import sys, os
basedirs = [os.getcwd()]

for basedir in basedirs:
    paths_to_ensure = [
        basedir,
        basedir + '/extensions/sd-cn-animation/scripts',
        basedir + '/extensions/SD-CN-Animation/scripts',
        basedir + '/extensions/sd-cn-animation/RAFT',
        basedir + '/extensions/SD-CN-Animation/RAFT'
        ]

    for scripts_path_fix in paths_to_ensure:
        if not scripts_path_fix in sys.path:
            sys.path.extend([scripts_path_fix])

import numpy as np
import cv2

from collections import namedtuple
import torch
import argparse
from RAFT.raft import RAFT
from RAFT.utils.utils import InputPadder

import modules.paths as ph
import gc

RAFT_model = None
fgbg = cv2.createBackgroundSubtractorMOG2(history=500, varThreshold=16, detectShadows=True)

def background_subtractor(frame, fgbg):
  fgmask = fgbg.apply(frame)
  return cv2.bitwise_and(frame, frame, mask=fgmask)

def RAFT_clear_memory():
  global RAFT_model
  del RAFT_model
  gc.collect()
  torch.cuda.empty_cache()
  RAFT_model = None

def RAFT_estimate_flow(frame1, frame2, device='cuda'):
  global RAFT_model

  org_size = frame1.shape[1], frame1.shape[0]
  size = frame1.shape[1] // 16 * 16, frame1.shape[0] // 16 * 16
  frame1 = cv2.resize(frame1, size)
  frame2 = cv2.resize(frame2, size)

  model_path = ph.models_path + '/RAFT/raft-things.pth'
  remote_model_path = 'https://drive.google.com/uc?id=1MqDajR89k-xLV0HIrmJ0k-n8ZpG6_suM'

  if not os.path.isfile(model_path):
    from basicsr.utils.download_util import load_file_from_url
    os.makedirs(os.path.dirname(model_path), exist_ok=True)
    load_file_from_url(remote_model_path, file_name=model_path)

  if RAFT_model is None:
    args = argparse.Namespace(**{
        'model': ph.models_path + '/RAFT/raft-things.pth',
        'mixed_precision': True,
        'small': False,
        'alternate_corr': False,
        'path': ""
    })

    RAFT_model = torch.nn.DataParallel(RAFT(args))
    RAFT_model.load_state_dict(torch.load(args.model))

    RAFT_model = RAFT_model.module
    RAFT_model.to(device)
    RAFT_model.eval()

  with torch.no_grad():
    frame1_torch = torch.from_numpy(frame1).permute(2, 0, 1).float()[None].to(device)
    frame2_torch = torch.from_numpy(frame2).permute(2, 0, 1).float()[None].to(device)

    padder = InputPadder(frame1_torch.shape)
    image1, image2 = padder.pad(frame1_torch, frame2_torch)

    # estimate optical flow
    _, next_flow = RAFT_model(image1, image2, iters=20, test_mode=True)
    _, prev_flow = RAFT_model(image2, image1, iters=20, test_mode=True)

    next_flow = next_flow[0].permute(1, 2, 0).cpu().numpy()
    prev_flow = prev_flow[0].permute(1, 2, 0).cpu().numpy()

    fb_flow = next_flow + prev_flow
    fb_norm = np.linalg.norm(fb_flow, axis=2)

    occlusion_mask = fb_norm[..., None].repeat(3, axis=-1)

  next_flow = cv2.resize(next_flow, org_size)
  prev_flow = cv2.resize(prev_flow, org_size)

  return next_flow, prev_flow, occlusion_mask

def compute_diff_map(next_flow, prev_flow, prev_frame, cur_frame, prev_frame_styled, args_dict):
  h, w = cur_frame.shape[:2]
  fl_w, fl_h = next_flow.shape[:2]

