import numpy as np
import cv2
import onnx
from onnx import numpy_helper

from insightface import model_zoo
from insightface.utils import face_align
from .base_swapper import BaseSwapper

from dofaker.utils import download_file, get_model_url


class InSwapper(BaseSwapper):

    def __init__(self, name='inswapper', root='weights/models'):
        _, model_file = download_file(get_model_url(name),
                                      save_dir=root,
                                      overwrite=False)
        providers = model_zoo.model_zoo.get_default_providers()
        self.session = model_zoo.model_zoo.PickableInferenceSession(
            model_file, providers=providers)

        model = onnx.load(model_file)
        graph = model.graph
        self.emap = numpy_helper.to_array(graph.initializer[-1])
        self.input_mean = 0.0
        self.input_std = 255.0

        inputs = self.session.get_inputs()
        self.input_names = []
        for inp in inputs:
            self.input_names.append(inp.name)
        outputs = self.session.get_outputs()
        output_names = []
        for out in outputs:
            output_names.append(out.name)
        self.output_names = output_names
        assert len(
            self.output_names
        ) == 1, "The output number of inswapper model should be 1, but got {}, please check your model.".format(
            len(self.output_names))
        output_shape = outputs[0].shape
        input_cfg = inputs[0]
        input_shape = input_cfg.shape
        self.input_shape = input_shape
        print('inswapper-shape:', self.input_shape)
        self.input_size = tuple(input_shape[2:4][::-1])

    def forward(self, img, latent):
        img = (img - self.input_mean) / self.input_std
        pred = self.session.run(self.output_names, {
            self.input_names[0]: img,
            self.input_names[1]: latent
        })[0]
        return pred

    def get(self, img, target_face, source_face, paste_back=True):
        aimg, M = face_align.norm_crop2(img, target_face.kps,
                                        self.input_size[0])
        blob = cv2.dnn.blobFromImage(
            aimg,
            1.0 / self.input_std,
            self.input_size,
            (self.input_mean, self.input_mean, self.input_mean),
            swapRB=True)
        latent = source_face.normed_embedding.reshape((1, -1))
        latent = np.dot(latent, self.emap)
        latent /= np.linalg.norm(latent)
        pred = self.session.run(self.output_names, {
            self.input_names[0]: blob,
            self.input_names[1]: latent
        })[0]
        img_fake = pred.transpose((0, 2, 3, 1))[0]
        bgr_fake = np.clip(255 * img_fake, 0, 255).astype(np.uint8)[:, :, ::-1]
        if not paste_back:
            return bgr_fake, M
        else:
            target_img = img
            fake_diff = bgr_fake.astype(np.float32) - aimg.astype(np.float32)
            fake_diff = np.abs(fake_diff).mean(axis=2)
            fake_diff[:2, :] = 0
            fake_diff[-2:, :] = 0
            fake_diff[:, :2] = 0
            fake_diff[:, -2:] = 0
            IM = cv2.invertAffineTransform(M)
            img_white = np.full((aimg.shape[0], aimg.shape[1]),
                                255,
                                dtype=np.float32)
            bgr_fake = cv2.warpAffine(
                bgr_fake,
                IM, (target_img.shape[1], target_img.shape[0]),
                borderValue=0.0)
            img_white = cv2.warpAffine(
                img_white,
                IM, (target_img.shape[1], target_img.shape[0]),
                borderValue=0.0)
            fake_diff = cv2.warpAffine(
                fake_diff,
                IM, (target_img.shape[1], target_img.shape[0]),
                borderValue=0.0)
            img_white[img_white > 20] = 255
            fthresh = 10
            fake_diff[fake_diff < fthresh] = 0
            fake_diff[fake_diff >= fthresh] = 255
            img_mask = img_white
            mask_h_inds, mask_w_inds = np.where(img_mask == 255)
            mask_h = np.max(mask_h_inds) - np.min(mask_h_inds)
            mask_w = np.max(mask_w_inds) - np.min(mask_w_inds)
            mask_size = int(np.sqrt(mask_h * mask_w))
            k = max(mask_size // 10, 10)
            #k = max(mask_size//20, 6)
            #k = 6
            kernel = np.ones((k, k), np.uint8)
            img_mask = cv2.erode(img_mask, kernel, iterations=1)
            kernel = np.ones((2, 2), np.uint8)
            fake_diff = cv2.dilate(fake_diff, kernel, iterations=1)
            k = max(mask_size // 20, 5)
            #k = 3
            #k = 3
            kernel_size = (k, k)
            blur_size = tuple(2 * i + 1 for i in kernel_size)
            img_mask = cv2.GaussianBlur(img_mask, blur_size, 0)
            k = 5
            kernel_size = (k, k)
            blur_size = tuple(2 * i + 1 for i in kernel_size)
            fake_diff = cv2.GaussianBlur(fake_diff, blur_size, 0)
            img_mask /= 255
            fake_diff /= 255
            #img_mask = fake_diff
            img_mask = np.reshape(img_mask,
                                  [img_mask.shape[0], img_mask.shape[1], 1])
            fake_merged = img_mask * bgr_fake + (
                1 - img_mask) * target_img.astype(np.float32)
            fake_merged = fake_merged.astype(np.uint8)
            return fake_merged