lip_utils.py

import os
import json
#from glibvision.cv2_utils import get_numpy_text
from glibvision.numpy_utils import bulge_polygon
import numpy as np
import math
USE_CACHE = True

# face structures are same
# MIT LICENSED
# https://github.com/ageitgey/face_recognition

TOP_LIP = "top_lip"
BOTTOM_LIP = "bottom_lip"
PARTS_CHIN ="chin"
PARTS_LEFT_EYEBROW ="left_eyebrow"
PARTS_RIGHT_EYEBROW ="right_eyebrow"
PARTS_LEFT_EYE ="left_eye"
PARTS_RIGHT_EYE ="right_eye"

POINTS_TOP_LIP = "top_lip"
POINTS_BOTTOM_LIP = "bottom_lip"
POINTS_CHIN = "chin"

COLOR_WHITE=(255,255,255)
COLOR_BLACK=(0,0,0)
COLOR_ALPHA=(0,0,0,0)

DEBUG = False
DEBUG_CHIN = False
face_recognition = None

def load_image_file(path):
    
    image = face_recognition.load_image_file(path)
    data_path=path+".json"
    if USE_CACHE and os.path.exists(data_path):
        with open(data_path, "r") as f:
            face_landmarks_list = json.loads(f.read())
    else:
        face_landmarks_list = image_to_landmarks_list(image)
        if USE_CACHE:
            json_data = json.dumps(face_landmarks_list)
            with open(data_path, "w") as f:
                f.write(json_data)

    return image,face_landmarks_list

def save_landmarks(face_landmarks,out_path):
     json_data = json.dumps(face_landmarks)
     with open(out_path, "w") as f:
        f.write(json_data)

def load_landmarks(input_path):
    with open(input_path, "r") as f:
        face_landmarks_list = json.loads(f.read())
    return face_landmarks_list



def image_to_landmarks_list(image):
    face_landmarks_list = face_recognition.face_landmarks(image)
    return face_landmarks_list

def fill_polygon(image,face_landmarks_list,key,thickness=1,line_color=(255,255,255),fill_color = (255,255,255)):
    points=get_landmark_points(face_landmarks_list,key)
    np_points = np.array(points,dtype=np.int32)
    cv2.fillPoly(image, [np_points], fill_color)
    cv2.polylines(image, [np_points], isClosed=True, color=line_color, thickness=thickness)

def fill_lip(image,face_landmarks_list,thickness=1,line_color=(255,255,255),fill_color = (255,255,255)):
    points1=get_landmark_points(face_landmarks_list,TOP_LIP)[0:7]
    points2=get_landmark_points(face_landmarks_list,BOTTOM_LIP)[0:7]
    
    np_points = np.array(points1+points2[::-1],dtype=np.int32)
    
    
    cv2.fillPoly(image, [np_points], fill_color)
    if thickness > 0:
        cv2.polylines(image, [np_points], isClosed=False, color=line_color, thickness=thickness)

def fill_top(image,face_landmarks_list,thickness=1,line_color=(255,255,255),fill_color = (255,255,255)):
    points1=get_landmark_points(face_landmarks_list,TOP_LIP)[0:7]
    
    np_points = np.array(points1,dtype=np.int32)
    
    
    cv2.fillPoly(image, [np_points], fill_color)
    if thickness > 0:
        cv2.polylines(image, [np_points], isClosed=False, color=line_color, thickness=thickness)

def fill_top_lower(image,face_landmarks_list,thickness=1,line_color=(255,255,255),fill_color = (255,255,255)):
    top_lip_points=get_landmark_points(face_landmarks_list,TOP_LIP) # 5 to 7 ,1 t- 11
    points1 = [lerp_points(top_lip_points[5],top_lip_points[7],0.7)]+ \
        [mid_points(top_lip_points[7],top_lip_points[8])]+ \
        list(top_lip_points[8:11]) +\
         [mid_points(top_lip_points[10],top_lip_points[11])]+ \
        [lerp_points(top_lip_points[1],top_lip_points[11],0.7)]+\
        [mid_points(top_lip_points[2],top_lip_points[10])]+\
        [mid_points(top_lip_points[3],top_lip_points[9])]+\
        [mid_points(top_lip_points[4],top_lip_points[8])]
    
