mirror of
https://github.com/gaomingqi/Track-Anything.git
synced 2025-12-16 08:27:49 +01:00
558 lines
24 KiB
Python
558 lines
24 KiB
Python
import gradio as gr
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import argparse
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import gdown
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import cv2
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import numpy as np
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import os
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import sys
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sys.path.append(sys.path[0]+"/tracker")
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sys.path.append(sys.path[0]+"/tracker/model")
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from track_anything import TrackingAnything
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from track_anything import parse_augment
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import requests
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import json
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import torchvision
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import torch
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from tools.painter import mask_painter
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try:
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from mmcv.cnn import ConvModule
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except:
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os.system("mim install mmcv")
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# download checkpoints
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def download_checkpoint(url, folder, filename):
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os.makedirs(folder, exist_ok=True)
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filepath = os.path.join(folder, filename)
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if not os.path.exists(filepath):
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print("download checkpoints ......")
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response = requests.get(url, stream=True)
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with open(filepath, "wb") as f:
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for chunk in response.iter_content(chunk_size=8192):
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if chunk:
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f.write(chunk)
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print("download successfully!")
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return filepath
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def download_checkpoint_from_google_drive(file_id, folder, filename):
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os.makedirs(folder, exist_ok=True)
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filepath = os.path.join(folder, filename)
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if not os.path.exists(filepath):
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print("Downloading checkpoints from Google Drive... tips: If you cannot see the progress bar, please try to download it manuall \
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and put it in the checkpointes directory. E2FGVI-HQ-CVPR22.pth: https://github.com/MCG-NKU/E2FGVI(E2FGVI-HQ model)")
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url = f"https://drive.google.com/uc?id={file_id}"
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gdown.download(url, filepath, quiet=False)
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print("Downloaded successfully!")
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return filepath
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# convert points input to prompt state
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def get_prompt(click_state, click_input):
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inputs = json.loads(click_input)
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points = click_state[0]
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labels = click_state[1]
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for input in inputs:
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points.append(input[:2])
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labels.append(input[2])
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click_state[0] = points
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click_state[1] = labels
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prompt = {
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"prompt_type":["click"],
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"input_point":click_state[0],
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"input_label":click_state[1],
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"multimask_output":"True",
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}
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return prompt
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# extract frames from upload video
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def get_frames_from_video(video_input, video_state):
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"""
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Args:
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video_path:str
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timestamp:float64
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Return
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[[0:nearest_frame], [nearest_frame:], nearest_frame]
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"""
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video_path = video_input
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frames = []
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try:
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cap = cv2.VideoCapture(video_path)
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fps = cap.get(cv2.CAP_PROP_FPS)
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while cap.isOpened():
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ret, frame = cap.read()
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if ret == True:
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frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
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else:
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break
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except (OSError, TypeError, ValueError, KeyError, SyntaxError) as e:
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print("read_frame_source:{} error. {}\n".format(video_path, str(e)))
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image_size = (frames[0].shape[0],frames[0].shape[1])
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# initialize video_state
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video_state = {
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"video_name": os.path.split(video_path)[-1],
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"origin_images": frames,
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"painted_images": frames.copy(),
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"masks": [np.zeros((frames[0].shape[0],frames[0].shape[1]), np.uint8)]*len(frames),
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"logits": [None]*len(frames),
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"select_frame_number": 0,
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"fps": fps
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}
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video_info = "Video Name: {}, FPS: {}, Total Frames: {}, Image Size:{}".format(video_state["video_name"], video_state["fps"], len(frames), image_size)
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model.samcontroler.sam_controler.reset_image()
