int main(int argc, char *argv[])
{
struct Map_info In, Out;
static struct line_pnts *Points;
struct line_cats *Cats;
struct GModule *module; /* GRASS module for parsing arguments */
struct Option *map_in, *map_out;
struct Option *cat_opt, *field_opt, *where_opt, *abcol, *afcol;
struct Option *iter_opt, *error_opt;
struct Flag *geo_f, *add_f;
int chcat, with_z;
int layer, mask_type;
struct varray *varray;
dglGraph_s *graph;
int i, geo, nnodes, nlines, j, max_cat;
char buf[2000], *covered;
/* initialize GIS environment */
G_gisinit(argv[0]); /* reads grass env, stores program name to G_program_name() */
/* initialize module */
module = G_define_module();
module->keywords = _("vector, network, centrality measures");
module->description =
_("Computes degree, centrality, betweeness, closeness and eigenvector "
"centrality measures in the network.");
/* Define the different options as defined in gis.h */
map_in = G_define_standard_option(G_OPT_V_INPUT);
field_opt = G_define_standard_option(G_OPT_V_FIELD);
map_out = G_define_standard_option(G_OPT_V_OUTPUT);
cat_opt = G_define_standard_option(G_OPT_V_CATS);
cat_opt->guisection = _("Selection");
where_opt = G_define_standard_option(G_OPT_WHERE);
where_opt->guisection = _("Selection");
afcol = G_define_standard_option(G_OPT_COLUMN);
afcol->key = "afcolumn";
afcol->required = NO;
afcol->description =
_("Name of arc forward/both direction(s) cost column");
afcol->guisection = _("Cost");
abcol = G_define_standard_option(G_OPT_COLUMN);
abcol->key = "abcolumn";
abcol->required = NO;
abcol->description = _("Name of arc backward direction cost column");
abcol->guisection = _("Cost");
deg_opt = G_define_standard_option(G_OPT_COLUMN);
deg_opt->key = "degree";
deg_opt->required = NO;
deg_opt->description = _("Name of degree centrality column");
deg_opt->guisection = _("Columns");
close_opt = G_define_standard_option(G_OPT_COLUMN);
close_opt->key = "closeness";
close_opt->required = NO;
close_opt->description = _("Name of closeness centrality column");
close_opt->guisection = _("Columns");
betw_opt = G_define_standard_option(G_OPT_COLUMN);
betw_opt->key = "betweenness";
betw_opt->required = NO;
betw_opt->description = _("Name of betweenness centrality column");
betw_opt->guisection = _("Columns");
eigen_opt = G_define_standard_option(G_OPT_COLUMN);
eigen_opt->key = "eigenvector";
eigen_opt->required = NO;
eigen_opt->description = _("Name of eigenvector centrality column");
eigen_opt->guisection = _("Columns");
iter_opt = G_define_option();
iter_opt->key = "iterations";
iter_opt->answer = "1000";
iter_opt->type = TYPE_INTEGER;
iter_opt->required = NO;
iter_opt->description =
_("Maximum number of iterations to compute eigenvector centrality");
error_opt = G_define_option();
error_opt->key = "error";
error_opt->answer = "0.1";
error_opt->type = TYPE_DOUBLE;
error_opt->required = NO;
error_opt->description =
_("Cummulative error tolerance for eigenvector centrality");
geo_f = G_define_flag();
geo_f->key = 'g';
geo_f->description =
_("Use geodesic calculation for longitude-latitude locations");
add_f = G_define_flag();
add_f->key = 'a';
add_f->description = _("Add points on nodes");
/* options and flags parser */
if (G_parser(argc, argv))
exit(EXIT_FAILURE);
/* TODO: make an option for this */
mask_type = GV_LINE | GV_BOUNDARY;
Points = Vect_new_line_struct();
Cats = Vect_new_cats_struct();
Vect_check_input_output_name(map_in->answer, map_out->answer,
GV_FATAL_EXIT);
Vect_set_open_level(2);
if (1 > Vect_open_old(&In, map_in->answer, ""))
G_fatal_error(_("Unable to open vector map <%s>"), map_in->answer);
with_z = Vect_is_3d(&In);
if (0 > Vect_open_new(&Out, map_out->answer, with_z)) {
Vect_close(&In);
G_fatal_error(_("Unable to create vector map <%s>"), map_out->answer);
}
if (geo_f->answer) {
geo = 1;
if (G_projection() != PROJECTION_LL)
G_warning(_("The current projection is not longitude-latitude"));
}
else
geo = 0;
/* parse filter option and select appropriate lines */
layer = atoi(field_opt->answer);
chcat =
(NetA_initialise_varray
(&In, layer, mask_type, where_opt->answer, cat_opt->answer,
&varray) == 1);
/* Create table */
Fi = Vect_default_field_info(&Out, 1, NULL, GV_1TABLE);
Vect_map_add_dblink(&Out, 1, NULL, Fi->table, "cat", Fi->database,
Fi->driver);
db_init_string(&sql);
driver = db_start_driver_open_database(Fi->driver, Fi->database);
if (driver == NULL)
G_fatal_error(_("Unable to open database <%s> by driver <%s>"),
Fi->database, Fi->driver);
db_init_string(&tmp);
if (deg_opt->answer)
append_string(&tmp, deg_opt->answer);
if (close_opt->answer)
append_string(&tmp, close_opt->answer);
if (betw_opt->answer)
append_string(&tmp, betw_opt->answer);
if (eigen_opt->answer)
append_string(&tmp, eigen_opt->answer);
sprintf(buf,
"create table %s(cat integer%s)", Fi->table, db_get_string(&tmp));
db_set_string(&sql, buf);
G_debug(2, db_get_string(&sql));
if (db_execute_immediate(driver, &sql) != DB_OK) {
db_close_database_shutdown_driver(driver);
G_fatal_error(_("Unable to create table: '%s'"), db_get_string(&sql));
}
if (db_create_index2(driver, Fi->table, "cat") != DB_OK)
G_warning(_("Cannot create index"));
if (db_grant_on_table
(driver, Fi->table, DB_PRIV_SELECT, DB_GROUP | DB_PUBLIC) != DB_OK)
G_fatal_error(_("Cannot grant privileges on table <%s>"), Fi->table);
db_begin_transaction(driver);
Vect_copy_head_data(&In, &Out);
Vect_hist_copy(&In, &Out);
Vect_hist_command(&Out);
Vect_net_build_graph(&In, mask_type, atoi(field_opt->answer), 0,
afcol->answer, abcol->answer, NULL, geo, 0);
graph = &(In.graph);
nnodes = dglGet_NodeCount(graph);
deg = closeness = betw = eigen = NULL;
covered = (char *)G_calloc(nnodes + 1, sizeof(char));
if (!covered)
G_fatal_error(_("Out of memory"));
if (deg_opt->answer) {
deg = (double *)G_calloc(nnodes + 1, sizeof(double));
if (!deg)
G_fatal_error(_("Out of memory"));
}
if (close_opt->answer) {
closeness = (double *)G_calloc(nnodes + 1, sizeof(double));
if (!closeness)
G_fatal_error(_("Out of memory"));
}
if (betw_opt->answer) {
betw = (double *)G_calloc(nnodes + 1, sizeof(double));
if (!betw)
G_fatal_error(_("Out of memory"));
}
if (eigen_opt->answer) {
eigen = (double *)G_calloc(nnodes + 1, sizeof(double));
if (!eigen)
G_fatal_error(_("Out of memory"));
}
if (deg_opt->answer) {
G_message(_("Computing degree centrality measure"));
NetA_degree_centrality(graph, deg);
}
if (betw_opt->answer || close_opt->answer) {
G_message(_("Computing betweenness and/or closeness centrality measure"));
NetA_betweenness_closeness(graph, betw, closeness);
if (closeness)
for (i = 1; i <= nnodes; i++)
closeness[i] /= (double)In.cost_multip;
}
if (eigen_opt->answer) {
G_message(_("Computing eigenvector centrality measure"));
NetA_eigenvector_centrality(graph, atoi(iter_opt->answer),
atof(error_opt->answer), eigen);
}
nlines = Vect_get_num_lines(&In);
G_message(_("Writing data into the table..."));
G_percent_reset();
for (i = 1; i <= nlines; i++) {
G_percent(i, nlines, 1);
int type = Vect_read_line(&In, Points, Cats, i);
if (type == GV_POINT && (!chcat || varray->c[i])) {
int cat, node;
if (!Vect_cat_get(Cats, layer, &cat))
continue;
Vect_reset_cats(Cats);
Vect_cat_set(Cats, 1, cat);
Vect_write_line(&Out, type, Points, Cats);
Vect_get_line_nodes(&In, i, &node, NULL);
process_node(node, cat);
covered[node] = 1;
}
}
if (add_f->answer && !chcat) {
max_cat = 0;
for (i = 1; i <= nlines; i++) {
Vect_read_line(&In, NULL, Cats, i);
for (j = 0; j < Cats->n_cats; j++)
if (Cats->cat[j] > max_cat)
max_cat = Cats->cat[j];
}
max_cat++;
for (i = 1; i <= nnodes; i++)
if (!covered[i]) {
Vect_reset_cats(Cats);
Vect_cat_set(Cats, 1, max_cat);
NetA_add_point_on_node(&In, &Out, i, Cats);
process_node(i, max_cat);
max_cat++;
}
}
db_commit_transaction(driver);
db_close_database_shutdown_driver(driver);
G_free(covered);
if (deg)
G_free(deg);
if (closeness)
G_free(closeness);
if (betw)
G_free(betw);
if (eigen)
G_free(eigen);
Vect_build(&Out);
Vect_close(&In);
Vect_close(&Out);
exit(EXIT_SUCCESS);
}
int remove_small_clumps(struct globals *globals)
{
int row, col, i;
struct NB_TREE *nbtree;
int this_id;
struct ngbr_stats *Rbest;
int best_n_id, best_n_row, best_n_col;
int reg_size;
CELL *renumber, n_regions, min_rid;
/* two possible modes
* best (most similar) neighbor
* neighbor with longest shared boundary
*/
if (globals->min_segment_size < 2)
return 0;
G_message(_("Merging segments smaller than %d cells..."), globals->min_segment_size);
/* init renumber */
renumber = (CELL *) G_malloc(sizeof(CELL) * (globals->max_rid + 1));
for (i = 0; i <= globals->max_rid; i++)
renumber[i] = 0;
/* clear candidate flag */
flag_clear_all(globals->candidate_flag);
min_rid = globals->max_rid;
/* Set candidate flag to true/1 for all non-NULL cells */
for (row = globals->row_min; row < globals->row_max; row++) {
for (col = globals->col_min; col < globals->col_max; col++) {
if (!(FLAG_GET(globals->null_flag, row, col))) {
FLAG_SET(globals->candidate_flag, row, col);
Segment_get(&globals->rid_seg, (void *) &this_id, row, col);
/* renumber is region size */
if (renumber[this_id] <= globals->min_segment_size) {
renumber[this_id]++;
if (min_rid > this_id)
min_rid = this_id;
}
}
}
}
min_rid--;
nbtree = nbtree_create(globals->nbands, globals->datasize);
/* go through all cells */
G_percent_reset();
for (row = globals->row_min; row < globals->row_max; row++) {
G_percent(row - globals->row_min,
globals->row_max - globals->row_min, 2);
for (col = globals->col_min; col < globals->col_max; col++) {
if ((FLAG_GET(globals->null_flag, row, col)))
continue;
if (!(FLAG_GET(globals->candidate_flag, row, col)))
continue;
/* get this ID */
Segment_get(&globals->rid_seg, (void *) &this_id, row, col);
reg_size = renumber[this_id];
best_n_id = 1;
while (reg_size < globals->min_segment_size && best_n_id > 0) {
Rbest = NULL;
reg_size = 1;
best_n_row = best_n_col = -1;
best_n_id = find_best_neighbour(globals, row, col, this_id,
nbtree, ®_size, &Rbest,
&best_n_row, &best_n_col);
/* Rbest: pointer to most common neighbour
* best_n_id, best_n_[row|col]: most similar neighbour */
if (reg_size < globals->min_segment_size && best_n_id > 0) {
/* update rid */
update_rid(globals, row, col, best_n_id);
/* mark as merged */
renumber[best_n_id] += renumber[this_id];
reg_size = renumber[best_n_id];
renumber[this_id] = 0;
this_id = best_n_id;
}
}
}
}
G_percent(1, 1, 1);
n_regions = 0;
/* renumber becomes new region ID */
for (i = 1; i <= globals->max_rid; i++) {
if (renumber[i] > 0) {
renumber[i] = ++n_regions;
}
}
G_message(_("Renumbering remaining %d segments..."), n_regions);
for (row = globals->row_min; row < globals->row_max; row++) {
G_percent(row - globals->row_min,
globals->row_max - globals->row_min, 4);
for (col = globals->col_min; col < globals->col_max; col++) {
if ((FLAG_GET(globals->null_flag, row, col)))
continue;
/* get this ID */
Segment_get(&globals->rid_seg, (void *) &this_id, row, col);
if (Rast_is_c_null_value(&this_id) || this_id < 1)
continue;
this_id = renumber[this_id] + min_rid;
Segment_put(&globals->rid_seg, (void *) &this_id, row, col);
}
}
G_percent(1, 1, 1);
globals->max_rid = n_regions + min_rid;
G_free(renumber);
nbtree_clear(nbtree);
return 1;
}
int main(int argc, char *argv[])
{
struct Map_info In, Out, Error;
struct line_pnts *Points;
struct line_cats *Cats;
int i, type, iter;
struct GModule *module; /* GRASS module for parsing arguments */
struct Option *map_in, *map_out, *error_out, *thresh_opt, *method_opt,
*look_ahead_opt;
struct Option *iterations_opt, *cat_opt, *alpha_opt, *beta_opt, *type_opt;
struct Option *field_opt, *where_opt, *reduction_opt, *slide_opt;
struct Option *angle_thresh_opt, *degree_thresh_opt,
*closeness_thresh_opt;
struct Option *betweeness_thresh_opt;
struct Flag *notab_flag, *loop_support_flag;
int with_z;
int total_input, total_output; /* Number of points in the input/output map respectively */
double thresh, alpha, beta, reduction, slide, angle_thresh;
