[Mapserver-users] not all labels get rendered

[Vincent Schut]

--Boundary-00=_hs6r+wMGHJPmojY
Content-Type: text/plain;
  charset="us-ascii"
Content-Transfer-Encoding: 7bit
Content-Disposition: inline

Hi all,

I have a shapefile of the provinces of Indonesia.
I have a map file with 2 layers: one displays the provinces, each with a 
unique color (classes are generated on the fly using metadata and php), the 
second is the same as the first, but labels the provinces and does not show 
anything else then just the labels. It contains only one class, which 
contains the label section. Labels are drawn based on the same item used to 
class provinces by color.
Now the first layer draws ok, every province is drawn with its according 
color. The second layer displays not ok. It shows some labels, some labels 
not. I have tried anything I can think of, I have force true in the label 
object, etc. etc., but some labels simply do not get drawn. And yes, I do 
call drawLabelCache before saveWebImage. I wil attach the generated image and 
my mapfile, and a save of the mapobject after creating the classes (thus the 
mapfile including the color classes). The image clearly shows that some 
provinces get drawn but not labeled. (For those that know their topo lessons: 
north-west sumatra (next to Aceh) is missing some, east & central Sulasesi 
too...).

Hope someone can help.

Regards,
Vincent Schut.
-- 
______________________________________
Vincent Schut (schut at sarvision.com)
Sarvision B.V.
Wageningen, The Netherlands
www.sarvision.com
--Boundary-00=_hs6r+wMGHJPmojY
Content-Type: image/png;
  name="labelproblem.png"
Content-Transfer-Encoding: base64
Content-Disposition: attachment; filename="labelproblem.png"
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--Boundary-00=_hs6r+wMGHJPmojY
Content-Type: text/plain;
  charset="us-ascii";
  name="province2.map"
Content-Transfer-Encoding: 7bit
Content-Disposition: attachment; filename="province2.map"

map

	name "Provinces of Indonesia"
	status on
	#extent 92 -12 170 29
	#extent 94.6741073 -12.0044661 162.5312532 9.9598206
	extent 94 -12 142 7
	size 600 240
	shapepath "/var/mapserver/data/spotvegetation/"
	symbolset "/var/mapserver/fonts/symbols.sym"
	fontset "/var/mapserver/fonts/fonts.txt"
	#imagecolor 150 150 255
	units DD
	imagetype png24
	#imagetype swf-single
	#imagetype swf-multiple
	#imagetype gtif
	#imagetype png_rgba
	
	web
		imagepath "/var/mapserver/ms_tmp/"
		imageurl "/ms_tmp/"
		log "/var/log/mapserver/indo.log"
	end

	projection
		"proj=latlong" 
		"ellps=WGS84" 
		"datum=WGS84" 
		"no_defs" 
	end
	
	OUTPUTFORMAT
		NAME gtif
		DRIVER "GDAL/GTiff"
		IMAGEMODE PC256
		FORMATOPTION "WORLDFILE=YES"
		formatoption "TRANSPARENCY=ON"
	END
	
	outputformat
		name swf-single
		mimetype "application/x-shockwave-flash"
		driver swf
		imagemode PC256
		formatoption "OUTPUT_MOVIE=SINGLE"
		formatoption "TRANSPARENCY=ON"
	end
	
	outputformat
		name swf-multiple
		mimetype "application/x-shockwave-flash"
		driver swf
		imagemode PC256
		formatoption "OUTPUT_MOVIE=MULTIPLE"
		formatoption "TRANSPARENCY=ON"
	end

	OUTPUTFORMAT
		NAME png8
		DRIVER "GD/PNG"
		MIMETYPE "image/png"
		IMAGEMODE PC256
		formatoption "TRANSPARENCY=ON"
	END

	outputformat
		name png24
		driver "GD/PNG"
		mimetype "image/png"
		imagemode RGB
		formatoption "TRANSPARENCY=ON"
		FORMATOPTION "QUALITY=80"
	end
	
	outputformat
		name png_rgba
		driver "GD/PNG"
		mimetype "image/png"
		imagemode RGBA
		#formatoption "TRANSPARENCY=ON"
	end
	
