/**
* im_scaleps:
* @in: input image
* @out: output image
*
* Scale a power spectrum. Transform with log10(1.0 + pow(x, 0.25)) + .5,
* then scale so max == 255.
*
* See also: im_scale().
*
* Returns: 0 on success, -1 on error
*/
int
im_scaleps( IMAGE *in, IMAGE *out )
{
IMAGE *t[4];
double mx;
double scale;
if( im_open_local_array( out, t, 4, "im_scaleps-1", "p" ) ||
im_max( in, &mx ) )
return( -1 );
if( mx <= 0.0 )
/* Range of zero: just return black.
*/
return( im_black( out, in->Xsize, in->Ysize, in->Bands ) );
scale = 255.0 / log10( 1.0 + pow( mx, .25 ) );
/* Transform!
*/
if( im_powtra( in, t[0], 0.25 ) ||
im_lintra( 1.0, t[0], 1.0, t[1] ) ||
im_log10tra( t[1], t[2] ) ||
im_lintra( scale, t[2], 0.0, t[3] ) ||
im_clip2fmt( t[3], out, IM_BANDFMT_UCHAR ) )
return( -1 );
return( 0 );
}
/* Call im_black via arg vector.
*/
static int
black_vec( im_object *argv )
{
int xs = *((int *) argv[1]);
int ys = *((int *) argv[2]);
int bands = *((int *) argv[3]);
return( im_black( argv[0], xs, ys, bands ) );
}
/**
* im_label_regions:
* @test: image to test
* @mask: write labelled regions here
* @segments: return number of regions here
*
* im_label_regions() repeatedly scans @test for regions of 4-connected pixels
* with the same pixel value. Every time a region is discovered, those
* pixels are marked in @mask with a unique serial number. Once all pixels
* have been labelled, the operation returns, setting @segments to the number
* of discrete regions which were detected.
*
* @mask is always a 1-band %IM_BANDFMT_UINT image of the same dimensions as
* @test.
*
* This operation is useful for, for example, blob counting. You can use the
* morphological operators to detect and isolate a series of objects, then use
* im_label_regions() to number them all.
*
* Use im_histindexed() to (for example) find blob coordinates.
*
* See also: im_histindexed()
*
* Returns: 0 on success, -1 on error.
*/
int
im_label_regions( IMAGE *test, IMAGE *mask, int *segments )
{
IMAGE *t[2];
int serial;
int *m;
int x, y;
/* Create the zero mask image.
*/
if( im_open_local_array( mask, t, 2, "im_label_regions", "p" ) ||
im_black( t[0], test->Xsize, test->Ysize, 1 ) ||
im_clip2fmt( t[0], t[1], IM_BANDFMT_INT ) )
return( -1 );
/* Search the mask image, flooding as we find zero pixels.
*/
if( im_rwcheck( t[1] ) )
return( -1 );
serial = 0;
m = (int *) t[1]->data;
for( y = 0; y < test->Ysize; y++ ) {
for( x = 0; x < test->Xsize; x++ ) {
if( !m[x] ) {
/*
if( im_flood_other_old( t[1], test,
x, y, serial ) )
*/
if( im_flood_other( test, t[1],
x, y, serial, NULL ) )
// if( im_flood_other_old( t[1], test,
// x, y, serial ) )
return( -1 );
serial += 1;
}
}
m += test->Xsize;
}
/* Copy result to mask.
*/
if( im_copy( t[1], mask ) )
return( -1 );
if( segments )
*segments = serial;
return( 0 );
}
/**
* im_zerox:
* @in: input image
* @out: output image
* @sign: detect positive or negative zero crossings
*
* im_zerox() detects the positive or negative zero crossings @in,
* depending on @sign. If @sign is -1, negative zero crossings are returned,
* if @sign is 1, positive zero crossings are returned.
*
* The output image is byte with zero crossing set to 255 and all other values
* set to zero. Input can have any number of channels, and be any non-complex
* type.
*
* See also: im_conv(), im_rot90.
