ai个性化国庆头像合成

源码地址 https://github.com/tencentmusic/cube-studio/tree/master/aihub/deep-learning/face-paint/web

实现过程将个人头像人体人脸识别，裁剪出肩膀以上部分，并将图片进行卡通化处理，最后与背景图片和文字合成，效果如下

实现代码：

import argparse
import base64
import datetime
import os
import sysimport cv2
import numpy as np
from tqdm import tqdm
import pysnooper
from PIL import ImageFont
from PIL import Image
from PIL import ImageDrawfrom paddleseg.utils import get_sys_env, logger, get_image_listfrom infer import Predictorimport os
import dlib
import collections
from typing import Union, List
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import torchdef get_bg_img(bg_img_path, img_shape):if bg_img_path is None:bg = 255 * np.ones(img_shape)elif not os.path.exists(bg_img_path):raise Exception('The --bg_img_path is not existed: {}'.format(bg_img_path))else:bg = cv2.imread(bg_img_path)return bgdef makedirs(save_dir):dirname = save_dir if os.path.isdir(save_dir) else \os.path.dirname(save_dir)if not os.path.exists(dirname):os.makedirs(dirname)@pysnooper.snoop()
def seg_image(args):assert os.path.exists(args['img_path']), \"The --img_path is not existed: {}.".format(args['img_path'])logger.info("Input: image")logger.info("Create predictor...")predictor = Predictor(args)logger.info("Start predicting...")img = cv2.imread(args['re_save_path'])bg_img = get_bg_img(args['bg_img_path'], img.shape)out_img = predictor.run(img, bg_img)# print(type(out_img))cv2.imwrite(args['save_path'], out_img)img_draw_text = Image.open(args['save_path'])draw = ImageDraw.Draw(img_draw_text)x,y = img_draw_text.sizeft = ImageFont.truetype("/home/ubuntu/PaddleSeg-release-2.6/contrib/PP-HumanSeg/src/1.ttf", args['text_height'])draw.text(args['text_position'], args['text'], font=ft, fill=args['text_color'])file = open(args['save_path'], 'rb')base64_str = base64.b64encode(file.read()).decode('utf-8')print(len(base64_str))return base64_str# img_ = Image.open(out_img)# print(img_)def get_dlib_face_detector(predictor_path: str = "shape_predictor_68_face_landmarks.dat"):if not os.path.isfile(predictor_path):model_file = "shape_predictor_68_face_landmarks.dat.bz2"os.system(f"wget http://dlib.net/files/{model_file}")os.system(f"bzip2 -dk {model_file}")detector = dlib.get_frontal_face_detector()shape_predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')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 display_facial_landmarks(img: Image,landmarks: List[np.ndarray],fig_size=[15, 15]
):plot_style = dict(marker='o',markersize=4,linestyle='-',lw=2)pred_type = collections.namedtuple('prediction_type', ['slice', 'color'])pred_types = {'face': pred_type(slice(0, 17), (0.682, 0.780, 0.909, 0.5)),'eyebrow1': pred_type(slice(17, 22), (1.0, 0.498, 0.055, 0.4)),'eyebrow2': pred_type(slice(22, 27), (1.0, 0.498, 0.055, 0.4)),'nose': pred_type(slice(27, 31), (0.345, 0.239, 0.443, 0.4)),'nostril': pred_type(slice(31, 36), (0.345, 0.239, 0.443, 0.4)),'eye1': pred_type(slice(36, 42), (0.596, 0.875, 0.541, 0.3)),'eye2': pred_type(slice(42, 48), (0.596, 0.875, 0.541, 0.3)),'lips': pred_type(slice(48, 60), (0.596, 0.875, 0.541, 0.3)),'teeth': pred_type(slice(60, 68), (0.596, 0.875, 0.541, 0.4))}fig = plt.figure(figsize=fig_size)ax = fig.add_subplot(1, 1, 1)ax.imshow(img)ax.axis('off')for face in landmarks:for pred_type in pred_types.values():ax.plot(face[pred_type.slice, 0],face[pred_type.slice, 1],color=pred_type.color, **plot_style)plt.show()import PIL.Image
import PIL.ImageFile
import numpy as np
import scipy.ndimagedef 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 imgimport pysnooper
@pysnooper.snoop()