    np_points = np.array(points1,dtype=np.int32)
    
    
    cv2.fillPoly(image, [np_points], fill_color)
    if thickness > 0:
        cv2.polylines(image, [np_points], isClosed=False, color=line_color, thickness=thickness)

def get_lip_mask_points(face_landmarks_list):
    points1=get_landmark_points(face_landmarks_list,TOP_LIP)[0:7]
    points2=get_landmark_points(face_landmarks_list,BOTTOM_LIP)[0:7]
    return points1+points2



from scipy.special import comb

def bernstein_poly(i, n, t):
    """
    n 次ベジェ曲線の i 番目の Bernstein 基底関数を計算する
    """
    return comb(n, i) * (t**(n-i)) * (1 - t)**i

def bezier_curve(points, num_points=100):
    """
    与えられた点からベジェ曲線を計算する
    """
    nPoints = len(points)
    xPoints = np.array([p[0] for p in points])
    yPoints = np.array([p[1] for p in points])

    t = np.linspace(0.0, 1.0, num_points)

    polynomial_array = np.array([bernstein_poly(i, nPoints-1, t) for i in range(0, nPoints)])

    xvals = np.dot(xPoints, polynomial_array)
    yvals = np.dot(yPoints, polynomial_array)

    return np.array(list(zip(xvals, yvals)))
import cv2
import numpy as np


    



def fill_eyes(image,face_landmarks_list,thickness=1,line_color=(255,255,255),fill_color = (255,255,255)):
    points1=get_landmark_points(face_landmarks_list,PARTS_LEFT_EYE)
    points2=get_landmark_points(face_landmarks_list,PARTS_RIGHT_EYE)
    
    for points in [points1,points2]:
        #points = bezier_curve(points, num_points=10)
        #print(points)
        points = bulge_polygon(points, bulge_factor=0.2)
        #print(points)
        np_points = np.array(points,dtype=np.int32)
        
        cv2.fillPoly(image, [np_points], fill_color)
        if thickness > 0:
            cv2.polylines(image, [np_points], isClosed=False, color=line_color, thickness=thickness)





def fill_face(image,face_landmarks_list,thickness=1,line_color=(255,255,255),fill_color = (255,255,255)):
    points1=get_landmark_points(face_landmarks_list,PARTS_LEFT_EYEBROW)
    points2=get_landmark_points(face_landmarks_list,PARTS_RIGHT_EYEBROW)
    points3=get_landmark_points(face_landmarks_list,PARTS_CHIN)
    
    np_points = np.array(points1+points2+points3[::-1],dtype=np.int32)
    
    
    cv2.fillPoly(image, [np_points], fill_color)
    cv2.polylines(image, [np_points], isClosed=False, color=line_color, thickness=thickness) 

def fill_face_inside(image,face_landmarks_list,thickness=1,line_color=(255,255,255),fill_color = (255,255,255)):
    print("not support yet")
    return None
    points1=get_landmark_points(face_landmarks_list,PARTS_LEFT_EYEBROW)
    points2=get_landmark_points(face_landmarks_list,PARTS_RIGHT_EYEBROW)
    points3=get_landmark_points(face_landmarks_list,PARTS_CHIN)
    points3=get_landmark_points(face_landmarks_list,PARTS_CHIN)
    points3=get_landmark_points(face_landmarks_list,PARTS_CHIN)
    
    np_points = np.array(points1+points2+points3[::-1],dtype=np.int32)
    
    
    cv2.fillPoly(image, [np_points], fill_color)
    cv2.polylines(image, [np_points], isClosed=False, color=line_color, thickness=thickness)   

def half_pt(point1,point2):
    return [sum(x) / 2 for x in zip(point1, point2)]


def line_lip(image,face_landmarks_list,key,thickness=1,line_color=(255,255,255)):
    points=get_landmark_points(face_landmarks_list,key)
    print(len(points))
    #st=[(points[0]+points[11])/2]
    st = [sum(x) / 2 for x in zip(points[0], points[11])]

