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# Copyright (c) SenseTime Research. All rights reserved.
import numpy as np
import PIL
import PIL.Image
import scipy
import scipy.ndimage
import dlib
import copy
from PIL import Image
def get_landmark(img, detector, predictor):
"""get landmark with dlib
:return: np.array shape=(68, 2)
"""
# detector = dlib.get_frontal_face_detector()
# dets, _, _ = detector.run(img, 1, -1)
dets = detector(img, 1)
for k, d in enumerate(dets):
shape = predictor(img, d.rect)
t = list(shape.parts())
a = []
for tt in t:
a.append([tt.x, tt.y])
lm = np.array(a)
# face rect
face_rect = [dets[0].rect.left(), dets[0].rect.top(), dets[0].rect.right(), dets[0].rect.bottom()]
return lm, face_rect
def align_face_for_insetgan(img, detector, predictor, output_size=256):
"""
:param img: numpy array rgb
:return: PIL Image
"""
img_cp = copy.deepcopy(img)
lm, face_rect = get_landmark(img, detector, predictor)
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
# opencv to PIL
img = PIL.Image.fromarray(img_cp)
# img = PIL.Image.open(filepath)
transform_size = output_size
enable_padding = False
# 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]))
# img.save("debug/raw.jpg")
if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
img = img.crop(crop)
quad -= crop[0:2]
# img.save("debug/crop.jpg")
# 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.
# crop shape to transform shape
# nw =
# print(img.size, quad+0.5, np.bound((quad+0.5).flatten()))
# assert False
# img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR)
# img.save("debug/transform.jpg")
# if output_size < transform_size:
img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS)
# img.save("debug/resize.jpg")
# print((quad+crop[0:2]).flatten())
# assert False
# Return aligned image.
return img, crop, face_rect
def align_face_for_projector(img, detector, predictor, output_size):
"""
:param filepath: str
:return: PIL Image
"""
img_cp = copy.deepcopy(img)
lm, face_rect = get_landmark(img, detector, predictor)
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.fromarray(img_cp)
transform_size = output_size
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)
# Return aligned image.
return img
def reverse_quad_transform(image, quad_to_map_to, alpha):
# forward mapping, for simplicity
result = Image.new("RGBA",image.size)
result_pixels = result.load()
width, height = result.size
for y in range(height):
for x in range(width):
result_pixels[x,y] = (0,0,0,0)
p1 = (quad_to_map_to[0],quad_to_map_to[1])
p2 = (quad_to_map_to[2],quad_to_map_to[3])
p3 = (quad_to_map_to[4],quad_to_map_to[5])
p4 = (quad_to_map_to[6],quad_to_map_to[7])
p1_p2_vec = (p2[0] - p1[0],p2[1] - p1[1])
p4_p3_vec = (p3[0] - p4[0],p3[1] - p4[1])
for y in range(height):
for x in range(width):
pixel = image.getpixel((x,y))
y_percentage = y / float(height)
x_percentage = x / float(width)
# interpolate vertically
pa = (p1[0] + p1_p2_vec[0] * y_percentage, p1[1] + p1_p2_vec[1] * y_percentage)
pb = (p4[0] + p4_p3_vec[0] * y_percentage, p4[1] + p4_p3_vec[1] * y_percentage)
pa_to_pb_vec = (pb[0] - pa[0],pb[1] - pa[1])
# interpolate horizontally
p = (pa[0] + pa_to_pb_vec[0] * x_percentage, pa[1] + pa_to_pb_vec[1] * x_percentage)
try:
result_pixels[p[0],p[1]] = (pixel[0],pixel[1],pixel[2],min(int(alpha * 255),pixel[3]))
except Exception:
pass
return result |