double degree_thresh, closeness_thresh, betweeness_thresh;
int method;
int look_ahead, iterations;
int loop_support;
int layer;
int n_lines;
int simplification, mask_type;
struct cat_list *cat_list = NULL;
char *s, *descriptions;
/* initialize GIS environment */
G_gisinit(argv[0]); /* reads grass env, stores program name to G_program_name() */
/* initialize module */
module = G_define_module();
G_add_keyword(_("vector"));
G_add_keyword(_("generalization"));
G_add_keyword(_("simplification"));
G_add_keyword(_("smoothing"));
G_add_keyword(_("displacement"));
G_add_keyword(_("network generalization"));
module->description = _("Performs vector based generalization.");
/* Define the different options as defined in gis.h */
map_in = G_define_standard_option(G_OPT_V_INPUT);
field_opt = G_define_standard_option(G_OPT_V_FIELD_ALL);
type_opt = G_define_standard_option(G_OPT_V_TYPE);
type_opt->options = "line,boundary,area";
type_opt->answer = "line,boundary,area";
type_opt->guisection = _("Selection");
map_out = G_define_standard_option(G_OPT_V_OUTPUT);
error_out = G_define_standard_option(G_OPT_V_OUTPUT);
error_out->key = "error";
error_out->required = NO;
error_out->description =
_("Error map of all lines and boundaries not being generalized due to topology issues or over-simplification");
method_opt = G_define_option();
method_opt->key = "method";
method_opt->type = TYPE_STRING;
method_opt->required = YES;
method_opt->multiple = NO;
method_opt->options =
"douglas,douglas_reduction,lang,reduction,reumann,boyle,sliding_averaging,distance_weighting,chaiken,hermite,snakes,network,displacement";
descriptions = NULL;
G_asprintf(&descriptions,
"douglas;%s;"
"douglas_reduction;%s;"
"lang;%s;"
"reduction;%s;"
"reumann;%s;"
"boyle;%s;"
"sliding_averaging;%s;"
"distance_weighting;%s;"
"chaiken;%s;"
"hermite;%s;"
"snakes;%s;"
"network;%s;"
"displacement;%s;",
_("Douglas-Peucker Algorithm"),
_("Douglas-Peucker Algorithm with reduction parameter"),
_("Lang Simplification Algorithm"),
_("Vertex Reduction Algorithm eliminates points close to each other"),
_("Reumann-Witkam Algorithm"),
_("Boyle's Forward-Looking Algorithm"),
_("McMaster's Sliding Averaging Algorithm"),
_("McMaster's Distance-Weighting Algorithm"),
_("Chaiken's Algorithm"),
_("Interpolation by Cubic Hermite Splines"),
_("Snakes method for line smoothing"),
_("Network generalization"),
_("Displacement of lines close to each other"));
method_opt->descriptions = G_store(descriptions);
method_opt->description = _("Generalization algorithm");
thresh_opt = G_define_option();
thresh_opt->key = "threshold";
thresh_opt->type = TYPE_DOUBLE;
thresh_opt->required = YES;
thresh_opt->options = "0-1000000000";
thresh_opt->description = _("Maximal tolerance value");
look_ahead_opt = G_define_option();
look_ahead_opt->key = "look_ahead";
look_ahead_opt->type = TYPE_INTEGER;
look_ahead_opt->required = NO;
look_ahead_opt->answer = "7";
look_ahead_opt->description = _("Look-ahead parameter");
reduction_opt = G_define_option();
reduction_opt->key = "reduction";
reduction_opt->type = TYPE_DOUBLE;
reduction_opt->required = NO;
reduction_opt->answer = "50";
reduction_opt->options = "0-100";
reduction_opt->description =
_("Percentage of the points in the output of 'douglas_reduction' algorithm");
slide_opt = G_define_option();
slide_opt->key = "slide";
slide_opt->type = TYPE_DOUBLE;
slide_opt->required = NO;
slide_opt->answer = "0.5";
slide_opt->options = "0-1";
slide_opt->description =
_("Slide of computed point toward the original point");
angle_thresh_opt = G_define_option();
angle_thresh_opt->key = "angle_thresh";
angle_thresh_opt->type = TYPE_DOUBLE;
angle_thresh_opt->required = NO;
angle_thresh_opt->answer = "3";
angle_thresh_opt->options = "0-180";
angle_thresh_opt->description =
_("Minimum angle between two consecutive segments in Hermite method");
degree_thresh_opt = G_define_option();
degree_thresh_opt->key = "degree_thresh";
degree_thresh_opt->type = TYPE_INTEGER;
degree_thresh_opt->required = NO;
degree_thresh_opt->answer = "0";
degree_thresh_opt->description =
_("Degree threshold in network generalization");
closeness_thresh_opt = G_define_option();
closeness_thresh_opt->key = "closeness_thresh";
closeness_thresh_opt->type = TYPE_DOUBLE;
closeness_thresh_opt->required = NO;
closeness_thresh_opt->answer = "0";
closeness_thresh_opt->options = "0-1";
closeness_thresh_opt->description =
_("Closeness threshold in network generalization");
betweeness_thresh_opt = G_define_option();
betweeness_thresh_opt->key = "betweeness_thresh";
betweeness_thresh_opt->type = TYPE_DOUBLE;
betweeness_thresh_opt->required = NO;
betweeness_thresh_opt->answer = "0";
betweeness_thresh_opt->description =
_("Betweeness threshold in network generalization");
alpha_opt = G_define_option();
alpha_opt->key = "alpha";
alpha_opt->type = TYPE_DOUBLE;
alpha_opt->required = NO;
alpha_opt->answer = "1.0";
alpha_opt->description = _("Snakes alpha parameter");
beta_opt = G_define_option();
beta_opt->key = "beta";
beta_opt->type = TYPE_DOUBLE;
beta_opt->required = NO;
beta_opt->answer = "1.0";
beta_opt->description = _("Snakes beta parameter");
iterations_opt = G_define_option();
iterations_opt->key = "iterations";
iterations_opt->type = TYPE_INTEGER;
iterations_opt->required = NO;
iterations_opt->answer = "1";
iterations_opt->description = _("Number of iterations");
cat_opt = G_define_standard_option(G_OPT_V_CATS);
cat_opt->guisection = _("Selection");
where_opt = G_define_standard_option(G_OPT_DB_WHERE);
where_opt->guisection = _("Selection");
loop_support_flag = G_define_flag();
loop_support_flag->key = 'l';
loop_support_flag->label = _("Disable loop support");
loop_support_flag->description = _("Do not modify end points of lines forming a closed loop");
notab_flag = G_define_standard_flag(G_FLG_V_TABLE);
notab_flag->description = _("Do not copy attributes");
notab_flag->guisection = _("Attributes");
/* options and flags parser */
if (G_parser(argc, argv))
exit(EXIT_FAILURE);
thresh = atof(thresh_opt->answer);
look_ahead = atoi(look_ahead_opt->answer);
alpha = atof(alpha_opt->answer);
beta = atof(beta_opt->answer);
reduction = atof(reduction_opt->answer);
iterations = atoi(iterations_opt->answer);
slide = atof(slide_opt->answer);
angle_thresh = atof(angle_thresh_opt->answer);
degree_thresh = atof(degree_thresh_opt->answer);
closeness_thresh = atof(closeness_thresh_opt->answer);
betweeness_thresh = atof(betweeness_thresh_opt->answer);
mask_type = type_mask(type_opt);
G_debug(3, "Method: %s", method_opt->answer);
s = method_opt->answer;
if (strcmp(s, "douglas") == 0)
method = DOUGLAS;
else if (strcmp(s, "lang") == 0)
method = LANG;
else if (strcmp(s, "reduction") == 0)
method = VERTEX_REDUCTION;
else if (strcmp(s, "reumann") == 0)
method = REUMANN;
else if (strcmp(s, "boyle") == 0)
method = BOYLE;
else if (strcmp(s, "distance_weighting") == 0)
method = DISTANCE_WEIGHTING;
else if (strcmp(s, "chaiken") == 0)
method = CHAIKEN;
else if (strcmp(s, "hermite") == 0)
method = HERMITE;
else if (strcmp(s, "snakes") == 0)
method = SNAKES;
else if (strcmp(s, "douglas_reduction") == 0)
method = DOUGLAS_REDUCTION;
else if (strcmp(s, "sliding_averaging") == 0)
method = SLIDING_AVERAGING;
else if (strcmp(s, "network") == 0)
method = NETWORK;
else if (strcmp(s, "displacement") == 0) {
method = DISPLACEMENT;
/* we can displace only the lines */
mask_type = GV_LINE;
}
else {
G_fatal_error(_("Unknown method"));
exit(EXIT_FAILURE);
}
/* simplification or smoothing? */
switch (method) {
case DOUGLAS:
case DOUGLAS_REDUCTION:
case LANG:
case VERTEX_REDUCTION:
case REUMANN:
simplification = 1;
break;
default:
simplification = 0;
break;
}
Points = Vect_new_line_struct();
Cats = Vect_new_cats_struct();
Vect_check_input_output_name(map_in->answer, map_out->answer,
G_FATAL_EXIT);
Vect_set_open_level(2);
if (Vect_open_old2(&In, map_in->answer, "", field_opt->answer) < 1)
G_fatal_error(_("Unable to open vector map <%s>"), map_in->answer);
if (Vect_get_num_primitives(&In, mask_type) == 0) {
G_warning(_("No lines found in input map <%s>"), map_in->answer);
Vect_close(&In);
exit(EXIT_SUCCESS);
}
with_z = Vect_is_3d(&In);
if (0 > Vect_open_new(&Out, map_out->answer, with_z)) {
Vect_close(&In);
G_fatal_error(_("Unable to create vector map <%s>"), map_out->answer);
}
if (error_out->answer) {
if (0 > Vect_open_new(&Error, error_out->answer, with_z)) {
Vect_close(&In);
G_fatal_error(_("Unable to create error vector map <%s>"), error_out->answer);
}
}
Vect_copy_head_data(&In, &Out);
Vect_hist_copy(&In, &Out);
Vect_hist_command(&Out);
total_input = total_output = 0;
layer = Vect_get_field_number(&In, field_opt->answer);
/* parse filter options */
if (layer > 0)
cat_list = Vect_cats_set_constraint(&In, layer,
where_opt->answer, cat_opt->answer);
if (method == DISPLACEMENT) {
/* modifies only lines, all other features including boundaries are preserved */
/* options where, cats, and layer are respected */
G_message(_("Displacement..."));
snakes_displacement(&In, &Out, thresh, alpha, beta, 1.0, 10.0,
iterations, cat_list, layer);
}
/* TODO: rearrange code below. It's really messy */
if (method == NETWORK) {
/* extracts lines of selected type, all other features are discarded */
/* options where, cats, and layer are ignored */
G_message(_("Network generalization..."));
total_output =
graph_generalization(&In, &Out, mask_type, degree_thresh,
closeness_thresh, betweeness_thresh);
}
/* copy tables here because method == NETWORK is complete and
* tables for Out may be needed for parse_filter_options() below */
if (!notab_flag->answer) {
if (method == NETWORK)
copy_tables_by_cats(&In, &Out);
else
Vect_copy_tables(&In, &Out, -1);
}
else if (where_opt->answer && method < NETWORK) {
G_warning(_("Attributes are needed for 'where' option, copying table"));
Vect_copy_tables(&In, &Out, -1);
}
/* smoothing/simplification */
if (method < NETWORK) {
/* modifies only lines of selected type, all other features are preserved */
int not_modified_boundaries = 0, n_oversimplified = 0;
struct line_pnts *APoints; /* original Points */
set_topo_debug();
Vect_copy_map_lines(&In, &Out);
Vect_build_partial(&Out, GV_BUILD_CENTROIDS);
G_message("-----------------------------------------------------");
G_message(_("Generalization (%s)..."), method_opt->answer);
G_message(_("Using threshold: %g %s"), thresh, G_database_unit_name(1));
G_percent_reset();
APoints = Vect_new_line_struct();
n_lines = Vect_get_num_lines(&Out);
for (i = 1; i <= n_lines; i++) {
int after = 0;
G_percent(i, n_lines, 1);
type = Vect_read_line(&Out, APoints, Cats, i);
if (!(type & GV_LINES) || !(mask_type & type))
continue;
if (layer > 0) {
if ((type & GV_LINE) &&
!Vect_cats_in_constraint(Cats, layer, cat_list))
continue;
else if ((type & GV_BOUNDARY)) {
int do_line = 0;
int left, right;
do_line = Vect_cats_in_constraint(Cats, layer, cat_list);
if (!do_line) {
/* check if any of the centroids is selected */
Vect_get_line_areas(&Out, i, &left, &right);
if (left < 0)
left = Vect_get_isle_area(&Out, abs(left));
if (right < 0)
right = Vect_get_isle_area(&Out, abs(right));
if (left > 0) {
Vect_get_area_cats(&Out, left, Cats);
do_line = Vect_cats_in_constraint(Cats, layer, cat_list);
}
if (!do_line && right > 0) {
Vect_get_area_cats(&Out, right, Cats);
do_line = Vect_cats_in_constraint(Cats, layer, cat_list);
}
}
if (!do_line)
continue;
}
}
Vect_line_prune(APoints);
if (APoints->n_points < 2)
/* Line of length zero, delete if boundary ? */
continue;
total_input += APoints->n_points;
/* copy points */
Vect_reset_line(Points);
Vect_append_points(Points, APoints, GV_FORWARD);
loop_support = 0;
if (!loop_support_flag->answer) {
int n1, n2;
Vect_get_line_nodes(&Out, i, &n1, &n2);
if (n1 == n2) {
if (Vect_get_node_n_lines(&Out, n1) == 2) {
if (abs(Vect_get_node_line(&Out, n1, 0)) == i &&
abs(Vect_get_node_line(&Out, n1, 1)) == i)
loop_support = 1;
}
}
}
for (iter = 0; iter < iterations; iter++) {
