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import os.path
import logging
import torch

from utils import utils_logger
from utils import utils_image as util
# from utils import utils_model
from models.network_rrdbnet import RRDBNet as net


"""
Spyder (Python 3.6-3.7)
PyTorch 1.4.0-1.8.1
Windows 10 or Linux
Kai Zhang (cskaizhang@gmail.com)
github: https://github.com/cszn/BSRGAN
        https://github.com/cszn/KAIR
If you have any question, please feel free to contact with me.
Kai Zhang (e-mail: cskaizhang@gmail.com)
by Kai Zhang ( March/2020 --> March/2021 --> )
This work was previously submitted to CVPR2021.

# --------------------------------------------
@inproceedings{zhang2021designing,
  title={Designing a Practical Degradation Model for Deep Blind Image Super-Resolution},
  author={Zhang, Kai and Liang, Jingyun and Van Gool, Luc and Timofte, Radu},
  booktitle={arxiv},
  year={2021}
}
# --------------------------------------------

"""

testsets = 'testsets'
testset_H = 'H_Image_Lora'
model_names = ['BSRGAN']
save_results = True
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

def getModel():

    utils_logger.logger_info('blind_sr_log', log_path='blind_sr_log.log')
    logger = logging.getLogger('blind_sr_log')

#    print(torch.__version__)               # pytorch version
#    print(torch.version.cuda)              # cuda version
#    print(torch.backends.cudnn.version())  # cudnn version

    #testsets = 'testsets'       # fixed, set path of testsets
    #testset_H = 'H_Image_Lora'  # ['RealSRSet','DPED']

    #model_names = ['RRDB','ESRGAN','FSSR_DPED','FSSR_JPEG','RealSR_DPED','RealSR_JPEG']
    #model_names = ['BSRGAN']    # 'BSRGANx2' for scale factor 2



    #save_results = True
    sf = 4
    #device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

    for model_name in model_names:
        if model_name in ['BSRGANx2']:
            sf = 2
        model_path = os.path.join('model_zoo', model_name+'.pth')          # set model path
        logger.info('{:>16s} : {:s}'.format('Model Name', model_name))

        # torch.cuda.set_device(0)      # set GPU ID
        logger.info('{:>16s} : {:<d}'.format('GPU ID', torch.cuda.current_device()))
        torch.cuda.empty_cache()

        # --------------------------------
        # define network and load model
        # --------------------------------
        model = net(in_nc=3, out_nc=3, nf=64, nb=23, gc=32, sf=sf)  # define network

#            model_old = torch.load(model_path)
#            state_dict = model.state_dict()
#            for ((key, param),(key2, param2)) in zip(model_old.items(), state_dict.items()):
#                state_dict[key2] = param
#            model.load_state_dict(state_dict, strict=True)

        model.load_state_dict(torch.load(model_path), strict=True)
        model.eval()
        for k, v in model.named_parameters():
            v.requires_grad = False
        model = model.to(device)
        torch.cuda.empty_cache()
        
        return model
        
        '''
        for testset_L in testset_Ls:

            L_path = os.path.join(testsets, testset_L)
            #E_path = os.path.join(testsets, testset_L+'_'+model_name)
            E_path = os.path.join(testsets, testset_L+'_results_x'+str(sf))
            util.mkdir(E_path)

            logger.info('{:>16s} : {:s}'.format('Input Path', L_path))
            logger.info('{:>16s} : {:s}'.format('Output Path', E_path))
            idx = 0

            for img in util.get_image_paths(L_path):
                print('test print image')
                print(img)

                # --------------------------------
                # (1) img_L
                # --------------------------------
                idx += 1
                img_name, ext = os.path.splitext(os.path.basename(img))
                logger.info('{:->4d} --> {:<s} --> x{:<d}--> {:<s}'.format(idx, model_name, sf, img_name+ext))

                img_L = util.imread_uint(img, n_channels=3)
                img_L = util.uint2tensor4(img_L)
                img_L = img_L.to(device)

                # --------------------------------
                # (2) inference
                # --------------------------------
                img_E = model(img_L)

                # --------------------------------
                # (3) img_E
                # --------------------------------
                img_E = util.tensor2uint(img_E)
                if save_results:
                    util.imsave(img_E, os.path.join(E_path, img_name+'_'+model_name+'.png'))
        '''

import sys

def main(img):
    #print('main')
    #img = '/home/nvidia/BSRGAN/testsets/L_Image_Lora/Lincoln.png'
    try:

        OutPath = os.path.join(testsets, testset_H)
        img_name, ext = os.path.splitext(os.path.basename(img))

        img_L = util.imread_uint(img, n_channels=3)
        img_L = util.uint2tensor4(img_L)
        img_L = img_L.to(device)

        #model = getModel()
    
        img_E = model(img_L)
        img_E = util.tensor2uint(img_E)
    
        util.imsave(img_E, os.path.join(OutPath, img_name+'_' + 'H' + '.png'))
        print('written')
    except Interrupt:
        return

def RUN():
    import serial
    import time
    from os.path import join, dirname, realpath
    import os
    import threading
    import filetype


    port = serial.Serial(
        port='/dev/ttyUSB0',\
        baudrate=9600,\
        parity=serial.PARITY_NONE,\
        stopbits=serial.STOPBITS_ONE,\
        bytesize=serial.EIGHTBITS,\
        timeout=0
    )

    print('Connected to : ' + port.port)

    portBuffer = []

    recvTime = 0
    numPacket = 0
    imgNum = 0

    rand_name = 'image_' + str(time.time()) + '.jpg'
    path = join(join(dirname(realpath(__file__)), 'testsets/L_Image_Lora'), rand_name)

    f = open(path, 'wb')
    
    while True:
        try:
            dataShow = []
            while port.inWaiting() > 0:
                recvTime = time.time()
                readByte = port.read()
                portBuffer.append(readByte)
                dataShow.append(readByte)
                if len(dataShow) > 20:
                    dataShow = []
            time.sleep(1)

            if time.time() - recvTime > 4 and len(portBuffer) != 0:
                rand_name = 'image_' + str(time.time()) + '.jpg'
                imageBuffer = []
                imgNum += 1
                dataPath = join(join(dirname(realpath(__file__)), 'testsets/L_Image_Lora'), rand_name)
                print('len portBuffer: ', len(portBuffer))

                if f.closed:
                    print('closed')
                    f = open(dataPath, 'wb')
                for i in range(0, len(portBuffer)):
                    if i < len(portBuffer) - 5:
                        if portBuffer[i] == b'\x05' and portBuffer[i+1] == b'\x00' and portBuffer[i+2] == b'\x82':
                            numPacket += 1
                            for j in range(0, int.from_bytes(portBuffer[i+7], 'big') - 2):
                                imageBuffer.append(portBuffer[i+10+j])
                                f.write(portBuffer[i+10+j])
                print('Number of packet = %d' % numPacket)
                numPacket = 0
                print('image size = %d' % len(imageBuffer))
                if len(imageBuffer) < 100:
                    print('closed file')
                    nameFile = f.name
                    f.close()

                    kind = filetype.guess(nameFile)
                    if kind is None:
                        print('failed')
                    else:
                        print(kind.mime)
                        print('call high resolution module')
                        main(nameFile)

                portBuffer = []

        except KeyBoardInterrupt:
            print('Good bye')
            break




if __name__ == '__main__':
    param1 = '/home/nvidia/BSRGAN/testsets/L_Image_Lora/Lincoln.png'

    if len(sys.argv) > 1:
        param1 = sys.argv[1]
    
    model = getModel()

    #main(param1)

    RUN()