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model.samcontroler.sam_controler.set_image(video_state["origin_images"][0])
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return video_state, video_info, video_state["origin_images"][0], gr.update(visible=True, maximum=len(frames), value=1), gr.update(visible=True, maximum=len(frames), value=len(frames)), \
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gr.update(visible=True), gr.update(visible=True), \
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gr.update(visible=True), gr.update(visible=True), \
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gr.update(visible=True), gr.update(visible=True), \
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gr.update(visible=True), gr.update(visible=True), \
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gr.update(visible=True), gr.update(visible=True)
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def run_example(example):
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return video_input
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# get the select frame from gradio slider
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def select_template(image_selection_slider, video_state, interactive_state):
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# images = video_state[1]
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image_selection_slider -= 1
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video_state["select_frame_number"] = image_selection_slider
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# once select a new template frame, set the image in sam
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model.samcontroler.sam_controler.reset_image()
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model.samcontroler.sam_controler.set_image(video_state["origin_images"][image_selection_slider])
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# update the masks when select a new template frame
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# if video_state["masks"][image_selection_slider] is not None:
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# video_state["painted_images"][image_selection_slider] = mask_painter(video_state["origin_images"][image_selection_slider], video_state["masks"][image_selection_slider])
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return video_state["painted_images"][image_selection_slider], video_state, interactive_state
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# set the tracking end frame
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def get_end_number(track_pause_number_slider, video_state, interactive_state):
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interactive_state["track_end_number"] = track_pause_number_slider
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return video_state["painted_images"][track_pause_number_slider],interactive_state
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def get_resize_ratio(resize_ratio_slider, interactive_state):
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interactive_state["resize_ratio"] = resize_ratio_slider
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return interactive_state
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# use sam to get the mask
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def sam_refine(video_state, point_prompt, click_state, interactive_state, evt:gr.SelectData):
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"""
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Args:
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template_frame: PIL.Image
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point_prompt: flag for positive or negative button click
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click_state: [[points], [labels]]
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"""
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if point_prompt == "Positive":
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coordinate = "[[{},{},1]]".format(evt.index[0], evt.index[1])
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interactive_state["positive_click_times"] += 1
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else:
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coordinate = "[[{},{},0]]".format(evt.index[0], evt.index[1])
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interactive_state["negative_click_times"] += 1
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# prompt for sam model
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prompt = get_prompt(click_state=click_state, click_input=coordinate)
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mask, logit, painted_image = model.first_frame_click(
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image=video_state["origin_images"][video_state["select_frame_number"]],
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points=np.array(prompt["input_point"]),
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labels=np.array(prompt["input_label"]),
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multimask=prompt["multimask_output"],
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)
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video_state["masks"][video_state["select_frame_number"]] = mask
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video_state["logits"][video_state["select_frame_number"]] = logit
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video_state["painted_images"][video_state["select_frame_number"]] = painted_image
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return painted_image, video_state, interactive_state
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def add_multi_mask(video_state, interactive_state, mask_dropdown):
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mask = video_state["masks"][video_state["select_frame_number"]]
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interactive_state["multi_mask"]["masks"].append(mask)
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interactive_state["multi_mask"]["mask_names"].append("mask_{:03d}".format(len(interactive_state["multi_mask"]["masks"])))
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mask_dropdown.append("mask_{:03d}".format(len(interactive_state["multi_mask"]["masks"])))
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select_frame = show_mask(video_state, interactive_state, mask_dropdown)
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return interactive_state, gr.update(choices=interactive_state["multi_mask"]["mask_names"], value=mask_dropdown), select_frame, [[],[]]
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def clear_click(video_state, click_state):
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click_state = [[],[]]
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template_frame = video_state["origin_images"][video_state["select_frame_number"]]
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return template_frame, click_state
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def remove_multi_mask(interactive_state):
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interactive_state["multi_mask"]["mask_names"]= []
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interactive_state["multi_mask"]["masks"] = []
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return interactive_state
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def show_mask(video_state, interactive_state, mask_dropdown):
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mask_dropdown.sort()
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select_frame = video_state["origin_images"][video_state["select_frame_number"]]
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for i in range(len(mask_dropdown)):
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mask_number = int(mask_dropdown[i].split("_")[1]) - 1
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mask = interactive_state["multi_mask"]["masks"][mask_number]
