import gradio as gr from demo import automask_image_app, automask_video_app, sahi_autoseg_app import argparse import cv2 import time from PIL import Image 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 import requests import json import torchvision import torch import concurrent.futures import queue 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 pause_video(play_state): print("user pause_video") play_state.append(time.time()) return play_state def play_video(play_state): print("user play_video") play_state.append(time.time()) return play_state # 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 def get_frames_from_video(video_input, play_state): """ Args: video_path:str timestamp:float64 Return [[0:nearest_frame], [nearest_frame:], nearest_frame] """ video_path = video_input timestamp = play_state[1] - play_state[0] frames = [] try: cap = cv2.VideoCapture(video_path) fps = cap.get(cv2.CAP_PROP_FPS) while cap.isOpened(): ret, frame = cap.read() if ret == True: frames.append(frame) else: break except (OSError, TypeError, ValueError, KeyError, SyntaxError) as e: print("read_frame_source:{} error. {}\n".format(video_path, str(e))) for index, frame in enumerate(frames): frames[index] = np.asarray(Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))) key_frame_index = int(timestamp * fps) nearest_frame = frames[key_frame_index] frames_split = [frames[:key_frame_index], frames[key_frame_index:], nearest_frame] # output_path='./seperate.mp4' # torchvision.io.write_video(output_path, frames[1], fps=fps, video_codec="libx264") # set image in sam when select the template frame model.samcontroler.sam_controler.set_image(nearest_frame) return frames_split, nearest_frame, nearest_frame def generate_video_from_frames(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)) # for frame in frames: # video.write(frame) # video.release() frames = torch.from_numpy(np.asarray(frames)) output_path='./output.mp4' torchvision.io.write_video(output_path, frames, fps=fps, video_codec="libx264") return output_path def sam_refine(origin_frame, point_prompt, click_state, logit, 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]) else: coordinate = "[[{},{},0]]".format(evt.index[0], evt.index[1]) # prompt for sam model prompt = get_prompt(click_state=click_state, click_input=coordinate) # default value # points = np.array([[evt.index[0],evt.index[1]]]) # labels= np.array([1]) if len(logit)==0: logit = None mask, logit, painted_image = model.first_frame_click( image=origin_frame, points=np.array(prompt["input_point"]), labels=np.array(prompt["input_label"]), multimask=prompt["multimask_output"], ) yield painted_image, click_state, logit, mask def vos_tracking_video(video_state, template_mask): masks, logits, painted_images = model.generator(images=video_state[1], mask=template_mask) video_output = generate_video_from_frames(painted_images, output_path="./output.mp4") return video_output def vos_tracking_image(video_state, template_mask, result_queue, done_queue): images = video_state[1] images = images[:5] for i in range(len(images)): if i ==0: mask, logit, painted_image = model.xmem.track(images[i], template_mask) result_queue['images'].put(images[i]) result_queue['masks'].put(mask) result_queue['logits'].put(logit) result_queue['painted'].put(painted_image) else: mask, logit, painted_image = model.xmem.track(images[i]) result_queue['images'].put(images[i]) result_queue['masks'].put(mask) result_queue['logits'].put(logit) result_queue['painted'].put(painted_image) done_queue.put(False) time.sleep(1) done_queue.put(True) def update_gradio_image(result_queue, done_queue): print("update_gradio_image") while True: if not done_queue.empty(): if done_queue.get(): break if not result_queue.empty(): image = result_queue['images'].get() mask = result_queue['masks'].get() logit = result_queue['logits'].get() painted_image = result_queue['painted'].get() yield painted_image def parallel_tracking(video_state, template_mask): with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor: executor.submit(vos_tracking_image, video_state, template_mask, result_queue, done_queue) executor.submit(update_gradio_image, result_queue, done_queue) # check and download checkpoints if needed SAM_checkpoint = "sam_vit_h_4b8939.pth" sam_checkpoint_url = "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth" xmem_checkpoint = "XMem-s012.pth" xmem_checkpoint_url = "https://github.com/hkchengrex/XMem/releases/download/v1.0/XMem-s012.pth" folder ="./checkpoints" SAM_checkpoint = download_checkpoint(sam_checkpoint_url, folder, SAM_checkpoint) xmem_checkpoint = download_checkpoint(xmem_checkpoint_url, folder, xmem_checkpoint) # args, defined in track_anything.py args = parse_augment() args.port = 12214 model = TrackingAnything(SAM_checkpoint, xmem_checkpoint, args) result_queue = {"images": queue.Queue(), "masks": queue.Queue(), "logits": queue.Queue(), "painted": queue.Queue()} done_queue = queue.Queue() with gr.Blocks() as iface: """ state for """ state = gr.State([]) play_state = gr.State([]) video_state = gr.State([[],[],[]]) click_state = gr.State([[],[]]) logits = gr.State([]) origin_image = gr.State(None) template_mask = gr.State(None) # queue value for image refresh, origin image, mask, logits, painted image with gr.Row(): # for user video input with gr.Column(scale=1.0): video_input = gr.Video().style(height=720) # listen to the user action for play and pause input video video_input.play(fn=play_video, inputs=play_state, outputs=play_state, scroll_to_output=True, show_progress=True) video_input.pause(fn=pause_video, inputs=play_state, outputs=play_state) with gr.Row(scale=1): # put the template frame under the radio button with gr.Column(scale=0.5): # click points settins, negative or positive, mode continuous or single with gr.Row(): with gr.Row(scale=0.5): point_prompt = gr.Radio( choices=["Positive", "Negative"], value="Positive", label="Point Prompt", interactive=True) click_mode = gr.Radio( choices=["Continuous", "Single"], value="Continuous", label="Clicking Mode", interactive=True) with gr.Row(scale=0.5): clear_button_clike = gr.Button(value="Clear Clicks", interactive=True).style(height=160) clear_button_image = gr.Button(value="Clear Image", interactive=True) template_frame = gr.Image(type="pil",interactive=True, elem_id="template_frame").style(height=360) with gr.Column(): template_select_button = gr.Button(value="Template select", interactive=True, variant="primary") with gr.Column(scale=0.5): # for intermedia result check and correction # intermedia_image = gr.Image(type="pil", interactive=True, elem_id="intermedia_frame").style(height=360) video_output = gr.Video().style(height=360) tracking_video_predict_button = gr.Button(value="Video") image_output = gr.Image(type="pil", interactive=True, elem_id="image_output").style(height=360) tracking_image_predict_button = gr.Button(value="Tracking") template_frame.select( fn=sam_refine, inputs=[ origin_image, point_prompt, click_state, logits ], outputs=[ template_frame, click_state, logits, template_mask ] ) template_select_button.click( fn=get_frames_from_video, inputs=[ video_input, play_state ], outputs=[video_state, template_frame, origin_image], ) tracking_video_predict_button.click( fn=vos_tracking_video, inputs=[video_state, template_mask], outputs=[video_output] ) tracking_image_predict_button.click( fn=parallel_tracking, inputs=[video_state, template_mask], outputs=[image_output] ) # clear # clear_button_clike.click( # lambda x: ([[], [], []], x, ""), # [origin_image], # [click_state, image_input, wiki_output], # queue=False, # show_progress=False # ) # clear_button_image.click( # lambda: (None, [], [], [[], [], []], "", ""), # [], # [image_input, chatbot, state, click_state, wiki_output, origin_image], # queue=False, # show_progress=False # ) video_input.clear( lambda: (None, [], [], [[], [], []], None), [], [video_input, state, play_state, video_state, template_frame], queue=False, show_progress=False ) iface.queue(concurrency_count=1) iface.launch(debug=True, enable_queue=True, server_port=args.port, server_name="0.0.0.0")