def fill(self, photo_list, max_photo_usage=20):
rand_filelist = []
dist_threshold = 12
print ("Starting fill…")
colrowList = []
for rowi in range (self.height):
for coli in range (self.width):
colrowList.append( (coli, rowi) )
random.shuffle(colrowList)
# We fill pixels in random order so we can easily limit
# the number of usages of a photo
for index, (coli, rowi) in enumerate(colrowList):
if index % self.width == 0:
print ("%i/%i points processed (%i%%)" % (index, self.width*self.height, index/(self.width*self.height)))
pix = self.array[coli,rowi]
min_distance = 10000
around_list = []
for c in (coli-1, coli, coli+1):
for r in (rowi-1, rowi, rowi+1):
try:
around_list.append( self.array[c,r].photo )
except KeyError:
None
choosen = None
for min_image in photo_list:
if min_image.used < max_photo_usage and min_image not in around_list:
tempdist = color.get_distance(pix.target_color, min_image.color)
if tempdist < min_distance:
min_distance=tempdist
choosen = min_image
self.array[coli,rowi].set_photo(choosen, min_distance)
def add_missing(self, photo_list, min_photo_usage=1):
"""
Add some photos in the mosaic to respect the min usage parameter
"""
to_be_corrected = []
for min_image in photo_list:
if min_image.used < min_photo_usage:
to_be_corrected.append(min_image)
for min_image in to_be_corrected:
for nb in range(min_photo_usage-min_image.used):
min_distance = 10000
for rowi in range (self.height):
for coli in range (self.width):
if self.array[coli,rowi].photo.used > min_photo_usage:
pix = self.array[coli,rowi]
tempdist = color.get_distance(pix.target_color, min_image.color)
if tempdist < min_distance:
min_distance=tempdist
choosen_location = (coli,rowi)
self.array[choosen_location].set_photo(min_image, min_distance)
print ( "Missing " + min_image.filename + " set at (" + str(choosen_location) + ")" )