	LAYER
		NAME "province"
		#connectiontype postgis
		#connection "user=mapserver dbname=indo_mof_gis"
		DATA "province_full.shp"
		#DATA "the_geom from (SELECT oid, * from province_full) AS foo using unique province.oid"
		STATUS ON
		TYPE POLYGON
		CLASSITEM "name"
		
		METADATA
			"ClassRampItem"			"name"
			"ClassRampType"			"linear"
			"ClassRampColorModel"		"hsv"
			"ClassRampColorhStart"	"0"
			"ClassRampColorhEnd"		"7100"
			"ClassRampColorsStart"	"1.0"
			"ClassRampColorsEnd"		"0.8"
			"ClassRampColorvStart"	"0.7"
			"ClassRampColorvEnd"		"0.9"
		END # METADATA
		
		
		
	END # LAYER PROVINCE
	
	LAYER
		NAME "province_labels"
		#connectiontype postgis
		#connection "user=mapserver dbname=indo_mof_gis"
		DATA "province_full.shp"
		#DATA "the_geom from (SELECT oid, * from province_full) AS foo using unique province.oid"
		STATUS ON
		TYPE POLYGON
		LABELITEM "name"
		LABELCACHE ON
		CLASS
			STATUS ON
			#COLOR 200 0 0
			#OUTLINECOLOR 0 0 100
			#TEXT "Testlabel"
			#EXPRESSION ('[name]' != '')
			LABEL
				ANTIALIAS TRUE
				COLOR 255 255 255
				SHADOWCOLOR 30 30 30
				SHADOWSIZE 1 1
				FONT "fritqat"
				TYPE TRUETYPE
				SIZE 8
				FORCE TRUE
				#POSITION cc
				MINFEATURESIZE 0
				MINDISTANCE 0
			END #LABEL
		END # CLASS
	END # LAYER
	
END # MAP
		
		

--Boundary-00=_hs6r+wMGHJPmojY
Content-Type: text/plain;
  charset="us-ascii";
  name="result.map"
Content-Transfer-Encoding: 7bit
Content-Disposition: attachment; filename="result.map"

MAP
  EXTENT 94 -12 142 7
  FONTSET "/var/mapserver/fonts/fonts.txt"
  IMAGECOLOR 255 255 255
  IMAGETYPE png24
  SYMBOLSET "/var/mapserver/fonts/symbols.sym"
  SHAPEPATH "/var/mapserver/data/spotvegetation/"
  SIZE 600 240
  STATUS ON
  UNITS DD
  NAME "Provinces of Indonesia"

  PROJECTION
    "proj=latlong"
    "ellps=WGS84"
    "datum=WGS84"
    "no_defs"
  END
  LEGEND
      IMAGECOLOR 255 255 255
    KEYSIZE 20 10
    KEYSPACING 5 5
    LABEL
      SIZE MEDIUM
      TYPE BITMAP
      BUFFER 0
      COLOR 0 0 0
      FORCE FALSE
      MINDISTANCE -1
      MINFEATURESIZE -1
      OFFSET 0 0
      PARTIALS TRUE
      POSITION CC
    END
    POSITION LL
    STATUS OFF
  END

  QUERYMAP
      COLOR 255 255 0
    SIZE -1 -1
    STATUS OFF
    STYLE HILITE
  END

  SCALEBAR
      COLOR 0 0 0
    IMAGECOLOR 255 255 255
    INTERVALS 4
    LABEL
      SIZE MEDIUM
      TYPE BITMAP
      BUFFER 0
      COLOR 0 0 0
      FORCE FALSE
      MINDISTANCE -1
      MINFEATURESIZE -1
      OFFSET 0 0
      PARTIALS TRUE
    END
    POSITION LL
    SIZE 200 3
    STATUS OFF
    STYLE 0
    UNITS MILES
  END

  WEB
    IMAGEPATH "/var/mapserver/ms_tmp/"
    IMAGEURL "/ms_tmp/"
    LOG "/var/log/mapserver/indo.log"
  END