    #et=[(points[6]+points[7])/2]
    et = [sum(x) / 2 for x in zip(points[6], points[7])]
    print(et)
    print(points)
    np_points = np.array([st]+points[1:6]+[et],dtype=np.int32)
    #if key == TOP_LIP:
    cv2.polylines(image, [np_points], isClosed=False, color=line_color, thickness=thickness)

def get_lip_hole_points(face_landmarks_list):
    top_points=get_landmark_points(face_landmarks_list,TOP_LIP)
    bottom_points=get_landmark_points(face_landmarks_list,BOTTOM_LIP)
    return top_points[7:]+bottom_points[7:]#[::-1]
    #np_points = np.array(top_points[7:]+bottom_points[7:][::-1],dtype=np.int32)

def get_lip_hole_top_points(face_landmarks_list):
    top_points=get_landmark_points(face_landmarks_list,TOP_LIP)
    #bottom_points=get_landmark_points(face_landmarks_list,BOTTOM_LIP)
    return top_points[7:]

def get_lip_hole_bottom_points(face_landmarks_list):
    #top_points=get_landmark_points(face_landmarks_list,TOP_LIP)
    bottom_points=get_landmark_points(face_landmarks_list,BOTTOM_LIP)
    #inverted for connect top
    return bottom_points[7:][::-1]

#for hide too long tooth
def get_lip_hole_bottom_half_points(face_landmarks_list):
    #top_points=get_landmark_points(face_landmarks_list,TOP_LIP)
    bottom_points=get_landmark_points(face_landmarks_list,BOTTOM_LIP)
    #inverted for connect top
    st = [sum(x) / 2 for x in zip(bottom_points[7], bottom_points[8])]
    et = [sum(x) / 2 for x in zip(bottom_points[10], bottom_points[11])]
    points = [st]+bottom_points[8:11]+[et]
    #print(points)
    return points[::-1]

def fill_points(points,image,thickness=1,line_color=(255,255,255),fill_color = (255,255,255)):
    np_points = np.array(points,dtype=np.int32)
    
    

    cv2.fillPoly(image, [np_points], fill_color)
    if thickness>0:
        cv2.polylines(image, [np_points], isClosed=False, color=line_color, thickness=thickness) 

def fill_lip_hole_top(image,face_landmarks_list,thickness=1,line_color=(255,255,255),fill_color = (255,255,255)):
    np_points = np.array(get_lip_hole_top_points(face_landmarks_list),dtype=np.int32)
    
    cv2.fillPoly(image, [np_points], fill_color)
    cv2.polylines(image, [np_points], isClosed=False, color=line_color, thickness=thickness) 



def fill_lip_hole(image,face_landmarks_list,thickness=1,line_color=(255,255,255),fill_color = (255,255,255)):
    np_points = np.array(get_lip_hole_points(face_landmarks_list),dtype=np.int32)
    #print(np_points)
    cv2.fillPoly(image, [np_points], fill_color)
    cv2.polylines(image, [np_points], isClosed=False, color=line_color, thickness=thickness) 



def get_landmark_points(face_landmarks_list,key):
    matching_landmark_points = []
    for face_landmarks in face_landmarks_list:
      for landmark_name, landmark_points in face_landmarks.items():
        #matching_landmark_points = landmark_points.copy()
        if landmark_name ==key:
            for value in landmark_points:
                matching_landmark_points.append([value[0],value[1]])
            return tuple(matching_landmark_points)

def get_image_size(cv2_image):
    return cv2_image.shape[:2]

def get_top_lip_box(face_landmarks_list,margin = 0):
    print(f"get_top_lip_box margin = {margin}")
    points = get_landmark_points(face_landmarks_list,TOP_LIP)
    box= points_to_box(points)
    if margin>0:
        return ((box[0][0] - margin,box[0][1] - margin),(box[1][0] + margin, box[1][1] + margin))
    else:
        return box

def get_points_box(face_landmarks_list,key,margin = 0):
    print(f"margin = {margin}")
    points = get_landmark_points(face_landmarks_list,key)
    box= points_to_box(points)
    if margin>0:
        return ((box[0][0] - margin,box[0][1] - margin),(box[1][0] + margin, box[1][1] + margin))
    else:
        return box

