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face_preprocessing.py
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face_preprocessing.py
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import os
import dlib
import PIL.Image
import PIL.ImageFile
import numpy as np
import scipy.ndimage
import os.path as path
# Reference: https://github.com/NVlabs/ffhq-dataset/blob/master/download_ffhq.py
def detect_face_landmarks(face_file_path=None,
predictor_path=None,
img=None):
# References:
# - http://dlib.net/face_landmark_detection.py.html
# - http://dlib.net/face_alignment.py.html
if predictor_path is None:
predictor_path = './utils/shape_predictor_68_face_landmarks.dat'
# Load all the models we need: a detector to find the faces, a shape predictor
# to find face landmarks so we can precisely localize the face
detector = dlib.get_frontal_face_detector()
shape_predictor = dlib.shape_predictor(predictor_path)
if img is None:
# Load the image using Dlib
print("Processing file: {}".format(face_file_path))
img = dlib.load_rgb_image(face_file_path)
shapes = list()
# Ask the detector to find the bounding boxes of each face. The 1 in the
# second argument indicates that we should upsample the image 1 time. This
# will make everything bigger and allow us to detect more faces.
dets = detector(img, 1)
num_faces = len(dets)
print("Number of faces detected: {}".format(num_faces))
if num_faces < 1:
raise Exception('No face found!')
# Find the face landmarks we need to do the alignment.
faces = dlib.full_object_detections()
for d in dets:
print("Left: {} Top: {} Right: {} Bottom: {}".format(
d.left(), d.top(), d.right(), d.bottom()
))
shape = shape_predictor(img, d)
faces.append(shape)
return faces
def recreate_aligned_images(json_data,
dst_dir='./temp-faces/',
output_size=1024,
transform_size=4096,
enable_padding=True):
print('Recreating aligned images...')
if dst_dir:
os.makedirs(dst_dir, exist_ok=True)
for item_idx, item in enumerate(json_data.values()):
print('\r%d / %d ... ' % (item_idx, len(json_data)), end='', flush=True)
# Parse landmarks.
# pylint: disable=unused-variable
lm = np.array(item['in_the_wild']['face_landmarks'])
filename = item['in_the_wild']['file_path']
filename = path.split(filename)[-1]
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.
print(eye_to_mouth.shape)
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
# Load in-the-wild image.
src_file = item['in_the_wild']['file_path']
img = PIL.Image.open(src_file)
# 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.
# dst_subdir = os.path.join(
# dst_dir, '%05d' % (item_idx - item_idx % 1000))
os.makedirs(dst_dir, exist_ok=True)
print(f'Saving {os.path.join(dst_dir, filename)}')
#img.save(os.path.join(dst_subdir, '%05d.png' % item_idx))
img.save(os.path.join(dst_dir, filename))
# All done.
print('\r%d / %d ... done' % (len(json_data), len(json_data)))
return
def face_extraction(face_file_path):
faces = detect_face_landmarks(face_file_path)
img = dlib.load_rgb_image(face_file_path)
thumbnail_size = 512
thumbnails = dlib.get_face_chips(img, faces, size=thumbnail_size)
# The first face which is detected:
# NB: we assume that there is exactly one face per picture!
f = faces[0]
parts = f.parts()
num_face_landmarks=68
v = np.zeros(shape=(num_face_landmarks, 2))
for k, e in enumerate(parts):
v[k, :] = [e.x, e.y]
json_data = dict()
item_idx = 0
json_data[item_idx] = dict()
json_data[item_idx]['in_the_wild'] = dict()
json_data[item_idx]['in_the_wild']['file_path'] = face_file_path
json_data[item_idx]['in_the_wild']['face_landmarks'] = v
recreate_aligned_images(json_data)