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select_frame = mask_painter(select_frame, mask.astype('uint8'), mask_color=mask_number+2)
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return select_frame
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# tracking vos
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def vos_tracking_video(video_state, interactive_state, mask_dropdown):
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model.xmem.clear_memory()
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if interactive_state["track_end_number"]:
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following_frames = video_state["origin_images"][video_state["select_frame_number"]:interactive_state["track_end_number"]]
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else:
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following_frames = video_state["origin_images"][video_state["select_frame_number"]:]
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if interactive_state["multi_mask"]["masks"]:
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if len(mask_dropdown) == 0:
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mask_dropdown = ["mask_001"]
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mask_dropdown.sort()
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template_mask = interactive_state["multi_mask"]["masks"][int(mask_dropdown[0].split("_")[1]) - 1] * (int(mask_dropdown[0].split("_")[1]))
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for i in range(1,len(mask_dropdown)):
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mask_number = int(mask_dropdown[i].split("_")[1]) - 1
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template_mask = np.clip(template_mask+interactive_state["multi_mask"]["masks"][mask_number]*(mask_number+1), 0, mask_number+1)
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video_state["masks"][video_state["select_frame_number"]]= template_mask
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else:
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template_mask = video_state["masks"][video_state["select_frame_number"]]
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fps = video_state["fps"]
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masks, logits, painted_images = model.generator(images=following_frames, template_mask=template_mask)
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if interactive_state["track_end_number"]:
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video_state["masks"][video_state["select_frame_number"]:interactive_state["track_end_number"]] = masks
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video_state["logits"][video_state["select_frame_number"]:interactive_state["track_end_number"]] = logits
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video_state["painted_images"][video_state["select_frame_number"]:interactive_state["track_end_number"]] = painted_images
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else:
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video_state["masks"][video_state["select_frame_number"]:] = masks
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video_state["logits"][video_state["select_frame_number"]:] = logits
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video_state["painted_images"][video_state["select_frame_number"]:] = painted_images
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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
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interactive_state["inference_times"] += 1
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print("For generating this tracking result, inference times: {}, click times: {}, positive: {}, negative: {}".format(interactive_state["inference_times"],
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interactive_state["positive_click_times"]+interactive_state["negative_click_times"],
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interactive_state["positive_click_times"],
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interactive_state["negative_click_times"]))
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#### shanggao code for mask save
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if interactive_state["mask_save"]:
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if not os.path.exists('./result/mask/{}'.format(video_state["video_name"].split('.')[0])):
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os.makedirs('./result/mask/{}'.format(video_state["video_name"].split('.')[0]))
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i = 0
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print("save mask")
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for mask in video_state["masks"]:
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np.save(os.path.join('./result/mask/{}'.format(video_state["video_name"].split('.')[0]), '{:05d}.npy'.format(i)), mask)
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i+=1
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# save_mask(video_state["masks"], video_state["video_name"])
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#### shanggao code for mask save
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return video_output, video_state, interactive_state
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# extracting masks from mask_dropdown
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# def extract_sole_mask(video_state, mask_dropdown):
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# combined_masks =
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# unique_masks = np.unique(combined_masks)
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# return 0
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# inpaint
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def inpaint_video(video_state, interactive_state, mask_dropdown):
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frames = np.asarray(video_state["origin_images"])
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fps = video_state["fps"]
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inpaint_masks = np.asarray(video_state["masks"])
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if len(mask_dropdown) == 0:
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mask_dropdown = ["mask_001"]
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mask_dropdown.sort()
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# convert mask_dropdown to mask numbers
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inpaint_mask_numbers = [int(mask_dropdown[i].split("_")[1]) for i in range(len(mask_dropdown))]
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# interate through all masks and remove the masks that are not in mask_dropdown
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unique_masks = np.unique(inpaint_masks)
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num_masks = len(unique_masks) - 1
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for i in range(1, num_masks + 1):
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if i in inpaint_mask_numbers:
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continue
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inpaint_masks[inpaint_masks==i] = 0
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# inpaint for videos
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inpainted_frames = model.baseinpainter.inpaint(frames, inpaint_masks, ratio=interactive_state["resize_ratio"]) # numpy array, T, H, W, 3
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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
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return video_output
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# generate video after vos inference
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def generate_video_from_frames(frames, output_path, fps=30):
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"""
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Generates a video from a list of frames.
