# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import cv2 import scipy import random import numpy as np import paddle import paddle.vision.transforms as T import ppgan.faceutils as futils from ppgan.models.generators import Pixel2Style2Pixel from ppgan.utils.download import get_path_from_url from PIL import Image model_cfgs = { 'ffhq-inversion': { 'model_urls': 'https://paddlegan.bj.bcebos.com/models/pSp-ffhq-inversion.pdparams', 'transform': T.Compose([T.Resize((256, 256)), T.Transpose(), T.Normalize([127.5, 127.5, 127.5], [127.5, 127.5, 127.5])]), 'size': 1024, 'style_dim': 512, 'n_mlp': 8, 'channel_multiplier': 2 }, 'ffhq-toonify': { 'model_urls': 'https://paddlegan.bj.bcebos.com/models/pSp-ffhq-toonify.pdparams', 'transform': T.Compose([T.Resize((256, 256)), T.Transpose(), T.Normalize([127.5, 127.5, 127.5], [127.5, 127.5, 127.5])]), 'size': 1024, 'style_dim': 512, 'n_mlp': 8, 'channel_multiplier': 2 }, 'default': { 'transform': T.Compose([T.Resize((256, 256)), T.Transpose(), T.Normalize([127.5, 127.5, 127.5], [127.5, 127.5, 127.5])]) } } def run_alignment(image): img = Image.fromarray(image).convert("RGB") face = futils.dlib.detect(img) if not face: raise Exception('Could not find a face in the given image.') face_on_image = face[0] lm = futils.dlib.landmarks(img, face_on_image) lm = np.array(lm)[:, ::-1] lm_eye_left = lm[36:42] lm_eye_right = lm[42:48] lm_mouth_outer = lm[48:60] output_size = 1024 transform_size = 4096 enable_padding = True # 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 # 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 += (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 = Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB') quad += pad[:2] # Transform. img = img.transform((transform_size, transform_size), Image.QUAD, (quad + 0.5).flatten(), Image.BILINEAR) return img class AttrDict(dict): def __init__(self, *args, **kwargs): super(AttrDict, self).__init__(*args, **kwargs) self.__dict__ = self class Pixel2Style2PixelPredictor: def __init__(self, weight_path=None, model_type=None, seed=None, size=1024, style_dim=512, n_mlp=8, channel_multiplier=2): if weight_path is None and model_type != 'default': if model_type in model_cfgs.keys(): weight_path = get_path_from_url(model_cfgs[model_type]['model_urls']) size = model_cfgs[model_type].get('size', size) style_dim = model_cfgs[model_type].get('style_dim', style_dim) n_mlp = model_cfgs[model_type].get('n_mlp', n_mlp) channel_multiplier = model_cfgs[model_type].get('channel_multiplier', channel_multiplier) checkpoint = paddle.load(weight_path) else: raise ValueError('Predictor need a weight path or a pretrained model type') else: checkpoint = paddle.load(weight_path) opts = checkpoint.pop('opts') opts = AttrDict(opts) opts['size'] = size opts['style_dim'] = style_dim opts['n_mlp'] = n_mlp opts['channel_multiplier'] = channel_multiplier self.generator = Pixel2Style2Pixel(opts) self.generator.set_state_dict(checkpoint) self.generator.eval() if seed is not None: paddle.seed(seed) random.seed(seed) np.random.seed(seed) self.model_type = 'default' if model_type is None else model_type def run(self, image): src_img = run_alignment(image) src_img = np.asarray(src_img) transformed_image = model_cfgs[self.model_type]['transform'](src_img) dst_img, latents = self.generator( paddle.to_tensor(transformed_image[None, ...]), resize=False, return_latents=True) dst_img = (dst_img * 0.5 + 0.5)[0].numpy() * 255 dst_img = dst_img.transpose((1, 2, 0)) dst_npy = latents[0].numpy() return dst_img, dst_npy