  LAYER
    CLASSITEM "name"
    DATA "province_full.shp"
      METADATA
        "ClassRampColorsStart"	"1.0"
        "ClassRampColorsEnd"	"0.8"
        "ClassRampColorModel"	"hsv"
        "ClassRampType"	"linear"
        "ClassRampColorvStart"	"0.7"
        "ClassRampItem"	"name"
        "ClassRampColorvEnd"	"0.9"
        "ClassRampColorhStart"	"0"
        "ClassRampColorhEnd"	"7100"
      END
    NAME "province"
    SIZEUNITS PIXELS
    STATUS ON
    TOLERANCE 0
    TOLERANCEUNITS PIXELS
    TYPE POLYGON
    UNITS METERS
    CLASS
      NAME "Aceh"
      EXPRESSION ('[name]'='Aceh')
      STYLE
          COLOR 178 0 0
        MAXSIZE 100
        MINSIZE 1
        SIZE 1
        SYMBOL 0
      END
    END
    CLASS
      NAME "Bali"
      EXPRESSION ('[name]'='Bali')
      STYLE
          COLOR 66 1 180
        MAXSIZE 100
        MINSIZE 1
        SIZE 1
        SYMBOL 0
      END
    END
    CLASS
      NAME "Bangka"
      EXPRESSION ('[name]'='Bangka')
      STYLE
          COLOR 2 182 137
        MAXSIZE 100
        MINSIZE 1
        SIZE 1
        SYMBOL 0
      END
    END
    CLASS
      NAME "Banten"
      EXPRESSION ('[name]'='Banten')
      STYLE
          COLOR 160 184 4
        MAXSIZE 100
        MINSIZE 1
        SIZE 1
        SYMBOL 0
      END
    END
    CLASS
      NAME "Bengkulu"
      EXPRESSION ('[name]'='Bengkulu')
      STYLE
          COLOR 186 5 92
        MAXSIZE 100
        MINSIZE 1
        SIZE 1
        SYMBOL 0
      END
    END
    CLASS
      NAME "DKI"
      EXPRESSION ('[name]'='DKI')
      STYLE
          COLOR 6 25 187
        MAXSIZE 100
        MINSIZE 1
        SIZE 1
        SYMBOL 0
      END
    END
    CLASS
      NAME "Goro"
      EXPRESSION ('[name]'='Goro')
      STYLE
          COLOR 8 189 59
        MAXSIZE 100
        MINSIZE 1
        SIZE 1
        SYMBOL 0
      END
    END
    CLASS
      NAME "Irian"
      EXPRESSION ('[name]'='Irian')
      STYLE
          COLOR 191 131 9
        MAXSIZE 100
        MINSIZE 1
        SIZE 1
        SYMBOL 0
      END
    END
    CLASS
      NAME "Jabar"
      EXPRESSION ('[name]'='Jabar')
      STYLE
          COLOR 193 11 184
        MAXSIZE 100
        MINSIZE 1
        SIZE 1
        SYMBOL 0
      END
    END
    CLASS
      NAME "Jambi"
      EXPRESSION ('[name]'='Jambi')
      STYLE
          COLOR 13 116 195
        MAXSIZE 100
        MINSIZE 1
        SIZE 1
        SYMBOL 0
      END
    END
    CLASS
      NAME "Jateng"
      EXPRESSION ('[name]'='Jateng')
      STYLE
          COLOR 48 197 14
        MAXSIZE 100
        MINSIZE 1
        SIZE 1
        SYMBOL 0
      END
    END
    CLASS
      NAME "Jatim"
      EXPRESSION ('[name]'='Jatim')
      STYLE
          COLOR 199 52 16
        MAXSIZE 100
        MINSIZE 1
        SIZE 1
        SYMBOL 0
      END
    END
    CLASS
      NAME "Kalbar"
      EXPRESSION ('[name]'='Kalbar')
      STYLE
          COLOR 124 17 201
        MAXSIZE 100
        MINSIZE 1
        SIZE 1
        SYMBOL 0
      END
    END
    CLASS
      NAME "Kalsel"
      EXPRESSION ('[name]'='Kalsel')
      STYLE
          COLOR 19 203 196
        MAXSIZE 100
        MINSIZE 1
        SIZE 1
        SYMBOL 0
      END
    END
    CLASS
      NAME "Kalteng"
      EXPRESSION ('[name]'='Kalteng')
