Add new functions to locallab - not used
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Desmis
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@ -298,6 +298,10 @@ public:
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void idirpyr(LabImage* data_coarse, LabImage* data_fine, int level, LUTf &rangefn_L, LUTf & nrwt_l, LUTf & nrwt_ab,
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int pitch, int scale, const int luma, const int chroma/*, LUTf & Lcurve, LUTf & abcurve*/);
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//locallab
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void normalize_mean_dt(float *data, const float *ref, size_t size);
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void retinex_pde(float *datain, float * dataout, int bfw, int bfh, float thresh, float multy, int numThreads);
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void fftw_convol_blur(float *input, float *output, int bfw, int bfh, float radius, int sigmafft);
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void MSRLocal(int sp, int lum, LabImage * bufreti, LabImage * bufmask, LabImage * buforig, LabImage * buforigmas, float** luminance, float** templ, const float* const *originalLuminance, const int width, const int height, const procparams::LocallabParams &loc, const int skip, const LocretigainCurve &locRETgainCcurve, const int chrome, const int scall, const float krad, float &minCD, float &maxCD, float &mini, float &maxi, float &Tmean, float &Tsigma, float &Tmin, float &Tmax,
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const LocCCmaskretiCurve & locccmasretiCurve, bool &lcmasretiutili, const LocLLmaskretiCurve & locllmasretiCurve, bool &llmasretiutili, const LocHHmaskretiCurve & lochhmasretiCurve, bool & lhmasretiutili, int llretiMask, LabImage * transformed, bool retiMasktmap, bool retiMask);
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void calc_ref(int sp, LabImage* original, LabImage* transformed, int cx, int cy, int oW, int oH, int sk, double &huerefblur, double &chromarefblur, double &lumarefblur, double &hueref, double &chromaref, double &lumaref, double &sobelref, float &avg);
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@ -3570,6 +3570,281 @@ void ImProcFunctions::calc_ref(int sp, LabImage * original, LabImage * transform
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}
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}
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static double *cos_table(size_t size)
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{
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double *table = NULL;
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double pi_size;
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size_t i;
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/* allocate the cosinus table */
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if (NULL == (table = (double *) malloc(sizeof(double) * size))) {
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fprintf(stderr, "allocation error\n");
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abort();
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}
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/*
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* fill the cosinus table,
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* table[i] = cos(i Pi / n) for i in [0..n[
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*/
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pi_size = rtengine::RT_PI / size;
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for (i = 0; i < size; i++)
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table[i] = cos(pi_size * i);
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return table;
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}
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static void mean_dt(const float *data, size_t size, double *mean_p, double *dt_p)
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{
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double mean, dt;
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const float *ptr_data;
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size_t i;
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mean = 0.;
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dt = 0.;
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ptr_data = data;
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for (i = 0; i < size; i++) {
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mean += *ptr_data;
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dt += (*ptr_data) * (*ptr_data);
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ptr_data++;
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}
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mean /= (double) size;
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dt /= (double) size;
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dt -= (mean * mean);
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dt = sqrt(dt);
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*mean_p = mean;
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*dt_p = dt;
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return;
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}
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void ImProcFunctions::normalize_mean_dt(float *data, const float *ref, size_t size)
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{
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double mean_ref, mean_data, dt_ref, dt_data;
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double a, b;
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size_t i;
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float *ptr_data;
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if (NULL == data || NULL == ref) {
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fprintf(stderr, "a pointer is NULL and should not be so\n");
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abort();
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}
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/* compute mean and variance of the two arrays */
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mean_dt(ref, size, &mean_ref, &dt_ref);
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mean_dt(data, size, &mean_data, &dt_data);
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/* compute the normalization coefficients */
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a = dt_ref / dt_data;
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b = mean_ref - a * mean_data;
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/* normalize the array */
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ptr_data = data;
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for (i = 0; i < size; i++) {
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*ptr_data = a * *ptr_data + b;
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ptr_data++;
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}
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return;
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}
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static float *retinex_poisson_dct(float *data, size_t nx, size_t ny, double m)
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{
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double *cosx = NULL, *cosy = NULL;
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size_t i;
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double m2;
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/*
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* get the cosinus tables
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* cosx[i] = cos(i Pi / nx) for i in [0..nx[
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* cosy[i] = cos(i Pi / ny) for i in [0..ny[
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*/
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cosx = cos_table(nx);
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cosy = cos_table(ny);
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/*
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* we will now multiply data[i, j] by
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* m / (4 - 2 * cosx[i] - 2 * cosy[j]))
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* and set data[0, 0] to 0
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*/
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m2 = m / 2.;
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/*
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* handle the first value, data[0, 0] = 0
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* after that, by construction, we always have
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* cosx[] + cosy[] != 2.
