import gradio as gr
import argparse
import gdown
import cv2
import numpy as np
import os
import sys
sys.path.append(sys.path[0]+"/tracker")
sys.path.append(sys.path[0]+"/tracker/model")
from track_anything import TrackingAnything
from track_anything import parse_augment, save_image_to_userfolder, read_image_from_userfolder
import requests
import json
import torchvision
import torch
from tools.painter import mask_painter
import psutil
import time
try:
from mmcv.cnn import ConvModule
except:
os.system("mim install mmcv")
# download checkpoints
def download_checkpoint(url, folder, filename):
os.makedirs(folder, exist_ok=True)
filepath = os.path.join(folder, filename)
if not os.path.exists(filepath):
print("download checkpoints ......")
response = requests.get(url, stream=True)
with open(filepath, "wb") as f:
for chunk in response.iter_content(chunk_size=8192):
if chunk:
f.write(chunk)
print("download successfully!")
return filepath
def download_checkpoint_from_google_drive(file_id, folder, filename):
os.makedirs(folder, exist_ok=True)
filepath = os.path.join(folder, filename)
if not os.path.exists(filepath):
print("Downloading checkpoints from Google Drive... tips: If you cannot see the progress bar, please try to download it manuall \
and put it in the checkpointes directory. E2FGVI-HQ-CVPR22.pth: https://github.com/MCG-NKU/E2FGVI(E2FGVI-HQ model)")
url = f"https://drive.google.com/uc?id={file_id}"
gdown.download(url, filepath, quiet=False)
print("Downloaded successfully!")
return filepath
# convert points input to prompt state
def get_prompt(click_state, click_input):
inputs = json.loads(click_input)
points = click_state[0]
labels = click_state[1]
for input in inputs:
points.append(input[:2])
labels.append(input[2])
click_state[0] = points
click_state[1] = labels
prompt = {
"prompt_type":["click"],
"input_point":click_state[0],
"input_label":click_state[1],
"multimask_output":"True",
}
return prompt
# extract frames from upload video
def get_frames_from_video(video_input, video_state):
"""
Args:
video_path:str
timestamp:float64
Return
[[0:nearest_frame], [nearest_frame:], nearest_frame]
"""
video_path = video_input
frames = [] # save image path
user_name = time.time()
video_state["video_name"] = os.path.split(video_path)[-1]
video_state["user_name"] = user_name
os.makedirs(os.path.join("/tmp/{}/originimages/{}".format(video_state["user_name"], video_state["video_name"])), exist_ok=True)
os.makedirs(os.path.join("/tmp/{}/paintedimages/{}".format(video_state["user_name"], video_state["video_name"])), exist_ok=True)
operation_log = [("",""),("Upload video already. Try click the image for adding targets to track and inpaint.","Normal")]
try:
cap = cv2.VideoCapture(video_path)
fps = cap.get(cv2.CAP_PROP_FPS)
if not cap.isOpened():
operation_log = [("No frames extracted, please input video file with '.mp4.' '.mov'.", "Error")]
print("No frames extracted, please input video file with '.mp4.' '.mov'.")
return None, None, None, None, \
None, None, None, None, \
None, None, None, None, \
None, None, gr.update(visible=True, value=operation_log)
image_index = 0
while cap.isOpened():
ret, frame = cap.read()
if ret == True:
current_memory_usage = psutil.virtual_memory().percent
# try solve memory usage problem, save image to disk instead of memory
frames.append(save_image_to_userfolder(video_state, image_index, frame, True))
image_index +=1
# frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
if current_memory_usage > 90:
operation_log = [("Memory usage is too high (>90%). Stop the video extraction. Please reduce the video resolution or frame rate.", "Error")]
print("Memory usage is too high (>90%). Please reduce the video resolution or frame rate.")