  # normalize flow
  next_flow = next_flow / np.array([fl_h,fl_w]) 
  prev_flow = prev_flow / np.array([fl_h,fl_w])

  # compute occlusion mask
  fb_flow = next_flow + prev_flow
  fb_norm = np.linalg.norm(fb_flow , axis=2) 

  zero_flow_mask = np.clip(1 - np.linalg.norm(prev_flow, axis=-1)[...,None] * 20, 0, 1)
  diff_mask_flow = fb_norm[..., None] * zero_flow_mask

  # resize flow
  next_flow = cv2.resize(next_flow, (w, h)) 
  next_flow = (next_flow * np.array([h,w])).astype(np.float32)
  prev_flow = cv2.resize(prev_flow, (w, h))
  prev_flow = (prev_flow  * np.array([h,w])).astype(np.float32)

  # Generate sampling grids
  grid_y, grid_x = torch.meshgrid(torch.arange(0, h), torch.arange(0, w))
  flow_grid = torch.stack((grid_x, grid_y), dim=0).float()
  flow_grid += torch.from_numpy(prev_flow).permute(2, 0, 1)
  flow_grid = flow_grid.unsqueeze(0)
  flow_grid[:, 0, :, :] = 2 * flow_grid[:, 0, :, :] / (w - 1) - 1
  flow_grid[:, 1, :, :] = 2 * flow_grid[:, 1, :, :] / (h - 1) - 1
  flow_grid = flow_grid.permute(0, 2, 3, 1)

  
  prev_frame_torch = torch.from_numpy(prev_frame).float().unsqueeze(0).permute(0, 3, 1, 2) #N, C, H, W
  prev_frame_styled_torch = torch.from_numpy(prev_frame_styled).float().unsqueeze(0).permute(0, 3, 1, 2) #N, C, H, W

  warped_frame = torch.nn.functional.grid_sample(prev_frame_torch, flow_grid, padding_mode="reflection").permute(0, 2, 3, 1)[0].numpy()
  warped_frame_styled = torch.nn.functional.grid_sample(prev_frame_styled_torch, flow_grid, padding_mode="reflection").permute(0, 2, 3, 1)[0].numpy()

  #warped_frame = cv2.remap(prev_frame, flow_map, None, cv2.INTER_NEAREST, borderMode = cv2.BORDER_REFLECT)
  #warped_frame_styled = cv2.remap(prev_frame_styled, flow_map, None, cv2.INTER_NEAREST, borderMode = cv2.BORDER_REFLECT)

  
  diff_mask_org = np.abs(warped_frame.astype(np.float32) - cur_frame.astype(np.float32)) / 255
  diff_mask_org = diff_mask_org.max(axis = -1, keepdims=True)

  diff_mask_stl = np.abs(warped_frame_styled.astype(np.float32) - cur_frame.astype(np.float32)) / 255
  diff_mask_stl = diff_mask_stl.max(axis = -1, keepdims=True)

  alpha_mask = np.maximum.reduce([diff_mask_flow * args_dict['occlusion_mask_flow_multiplier'] * 10, \
                                  diff_mask_org * args_dict['occlusion_mask_difo_multiplier'], \
                                  diff_mask_stl * args_dict['occlusion_mask_difs_multiplier']]) #
  alpha_mask = alpha_mask.repeat(3, axis = -1)

  #alpha_mask_blured = cv2.dilate(alpha_mask, np.ones((5, 5), np.float32))
  if args_dict['occlusion_mask_blur'] > 0:
    blur_filter_size = min(w,h) // 15 | 1
    alpha_mask = cv2.GaussianBlur(alpha_mask, (blur_filter_size, blur_filter_size) , args_dict['occlusion_mask_blur'], cv2.BORDER_REFLECT)

  alpha_mask = np.clip(alpha_mask, 0, 1)

  return alpha_mask, warped_frame_styled

def frames_norm(occl): return occl / 127.5 - 1

def flow_norm(flow): return flow / 255

def occl_norm(occl): return occl / 127.5 - 1

def flow_renorm(flow): return flow * 255

def occl_renorm(occl): return (occl + 1) * 127.5