
import cv2
import torch
import fractions
import numpy as np
from PIL import Image
import torch.nn.functional as F
from torchvision import transforms
from models.models import create_model
from options.test_options import TestOptions
from insightface_func.face_detect_crop_multi import Face_detect_crop
from util.reverse2original import reverse2wholeimage
import os
from util.add_watermark import watermark_image
import torch.nn as nn
from util.norm import SpecificNorm

def lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0

transformer_Arcface = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])

def _totensor(array):
    tensor = torch.from_numpy(array)
    img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
    return img.float().div(255)

def _toarctensor(array):
    tensor = torch.from_numpy(array)
    img = tensor.transpose(0, 1).transpose(0, 2).contiguous()
    return img.float().div(255)

if __name__ == '__main__':
    opt = TestOptions().parse()

    start_epoch, epoch_iter = 1, 0
    crop_size = 224

    torch.nn.Module.dump_patches = True
    logoclass = watermark_image('./simswaplogo/simswaplogo.png')
    model = create_model(opt)
    model.eval()
    mse = torch.nn.MSELoss().cuda()

    spNorm =SpecificNorm()


    app = Face_detect_crop(name='antelope', root='./insightface_func/models')
    app.prepare(ctx_id= 0, det_thresh=0.6, det_size=(640,640))

    pic_a = opt.pic_a_path
    pic_specific = opt.pic_specific_path

    # The person who provides id information 
    img_a_whole = cv2.imread(pic_a)
    img_a_align_crop, _ = app.get(img_a_whole,crop_size)
    img_a_align_crop_pil = Image.fromarray(cv2.cvtColor(img_a_align_crop[0],cv2.COLOR_BGR2RGB)) 
    img_a = transformer_Arcface(img_a_align_crop_pil)
    img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])

    # convert numpy to tensor
    img_id = img_id.cuda()

    #create latent id
    img_id_downsample = F.interpolate(img_id, scale_factor=0.5)
    latend_id = model.netArc(img_id_downsample)
    latend_id = F.normalize(latend_id, p=2, dim=1)


    # The specific person to be swapped
    specific_person_whole = cv2.imread(pic_specific)
    specific_person_align_crop, _ = app.get(specific_person_whole,crop_size)
    specific_person_align_crop_pil = Image.fromarray(cv2.cvtColor(specific_person_align_crop[0],cv2.COLOR_BGR2RGB)) 
    specific_person = transformer_Arcface(specific_person_align_crop_pil)
    specific_person = specific_person.view(-1, specific_person.shape[0], specific_person.shape[1], specific_person.shape[2])

    # convert numpy to tensor
    specific_person = specific_person.cuda()

    #create latent id
    specific_person_downsample = F.interpolate(specific_person, scale_factor=0.5)
    specific_person_id_nonorm = model.netArc(specific_person_downsample)
    # specific_person_id_norm = F.normalize(specific_person_id_nonorm, p=2, dim=1)

    ############## Forward Pass ######################

    pic_b = opt.pic_b_path
    img_b_whole = cv2.imread(pic_b)

    img_b_align_crop_list, b_mat_list = app.get(img_b_whole,crop_size)
    # detect_results = None
    swap_result_list = []

    id_compare_values = [] 
    b_align_crop_tenor_list = []
    for b_align_crop in img_b_align_crop_list:

        b_align_crop_tenor = _totensor(cv2.cvtColor(b_align_crop,cv2.COLOR_BGR2RGB))[None,...].cuda()

        b_align_crop_tenor_arcnorm = spNorm(b_align_crop_tenor)
        b_align_crop_tenor_arcnorm_downsample = F.interpolate(b_align_crop_tenor_arcnorm, scale_factor=0.5)
        b_align_crop_id_nonorm = model.netArc(b_align_crop_tenor_arcnorm_downsample)

        id_compare_values.append(mse(b_align_crop_id_nonorm,specific_person_id_nonorm).detach().cpu().numpy())
        b_align_crop_tenor_list.append(b_align_crop_tenor)

    id_compare_values_array = np.array(id_compare_values)
    min_index = np.argmin(id_compare_values_array)
    min_value = id_compare_values_array[min_index]

    if min_value < opt.id_thres:

        swap_result = model(None, b_align_crop_tenor_list[min_index], latend_id, None, True)[0]

        reverse2wholeimage([swap_result], [b_mat_list[min_index]], crop_size, img_b_whole, logoclass, os.path.join(opt.output_path, 'result_whole_swapspecific.jpg'), opt.no_simswaplogo)

        print(' ')

        print('************ Done ! ************')

    else:
        print('The person you specified is not found on the picture: {}'.format(pic_b))
