utils.py

import numpy as np
import mediapipe as mp
import cv2


# landmarks of features from mediapipe
face_points={
"BLUSH_LEFT": [50],
"BLUSH_RIGHT": [280],
"LEFT_EYE": [33, 246, 161, 160, 159, 158, 157, 173, 133, 155, 154, 153, 145, 144, 163, 7, 33],
"RIGHT_EYE": [362, 298, 384, 385, 386, 387, 388, 466, 263, 249, 390, 373, 374, 380, 381, 382, 362],
"EYELINER_LEFT": [243, 112, 26, 22, 23, 24, 110, 25, 226, 130, 33, 7, 163, 144, 145, 153, 154, 155, 133, 243],
"EYELINER_RIGHT": [463, 362, 382, 381, 380, 374, 373, 390, 249, 263, 359, 446, 255, 339, 254, 253, 252, 256, 341, 463],
"EYESHADOW_LEFT": [226, 247, 30, 29, 27, 28, 56, 190, 243, 173, 157, 158, 159, 160, 161, 246, 33, 130, 226],
"EYESHADOW_RIGHT": [463, 414, 286, 258, 257, 259, 260, 467, 446, 359, 263, 466, 388, 387, 386, 385, 384, 398, 362, 463],
"FACE": [152, 148, 176, 149, 150, 136, 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109, 10, 338, 297, 332, 284, 251, 389, 454, 323, 401, 361, 435, 288, 397, 365, 379, 378, 400, 377, 152],
"LIP_UPPER": [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291, 308, 415, 310, 312, 13, 82, 81, 80, 191, 78],
"LIP_LOWER": [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, 308, 324, 402, 317, 14, 87, 178, 88, 95, 78, 61],
"EYEBROW_LEFT": [55, 107, 66, 105, 63, 70, 46, 53, 52, 65, 55],
"EYEBROW_RIGHT": [285, 336, 296, 334, 293, 300, 276, 283, 295, 285]
}

# initialize mediapipe functions
mp_face_mesh = mp.solutions.face_mesh
mp_drawing = mp.solutions.drawing_utils


# to display image in cv2 window
def show_image(image: np.array, msg: str = "Loaded Image"):
    """
    image : image as np array
    msg : cv2 window name
    """
    image_copy = image.copy()
    cv2.imshow(msg, image_copy)
    cv2.waitKey(0)
    cv2.destroyAllWindows()


def read_landmarks(image: np.array):
    """
    image : image as np.array
    """
    landmark_cordinates = {}
    # load mediapipe facemesh and detect facial landmarks
    # face landmarks returns normalized points of all facial landmarks from 0 to 477
    with mp_face_mesh.FaceMesh(refine_landmarks=True) as face_mesh:
        results = face_mesh.process(image)
        face_landmarks = results.multi_face_landmarks[0].landmark

    # convert normalized points w.r.to image dimensions
    for idx, landmark in enumerate(face_landmarks):
        landmark_px = mp_drawing._normalized_to_pixel_coordinates(
            landmark.x, landmark.y, image.shape[1], image.shape[0]
        )
        # create a map of facial landmarks to (x,y) coordinates
        if landmark_px:
            landmark_cordinates[idx] = landmark_px
    return landmark_cordinates


# based on input facial features create make w.r.to colors
def add_mask(
    mask: np.array, idx_to_coordinates: dict, face_connections: list, colors: list
):
    """
    mask: image filled with 0's
    idx_to_coordinates : dict with (x,y) cordinates for each face landmarks
    face_connections : list of (x,y) cordinates for each facial features
    colors : list of [B,G,R] color for each features
    """
    for i, connection in enumerate(face_connections):
        # extract (x,y) w.r.to image for each cordinates
        points = np.array([idx_to_coordinates[idx] for idx in connection])
        # make a shape of feature in the mask and add color
        cv2.fillPoly(mask, [points], colors[i])

    # smoothening of image
    mask = cv2.GaussianBlur(mask, (7, 7), 4)
    return mask