from matplotlib import pyplot as plt import torch import torch.nn.functional as F import os import cv2 import dlib from PIL import Image import numpy as np import math import torchvision import scipy import scipy.ndimage import torchvision.transforms as transforms from huggingface_hub import hf_hub_download shape_predictor_path = hf_hub_download(repo_id="aijackliu/jojogan", filename="face_landmarks.dat") google_drive_paths = { } @torch.no_grad() def load_model(generator, model_file_path): ensure_checkpoint_exists(model_file_path) ckpt = torch.load(model_file_path, map_location=lambda storage, loc: storage) generator.load_state_dict(ckpt["g_ema"], strict=False) return generator.mean_latent(50000) def ensure_checkpoint_exists(model_weights_filename): if not os.path.isfile(model_weights_filename) and ( model_weights_filename in google_drive_paths ): gdrive_url = google_drive_paths[model_weights_filename] try: from gdown import download as drive_download drive_download(gdrive_url, model_weights_filename, quiet=False) except ModuleNotFoundError: print( "gdown module not found.", "pip3 install gdown or, manually download the checkpoint file:", gdrive_url ) if not os.path.isfile(model_weights_filename) and ( model_weights_filename not in google_drive_paths ): print( model_weights_filename, " not found, you may need to manually download the model weights." ) # given a list of filenames, load the inverted style code @torch.no_grad() def load_source(files, generator, device='cuda'): sources = [] for file in files: source = torch.load(f'./inversion_codes/{file}.pt')['latent'].to(device) if source.size(0) != 1: source = source.unsqueeze(0) if source.ndim == 3: source = generator.get_latent(source, truncation=1, is_latent=True) source = list2style(source) sources.append(source) sources = torch.cat(sources, 0) if type(sources) is not list: sources = style2list(sources) return sources def display_image(image, size=None, mode='nearest', unnorm=False, title=''): # image is [3,h,w] or [1,3,h,w] tensor [0,1] if not isinstance(image, torch.Tensor): image = transforms.ToTensor()(image).unsqueeze(0) if image.is_cuda: image = image.cpu() if size is not None and image.size(-1) != size: image = F.interpolate(image, size=(size,size), mode=mode) if image.dim() == 4: image = image[0] image = image.permute(1, 2, 0).detach().numpy() plt.figure() plt.title(title) plt.axis('off') plt.imshow(image) def get_landmark(filepath, predictor): """get landmark with dlib :return: np.array shape=(68, 2) """ detector = dlib.get_frontal_face_detector() img = dlib.load_rgb_image(filepath) dets = detector(img, 1) assert len(dets) > 0, "Face not detected, try another face image" for k, d in enumerate(dets): shape = predictor(img, d) t = list(shape.parts()) a = [] for tt in t: a.append([tt.x, tt.y]) lm = np.array(a) return lm def align_face(filepath, output_size=256, transform_size=1024, enable_padding=True): """ :param filepath: str :return: PIL Image """ predictor = dlib.shape_predictor(shape_predictor_path) lm = get_landmark(filepath, predictor) lm_chin = lm[0: 17] # left-right lm_eyebrow_left = lm[17: 22] # left-right lm_eyebrow_right = lm[22: 27] # left-right lm_nose = lm[27: 31] # top-down lm_nostrils = lm[31: 36] # top-down lm_eye_left = lm[36: 42] # left-clockwise lm_eye_right = lm[42: 48] # left-clockwise lm_mouth_outer = lm[48: 60] # left-clockwise lm_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) * 0.5 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 # read image img = Image.open(filepath) transform_size = output_size enable_padding = True # 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) if output_size < transform_size: img = img.resize((output_size, output_size), Image.ANTIALIAS) # Return aligned image. return img def strip_path_extension(path): return os.path.splitext(path)[0]