switch (method) {
case DOUGLAS:
douglas_peucker(Points, thresh, with_z);
break;
case DOUGLAS_REDUCTION:
douglas_peucker_reduction(Points, thresh, reduction,
with_z);
break;
case LANG:
lang(Points, thresh, look_ahead, with_z);
break;
case VERTEX_REDUCTION:
vertex_reduction(Points, thresh, with_z);
break;
case REUMANN:
reumann_witkam(Points, thresh, with_z);
break;
case BOYLE:
boyle(Points, look_ahead, loop_support, with_z);
break;
case SLIDING_AVERAGING:
sliding_averaging(Points, slide, look_ahead, loop_support, with_z);
break;
case DISTANCE_WEIGHTING:
distance_weighting(Points, slide, look_ahead, loop_support, with_z);
break;
case CHAIKEN:
chaiken(Points, thresh, loop_support, with_z);
break;
case HERMITE:
hermite(Points, thresh, angle_thresh, loop_support, with_z);
break;
case SNAKES:
snakes(Points, alpha, beta, loop_support, with_z);
break;
}
}
if (loop_support == 0) {
/* safety check, BUG in method if not passed */
if (APoints->x[0] != Points->x[0] ||
APoints->y[0] != Points->y[0] ||
APoints->z[0] != Points->z[0])
G_fatal_error(_("Method '%s' did not preserve first point"), method_opt->answer);
if (APoints->x[APoints->n_points - 1] != Points->x[Points->n_points - 1] ||
APoints->y[APoints->n_points - 1] != Points->y[Points->n_points - 1] ||
APoints->z[APoints->n_points - 1] != Points->z[Points->n_points - 1])
G_fatal_error(_("Method '%s' did not preserve last point"), method_opt->answer);
}
else {
/* safety check, BUG in method if not passed */
if (Points->x[0] != Points->x[Points->n_points - 1] ||
Points->y[0] != Points->y[Points->n_points - 1] ||
Points->z[0] != Points->z[Points->n_points - 1])
G_fatal_error(_("Method '%s' did not preserve loop"), method_opt->answer);
}
Vect_line_prune(Points);
/* oversimplified line */
if (Points->n_points < 2) {
after = APoints->n_points;
n_oversimplified++;
if (error_out->answer)
Vect_write_line(&Error, type, APoints, Cats);
}
/* check for topology corruption */
else if (type == GV_BOUNDARY) {
if (!check_topo(&Out, i, APoints, Points, Cats)) {
after = APoints->n_points;
not_modified_boundaries++;
if (error_out->answer)
Vect_write_line(&Error, type, APoints, Cats);
}
else
after = Points->n_points;
}
else {
/* type == GV_LINE */
Vect_rewrite_line(&Out, i, type, Points, Cats);
after = Points->n_points;
}
total_output += after;
}
if (not_modified_boundaries > 0)
G_warning(_("%d boundaries were not modified because modification would damage topology"),
not_modified_boundaries);
if (n_oversimplified > 0)
G_warning(_("%d lines/boundaries were not modified due to over-simplification"),
n_oversimplified);
G_message("-----------------------------------------------------");
/* make sure that clean topo is built at the end */
Vect_build_partial(&Out, GV_BUILD_NONE);
if (error_out->answer)
Vect_build_partial(&Error, GV_BUILD_NONE);
}
Vect_build(&Out);
if (error_out->answer)
Vect_build(&Error);
Vect_close(&In);
Vect_close(&Out);
if (error_out->answer)
Vect_close(&Error);
G_message("-----------------------------------------------------");
if (total_input != 0 && total_input != total_output)
G_done_msg(_("Number of vertices for selected features %s from %d to %d (%d%% remaining)"),
simplification ? _("reduced") : _("changed"),
total_input, total_output,
(total_output * 100) / total_input);
else
G_done_msg(" ");
exit(EXIT_SUCCESS);
}
int mean_shift(struct globals *globals)
{
int row, col, t, n;
int mwrow, mwrow1, mwrow2, mwnrows, mwcol, mwcol1, mwcol2, mwncols, radiusc;
double hspat, hspec, hspat2, hspec2, sigmaspat2, sigmaspec2;
double hspecad, hspecad2;
double ka2;
double w, wsum;
LARGEINT n_changes;
double alpha2, maxdiff2;
struct ngbr_stats Rin, Rout, Rn;
double diff, diff2;
SEGMENT *seg_tmp;
double mindiff, mindiffzero, mindiffavg, mindiffzeroavg;
double avgdiff, avgdiffavg;
LARGEINT nvalid, count;
int do_gauss = 0, do_adaptive, do_progressive;
Rin.mean = G_malloc(globals->datasize);
Rout.mean = G_malloc(globals->datasize);
Rn.mean = G_malloc(globals->datasize);
alpha2 = globals->alpha * globals->alpha;
do_adaptive = globals->ms_adaptive;
do_progressive = globals->ms_progressive;
globals->candidate_count = 0;
flag_clear_all(globals->candidate_flag);
/* Set candidate flag to true/1 for all non-NULL cells */
for (row = globals->row_min; row < globals->row_max; row++) {
for (col = globals->col_min; col < globals->col_max; col++) {
if (!(FLAG_GET(globals->null_flag, row, col))) {
FLAG_SET(globals->candidate_flag, row, col);
globals->candidate_count++;
}
}
}
/* spatial bandwidth */
hspat = globals->hs;
if (hspat < 1) {
hspat = 1.5;
globals->hs = hspat;
}
hspat2 = hspat * hspat;
sigmaspat2 = hspat2 / 9.;
radiusc = hspat; /* radius in cells truncated to integer */
mwnrows = mwncols = radiusc * 2 + 1;
/* estimate spectral bandwidth for given spatial bandwidth */
mindiffavg = mindiffzeroavg = 0;
avgdiffavg = 0;
nvalid = 0;
G_message(_("Estimating spectral bandwidth for spatial bandwidth %g..."), hspat);
G_percent_reset();
for (row = globals->row_min; row < globals->row_max; row++) {
G_percent(row - globals->row_min,
globals->row_max - globals->row_min, 4);
mwrow1 = row - radiusc;
mwrow2 = mwrow1 + mwnrows;
if (mwrow1 < globals->row_min)
mwrow1 = globals->row_min;
if (mwrow2 > globals->row_max)
mwrow2 = globals->row_max;
for (col = globals->col_min; col < globals->col_max; col++) {
if ((FLAG_GET(globals->null_flag, row, col)))
continue;
/* get current band values */
Segment_get(globals->bands_in, (void *)Rin.mean,
row, col);
mwcol1 = col - radiusc;
mwcol2 = mwcol1 + mwncols;
if (mwcol1 < globals->col_min)
mwcol1 = globals->col_min;
if (mwcol2 > globals->col_max)
mwcol2 = globals->col_max;
/* get minimum spectral distance for this cell */
count = 0;
mindiff = globals->max_diff;
mindiffzero = globals->max_diff;
avgdiff = 0;
for (mwrow = mwrow1; mwrow < mwrow2; mwrow++) {
for (mwcol = mwcol1; mwcol < mwcol2; mwcol++) {
if ((FLAG_GET(globals->null_flag, mwrow, mwcol)))
continue;
if (mwrow == row && mwcol == col)
continue;
diff = mwrow - row;
diff2 = diff * diff;
diff = mwcol - col;
diff2 += diff * diff;
if (diff2 <= hspat2) {
Segment_get(globals->bands_in, (void *)Rn.mean,
mwrow, mwcol);
/* get spectral distance */
diff2 = (globals->calculate_similarity)(&Rin, &Rn, globals);
if (mindiff > diff2)
mindiff = diff2;
if (mindiffzero > diff2 && diff2 > 0)
mindiffzero = diff2;
avgdiff += sqrt(diff2);
count++;
}
}
}
if (count) {
nvalid++;
if (mindiff > 0)
mindiffavg += sqrt(mindiff);
mindiffzeroavg += sqrt(mindiffzero);
if (avgdiff > 0)
avgdiffavg += avgdiff / count;
}
}
}
G_percent(1, 1, 1);
if (!nvalid) {
G_fatal_error(_("Empty moving windows"));
}
mindiffavg /= nvalid;
mindiffzeroavg /= nvalid;
avgdiffavg /= nvalid;
G_debug(1, "Average minimum difference to neighbours: %g", mindiffavg);
G_debug(1, "Average minimum difference excl zero to neighbours: %g", mindiffzeroavg);
G_debug(1, "Average average difference to neighbours: %g", avgdiffavg);
/* use avgdiffavg as hspec for adaptive bandwidth */
hspec = globals->hr;
if (hspec < 0 || hspec >= 1) {
hspec = sqrt(avgdiffavg / 10.0);
hspec = avgdiffavg;
hspec = mindiffzeroavg;
if (do_progressive)
G_message(_("Initial range bandwidth: %g"), hspec);
else
G_message(_("Estimated range bandwidth: %g"), hspec);
globals->hr = hspec;
}
else {
G_message(_("Estimated range bandwidth: %g"), mindiffzeroavg);
}
if (do_adaptive) {
/* bandwidth is now standard deviation for adaptive bandwidth
* using a gaussian function with range bandwith used as
* bandwidth for the gaussian function
* the aim is to produce similar but improved results with
* adaptive bandwidth
* thus increase bandwidth */
hspec = sqrt(hspec);
}
hspec2 = hspec * hspec;
sigmaspec2 = hspec2 / 9.;
if (!do_progressive) {
G_message(_("Spatial bandwidth: %g"), hspat);
G_message(_("Range bandwidth: %g"), hspec);
}
G_debug(4, "Starting to process %ld candidate cells",
globals->candidate_count);
t = 0;
n_changes = 1;
maxdiff2 = 0;
while (t < globals->end_t && n_changes > 0) {
G_message(_("Processing pass %d..."), ++t);
/* cells within an object should become more similar with each pass
* therefore the spectral bandwidth could be decreased
* and the spatial bandwidth could be increased */
/* spatial bandwidth: double the area covered by the moving window
* area = M_PI * hspat * hspat
* new hspat = sqrt(M_PI * hspat * hspat * 2 / M_PI)
* no good, too large increases */
if (do_progressive) {
if (t > 1)
hspat *= 1.1;
hspat2 = hspat * hspat;
sigmaspat2 = hspat2 / 9.;
radiusc = hspat; /* radius in cells truncated to integer */
mwnrows = mwncols = radiusc * 2 + 1;
/* spectral bandwidth: reduce by 0.7 */
if (t > 1)
hspec *= 0.9;
hspec2 = hspec * hspec;
sigmaspec2 = hspec2 / 9.;
G_verbose_message(_("Spatial bandwidth: %g"), hspat);
G_verbose_message(_("Range bandwidth: %g"), hspec);
}
n_changes = 0;
maxdiff2 = 0;
/* swap input and output */
seg_tmp = globals->bands_in;
globals->bands_in = globals->bands_out;
globals->bands_out = seg_tmp;
/*process candidate cells */
G_percent_reset();
for (row = globals->row_min; row < globals->row_max; row++) {
G_percent(row - globals->row_min,
globals->row_max - globals->row_min, 4);
mwrow1 = row - radiusc;
mwrow2 = mwrow1 + mwnrows;
if (mwrow1 < globals->row_min)
mwrow1 = globals->row_min;
if (mwrow2 > globals->row_max)
mwrow2 = globals->row_max;
for (col = globals->col_min; col < globals->col_max; col++) {
if ((FLAG_GET(globals->null_flag, row, col)))
continue;
/* get current band values */
Segment_get(globals->bands_in, (void *)Rin.mean,
row, col);
/* init output */
for (n = 0; n < globals->nbands; n++)
Rout.mean[n] = 0.;
mwcol1 = col - radiusc;
mwcol2 = mwcol1 + mwncols;
if (mwcol1 < globals->col_min)
mwcol1 = globals->col_min;
if (mwcol2 > globals->col_max)
mwcol2 = globals->col_max;
hspecad2 = hspec2;
if (do_adaptive) {
/* adapt initial range bandwith */
ka2 = hspec2; /* OTB: conductance parameter */
avgdiff = 0;
count = 0;
for (mwrow = mwrow1; mwrow < mwrow2; mwrow++) {
for (mwcol = mwcol1; mwcol < mwcol2; mwcol++) {
if ((FLAG_GET(globals->null_flag, mwrow, mwcol)))
continue;
if (mwrow == row && mwcol == col)
continue;
diff = mwrow - row;
diff2 = diff * diff;
diff = mwcol - col;
diff2 += diff * diff;
if (diff2 <= hspat2) {
Segment_get(globals->bands_in, (void *)Rn.mean,
mwrow, mwcol);
/* get spectral distance */
diff2 = (globals->calculate_similarity)(&Rin, &Rn, globals);
avgdiff += sqrt(diff2);
count++;
}
}
}
hspecad2 = 0;
if (avgdiff > 0) {
avgdiff /= count;
hspecad = hspec;
/* OTB-like, contrast enhancing */
hspecad = exp(-avgdiff * avgdiff / (2 * ka2)) * avgdiff;
/* preference for large regions, from Perona Malik 1990
* if the settings are right, it could be used to reduce noise */
/* hspecad = 1 / (1 + (avgdiff * avgdiff / (2 * hspec2))); */
hspecad2 = hspecad * hspecad;
G_debug(1, "avg spectral diff: %g", avgdiff);
G_debug(1, "initial hspec2: %g", hspec2);
G_debug(1, "adapted hspec2: %g", hspecad2);
}
}
/* actual mean shift */
wsum = 0;
for (mwrow = mwrow1; mwrow < mwrow2; mwrow++) {
for (mwcol = mwcol1; mwcol < mwcol2; mwcol++) {
if ((FLAG_GET(globals->null_flag, mwrow, mwcol)))
continue;
diff = mwrow - row;
diff2 = diff * diff;
diff = mwcol - col;
diff2 += diff * diff;
if (diff2 <= hspat2) {
w = 1;
if (do_gauss)
w = gauss_kernel(diff2, sigmaspat2);
Segment_get(globals->bands_in, (void *)Rn.mean,
mwrow, mwcol);
/* check spectral distance */
diff2 = (globals->calculate_similarity)(&Rin, &Rn, globals);
if (diff2 <= hspecad2) {
if (do_gauss)
w *= gauss_kernel(diff2, sigmaspec2);
wsum += w;
for (n = 0; n < globals->nbands; n++)
Rout.mean[n] += w * Rn.mean[n];
}
}
}
}
if (wsum > 0) {
for (n = 0; n < globals->nbands; n++)
Rout.mean[n] /= wsum;
}
else {
for (n = 0; n < globals->nbands; n++)
Rout.mean[n] = Rin.mean[n];
}
/* put new band values */
Segment_put(globals->bands_out, (void *)Rout.mean,
row, col);
/* if the squared difference between old and new band values
* is larger than alpha2, then increase n_changes */