def start(config=r'/home/ubuntu/PaddleSeg-release-2.6/contrib/PP-HumanSeg/src/inference_models/portrait_pp_humansegv2_lite_256x144_inference_model_with_softmax/deploy.yaml',img_path=r'/home/ubuntu/PaddleSeg-release-2.6/contrib/PP-HumanSeg/src/data/images/1.jpg',re_save_path=r'temp/1_.jpg',save_path=r'temp/1.jpg',text='陈',use_gpu=False,test_speed=False, use_optic_flow=False, use_post_process=False):args = {'config': config,'img_path': img_path,'re_save_path': re_save_path,'save_path': save_path,'use_gpu': use_gpu,'test_speed': test_speed,"text":text,'use_optic_flow': use_optic_flow,'use_post_process': use_post_process}print(type(args))# 先动漫化后增加背景效果更佳# 加载网络或本地文件save_ = datetime.datetime.now().strftime('%Y%m%d%H%M%S')os.mkdir(save_)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)img = Image.open(args['img_path']).convert("RGB")# img = Image.open("/content/sample.jpg").convert("RGB")face_detector = get_dlib_face_detector()landmarks = face_detector(img)out = ''for landmark in landmarks:face = align_and_crop_face(img, landmark, expand=1.3)p_face = face2paint(model=model, img=face, size=512)# display(p_face)p_face.save('temp/input_cartoon.jpg') # 此输出为对比图片# 裁剪为需要的部分输出x_, y_ = p_face.sizeout = p_face.crop((int(x_/2), 0, x_, y_))  # 后面一半是卡通效果图img_ = outx, y = img_.sizeprint(x, y)all_list = []all_backgroud=[{"path":"icon/1.jpg","position":[410,260],"height":340,"text_position":(260,200),"text_height":200,"color":(149,6,2),"text_color": "white"},{"path": "icon/2.jpg","position": [70, 200],"height": 380,"text_position": (450, 300),"text_height": 180,"color": (182,28,30),"text_color": "white"},{"path": "icon/3.jpg","position": [330, 150],"height": 370,"text_position": (150, 350),"text_height": 150,"color": (114,10,11),"text_color": "white"},{"path": "icon/4.jpg","position": [350, 270],"height": 340,"text_position": (140, 400),"text_height": 150,"color": (152, 16, 0),"text_color": "white"},{"path": "icon/5.jpg","position": [260, 230],"height": 570,"text_position": (80, 430),"text_height": 180,"color": (240, 105, 116),"text_color": "white"},{"path": "icon/6.jpg","position": [100, 230],"height": 320,"text_position": (460, 310),"text_height": 170,"color": (194, 0, 8),"text_color": "white"},{"path": "icon/7.jpg","position": [240, 210],"height": 400,"text_position": (130, 290),"text_height": 140,"color": (244, 214, 190),"text_color": "white"},{"path": "icon/8.jpg","position": [120, 190],"height": 330,"text_position": (460, 325),"text_height": 150,"color": (216, 18, 19),"text_color": "white"},{"path": "icon/9.jpg","position": [380, 260],"height": 400,"text_position": (90, 330),"text_height": 185,"color": (250, 250, 250),"text_color": "red"},{"path": "icon/10.jpg","position": [100, 200],"height": 320,"text_position": (410, 290),"text_height": 180,"color": (250, 250, 250),"text_color": "red"}]for pic in all_backgroud:img_=img_.resize((int((pic['height']/y)*x),pic['height']),Image.ANTIALIAS)newIm = Image.new('RGB', (800, 800), pic['color'])newIm.paste(img_, pic['position'])  # 把人体复制进去newIm.save(args['re_save_path'])args['bg_img_path']=pic['path']args['position']=pic['position']args['height']=pic['height']args['text_position']=pic['text_position']args['text_height']=pic['text_height']args['text_color'] = pic['text_color']base64_ = seg_image(args)all_list.append(base64_)return all_listif __name__ == "__main__":image_path = r'/home/ubuntu/PaddleSeg-release-2.6/contrib/PP-HumanSeg/src/data/images/human.jpg'file_after = open(image_path, 'rb')base64_after_str = base64.b64encode(file_after.read()).decode('utf-8')print(len(base64_after_str))imgdata = base64.b64decode(base64_after_str)# 将图片保存为文件if os.path.exists('temp'):passelse:os.mkdir('temp')name_ = datetime.datetime.now().strftime('%Y%m%d%H%M%S')new_image_path = f'temp/{name_}.jpg'with open(new_image_path, 'wb') as f:f.write(imgdata)start(# config=r'E:\PaddleSeg-release-2.6\contrib\PP-HumanSeg\src\inference_models\portrait_pp_humansegv2_lite_256x144_inference_model_with_softmax\deploy.yaml',img_path=new_image_path,# bg_img_path=r'E:\PaddleSeg-release-2.6\contrib\PP-HumanSeg\src\data\images\bg_1.jpg',# re_save_path=r'E:\PaddleSeg-release-2.6\contrib\PP-HumanSeg\src\data\images\_1.jpg',# save_path=r'E:\PaddleSeg-release-2.6\contrib\PP-HumanSeg\src\data\images_result\1.jpg',# use_gpu=True,# test_speed=False, use_optic_flow=False, use_post_process=False))

详情参考github中aihub功能：https://github.com/tencentmusic/cube-studio/tree/master/aihub/deep-learning/face-paint

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