
else if (color_type == PNG_COLOR_TYPE_GRAY)
{
if(bit_depth < 8)
{
png_set_expand_gray_1_2_4_to_8(png_ptr);
}
depth = 1;
}
else if (color_type == PNG_COLOR_TYPE_GA)
depth = 2;
else if (color_type == PNG_COLOR_TYPE_PALETTE)
{
depth = 3;
png_set_expand(png_ptr);
}
else
luaL_error(L, "[read_png_file] Unknown color space");
if(bit_depth < 8)
{
png_set_strip_16(png_ptr);
}
png_read_update_info(png_ptr, info_ptr);
/* read file */
if (setjmp(png_jmpbuf(png_ptr)))
luaL_error(L, "[read_png_file] Error during read_image: %s", errmsg.str);
/* alloc tensor */
THTensor *tensor = THTensor_(newWithSize3d)(depth, height, width);
real *tensor_data = THTensor_(data)(tensor);
/* alloc data in lib format */
row_pointers = (png_bytep*) malloc(sizeof(png_bytep) * height);
int y;
for (y=0; y<height; y++)
row_pointers[y] = (png_byte*) malloc(png_get_rowbytes(png_ptr,info_ptr));
/* read image in */
png_read_image(png_ptr, row_pointers);
/* convert image to dest tensor */
int x,k;
if ((bit_depth == 16) && (sizeof(real) > 1)) {
for (k=0; k<depth; k++) {
for (y=0; y<height; y++) {
png_byte* row = row_pointers[y];
for (x=0; x<width; x++) {
// PNG is big-endian
int val = ((int)row[(x*depth+k)*2] << 8) + row[(x*depth+k)*2+1];
*tensor_data++ = (real)val;
}
}
}
} else {
int stride = 1;
if (bit_depth == 16) {
/* PNG has 16 bit color depth, but the tensor type is byte. */
stride = 2;
}
for (k=0; k<depth; k++) {
for (y=0; y<height; y++) {
png_byte* row = row_pointers[y];
for (x=0; x<width; x++) {
*tensor_data++ = (real)row[(x*depth+k)*stride];
//png_byte val = row[x*depth+k];
//THTensor_(set3d)(tensor, k, y, x, (real)val);
}
}
}
}
/* cleanup heap allocation */
for (y=0; y<height; y++)
free(row_pointers[y]);
free(row_pointers);
/* cleanup png structs */
png_read_end(png_ptr, NULL);
png_destroy_read_struct(&png_ptr, &info_ptr, NULL);
/* done with file */
if (fp) {
fclose(fp);
}
/* return tensor */
luaT_pushudata(L, tensor, torch_Tensor);
if (bit_depth < 8) {
bit_depth = 8;
}
lua_pushnumber(L, bit_depth);
return 2;
}
