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| import gradio | |
| from huggingface_hub import Repository | |
| import os | |
| from utils.utils import norm_crop, estimate_norm, inverse_estimate_norm, transform_landmark_points, get_lm | |
| from networks.layers import AdaIN, AdaptiveAttention | |
| from tensorflow_addons.layers import InstanceNormalization | |
| import numpy as np | |
| import cv2 | |
| from scipy.ndimage import gaussian_filter | |
| from tensorflow.keras.models import load_model | |
| from options.swap_options import SwapOptions | |
| # . | |
| # token = os.environ['model_fetch'] | |
| opt = SwapOptions().parse() | |
| token = os.environ['token'] | |
| retina_repo = Repository(local_dir="retina_models", clone_from="felixrosberg/RetinaFace") | |
| from retinaface.models import * | |
| RetinaFace = load_model("retina_models/RetinaFace-Res50.h5", | |
| custom_objects={"FPN": FPN, | |
| "SSH": SSH, | |
| "BboxHead": BboxHead, | |
| "LandmarkHead": LandmarkHead, | |
| "ClassHead": ClassHead} | |
| ) | |
| arc_repo = Repository(local_dir="arcface_model", clone_from="felixrosberg/ArcFace") | |
| ArcFace = load_model("arcface_model/ArcFace-Res50.h5") | |
| ArcFaceE = load_model("arcface_model/ArcFacePerceptual-Res50.h5") | |
| g_repo = Repository(local_dir="g_model_c_hq", clone_from="felixrosberg/FaceDancer",use_auth_token=token) | |
| G = load_model("g_model_c_hq/FaceDancer_config_c_HQ.h5", custom_objects={"AdaIN": AdaIN, | |
| "AdaptiveAttention": AdaptiveAttention, | |
| "InstanceNormalization": InstanceNormalization}) | |
| # r_repo = Repository(local_dir="reconstruction_attack", clone_from="felixrosberg/reconstruction_attack", | |
| # private=True, use_auth_token=token) | |
| # R = load_model("reconstruction_attack/reconstructor_42.h5", custom_objects={"AdaIN": AdaIN, | |
| # "AdaptiveAttention": AdaptiveAttention, | |
| # "InstanceNormalization": InstanceNormalization}) | |
| # permuter_repo = Repository(local_dir="identity_permuter", clone_from="felixrosberg/identitypermuter", | |
| # private=True, use_auth_token=token, git_user="felixrosberg") | |
| # from identity_permuter.id_permuter import identity_permuter | |
| # IDP = identity_permuter(emb_size=32, min_arg=False) | |
| # IDP.load_weights("identity_permuter/id_permuter.h5") | |
| blend_mask_base = np.zeros(shape=(256, 256, 1)) | |
| blend_mask_base[80:244, 32:224] = 1 | |
| blend_mask_base = gaussian_filter(blend_mask_base, sigma=7) | |
| def run_inference(target, source, slider, adv_slider, settings): | |
| try: | |
| source = np.array(source) | |
| target = np.array(target) | |
| # Prepare to load video | |
| if "anonymize" not in settings: | |
| source_a = RetinaFace(np.expand_dims(source, axis=0)).numpy()[0] | |
| source_h, source_w, _ = source.shape | |
| source_lm = get_lm(source_a, source_w, source_h) | |
| source_aligned = norm_crop(source, source_lm, image_size=256) | |
| source_z = ArcFace.predict(np.expand_dims(tf.image.resize(source_aligned, [112, 112]) / 255.0, axis=0)) | |
| else: | |
| source_z = None | |
| # read frame | |
| im = target | |
| im_h, im_w, _ = im.shape | |
| im_shape = (im_w, im_h) | |
| detection_scale = im_w // 640 if im_w > 640 else 1 | |
| faces = RetinaFace(np.expand_dims(cv2.resize(im, | |
| (im_w // detection_scale, | |
| im_h // detection_scale)), axis=0)).numpy() | |
| total_img = im / 255.0 | |
| for annotation in faces: | |
| lm_align = np.array([[annotation[4] * im_w, annotation[5] * im_h], | |
| [annotation[6] * im_w, annotation[7] * im_h], | |
| [annotation[8] * im_w, annotation[9] * im_h], | |
| [annotation[10] * im_w, annotation[11] * im_h], | |
| [annotation[12] * im_w, annotation[13] * im_h]], | |
| dtype=np.float32) | |
| # align the detected face | |
| M, pose_index = estimate_norm(lm_align, 256, "arcface", shrink_factor=1.0) | |
| im_aligned = (cv2.warpAffine(im, M, (256, 256), borderValue=0.0) - 127.5) / 127.5 | |
