图片动漫化 - 面部

图片处理

#!/usr/bin/env python
# coding: utf-8import torch
from PIL import Imageimport os
import dlib
import collections
from typing import Union, List
import numpy as np
from PIL import Image
import matplotlib.pyplot as pltimport PIL.Image
import PIL.ImageFile
import numpy as np
import scipy.ndimagedef get_dlib_face_detector(predictor_path: str = "shape_predictor_68_face_landmarks.dat"):detector = dlib.get_frontal_face_detector()shape_predictor = dlib.shape_predictor(predictor_path)def detect_face_landmarks(img: Union[Image.Image, np.ndarray]):if isinstance(img, Image.Image):img = np.array(img)faces = []dets = detector(img)for d in dets:shape = shape_predictor(img, d)faces.append(np.array([[v.x, v.y] for v in shape.parts()]))return facesreturn detect_face_landmarksdef align_and_crop_face(img: Image.Image,landmarks: np.ndarray,expand: float = 1.0,output_size: int = 1024, transform_size: int = 4096,enable_padding: bool = True,):# Parse landmarks.# pylint: disable=unused-variablelm = landmarkslm_chin          = lm[0  : 17]  # left-rightlm_eyebrow_left  = lm[17 : 22]  # left-rightlm_eyebrow_right = lm[22 : 27]  # left-rightlm_nose          = lm[27 : 31]  # top-downlm_nostrils      = lm[31 : 36]  # top-downlm_eye_left      = lm[36 : 42]  # left-clockwiselm_eye_right     = lm[42 : 48]  # left-clockwiselm_mouth_outer   = lm[48 : 60]  # left-clockwiselm_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) * 0.5eye_to_eye   = eye_right - eye_leftmouth_left   = lm_mouth_outer[0]mouth_right  = lm_mouth_outer[6]mouth_avg    = (mouth_left + mouth_right) * 0.5eye_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)x *= expandy = np.flipud(x) * [-1, 1]c = eye_avg + eye_to_mouth * 0.1quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])qsize = np.hypot(*x) * 2# 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 /= shrinkqsize /= 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.shapey, 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 * 0.02img += (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 img

图片测试

# 加载模型和面部检测
device = "cuda" if torch.cuda.is_available() else "cpu"
model = torch.hub.load("bryandlee/animegan2-pytorch:main", "generator", device=device).eval()
face2paint = torch.hub.load("bryandlee/animegan2-pytorch:main", "face2paint", device=device, side_by_side=True)
face_detector = get_dlib_face_detector()# 加载图片并动漫化
img = Image.open("1.jpeg").convert("RGB")
landmarks = face_detector(img)
for landmark in landmarks:face = align_and_crop_face(img, landmark, expand=1.3)plt.imshow(face2paint(model=model, img=face, size=512))plt.axis('off')plt.show()

结果

1.jpeg

输出

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图片动漫化 - 面部 - AnimeGAN

图片处理

图片测试

结果

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