def main(in_img_path):
rgb_img = cv2.imread(in_img_path)
overlay_img = cv2.imread(OVERLAY_IMG_PATH, -1)
cascade = cv2.CascadeClassifier(CASCADE_PATH)
faces = cascade.detectMultiScale(cv2.cvtColor(rgb_img,
cv2.COLOR_BGR2GRAY),
scaleFactor=1.1,
minNeighbors=1,
minSize=(1, 1))
if len(faces) <= 0:
check_img(rgb_img, 'total', True, OUT_IMG_PATH)
return
for (x, y, w, h) in faces:
face_img = rgb_img[y:y + h, x:x + w]
if not is_skin(face_img):
continue
# color = hsv_to_bgr(get_hair_color_hsv(face_img))
name, hsv = detect_aikatsu_charactors['anime_based']['bgr_diff'](
get_hair_color_hsv(face_img))
color = hsv_to_bgr(hsv)
cv2.rectangle(rgb_img, (x, y), (x + w, y + h), color, thickness=7)
check_img(rgb_img, 'total', True, OUT_IMG_PATH)
def main(in_img_path):
rgb_img = cv2.imread(in_img_path)
overlay_img = cv2.imread(OVERLAY_IMG_PATH, -1)
cascade = cv2.CascadeClassifier(CASCADE_PATH)
faces = cascade.detectMultiScale(cv2.cvtColor(rgb_img, cv2.COLOR_BGR2GRAY),
scaleFactor=1.1, minNeighbors=1, minSize=(1, 1))
if len(faces) <= 0:
return
for (x, y, w, h) in faces:
face_img = rgb_img[y:y + h, x:x + w]
check_img(face_img, 'face_img', CHECK_IMG_FLAG, OUT_IMG_PATH)
if not is_skin(face_img):
continue
alpha_channel = get_alphachannel(face_img)
check_img(alpha_channel, 'alpha_channel', CHECK_IMG_FLAG, OUT_IMG_PATH)
alpha_channel = fill_void(alpha_channel)
resized_overlay_img = cv2.resize(overlay_img, tuple((w, h)))
mask_img = cv2.bitwise_and(resized_overlay_img, resized_overlay_img,
mask=alpha_channel)
check_img(mask_img, 'mask_img', CHECK_IMG_FLAG, OUT_IMG_PATH)
for i, j in [(i, j) for i in range(h) for j in range(w)]:
if any(mask_img[i, j]):
rgb_img[y + i, x + j] = mask_img[i, j]
check_img(rgb_img, 'total', CHECK_IMG_FLAG, OUT_IMG_PATH)
def main(in_img_path):
rgb_img = cv2.imread(in_img_path)
overlay_img = cv2.imread(OVERLAY_IMG_PATH, -1)
cascade = cv2.CascadeClassifier(CASCADE_PATH)
faces = cascade.detectMultiScale(cv2.cvtColor(rgb_img, cv2.COLOR_BGR2GRAY),
scaleFactor=1.1,
minNeighbors=1,
minSize=(1, 1))
if len(faces) <= 0:
return
for (x, y, w, h) in faces:
face_img = rgb_img[y:y + h, x:x + w]
check_img(face_img, 'face_img', CHECK_IMG_FLAG, OUT_IMG_PATH)
if not is_skin(face_img):
continue
alpha_channel = get_alphachannel(face_img)
check_img(alpha_channel, 'alpha_channel', CHECK_IMG_FLAG, OUT_IMG_PATH)
alpha_channel = fill_void(alpha_channel)
resized_overlay_img = cv2.resize(overlay_img, tuple((w, h)))
mask_img = cv2.bitwise_and(resized_overlay_img,
resized_overlay_img,
mask=alpha_channel)
check_img(mask_img, 'mask_img', CHECK_IMG_FLAG, OUT_IMG_PATH)
for i, j in [(i, j) for i in range(h) for j in range(w)]:
if any(mask_img[i, j]):
rgb_img[y + i, x + j] = mask_img[i, j]
check_img(rgb_img, 'total', CHECK_IMG_FLAG, OUT_IMG_PATH)
def __init__(self,
group,
x,
y,
level,
damage=1,
health=10,
size=(1, 2),
speed=(2, 2),
image=None):
super().__init__(group)
self.x, self.y = x, y # In cell
self.size_x, self.size_y = size # In cell
cell = level.cell_size
self.rect = [x * cell, y * cell, size[0] * cell, size[1] * cell]
self.location = level
self.surface = level.surface
self.image = check_img(
"hello", (level.cell_size * size[0], level.cell_size * size[1]))
self.speed = speed # Speed in move (module)
self.damage = damage
self.hp = health
def __init__(self, surface, x, y, level, size=(1, 2), image=None):
self.surface = surface
self.x, self.y = x, y
self.size_x, self.size_y = size
self.location = level
self.img = check_img(image, (level.cell_size, level.cell_size * 2))
def __init__(self,
surface,
width,
height,
s_x=0,
s_y=0,
text="Menu",
image=None):
self.surface = surface
self.width = width
self.height = height
self.buttons = [
Button_menu(surface,
490,
200,
300,
100,
"Play",
image="resource/menu/background.jpg"),
Button_menu(surface, 490, 350, 300, 100, "Settings"),
Button_menu(surface, 490, 500, 300, 100, "Exit")
]
self.x, self.y = s_x, s_y
self.text = text
self.img = check_img(image, (width - s_x, height - s_y))
def __init__(self, surface, start_and_size, cell_size, image=None):
self.surface = surface
self.rect = start_and_size # (x, y, size_x, size_y)
self.cell_size = cell_size
x, y, size_x, size_y = start_and_size
self.img = check_img(image, (size_x, size_y))
self.objects = {"wall": []}
def main(in_img_path):
rgb_img = cv2.imread(in_img_path)
overlay_img = cv2.imread(OVERLAY_IMG_PATH, -1)
cascade = cv2.CascadeClassifier(CASCADE_PATH)
faces = cascade.detectMultiScale(cv2.cvtColor(rgb_img, cv2.COLOR_BGR2GRAY),
scaleFactor=1.1, minNeighbors=1, minSize=(1, 1))
if len(faces) <= 0:
check_img(rgb_img, 'total', True, OUT_IMG_PATH)
return
for (x, y, w, h) in faces:
face_img = rgb_img[y:y + h, x:x + w]
if not is_skin(face_img):
continue
# color = hsv_to_bgr(get_hair_color_hsv(face_img))
name, hsv = detect_aikatsu_charactors['anime_based']['bgr_diff'](get_hair_color_hsv(face_img))
color = hsv_to_bgr(hsv)
cv2.rectangle(rgb_img, (x, y), (x + w, y + h), color, thickness=7)
check_img(rgb_img, 'total', True, OUT_IMG_PATH)
def set_size(self, size):
self.size_x, self.size_y = size
self.img = check_img(self.img, size)
def set_image(self, img):
self.img = check_img(img, (self.size_x, self.size_y))