break
else:
break
except (OSError, TypeError, ValueError, KeyError, SyntaxError) as e:
# except:
operation_log = [("read_frame_source:{} error. {}\n".format(video_path, str(e)), "Error")]
print("read_frame_source:{} error. {}\n".format(video_path, str(e)))
return None, None, None, None, \
None, None, None, None, \
None, None, None, None, \
None, None, gr.update(visible=True, value=operation_log)
first_image = read_image_from_userfolder(frames[0])
image_size = (first_image.shape[0], first_image.shape[1])
# initialize video_state
video_state = {
"user_name": user_name,
"video_name": os.path.split(video_path)[-1],
"origin_images": frames,
"painted_images": frames.copy(),
"masks": [np.zeros((image_size[0], image_size[1]), np.uint8)]*len(frames),
"logits": [None]*len(frames),
"select_frame_number": 0,
"fps": fps
}
video_info = "Video Name: {}, FPS: {}, Total Frames: {}, Image Size:{}".format(video_state["video_name"], video_state["fps"], len(frames), image_size)
model.samcontroler.sam_controler.reset_image()
model.samcontroler.sam_controler.set_image(first_image)
return video_state, video_info, first_image, gr.update(visible=True, maximum=len(frames), value=1), \
gr.update(visible=True, maximum=len(frames), value=len(frames)), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), \
gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), \
gr.update(visible=True), gr.update(visible=True), gr.update(visible=True, value=operation_log),
def run_example(example):
return example
# get the select frame from gradio slider
def select_template(image_selection_slider, video_state, interactive_state):
# images = video_state[1]
image_selection_slider -= 1
video_state["select_frame_number"] = image_selection_slider
# once select a new template frame, set the image in sam
model.samcontroler.sam_controler.reset_image()
model.samcontroler.sam_controler.set_image(read_image_from_userfolder(video_state["origin_images"][image_selection_slider]))
# update the masks when select a new template frame
# if video_state["masks"][image_selection_slider] is not None:
# video_state["painted_images"][image_selection_slider] = mask_painter(video_state["origin_images"][image_selection_slider], video_state["masks"][image_selection_slider])
operation_log = [("",""), ("Select frame {}. Try click image and add mask for tracking.".format(image_selection_slider),"Normal")]
return read_image_from_userfolder(video_state["painted_images"][image_selection_slider]), video_state, interactive_state, operation_log
# set the tracking end frame
def get_end_number(track_pause_number_slider, video_state, interactive_state):
track_pause_number_slider -= 1
interactive_state["track_end_number"] = track_pause_number_slider
operation_log = [("",""),("Set the tracking finish at frame {}".format(track_pause_number_slider),"Normal")]
return read_image_from_userfolder(video_state["painted_images"][track_pause_number_slider]),interactive_state, operation_log
def get_resize_ratio(resize_ratio_slider, interactive_state):
interactive_state["resize_ratio"] = resize_ratio_slider
return interactive_state
# use sam to get the mask
def sam_refine(video_state, point_prompt, click_state, interactive_state, evt:gr.SelectData):
"""
Args:
template_frame: PIL.Image
point_prompt: flag for positive or negative button click
click_state: [[points], [labels]]
"""
if point_prompt == "Positive":
coordinate = "[[{},{},1]]".format(evt.index[0], evt.index[1])
interactive_state["positive_click_times"] += 1
else:
coordinate = "[[{},{},0]]".format(evt.index[0], evt.index[1])
interactive_state["negative_click_times"] += 1
# prompt for sam model
model.samcontroler.sam_controler.reset_image()
model.samcontroler.sam_controler.set_image(read_image_from_userfolder(video_state["origin_images"][video_state["select_frame_number"]]))
prompt = get_prompt(click_state=click_state, click_input=coordinate)
mask, logit, painted_image = model.first_frame_click(
image=read_image_from_userfolder(video_state["origin_images"][video_state["select_frame_number"]]),
points=np.array(prompt["input_point"]),
labels=np.array(prompt["input_label"]),
multimask=prompt["multimask_output"],
)
video_state["masks"][video_state["select_frame_number"]] = mask
video_state["logits"][video_state["select_frame_number"]] = logit