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*/
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data[0] = 0.;
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/*
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* continue with all the array:
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* i % nx is the position on the x axis (column number)
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* i / nx is the position on the y axis (row number)
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*/
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for (i = 1; i < nx * ny; i++)
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data[i] *= m2 / (2. - cosx[i % nx] - cosy[i / nx]);
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free(cosx);
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free(cosy);
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return data;
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}
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static float *discrete_laplacian_threshold(float *data_out, const float *data_in, size_t nx, size_t ny, float t)
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{
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size_t i, j;
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float *ptr_out;
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float diff;
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/* pointers to the current and neighbour values */
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const float *ptr_in, *ptr_in_xm1, *ptr_in_xp1, *ptr_in_ym1, *ptr_in_yp1;
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if (NULL == data_in || NULL == data_out) {
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fprintf(stderr, "a pointer is NULL and should not be so\n");
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abort();
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}
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/* pointers to the data and neighbour values */
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/*
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* y-1
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* x-1 ptr x+1
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* y+1
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* <---------------------nx------->
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*/
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ptr_in = data_in;
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ptr_in_xm1 = data_in - 1;
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ptr_in_xp1 = data_in + 1;
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ptr_in_ym1 = data_in - nx;
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ptr_in_yp1 = data_in + nx;
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ptr_out = data_out;
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for (j = 0; j < ny; j++) {
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for (i = 0; i < nx; i++) {
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*ptr_out = 0.;
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/* row differences */
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if (0 < i) {
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diff = *ptr_in - *ptr_in_xm1;
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if (fabs(diff) > t)
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*ptr_out += diff;
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}
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if (nx - 1 > i) {
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diff = *ptr_in - *ptr_in_xp1;
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if (fabs(diff) > t)
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*ptr_out += diff;
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}
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/* column differences */
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if (0 < j) {
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diff = *ptr_in - *ptr_in_ym1;
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if (fabs(diff) > t)
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*ptr_out += diff;
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}
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if (ny - 1 > j) {
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diff = *ptr_in - *ptr_in_yp1;
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if (fabs(diff) > t)
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*ptr_out += diff;
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}
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ptr_in++;
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ptr_in_xm1++;
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ptr_in_xp1++;
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ptr_in_ym1++;
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ptr_in_yp1++;
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ptr_out++;
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}
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}
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return data_out;
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}
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void ImProcFunctions::retinex_pde(float *datain, float * dataout, int bfw, int bfh, float thresh, float multy, int numThreads)
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{
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fftwf_plan dct_fw, dct_bw;
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float *data_fft, *data_tmp, *data;
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if (NULL == (data_tmp = (float *) fftwf_malloc(sizeof(float) * bfw * bfh))) {
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fprintf(stderr, "allocation error\n");
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abort();
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}
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(void) discrete_laplacian_threshold(data_tmp, datain, bfw, bfh, thresh /*0.05f * fabs(lp.ligh)*/);
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if (NULL == (data_fft = (float *) fftwf_malloc(sizeof(float) * bfw * bfh))) {
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fprintf(stderr, "allocation error\n");
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abort();
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}
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if (NULL == (data = (float *) fftwf_malloc(sizeof(float) * bfw * bfh))) {
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fprintf(stderr, "allocation error\n");
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abort();
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}
