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import os
import cv2
import imutils
import torch
import numpy as np
import gradio as gr
from cotracker.utils.visualizer import Visualizer
def parse_video(video_file):
vs = cv2.VideoCapture(video_file)
frames = []
while True:
(gotit, frame) = vs.read()
if frame is not None:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frames.append(frame)
if not gotit:
break
return np.stack(frames)
def cotracker_demo(
input_video,
grid_size: int = 10,
tracks_leave_trace: bool = False,
):
load_video = parse_video(input_video)
load_video = torch.from_numpy(load_video).permute(0, 3, 1, 2)[None].float()
model = torch.hub.load("facebookresearch/co-tracker", "cotracker2_online")
if torch.cuda.is_available():
model = model.cuda()
load_video = load_video.cuda()
model(video_chunk=load_video, is_first_step=True, grid_size=grid_size)
for ind in range(0, load_video.shape[1] - model.step, model.step):
pred_tracks, pred_visibility = model(
video_chunk=load_video[:, ind : ind + model.step * 2]
) # B T N 2, B T N 1
linewidth = 2
if grid_size < 10:
linewidth = 4
elif grid_size < 20:
linewidth = 3
vis = Visualizer(
save_dir=os.path.join(os.path.dirname(__file__), "results"),
grayscale=False,
pad_value=100,
fps=10,
linewidth=linewidth,
show_first_frame=5,
tracks_leave_trace=-1 if tracks_leave_trace else 0,
)
import time
def current_milli_time():
return round(time.time() * 1000)
filename = str(current_milli_time())
vis.visualize(
load_video.cpu(),
tracks=pred_tracks.cpu(),
visibility=pred_visibility.cpu(),
filename=f"{filename}_pred_track",
)
return os.path.join(
os.path.dirname(__file__), "results", f"{filename}_pred_track.mp4"
)
apple = os.path.join(os.path.dirname(__file__), "videos", "apple.mp4")
bear = os.path.join(os.path.dirname(__file__), "videos", "bear.mp4")
paragliding_launch = os.path.join(
os.path.dirname(__file__), "videos", "paragliding-launch.mp4"
)
paragliding = os.path.join(os.path.dirname(__file__), "videos", "paragliding.mp4")
app = gr.Interface(
title="🎨 CoTracker: It is Better to Track Together",
description="<div style='text-align: left;'> \
<p>Welcome to <a href='http://co-tracker.github.io' target='_blank'>CoTracker</a>! This space demonstrates point (pixel) tracking in videos. \
Points are sampled on a regular grid and are tracked jointly. </p> \
<p> To get started, simply upload your <b>.mp4</b> video in landscape orientation or click on one of the example videos to load them. The shorter the video, the faster the processing. We recommend submitting short videos of length <b>2-7 seconds</b>.</p> \
<ul style='display: inline-block; text-align: left;'> \
<li>The total number of grid points is the square of <b>Grid Size</b>.</li> \
<li>Check <b>Visualize Track Traces</b> to visualize traces of all the tracked points. </li> \
</ul> \
<p style='text-align: left'>For more details, check out our <a href='https://github.com/facebookresearch/co-tracker' target='_blank'>GitHub Repo</a> ⭐</p> \
</div>",
fn=cotracker_demo,
inputs=[
gr.Video(type="file", label="Input video", interactive=True),
gr.Slider(minimum=10, maximum=80, step=1, value=10, label="Grid Size"),
gr.Checkbox(label="Visualize Track Traces"),
],
outputs=gr.Video(label="Video with predicted tracks"),
examples=[
[apple, 30, False],
[apple, 10, True],
[bear, 10, False],
[paragliding, 10, False],
[paragliding_launch, 10, False],
],
cache_examples=True,
allow_flagging=False,
)
app.queue(max_size=20, concurrency_count=2).launch(debug=True)
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