diff2 = (globals->calculate_similarity)(&Rin, &Rout, globals);
if (diff2 > alpha2)
n_changes++;
if (maxdiff2 < diff2)
maxdiff2 = diff2;
}
}
G_percent(1, 1, 1);
G_message(_("Changes > threshold: %d, largest change: %g"), n_changes, sqrt(maxdiff2));
}
if (n_changes > 1)
G_message(_("Mean shift stopped at %d due to reaching max iteration limit, more changes may be possible"), t);
else
G_message(_("Mean shift converged after %d iterations"), t);
/* identify connected components */
cluster_bands(globals);
/* remove small regions */
remove_small_clumps(globals);
return TRUE;
}
int main(int argc, char *argv[])
{
FILE *in_fp;
int out_fd;
char *infile, *outmap;
int xcol, ycol, zcol, max_col, percent;
int do_zfilter;
int method = -1;
int bin_n, bin_min, bin_max, bin_sum, bin_sumsq, bin_index;
double zrange_min, zrange_max, d_tmp;
char *fs; /* field delim */
off_t filesize;
int linesize;
long estimated_lines;
int from_stdin;
int can_seek;
RASTER_MAP_TYPE rtype;
struct History history;
char title[64];
void *n_array, *min_array, *max_array, *sum_array, *sumsq_array,
*index_array;
void *raster_row, *ptr;
struct Cell_head region;
int rows, cols; /* scan box size */
int row, col; /* counters */
int pass, npasses;
unsigned long line;
char buff[BUFFSIZE];
double x, y, z;
char **tokens;
int ntokens; /* number of tokens */
double pass_north, pass_south;
int arr_row, arr_col;
unsigned long count, count_total;
double min = 0.0 / 0.0; /* init as nan */
double max = 0.0 / 0.0; /* init as nan */
double zscale = 1.0;
size_t offset, n_offset;
int n = 0;
double sum = 0.;
double sumsq = 0.;
double variance, mean, skew, sumdev;
int pth = 0;
double trim = 0.0;
int j, k;
int head_id, node_id;
int r_low, r_up;
struct GModule *module;
struct Option *input_opt, *output_opt, *delim_opt, *percent_opt,
*type_opt;
struct Option *method_opt, *xcol_opt, *ycol_opt, *zcol_opt, *zrange_opt,
*zscale_opt;
struct Option *trim_opt, *pth_opt;
struct Flag *scan_flag, *shell_style, *skipline;
G_gisinit(argv[0]);
module = G_define_module();
module->keywords = _("raster, import, LIDAR");
module->description =
_("Create a raster map from an assemblage of many coordinates using univariate statistics.");
input_opt = G_define_standard_option(G_OPT_F_INPUT);
input_opt->description =
_("ASCII file containing input data (or \"-\" to read from stdin)");
output_opt = G_define_standard_option(G_OPT_R_OUTPUT);
method_opt = G_define_option();
method_opt->key = "method";
method_opt->type = TYPE_STRING;
method_opt->required = NO;
method_opt->description = _("Statistic to use for raster values");
method_opt->options =
"n,min,max,range,sum,mean,stddev,variance,coeff_var,median,percentile,skewness,trimmean";
method_opt->answer = "mean";
method_opt->guisection = _("Statistic");
type_opt = G_define_option();
type_opt->key = "type";
type_opt->type = TYPE_STRING;
type_opt->required = NO;
type_opt->options = "CELL,FCELL,DCELL";
type_opt->answer = "FCELL";
type_opt->description = _("Storage type for resultant raster map");
delim_opt = G_define_standard_option(G_OPT_F_SEP);
delim_opt->guisection = _("Input");
xcol_opt = G_define_option();
xcol_opt->key = "x";
xcol_opt->type = TYPE_INTEGER;
xcol_opt->required = NO;
xcol_opt->answer = "1";
xcol_opt->description =
_("Column number of x coordinates in input file (first column is 1)");
xcol_opt->guisection = _("Input");
ycol_opt = G_define_option();
ycol_opt->key = "y";
ycol_opt->type = TYPE_INTEGER;
ycol_opt->required = NO;
ycol_opt->answer = "2";
ycol_opt->description = _("Column number of y coordinates in input file");
ycol_opt->guisection = _("Input");
zcol_opt = G_define_option();
zcol_opt->key = "z";
zcol_opt->type = TYPE_INTEGER;
zcol_opt->required = NO;
zcol_opt->answer = "3";
zcol_opt->description = _("Column number of data values in input file");
zcol_opt->guisection = _("Input");
zrange_opt = G_define_option();
zrange_opt->key = "zrange";
zrange_opt->type = TYPE_DOUBLE;
zrange_opt->required = NO;
zrange_opt->key_desc = "min,max";
zrange_opt->description = _("Filter range for z data (min,max)");
zscale_opt = G_define_option();
zscale_opt->key = "zscale";
zscale_opt->type = TYPE_DOUBLE;
zscale_opt->required = NO;
zscale_opt->answer = "1.0";
zscale_opt->description = _("Scale to apply to z data");
percent_opt = G_define_option();
percent_opt->key = "percent";
percent_opt->type = TYPE_INTEGER;
percent_opt->required = NO;
percent_opt->answer = "100";
percent_opt->options = "1-100";
percent_opt->description = _("Percent of map to keep in memory");
pth_opt = G_define_option();
pth_opt->key = "pth";
pth_opt->type = TYPE_INTEGER;
pth_opt->required = NO;
pth_opt->options = "1-100";
pth_opt->description = _("pth percentile of the values");
pth_opt->guisection = _("Statistic");
trim_opt = G_define_option();
trim_opt->key = "trim";
trim_opt->type = TYPE_DOUBLE;
trim_opt->required = NO;
trim_opt->options = "0-50";
trim_opt->description =
_("Discard <trim> percent of the smallest and <trim> percent of the largest observations");
trim_opt->guisection = _("Statistic");
scan_flag = G_define_flag();
scan_flag->key = 's';
scan_flag->description = _("Scan data file for extent then exit");
shell_style = G_define_flag();
shell_style->key = 'g';
shell_style->description =
_("In scan mode, print using shell script style");
skipline = G_define_flag();
skipline->key = 'i';
skipline->description = _("Ignore broken lines");
if (G_parser(argc, argv))
exit(EXIT_FAILURE);
/* parse input values */
infile = input_opt->answer;
outmap = output_opt->answer;
if (shell_style->answer && !scan_flag->answer) {
scan_flag->answer = 1;
}
fs = delim_opt->answer;
if (strcmp(fs, "\\t") == 0)
fs = "\t";
if (strcmp(fs, "tab") == 0)
fs = "\t";
if (strcmp(fs, "space") == 0)
fs = " ";
xcol = atoi(xcol_opt->answer);
ycol = atoi(ycol_opt->answer);
zcol = atoi(zcol_opt->answer);
if ((xcol < 0) || (ycol < 0) || (zcol < 0))
G_fatal_error(_("Please specify a reasonable column number."));
max_col = (xcol > ycol) ? xcol : ycol;
max_col = (zcol > max_col) ? zcol : max_col;
percent = atoi(percent_opt->answer);
zscale = atof(zscale_opt->answer);
/* parse zrange */
do_zfilter = FALSE;
if (zrange_opt->answer != NULL) {
if (zrange_opt->answers[0] == NULL)
G_fatal_error(_("Invalid zrange"));
sscanf(zrange_opt->answers[0], "%lf", &zrange_min);
sscanf(zrange_opt->answers[1], "%lf", &zrange_max);
do_zfilter = TRUE;
if (zrange_min > zrange_max) {
d_tmp = zrange_max;
zrange_max = zrange_min;
zrange_min = d_tmp;
}
}
/* figure out what maps we need in memory */
/* n n
min min
max max
range min max max - min
sum sum
mean sum n sum/n
stddev sum sumsq n sqrt((sumsq - sum*sum/n)/n)
variance sum sumsq n (sumsq - sum*sum/n)/n
coeff_var sum sumsq n sqrt((sumsq - sum*sum/n)/n) / (sum/n)
median n array index to linked list
percentile n array index to linked list
skewness n array index to linked list
trimmean n array index to linked list
*/
bin_n = FALSE;
bin_min = FALSE;
bin_max = FALSE;
bin_sum = FALSE;
bin_sumsq = FALSE;
bin_index = FALSE;
if (strcmp(method_opt->answer, "n") == 0) {
method = METHOD_N;
bin_n = TRUE;
}
if (strcmp(method_opt->answer, "min") == 0) {
method = METHOD_MIN;
bin_min = TRUE;
}
if (strcmp(method_opt->answer, "max") == 0) {
method = METHOD_MAX;
bin_max = TRUE;
}
if (strcmp(method_opt->answer, "range") == 0) {
method = METHOD_RANGE;
bin_min = TRUE;
bin_max = TRUE;
}
if (strcmp(method_opt->answer, "sum") == 0) {
method = METHOD_SUM;
bin_sum = TRUE;
}
if (strcmp(method_opt->answer, "mean") == 0) {
method = METHOD_MEAN;
bin_sum = TRUE;
bin_n = TRUE;
}
if (strcmp(method_opt->answer, "stddev") == 0) {
method = METHOD_STDDEV;
bin_sum = TRUE;
bin_sumsq = TRUE;
bin_n = TRUE;
}
if (strcmp(method_opt->answer, "variance") == 0) {
method = METHOD_VARIANCE;
bin_sum = TRUE;
bin_sumsq = TRUE;
bin_n = TRUE;
}
if (strcmp(method_opt->answer, "coeff_var") == 0) {
method = METHOD_COEFF_VAR;
bin_sum = TRUE;
bin_sumsq = TRUE;
bin_n = TRUE;
}
if (strcmp(method_opt->answer, "median") == 0) {
method = METHOD_MEDIAN;
bin_index = TRUE;
}
if (strcmp(method_opt->answer, "percentile") == 0) {
if (pth_opt->answer != NULL)
pth = atoi(pth_opt->answer);
else
G_fatal_error(_("Unable to calculate percentile without the pth option specified!"));
method = METHOD_PERCENTILE;
bin_index = TRUE;
}
if (strcmp(method_opt->answer, "skewness") == 0) {
method = METHOD_SKEWNESS;
bin_index = TRUE;
}
if (strcmp(method_opt->answer, "trimmean") == 0) {
if (trim_opt->answer != NULL)
trim = atof(trim_opt->answer) / 100.0;
else
G_fatal_error(_("Unable to calculate trimmed mean without the trim option specified!"));
method = METHOD_TRIMMEAN;
bin_index = TRUE;
}
if (strcmp("CELL", type_opt->answer) == 0)
rtype = CELL_TYPE;
else if (strcmp("DCELL", type_opt->answer) == 0)
rtype = DCELL_TYPE;
else
rtype = FCELL_TYPE;
if (method == METHOD_N)
rtype = CELL_TYPE;
G_get_window(®ion);
rows = (int)(region.rows * (percent / 100.0));
cols = region.cols;
G_debug(2, "region.n=%f region.s=%f region.ns_res=%f", region.north,
region.south, region.ns_res);
G_debug(2, "region.rows=%d [box_rows=%d] region.cols=%d", region.rows,
rows, region.cols);
npasses = (int)ceil(1.0 * region.rows / rows);
if (!scan_flag->answer) {
/* allocate memory (test for enough before we start) */
if (bin_n)
n_array = G_calloc(rows * (cols + 1), G_raster_size(CELL_TYPE));
if (bin_min)
min_array = G_calloc(rows * (cols + 1), G_raster_size(rtype));
if (bin_max)
max_array = G_calloc(rows * (cols + 1), G_raster_size(rtype));
if (bin_sum)
sum_array = G_calloc(rows * (cols + 1), G_raster_size(rtype));
if (bin_sumsq)
sumsq_array = G_calloc(rows * (cols + 1), G_raster_size(rtype));
if (bin_index)
index_array =
G_calloc(rows * (cols + 1), G_raster_size(CELL_TYPE));
/* and then free it again */
if (bin_n)
G_free(n_array);
if (bin_min)
G_free(min_array);
if (bin_max)
G_free(max_array);
if (bin_sum)
G_free(sum_array);
if (bin_sumsq)
G_free(sumsq_array);
if (bin_index)
G_free(index_array);
/** end memory test **/
}
/* open input file */
if (strcmp("-", infile) == 0) {
from_stdin = TRUE;
in_fp = stdin;
infile = G_store("stdin"); /* filename for history metadata */
}
else {
if ((in_fp = fopen(infile, "r")) == NULL)
G_fatal_error(_("Unable to open input file <%s>"), infile);
}
can_seek = fseek(in_fp, 0, SEEK_SET) == 0;
/* can't rewind() non-files */
if (!can_seek && npasses != 1) {
G_warning(_("If input is not from a file it is only possible to perform a single pass."));
npasses = 1;
}
if (scan_flag->answer) {
if (zrange_opt->answer)
G_warning(_("zrange will not be taken into account during scan"));
scan_bounds(in_fp, xcol, ycol, zcol, fs, shell_style->answer,
skipline->answer, zscale);
if (!from_stdin)
fclose(in_fp);
exit(EXIT_SUCCESS);
}
/* open output map */
out_fd = G_open_raster_new(outmap, rtype);
if (out_fd < 0)
G_fatal_error(_("Unable to create raster map <%s>"), outmap);
if (can_seek) {
/* guess at number of lines in the file without actually reading it all in */
for (line = 0; line < 10; line++) { /* arbitrarily use 10th line for guess */
if (0 == G_getl2(buff, BUFFSIZE - 1, in_fp))
break;
linesize = strlen(buff) + 1;
}
fseek(in_fp, 0L, SEEK_END);
filesize = ftell(in_fp);
rewind(in_fp);
if (linesize < 6) /* min possible: "0,0,0\n" */
linesize = 6;
estimated_lines = filesize / linesize;
G_debug(2, "estimated number of lines in file: %ld", estimated_lines);
}
else
estimated_lines = -1;
/* allocate memory for a single row of output data */
raster_row = G_allocate_raster_buf(rtype);
G_message(_("Reading data ..."));
count_total = 0;
/* main binning loop(s) */
for (pass = 1; pass <= npasses; pass++) {
if (npasses > 1)
G_message(_("Pass #%d (of %d) ..."), pass, npasses);
if (can_seek)
rewind(in_fp);
/* figure out segmentation */
pass_north = region.north - (pass - 1) * rows * region.ns_res;
if (pass == npasses)
rows = region.rows - (pass - 1) * rows;
pass_south = pass_north - rows * region.ns_res;
G_debug(2, "pass=%d/%d pass_n=%f pass_s=%f rows=%d",