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Args:
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frames (list of numpy arrays): The frames to include in the video.
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output_path (str): The path to save the generated video.
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fps (int, optional): The frame rate of the output video. Defaults to 30.
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"""
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# height, width, layers = frames[0].shape
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# fourcc = cv2.VideoWriter_fourcc(*"mp4v")
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# video = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
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# print(output_path)
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# for frame in frames:
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# video.write(frame)
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# video.release()
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frames = torch.from_numpy(np.asarray(frames))
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if not os.path.exists(os.path.dirname(output_path)):
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os.makedirs(os.path.dirname(output_path))
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torchvision.io.write_video(output_path, frames, fps=fps, video_codec="libx264")
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return output_path
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# args, defined in track_anything.py
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args = parse_augment()
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# check and download checkpoints if needed
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SAM_checkpoint_dict = {
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'vit_h': "sam_vit_h_4b8939.pth",
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'vit_l': "sam_vit_l_0b3195.pth",
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"vit_b": "sam_vit_b_01ec64.pth"
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}
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SAM_checkpoint_url_dict = {
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'vit_h': "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth",
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'vit_l': "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth",
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'vit_b': "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth"
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}
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sam_checkpoint = SAM_checkpoint_dict[args.sam_model_type]
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sam_checkpoint_url = SAM_checkpoint_url_dict[args.sam_model_type]
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xmem_checkpoint = "XMem-s012.pth"
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xmem_checkpoint_url = "https://github.com/hkchengrex/XMem/releases/download/v1.0/XMem-s012.pth"
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e2fgvi_checkpoint = "E2FGVI-HQ-CVPR22.pth"
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e2fgvi_checkpoint_id = "10wGdKSUOie0XmCr8SQ2A2FeDe-mfn5w3"
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folder ="./checkpoints"
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SAM_checkpoint = download_checkpoint(sam_checkpoint_url, folder, sam_checkpoint)
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xmem_checkpoint = download_checkpoint(xmem_checkpoint_url, folder, xmem_checkpoint)
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e2fgvi_checkpoint = download_checkpoint_from_google_drive(e2fgvi_checkpoint_id, folder, e2fgvi_checkpoint)
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# args.port = 12315
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# args.device = "cuda:2"
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# args.mask_save = True
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# initialize sam, xmem, e2fgvi models
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model = TrackingAnything(SAM_checkpoint, xmem_checkpoint, e2fgvi_checkpoint,args)
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with gr.Blocks() as iface:
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"""
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state for
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"""
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click_state = gr.State([[],[]])
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interactive_state = gr.State({
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"inference_times": 0,
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"negative_click_times" : 0,
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"positive_click_times": 0,
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"mask_save": args.mask_save,
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"multi_mask": {
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"mask_names": [],
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"masks": []
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},
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"track_end_number": None,
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"resize_ratio": 1
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}
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)
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video_state = gr.State(
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{
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"video_name": "",
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"origin_images": None,
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"painted_images": None,
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"masks": None,
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"inpaint_masks": None,
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"logits": None,
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"select_frame_number": 0,
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"fps": 30
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}
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)
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with gr.Row():
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# for user video input
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with gr.Column():
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with gr.Row(scale=0.4):
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video_input = gr.Video(autosize=True)
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with gr.Column():
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video_info = gr.Textbox()
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resize_info = gr.Textbox(value="If you want to use the inpaint function, it is best to download and use a machine with more VRAM locally. \
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Alternatively, you can use the resize ratio slider to scale down the original image to around 360P resolution for faster processing.")