#for size up 

def create_moved_image(image,src_points,dst_points,force_size=None):
    # keep top of lip stable but affing must be 4 point
    #print(f"src = {src_points}")
    #print(f"dst = {dst_points}")
    src_pts=np.array([src_points],dtype=np.float32)
    dst_pts=np.array([dst_points],dtype=np.float32)
    #BORDER_REPLICATE
    return warp_with_auto_resize(image, src_pts, dst_pts,cv2.BORDER_REPLICATE,force_size)

# lip-index
"""
 1 2 3 4 5
0           6
 11 10 9 8 7
"""
def get_top_lip_align_points(face_landmarks_list):
    landmark=get_landmark_points(face_landmarks_list,TOP_LIP)
    index_center = 3
    index_right= 0 #mirror
    #index_ritht_top= 2 #mirror
    #index_left_top= 4 #mirror
    index_left = 6
    #if landmark_name ==key:
    # 0 is right edge
    x1 = landmark[index_right][0]
    y1 = landmark[index_right][1]
    # 6 is left edge
    x2 = landmark[index_left][0]
    y2 = landmark[index_left][1]

    #left_top = landmark[index_left_top][1]
    #right_top = landmark[index_ritht_top][1]
    #top = left_top if left_top<right_top else right_top

    # bottom center position
    cx = (x1+x2)/2
    cy = (y1+y2)/2

   
    diffx=(landmark[index_center][0]-cx)
    diffy=(landmark[index_center][1]-cy)

    #print(f"x1={x1} y1={y1} x2={x2} y2={y2} cx={cx} cy={cy} diffx={diffx} diffy={diffy}")

    #plt.scatter(cx,cy, c='r', s=10)
    return ((int(x1+diffx),int(y1+diffy)),(int(x2+diffx),int(y2+diffy)),(x1,y1),(x2,y2))


def calculate_new_point(start_point, distance, angle):
    x1, y1 = start_point
    angle_rad = math.radians(angle)
    
    # 新しい点の座標を計算
    new_x = x1 + distance * math.cos(angle_rad)
    new_y = y1 + distance * math.sin(angle_rad)
    
    return (new_x, new_y)

def calculate_clockwise_angle(point1, point2):
    x1, y1 = point1
    x2, y2 = point2
    
    # atan2を使用して角度を計算
    angle_rad = math.atan2(y2 - y1, x2 - x1)
    
    # 反時計回りから時計回りに変換
    if angle_rad < 0:
        angle_rad += 2 * math.pi
    
    # ラジアンから度に変換
    angle_deg = math.degrees(angle_rad)
    
    return angle_deg

def get_bottom_lip_align_points(landmarks_list):
    points =  get_landmark_points(landmarks_list,POINTS_BOTTOM_LIP)
    return (points[0],points[3],points[6],points[9])

def get_bottom_lip_width_height(landmarks_list):
    points =  get_landmark_points(landmarks_list,POINTS_BOTTOM_LIP)
    return (points[0][0] -points[6][0],points[3][1] -points[9][1])


def crop_image(image,x1,y1,x2,y2):
    return image[y1:y2, x1:x2]

def crop_cv2_image_by_box(image,box):
    return crop_image_by_box(image,box)

def crop_image_by_box(image,box):
    print(f"crop_cv2_image_by_box yy2 xx2 {box[0][1]}:{box[1][1]},{box[0][0]}:{box[1][0]}")
    return image[box[0][1]:box[1][1], box[0][0]:box[1][0]]

def get_top_lip_datas(img,margin=4):
      landmarks_list=image_to_landmarks_list(img)
      box = get_top_lip_box(landmarks_list,margin)
      cropped_img = crop_cv2_image_by_box(img,box)
      points = get_top_lip_points(landmarks_list) #its rectangle but not square
      return landmarks_list,cropped_img,points,box

