import os
import torch

from base64 import b64encode
from cotracker.utils.visualizer import Visualizer, read_video_from_path
import numpy as np
from PIL import Image
import time

device = torch.device('cuda:3' if torch.cuda.is_available() else 'cpu')

start_time = time.time()
print(f'Using device: {device}')
print(f'start loading video')
video = read_video_from_path('./assets/F1_shorts.mp4')
print(f'video shape: {video.shape}')
# video = torch.from_numpy(video).permute(0, 3, 1, 2)[None].float().to(device)
video = torch.from_numpy(video).permute(0, 3, 1, 2)[None].float()
end_time = time.time()
print(f'video shape after permute: {video.shape}')
print("Load video Time taken: {:.2f} seconds".format(end_time - start_time))

from cotracker.predictor import CoTrackerPredictor


model = CoTrackerPredictor(
    checkpoint=os.path.join(
        './checkpoints/cotracker2.pth'
    )
)

# pred_tracks, pred_visibility = model(video, grid_size=30)

# vis = Visualizer(save_dir='./videos', pad_value=100)
# vis.visualize(video=video, tracks=pred_tracks, visibility=pred_visibility, filename='teaser');

grid_query_frame=20

import torch.nn.functional as F
# video_interp = F.interpolate(video[0], [200, 360], mode="bilinear")[None].to(device)
interp_size = (720, 1280)
video_interp = F.interpolate(video[0], [interp_size[0], interp_size[1]], mode="bilinear")[None].to(device)
print(f'video_interp shape: {video_interp.shape}')

start_time = time.time()
# pred_tracks, pred_visibility = model(video_interp, 
input_mask='./assets/F1_mask.png'
segm_mask = Image.open(input_mask)
interp_size = (interp_size[1], interp_size[0])
segm_mask = segm_mask.resize(interp_size, Image.BILINEAR)
segm_mask = np.array(Image.open(input_mask))
segm_mask = torch.tensor(segm_mask).to(device)
# pred_tracks, pred_visibility = model(video, 
pred_tracks, pred_visibility = model(video_interp, 
                                     grid_query_frame=grid_query_frame, backward_tracking=True,
                                     segm_mask=segm_mask )
end_time = time.time() 

print("Time taken: {:.2f} seconds".format(end_time - start_time))

start_time = time.time()
print(f'start visualizing')
vis = Visualizer(
    save_dir='./videos',
    pad_value=20,
    linewidth=1,
    mode='optical_flow'
)
print(f'vis initialized')
end_time = time.time()
print("Time taken: {:.2f} seconds".format(end_time - start_time))
start_time = time.time()
print(f'start visualize')
vis.visualize(
    video=video_interp,
    # video=video,
    tracks=pred_tracks,
    visibility=pred_visibility,
    filename='dense2');
print(f'done')
end_time = time.time()
print("Time taken: {:.2f} seconds".format(end_time - start_time))