mirror of
https://github.com/gaomingqi/Track-Anything.git
synced 2025-12-16 00:17:50 +01:00
382 lines
14 KiB
Python
382 lines
14 KiB
Python
import gradio as gr
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from demo import automask_image_app, automask_video_app, sahi_autoseg_app
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import argparse
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import cv2
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import time
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from PIL import Image
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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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import concurrent.futures
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import queue
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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 pause_video(play_state):
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print("user pause_video")
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play_state.append(time.time())
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return play_state
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def play_video(play_state):
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print("user play_video")
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play_state.append(time.time())
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return play_state
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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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def get_frames_from_video(video_input, play_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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# video_name = video_path.split('/')[-1]
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try:
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timestamp = play_state[1] - play_state[0]
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except:
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timestamp = 0
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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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# for index, frame in enumerate(frames):
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# frames[index] = np.asarray(Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)))
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key_frame_index = int(timestamp * fps)
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nearest_frame = frames[key_frame_index]
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frames_split = [frames[:key_frame_index], frames[key_frame_index:], nearest_frame]
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# output_path='./seperate.mp4'
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# torchvision.io.write_video(output_path, frames[1], fps=fps, video_codec="libx264")
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# set image in sam when select the template frame
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model.samcontroler.sam_controler.set_image(nearest_frame)
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return frames_split, nearest_frame, nearest_frame, fps
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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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# 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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output_path='./output.mp4'
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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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def model_reset():
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model.xmem.clear_memory()
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return None
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def sam_refine(origin_frame, point_prompt, click_state, logit, 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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else:
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coordinate = "[[{},{},0]]".format(evt.index[0], evt.index[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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# default value
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# points = np.array([[evt.index[0],evt.index[1]]])
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# labels= np.array([1])
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if len(logit)==0:
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logit = None
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mask, logit, painted_image = model.first_frame_click(
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image=origin_frame,
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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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return painted_image, click_state, logit, mask
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def vos_tracking_video(video_state, template_mask,fps,video_input):
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masks, logits, painted_images = model.generator(images=video_state[1], template_mask=template_mask)
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video_output = generate_video_from_frames(painted_images, output_path="./output.mp4", fps=fps)
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# image_selection_slider = gr.Slider(minimum=1, maximum=len(video_state[1]), value=1, label="Image Selection", interactive=True)
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video_name = video_input.split('/')[-1].split('.')[0]
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result_path = os.path.join('/hhd3/gaoshang/Track-Anything/results/'+video_name)
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if not os.path.exists(result_path):
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os.makedirs(result_path)
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i=0
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for mask in masks:
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np.save(os.path.join(result_path,'{:05}.npy'.format(i)), mask)
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i+=1
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return video_output, painted_images, masks, logits
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def vos_tracking_image(image_selection_slider, painted_images):
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# images = video_state[1]
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percentage = image_selection_slider / 100
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select_frame_num = int(percentage * len(painted_images))
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return painted_images[select_frame_num], select_frame_num
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def interactive_correction(video_state, point_prompt, click_state, select_correction_frame, 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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refine_image = video_state[1][select_correction_frame]
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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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else:
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coordinate = "[[{},{},0]]".format(evt.index[0], evt.index[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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model.samcontroler.seg_again(refine_image)
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corrected_mask, corrected_logit, corrected_painted_image = model.first_frame_click(
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image=refine_image,
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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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return corrected_painted_image, [corrected_mask, corrected_logit, corrected_painted_image]
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def correct_track(video_state, select_correction_frame, corrected_state, masks, logits, painted_images, fps, video_input):
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model.xmem.clear_memory()
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# inference the following images
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following_images = video_state[1][select_correction_frame:]
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corrected_masks, corrected_logits, corrected_painted_images = model.generator(images=following_images, template_mask=corrected_state[0])
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masks = masks[:select_correction_frame] + corrected_masks
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logits = logits[:select_correction_frame] + corrected_logits
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painted_images = painted_images[:select_correction_frame] + corrected_painted_images
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video_output = generate_video_from_frames(painted_images, output_path="./output.mp4", fps=fps)
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video_name = video_input.split('/')[-1].split('.')[0]
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result_path = os.path.join('/hhd3/gaoshang/Track-Anything/results/'+video_name)
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if not os.path.exists(result_path):
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os.makedirs(result_path)
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i=0
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for mask in masks:
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np.save(os.path.join(result_path,'{:05}.npy'.format(i)), mask)
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i+=1
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return video_output, painted_images, logits, masks