def get_bottom_lip_datas(img,margin=4):
      landmarks_list=image_to_landmarks_list(img)
      box = get_points_box(landmarks_list,POINTS_BOTTOM_LIP,margin)
      cropped_img = crop_cv2_image_by_box(img,box)
      points = get_bottom_lip_align_points(landmarks_list) #its rectangle but not square
      return landmarks_list,cropped_img,points,box

def offset_points(points,offset):
    new_points = []
    for point in points:
        new_points.append((point[0]-offset[0],point[1]-offset[1]))
    return new_points


def points_to_box(points):
   min_x = 0
   min_y = 0
   min_x  = float('inf')
   min_y  = float('inf')
   max_x= 0
   max_y= 0
   for point in points:
      if point[0]>max_x:
            max_x=int(point[0])
      if point[1]>max_y:
            max_y=int(point[1])
      if point[0]<min_x:
            min_x=int(point[0])
      if point[1]<min_y:
            min_y=int(point[1])
   return ((min_x,min_y),(max_x,max_y))



import cv2
import numpy as np

def warp_with_auto_resize(img, src_pts, dst_pts, borderMode=cv2.BORDER_TRANSPARENT, force_size=None):
  """
  画像を WRAP 変換し、はみ出した場合は自動的にサイズを調整します。

  Args:
      img: 変換対象の画像 (numpy array)
      src_pts: 変換元の四角形の頂点 (numpy array)
      dst_pts: 変換先の四角形の頂点 (numpy array)

  Returns:
      変換後の画像 (numpy array)
  """
  # 変換行列を計算
  mat = cv2.getPerspectiveTransform(src_pts, dst_pts)

  # 変換後の画像サイズを計算
  h, w = img.shape[:2]
  corners = np.float32([[0, 0], [w, 0], [w, h], [0, h]])
  warped_corners = cv2.perspectiveTransform(corners.reshape(-1, 1, 2), mat).reshape(-1, 2)

  # 変換後の画像の最小矩形を計算 (元の画像の四隅も含めて計算)
  min_x = np.min(warped_corners[:, 0])
  min_y = np.min(warped_corners[:, 1])
  max_x = np.max(warped_corners[:, 0])
  max_y = np.max(warped_corners[:, 1])
  new_w, new_h = int(max_x - min_x), int(max_y - min_y)

  # 変換行列を更新 (平行移動成分を追加)
  mat[0, 2] += -min_x
  mat[1, 2] += -min_y

  if force_size:
    new_w = force_size[0]
    new_h = force_size[1]

  warped_img = cv2.warpPerspective(img, mat, (new_w, new_h), flags=cv2.INTER_LANCZOS4, borderMode=borderMode)

  return warped_img

def warp_with_auto_resize1(img, src_pts, dst_pts,borderMode= cv2.BORDER_TRANSPARENT,force_size=None):
  """
  画像を WRAP 変換し、はみ出した場合は自動的にサイズを調整します。

  Args:
      img: 変換対象の画像 (numpy array)
      src_pts: 変換元の四角形の頂点 (numpy array)
      dst_pts: 変換先の四角形の頂点 (numpy array)

  Returns:
      変換後の画像 (numpy array)
  """
  # 変換行列を計算
  mat = cv2.getPerspectiveTransform(src_pts, dst_pts)

  # 変換後の画像サイズを計算
  h, w = img.shape[:2]
  #print(f"img w{w} h{h}")
  corners = np.float32([[0, 0], [w, 0], [w, h], [0, h]])
  warped_corners = cv2.perspectiveTransform(corners.reshape(-1, 1, 2), mat).reshape(-1, 2)

  # 変換後の画像の最小矩形を計算
  min_x, min_y = np.min(warped_corners, axis=0)
 
  max_x, max_y = np.max(warped_corners, axis=0)
  new_w, new_h = int(max_x - min_x), int(max_y - min_y)
  #print(f"min x {min_x} min y {min_y}")
  #print(f"max x {max_x} max y {max_y}")
  # 変換行列を更新 (平行移動成分を追加)
  mat[0, 2] += -min_x
  mat[1, 2] += -min_y