video_state["painted_images"][video_state["select_frame_number"]] = save_image_to_userfolder(video_state, index=video_state["select_frame_number"], image=cv2.cvtColor(np.asarray(painted_image),cv2.COLOR_BGR2RGB),type=False)
operation_log = [("",""), ("Use SAM for segment. You can try add positive and negative points by clicking. Or press Clear clicks button to refresh the image. Press Add mask button when you are satisfied with the segment","Normal")]
return painted_image, video_state, interactive_state, operation_log
def add_multi_mask(video_state, interactive_state, mask_dropdown):
try:
mask = video_state["masks"][video_state["select_frame_number"]]
interactive_state["multi_mask"]["masks"].append(mask)
interactive_state["multi_mask"]["mask_names"].append("mask_{:03d}".format(len(interactive_state["multi_mask"]["masks"])))
mask_dropdown.append("mask_{:03d}".format(len(interactive_state["multi_mask"]["masks"])))
select_frame, run_status = show_mask(video_state, interactive_state, mask_dropdown)
operation_log = [("",""),("Added a mask, use the mask select for target tracking or inpainting.","Normal")]
except:
operation_log = [("Please click the left image to generate mask.", "Error"), ("","")]
return interactive_state, gr.update(choices=interactive_state["multi_mask"]["mask_names"], value=mask_dropdown), select_frame, [[],[]], operation_log
def clear_click(video_state, click_state):
click_state = [[],[]]
template_frame = read_image_from_userfolder(video_state["origin_images"][video_state["select_frame_number"]])
operation_log = [("",""), ("Clear points history and refresh the image.","Normal")]
return template_frame, click_state, operation_log
def remove_multi_mask(interactive_state, mask_dropdown):
interactive_state["multi_mask"]["mask_names"]= []
interactive_state["multi_mask"]["masks"] = []
operation_log = [("",""), ("Remove all mask, please add new masks","Normal")]
return interactive_state, gr.update(choices=[],value=[]), operation_log
def show_mask(video_state, interactive_state, mask_dropdown):
mask_dropdown.sort()
select_frame = read_image_from_userfolder(video_state["origin_images"][video_state["select_frame_number"]])
for i in range(len(mask_dropdown)):
mask_number = int(mask_dropdown[i].split("_")[1]) - 1
mask = interactive_state["multi_mask"]["masks"][mask_number]
select_frame = mask_painter(select_frame, mask.astype('uint8'), mask_color=mask_number+2)
operation_log = [("",""), ("Select {} for tracking or inpainting".format(mask_dropdown),"Normal")]
return select_frame, operation_log
# tracking vos
def vos_tracking_video(video_state, interactive_state, mask_dropdown):
operation_log = [("",""), ("Track the selected masks, and then you can select the masks for inpainting.","Normal")]
model.xmem.clear_memory()
if interactive_state["track_end_number"]:
following_frames = video_state["origin_images"][video_state["select_frame_number"]:interactive_state["track_end_number"]]
else:
following_frames = video_state["origin_images"][video_state["select_frame_number"]:]
if interactive_state["multi_mask"]["masks"]:
if len(mask_dropdown) == 0:
mask_dropdown = ["mask_001"]
mask_dropdown.sort()
template_mask = interactive_state["multi_mask"]["masks"][int(mask_dropdown[0].split("_")[1]) - 1] * (int(mask_dropdown[0].split("_")[1]))
for i in range(1,len(mask_dropdown)):
mask_number = int(mask_dropdown[i].split("_")[1]) - 1
template_mask = np.clip(template_mask+interactive_state["multi_mask"]["masks"][mask_number]*(mask_number+1), 0, mask_number+1)
video_state["masks"][video_state["select_frame_number"]]= template_mask
else:
template_mask = video_state["masks"][video_state["select_frame_number"]]
fps = video_state["fps"]
# operation error
if len(np.unique(template_mask))==1:
template_mask[0][0]=1
operation_log = [("Error! Please add at least one mask to track by clicking the left image.","Error"), ("","")]
# return video_output, video_state, interactive_state, operation_error
masks, logits, painted_images_path = model.generator(images=following_frames, template_mask=template_mask, video_state=video_state)
# clear GPU memory
model.xmem.clear_memory()
if interactive_state["track_end_number"]:
video_state["masks"][video_state["select_frame_number"]:interactive_state["track_end_number"]] = masks
video_state["logits"][video_state["select_frame_number"]:interactive_state["track_end_number"]] = logits
