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import numpy as np
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import cv2
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def expand_eyebrows(lmrks, eyebrows_expand_mod=1.0):
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    lmrks = np.array( lmrks.copy(), dtype=np.int32 )
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    # Top of the eye arrays
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    bot_l = lmrks[[35, 41, 40, 42, 39]]
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    bot_r = lmrks[[89, 95, 94, 96, 93]]
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    # Eyebrow arrays
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    top_l = lmrks[[43, 48, 49, 51, 50]]
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    top_r = lmrks[[102, 103, 104, 105, 101]]
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    # Adjust eyebrow arrays
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    lmrks[[43, 48, 49, 51, 50]] = top_l + eyebrows_expand_mod * 0.5 * (top_l - bot_l)
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    lmrks[[102, 103, 104, 105, 101]] = top_r + eyebrows_expand_mod * 0.5 * (top_r - bot_r)
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    return lmrks
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def get_mask(image: np.ndarray, landmarks: np.ndarray) -> np.ndarray:
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    """
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    Get face mask of image size using given landmarks of person
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    """
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    img_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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    mask = np.zeros_like(img_gray)
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    points = np.array(landmarks, np.int32)
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    convexhull = cv2.convexHull(points)
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    cv2.fillConvexPoly(mask, convexhull, 255)
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    return mask
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def face_mask_static(image: np.ndarray, landmarks: np.ndarray, landmarks_tgt: np.ndarray, params = None) -> np.ndarray:
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    """
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    Get the final mask, using landmarks and applying blur
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    """
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    if params is None:
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        left = np.sum((landmarks[1][0]-landmarks_tgt[1][0], landmarks[2][0]-landmarks_tgt[2][0], landmarks[13][0]-landmarks_tgt[13][0]))
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        right = np.sum((landmarks_tgt[17][0]-landmarks[17][0], landmarks_tgt[18][0]-landmarks[18][0], landmarks_tgt[29][0]-landmarks[29][0]))
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        offset = max(left, right)
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        if offset > 6:
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            erode = 15
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            sigmaX = 15
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            sigmaY = 10
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        elif offset > 3:
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            erode = 10
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            sigmaX = 10
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            sigmaY = 8
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        elif offset < -3:
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            erode = -5
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            sigmaX = 5
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            sigmaY = 10
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        else:
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            erode = 5
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            sigmaX = 5
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            sigmaY = 5
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    else:
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        erode = params[0]
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        sigmaX = params[1]
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        sigmaY = params[2]
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    if erode == 15:
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        eyebrows_expand_mod=2.7
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    elif erode == -5:
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        eyebrows_expand_mod=0.5
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    else:
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        eyebrows_expand_mod=2.0
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    landmarks = expand_eyebrows(landmarks, eyebrows_expand_mod=eyebrows_expand_mod)
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    mask = get_mask(image, landmarks)
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    mask = erode_and_blur(mask, erode, sigmaX, sigmaY, True)
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    if params is None:
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        return mask/255, [erode, sigmaX, sigmaY]
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    return mask/255
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def erode_and_blur(mask_input, erode, sigmaX, sigmaY, fade_to_border = True):
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    mask = np.copy(mask_input)
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    if erode > 0:
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        kernel = np.ones((erode, erode), 'uint8')
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        mask = cv2.erode(mask, kernel, iterations=1)
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    else:
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        kernel = np.ones((-erode, -erode), 'uint8')
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        mask = cv2.dilate(mask, kernel, iterations=1)
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    if fade_to_border:
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        clip_size = sigmaY * 2
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        mask[:clip_size,:] = 0
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        mask[-clip_size:,:] = 0
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        mask[:,:clip_size] = 0
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        mask[:,-clip_size:] = 0
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    mask = cv2.GaussianBlur(mask, (0, 0), sigmaX = sigmaX, sigmaY = sigmaY)
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    return mask
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