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import dlib
import numpy as np
from skimage import io
import random as r #imported to make the file name randomized
class GetPoints: #class container so that code can be reused easily, and is modular
def __init__(self, image):
self.predictor_path = "shape_predictor_68_face_landmarks.dat"
self.detector = dlib.get_frontal_face_detector()
self.predictor = dlib.shape_predictor(self.predictor_path)
self.img = io.imread(image)
self.dets = self.detector(self.img)
def get_coors(self):
save_coordinates = open(str(r.randint(0,100)) + 'coordinates.txt', 'a') #randint changes the name of the text file
for k, d in enumerate(self.dets):
shape = self.predictor(self.img, d)
vec = np.empty([68, 2], dtype = int)
for b in range(68): #gets the coordinates of the picture passed in
vec[b][0] = shape.part(b).x
vec[b][1] = shape.part(b).y
save_coordinates.write(str(vec[b][0]) + " "+ str(vec[b][1]) + "\n") #adds them to the randomized text file
extra_points = ['514 360', '294 676', '0 680', '599 588', '0 0', '0 400', '0 799', '300 799',
'599 799', '599 400', '599 0' , '300 0']
for i in range(len(extra_points)): #this accesses the array of extra points and writes them into the text file
save_coordinates.write(extra_points[i] + "\n")
save_coordinates.close() #this closes the picture
name = "ted_cruz" #this will have to be gotten from the user instead of hardcoding
image = name + ".jpg"
x = GetPoints(image) #pass the file name into the class to be converted into a text file
x.get_coors() #this calls the object so that files can be used appropriately
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