pass, npasses, pass_north, pass_south, rows);
if (bin_n) {
G_debug(2, "allocating n_array");
n_array = G_calloc(rows * (cols + 1), G_raster_size(CELL_TYPE));
blank_array(n_array, rows, cols, CELL_TYPE, 0);
}
if (bin_min) {
G_debug(2, "allocating min_array");
min_array = G_calloc(rows * (cols + 1), G_raster_size(rtype));
blank_array(min_array, rows, cols, rtype, -1); /* fill with NULLs */
}
if (bin_max) {
G_debug(2, "allocating max_array");
max_array = G_calloc(rows * (cols + 1), G_raster_size(rtype));
blank_array(max_array, rows, cols, rtype, -1); /* fill with NULLs */
}
if (bin_sum) {
G_debug(2, "allocating sum_array");
sum_array = G_calloc(rows * (cols + 1), G_raster_size(rtype));
blank_array(sum_array, rows, cols, rtype, 0);
}
if (bin_sumsq) {
G_debug(2, "allocating sumsq_array");
sumsq_array = G_calloc(rows * (cols + 1), G_raster_size(rtype));
blank_array(sumsq_array, rows, cols, rtype, 0);
}
if (bin_index) {
G_debug(2, "allocating index_array");
index_array =
G_calloc(rows * (cols + 1), G_raster_size(CELL_TYPE));
blank_array(index_array, rows, cols, CELL_TYPE, -1); /* fill with NULLs */
}
line = 0;
count = 0;
G_percent_reset();
while (0 != G_getl2(buff, BUFFSIZE - 1, in_fp)) {
line++;
if (line % 10000 == 0) { /* mod for speed */
if (!can_seek)
G_clicker();
else if (line < estimated_lines)
G_percent(line, estimated_lines, 3);
}
if ((buff[0] == '#') || (buff[0] == '\0')) {
continue; /* line is a comment or blank */
}
G_chop(buff); /* remove leading and trailing whitespace from the string. unneded?? */
tokens = G_tokenize(buff, fs);
ntokens = G_number_of_tokens(tokens);
if ((ntokens < 3) || (max_col > ntokens)) {
if (skipline->answer) {
G_warning(_("Not enough data columns. "
"Incorrect delimiter or column number? "
"Found the following character(s) in row %lu:\n[%s]"),
line, buff);
G_warning(_("Line ignored as requested"));
continue; /* line is garbage */
}
else {
G_fatal_error(_("Not enough data columns. "
"Incorrect delimiter or column number? "
"Found the following character(s) in row %lu:\n[%s]"),
line, buff);
}
}
/* too slow?
if ( G_projection() == PROJECTION_LL ) {
G_scan_easting( tokens[xcol-1], &x, region.proj);
G_scan_northing( tokens[ycol-1], &y, region.proj);
}
else {
*/
if (1 != sscanf(tokens[ycol - 1], "%lf", &y))
G_fatal_error(_("Bad y-coordinate line %lu column %d. <%s>"),
line, ycol, tokens[ycol - 1]);
if (y <= pass_south || y > pass_north) {
G_free_tokens(tokens);
continue;
}
if (1 != sscanf(tokens[xcol - 1], "%lf", &x))
G_fatal_error(_("Bad x-coordinate line %lu column %d. <%s>"),
line, xcol, tokens[xcol - 1]);
if (x < region.west || x > region.east) {
G_free_tokens(tokens);
continue;
}
if (1 != sscanf(tokens[zcol - 1], "%lf", &z))
G_fatal_error(_("Bad z-coordinate line %lu column %d. <%s>"),
line, zcol, tokens[zcol - 1]);
z = z * zscale;
if (zrange_opt->answer) {
if (z < zrange_min || z > zrange_max) {
G_free_tokens(tokens);
continue;
}
}
count++;
/* G_debug(5, "x: %f, y: %f, z: %f", x, y, z); */
G_free_tokens(tokens);
/* find the bin in the current array box */
arr_row = (int)((pass_north - y) / region.ns_res);
arr_col = (int)((x - region.west) / region.ew_res);
/* G_debug(5, "arr_row: %d arr_col: %d", arr_row, arr_col); */
/* The range should be [0,cols-1]. We use (int) to round down,
but if the point exactly on eastern edge arr_col will be /just/
on the max edge .0000000 and end up on the next row.
We could make above bounds check "if(x>=region.east) continue;"
But instead we go to all sorts of trouble so that not one single
data point is lost. GE is too small to catch them all.
We don't try to make y happy as percent segmenting will make some
points happen twice that way; so instead we use the y<= test above.
*/
if (arr_col >= cols) {
if (((x - region.west) / region.ew_res) - cols <
10 * GRASS_EPSILON)
arr_col--;
else { /* oh well, we tried. */
G_debug(3,
"skipping extraneous data point [%.3f], column %d of %d",
x, arr_col, cols);
continue;
}
}
if (bin_n)
update_n(n_array, cols, arr_row, arr_col);
if (bin_min)
update_min(min_array, cols, arr_row, arr_col, rtype, z);
if (bin_max)
update_max(max_array, cols, arr_row, arr_col, rtype, z);
if (bin_sum)
update_sum(sum_array, cols, arr_row, arr_col, rtype, z);
if (bin_sumsq)
update_sumsq(sumsq_array, cols, arr_row, arr_col, rtype, z);
if (bin_index) {
ptr = index_array;
ptr =
G_incr_void_ptr(ptr,
((arr_row * cols) +
arr_col) * G_raster_size(CELL_TYPE));
if (G_is_null_value(ptr, CELL_TYPE)) { /* first node */
head_id = new_node();
nodes[head_id].next = -1;
nodes[head_id].z = z;
G_set_raster_value_c(ptr, head_id, CELL_TYPE); /* store index to head */
}
else { /* head is already there */
head_id = G_get_raster_value_c(ptr, CELL_TYPE); /* get index to head */
head_id = add_node(head_id, z);
if (head_id != -1)
G_set_raster_value_c(ptr, head_id, CELL_TYPE); /* store index to head */
}
}
} /* while !EOF */
G_percent(1, 1, 1); /* flush */
G_debug(2, "pass %d finished, %lu coordinates in box", pass, count);
count_total += count;
/* calc stats and output */
G_message(_("Writing to map ..."));
for (row = 0; row < rows; row++) {
switch (method) {
case METHOD_N: /* n is a straight copy */
G_raster_cpy(raster_row,
n_array +
(row * cols * G_raster_size(CELL_TYPE)), cols,
CELL_TYPE);
break;
case METHOD_MIN:
G_raster_cpy(raster_row,
min_array + (row * cols * G_raster_size(rtype)),
cols, rtype);
break;
case METHOD_MAX:
G_raster_cpy(raster_row,
max_array + (row * cols * G_raster_size(rtype)),
cols, rtype);
break;
case METHOD_SUM:
G_raster_cpy(raster_row,
sum_array + (row * cols * G_raster_size(rtype)),
cols, rtype);
break;
case METHOD_RANGE: /* (max-min) */
ptr = raster_row;
for (col = 0; col < cols; col++) {
offset = (row * cols + col) * G_raster_size(rtype);
min = G_get_raster_value_d(min_array + offset, rtype);
max = G_get_raster_value_d(max_array + offset, rtype);
G_set_raster_value_d(ptr, max - min, rtype);
ptr = G_incr_void_ptr(ptr, G_raster_size(rtype));
}
break;
case METHOD_MEAN: /* (sum / n) */
ptr = raster_row;
for (col = 0; col < cols; col++) {
offset = (row * cols + col) * G_raster_size(rtype);
n_offset = (row * cols + col) * G_raster_size(CELL_TYPE);
n = G_get_raster_value_c(n_array + n_offset, CELL_TYPE);
sum = G_get_raster_value_d(sum_array + offset, rtype);
if (n == 0)
G_set_null_value(ptr, 1, rtype);
else
G_set_raster_value_d(ptr, (sum / n), rtype);
ptr = G_incr_void_ptr(ptr, G_raster_size(rtype));
}
break;
case METHOD_STDDEV: /* sqrt(variance) */
case METHOD_VARIANCE: /* (sumsq - sum*sum/n)/n */
case METHOD_COEFF_VAR: /* 100 * stdev / mean */
ptr = raster_row;
for (col = 0; col < cols; col++) {
offset = (row * cols + col) * G_raster_size(rtype);
n_offset = (row * cols + col) * G_raster_size(CELL_TYPE);
n = G_get_raster_value_c(n_array + n_offset, CELL_TYPE);
sum = G_get_raster_value_d(sum_array + offset, rtype);
sumsq = G_get_raster_value_d(sumsq_array + offset, rtype);
if (n == 0)
G_set_null_value(ptr, 1, rtype);
else {
variance = (sumsq - sum * sum / n) / n;
if (variance < GRASS_EPSILON)
variance = 0.0;
if (method == METHOD_STDDEV)
G_set_raster_value_d(ptr, sqrt(variance), rtype);
else if (method == METHOD_VARIANCE)
G_set_raster_value_d(ptr, variance, rtype);
else if (method == METHOD_COEFF_VAR)
G_set_raster_value_d(ptr,
100 * sqrt(variance) / (sum /
n),
rtype);
}
ptr = G_incr_void_ptr(ptr, G_raster_size(rtype));
}
break;
case METHOD_MEDIAN: /* median, if only one point in cell we will use that */
ptr = raster_row;
for (col = 0; col < cols; col++) {
n_offset = (row * cols + col) * G_raster_size(CELL_TYPE);
if (G_is_null_value(index_array + n_offset, CELL_TYPE)) /* no points in cell */
G_set_null_value(ptr, 1, rtype);
else { /* one or more points in cell */
head_id =
G_get_raster_value_c(index_array + n_offset,
CELL_TYPE);
node_id = head_id;
n = 0;
while (node_id != -1) { /* count number of points in cell */
n++;
node_id = nodes[node_id].next;
}
if (n == 1) /* only one point, use that */
G_set_raster_value_d(ptr, nodes[head_id].z,
rtype);
else if (n % 2 != 0) { /* odd number of points: median_i = (n + 1) / 2 */
n = (n + 1) / 2;
node_id = head_id;
for (j = 1; j < n; j++) /* get "median element" */
node_id = nodes[node_id].next;
G_set_raster_value_d(ptr, nodes[node_id].z,
rtype);
}
else { /* even number of points: median = (val_below + val_above) / 2 */
z = (n + 1) / 2.0;
n = floor(z);
node_id = head_id;
for (j = 1; j < n; j++) /* get element "below" */
node_id = nodes[node_id].next;
z = (nodes[node_id].z +
nodes[nodes[node_id].next].z) / 2;
G_set_raster_value_d(ptr, z, rtype);
}
}
ptr = G_incr_void_ptr(ptr, G_raster_size(rtype));
}
break;
case METHOD_PERCENTILE: /* rank = (pth*(n+1))/100; interpolate linearly */
ptr = raster_row;
for (col = 0; col < cols; col++) {
n_offset = (row * cols + col) * G_raster_size(CELL_TYPE);
if (G_is_null_value(index_array + n_offset, CELL_TYPE)) /* no points in cell */
G_set_null_value(ptr, 1, rtype);
else {
head_id =
G_get_raster_value_c(index_array + n_offset,
CELL_TYPE);
node_id = head_id;
n = 0;
while (node_id != -1) { /* count number of points in cell */
n++;
node_id = nodes[node_id].next;
}
z = (pth * (n + 1)) / 100.0;
r_low = floor(z); /* lower rank */
if (r_low < 1)
r_low = 1;
else if (r_low > n)
r_low = n;
r_up = ceil(z); /* upper rank */
if (r_up > n)
r_up = n;
node_id = head_id;
for (j = 1; j < r_low; j++) /* search lower value */
node_id = nodes[node_id].next;
z = nodes[node_id].z; /* save lower value */
node_id = head_id;
for (j = 1; j < r_up; j++) /* search upper value */
node_id = nodes[node_id].next;
z = (z + nodes[node_id].z) / 2;
G_set_raster_value_d(ptr, z, rtype);
}
ptr = G_incr_void_ptr(ptr, G_raster_size(rtype));
}
break;
case METHOD_SKEWNESS: /* skewness = sum(xi-mean)^3/(N-1)*s^3 */
ptr = raster_row;
for (col = 0; col < cols; col++) {
n_offset = (row * cols + col) * G_raster_size(CELL_TYPE);
if (G_is_null_value(index_array + n_offset, CELL_TYPE)) /* no points in cell */
G_set_null_value(ptr, 1, rtype);
else {
head_id =
G_get_raster_value_c(index_array + n_offset,
CELL_TYPE);
node_id = head_id;
n = 0; /* count */
sum = 0.0; /* sum */
sumsq = 0.0; /* sum of squares */
sumdev = 0.0; /* sum of (xi - mean)^3 */
skew = 0.0; /* skewness */
while (node_id != -1) {
z = nodes[node_id].z;
n++;
sum += z;
sumsq += (z * z);
node_id = nodes[node_id].next;
}
if (n > 1) { /* if n == 1, skew is "0.0" */
mean = sum / n;
node_id = head_id;
while (node_id != -1) {
z = nodes[node_id].z;
sumdev += pow((z - mean), 3);
node_id = nodes[node_id].next;
}
variance = (sumsq - sum * sum / n) / n;
if (variance < GRASS_EPSILON)
skew = 0.0;
else
skew =
sumdev / ((n - 1) *
pow(sqrt(variance), 3));
}
G_set_raster_value_d(ptr, skew, rtype);
}
ptr = G_incr_void_ptr(ptr, G_raster_size(rtype));
}
break;
case METHOD_TRIMMEAN:
ptr = raster_row;
for (col = 0; col < cols; col++) {
n_offset = (row * cols + col) * G_raster_size(CELL_TYPE);
if (G_is_null_value(index_array + n_offset, CELL_TYPE)) /* no points in cell */
G_set_null_value(ptr, 1, rtype);
else {
head_id =
G_get_raster_value_c(index_array + n_offset,
CELL_TYPE);
node_id = head_id;
n = 0;
while (node_id != -1) { /* count number of points in cell */
n++;
node_id = nodes[node_id].next;
}
if (1 == n)
mean = nodes[head_id].z;
else {
k = floor(trim * n + 0.5); /* number of ranks to discard on each tail */
if (k > 0 && (n - 2 * k) > 0) { /* enough elements to discard */
node_id = head_id;
for (j = 0; j < k; j++) /* move to first rank to consider */
node_id = nodes[node_id].next;
j = k + 1;
k = n - k;
n = 0;
sum = 0.0;
while (j <= k) { /* get values in interval */
n++;
sum += nodes[node_id].z;
node_id = nodes[node_id].next;
j++;
}
}
else {
node_id = head_id;
n = 0;
sum = 0.0;
while (node_id != -1) {
n++;
sum += nodes[node_id].z;
node_id = nodes[node_id].next;
}
}
mean = sum / n;
}
G_set_raster_value_d(ptr, mean, rtype);
}
ptr = G_incr_void_ptr(ptr, G_raster_size(rtype));
}
break;
default:
G_fatal_error("?");
}
/* write out line of raster data */