| if "adversarial defense" in settings: | |
| eps = adv_slider / 200 | |
| X = tf.convert_to_tensor(np.expand_dims(im_aligned, axis=0)) | |
| with tf.GradientTape() as tape: | |
| tape.watch(X) | |
| X_z = ArcFaceE(tf.image.resize(X * 0.5 + 0.5, [112, 112])) | |
| output = R([X, X_z]) | |
| loss = tf.reduce_mean(tf.abs(0 - output)) | |
| gradient = tf.sign(tape.gradient(loss, X)) | |
| adv_x = X + eps * gradient | |
| im_aligned = tf.clip_by_value(adv_x, -1, 1)[0] | |
| if "anonymize" in settings and "reconstruction attack" not in settings: | |
| """source_z = ArcFace.predict(np.expand_dims(tf.image.resize(im_aligned, [112, 112]) / 255.0, axis=0)) | |
| anon_ratio = int(512 * (slider / 100)) | |
| anon_vector = np.ones(shape=(1, 512)) | |
| anon_vector[:, :anon_ratio] = -1 | |
| np.random.shuffle(anon_vector) | |
| source_z *= anon_vector""" | |
| slider_weight = slider / 100 | |
| target_z = ArcFace.predict(np.expand_dims(tf.image.resize(im_aligned, [112, 112]) * 0.5 + 0.5, axis=0)) | |
| # source_z = IDP.predict(target_z) | |
| source_z = slider_weight * source_z + (1 - slider_weight) * target_z | |
| if "reconstruction attack" in settings: | |
| source_z = ArcFaceE.predict(np.expand_dims(tf.image.resize(im_aligned, [112, 112]) * 0.5 + 0.5, axis=0)) | |
| # face swap | |
| if "reconstruction attack" not in settings: | |
| changed_face_cage = G.predict([np.expand_dims(im_aligned, axis=0), | |
| source_z]) | |
| changed_face = changed_face_cage[0] * 0.5 + 0.5 | |
| # get inverse transformation landmarks | |
| transformed_lmk = transform_landmark_points(M, lm_align) | |
| # warp image back | |
| iM, _ = inverse_estimate_norm(lm_align, transformed_lmk, 256, "arcface", shrink_factor=1.0) | |
| iim_aligned = cv2.warpAffine(changed_face, iM, im_shape, borderValue=0.0) | |
| # blend swapped face with target image | |
| blend_mask = cv2.warpAffine(blend_mask_base, iM, im_shape, borderValue=0.0) | |
| blend_mask = np.expand_dims(blend_mask, axis=-1) | |
| total_img = (iim_aligned * blend_mask + total_img * (1 - blend_mask)) | |
| else: | |
| changed_face_cage = R.predict([np.expand_dims(im_aligned, axis=0), | |
| source_z]) | |
| changed_face = changed_face_cage[0] * 0.5 + 0.5 | |
| # get inverse transformation landmarks | |
| transformed_lmk = transform_landmark_points(M, lm_align) | |
| # warp image back | |
| iM, _ = inverse_estimate_norm(lm_align, transformed_lmk, 256, "arcface", shrink_factor=1.0) | |
| iim_aligned = cv2.warpAffine(changed_face, iM, im_shape, borderValue=0.0) | |
| # blend swapped face with target image | |
| blend_mask = cv2.warpAffine(blend_mask_base, iM, im_shape, borderValue=0.0) | |
| blend_mask = np.expand_dims(blend_mask, axis=-1) | |
| total_img = (iim_aligned * blend_mask + total_img * (1 - blend_mask)) | |
| if "compare" in settings: | |
| total_img = np.concatenate((im / 255.0, total_img), axis=1) | |
| total_img = np.clip(total_img, 0, 1) | |
| total_img *= 255.0 | |
| total_img = total_img.astype('uint8') | |
| return total_img | |
| except Exception as e: | |
| print(e) | |
| return None | |
| description = "Not Working" | |
| examples = [] | |
| article = """ | |
| Demo is based of recent research from my Ph.D work. Results expects to be published in the coming months. | |
| """ | |
| iface = gradio.Interface(run_inference, | |
| [gradio.Image(shape=None, type="pil", label='Target'), | |
| gradio.Image(shape=None, type="pil", label='Source'), | |
| gradio.Slider(0, 100, default=100, label="Anonymization ratio (%)"), | |
| gradio.Slider(0, 100, default=100, label="Adversarial defense ratio (%)"), | |
| gradio.CheckboxGroup(["compare", | |
| "anonymize", | |
| "reconstruction attack", | |
| "adversarial defense"], | |
| label='Options')], | |
| "image", | |
| title="Not Working", | |
| description=description, | |
| examples=examples, | |
| article=article, | |
| layout="vertical") | |
| iface.launch() | |