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dct_fw = fftwf_plan_r2r_2d(bfh, bfw, data_tmp, data_fft, FFTW_REDFT10, FFTW_REDFT10, FFTW_ESTIMATE | FFTW_DESTROY_INPUT);
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fftwf_execute(dct_fw);
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fftwf_free(data_tmp);
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/* solve the Poisson PDE in Fourier space */
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/* 1. / (float) (nx * ny)) is the DCT normalisation term, see libfftw */
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(void) retinex_poisson_dct(data_fft, bfw, bfh, 1./(double) (bfw * bfh));
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dct_bw = fftwf_plan_r2r_2d(bfh, bfw, data_fft, data, FFTW_REDFT01, FFTW_REDFT01, FFTW_ESTIMATE | FFTW_DESTROY_INPUT);
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fftwf_execute(dct_bw);
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fftwf_destroy_plan(dct_fw);
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fftwf_destroy_plan(dct_bw);
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fftwf_free(data_fft);
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fftwf_cleanup();
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normalize_mean_dt(data, datain, bfw * bfh);
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#ifdef _OPENMP
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#pragma omp parallel for
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#endif
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for (int y = 0; y < bfh ; y++) {
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for (int x = 0; x < bfw; x++) {
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dataout[y * bfw + x] = CLIPLOC(multy * data[y * bfw + x]);
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}
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}
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}
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void ImProcFunctions::fftw_convol_blur(float *input, float *output, int bfw, int bfh, float radius, int sigmafft)
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{
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float *out; // *outfft;
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fftwf_plan p;// ps;
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int image_size, image_sizechange;
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int i, j, index;
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double norm_x, norm_y;
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out = (float*) fftwf_malloc(sizeof(float) * (bfw * bfh));
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/*compute the Fourier transform of the input data*/
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p = fftwf_plan_r2r_2d(bfh, bfw, input, out, FFTW_REDFT10, FFTW_REDFT10,FFTW_ESTIMATE);
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fftwf_execute(p);
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fftwf_destroy_plan(p);
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/*define the gaussian constants for the convolution*/
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norm_x = rtengine::RT_PI/(double) bfw;
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norm_y = rtengine::RT_PI/(double) bfh;
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norm_x = norm_x * norm_x;
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norm_y = norm_y * norm_y;
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image_size = bfw * bfh;
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image_sizechange = 4 * image_size;
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for(j=0; j< bfh; j++){
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index = j * bfw;
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for(i=0; i< bfh; i++)
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out[i+index]*= exp((float)(-radius)*(norm_x * i * i + norm_y * j * j));
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}
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p = fftwf_plan_r2r_2d(bfh, bfw, out, output, FFTW_REDFT01, FFTW_REDFT01, FFTW_ESTIMATE);
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fftwf_execute(p);
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for(index=0; index < image_size; index++)
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output[index] /= image_sizechange;
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fftwf_destroy_plan(p);
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fftwf_free(out);
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}
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void ImProcFunctions::fftw_denoise(int GW, int GH, int max_numblox_W, int min_numblox_W, float **tmp1, array2D<float> *Lin, int numThreads, const struct local_params & lp, int chrom)
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{
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@ -881,16 +881,17 @@ void ImProcFunctions::MSRLocal(int sp, int lum, LabImage * bufreti, LabImage * b
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//to test on several images
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int nei = (int)(krad * loc.spots.at(sp).neigh);
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//several test to find good values ???!!!
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//very difficult to do because 4 factor are correlate with skip and cannot been solved
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//very difficult to do because 4 factor are correlate with skip and cannot been solved easily
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// size of spots
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// radius - neigh
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// scal
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// variance vart
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//not too bad proposition
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if (skip >= 4) {
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nei = (int)(nei / (1.5f * skip) + 2.f)/ sqrt(scal / 3.f); //not too bad
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nei = (int)(nei / (1.5f * skip) + 2.f)/ sqrt(scal / 3.f);
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vart *= skip;
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} else if (skip > 1 && skip < 4) {
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nei = (int)(nei / skip + 10.f) / sqrt(scal / 3.f);
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nei = (int)(nei / skip + 3.f) / sqrt(scal / 3.f);
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vart *= skip;
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}
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