      STYLE
          COLOR 140 204 21
        MAXSIZE 100
        MINSIZE 1
        SIZE 1
        SYMBOL 0
      END
    END
    CLASS
      NAME "Kaltim"
      EXPRESSION ('[name]'='Kaltim')
      STYLE
          COLOR 206 22 72
        MAXSIZE 100
        MINSIZE 1
        SIZE 1
        SYMBOL 0
      END
    END
    CLASS
      NAME "Lampung"
      EXPRESSION ('[name]'='Lampung')
      STYLE
          COLOR 46 24 208
        MAXSIZE 100
        MINSIZE 1
        SIZE 1
        SYMBOL 0
      END
    END
    CLASS
      NAME "Maluku"
      EXPRESSION ('[name]'='Maluku')
      STYLE
          COLOR 26 210 118
        MAXSIZE 100
        MINSIZE 1
        SIZE 1
        SYMBOL 0
      END
    END
    CLASS
      NAME "NTB"
      EXPRESSION ('[name]'='NTB')
      STYLE
          COLOR 212 191 28
        MAXSIZE 100
        MINSIZE 1
        SIZE 1
        SYMBOL 0
      END
    END
    CLASS
      NAME "NTT"
      EXPRESSION ('[name]'='NTT')
      STYLE
          COLOR 214 30 165
        MAXSIZE 100
        MINSIZE 1
        SIZE 1
        SYMBOL 0
      END
    END
    CLASS
      NAME "Riau"
      EXPRESSION ('[name]'='Riau')
      STYLE
          COLOR 32 96 216
        MAXSIZE 100
        MINSIZE 1
        SIZE 1
        SYMBOL 0
      END
    END
    CLASS
      NAME "Sumbar"
      EXPRESSION ('[name]'='Sumbar')
      STYLE
          COLOR 33 218 40
        MAXSIZE 100
        MINSIZE 1
        SIZE 1
        SYMBOL 0
      END
    END
    CLASS
      NAME "Sumsel"
      EXPRESSION ('[name]'='Sumsel')
      STYLE
          COLOR 220 112 35
        MAXSIZE 100
        MINSIZE 1
        SIZE 1
        SYMBOL 0
      END
    END
    CLASS
      NAME "Sumut"
      EXPRESSION ('[name]'='Sumut')
      STYLE
          COLOR 185 37 221
        MAXSIZE 100
        MINSIZE 1
        SIZE 1
        SYMBOL 0
      END
    END
    CLASS
      NAME "Sulsel"
      EXPRESSION ('[name]'='Sulsel')
      STYLE
          COLOR 39 190 223
        MAXSIZE 100
        MINSIZE 1
        SIZE 1
        SYMBOL 0
      END
    END
    CLASS
      NAME "Sulteng"
      EXPRESSION ('[name]'='Sulteng')
      STYLE
          COLOR 121 225 41
        MAXSIZE 100
        MINSIZE 1
        SIZE 1
        SYMBOL 0
      END
    END
    CLASS
      NAME "Sultra"
      EXPRESSION ('[name]'='Sultra')
      STYLE
          COLOR 227 43 53
        MAXSIZE 100
        MINSIZE 1
        SIZE 1
        SYMBOL 0
      END
    END
    CLASS
      NAME "Yogya"
      EXPRESSION ('[name]'='Yogya')
      STYLE
          COLOR 107 45 229
        MAXSIZE 100
        MINSIZE 1
        SIZE 1
        SYMBOL 0
      END
    END
  END

  LAYER
    DATA "province_full.shp"
    LABELITEM "name"
    NAME "province_labels"
    SIZEUNITS PIXELS
    STATUS ON
    TOLERANCE 0
    TOLERANCEUNITS PIXELS
    TYPE POLYGON
    UNITS METERS
    CLASS
      LABEL
        ANGLE 0.000000
        ANTIALIAS TRUE
        FONT fritqat
        MAXSIZE 256
        MINSIZE 4
        SIZE 8
        TYPE TRUETYPE
        BUFFER 0
        COLOR 255 255 255
        FORCE TRUE
        MINDISTANCE 0
        MINFEATURESIZE 0
        OFFSET 0 0
        PARTIALS TRUE
        POSITION CC
        SHADOWCOLOR 30 30 30
      END
    END
  END

END

--Boundary-00=_hs6r+wMGHJPmojY--