if (1 != G_put_raster_row(out_fd, raster_row, rtype)) {
G_close_cell(out_fd);
G_fatal_error(_("Writing map, row %d"),
((pass - 1) * rows) + row);
}
}
/* free memory */
if (bin_n)
G_free(n_array);
if (bin_min)
G_free(min_array);
if (bin_max)
G_free(max_array);
if (bin_sum)
G_free(sum_array);
if (bin_sumsq)
G_free(sumsq_array);
if (bin_index) {
G_free(index_array);
G_free(nodes);
num_nodes = 0;
max_nodes = 0;
nodes = NULL;
}
} /* passes loop */
G_percent(1, 1, 1); /* flush */
G_free(raster_row);
/* close input file */
if (!from_stdin)
fclose(in_fp);
/* close raster file & write history */
G_close_cell(out_fd);
sprintf(title, "Raw x,y,z data binned into a raster grid by cell %s",
method_opt->answer);
G_put_cell_title(outmap, title);
G_short_history(outmap, "raster", &history);
G_command_history(&history);
strncpy(history.datsrc_1, infile, RECORD_LEN);
history.datsrc_1[RECORD_LEN - 1] = '\0'; /* strncpy() doesn't null terminate if maxfill */
G_write_history(outmap, &history);
sprintf(buff, _("%lu points found in region."), count_total);
G_done_msg(buff);
G_debug(1, "Processed %lu lines.", line);
exit(EXIT_SUCCESS);
}
int main(int argc, char *argv[])
{
int out_fd, base_raster;
char *infile, *outmap;
int percent;
double zrange_min, zrange_max, d_tmp;
double irange_min, irange_max;
unsigned long estimated_lines;
RASTER_MAP_TYPE rtype, base_raster_data_type;
struct History history;
char title[64];
SEGMENT base_segment;
struct PointBinning point_binning;
void *base_array;
void *raster_row;
struct Cell_head region;
struct Cell_head input_region;
int rows, last_rows, row0, cols; /* scan box size */
int row; /* counters */
int pass, npasses;
unsigned long line, line_total;
unsigned int counter;
unsigned long n_invalid;
char buff[BUFFSIZE];
double x, y, z;
double intensity;
int arr_row, arr_col;
unsigned long count, count_total;
int point_class;
double zscale = 1.0;
double iscale = 1.0;
double res = 0.0;
struct BinIndex bin_index_nodes;
bin_index_nodes.num_nodes = 0;
bin_index_nodes.max_nodes = 0;
bin_index_nodes.nodes = 0;
struct GModule *module;
struct Option *input_opt, *output_opt, *percent_opt, *type_opt, *filter_opt, *class_opt;
struct Option *method_opt, *base_raster_opt;
struct Option *zrange_opt, *zscale_opt;
struct Option *irange_opt, *iscale_opt;
struct Option *trim_opt, *pth_opt, *res_opt;
struct Option *file_list_opt;
struct Flag *print_flag, *scan_flag, *shell_style, *over_flag, *extents_flag;
struct Flag *intens_flag, *intens_import_flag;
struct Flag *set_region_flag;
struct Flag *base_rast_res_flag;
struct Flag *only_valid_flag;
/* LAS */
LASReaderH LAS_reader;
LASHeaderH LAS_header;
LASSRSH LAS_srs;
LASPointH LAS_point;
int return_filter;
const char *projstr;
struct Cell_head cellhd, loc_wind;
unsigned int n_filtered;
G_gisinit(argv[0]);
module = G_define_module();
G_add_keyword(_("raster"));
G_add_keyword(_("import"));
G_add_keyword(_("LIDAR"));
G_add_keyword(_("statistics"));
G_add_keyword(_("conversion"));
G_add_keyword(_("aggregation"));
G_add_keyword(_("binning"));
module->description =
_("Creates a raster map from LAS LiDAR points using univariate statistics.");
input_opt = G_define_standard_option(G_OPT_F_BIN_INPUT);
input_opt->required = NO;
input_opt->label = _("LAS input file");
input_opt->description = _("LiDAR input files in LAS format (*.las or *.laz)");
input_opt->guisection = _("Input");
output_opt = G_define_standard_option(G_OPT_R_OUTPUT);
output_opt->required = NO;
output_opt->guisection = _("Output");
file_list_opt = G_define_standard_option(G_OPT_F_INPUT);
file_list_opt->key = "file";
file_list_opt->label = _("File containing names of LAS input files");
file_list_opt->description = _("LiDAR input files in LAS format (*.las or *.laz)");
file_list_opt->required = NO;
file_list_opt->guisection = _("Input");
method_opt = G_define_option();
method_opt->key = "method";
method_opt->type = TYPE_STRING;
method_opt->required = NO;
method_opt->description = _("Statistic to use for raster values");
method_opt->options =
"n,min,max,range,sum,mean,stddev,variance,coeff_var,median,percentile,skewness,trimmean";
method_opt->answer = "mean";
method_opt->guisection = _("Statistic");
G_asprintf((char **)&(method_opt->descriptions),
"n;%s;"
"min;%s;"
"max;%s;"
"range;%s;"
"sum;%s;"
"mean;%s;"
"stddev;%s;"
"variance;%s;"
"coeff_var;%s;"
"median;%s;"
"percentile;%s;"
"skewness;%s;"
"trimmean;%s",
_("Number of points in cell"),
_("Minimum value of point values in cell"),
_("Maximum value of point values in cell"),
_("Range of point values in cell"),
_("Sum of point values in cell"),
_("Mean (average) value of point values in cell"),
_("Standard deviation of point values in cell"),
_("Variance of point values in cell"),
_("Coefficient of variance of point values in cell"),
_("Median value of point values in cell"),
_("pth (nth) percentile of point values in cell"),
_("Skewness of point values in cell"),
_("Trimmed mean of point values in cell"));
type_opt = G_define_standard_option(G_OPT_R_TYPE);
type_opt->required = NO;
type_opt->answer = "FCELL";
base_raster_opt = G_define_standard_option(G_OPT_R_INPUT);
base_raster_opt->key = "base_raster";
base_raster_opt->required = NO;
base_raster_opt->label =
_("Subtract raster values from the Z coordinates");
base_raster_opt->description =
_("The scale for Z is applied beforehand, the range filter for"
" Z afterwards");
base_raster_opt->guisection = _("Transform");
zrange_opt = G_define_option();
zrange_opt->key = "zrange";
zrange_opt->type = TYPE_DOUBLE;
zrange_opt->required = NO;
zrange_opt->key_desc = "min,max";
zrange_opt->description = _("Filter range for Z data (min,max)");
zrange_opt->guisection = _("Selection");
zscale_opt = G_define_option();
zscale_opt->key = "zscale";
zscale_opt->type = TYPE_DOUBLE;
zscale_opt->required = NO;
zscale_opt->answer = "1.0";
zscale_opt->description = _("Scale to apply to Z data");
zscale_opt->guisection = _("Transform");
irange_opt = G_define_option();
irange_opt->key = "intensity_range";
irange_opt->type = TYPE_DOUBLE;
irange_opt->required = NO;
irange_opt->key_desc = "min,max";
irange_opt->description = _("Filter range for intensity values (min,max)");
irange_opt->guisection = _("Selection");
iscale_opt = G_define_option();
iscale_opt->key = "intensity_scale";
iscale_opt->type = TYPE_DOUBLE;
iscale_opt->required = NO;
iscale_opt->answer = "1.0";
iscale_opt->description = _("Scale to apply to intensity values");
iscale_opt->guisection = _("Transform");
percent_opt = G_define_option();
percent_opt->key = "percent";
percent_opt->type = TYPE_INTEGER;
percent_opt->required = NO;
percent_opt->answer = "100";
percent_opt->options = "1-100";
percent_opt->description = _("Percent of map to keep in memory");
/* I would prefer to call the following "percentile", but that has too
* much namespace overlap with the "percent" option above */
pth_opt = G_define_option();
pth_opt->key = "pth";
pth_opt->type = TYPE_INTEGER;
pth_opt->required = NO;
pth_opt->options = "1-100";
pth_opt->description = _("pth percentile of the values");
pth_opt->guisection = _("Statistic");
trim_opt = G_define_option();
trim_opt->key = "trim";
trim_opt->type = TYPE_DOUBLE;
trim_opt->required = NO;
trim_opt->options = "0-50";
trim_opt->label = _("Discard given percentage of the smallest and largest values");
trim_opt->description =
_("Discard <trim> percent of the smallest and <trim> percent of the largest observations");
trim_opt->guisection = _("Statistic");
res_opt = G_define_option();
res_opt->key = "resolution";
res_opt->type = TYPE_DOUBLE;
res_opt->required = NO;
res_opt->description =
_("Output raster resolution");
res_opt->guisection = _("Output");
filter_opt = G_define_option();
filter_opt->key = "return_filter";
filter_opt->type = TYPE_STRING;
filter_opt->required = NO;
filter_opt->label = _("Only import points of selected return type");
filter_opt->description = _("If not specified, all points are imported");
filter_opt->options = "first,last,mid";
filter_opt->guisection = _("Selection");
class_opt = G_define_option();
class_opt->key = "class_filter";
class_opt->type = TYPE_INTEGER;
class_opt->multiple = YES;
class_opt->required = NO;
class_opt->label = _("Only import points of selected class(es)");
class_opt->description = _("Input is comma separated integers. "
"If not specified, all points are imported.");
class_opt->guisection = _("Selection");
print_flag = G_define_flag();
print_flag->key = 'p';
print_flag->description =
_("Print LAS file info and exit");
extents_flag = G_define_flag();
extents_flag->key = 'e';
extents_flag->label =
_("Use the extent of the input for the raster extent");
extents_flag->description =
_("Set internally computational region extents based on the"
" point cloud");
extents_flag->guisection = _("Output");
set_region_flag = G_define_flag();
set_region_flag->key = 'n';
set_region_flag->label =
_("Set computation region to match the new raster map");
set_region_flag->description =
_("Set computation region to match the 2D extent and resolution"
" of the newly created new raster map");
set_region_flag->guisection = _("Output");
over_flag = G_define_flag();
over_flag->key = 'o';
over_flag->label =
_("Override projection check (use current location's projection)");
over_flag->description =
_("Assume that the dataset has same projection as the current location");
scan_flag = G_define_flag();
scan_flag->key = 's';
scan_flag->description = _("Scan data file for extent then exit");
shell_style = G_define_flag();
shell_style->key = 'g';
shell_style->description =
_("In scan mode, print using shell script style");
intens_flag = G_define_flag();
intens_flag->key = 'i';
intens_flag->label =
_("Use intensity values rather than Z values");
intens_flag->description =
_("Uses intensity values everywhere as if they would be Z"
" coordinates");
intens_import_flag = G_define_flag();
intens_import_flag->key = 'j';
intens_import_flag->description =
_("Use Z values for filtering, but intensity values for statistics");
base_rast_res_flag = G_define_flag();
base_rast_res_flag->key = 'd';
base_rast_res_flag->label =
_("Use base raster resolution instead of computational region");
base_rast_res_flag->description =
_("For getting values from base raster, use its actual"
" resolution instead of computational region resolution");
only_valid_flag = G_define_flag();
only_valid_flag->key = 'v';
only_valid_flag->label = _("Use only valid points");
only_valid_flag->description =
_("Points invalid according to APSRS LAS specification will be"
" filtered out");
only_valid_flag->guisection = _("Selection");
G_option_required(input_opt, file_list_opt, NULL);
G_option_exclusive(input_opt, file_list_opt, NULL);
G_option_required(output_opt, print_flag, scan_flag, shell_style, NULL);
G_option_exclusive(intens_flag, intens_import_flag, NULL);
G_option_requires(base_rast_res_flag, base_raster_opt, NULL);
if (G_parser(argc, argv))
exit(EXIT_FAILURE);
int only_valid = FALSE;
n_invalid = 0;
if (only_valid_flag->answer)
only_valid = TRUE;
/* we could use rules but this gives more info and allows continuing */
if (set_region_flag->answer && !(extents_flag->answer || res_opt->answer)) {
G_warning(_("Flag %c makes sense only with %s option or -%c flag"),
set_region_flag->key, res_opt->key, extents_flag->key);
/* avoid the call later on */
set_region_flag->answer = '\0';
}
struct StringList infiles;
if (file_list_opt->answer) {
if (access(file_list_opt->answer, F_OK) != 0)
G_fatal_error(_("File <%s> does not exist"), file_list_opt->answer);
string_list_from_file(&infiles, file_list_opt->answer);
}
else {
string_list_from_one_item(&infiles, input_opt->answer);
}
/* parse input values */
outmap = output_opt->answer;
if (shell_style->answer && !scan_flag->answer) {
scan_flag->answer = 1; /* pointer not int, so set = shell_style->answer ? */
}
/* check zrange and extent relation */
if (scan_flag->answer || extents_flag->answer) {