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resize_ratio_slider = gr.Slider(minimum=0.02, maximum=1, step=0.02, value=1, label="Resize ratio", visible=True)
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with gr.Row():
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# put the template frame under the radio button
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with gr.Column():
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# extract frames
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with gr.Column():
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extract_frames_button = gr.Button(value="Get video info", interactive=True, variant="primary")
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# click points settins, negative or positive, mode continuous or single
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with gr.Row():
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with gr.Row():
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point_prompt = gr.Radio(
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choices=["Positive", "Negative"],
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value="Positive",
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label="Point Prompt",
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interactive=True,
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visible=False)
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click_mode = gr.Radio(
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choices=["Continuous", "Single"],
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value="Continuous",
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label="Clicking Mode",
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interactive=True,
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visible=False)
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with gr.Row():
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clear_button_click = gr.Button(value="Clear Clicks", interactive=True, visible=False).style(height=160)
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Add_mask_button = gr.Button(value="Add mask", interactive=True, visible=False)
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template_frame = gr.Image(type="pil",interactive=True, elem_id="template_frame", visible=False).style(height=360)
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image_selection_slider = gr.Slider(minimum=1, maximum=100, step=1, value=1, label="Image Selection", visible=False)
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track_pause_number_slider = gr.Slider(minimum=1, maximum=100, step=1, value=1, label="Track end frames", visible=False)
|
|
|
|
with gr.Column():
|
|
mask_dropdown = gr.Dropdown(multiselect=True, value=[], label="Mask_select", info=".", visible=False)
|
|
remove_mask_button = gr.Button(value="Remove mask", interactive=True, 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)
|
|
|
|
# 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, click_mode, clear_button_click, Add_mask_button, template_frame,
|
|
tracking_video_predict_button, video_output, mask_dropdown, remove_mask_button, inpaint_video_predict_button]
|
|
)
|
|
|
|
# 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], 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], 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]
|
|
)
|
|
|
|
# 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]
|
|
)
|
|
|
|
remove_mask_button.click(
|
|
fn=remove_multi_mask,
|
|
inputs=[interactive_state],
|
|
outputs=[interactive_state]
|
|
)
|
|
|
|
# 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]
|
|
)
|
|
|
|
# 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]
|
|
)
|
|
|
|
# click to get mask
|
|
mask_dropdown.change(
|
|
fn=show_mask,
|
|
inputs=[video_state, interactive_state, mask_dropdown],
|
|
outputs=[template_frame]
|
|
)
|
|
|
|
# clear input
|
|
video_input.clear(
|
|
lambda: (
|
|
{
|
|
"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": 1
|
|
},
|
|
[[],[]],
|
|
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), gr.update(visible=False, value=[]), gr.update(visible=False), gr.update(visible=False) \
|
|
|
|
),
|
|
[],
|
|
[
|
|
video_state,
|
|
interactive_state,
|
|
click_state,
|
|
video_output,
|
|
template_frame,
|
|
tracking_video_predict_button, image_selection_slider , track_pause_number_slider,point_prompt, click_mode, clear_button_click,
|
|
Add_mask_button, template_frame, tracking_video_predict_button, video_output, mask_dropdown, remove_mask_button,inpaint_video_predict_button
|
|
],
|
|
queue=False,
|
|
show_progress=False)
|
|
|
|
# points clear
|
|
clear_button_click.click(
|
|
fn = clear_click,
|
|
inputs = [video_state, click_state,],
|
|
outputs = [template_frame,click_state],
|
|
)
|
|
# 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,
|
|
)
|
|
iface.queue(concurrency_count=1)
|
|
iface.launch(debug=True, enable_queue=True, server_port=args.port, server_name="0.0.0.0")
|
|
|
|
|
|
|