mirror of
https://github.com/gaomingqi/Track-Anything.git
synced 2025-12-16 16:37:58 +01:00
252 lines
8.1 KiB
Python
252 lines
8.1 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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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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# 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=12219
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model = TrackingAnything(SAM_checkpoint, xmem_checkpoint, args)
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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(inputs, click_state):
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points = []
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labels = []
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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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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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timestamp = play_state[1] - play_state[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(frame)
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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] = 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 = [frames[:key_frame_index], frames[key_frame_index:], nearest_frame]
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return frames, nearest_frame
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def inference_all(template_frame, click_state, evt:gr.SelectData):
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coordinate = "[[{},{},1]]".format(evt.index[0], evt.index[1])
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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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mask, logit, painted_image = model.inference_step(first_flag=True, interact_flag=False, image=np.asarray(template_frame), same_image_flag=False,points=points, labels=labels,logits=None,multimask=True)
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return painted_image
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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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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)
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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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# seg_automask_video_points_per_batch = gr.Slider(
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# minimum=0,
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# maximum=64,
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# step=2,
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# value=64,
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# label="Points per Batch",
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# )
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seg_automask_video_predict = gr.Button(value="Generator")
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# Display the first frame
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# with gr.Column():
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# first_frame = gr.Image(type="pil", interactive=True, elem_id="first_frame")
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# seg_automask_firstframe = gr.Button(value="Find target")
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# video_input = gr.inputs.Video(type="mp4")
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# output = gr.outputs.Image(type="pil")
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# gr.Interface(fn=capture_frame, inputs=seg_automask_video_file, outputs=first_frame)
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# seg_automask_video_predict.click(
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# fn=automask_video_app,
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# inputs=[
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# seg_automask_video_file,
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# seg_automask_video_model_type,
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# seg_automask_video_points_per_side,
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# seg_automask_video_points_per_batch,
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# seg_automask_video_min_area,
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# ],
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# outputs=[output_video],
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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],
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)
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template_frame.select(
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fn=inference_all,
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inputs=[
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template_frame
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],
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outputs=[
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template_frame
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]
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)
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# clear
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# clear_button_clike.click(
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# lambda x: ([[], [], []], x, ""),
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# [origin_image],
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# [click_state, image_input, wiki_output],
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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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# lambda: (None, [], [], [[], [], []], "", ""),
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# [],
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# [image_input, chatbot, state, click_state, wiki_output, origin_image],
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# queue=False,
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# show_progress=False
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# )
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video_input.clear(
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lambda: (None, [], [], [[], [], []], None),
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[],
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[video_input, state, play_state, video_state, template_frame],
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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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