*
* Returns: 0 on success, -1 on error
*/
int
im_zerox( IMAGE *in, IMAGE *out, int sign )
{
IMAGE *t1;
if( sign != -1 && sign != 1 ) {
im_error( "im_zerox", "%s", _( "flag not -1 or 1" ) );
return( -1 );
}
if( in->Xsize < 2 ) {
im_error( "im_zerox", "%s", _( "image too narrow" ) );
return( -1 );
}
if( !(t1 = im_open_local( out, "im_zerox" , "p" )) ||
im_piocheck( in, t1 ) ||
im_check_uncoded( "im_zerox", in ) ||
im_check_noncomplex( "im_zerox", in ) )
return( -1 );
if( vips_bandfmt_isuint( in->BandFmt ) )
/* Unsigned type, therefore there will be no zero-crossings.
*/
return( im_black( out, in->Xsize, in->Ysize, in->Bands ) );
/* Force output to be BYTE. Output is narrower than input by 1 pixel.
*/
if( im_cp_desc( t1, in ) )
return( -1 );
t1->BandFmt = IM_BANDFMT_UCHAR;
t1->Xsize -= 1;
/* Set hints - THINSTRIP is ok with us.
*/
if( im_demand_hint( t1, IM_THINSTRIP, NULL ) )
return( -1 );
/* Generate image.
*/
if( im_generate( t1, im_start_one, zerox_gen, im_stop_one,
in, GINT_TO_POINTER( sign ) ) )
return( -1 );
/* Now embed it in a larger image.
*/
if( im_embed( t1, out, 0, 0, 0, in->Xsize, in->Ysize ) )
return( -1 );
return( 0 );
}
/* Find stats on an area of an IMAGE ... consider only pixels for which the
* mask is true.
*/
static DOUBLEMASK *
find_image_stats( IMAGE *in, IMAGE *mask, Rect *area )
{
DOUBLEMASK *stats;
IMAGE *t[4];
gint64 count;
/* Extract area, build black image, mask out pixels we want.
*/
if( im_open_local_array( in, t, 4, "find_image_stats", "p" ) ||
extract_rect( in, t[0], area ) ||
im_black( t[1], t[0]->Xsize, t[0]->Ysize, t[0]->Bands ) ||
im_clip2fmt( t[1], t[2], t[0]->BandFmt ) ||
im_ifthenelse( mask, t[0], t[2], t[3] ) )
return( NULL );
/* Get stats from masked image.
*/
if( !(stats = local_mask( in, im_stats( t[3] ) )) )
return( NULL );
/* Number of non-zero pixels in mask.
*/
if( count_nonzero( mask, &count ) )
return( NULL );
/* And scale masked average to match.
*/
stats->coeff[4] *= (double) count /
((double) mask->Xsize * mask->Ysize);
/* Yuk! Zap the deviation column with the pixel count. Used later to
* determine if this is likely to be a significant overlap.
*/
stats->coeff[5] = count;
#ifdef DEBUG
if( count == 0 )
im_warn( "global_balance", _( "empty overlap!" ) );
#endif /*DEBUG*/
return( stats );
}
/* Transform a 1 band image with vips's built-in fft routine.
*/
static int
fwfft1( IMAGE *dummy, IMAGE *in, IMAGE *out )
{
int size = in->Xsize * in->Ysize;
int bpx = im_ispoweroftwo( in->Xsize );
int bpy = im_ispoweroftwo( in->Ysize );
float *buf, *q, *p1, *p2;
int x, y;
/* Buffers for real and imaginary parts.
*/
IMAGE *real = im_open_local( dummy, "fwfft1:1", "t" );
IMAGE *imag = im_open_local( dummy, "fwfft1:2", "t" );
/* Temporaries.
*/
IMAGE *t1 = im_open_local( dummy, "fwfft1:3", "p" );
if( !real || !imag || !t1 )
return( -1 );
if( im_pincheck( in ) || im_outcheck( out ) )
return( -1 );
if( in->Coding != IM_CODING_NONE || in->Bands != 1 ||
im_iscomplex( in ) ) {
im_error( "im_fwfft",
_( "one band non-complex uncoded only" ) );
return( -1 );
}
if( !bpx || !bpy ) {
im_error( "im_fwfft", _( "sides must be power of 2" ) );
return( -1 );
}
/* Make sure we have a float input image.
