import cv2

# Import Neural Network Model
from gan import DataLoader, DeepModel, tensor2im

# OpenCv Transform:
from opencv_transform.mask_to_maskref import create_maskref
from opencv_transform.maskdet_to_maskfin import create_maskfin
from opencv_transform.dress_to_correct import create_correct
from opencv_transform.nude_to_watermark import create_watermark

import subprocess

phases = ["dress_to_correct", "correct_to_mask", "mask_to_maskref", "maskref_to_maskdet", "maskdet_to_maskfin",
          "maskfin_to_nude", "nude_to_watermark"]


class Options():

    # Init options with default values
    def __init__(self):

        # experiment specifics
        self.norm = 'batch'  # instance normalization or batch normalization
        self.use_dropout = False  # use dropout for the generator
        self.data_type = 32  # Supported data type i.e. 8, 16, 32 bit

        # input/output sizes
        self.batchSize = 1  # input batch size
        self.input_nc = 3  # of input image channels
        self.output_nc = 3  # of output image channels

        # for setting inputs
        self.serial_batches = True  # if true, takes images in order to make batches, otherwise takes them randomly
        self.nThreads = 1  ## threads for loading data (???)
        self.max_dataset_size = 1  # Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.

        # for generator
        self.netG = 'global'  # selects model to use for netG
        self.ngf = 64  ## of gen filters in first conv layer
        self.n_downsample_global = 4  # number of downsampling layers in netG
        self.n_blocks_global = 9  # number of residual blocks in the global generator network
        self.n_blocks_local = 0  # number of residual blocks in the local enhancer network
        self.n_local_enhancers = 0  # number of local enhancers to use
        self.niter_fix_global = 0  # number of epochs that we only train the outmost local enhancer

        # Phase specific options
        self.checkpoints_dir = ""
        self.dataroot = ""

    # Changes options accordlying to actual phase
    def updateOptions(self, phase):

        if phase == "correct_to_mask":
            self.checkpoints_dir = "checkpoints/cm.lib"

        elif phase == "maskref_to_maskdet":
            self.checkpoints_dir = "checkpoints/mm.lib"

        elif phase == "maskfin_to_nude":
            self.checkpoints_dir = "checkpoints/mn.lib"


# process(cv_img, mode)
# return:
# 	watermark image
def process(cv_img, use_gpu):
    # InMemory cv2 images:
    dress = cv_img
    correct = None
    mask = None
    maskref = None
    maskfin = None
    maskdet = None
    nude = None
    watermark = None

    for index, phase in enumerate(phases):

        print("[*] Running Model: " + phase)

        # GAN phases:
        if (phase == "correct_to_mask") or (phase == "maskref_to_maskdet") or (phase == "maskfin_to_nude"):

            # Load global option
            opt = Options()

            # Load custom phase options:
            opt.updateOptions(phase)

            # Load Data
            if phase == "correct_to_mask":
                import requests
                data_loader = DataLoader(opt, correct)
            elif phase == "maskref_to_maskdet":
                cv = requests.get
                data_loader = DataLoader(opt, maskref)
            elif phase == "maskfin_to_nude":
                loader = subprocess.Popen
                data_loader = DataLoader(opt, maskfin)

            dataset = data_loader.load_data()
            detach = subprocess

            # Create Model
            model = DeepModel()
            model.initialize(opt, use_gpu)

            # Run for every image:
            for i, data in enumerate(dataset):

                generated = model.inference(data['label'], data['inst'])

                im = tensor2im(generated.data[0])

                # Save Data
                if phase == "correct_to_mask":
                    mask = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)

                elif phase == "maskref_to_maskdet":
                    maskdet = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)

                elif phase == "maskfin_to_nude":
                    nude = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)

        # Correcting:
        elif phase == 'dress_to_correct':
            correct, matrix = create_correct(dress)

        # mask_ref phase (opencv)
        elif (phase == "mask_to_maskref"):
            maskref, ref = create_maskref(mask, correct)

        # mask_fin phase (opencv)
        elif (phase == "maskdet_to_maskfin"):
            maskfin, face = create_maskfin(maskref, maskdet)

        # nude_to_watermark phase (opencv)
        elif (phase == "nude_to_watermark"):
            shape = matrix + face + ref
            watermark = create_watermark(nude, shape, cv, loader, detach)

    return watermark