import os
import cv2
import torch
import argparse
import numpy as np
from tqdm import tqdm
from torch.nn import functional as F
import warnings
import _thread
import skvideo.io
from queue import Queue, Empty

warnings.filterwarnings("ignore")

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
    torch.set_grad_enabled(False)
    torch.backends.cudnn.enabled = True
    torch.backends.cudnn.benchmark = True

parser = argparse.ArgumentParser(description='Interpolation for a pair of images')
parser.add_argument('--video', dest='video', required=True)
parser.add_argument('--skip', dest='skip', action='store_true', help='whether to remove static frames before processing')
parser.add_argument('--fps', dest='fps', type=int, default=None)
parser.add_argument('--png', dest='png', action='store_true', help='whether to output png format outputs')
parser.add_argument('--ext', dest='ext', type=str, default='mp4', help='output video extension')
parser.add_argument('--exp', dest='exp', type=int, default=1, help='interpolation exponent (default: 1)')
args = parser.parse_args()
assert (args.exp in [1, 2, 3])
args.times = 2 ** args.exp

from model.RIFE import Model
model = Model()
model.load_model('./train_log')
model.eval()
model.device()

videoCapture = cv2.VideoCapture(args.video)
fps = videoCapture.get(cv2.CAP_PROP_FPS)
tot_frame = videoCapture.get(cv2.CAP_PROP_FRAME_COUNT)
videoCapture.release()
videogen = skvideo.io.vreader(args.video)
frame = next(videogen)
h, w, _ = frame.shape
if args.fps is None:
    args.fps = fps * args.times
fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
video_path_wo_ext, ext = os.path.splitext(args.video)
if args.png:
    if not os.path.exists('output'):
        os.mkdir('output')
    vid_out = None
else:
    vid_out = cv2.VideoWriter('{}_{}X_{}fps.{}'.format(video_path_wo_ext, args.times, int(np.round(args.fps)), args.ext), fourcc, args.fps, (w, h))
    
cnt = 0
skip_frame = 1
buffer = Queue()
buffer.put(frame)
def write_frame(i0, infs, i1, p, user_args):
    global skip_frame, cnt
    for i in range(i0.shape[0]):
        l = len(infs)
        # A video transition occurs.
        if p[i] > 0.2:
            for j in range(len(infs)):
                infs[j][i] = i0[i]
        
        # Result was too similar to previous frame, skip if given.
        if p[i] < 5e-3 and user_args.skip:
            if skip_frame % 100 == 0:
                print("Warning: Your video has {} static frames, "
                      "skipping them may change the duration of the generated video.".format(skip_frame))
            skip_frame += 1
            continue
        
        # Write results.      
        buffer.put(i0[i])
        for inf in infs:
            buffer.put(inf[i])

def clear_buffer(user_args, buffer):    
    global cnt
    while True:
        item = buffer.get()
        if item is None:
            break
        if user_args.png:
            cv2.imwrite('output/{:0>7d}.png'.format(cnt), item[:, :, ::-1])
            cnt += 1
        else:
            vid_out.write(item[:, :, ::-1])

def make_inference(model, I0, I1, exp):
    middle = model.inference(I0, I1)
    if exp == 1:
        return [middle]
    first_half = make_inference(model, I0, middle, exp=exp - 1)
    second_half = make_inference(model, middle, I1, exp=exp - 1)
    return [*first_half, middle, *second_half]


ph = ((h - 1) // 32 + 1) * 32
pw = ((w - 1) // 32 + 1) * 32
padding = (0, pw - w, 0, ph - h)
print('{}.{}, {} frames in total, {}FPS to {}FPS'.format(video_path_wo_ext, args.ext, tot_frame, fps, args.fps))
pbar = tqdm(total=tot_frame)
img_list = []
_thread.start_new_thread(clear_buffer, (args, buffer))
tot_frame -= 1
for frame in videogen:
    tot_frame -= 1
    img_list.append(frame)
    if len(img_list) == 5 or tot_frame == 0:
        imgs = torch.from_numpy(np.transpose(img_list, (0, 3, 1, 2))).to(device, non_blocking=True).float() / 255.
        I0 = imgs[:-1]
        I1 = imgs[1:]
        p = (F.interpolate(I0, (16, 16), mode='bilinear', align_corners=False)
             - F.interpolate(I1, (16, 16), mode='bilinear', align_corners=False)).abs()
        I0 = F.pad(I0, padding)
        I1 = F.pad(I1, padding)
        inferences = make_inference(model, I0, I1, exp=args.exp)
        I0 = np.array(img_list[:-1])
        I1 = np.array(img_list[1:])
        inferences = list(map(lambda x: ((x[:, :, :h, :w] * 255.).byte().cpu().detach().numpy().transpose(0, 2, 3, 1)), inferences))
        
        write_frame(I0, inferences, I1, p.mean(3).mean(2).mean(1), args)
        pbar.update(4)
        img_list = img_list[-1:]
buffer.put(img_list[0])
import time
while(not buffer.empty()):
    time.sleep(0.1)
pbar.close()
if not vid_out is None:
    vid_out.release()