
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


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

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

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

# detransformer = transforms.Compose([
#         transforms.Normalize([0, 0, 0], [1/0.229, 1/0.224, 1/0.225]),
#         transforms.Normalize([-0.485, -0.456, -0.406], [1, 1, 1])
#     ])
if __name__ == '__main__':
    opt = TestOptions().parse()

    start_epoch, epoch_iter = 1, 0

    torch.nn.Module.dump_patches = True
    model = create_model(opt)
    model.eval()


    pic_a = opt.pic_a_path
    img_a = Image.open(pic_a).convert('RGB')
    img_a = transformer_Arcface(img_a)
    img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])

    pic_b = opt.pic_b_path

    img_b = Image.open(pic_b).convert('RGB')
    img_b = transformer(img_b)
    img_att = img_b.view(-1, img_b.shape[0], img_b.shape[1], img_b.shape[2])

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

    #create latent id
    img_id_downsample = F.interpolate(img_id, scale_factor=0.5)
    latend_id = model.netArc(img_id_downsample)
    latend_id = latend_id.detach().to('cpu')
    latend_id = latend_id/np.linalg.norm(latend_id,axis=1,keepdims=True)
    latend_id = latend_id.to('cuda')


    ############## Forward Pass ######################
    img_fake = model(img_id, img_att, latend_id, latend_id, True)


    for i in range(img_id.shape[0]):
        if i == 0:
            row1 = img_id[i]
            row2 = img_att[i]
            row3 = img_fake[i]
        else:
            row1 = torch.cat([row1, img_id[i]], dim=2)
            row2 = torch.cat([row2, img_att[i]], dim=2)
            row3 = torch.cat([row3, img_fake[i]], dim=2)

    #full = torch.cat([row1, row2, row3], dim=1).detach()
    full = row3.detach()
    full = full.permute(1, 2, 0)
    output = full.to('cpu')
    output = np.array(output)
    output = output[..., ::-1]

    output = output*255

    cv2.imwrite(opt.output_path + 'result.jpg',output)