Update app.py

app.py

@@ -1,7 +1,355 @@
 import gradio as gr
+from huggingface_hub import Repository
+import os
 
-def greet(name):
-    return "Hello " + name + "!!"
+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
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+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.Base(
+        primary_hue="neutral",
+        radius_size="none",
+    ).set(
+        embed_radius='*radius_xxs',
+
+        loader_color="#303A3F",
+        loader_color_dark="#303A3F",
+
+        body_text_color='*neutral_800',
+        body_text_color_dark='*neutral_800',
+
+        body_text_color_subdued="*neutral_400",
+        body_text_color_subdued_dark="*neutral_400",
+
+        button_primary_background_fill='*primary_700',
+        button_primary_background_fill_dark='*primary_700',
+
+        button_primary_background_fill_hover='*primary_400',
+        button_primary_background_fill_hover_dark='*primary_400',
+
+        button_primary_border_color='*primary_200',
+        button_primary_border_color_dark='*primary_200',
+
+        button_primary_text_color='#303A3F',
+        button_primary_text_color_dark='#303A3F',
+
+        button_secondary_text_color="#303A3F",
+        button_secondary_text_color_dark="#303A3F",
+
+        button_primary_text_color_hover='*primary_50',
+        button_primary_text_color_hover_dark='*primary_50',
+
+        button_secondary_background_fill="*primary_200",
+        button_secondary_background_fill_dark="*primary_200",
+
+        button_secondary_background_fill_hover='#667D88',
+        button_secondary_background_fill_hover_dark='#667D88',
+
+        input_background_fill="*neutral_100",
+        input_background_fill_dark="*neutral_100",
+
+        input_background_fill_focus="*secondary_500",
+        input_background_fill_focus_dark="*secondary_500",
+
+        body_background_fill="#FFFFFF",
+        body_background_fill_dark="#FFFFFF",
+
+        background_fill_secondary="*neutral_50",
+        background_fill_secondary_dark="*neutral_50",
+
+        border_color_accent="#303A3F",
+        border_color_accent_dark="#303A3F",
+
+        border_color_primary="#303A3F",
+        border_color_primary_dark="#303A3F",
+
+        color_accent="*primary_500",
+        color_accent_soft="*primary_50",
+        color_accent_soft_dark="*primary_50",
+
+        background_fill_primary="#FFFFFF",
+        background_fill_primary_dark="#FFFFFF",
+
+        block_title_background_fill="#FFFFFF",
+        block_title_background_fill_dark="#FFFFFF",
+
+        block_background_fill="#FFFFFF",
+        block_background_fill_dark="#FFFFFF",
+
+        block_label_border_color="*neutral_200",
+        block_label_border_color_dark="*neutral_200",
+
+        block_label_text_color="*neutral_500",
+        block_label_text_color_dark="*neutral_500",
+
+        block_info_text_color="*neutral_400",
+        block_info_text_color_dark="*neutral_400",
+
+        block_border_color="*neutral_700",
+        block_border_color_dark="*neutral_700",
+
+        block_border_width="1px",
+        block_border_width_dark="1px",
+
+        block_label_background_fill="#FFFFFF",
+        block_label_background_fill_dark="#FFFFFF",
+
+        block_label_border_width="1px",
+        block_label_border_width_dark="1px",
+
+        block_title_text_color="*neutral_500",
+        block_title_text_color_dark="*neutral_500",
+
+        checkbox_background_color="#FFFFFF",
+        checkbox_background_color_dark="#FFFFFF",
+
+        checkbox_background_color_focus="#FFFFFF",
+        checkbox_background_color_focus_dark="#FFFFFF",
+
+        checkbox_background_color_hover="#FFFFFF",
+        checkbox_background_color_hover_dark="#FFFFFF",
+
+        panel_background_fill="*neutral_50",
+        panel_background_fill_dark="*neutral_50",
+
+        slider_color="#303A3F",
+        slider_color_dark="#303A3F",
+    )
+
+
+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")
+    gr.Markdown(value="# Paper: [FaceDancer: Pose- and Occlusion-Aware High Fidelity Face Swapping](https://arxiv.org/abs/2210.10473)")
+    gr.Markdown(value="# Check out the code [here](https://github.com/felixrosberg/FaceDancer)")
+    with gr.Row():
+        with gr.Column():
+            with gr.Group():
+                trg_in = gr.Image(type="pil", label='Target')
+                src_in = gr.Image(type="pil", label='Source')
+            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.Group():
+                ano_out = gr.Image(type="pil", label='Output')
+
+    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()