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| 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="akhaliq/jojogan_dlib", filename="shape_predictor_68_face_landmarks.dat") | |
| google_drive_paths = { | |
| "models/stylegan2-ffhq-config-f.pt": "https://drive.google.com/uc?id=1Yr7KuD959btpmcKGAUsbAk5rPjX2MytK", | |
| "models/dlibshape_predictor_68_face_landmarks.dat": "https://drive.google.com/uc?id=11BDmNKS1zxSZxkgsEvQoKgFd8J264jKp", | |
| "models/e4e_ffhq_encode.pt": "https://drive.google.com/uc?id=1o6ijA3PkcewZvwJJ73dJ0fxhndn0nnh7", | |
| "models/restyle_psp_ffhq_encode.pt": "https://drive.google.com/uc?id=1nbxCIVw9H3YnQsoIPykNEFwWJnHVHlVd", | |
| "models/arcane_caitlyn.pt": "https://drive.google.com/uc?id=1gOsDTiTPcENiFOrhmkkxJcTURykW1dRc", | |
| "models/arcane_caitlyn_preserve_color.pt": "https://drive.google.com/uc?id=1cUTyjU-q98P75a8THCaO545RTwpVV-aH", | |
| "models/arcane_jinx_preserve_color.pt": "https://drive.google.com/uc?id=1jElwHxaYPod5Itdy18izJk49K1nl4ney", | |
| "models/arcane_jinx.pt": "https://drive.google.com/uc?id=1quQ8vPjYpUiXM4k1_KIwP4EccOefPpG_", | |
| "models/disney.pt": "https://drive.google.com/uc?id=1zbE2upakFUAx8ximYnLofFwfT8MilqJA", | |
| "models/disney_preserve_color.pt": "https://drive.google.com/uc?id=1Bnh02DjfvN_Wm8c4JdOiNV4q9J7Z_tsi", | |
| "models/jojo.pt": "https://drive.google.com/uc?id=13cR2xjIBj8Ga5jMO7gtxzIJj2PDsBYK4", | |
| "models/jojo_preserve_color.pt": "https://drive.google.com/uc?id=1ZRwYLRytCEKi__eT2Zxv1IlV6BGVQ_K2", | |
| "models/jojo_yasuho.pt": "https://drive.google.com/uc?id=1grZT3Gz1DLzFoJchAmoj3LoM9ew9ROX_", | |
| "models/jojo_yasuho_preserve_color.pt": "https://drive.google.com/uc?id=1SKBu1h0iRNyeKBnya_3BBmLr4pkPeg_L", | |
| "models/supergirl.pt": "https://drive.google.com/uc?id=1L0y9IYgzLNzB-33xTpXpecsKU-t9DpVC", | |
| "models/supergirl_preserve_color.pt": "https://drive.google.com/uc?id=1VmKGuvThWHym7YuayXxjv0fSn32lfDpE", | |
| } | |
| 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 | |
| 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] | |