def __init__(self):
self.skin_histogram = HistogramUV()
self.non_skin_histogram = HistogramUV()
self.skin_dist = ProbDist()
self.theta_max = 0.4
self.lower_boundary_skin = [0, 0, 0]
self.upper_boundary_skin = [255, 255, 255]
self.masks = []
class SkinClassifierUV:
def __init__(self):
self.skin_histogram = HistogramUV()
self.non_skin_histogram = HistogramUV()
self.skin_dist = ProbDist()
self.theta_max = 0.4
self.lower_boundary_skin = [0, 0, 0]
self.upper_boundary_skin = [255, 255, 255]
self.masks = []
def load_rgb(self, url):
return cv2.imread(url, 1)
def load_yuv(self, url):
img_in = cv2.imread(url, 1)
img_out = cv2.cvtColor(img_in, cv2.COLOR_BGR2YUV)
return img_out
def learn_from_database(self):
for i in range(1,60):
# img_name = 'img (' + str(i) + ').jpg'
img_name = 'img (' + str(i) + ').jpg'
img_original = self.load_rgb("sfa_database/original/" + img_name)
img_skin = self.load_rgb("sfa_database/skin_extracted/" + img_name)
img_yuv = self.load_yuv("sfa_database/original/" + img_name)
height, width, channels = img_original.shape
v, u, y = cv2.split(img_yuv)
for i in range(0, width):
for j in range(0, height):
key = u[j][i]//8, v[j][i]//8
if self.is_background_color(img_skin[j][i]):
self.non_skin_histogram.fill_bin(key)
else:
self.skin_histogram.fill_bin(key)
print("[LEARNING] " + img_name + " loaded")
self.calculate_prob_dist()
self.calculate_boundaries()
def is_background_color(self, color):
return color[0] == 0 and color[1] == 0 and color[2] == 0
def calculate_prob_dist(self):
for u in range(0,32):
for v in range (0, 32):
key = u, v
is_skin = self.skin_histogram.get_probability(key)
is_non_skin = self.non_skin_histogram.get_probability(key)
if is_skin == 0 or is_non_skin == 0:
continue
T_skin = self.skin_histogram.count / (self.skin_histogram.count + self.non_skin_histogram.count)
T_non_skin = 1.0 - T_skin
result = (is_skin * T_skin) / (is_skin * T_skin + is_non_skin * T_non_skin)
self.skin_dist.fill_bin(key,result)
print('bin: ', u, v, 'prob', result)
def calculate_boundaries(self):
self.masks = []
U_min_bin = 0
U_max_bin = 32
V_min_bin = 0
V_max_bin = 32
U_skin_bins = []
V_skin_bins = []
temp = []
# Collect bins with skin colors
for u in range(0,32):
for v in range(0,32):
key = u, v
if self.skin_dist.get_probability(key) > self.theta_max:
U_skin_bins.append(u)
V_skin_bins.append(v)
temp.append([u,v])
U_min_bin = min(int(s) for s in U_skin_bins)
V_min_bin = min(int(s) for s in V_skin_bins)
U_max_bin = max(int(s) for s in U_skin_bins)
V_max_bin = max(int(s) for s in V_skin_bins)
# print('mins: ', U_min_bin, V_min_bin, 'maxs: ', U_max_bin, V_max_bin)
# print(temp)
for mask in temp:
lower = np.array([mask[1]*8, mask[0]*8,0])
upper = np.array([mask[1]*8 + 8, mask[0]*8 + 8,255])
self.masks.append([lower, upper])
# print(self.masks)
def test(self):
for i in range(57,65):
img_name = 'img (' + str(i) + ').jpg'
print('[TESTING]',img_name)
img_yuv = self.load_yuv("sfa_database/original/" + img_name)
height, width, channels = img_yuv.shape
v, u, y = cv2.split(img_yuv)
for i in range(0, width):
for j in range(0, height):
key = u[j][i]//8, v[j][i]//8
prob = self.skin_dist.get_probability(key)
if(prob > self.theta_max):
img_yuv[j][i] = [255, 255, 255]
cv2.imshow('image',img_yuv)
cv2.waitKey(0)
cv2.destroyAllWindows()
def mark_skin(self, img_rgb):
img_yuv = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2YUV)
# e1 = cv2.getTickCount()
############## TEST ########################
# just for one mask
new_masks = []
for mask in self.masks:
new_masks.append(cv2.inRange(img_yuv, mask[0], mask[1]))
final_mask = new_masks[0]
for i in range(1, len(new_masks)):
final_mask = cv2.bitwise_or(final_mask, new_masks[i])
img_rgb = cv2.bitwise_and(img_rgb,img_rgb, mask = final_mask)
# e2 = cv2.getTickCount()
# t = (e2 - e1)/cv2.getTickFrequency()
# print(t)
return img_rgb
def increase_theta(self):
self.theta_max += 0.1
print('theta_max:',self.theta_max)