if (zrange_opt->answer)
G_warning(_("zrange will not be taken into account during scan"));
}
Rast_get_window(®ion);
/* G_get_window seems to be unreliable if the location has been changed */
G_get_set_window(&loc_wind); /* TODO: v.in.lidar uses G_get_default_window() */
estimated_lines = 0;
int i;
for (i = 0; i < infiles.num_items; i++) {
infile = infiles.items[i];
/* don't if file not found */
if (access(infile, F_OK) != 0)
G_fatal_error(_("Input file <%s> does not exist"), infile);
/* Open LAS file*/
LAS_reader = LASReader_Create(infile);
if (LAS_reader == NULL)
G_fatal_error(_("Unable to open file <%s> as a LiDAR point cloud"),
infile);
LAS_header = LASReader_GetHeader(LAS_reader);
if (LAS_header == NULL) {
G_fatal_error(_("Unable to read LAS header of <%s>"), infile);
}
LAS_srs = LASHeader_GetSRS(LAS_header);
/* print info or check projection if we are actually importing */
if (print_flag->answer) {
/* print filename when there is more than one file */
if (infiles.num_items > 1)
fprintf(stdout, "File: %s\n", infile);
/* Print LAS header */
print_lasinfo(LAS_header, LAS_srs);
}
else {
/* report that we are checking more files */
if (i == 1)
G_message(_("First file's projection checked,"
" checking projection of the other files..."));
/* Fetch input map projection in GRASS form. */
projstr = LASSRS_GetWKT_CompoundOK(LAS_srs);
/* we are printing the non-warning messages only for first file */
projection_check_wkt(cellhd, loc_wind, projstr, over_flag->answer,
shell_style->answer || i);
/* if there is a problem in some other file, first OK message
* is printed but than a warning, this is not ideal but hopefully
* not so confusing when importing multiple files */
}
if (scan_flag->answer || extents_flag->answer) {
/* we assign to the first one (i==0) but update for the rest */
scan_bounds(LAS_reader, shell_style->answer, extents_flag->answer, i,
zscale, ®ion);
}
/* number of estimated point across all files */
/* TODO: this should be ull which won't work with percent report */
estimated_lines += LASHeader_GetPointRecordsCount(LAS_header);
/* We are closing all again and we will be opening them later,
* so we don't have to worry about limit for open files. */
LASSRS_Destroy(LAS_srs);
LASHeader_Destroy(LAS_header);
LASReader_Destroy(LAS_reader);
}
/* if we are not importing, end */
if (print_flag->answer || scan_flag->answer)
exit(EXIT_SUCCESS);
return_filter = LAS_ALL;
if (filter_opt->answer) {
if (strcmp(filter_opt->answer, "first") == 0)
return_filter = LAS_FIRST;
else if (strcmp(filter_opt->answer, "last") == 0)
return_filter = LAS_LAST;
else if (strcmp(filter_opt->answer, "mid") == 0)
return_filter = LAS_MID;
else
G_fatal_error(_("Unknown filter option <%s>"), filter_opt->answer);
}
struct ReturnFilter return_filter_struct;
return_filter_struct.filter = return_filter;
struct ClassFilter class_filter;
class_filter_create_from_strings(&class_filter, class_opt->answers);
percent = atoi(percent_opt->answer);
/* TODO: we already used zscale */
/* TODO: we don't report intensity range */
if (zscale_opt->answer)
zscale = atof(zscale_opt->answer);
if (iscale_opt->answer)
iscale = atof(iscale_opt->answer);
/* parse zrange */
if (zrange_opt->answer != NULL) {
if (zrange_opt->answers[0] == NULL)
G_fatal_error(_("Invalid zrange"));
sscanf(zrange_opt->answers[0], "%lf", &zrange_min);
sscanf(zrange_opt->answers[1], "%lf", &zrange_max);
if (zrange_min > zrange_max) {
d_tmp = zrange_max;
zrange_max = zrange_min;
zrange_min = d_tmp;
}
}
/* parse irange */
if (irange_opt->answer != NULL) {
if (irange_opt->answers[0] == NULL)
G_fatal_error(_("Invalid %s"), irange_opt->key);
sscanf(irange_opt->answers[0], "%lf", &irange_min);
sscanf(irange_opt->answers[1], "%lf", &irange_max);
if (irange_min > irange_max) {
d_tmp = irange_max;
irange_max = irange_min;
irange_min = d_tmp;
}
}
point_binning_set(&point_binning, method_opt->answer, pth_opt->answer,
trim_opt->answer, FALSE);
base_array = NULL;
if (strcmp("CELL", type_opt->answer) == 0)
rtype = CELL_TYPE;
else if (strcmp("DCELL", type_opt->answer) == 0)
rtype = DCELL_TYPE;
else
rtype = FCELL_TYPE;
if (point_binning.method == METHOD_N)
rtype = CELL_TYPE;
if (res_opt->answer) {
/* align to resolution */
res = atof(res_opt->answer);
if (!G_scan_resolution(res_opt->answer, &res, region.proj))
G_fatal_error(_("Invalid input <%s=%s>"), res_opt->key, res_opt->answer);
if (res <= 0)
G_fatal_error(_("Option '%s' must be > 0.0"), res_opt->key);
region.ns_res = region.ew_res = res;
region.north = ceil(region.north / res) * res;
region.south = floor(region.south / res) * res;
region.east = ceil(region.east / res) * res;
region.west = floor(region.west / res) * res;
G_adjust_Cell_head(®ion, 0, 0);
}
else if (extents_flag->answer) {
/* align to current region */
Rast_align_window(®ion, &loc_wind);
}
Rast_set_output_window(®ion);
rows = last_rows = region.rows;
npasses = 1;
if (percent < 100) {
rows = (int)(region.rows * (percent / 100.0));
npasses = region.rows / rows;
last_rows = region.rows - npasses * rows;
if (last_rows)
npasses++;
else
last_rows = rows;
}
cols = region.cols;
G_debug(2, "region.n=%f region.s=%f region.ns_res=%f", region.north,
region.south, region.ns_res);
G_debug(2, "region.rows=%d [box_rows=%d] region.cols=%d", region.rows,
rows, region.cols);
/* using row-based chunks (used for output) when input and output
* region matches and using segment library when they don't */
int use_segment = 0;
int use_base_raster_res = 0;
/* TODO: see if the input region extent is smaller than the raster
* if yes, the we need to load the whole base raster if the -e
* flag was defined (alternatively clip the regions) */
if (base_rast_res_flag->answer)
use_base_raster_res = 1;
if (base_raster_opt->answer && (res_opt->answer || use_base_raster_res
|| extents_flag->answer))
use_segment = 1;
if (base_raster_opt->answer && !use_segment) {
/* TODO: do we need to test existence first? mapset? */
base_raster = Rast_open_old(base_raster_opt->answer, "");
base_raster_data_type = Rast_get_map_type(base_raster);
base_array = G_calloc((size_t)rows * (cols + 1), Rast_cell_size(base_raster_data_type));
}
if (base_raster_opt->answer && use_segment) {
if (use_base_raster_res) {
/* read raster actual extent and resolution */
Rast_get_cellhd(base_raster_opt->answer, "", &input_region);
/* TODO: make it only as small as the output is or points are */
Rast_set_input_window(&input_region); /* we have split window */
} else {
Rast_get_input_window(&input_region);
}
rast_segment_open(&base_segment, base_raster_opt->answer, &base_raster_data_type);
}
if (!scan_flag->answer) {
if (!check_rows_cols_fit_to_size_t(rows, cols))
G_fatal_error(_("Unable to process the hole map at once. "
"Please set the '%s' option to some value lower than 100."),
percent_opt->key);
point_binning_memory_test(&point_binning, rows, cols, rtype);
}
/* open output map */
out_fd = Rast_open_new(outmap, rtype);
/* allocate memory for a single row of output data */
raster_row = Rast_allocate_output_buf(rtype);
G_message(_("Reading data ..."));
count_total = line_total = 0;
/* main binning loop(s) */
for (pass = 1; pass <= npasses; pass++) {
if (npasses > 1)
G_message(_("Pass #%d (of %d) ..."), pass, npasses);
/* figure out segmentation */
row0 = (pass - 1) * rows;
if (pass == npasses) {
rows = last_rows;
}
if (base_array) {
G_debug(2, "filling base raster array");
for (row = 0; row < rows; row++) {
Rast_get_row(base_raster, base_array + ((size_t) row * cols * Rast_cell_size(base_raster_data_type)), row, base_raster_data_type);
}
}
G_debug(2, "pass=%d/%d rows=%d", pass, npasses, rows);
point_binning_allocate(&point_binning, rows, cols, rtype);
line = 0;
count = 0;
counter = 0;
G_percent_reset();
/* loop of input files */
for (i = 0; i < infiles.num_items; i++) {
infile = infiles.items[i];
/* we already know file is there, so just do basic checks */
LAS_reader = LASReader_Create(infile);
if (LAS_reader == NULL)
G_fatal_error(_("Unable to open file <%s>"), infile);
while ((LAS_point = LASReader_GetNextPoint(LAS_reader)) != NULL) {
line++;
counter++;
if (counter == 100000) { /* speed */
if (line < estimated_lines)
G_percent(line, estimated_lines, 3);
counter = 0;
}
/* We always count them and report because behavior
* changed in between 7.0 and 7.2 from undefined (but skipping
* invalid points) to filtering them out only when requested. */
if (!LASPoint_IsValid(LAS_point)) {
n_invalid++;
if (only_valid)
continue;
}
x = LASPoint_GetX(LAS_point);
y = LASPoint_GetY(LAS_point);
if (intens_flag->answer)
/* use intensity as z here to allow all filters (and
* modifications) below to be applied for intensity */
z = LASPoint_GetIntensity(LAS_point);
else
z = LASPoint_GetZ(LAS_point);
int return_n = LASPoint_GetReturnNumber(LAS_point);
int n_returns = LASPoint_GetNumberOfReturns(LAS_point);
if (return_filter_is_out(&return_filter_struct, return_n, n_returns)) {
n_filtered++;
continue;
}
point_class = (int) LASPoint_GetClassification(LAS_point);
if (class_filter_is_out(&class_filter, point_class))
continue;
if (y <= region.south || y > region.north) {
continue;
}
if (x < region.west || x >= region.east) {
continue;
}
/* find the bin in the current array box */
arr_row = (int)((region.north - y) / region.ns_res) - row0;
if (arr_row < 0 || arr_row >= rows)
continue;
arr_col = (int)((x - region.west) / region.ew_res);
z = z * zscale;
if (base_array) {
double base_z;
if (row_array_get_value_row_col(base_array, arr_row, arr_col,
cols, base_raster_data_type,
&base_z))
z -= base_z;
else
continue;
}
else if (use_segment) {
double base_z;
if (rast_segment_get_value_xy(&base_segment, &input_region,
base_raster_data_type, x, y,
&base_z))
z -= base_z;
else
continue;
}
if (zrange_opt->answer) {
if (z < zrange_min || z > zrange_max) {
continue;
}
}
if (intens_import_flag->answer || irange_opt->answer) {
intensity = LASPoint_GetIntensity(LAS_point);
intensity *= iscale;
if (irange_opt->answer) {
if (intensity < irange_min || intensity > irange_max) {
continue;
}
}
/* use intensity for statistics */
if (intens_import_flag->answer)
z = intensity;
}
count++;
/* G_debug(5, "x: %f, y: %f, z: %f", x, y, z); */
update_value(&point_binning, &bin_index_nodes, cols,
arr_row, arr_col, rtype, x, y, z);
} /* while !EOF of one input file */
/* close input LAS file */
LASReader_Destroy(LAS_reader);
} /* end of loop for all input files files */
G_percent(1, 1, 1); /* flush */
G_debug(2, "pass %d finished, %lu coordinates in box", pass, count);
count_total += count;
line_total += line;
/* calc stats and output */
G_message(_("Writing to map ..."));
for (row = 0; row < rows; row++) {
/* potentially vector writing can be independent on the binning */
write_values(&point_binning, &bin_index_nodes, raster_row, row,
cols, rtype, NULL);
/* write out line of raster data */
Rast_put_row(out_fd, raster_row, rtype);
}
/* free memory */
point_binning_free(&point_binning, &bin_index_nodes);
} /* passes loop */
if (base_array)
Rast_close(base_raster);
if (use_segment)
Segment_close(&base_segment);
G_percent(1, 1, 1); /* flush */
G_free(raster_row);
/* close raster file & write history */
Rast_close(out_fd);
sprintf(title, "Raw X,Y,Z data binned into a raster grid by cell %s",
method_opt->answer);
Rast_put_cell_title(outmap, title);
Rast_short_history(outmap, "raster", &history);
Rast_command_history(&history);
Rast_set_history(&history, HIST_DATSRC_1, infile);
Rast_write_history(outmap, &history);
/* set computation region to the new raster map */
/* TODO: should be in the done message */
if (set_region_flag->answer)
G_put_window(®ion);
if (n_invalid && only_valid)
G_message(_("%lu input points were invalid and filtered out"),
n_invalid);
if (n_invalid && !only_valid)
G_message(_("%lu input points were invalid, use -%c flag to filter"
" them out"), n_invalid, only_valid_flag->key);
if (infiles.num_items > 1) {
sprintf(buff, _("Raster map <%s> created."