video_state["painted_images"][video_state["select_frame_number"]:interactive_state["track_end_number"]] = painted_images_path
else:
video_state["masks"][video_state["select_frame_number"]:] = masks
video_state["logits"][video_state["select_frame_number"]:] = logits
video_state["painted_images"][video_state["select_frame_number"]:] = painted_images_path
video_output = generate_video_from_frames(video_state["painted_images"], output_path="./result/track/{}".format(video_state["video_name"]), fps=fps) # import video_input to name the output video
interactive_state["inference_times"] += 1
print("For generating this tracking result, inference times: {}, click times: {}, positive: {}, negative: {}".format(interactive_state["inference_times"],
interactive_state["positive_click_times"]+interactive_state["negative_click_times"],
interactive_state["positive_click_times"],
interactive_state["negative_click_times"]))
#### shanggao code for mask save
if interactive_state["mask_save"]:
if not os.path.exists('./result/mask/{}'.format(video_state["video_name"].split('.')[0])):
os.makedirs('./result/mask/{}'.format(video_state["video_name"].split('.')[0]))
i = 0
print("save mask")
for mask in video_state["masks"]:
np.save(os.path.join('./result/mask/{}'.format(video_state["video_name"].split('.')[0]), '{:05d}.npy'.format(i)), mask)
i+=1
#### shanggao code for mask save
return video_output, video_state, interactive_state, operation_log
# inpaint
def inpaint_video(video_state, interactive_state, mask_dropdown):
operation_log = [("",""), ("Removed the selected masks.","Normal")]
# solve memory
frames = np.asarray(video_state["origin_images"])
fps = video_state["fps"]
inpaint_masks = np.asarray(video_state["masks"])
if len(mask_dropdown) == 0:
mask_dropdown = ["mask_001"]
mask_dropdown.sort()
# convert mask_dropdown to mask numbers
inpaint_mask_numbers = [int(mask_dropdown[i].split("_")[1]) for i in range(len(mask_dropdown))]
# interate through all masks and remove the masks that are not in mask_dropdown
unique_masks = np.unique(inpaint_masks)
num_masks = len(unique_masks) - 1
for i in range(1, num_masks + 1):
if i in inpaint_mask_numbers:
continue
inpaint_masks[inpaint_masks==i] = 0
# inpaint for videos
try:
inpainted_frames = model.baseinpainter.inpaint(frames, inpaint_masks, ratio=interactive_state["resize_ratio"]) # numpy array, T, H, W, 3
video_output = generate_video_from_paintedframes(inpainted_frames, output_path="./result/inpaint/{}".format(video_state["video_name"]), fps=fps)
except:
operation_log = [("Error! You are trying to inpaint without masks input. Please track the selected mask first, and then press inpaint. If VRAM exceeded, please use the resize ratio to scaling down the image size.","Error"), ("","")]
inpainted_frames = video_state["origin_images"]
video_output = generate_video_from_frames(inpainted_frames, output_path="./result/inpaint/{}".format(video_state["video_name"]), fps=fps) # import video_input to name the output video
return video_output, operation_log
# generate video after vos inference
def generate_video_from_frames(frames_path, output_path, fps=30):
"""
Generates a video from a list of frames.
Args:
frames (list of numpy arrays): The frames to include in the video.
output_path (str): The path to save the generated video.
fps (int, optional): The frame rate of the output video. Defaults to 30.
"""
# height, width, layers = frames[0].shape
# fourcc = cv2.VideoWriter_fourcc(*"mp4v")
# video = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
# print(output_path)
# for frame in frames:
# video.write(frame)
# video.release()
frames = []
for file in frames_path:
frames.append(read_image_from_userfolder(file))
frames = torch.from_numpy(np.asarray(frames))
if not os.path.exists(os.path.dirname(output_path)):
os.makedirs(os.path.dirname(output_path))
torchvision.io.write_video(output_path, frames, fps=fps, video_codec="libx264")
return output_path
def generate_video_from_paintedframes(frames, output_path, fps=30):
"""
Generates a video from a list of frames.
Args:
frames (list of numpy arrays): The frames to include in the video.
output_path (str): The path to save the generated video.
fps (int, optional): The frame rate of the output video. Defaults to 30.
