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# 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.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 += (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.ANTIALIAS) | |
return img, quad_orig | |