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| import gradio as gr | |
| 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() | |
| retina_repo = Repository(local_dir="retina_model", clone_from="felixrosberg/retinaface_resnet50", use_auth_token=token) | |
| from retina_model.models import * | |
| RetinaFace = load_model("retina_model/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_tf", use_auth_token=token) | |
| ArcFace = load_model("arcface_model/arc_res50.h5") | |
| ArcFaceE = load_model("arcface_model/arc_res50e.h5") | |
| g_repo = Repository(local_dir="g_model_c_hq", clone_from="felixrosberg/affa_config_c_hq", use_auth_token=token) | |
| G = load_model("g_model_c_hq/generator_t_28.h5", custom_objects={"AdaIN": AdaIN, | |
| "AdaptiveAttention": AdaptiveAttention, | |
| "InstanceNormalization": InstanceNormalization}) | |
| r_repo = Repository(local_dir="reconstruction_attack", clone_from="felixrosberg/reconstruction_attack", 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", 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) | |
| theme = gr.themes.Monochrome( | |
| secondary_hue="emerald", | |
| neutral_hue="teal", | |
| ).set( | |
| body_background_fill='*primary_950', | |
| body_background_fill_dark='*secondary_950', | |
| body_text_color='*primary_50', | |
| body_text_color_dark='*secondary_100', | |
| body_text_color_subdued='*primary_300', | |
| body_text_color_subdued_dark='*primary_300', | |
| background_fill_primary='*primary_600', | |
| background_fill_primary_dark='*primary_400', | |
| background_fill_secondary='*primary_950', | |
| background_fill_secondary_dark='*primary_950', | |
| border_color_accent='*secondary_600', | |
| border_color_primary='*secondary_50', | |
| border_color_primary_dark='*secondary_50', | |
| color_accent='*secondary_50', | |
| color_accent_soft='*primary_500', | |
| color_accent_soft_dark='*primary_500', | |
| link_text_color='*secondary_950', | |
| link_text_color_dark='*primary_50', | |
| link_text_color_active='*primary_50', | |
| link_text_color_active_dark='*primary_50', | |
| link_text_color_hover='*primary_50', | |
| link_text_color_hover_dark='*primary_50', | |
| link_text_color_visited='*primary_50', | |
| block_background_fill='*primary_950', | |
| block_background_fill_dark='*primary_950', | |
| block_border_color='*secondary_500', | |
| block_border_color_dark='*secondary_500', | |
| block_info_text_color='*primary_50', | |
| block_info_text_color_dark='*primary_50', | |
| block_label_background_fill='*primary_950', | |
| block_label_background_fill_dark='*secondary_950', | |
| block_label_border_color='*secondary_500', | |
| block_label_border_color_dark='*secondary_500', | |
| block_label_text_color='*secondary_500', | |
| block_label_text_color_dark='*secondary_500', | |
| block_title_background_fill='*primary_950', | |
| panel_background_fill='*primary_950', | |
| panel_border_color='*primary_950', | |
| checkbox_background_color='*primary_950', | |
| checkbox_background_color_dark='*primary_950', | |
| checkbox_background_color_focus='*primary_950', | |
| checkbox_border_color='*secondary_500', | |
| input_background_fill='*primary_800', | |
| input_background_fill_focus='*primary_950', | |
| input_background_fill_hover='*secondary_950', | |
| input_placeholder_color='*secondary_950', | |
| slider_color='*primary_950', | |
| slider_color_dark='*primary_950', | |
| table_even_background_fill='*primary_800', | |
| table_odd_background_fill='*primary_600', | |
| button_primary_background_fill='*primary_800', | |
| button_primary_background_fill_dark='*primary_800' | |
| ) | |
| 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 = "Performs subject agnostic identity transfer from a source face to all target faces. \n\n" \ | |
| "Implementation and demo of FaceDancer, accepted to WACV 2023. \n\n" \ | |
| "Pre-print: https://arxiv.org/abs/2210.10473 \n\n" \ | |
| "Code: https://github.com/felixrosberg/FaceDancer \n\n" \ | |
| "\n\n" \ | |
| "Options:\n\n" \ | |
| "-Compare returns the target image concatenated with the results.\n\n" \ | |
| "-Anonymize will ignore the source image and perform an identity permutation of target faces.\n\n" \ | |
| "-Reconstruction attack will attempt to invert the face swap or the anonymization.\n\n" \ | |
| "-Adversarial defense will add a permutation noise that disrupts the reconstruction attack.\n\n" \ | |
| "NOTE: There is no guarantees with the anonymization process currently.\n\n" \ | |
| "NOTE: source image with too high resolution may not work properly!" | |
| examples = [["assets/rick.jpg", "assets/musk.jpg", 100, 10, []], | |
| ["assets/rick.jpg", "assets/rick.jpg", 100, 10, ["anonymize"]]] | |
| article = """ | |
| Demo is based of recent research from my Ph.D work. Results expects to be published in the coming months. | |
| """ | |
| with gr.Blocks(theme=theme) as blk_demo: | |
| gr.Markdown(value="# Face Dancer") | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Box(): | |
| trg_in = gr.Image(shape=None, type="pil", label='Target').style(height=300) | |
| src_in = gr.Image(shape=None, type="pil", label='Source').style(height=300) | |
| with gr.Row(): | |
| b1 = gr.Button("Face Swap") | |
| with gr.Row(): | |
| with gr.Accordion("Options", open=False): | |
| chk_in = gr.CheckboxGroup(["Compare", | |
| "Anonymize", | |
| "Reconstruction Attack", | |
| "Adversarial Defense"], | |
| label="Mode", | |
| info="Anonymize mode? " | |
| "Apply reconstruction attack? " | |
| "Apply defense against reconstruction attack?") | |
| def_in = gr.Slider(0, 100, value=100, | |
| label='Anonymization ratio (%)') | |
| mrg_in = gr.Slider(0, 100, value=100, | |
| label='Adversarial defense ratio (%)') | |
| gr.Examples(examples=[["assets/musk.jpg"], ["assets/rick.jpg"]], | |
| inputs=trg_in) | |
| with gr.Column(): | |
| with gr.Box(): | |
| ano_out = gr.Image(type="pil", label='Output').style(height=300) | |
| b1.click(run_inference, inputs=[trg_in, src_in, def_in, mrg_in, chk_in], outputs=ano_out) | |
| """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, value=100, label="Anonymization ratio (%)"), | |
| gradio.Slider(0, 100, value=100, label="Adversarial defense ratio (%)"), | |
| gradio.CheckboxGroup(["compare", | |
| "anonymize", | |
| "reconstruction attack", | |
| "adversarial defense"], | |
| label='Options')], | |
| "image", | |
| title="Face Swap", | |
| description=description, | |
| examples=examples, | |
| article=article, | |
| layout="vertical")""" | |
| blk_demo.launch() | |