*/
if( im_clip2f( in, real ) )
return( -1 );
/* Make a buffer of 0 floats of the same size for the imaginary part.
*/
if( im_black( t1, in->Xsize, in->Ysize, 1 ) )
return( -1 );
if( im_clip2f( t1, imag ) )
return( -1 );
/* Transform!
*/
if( im__fft_sp( (float *) real->data, (float *) imag->data,
bpx - 1, bpy - 1 ) ) {
im_error( "im_fwfft", _( "fft_sp failed" ) );
return( -1 );
}
/* WIO to out.
*/
if( im_cp_desc( out, in ) )
return( -1 );
out->Bbits = IM_BBITS_COMPLEX;
out->BandFmt = IM_BANDFMT_COMPLEX;
if( im_setupout( out ) )
return( -1 );
if( !(buf = (float *) IM_ARRAY( dummy,
IM_IMAGE_SIZEOF_LINE( out ), PEL )) )
return( -1 );
/* Gather together real and imag parts. We have to normalise output!
*/
for( p1 = (float *) real->data, p2 = (float *) imag->data,
y = 0; y < out->Ysize; y++ ) {
q = buf;
for( x = 0; x < out->Xsize; x++ ) {
q[0] = *p1++ / size;
q[1] = *p2++ / size;
q += 2;
}
if( im_writeline( y, out, (PEL *) buf ) )
return( -1 );
}
return( 0 );
}
/* The main part of the benchmark ... transform labq to labq. Chain several of
* these together to get a CPU-bound operation.
*/
static int
benchmark( IMAGE *in, IMAGE *out )
{
IMAGE *t[18];
double one[3] = { 1.0, 1.0, 1.0 };
double zero[3] = { 0.0, 0.0, 0.0 };
double darken[3] = { 1.0 / 1.18, 1.0, 1.0 };
double whitepoint[3] = { 1.06, 1.0, 1.01 };
double shadow[3] = { -2, 0, 0 };
double white[3] = { 100, 0, 0 };
DOUBLEMASK *d652d50 = im_create_dmaskv( "d652d50", 3, 3,
1.13529, -0.0604663, -0.0606321,
0.0975399, 0.935024, -0.0256156,
-0.0336428, 0.0414702, 0.994135 );
im_add_close_callback( out,
(im_callback_fn) im_free_dmask, d652d50, NULL );
return(
/* Set of descriptors for this operation.
*/
im_open_local_array( out, t, 18, "im_benchmark", "p" ) ||
/* Unpack to float.
*/
im_LabQ2Lab( in, t[0] ) ||
/* Crop 100 pixels off all edges.
*/
im_extract_area( t[0], t[1],
100, 100, t[0]->Xsize - 200, t[0]->Ysize - 200 ) ||
/* Shrink by 10%, bilinear interp.
*/
im_affinei_all( t[1], t[2],
vips_interpolate_bilinear_static(),
0.9, 0, 0, 0.9,
0, 0 ) ||
/* Find L ~= 100 areas (white surround).
*/
im_extract_band( t[2], t[3], 0 ) ||
im_moreconst( t[3], t[4], 99 ) ||
/* Adjust white point and shadows.
*/
im_lintra_vec( 3, darken, t[2], zero, t[5] ) ||
im_Lab2XYZ( t[5], t[6] ) ||
im_recomb( t[6], t[7], d652d50 ) ||
im_lintra_vec( 3, whitepoint, t[7], zero, t[8] ) ||
im_lintra( 1.5, t[8], 0.0, t[9] ) ||
im_XYZ2Lab( t[9], t[10] ) ||
im_lintra_vec( 3, one, t[10], shadow, t[11] ) ||
/* Make a solid white image.
*/
im_black( t[12], t[4]->Xsize, t[4]->Ysize, 3 ) ||
im_lintra_vec( 3, zero, t[12], white, t[13] ) ||
/* Reattach border.
*/
im_ifthenelse( t[4], t[13], t[11], t[14] ) ||
/* Sharpen.
*/
im_Lab2LabQ( t[14], t[15] ) ||
im_sharpen( t[15], out, 11, 2.5, 40, 20, 0.5, 1.5 )
);
}
