def __init__(self, metadata_file_path=None, ref_color_table_file_path=None,
color_ref_dir=None, decay_colors="", decay_percentage=0.7,
max_skin_percentage=0.6) :
metadata = None
if metadata_file_path is not None and len(metadata_file_path) > 0 :
with open(metadata_file_path, 'r') as m_file:
metadata = pickle.load(m_file)
m_file.close()
else :
assert color_ref_dir is not None and len(color_ref_dir) > 0
metadata = build_metadata(color_ref_dir)
self._initialize(metadata, decay_colors, decay_percentage, max_skin_percentage)
self._color_table_builder = ColorTableBuilder(self._color_ref_lab)
if ref_color_table_file_path is not None and len(ref_color_table_file_path) > 0 :
rgb_to_color = None
with open(ref_color_table_file_path, 'r') as r_file:
rgb_to_color = pickle.load(r_file)
print "load existed color table"
r_file.close()
self._color_table_builder.load_color_table(rgb_to_color)
class ColorClassifier(object) :
# Load metadatafrom metadata_file_path if # non-empty. Otherwise try build
# metadata from color_ref_dir.
def __init__(self, metadata_file_path=None, ref_color_table_file_path=None,
color_ref_dir=None, decay_colors="", decay_percentage=0.7,
max_skin_percentage=0.6) :
metadata = None
if metadata_file_path is not None and len(metadata_file_path) > 0 :
with open(metadata_file_path, 'r') as m_file:
metadata = pickle.load(m_file)
m_file.close()
else :
assert color_ref_dir is not None and len(color_ref_dir) > 0
metadata = build_metadata(color_ref_dir)
self._initialize(metadata, decay_colors, decay_percentage, max_skin_percentage)
self._color_table_builder = ColorTableBuilder(self._color_ref_lab)
if ref_color_table_file_path is not None and len(ref_color_table_file_path) > 0 :
rgb_to_color = None
with open(ref_color_table_file_path, 'r') as r_file:
rgb_to_color = pickle.load(r_file)
print "load existed color table"
r_file.close()
self._color_table_builder.load_color_table(rgb_to_color)
def _initialize(self, metadata, decay_colors, decay_percentage, max_skin_percentage) :
self._color_ref_rgb = metadata['color_ref_rgb'] # array of [r,g,b] for the reference colors.
self._color_ref_lab = metadata['color_ref_lab'] # array of lab object for the reference colors.
self._color_ref_name = metadata['color_ref_name'] # array of color names.
self._color_num = metadata['color_num'] # The total number of colors.
self._decay_color_index = set() # The index for colors to be decayed, e.g., white and silver.
decay_color_name = decay_colors.split(",")
for i in range(len(self._color_ref_name)) :
if self._color_ref_name[i] in decay_color_name :
print "Decaying color: %s" % self._color_ref_name[i]
self._decay_color_index.add(i)
self._decay_percentage = decay_percentage
self._max_skin_percentage = max_skin_percentage
def detect(self, image_file_path) :
img = cv2.imread(image_file_path)
start_time = time.time()
# generate the foreground
background_removed = remove_background(image_file_path) # background removal
skin_removed = skin_detect(img) # skin removal
foreground = cv2.bitwise_and(background_removed, background_removed, mask = skin_removed) # the foreground mask
skin_percentage = 1 - sum(map(sum, foreground))/1.0/(sum(map(sum, background_removed))+0.1)
# In case of clothes in skin color.
# print "%f skin detected." % skin_percentage
if skin_percentage > self._max_skin_percentage:
# print "Too much skin"
foreground = background_removed
# Find the nearest reference color for each pixel and count
color_histogram = [0] * self._color_num
image_foreground_pixel = 0
for i in range(len(foreground)):
for j in range(len(foreground[0])):
if foreground[i][j] != 255:
continue
image_foreground_pixel += 1
if self._color_table_builder.rgb_to_color[img[i][j][2], img[i][j][1], img[i][j][0]] < 0 :
self._color_table_builder.reset_color(img[i][j][2], img[i][j][1], img[i][j][0])
color_index = int(self._color_table_builder.rgb_to_color[img[i][j][2], img[i][j][1], img[i][j][0]])
color_histogram[color_index] += 1
# Decay colors.
for decay_color in self._decay_color_index :
color_histogram[decay_color] *= self._decay_percentage
max_color_count = max(color_histogram)
return (color_histogram.index(max_color_count),
color_histogram,
max_color_count / (float(image_foreground_pixel)+0.1),
foreground)
def color_index_to_name(self, index) :
return self._color_ref_name[int(index)]
def color_num(self) :
return self._color_num
