def inferHair(vag_list, enable):
hair_list = []
#70% of chanche to add hair
if enable:
for vag in vag_list:
#Hair rules:
hair_w = vag.w * random.uniform(0.4, 1.5)
hair_h = vag.h * random.uniform(0.4, 1.5)
#center:
x = vag.x
y = vag.y - (hair_h / 2) - (vag.h / 2)
#Calculate Bounding Box:
xmin = int(x - (hair_w / 2))
xmax = int(x + (hair_w / 2))
ymin = int(y - (hair_h / 2))
ymax = int(y + (hair_h / 2))
hair_list.append(
BodyPart("hair", xmin, ymin, xmax, ymax, x, y, hair_w, hair_h))
return hair_list
def inferNip(aur_list):
nip_list = []
for aur in aur_list:
#Nip rules:
# - circle (w == h)
# - min dim: 5
# - bigger if aur is bigger
nip_dim = int(5 + aur.w * random.uniform(0.03, 0.09))
#center:
x = aur.x
y = aur.y
#Calculate Bounding Box:
xmin = int(x - (nip_dim / 2))
xmax = int(x + (nip_dim / 2))
ymin = int(y - (nip_dim / 2))
ymax = int(y + (nip_dim / 2))
nip_list.append(
BodyPart("nip", xmin, ymin, xmax, ymax, x, y, nip_dim, nip_dim))
return nip_list
def resolveTitAurMissingProblems(tits_list, aur_list, problem_code):
if problem_code == 3:
random_tit_factor = random.randint(2, 5) #TOTEST
#Add the first tit:
new_w = aur_list[0].w * random_tit_factor #TOTEST
new_x = aur_list[0].x
new_y = aur_list[0].y
xmin = int(new_x - (new_w / 2))
xmax = int(new_x + (new_w / 2))
ymin = int(new_y - (new_w / 2))
ymax = int(new_y + (new_w / 2))
tits_list.append(
BodyPart("tit", xmin, ymin, xmax, ymax, new_x, new_y, new_w,
new_w))
#Add the second tit:
new_w = aur_list[1].w * random_tit_factor #TOTEST
new_x = aur_list[1].x
new_y = aur_list[1].y
xmin = int(new_x - (new_w / 2))
xmax = int(new_x + (new_w / 2))
ymin = int(new_y - (new_w / 2))
ymax = int(new_y + (new_w / 2))
tits_list.append(
BodyPart("tit", xmin, ymin, xmax, ymax, new_x, new_y, new_w,
new_w))
elif problem_code == 6:
#Find wich aur is full:
d1 = abs(tits_list[0].x - aur_list[0].x)
d2 = abs(tits_list[0].x - aur_list[1].x)
if d1 > d2:
#aur[0] is empty
new_x = aur_list[0].x
new_y = aur_list[0].y
else:
#aur[1] is empty
new_x = aur_list[1].x
new_y = aur_list[1].y
#Calculate Bounding Box:
xmin = int(new_x - (tits_list[0].w / 2))
xmax = int(new_x + (tits_list[0].w / 2))
ymin = int(new_y - (tits_list[0].w / 2))
ymax = int(new_y + (tits_list[0].w / 2))
tits_list.append(
BodyPart("tit", xmin, ymin, xmax, ymax, new_x, new_y,
tits_list[0].w, tits_list[0].w))
elif problem_code == 7:
#Add the first aur:
new_w = tits_list[0].w * random.uniform(0.03, 0.1) #TOTEST
new_x = tits_list[0].x
new_y = tits_list[0].y
xmin = int(new_x - (new_w / 2))
xmax = int(new_x + (new_w / 2))
ymin = int(new_y - (new_w / 2))
ymax = int(new_y + (new_w / 2))
aur_list.append(
BodyPart("aur", xmin, ymin, xmax, ymax, new_x, new_y, new_w,
new_w))
#Add the second aur:
new_w = tits_list[1].w * random.uniform(0.03, 0.1) #TOTEST
new_x = tits_list[1].x
new_y = tits_list[1].y
xmin = int(new_x - (new_w / 2))
xmax = int(new_x + (new_w / 2))
ymin = int(new_y - (new_w / 2))
ymax = int(new_y + (new_w / 2))
aur_list.append(
BodyPart("aur", xmin, ymin, xmax, ymax, new_x, new_y, new_w,
new_w))
elif problem_code == 8:
#Find wich tit is full:
d1 = abs(aur_list[0].x - tits_list[0].x)
d2 = abs(aur_list[0].x - tits_list[1].x)
if d1 > d2:
#tit[0] is empty
new_x = tits_list[0].x
new_y = tits_list[0].y
else:
#tit[1] is empty
new_x = tits_list[1].x
new_y = tits_list[1].y
#Calculate Bounding Box:
xmin = int(new_x - (aur_list[0].w / 2))
xmax = int(new_x + (aur_list[0].w / 2))
ymin = int(new_y - (aur_list[0].w / 2))
ymax = int(new_y + (aur_list[0].w / 2))
aur_list.append(
BodyPart("aur", xmin, ymin, xmax, ymax, new_x, new_y,
aur_list[0].w, aur_list[0].w))
def findBodyPart(image, part_name):
bodypart_list = [] #empty BodyPart list
#Get the correct color filter:
if part_name == "tit":
#Use combined color filter
f1 = np.asarray([0, 0, 0]) # tit color filter
f2 = np.asarray([10, 10, 10])
f3 = np.asarray([0, 0, 250]) # aur color filter
f4 = np.asarray([0, 0, 255])
color_mask1 = cv2.inRange(image, f1, f2)
color_mask2 = cv2.inRange(image, f3, f4)
color_mask = cv2.bitwise_or(color_mask1, color_mask2) #combine
elif part_name == "aur":
f1 = np.asarray([0, 0, 250]) # aur color filter
f2 = np.asarray([0, 0, 255])
color_mask = cv2.inRange(image, f1, f2)
elif part_name == "vag":
f1 = np.asarray([250, 0, 0]) # vag filter
f2 = np.asarray([255, 0, 0])
color_mask = cv2.inRange(image, f1, f2)
elif part_name == "belly":
f1 = np.asarray([250, 0, 250]) # belly filter
f2 = np.asarray([255, 0, 255])
color_mask = cv2.inRange(image, f1, f2)
#find contours:
contours, hierarchy = cv2.findContours(color_mask, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
#for every contour:
for cnt in contours:
if len(cnt) > 5: #at least 5 points to fit ellipse
#(x, y), (MA, ma), angle = cv2.fitEllipse(cnt)
ellipse = cv2.fitEllipse(cnt)
#Fit Result:
x = ellipse[0][0] #center x
y = ellipse[0][1] #center y
angle = ellipse[2] #angle
aMin = ellipse[1][0]
#asse minore
aMax = ellipse[1][1]
#asse maggiore
#Detect direction:
if angle == 0:
h = aMax
w = aMin
else:
h = aMin
w = aMax
#Normalize the belly size:
if part_name == "belly":
if w < 15:
w *= 2
if h < 15:
h *= 2
#Normalize the vag size:
if part_name == "vag":
if w < 15:
w *= 2
if h < 15:
h *= 2
#Calculate Bounding Box:
xmin = int(x - (w / 2))
xmax = int(x + (w / 2))
ymin = int(y - (h / 2))
ymax = int(y + (h / 2))
bodypart_list.append(
BodyPart(part_name, xmin, ymin, xmax, ymax, x, y, w, h))
return bodypart_list