"""
# height, width, layers = frames[0].shape
# fourcc = cv2.VideoWriter_fourcc(*"mp4v")
# video = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
# print(output_path)
# for frame in frames:
# video.write(frame)
# video.release()
frames = torch.from_numpy(np.asarray(frames))
if not os.path.exists(os.path.dirname(output_path)):
os.makedirs(os.path.dirname(output_path))
torchvision.io.write_video(output_path, frames, fps=fps, video_codec="libx264")
return output_path
# args, defined in track_anything.py
args = parse_augment()
# check and download checkpoints if needed
SAM_checkpoint_dict = {
'vit_h': "sam_vit_h_4b8939.pth",
'vit_l': "sam_vit_l_0b3195.pth",
"vit_b": "sam_vit_b_01ec64.pth"
}
SAM_checkpoint_url_dict = {
'vit_h': "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth",
'vit_l': "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth",
'vit_b': "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth"
}
sam_checkpoint = SAM_checkpoint_dict[args.sam_model_type]
sam_checkpoint_url = SAM_checkpoint_url_dict[args.sam_model_type]
xmem_checkpoint = "XMem-s012.pth"
xmem_checkpoint_url = "https://github.com/hkchengrex/XMem/releases/download/v1.0/XMem-s012.pth"
e2fgvi_checkpoint = "E2FGVI-HQ-CVPR22.pth"
e2fgvi_checkpoint_id = "10wGdKSUOie0XmCr8SQ2A2FeDe-mfn5w3"
folder ="./checkpoints"
SAM_checkpoint = download_checkpoint(sam_checkpoint_url, folder, sam_checkpoint)
xmem_checkpoint = download_checkpoint(xmem_checkpoint_url, folder, xmem_checkpoint)
e2fgvi_checkpoint = download_checkpoint_from_google_drive(e2fgvi_checkpoint_id, folder, e2fgvi_checkpoint)
# args.port = 12213
# args.device = "cuda:8"
# args.mask_save = True
# initialize sam, xmem, e2fgvi models
model = TrackingAnything(SAM_checkpoint, xmem_checkpoint, e2fgvi_checkpoint,args)
title = """Track-Anything
"""
description = """Gradio demo for Track Anything, a flexible and interactive tool for video object tracking, segmentation, and inpainting. To use it, simply upload your video, or click one of the examples to load them. Code: Track-Anything 
If you stuck in unknown errors, please feel free to watch the Tutorial video.
"""
with gr.Blocks() as iface:
"""
state for
"""
click_state = gr.State([[],[]])
interactive_state = gr.State({
"inference_times": 0,
"negative_click_times" : 0,
"positive_click_times": 0,
"mask_save": args.mask_save,
"multi_mask": {
"mask_names": [],
"masks": []
},
"track_end_number": None,
"resize_ratio": 0.6
}
)
video_state = gr.State(
{
"user_name": "",
"video_name": "",
"origin_images": None,
"painted_images": None,
"masks": None,
"inpaint_masks": None,
"logits": None,
"select_frame_number": 0,
"fps": 30
}
)
gr.Markdown(title)
gr.Markdown(description)
with gr.Row():
with gr.Column():
with gr.Tab("Test"):
# for user video input
with gr.Column():
with gr.Row(scale=0.4):
video_input = gr.Video(autosize=True)
with gr.Column():
video_info = gr.Textbox(label="Video Info")
resize_info = gr.Textbox(value="If you want to use the inpaint function, it is best to git clone the repo and use a machine with more VRAM locally. \
Alternatively, you can use the resize ratio slider to scale down the original image to around 360P resolution for faster processing.", label="Tips for running this demo.")
resize_ratio_slider = gr.Slider(minimum=0.02, maximum=1, step=0.02, value=0.6, label="Resize ratio", visible=True)
with gr.Row():
# put the template frame under the radio button
with gr.Column():
# extract frames
with gr.Column():
extract_frames_button = gr.Button(value="Get video info", interactive=True, variant="primary")
# click points settins, negative or positive, mode continuous or single
with gr.Row():
with gr.Row():
point_prompt = gr.Radio(
choices=["Positive", "Negative"],
value="Positive",
label="Point prompt",
interactive=True,
visible=False)
remove_mask_button = gr.Button(value="Remove mask", interactive=True, visible=False)
clear_button_click = gr.Button(value="Clear clicks", interactive=True, visible=False).style(height=160)
Add_mask_button = gr.Button(value="Add mask", interactive=True, visible=False)
template_frame = gr.Image(type="pil",interactive=True, elem_id="template_frame", visible=False).style(height=360)
image_selection_slider = gr.Slider(minimum=1, maximum=100, step=1, value=1, label="Track start frame", visible=False)
track_pause_number_slider = gr.Slider(minimum=1, maximum=100, step=1, value=1, label="Track end frame", visible=False)
with gr.Column():
run_status = gr.HighlightedText(value=[("Run","Error"),("Status","Normal")], visible=True)
mask_dropdown = gr.Dropdown(multiselect=True, value=[], label="Mask selection", info=".", visible=False)
