import gradio
from huggingface_hub import Repository

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 retinaface.models import *
from options.swap_options import SwapOptions


opt = SwapOptions().parse()


retina_repo = Repository(local_dir="retina_model", clone_from="felixrosberg/retinaface_resnet50",
                         private=True, use_auth_token="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", git_user="felixrosberg")
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",
                      private=True, use_auth_token="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")
ArcFace = load_model("arcface_model/arc_res50.h5")

g_repo = Repository(local_dir="g_model", clone_from="felixrosberg/affa_f",
                    private=True, use_auth_token="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")
G = load_model("g_model/affa_f_demo.h5", custom_objects={"AdaIN": AdaIN,
                                                         "AdaptiveAttention": AdaptiveAttention,
                                                         "InstanceNormalization": InstanceNormalization})

blend_mask_base = np.zeros(shape=(256, 256, 1))
blend_mask_base[100:240, 32:224] = 1
blend_mask_base = gaussian_filter(blend_mask_base, sigma=7)


def run_inference(target, source):
    source = np.array(source)
    target = np.array(target)

    # Prepare to load video
    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))

    # 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)

        # face swap
        changed_face_cage = G.predict([np.expand_dims((im_aligned - 127.5) / 127.5, axis=0),
                                       source_z])
        changed_face = (changed_face_cage[0] + 1) / 2

        # 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 opt.compare:
        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


iface = gradio.Interface(run_inference,
                         [gradio.inputs.Image(shape=None),
                          gradio.inputs.Image(shape=None)],
                         gradio.outputs.Image())
iface.launch()