File size: 5,693 Bytes
0e3fe1f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1c80236
0e3fe1f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1c80236
0e3fe1f
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
# Copyright (c) 2021 Justin Pinkney

import dlib
import numpy as np
import os
from PIL import Image
from PIL import ImageOps
from scipy.ndimage import gaussian_filter
import cv2


MODEL_PATH = "shape_predictor_5_face_landmarks.dat"
detector = dlib.get_frontal_face_detector()


def align(image_in, face_index=0, output_size=256):
    try:
        image_in = ImageOps.exif_transpose(image_in)
    except:
        print("exif problem, not rotating")

    landmarks = list(get_landmarks(image_in))
    n_faces = len(landmarks)
    face_index = min(n_faces-1, face_index)
    if n_faces == 0:
        aligned_image = image_in
        quad = None
    else:
        aligned_image, quad = image_align(image_in, landmarks[face_index], output_size=output_size)

    return aligned_image, n_faces, quad


def composite_images(quad, img, output):
    """Composite an image into and output canvas according to transformed co-ords"""
    output = output.convert("RGBA")
    img = img.convert("RGBA")
    input_size = img.size
    src = np.array(((0, 0), (0, input_size[1]), input_size, (input_size[0], 0)), dtype=np.float32)
    dst = np.float32(quad)
    mtx = cv2.getPerspectiveTransform(dst, src)
    img = img.transform(output.size, Image.PERSPECTIVE, mtx.flatten(), Image.BILINEAR)
    output.alpha_composite(img)

    return output.convert("RGB")
    

def get_landmarks(image):
    """Get landmarks from PIL image"""
    shape_predictor = dlib.shape_predictor(MODEL_PATH)

    max_size = max(image.size)
    reduction_scale = int(max_size/512)
    if reduction_scale == 0:
        reduction_scale = 1
    downscaled = image.reduce(reduction_scale)
    img = np.array(downscaled)
    detections = detector(img, 0)
    
    for detection in detections:
        try:
            face_landmarks = [(reduction_scale*item.x, reduction_scale*item.y) for item in shape_predictor(img, detection).parts()]
            yield face_landmarks
        except Exception as e:
            print(e)


def image_align(src_img, face_landmarks, output_size=512, transform_size=2048, enable_padding=True, x_scale=1, y_scale=1, em_scale=0.1, alpha=False):
        # Align function modified from ffhq-dataset
        # See https://github.com/NVlabs/ffhq-dataset for license

        lm = np.array(face_landmarks)
        lm_eye_left      = lm[2:3]  # left-clockwise
        lm_eye_right     = lm[0:1]  # 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   = 0.71*(eye_right - eye_left)
        mouth_avg    = lm[4]
        eye_to_mouth = 1.35*(mouth_avg - eye_avg)

        # Choose oriented crop rectangle.
        x = eye_to_eye.copy()
        x /= np.hypot(*x)
        x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
        x *= x_scale
        y = np.flipud(x) * [-y_scale, y_scale]
        c = eye_avg + eye_to_mouth * em_scale
        quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
        quad_orig = quad.copy()
        qsize = np.hypot(*x) * 2        
        
        img = src_img.convert('RGBA').convert('RGB')

        # 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, Image.Resampling.LANCZOS)
            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 += (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 = np.uint8(np.clip(np.rint(img), 0, 255))
            if alpha:
                mask = 1-np.clip(3.0 * mask, 0.0, 1.0)
                mask = np.uint8(np.clip(np.rint(mask*255), 0, 255))
                img = np.concatenate((img, mask), axis=2)
                img = Image.fromarray(img, 'RGBA')
            else:
                img = Image.fromarray(img, 'RGB')
            quad += pad[:2]

        # Transform.
        img = img.transform((transform_size, transform_size), Image.QUAD, (quad + 0.5).flatten(), Image.BILINEAR)
        if output_size < transform_size:
            img = img.resize((output_size, output_size), Image.Resampling.LANCZOS)

        return img, quad_orig