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import dlib
import yaml
import cv2
import os
import numpy as np
import imutils
from src.cv_utils import get_image
from typing import List, Union
from PIL import Image as PILImage
with open("parameters.yml", "r") as stream:
try:
parameters = yaml.safe_load(stream)
except yaml.YAMLError as exc:
print(exc)
class GetFaceProportions:
def __init__(self):
self.golden_ratio = 1.618
@staticmethod
def preprocess_image(image: np.array) -> np.array:
image = imutils.resize(image, width=500)
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
return gray_image
@staticmethod
def detect_face_landmarks(gray_image: np.array) -> List[Union[np.array, np.array]]:
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(parameters["face_landmarks"]["model"])
rects = detector(gray_image, 1)
for rect in rects:
shape = predictor(gray_image, rect)
shape = np.array([(shape.part(i).x, shape.part(i).y) for i in range(shape.num_parts)])
# Draw facial landmarks
for (x, y) in shape:
cv2.circle(gray_image, (x, y), 2, (0, 255, 0), -1)
return shape, gray_image
def compute_golden_ratios(self, shape: np.array) -> dict:
top_mouth, middle_mouth, bottom_mouth = shape[51], shape[62], shape[57]
top_nose, bottom_nose = shape[27], shape[33]
bottom_chin = shape[8]
# 1
top_nose_to_middle_mouth_dist = np.linalg.norm(top_nose - middle_mouth) # euclidean distance
middle_mouth_to_bottom_chin_dist = np.linalg.norm(middle_mouth - bottom_chin)
ratio_top_nose_to_middle_mouth_vs_middle_mouth_to_bottom_chin = top_nose_to_middle_mouth_dist/middle_mouth_to_bottom_chin_dist
# 2
top_mouth_to_middle_mouth_dist = np.linalg.norm(top_mouth - middle_mouth)
middle_mouth_to_bottom_mouth_dist = np.linalg.norm(middle_mouth - bottom_mouth)
ratio_middle_mouth_to_bottom_mouth_vs_top_mouth_to_middle_mouth = middle_mouth_to_bottom_mouth_dist/top_mouth_to_middle_mouth_dist
golden_ratios = {
"Ideal ratio (golden ratio)": self.golden_ratio,
"Top of nose to middle of mouth vs middle mouth to bottom of chin": ratio_top_nose_to_middle_mouth_vs_middle_mouth_to_bottom_chin,
"Middle of mouth to bottom of mouth vs top of mouth to middle of mouth": ratio_middle_mouth_to_bottom_mouth_vs_top_mouth_to_middle_mouth
}
return golden_ratios
@staticmethod
def compute_equal_ratios(shape: np.array) -> dict:
left_side_left_eye, right_side_left_eye, left_side_right_eye, right_side_right_eye = shape[36], shape[39], shape[42], shape[45]
left_eye_top, left_eye_bottom, right_eye_top, right_eye_bottom = shape[37], shape[41], shape[44], shape[46]
left_eyebrow_top, right_eyebrow_top = shape[19], shape[24]
left_eye_center = np.mean([shape[37], shape[38], shape[41], shape[40]], axis=0)
right_eye_center = np.mean([shape[43], shape[44], shape[47], shape[46]], axis=0)
left_mouth, right_mouth = shape[48], shape[54]
# 1
left_eye_dist = np.linalg.norm(left_side_left_eye - right_side_left_eye)
right_eye_dist = np.linalg.norm(left_side_right_eye - right_side_right_eye)
average_eye_dist = (left_eye_dist + right_eye_dist)/2
between_eye_dist = np.linalg.norm(right_side_left_eye - left_side_right_eye)
ratio_eyes_width_vs_between_eye = average_eye_dist/between_eye_dist
# 2
left_eye_to_eyebrow_dist = np.linalg.norm(left_eyebrow_top - left_eye_top)
right_eye_to_eyebrow_dist = np.linalg.norm(right_eyebrow_top - right_eye_top)
eye_to_eyebrow_dist = (left_eye_to_eyebrow_dist + right_eye_to_eyebrow_dist)/2
left_eye_height = np.linalg.norm(left_eye_top - left_eye_bottom)
right_eye_height = np.linalg.norm(right_eye_top - right_eye_bottom)
eye_height = (left_eye_height + right_eye_height)/2
ratio_eye_to_eyebrow_vs_eye_height = eye_to_eyebrow_dist/eye_height
# 3
left_to_right_eye_center_dist = np.linalg.norm(left_eye_center - right_eye_center)
mouth_width = np.linalg.norm(left_mouth - right_mouth)
ratio_left_to_right_eye_center_vs_mouth_width = left_to_right_eye_center_dist/mouth_width
equal_ratios = {
"Ideal ratio": 1,
"Eye width vs distance between eyes": ratio_eyes_width_vs_between_eye,
"Eye to eyebrows vs eye height": ratio_eye_to_eyebrow_vs_eye_height,
"Center of left to right eye vs mouth width": ratio_left_to_right_eye_center_vs_mouth_width
}
return equal_ratios
def main(self, image_input):
image = get_image(image_input)
gray_image = self.preprocess_image(image)
shape, image = self.detect_face_landmarks(gray_image)
golden_ratios = self.compute_golden_ratios(shape)
equal_ratios = self.compute_equal_ratios(shape)
image = PILImage.fromarray(image)
return golden_ratios, equal_ratios, image
if __name__ == "__main__":
path_to_images = "data/"
image_files = os.listdir(path_to_images)
for image in image_files:
print(image)
results = GetFaceProportions().main(path_to_images + image)
print(results) |