video_output = gr.Video(autosize=True, visible=False).style(height=360)
with gr.Row():
tracking_video_predict_button = gr.Button(value="Tracking", visible=False)
inpaint_video_predict_button = gr.Button(value="Inpaint", visible=False)
# set example
gr.Markdown("## Examples")
gr.Examples(
examples=[os.path.join(os.path.dirname(__file__), "./test_sample/", test_sample) for test_sample in ["test-sample8.mp4","test-sample4.mp4", \
"test-sample2.mp4","test-sample13.mp4"]],
fn=run_example,
inputs=[
video_input
],
outputs=[video_input],
# cache_examples=True,
)
with gr.Tab("Tutorial"):
with gr.Column():
with gr.Row(scale=0.4):
video_demo_operation = gr.Video(autosize=True)
# set example
gr.Markdown("## Operation tutorial video")
gr.Examples(
examples=[os.path.join(os.path.dirname(__file__), "./test_sample/", test_sample) for test_sample in ["huggingface_demo_operation.mp4"]],
fn=run_example,
inputs=[
video_demo_operation
],
outputs=[video_demo_operation],
# cache_examples=True,
)
# first step: get the video information
extract_frames_button.click(
fn=get_frames_from_video,
inputs=[
video_input, video_state
],
outputs=[video_state, video_info, template_frame, image_selection_slider,
track_pause_number_slider,point_prompt, clear_button_click, Add_mask_button,
template_frame, tracking_video_predict_button, video_output, mask_dropdown,
remove_mask_button, inpaint_video_predict_button, run_status]
)
# second step: select images from slider
image_selection_slider.release(fn=select_template,
inputs=[image_selection_slider, video_state, interactive_state],
outputs=[template_frame, video_state, interactive_state, run_status], api_name="select_image")
track_pause_number_slider.release(fn=get_end_number,
inputs=[track_pause_number_slider, video_state, interactive_state],
outputs=[template_frame, interactive_state, run_status], api_name="end_image")
resize_ratio_slider.release(fn=get_resize_ratio,
inputs=[resize_ratio_slider, interactive_state],
outputs=[interactive_state], api_name="resize_ratio")
# click select image to get mask using sam
template_frame.select(
fn=sam_refine,
inputs=[video_state, point_prompt, click_state, interactive_state],
outputs=[template_frame, video_state, interactive_state, run_status]
)
# add different mask
Add_mask_button.click(
fn=add_multi_mask,
inputs=[video_state, interactive_state, mask_dropdown],
outputs=[interactive_state, mask_dropdown, template_frame, click_state, run_status]
)
remove_mask_button.click(
fn=remove_multi_mask,
inputs=[interactive_state, mask_dropdown],
outputs=[interactive_state, mask_dropdown, run_status]
)
# tracking video from select image and mask
tracking_video_predict_button.click(
fn=vos_tracking_video,
inputs=[video_state, interactive_state, mask_dropdown],
outputs=[video_output, video_state, interactive_state, run_status]
)
# inpaint video from select image and mask
inpaint_video_predict_button.click(
fn=inpaint_video,
inputs=[video_state, interactive_state, mask_dropdown],
outputs=[video_output, run_status]
)
# click to get mask
mask_dropdown.change(
fn=show_mask,
inputs=[video_state, interactive_state, mask_dropdown],
outputs=[template_frame, run_status]
)
# clear input
video_input.clear(
lambda: (
{
"user_name": "",
"video_name": "",
"origin_images": None,
"painted_images": None,
"masks": None,
"inpaint_masks": None,
"logits": None,
"select_frame_number": 0,
"fps": 30
},
{
"inference_times": 0,
"negative_click_times" : 0,
"positive_click_times": 0,
"mask_save": args.mask_save,
"multi_mask": {
"mask_names": [],
"masks": []
},
"track_end_number": 0,
"resize_ratio": 0.6
},
[[],[]],
None,
None,
gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), \
gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), \
gr.update(visible=False), gr.update(visible=False), gr.update(visible=False, value=[]), gr.update(visible=False), \
gr.update(visible=False), gr.update(visible=True)
),
[],
[
video_state,
interactive_state,
click_state,
video_output,
template_frame,
tracking_video_predict_button, image_selection_slider , track_pause_number_slider,point_prompt, clear_button_click,
Add_mask_button, template_frame, tracking_video_predict_button, video_output, mask_dropdown, remove_mask_button,inpaint_video_predict_button, run_status
],
queue=False,
show_progress=False)
# points clear
clear_button_click.click(
fn = clear_click,
inputs = [video_state, click_state,],
outputs = [template_frame,click_state, run_status],
)
iface.queue(concurrency_count=1)
# iface.launch(debug=True, enable_queue=True, server_port=args.port, server_name="0.0.0.0")
iface.launch(debug=True, enable_queue=True)