Cut Fragments from Sprite

gistfile1.txt

#!/usr/bin/env python

import sys, os
import numpy as np
from matplotlib import cm
from scipy import ndimage, misc, spatial

OKGREEN = '\033[92m'
WARNING = '\033[93m'
ENDC = '\033[0m'
BOLD = '\033[1m'

filename = sys.argv[1]
fileroot = os.path.splitext(filename)[0]

image = misc.imread(filename)

if len(sys.argv) > 2:
	hx = sys.argv[2]
	channels = [hx[0:2], hx[2:4], hx[4:6], "FF"]
	crack_color = [int(c, 16) for c in channels]
else:
	crack_color = image[0,0]

print "Cracks with color: %s" % (crack_color)

cracks = np.all(image == crack_color, axis=-1)
transparent = image[:,:,3] == 0

regions = 1 - np.logical_or(cracks,	transparent)

labels, num_labels = ndimage.label(regions)


# Structuring element that expands to bottom right
# when used for dilation
strel = np.array([[0,0,0],
				  [0,1,1],
				  [1,1,1]])


# We're going to assign all the cracks to the closest region
# in a way so that each crack pixel belongs to exactly one region

# Create a list of coordinates
positions = np.indices(labels.shape).transpose([1,2,0])

# Filter that list by the pixels that belong to a region
positions = positions[labels > 0]

# A KDTree is super overkill but it does save on some code here.
# Feed the KDTree the coordinates of all non-zero label positions
kdtree = spatial.KDTree(positions)

# Get positions of crack pixels
crack_positions = np.transpose(np.nonzero(cracks))

# Offset the cracks a tiny bit to bias the cracks to stick to
# the closest pixel on the top-left
crack_positions_offset = crack_positions.astype(float) + [[-0.1, -0.4]]

dists, idxs = kdtree.query(crack_positions_offset)

closest_region_position = positions[idxs]
closest_region_label = labels[closest_region_position[:,0], closest_region_position[:,1]]

# Assign the regions to the label matrix
labels[ crack_positions[:,0], crack_positions[:,1] ] = closest_region_label

# Save image that are transparent except for the region
for label in range(1,num_labels+1):
	to_delete = (labels != label)
	isolated = np.copy(image)
	# Set all other pixels to transparent
	isolated[to_delete] = [0,0,0,0]
	# Compute a centroid
	cy, cx = np.average(np.transpose(np.nonzero(labels == label)), axis=0).astype(int)
	cy = round(cy / 4) * 4
	cx = round(cx / 4) * 4
	# Save the image using the centroid as name
	# We do this so that the names of unaffected pieces
	# stay the same if other pieces are merged or split
	savepath = '%s_s%03d_%03d.png' % (fileroot, cx, cy)
	if os.path.exists(savepath):
		status = WARNING + BOLD + "Overwriting" + ENDC
	else:
		status = OKGREEN + "New" + ENDC
	print "[%d/%d] %s %s" % (label, num_labels, status, os.path.basename(savepath))
	misc.imsave(savepath, isolated)

# Save an image of the generated regions
colormap = cm.get_cmap("Set3")
colorlabels = colormap(labels / float(np.max(labels)))
colorlabels[:,:,-1] = image[:,:,-1] / 255.0
misc.imsave('%s_labels.png' % (fileroot), colorlabels)