" %lu points from %d files found in region."),
outmap, count_total, infiles.num_items);
}
else {
sprintf(buff, _("Raster map <%s> created."
" %lu points found in region."),
outmap, count_total);
}
G_done_msg("%s", buff);
G_debug(1, "Processed %lu points.", line_total);
string_list_free(&infiles);
exit(EXIT_SUCCESS);
}
int main(int argc, char *argv[])
{
struct Map_info In, Out, cut_map;
static struct line_pnts *Points;
struct line_cats *Cats;
struct GModule *module; /* GRASS module for parsing arguments */
struct Option *map_in, *map_out, *cut_out;
struct Option *afield_opt, *nfield_opt, *abcol, *afcol, *ncol;
struct Option *catsource_opt, *wheresource_opt;
struct Option *catsink_opt, *wheresink_opt;
int with_z;
int afield, nfield, mask_type;
struct varray *varray_source, *varray_sink;
dglGraph_s *graph;
int i, nlines, *flow, total_flow;
struct ilist *source_list, *sink_list, *cut;
int find_cut;
char buf[2000];
/* Attribute table */
dbString sql;
dbDriver *driver;
struct field_info *Fi;
/* initialize GIS environment */
G_gisinit(argv[0]); /* reads grass env, stores program name to G_program_name() */
/* initialize module */
module = G_define_module();
G_add_keyword(_("vector"));
G_add_keyword(_("network"));
G_add_keyword(_("flow"));
module->description =
_("Computes the maximum flow between two sets of nodes in the network.");
/* Define the different options as defined in gis.h */
map_in = G_define_standard_option(G_OPT_V_INPUT);
afield_opt = G_define_standard_option(G_OPT_V_FIELD);
afield_opt->key = "arc_layer";
afield_opt->answer = "1";
afield_opt->label = _("Arc layer");
afield_opt->guisection = _("Cost");
nfield_opt = G_define_standard_option(G_OPT_V_FIELD);
nfield_opt->key = "node_layer";
nfield_opt->answer = "2";
nfield_opt->label = _("Node layer");
nfield_opt->guisection = _("Cost");
map_out = G_define_standard_option(G_OPT_V_OUTPUT);
cut_out = G_define_standard_option(G_OPT_V_OUTPUT);
cut_out->key = "cut";
cut_out->description =
_("Name for output vector map containing a minimum cut");
afcol = G_define_standard_option(G_OPT_DB_COLUMN);
afcol->key = "arc_column";
afcol->required = NO;
afcol->description =
_("Arc forward/both direction(s) cost column (number)");
afcol->guisection = _("Cost");
abcol = G_define_standard_option(G_OPT_DB_COLUMN);
abcol->key = "arc_backward_column";
abcol->required = NO;
abcol->description = _("Arc backward direction cost column (number)");
abcol->guisection = _("Cost");
ncol = G_define_standard_option(G_OPT_DB_COLUMN);
ncol->key = "node_column";
ncol->required = NO;
ncol->description = _("Node cost column (number)");
ncol->guisection = _("Cost");
catsource_opt = G_define_standard_option(G_OPT_V_CATS);
catsource_opt->key = "source_cats";
catsource_opt->label = _("Source category values");
catsource_opt->guisection = _("Source");
wheresource_opt = G_define_standard_option(G_OPT_DB_WHERE);
wheresource_opt->key = "source_where";
wheresource_opt->label =
_("Source WHERE conditions of SQL statement without 'where' keyword");
wheresource_opt->guisection = _("Source");
catsink_opt = G_define_standard_option(G_OPT_V_CATS);
catsink_opt->key = "sink_cats";
catsink_opt->label = _("Sink category values");
catsink_opt->guisection = _("Sink");
wheresink_opt = G_define_standard_option(G_OPT_DB_WHERE);
wheresink_opt->key = "sink_where";
wheresink_opt->label =
_("Sink WHERE conditions of SQL statement without 'where' keyword");
wheresink_opt->guisection = _("Sink");
/* options and flags parser */
if (G_parser(argc, argv))
exit(EXIT_FAILURE);
find_cut = (cut_out->answer[0]);
/* TODO: make an option for this */
mask_type = GV_LINE | GV_BOUNDARY;
Points = Vect_new_line_struct();
Cats = Vect_new_cats_struct();
Vect_check_input_output_name(map_in->answer, map_out->answer,
G_FATAL_EXIT);
Vect_set_open_level(2);
if (1 > Vect_open_old(&In, map_in->answer, ""))
G_fatal_error(_("Unable to open vector map <%s>"), map_in->answer);
with_z = Vect_is_3d(&In);
if (0 > Vect_open_new(&Out, map_out->answer, with_z)) {
Vect_close(&In);
G_fatal_error(_("Unable to create vector map <%s>"), map_out->answer);
}
if (find_cut && 0 > Vect_open_new(&cut_map, cut_out->answer, with_z)) {
Vect_close(&In);
Vect_close(&Out);
G_fatal_error(_("Unable to create vector map <%s>"), cut_out->answer);
}
/* parse filter option and select appropriate lines */
afield = Vect_get_field_number(&In, afield_opt->answer);
nfield = Vect_get_field_number(&In, nfield_opt->answer);
/* Create table */
Fi = Vect_default_field_info(&Out, 1, NULL, GV_1TABLE);
Vect_map_add_dblink(&Out, 1, NULL, Fi->table, GV_KEY_COLUMN, Fi->database,
Fi->driver);
db_init_string(&sql);
driver = db_start_driver_open_database(Fi->driver, Fi->database);
if (driver == NULL)
G_fatal_error(_("Unable to open database <%s> by driver <%s>"),
Fi->database, Fi->driver);
db_set_error_handler_driver(driver);
sprintf(buf, "create table %s (cat integer, flow double precision)",
Fi->table);
db_set_string(&sql, buf);
G_debug(2, "%s", db_get_string(&sql));
if (db_execute_immediate(driver, &sql) != DB_OK) {
db_close_database_shutdown_driver(driver);
G_fatal_error(_("Unable to create table: '%s'"), db_get_string(&sql));
}
if (db_create_index2(driver, Fi->table, GV_KEY_COLUMN) != DB_OK)
G_warning(_("Cannot create index"));
if (db_grant_on_table
(driver, Fi->table, DB_PRIV_SELECT, DB_GROUP | DB_PUBLIC) != DB_OK)
G_fatal_error(_("Cannot grant privileges on table <%s>"), Fi->table);
db_begin_transaction(driver);
source_list = Vect_new_list();
sink_list = Vect_new_list();
if (NetA_initialise_varray
(&In, nfield, GV_POINT,
wheresource_opt->answer, catsource_opt->answer, &varray_source) <= 0) {
G_fatal_error(_("No source features selected. "
"Please check options '%s', '%s'."),
catsource_opt->key, wheresource_opt->key);
}
if (NetA_initialise_varray
(&In, nfield, GV_POINT, wheresink_opt->answer,
catsink_opt->answer, &varray_sink) <= 0) {
G_fatal_error(_("No sink features selected. "
"Please check options '%s', '%s'."),
catsink_opt->key, wheresink_opt->key);
}
NetA_varray_to_nodes(&In, varray_source, source_list, NULL);
NetA_varray_to_nodes(&In, varray_sink, sink_list, NULL);
if (source_list->n_values == 0)
G_fatal_error(_("No sources"));
if (sink_list->n_values == 0)
G_fatal_error(_("No sinks"));
Vect_copy_head_data(&In, &Out);
Vect_hist_copy(&In, &Out);
Vect_hist_command(&Out);
if (0 != Vect_net_build_graph(&In, mask_type, afield, nfield, afcol->answer, abcol->answer,
ncol->answer, 0, 0))
G_fatal_error(_("Unable to build graph for vector map <%s>"), Vect_get_full_name(&In));
graph = Vect_net_get_graph(&In);
nlines = Vect_get_num_lines(&In);
flow = (int *)G_calloc(nlines + 1, sizeof(int));
if (!flow)
G_fatal_error(_("Out of memory"));
total_flow = NetA_flow(graph, source_list, sink_list, flow);
G_debug(3, "Max flow: %d", total_flow);
if (find_cut) {
cut = Vect_new_list();
total_flow = NetA_min_cut(graph, source_list, sink_list, flow, cut);
G_debug(3, "Min cut: %d", total_flow);
}
G_message(_("Writing the output..."));
G_percent_reset();
for (i = 1; i <= nlines; i++) {
G_percent(i, nlines, 1);
int type = Vect_read_line(&In, Points, Cats, i);
Vect_write_line(&Out, type, Points, Cats);
if (type == GV_LINE) {
int cat;
Vect_cat_get(Cats, afield, &cat);
if (cat == -1)
continue; /*TODO: warning? */
sprintf(buf, "insert into %s values (%d, %f)", Fi->table, cat,
flow[i] / (double)In.dgraph.cost_multip);
db_set_string(&sql, buf);
G_debug(3, "%s", db_get_string(&sql));
if (db_execute_immediate(driver, &sql) != DB_OK) {
db_close_database_shutdown_driver(driver);
G_fatal_error(_("Cannot insert new record: %s"),
db_get_string(&sql));
};
}
}
if (find_cut) {
for (i = 0; i < cut->n_values; i++) {
int type = Vect_read_line(&In, Points, Cats, cut->value[i]);
Vect_write_line(&cut_map, type, Points, Cats);
}
Vect_destroy_list(cut);
Vect_build(&cut_map);
Vect_close(&cut_map);
}
db_commit_transaction(driver);
db_close_database_shutdown_driver(driver);
G_free(flow);
Vect_destroy_list(source_list);
Vect_destroy_list(sink_list);
Vect_build(&Out);
Vect_close(&In);
Vect_close(&Out);
exit(EXIT_SUCCESS);
}
int mean_shift(struct globals *globals)
{
int row, col, t;
int n_changes;
double alpha2;
struct ngbr_stats Rin, Rout;
double diff2;
G_fatal_error(_("Mean shift is not yet implemented"));
return FALSE;
Rin.mean = G_malloc(globals->datasize);
Rout.mean = G_malloc(globals->datasize);
/* TODO: need another segment structure holding output
* for mean shift, output of the previous iteration becomes input of the next iteration
* initially, input and output are original band values */
alpha2 = globals->alpha * globals->alpha;
t = 0;
n_changes = 1;
while (t < globals->end_t && n_changes > 0) {
G_message(_("Processing pass %d..."), ++t);
n_changes = 0;
globals->candidate_count = 0;
flag_clear_all(globals->candidate_flag);
/* Set candidate flag to true/1 for all non-NULL cells */
for (row = globals->row_min; row < globals->row_max; row++) {
for (col = globals->col_min; col < globals->col_max; col++) {
if (!(FLAG_GET(globals->null_flag, row, col))) {
FLAG_SET(globals->candidate_flag, row, col);
globals->candidate_count++;
}
}
}
G_debug(4, "Starting to process %ld candidate cells",
globals->candidate_count);
/*process candidate cells */
G_percent_reset();
for (row = globals->row_min; row < globals->row_max; row++) {
G_percent(row - globals->row_min,
globals->row_max - globals->row_min, 4);
for (col = globals->col_min; col < globals->col_max; col++) {
if (!(FLAG_GET(globals->candidate_flag, row, col)))
continue;
/* get current band values */
Segment_get(&globals->bands_seg, (void *)Rin.mean,
row, col);
/* adapt initial spatial and range bandwiths */
/* calculate new band values */
/* if the squared difference between old and new band values
* is larger than alpha2, then increase n_changes */
diff2 = (globals->calculate_similarity)(&Rin, &Rout, globals);
if (diff2 > alpha2)
n_changes++;
}
}
}
if (n_changes > 1)
G_message(_("Mean shift stopped at %d due to reaching max iteration limit, more changes may be possible"), t);
else
G_message(_("Mean shift converged after %d iterations"), t);
/* identify connected components */
return TRUE;
}