  #print(f"audo w{new_w} h{new_h}")

  if force_size:
    new_w = force_size[0]
    new_h = force_size[1]

  warped_img = cv2.warpPerspective(img, mat, (new_w, new_h),flags=cv2.INTER_LANCZOS4, borderMode=borderMode)

  return warped_img

def get_channel(np_array):
    return np_array.shape[2] if np_array.ndim == 3 else 1 

def print_numpy(np_array,key=""):
    channel = get_channel(np_array)
    print(f"{key} shape = {np_array.shape} channel = {channel} ndim = {np_array.ndim} size = {np_array.size}")

def create_color_image(img,color=(255,255,255)):
    mask = np.zeros_like(img)
    h, w = img.shape[:2]
    cv2.rectangle(mask, (0, 0), (w, h), color, -1)
    return mask

def create_mask(img,color=(255,255,255)):
    mask = np.zeros_like(img)
    h, w = img.shape[:2]
    cv2.rectangle(mask, (0, 0), (w, h), color, -1)
    return mask

def create_rgba(width,height):
    return np.zeros((height, width, 4), dtype=np.uint8)

def create_rgb(width,height):
    return np.zeros((height, width, 3), dtype=np.uint8)

def create_gray(width,height):
    return np.zeros((height, width), dtype=np.uint8)


def copy_image(img1, img2, x, y):
    """
    Paste img2 onto img1 at position (x, y).
    If img2 extends beyond the bounds of img1, only the overlapping part is pasted.

    Parameters:
    img1 (numpy.ndarray): The base image to modify (H, W, C).
    img2 (numpy.ndarray): The image to paste onto img1 (h, w, C).
    x (int): The x-coordinate where img2 will be placed.
    y (int): The y-coordinate where img2 will be placed.

    Raises:
    TypeError: If img1 or img2 are not NumPy arrays.
    ValueError: If x or y are out of bounds of img1.
    ValueError: If img1 and img2 do not have the same number of channels or are not 3-dimensional arrays.
    """
    # Type check
    if not isinstance(img1, np.ndarray) or not isinstance(img2, np.ndarray):
        raise TypeError("img1 and img2 must be NumPy arrays.")

    # Channel count check
    if img1.ndim != 3 or img2.ndim != 3 or img1.shape[2] != img2.shape[2]:
        raise ValueError("img1 and img2 must have the same number of channels and be 3-dimensional arrays.")

    # Bounds check
    max_y, max_x, _ = img1.shape
    if not (0 <= y < max_y and 0 <= x < max_x):
        raise ValueError(f"x ({x}) and y ({y}) must be within the bounds of img1 ({max_x}, {max_y}).")

    # Calculate the height and width of the overlapping part
    h = min(img2.shape[0], max_y - y)
    w = min(img2.shape[1], max_x - x)

    # Paste the overlapping part
    img1[y:y+h, x:x+w] = img2[:h, :w]


def copy_color(img1,x,y,x2,y2,color):
    color_img = np.full((y2-y, x2-x, 4), color, dtype=np.uint8)
    img1[y:y2, x:x2] = color_img



def multiply_point(point,multiply):
    return int(point[0]*multiply),int(point[1]*multiply)

def get_resized_top_pos(points,multiply=0.5):
      diff_left = multiply_point((points[0][0]-points[2][0],points[0][1]-points[2][1]),multiply)
      diff_right = multiply_point((points[1][0]-points[3][0],points[1][1]-points[3][1]),multiply)
      return (diff_right,diff_left)


def get_alpha_image(base_image,landmarks_list,key,margin = 0,dilation_size = 2,gaussian_size = 2):
    box = get_points_box(landmarks_list,key,margin)
    # box expand margin
    cropped_img = crop_cv2_image_by_box(base_image,box)
    # convert RGBA
    if cropped_img.shape[2] == 3: # Check if the image has 3 channels (RGB)
        image_rgba = cv2.cvtColor(cropped_img, cv2.COLOR_BGR2BGRA)
        mask = np.zeros(cropped_img.shape[:2], dtype="uint8")
    else:
        print("already alpha skipped")
        image_rgba = cropped_img
        mask = np.zeros(cropped_img.shape[:2], dtype="uint8")
        #mask = cropped_img[:, :, 3].copy() # if you use this .some how block
    
    global_points = get_landmark_points(landmarks_list,key)
    
    local_points =  offset_points(global_points,box[0]) 
    print(local_points)
    # create Lip Mask
    np_points = np.array(local_points,dtype=np.int32)
    
    cv2.fillPoly(mask, [np_points], 255)

    kernel = np.ones((dilation_size, dilation_size), np.uint8)

    dilated_mask = cv2.dilate(mask, kernel, iterations=1)
    #dilated_mask = cv2.erode(mask, kernel, iterations=1) # TODO support dilation_size