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# check and download checkpoints if needed
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SAM_checkpoint = "sam_vit_h_4b8939.pth"
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sam_checkpoint_url = "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth"
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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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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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# args, defined in track_anything.py
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args = parse_augment()
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args.port = 12207
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args.device = "cuda:5"
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model = TrackingAnything(SAM_checkpoint, xmem_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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state = gr.State([])
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play_state = gr.State([])
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video_state = gr.State([[],[],[]])
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click_state = gr.State([[],[]])
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logits = gr.State([])
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masks = gr.State([])
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painted_images = gr.State([])
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origin_image = gr.State(None)
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template_mask = gr.State(None)
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select_correction_frame = gr.State(None)
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corrected_state = gr.State([[],[],[]])
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fps = gr.State([])
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# video_name = gr.State([])
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# queue value for image refresh, origin image, mask, logits, painted image
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with gr.Row():
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# for user video input
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with gr.Column(scale=1.0):
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video_input = gr.Video().style(height=720)
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# listen to the user action for play and pause input video
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video_input.play(fn=play_video, inputs=play_state, outputs=play_state, scroll_to_output=True, show_progress=True)
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video_input.pause(fn=pause_video, inputs=play_state, outputs=play_state)
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with gr.Row(scale=1):
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# put the template frame under the radio button
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with gr.Column(scale=0.5):
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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(scale=0.5):
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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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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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with gr.Row(scale=0.5):
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clear_button_clike = gr.Button(value="Clear Clicks", interactive=True).style(height=160)
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clear_button_image = gr.Button(value="Clear Image", interactive=True)
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template_frame = gr.Image(type="pil",interactive=True, elem_id="template_frame").style(height=360)
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with gr.Column():
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template_select_button = gr.Button(value="Template select", interactive=True, variant="primary")
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with gr.Column(scale=0.5):
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# for intermedia result check and correction
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# intermedia_image = gr.Image(type="pil", interactive=True, elem_id="intermedia_frame").style(height=360)
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video_output = gr.Video().style(height=360)
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tracking_video_predict_button = gr.Button(value="Tracking")
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image_output = gr.Image(type="pil", interactive=True, elem_id="image_output").style(height=360)
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image_selection_slider = gr.Slider(minimum=0, maximum=100, step=0.1, value=0, label="Image Selection", interactive=True)
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correct_track_button = gr.Button(value="Interactive Correction")
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template_frame.select(
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fn=sam_refine,
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inputs=[
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origin_image, point_prompt, click_state, logits
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],
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outputs=[
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template_frame, click_state, logits, template_mask
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]
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)
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template_select_button.click(
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fn=get_frames_from_video,
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inputs=[
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video_input,
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play_state
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],
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# outputs=[video_state, template_frame, origin_image, fps, video_name],
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outputs=[video_state, template_frame, origin_image, fps],
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)
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tracking_video_predict_button.click(
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fn=vos_tracking_video,
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inputs=[video_state, template_mask, fps, video_input],
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outputs=[video_output, painted_images, masks, logits]
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)
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image_selection_slider.release(fn=vos_tracking_image,
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inputs=[image_selection_slider, painted_images], outputs=[image_output, select_correction_frame], api_name="select_image")
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# correction
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image_output.select(
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fn=interactive_correction,
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inputs=[video_state, point_prompt, click_state, select_correction_frame],
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outputs=[image_output, corrected_state]
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)
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correct_track_button.click(
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fn=correct_track,
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inputs=[video_state, select_correction_frame, corrected_state, masks, logits, painted_images, fps,video_input],
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outputs=[video_output, painted_images, logits, masks ]
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)
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# clear input
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video_input.clear(
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lambda: ([], [], [[], [], []],
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None, "", "", "", "", "", "", "", [[],[]],
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None),
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[],
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[ state, play_state, video_state,
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template_frame, video_output, image_output, origin_image, template_mask, painted_images, masks, logits, click_state,
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select_correction_frame],
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queue=False,
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show_progress=False
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)
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clear_button_image.click(
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fn=model_reset
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)
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clear_button_clike.click(
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lambda: ([[],[]]),
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[],
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[click_state],
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queue=False,
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show_progress=False
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)
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iface.queue(concurrency_count=1)
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iface.launch(debug=True, enable_queue=True, server_port=args.port, server_name="0.0.0.0")
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