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Correct some spelling mistakes

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Automaticaly --> Automatically
Invalide --> Invalid
Strech --> Stretch
allows to --> allows one to
attachement --> attachment
contraints --> constraints
inconsistant --> inconsistent
occured --> occurred
occurences --> occurrences
permits to --> permits one to
postion --> position
regularily --> regularly
transfered --> transferred
This commit is contained in:
Nic Soudée
2019-01-24 08:32:58 -05:00
parent fc2839a3b6
commit 228e651b4f
13 changed files with 77 additions and 77 deletions
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20
src/op_c.c
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@@ -192,12 +192,12 @@ long Perceptual_lightness(T_Components *color)
19*color->B*19*color->B;
}
// Handlers for the occurences tables
// This table is used to count the occurence of an (RGB) pixel value in the
// Handlers for the occurrences tables
// This table is used to count the occurrence of an (RGB) pixel value in the
// source 24bit image. These count are then used by the median cut algorithm to
// decide which cluster to split.
/// Initialize an occurence table
/// Initialize an occurrence table
void OT_init(T_Occurrence_table * t)
{
int size;
@@ -206,7 +206,7 @@ void OT_init(T_Occurrence_table * t)
memset(t->table,0,size); // Set it to 0
}
/// Allocate an occurence table for given number of bits
/// Allocate an occurrence table for given number of bits
T_Occurrence_table * OT_new(int nbb_r,int nbb_g,int nbb_b)
{
T_Occurrence_table * n;
@@ -255,7 +255,7 @@ void OT_delete(T_Occurrence_table * t)
}
/// Get number of occurences for a given color
/// Get number of occurrences for a given color
int OT_get(T_Occurrence_table * t, byte r, byte g, byte b)
{
int index;
@@ -293,7 +293,7 @@ void OT_count_occurrences(T_Occurrence_table* t, T_Bitmap24B image, int size)
}
/// Count the total number of pixels in an occurence table
/// Count the total number of pixels in an occurrence table
int OT_count_colors(T_Occurrence_table * t)
{
int val; // Computed return value
@@ -341,7 +341,7 @@ void Cluster_pack(T_Cluster * c,const T_Occurrence_table * const to)
// Unoptimized code kept here for documentation purpose because the optimized
// one is unreadable : run over the whole cluster and find the min and max,
// and count the occurences at the same time.
// and count the occurrences at the same time.
/*
for (r=c->rmin<<to->dec_r;r<=c->rmax<<to->dec_r;r+=1<<to->dec_r)
for (g=c->vmin<<to->dec_g;g<=c->vmax<<to->dec_g;g+=1<<to->dec_g)
@@ -356,7 +356,7 @@ void Cluster_pack(T_Cluster * c,const T_Occurrence_table * const to)
else if (g>vmax) vmax=g;
if (b<bmin) bmin=b;
else if (b>bmax) bmax=b;
c->occurences+=nbocc;
c->occurrences+=nbocc;
}
}
*/
@@ -866,7 +866,7 @@ int CS_Set(T_Cluster_set * cs,T_Cluster * c)
/// This is the main median cut algorithm and the function actually called to
/// reduce the palette. We get the number of pixels for each collor in the
/// occurence table and generate the cluster set from it.
/// occurrence table and generate the cluster set from it.
// 1) RGB space is a big box
// 2) We seek the pixels with extreme values
// 3) We split the box in 2 parts on its longest axis
@@ -918,7 +918,7 @@ int CS_Generate(T_Cluster_set * cs, const T_Occurrence_table * const to, CT_Tree
if(CS_Set(cs,&Nouveau1) < 0)
return -1;
}
if (Nouveau2.occurences != 0) {
if(CS_Set(cs,&Nouveau2) < 0)
return -1;