    # Gaussian Blur
    if gaussian_size > 0:
        smooth_mask = cv2.GaussianBlur(dilated_mask, (0,0 ), sigmaX=gaussian_size, sigmaY=gaussian_size)
        expanded_mask = np.expand_dims(smooth_mask, axis=-1)
    else:
        expanded_mask = np.expand_dims(dilated_mask, axis=-1)

    #lip_utils.print_numpy(image_rgba,"rgba")
    #lip_utils.print_numpy(smooth_mask,"smooth")
    #lip_utils.print_numpy(expanded_mask,"expanded_mask")

    image_rgba[..., 3] = expanded_mask[..., 0]


    return image_rgba,box

def apply_mask(image,mask):
    if len(mask.shape) == 3:
        expanded_mask = mask
    else:
        expanded_mask = np.expand_dims(mask, axis=-1)

    if len(mask.shape)!=3:
        error = f"image must be shape 3 {image.shape}"
        raise ValueError(error)

    if get_channel(image)!=4:
        image_rgba = cv2.cvtColor(image, cv2.COLOR_BGR2BGRA) #why rgb to gray?
    else:
        image_rgba = image
    image_rgba[..., 3] = expanded_mask[..., 0]
    return image_rgba

def apply_mask_alpha(image,mask,invert=False):
    if len(mask.shape) == 3:
        expanded_mask = mask
    else:
        expanded_mask = np.expand_dims(mask, axis=-1)

    image_rgba = cv2.cvtColor(image, cv2.COLOR_BGR2BGRA)
    if invert:
        image_rgba[..., 3] = expanded_mask[..., 0]
    else:
        image_rgba[..., 3] = 255 - expanded_mask[..., 0]
    return image_rgba

def print_width_height(image,label):
    new_h,new_w = get_image_size(image)
    print(f"{label}:width = {new_w} height = {new_h}")


def create_mask_from_points(img,points,dilation_size=4,gaussian_size=4):
    np_points = np.array(points,dtype=np.int32)
    mask = np.zeros(img.shape[:2], dtype="uint8")
    cv2.fillPoly(mask, [np_points], 255)

    kernel = np.ones((abs(dilation_size),abs(dilation_size) ), np.uint8)
    if dilation_size > 0:
        dilated_mask = cv2.dilate(mask, kernel, iterations=1)
    else:
        dilated_mask = cv2.erode(mask, kernel, iterations=1) # TODO support dilation_size
    # Gaussian Blur
    if gaussian_size > 0:
        smooth_mask = cv2.GaussianBlur(dilated_mask, (0,0 ), sigmaX=gaussian_size, sigmaY=gaussian_size)
        expanded_mask = np.expand_dims(smooth_mask, axis=-1)
    else:
        expanded_mask = np.expand_dims(dilated_mask, axis=-1)
    return expanded_mask
    #lip_utils.print_numpy(image_rgba,"rgba")
    #lip_utils.print_numpy(smooth_mask,"smooth")
    #lip_utils.print_numpy(expanded_mask,"expanded_mask")

def mid_points(point1,point2):
    return [sum(x) / 2 for x in zip(point1,point2)]

def lerp_points(point1, point2, lerp):
    return [(1.0 - lerp) * p1 + lerp * p2 for p1, p2 in zip(point1, point2)]

def get_jaw_points(face_landmarks_list):
    chin_points = get_landmark_points(face_landmarks_list,POINTS_CHIN)
    bottom_lip_points = get_landmark_points(face_landmarks_list,POINTS_BOTTOM_LIP)

    points =[]
    
