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align_face.py
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align_face.py
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"""
brief: face alignment with FFHQ method (https://github.com/NVlabs/ffhq-dataset)
author: lzhbrian (https://lzhbrian.me)
date: 2020.1.5
note: code is heavily borrowed from
https://github.com/NVlabs/ffhq-dataset
http://dlib.net/face_landmark_detection.py.html
requirements:
apt install cmake
conda install Pillow numpy scipy
pip install dlib
# download face landmark model from:
# http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
"""
import numpy as np
import PIL
import PIL.Image
import sys
import os
import glob
import scipy
import scipy.ndimage
import dlib
# download model from: http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
predictor = dlib.shape_predictor("./shape_predictor_68_face_landmarks.dat")
def get_landmark(filepath):
"""get landmark with dlib
:return: np.array shape=(68, 2)
"""
detector = dlib.get_frontal_face_detector()
# img = dlib.load_rgb_image(filepath)
img = np.asarray(PIL.Image.open(filepath))
dets = detector(img, 1)
print("Number of faces detected: {}".format(len(dets)))
for k, d in enumerate(dets):
print(
"Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
k, d.left(), d.top(), d.right(), d.bottom()
)
)
# Get the landmarks/parts for the face in box d.
shape = predictor(img, d)
print("Part 0: {}, Part 1: {} ...".format(shape.part(0), shape.part(1)))
t = list(shape.parts())
a = []
for tt in t:
a.append([tt.x, tt.y])
lm = np.array(a)
# lm is a shape=(68,2) np.array
return lm
def align_face(filepath):
"""
:param filepath: str
:return: PIL Image
"""
lm = get_landmark(filepath)
lm_chin = lm[0:17] # left-right
lm_eyebrow_left = lm[17:22] # left-right
lm_eyebrow_right = lm[22:27] # left-right
lm_nose = lm[27:31] # top-down
lm_nostrils = lm[31:36] # top-down
lm_eye_left = lm[36:42] # left-clockwise
lm_eye_right = lm[42:48] # left-clockwise
lm_mouth_outer = lm[48:60] # left-clockwise
lm_mouth_inner = lm[60:68] # left-clockwise
# Calculate auxiliary vectors.
eye_left = np.mean(lm_eye_left, axis=0)
eye_right = np.mean(lm_eye_right, axis=0)
eye_avg = (eye_left + eye_right) * 0.5
eye_to_eye = eye_right - eye_left
mouth_left = lm_mouth_outer[0]
mouth_right = lm_mouth_outer[6]
mouth_avg = (mouth_left + mouth_right) * 0.5
eye_to_mouth = mouth_avg - eye_avg
# Choose oriented crop rectangle.
x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
x /= np.hypot(*x)
x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
y = np.flipud(x) * [-1, 1]
c = eye_avg + eye_to_mouth * 0.1
quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
qsize = np.hypot(*x) * 2
# read image
img = PIL.Image.open(filepath)
output_size = 1024
transform_size = 4096
enable_padding = True
# Shrink.
shrink = int(np.floor(qsize / output_size * 0.5))
if shrink > 1:
rsize = (
int(np.rint(float(img.size[0]) / shrink)),
int(np.rint(float(img.size[1]) / shrink)),
)
img = img.resize(rsize, PIL.Image.ANTIALIAS)
quad /= shrink
qsize /= shrink
# Crop.
border = max(int(np.rint(qsize * 0.1)), 3)
crop = (
int(np.floor(min(quad[:, 0]))),
int(np.floor(min(quad[:, 1]))),
int(np.ceil(max(quad[:, 0]))),
int(np.ceil(max(quad[:, 1]))),
)
crop = (
max(crop[0] - border, 0),
max(crop[1] - border, 0),
min(crop[2] + border, img.size[0]),
min(crop[3] + border, img.size[1]),
)
if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
img = img.crop(crop)
quad -= crop[0:2]
# Pad.
pad = (
int(np.floor(min(quad[:, 0]))),
int(np.floor(min(quad[:, 1]))),
int(np.ceil(max(quad[:, 0]))),
int(np.ceil(max(quad[:, 1]))),
)
pad = (
max(-pad[0] + border, 0),
max(-pad[1] + border, 0),
max(pad[2] - img.size[0] + border, 0),
max(pad[3] - img.size[1] + border, 0),
)
if enable_padding and max(pad) > border - 4:
pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
img = np.pad(
np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), "reflect"
)
h, w, _ = img.shape
y, x, _ = np.ogrid[:h, :w, :1]
mask = np.maximum(
1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]),
1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3]),
)
blur = qsize * 0.02
img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(
mask * 3.0 + 1.0, 0.0, 1.0
)
img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
img = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), "RGB")
quad += pad[:2]
# Transform.
img = img.transform(
(transform_size, transform_size),
PIL.Image.QUAD,
(quad + 0.5).flatten(),
PIL.Image.BILINEAR,
)
if output_size < transform_size:
img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS)
# Save aligned image.
return img
if __name__ == "__main__":
if len(sys.argv) > 1:
file = sys.argv[1]
img = align_face(file)
name, ext = os.path.splitext(file)
img_name = f"{name}_ffhq{ext}"
img.save(img_name)
else:
print("Need image filename as first arg")