Added a small tool to convert feature distribution maps into feature placement files for mapconv

Author: Beherith

tree_placer_springrts.py

@@ -0,0 +1,129 @@
+from PIL import Image
+import random
+import numpy as np
+import sys
+
+input_imagefile_name= "tetrad_v3_feature_distribution.png"
+base_treename = "allpinesb_ad0_%s_%s_%s" #color type size
+outputline = '{ name = \'%s\', x = %d, z = %d, rot = "%d" ,scale = 1.000000 },'
+colors = ['green','brown','snow','snowgreen']
+types  = ['a','b','c']
+sizes  = ['m','l','xl','xxl']
+snowy_pine_trees = []
+green_pine_trees = []
+for size in sizes:
+	for type in types:
+		for color in colors:
+			if 'snow' in color:
+				snowy_pine_trees.append(base_treename%(color,type,size))
+			else:
+				green_pine_trees.append(base_treename%(color,type,size))
+rocks = ['agorm_rock1', 'agorm_rock2', 'agorm_rock3', 'agorm_rock4', 'agorm_rock5']
+palm_trees = ['ad0_senegal_1', 'ad0_senegal_2', 'ad0_senegal_3', 'ad0_senegal_4', 'ad0_senegal_5', 'ad0_senegal_6', 'ad0_senegal_7', 'ad0_senegal_1_large', 'ad0_senegal_2_large', 'ad0_senegal_3_large', 'ad0_senegal_4_large', 'ad0_senegal_5_large', 'ad0_senegal_6_large', 'ad0_senegal_7_large' ]
+shrooms = ['mushroom0%d'%i for i in range(1,10)]
+ferns = ['cycas%d'%i for i in range(1,9)]
+
+# SPECIFY WHAT KIND OF FEATURES YOU WANT
+RED_FEATURES = shrooms
+GREEN_FEATURES = ferns
+BLUE_FEATURES = green_pine_trees
+
+# SPECIFY THE PROBABILITY THAT A FEATURE WILL BE PLACED
+red_probability = 0.03 *5
+green_probability = 0.005 *5
+blue_probability = 0.03 *5
+
+#SPECIFY WHETHER YOU WANT HIGHER INPUT PIXEL COLOR INTERPRETED AS A SIZE BIAS OR AS A PROBABILITY FOR PLACEMENT # TODO
+red_sizebias = False
+green_sizebias = False
+blue_sizebias = False
+
+
+
+geos = [] # list of geo clusters, [centerx, centery, [pointsx],[pointsy] ]
+print ("Usage: specify a .bmp image to work on\n Red, Green and Blue channels specify different feature classes that will be randomly placed\n Alpha channel small values mark geovents.\nEdit the feature lists in this file to change what kind of features you want.")
+if len(sys.argv) >=2:
+	input_imagefile_name = sys.argv[1]
+
+def clusterGeos(x, y):
+    newGeo = True
+    for geo in geos:
+        if (abs(geo[0]-x) + abs(geo[1]-y)) < 10:
+            geo[2].append(x)
+            geo[3].append(y)
+            geo[0] = int(sum(geo[2])/len(geo[2]))
+            geo[1] = int(sum(geo[3])/len(geo[3]))
+            newGeo = False
+    if newGeo:
+        geos.append([x,y,[x],[y]])
+
+
+def pixelvalue_choice(p,treegroup,sizebias = False):
+    if len(treegroup)> 0:
+        if sizebias:
+        	return treegroup[int((p)*len(treegroup))]
+        else:
+            return treegroup[int((p)*len(treegroup))]
+    else:
+        return None
+
+input_featureimage = Image.open(input_imagefile_name)
+imshape = input_featureimage.size
+input_pixels = input_featureimage.load()
+
+output_featureimage = Image.new('RGB', imshape, color=(0, 0, 0))
+output_pixels = output_featureimage.load()
+print ("Image shape is:",imshape, input_featureimage)
+new_features = []
+for x in range(imshape[0]):
+    for y in range(imshape[1]):
+        pix = input_pixels[x,y]
+        #print (pix)
+        cx = 8*x+4
+        cz = 8*y+4
+        if pix[0] > 0: #snowy
+            if random.random() < (red_probability * pix[0] / 255.0):
+                feature = [pixelvalue_choice(random.random(), RED_FEATURES), cx, cz]
+                new_features.append(feature)
+        if pix[1]>0:
+            if random.random() < (green_probability * pix[1] / 255.0):
+                feature = [pixelvalue_choice(random.random(), GREEN_FEATURES), cx, cz]
+                new_features.append(feature)
+        if pix[2]>0: #snowy
+            if random.random() < (blue_probability * pix[2] / 255.0):
+                feature = [pixelvalue_choice(random.random(), BLUE_FEATURES), cx, cz]
+                new_features.append(feature)
+        if len(pix)>=4:
+            if pix[3] < 10:
+                clusterGeos(x,y)
+
+for geo in geos:
+    cx = 8 * geo[0] + 4
+    cz = 8 * geo[1] + 4
+    new_features.append(['geovent',cx,cz])
+    print("Placing geovent at:", cx,cz)
+
+outf = open('feature_placer_springrts_featureplacement.lua','w')
+typecounts = {}
+for featurename, cx,cz in new_features:
+    if feature[0] is not None:
+        output_pixel = [0,0,0]
+        if featurename in RED_FEATURES:
+            output_pixel[0] = 255-RED_FEATURES.index(featurename)
+        if featurename in GREEN_FEATURES:
+            output_pixel[1] = 255-GREEN_FEATURES.index(featurename)
+        if featurename in BLUE_FEATURES:
+            output_pixel[2] = 255-BLUE_FEATURES.index(featurename)
+        output_pixels[int(cx/8),int(cz/8)] = tuple(output_pixel)
+        # { name = 'ad0_senegal_2', x = 7884, z = 228, rot = "3852" ,scale = 1.000000 },
+        outf.write(outputline % (featurename, cx,cz, random.randint(-32700, 32700)) + '\n')
+        typecounts[featurename] = 1 if featurename not in typecounts else typecounts[featurename] + 1
+
+#print ('\n'.join(['%s:%d'%(ftype,fcount) for ftype, fcount in typecounts.items()]))
+print ('\n'.join(['%s:%d'%(ftype,typecounts[ftype]) for ftype in sorted(typecounts.keys())]))
+outf.close()
+print ("total trees=",len(new_features))
+
+
+
+output_featureimage.save(input_imagefile_name+"_featuremapout.bmp")