    points.extend(chin_points[4:13])
    points.append(mid_points(chin_points[12],bottom_lip_points[0]))
    points.append(mid_points(chin_points[8],bottom_lip_points[3]))
    points.append(mid_points(chin_points[4],bottom_lip_points[6]))

    return points

def get_bottom_mid_drop_size(open_size_y,lip_height):
    # when full open case open_size_y 40 lip become half
    mid_lip_move_ratio = open_size_y/80.0 if open_size_y>0 else 0
    return mid_lip_move_ratio*lip_height


def fade_in_x(img,size):
    if size==0:
        return
    per_pixel = 1.0/size
    for y in range(img.shape[0]):
        for x in range(img.shape[1]):

            if x <size:
                alpha_base = per_pixel * x
                # アルファ値を変更し、ピクセルに設定
                #print(f"before x ={x} = {img[y,x,3]} after = {img[y,x,3] * alpha_base}")
                img[y, x, 3] =  img[y,x,3] * alpha_base
def fade_out_x(img,size):
    if size==0:
        return
    per_pixel = 1.0/size
    w = img.shape[1]

    for y in range(img.shape[0]):
        for x in range(img.shape[1]):

            if x >w:
                diff = x - w
                alpha_base = 1.0 - (per_pixel * x)
                # アルファ値を変更し、ピクセルに設定
                #print(f"before x ={x} = {img[y,x,3]} after = {img[y,x,3] * alpha_base}")
                img[y, x, 3] =  img[y,x,3] * alpha_base


def alpha_blend_with_image2_alpha(image1, image2):
    return cv2.addWeighted(image1, 1, image2, 1, 0)
def numpy_alpha_blend_with_image2_alpha(image1, image2,invert=False):
    """
    image1をimage2のアルファチャンネルを使用してアルファブレンディングします。
    """
    # 画像のサイズを確認し、必要に応じてリサイズします。
    if image1.shape[:2] != image2.shape[:2]:
        image1 = cv2.resize(image1, (image2.shape[1], image2.shape[0]))
    
    src1 = np.array(image1)
    src2 = np.array(image2)
    mask1 = np.array(image2[:, :, 3])
    mask1 = mask1 / 255
    mask1 = np.expand_dims(mask1, axis=-1)
    if invert:
        dst = src1 * (1-mask1) + src2 * mask1
    else:
        dst = src1 * mask1 + src2 * (1 - mask1)
    # アルファブレンディングを行います。
    #blended = cv2.cvtColor(dst, cv2.COLOR_BGRA2BGRA)
    dst = dst.astype(np.uint8)
    return dst

def distance_2d(point1, point2):
    return math.sqrt((point2[0] - point1[0])**2 + (point2[1] - point1[1])**2)

# points[index][x=0 y=1] index is see landmark image by plot2.py
def get_top_lip_thicks(landmarks_list,is_distance_base=False):
    points =  get_landmark_points(landmarks_list,POINTS_TOP_LIP)
    if is_distance_base:
        return (distance_2d(points[10],points[2]),distance_2d(points[9],points[3]),distance_2d(points[8],points[4]))
    return (points[10][1] -points[2][1],points[9][1] -points[3][1],points[8][1] -points[4][1])


def scale_down_values(data, scale_factor=0.25):
    """
    Scales down the values in a list of dictionaries by a given scale factor.
    
    Parameters:
    - data: A list of dictionaries where each dictionary represents facial landmarks.
    - scale_factor: The factor by which to scale down the values. Default is 0.25 (1/4).
    
    Returns:
    - A new list of dictionaries with scaled down values.
    """
    scaled_data = []
    for item in data:
        scaled_item = {}
        for key, values in item.items():
            scaled_values = [(int(x * scale_factor), int(y * scale_factor)) for x, y in values]
            scaled_item[key] = scaled_values
        scaled_data.append(scaled_item)
    return scaled_data

def save_landmarks(face_landmarks,out_path):
     json_data = json.dumps(face_landmarks)
     with open(out_path, "w") as f:
        f.write(json_data)

def load_landmarks(input_path):
    with open(input_path, "r") as f:
        face_landmarks_list = json.loads(f.read())
    return face_landmarks_list