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 # download checkpoints def download_checkpoint(url, folder, filename): os.makedirs(folder, exist_ok=True) filepath = os.path.join(folder, filename) if not os.path.exists(filepath): print("download checkpoints ......") response = requests.get(url, stream=True) with open(filepath, "wb") as f: for chunk in response.iter_content(chunk_size=8192): if chunk: f.write(chunk) print("download successfully!") return filepath # convert points input to prompt state def get_prompt(click_state, click_input): inputs = json.loads(click_input) points = click_state[0] labels = click_state[1] for input in inputs: points.append(input[:2]) labels.append(input[2]) click_state[0] = points click_state[1] = labels prompt = { "prompt_type":["click"], "input_point":click_state[0], "input_label":click_state[1], "multimask_output":"True", } return prompt # extract frames from upload video def get_frames_from_video(video_input, video_state): """ Args: video_path:str timestamp:float64 Return [[0:nearest_frame], [nearest_frame:], nearest_frame] """ video_path = video_input frames = [] 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(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) else: break except (OSError, TypeError, ValueError, KeyError, SyntaxError) as e: print("read_frame_source:{} error. {}\n".format(video_path, str(e))) # initialize video_state video_state = { "video_name": os.path.split(video_path)[-1], "origin_images": frames, "painted_images": frames.copy(), "masks": [None]*len(frames), "logits": [None]*len(frames), "select_frame_number": 0, "fps": 30 } return video_state, gr.update(visible=True, maximum=len(frames), value=1) # get the select frame from gradio slider def select_template(image_selection_slider, video_state): # images = video_state[1] image_selection_slider -= 1 video_state["select_frame_number"] = image_selection_slider # once select a new template frame, set the image in sam model.samcontroler.sam_controler.reset_image() model.samcontroler.sam_controler.set_image(video_state["origin_images"][image_selection_slider]) return video_state["painted_images"][image_selection_slider], video_state # use sam to get the mask def sam_refine(video_state, point_prompt, click_state, interactive_state, evt:gr.SelectData): """ Args: template_frame: PIL.Image point_prompt: flag for positive or negative button click click_state: [[points], [labels]] """ if point_prompt == "Positive": coordinate = "[[{},{},1]]".format(evt.index[0], evt.index[1]) interactive_state["positive_click_times"] += 1 else: coordinate = "[[{},{},0]]".format(evt.index[0], evt.index[1]) interactive_state["negative_click_times"] += 1 # prompt for sam model prompt = get_prompt(click_state=click_state, click_input=coordinate) mask, logit, painted_image = model.first_frame_click( image=video_state["origin_images"][video_state["select_frame_number"]], points=np.array(prompt["input_point"]), labels=np.array(prompt["input_label"]), multimask=prompt["multimask_output"], ) video_state["masks"][video_state["select_frame_number"]] = mask video_state["logits"][video_state["select_frame_number"]] = logit video_state["painted_images"][video_state["select_frame_number"]] = painted_image return painted_image, video_state, interactive_state # tracking vos def vos_tracking_video(video_state, interactive_state): model.xmem.clear_memory() following_frames = video_state["origin_images"][video_state["select_frame_number"]:] template_mask = video_state["masks"][video_state["select_frame_number"]] fps = video_state["fps"] masks, logits, painted_images = model.generator(images=following_frames, template_mask=template_mask) video_state["masks"][video_state["select_frame_number"]:] = masks video_state["logits"][video_state["select_frame_number"]:] = logits video_state["painted_images"][video_state["select_frame_number"]:] = painted_images video_output = generate_video_from_frames(video_state["painted_images"], output_path="./result/{}".format(video_state["video_name"]), fps=fps) # import video_input to name the output video interactive_state["inference_times"] += 1 print("For generating this tracking result, inference times: {}, click times: {}, positive: {}, negative: {}".format(interactive_state["inference_times"], interactive_state["positive_click_times"]+interactive_state["negative_click_times"], interactive_state["positive_click_times"], interactive_state["negative_click_times"])) #### shanggao code for mask save if interactive_state["mask_save"]: if not os.path.exists('./result/mask/{}'.format(video_state["video_name"].split('.')[0])): os.makedirs('./result/mask/{}'.format(video_state["video_name"].split('.')[0])) i = 0 print("save mask") for mask in video_state["masks"]: np.save(os.path.join('./result/mask/{}'.format(video_state["video_name"].split('.')[0]), '{:05d}.npy'.format(i)), mask) i+=1 # save_mask(video_state["masks"], video_state["video_name"]) #### shanggao code for mask save return video_output, video_state, interactive_state # generate video after vos inference 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. """ frames = torch.from_numpy(np.asarray(frames)) if not os.path.exists(os.path.dirname(output_path)): os.makedirs(os.path.dirname(output_path)) torchvision.io.write_video(output_path, frames, fps=fps, video_codec="libx264") return output_path # 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 = 12212 # args.device = "cuda:4" # args.mask_save = True model = TrackingAnything(SAM_checkpoint, xmem_checkpoint, args) with gr.Blocks() as iface: """ state for """ click_state = gr.State([[],[]]) interactive_state = gr.State({ "inference_times": 0, "negative_click_times" : 0, "positive_click_times": 0, "mask_save": args.mask_save }) video_state = gr.State( { "video_name": "", "origin_images": None, "painted_images": None, "masks": None, "logits": None, "select_frame_number": 0, "fps": 30 } ) with gr.Row(): # for user video input with gr.Column(scale=1.0): video_input = gr.Video().style(height=360) with gr.Row(scale=1): # put the template frame under the radio button with gr.Column(scale=0.5): # extract frames with gr.Column(): extract_frames_button = gr.Button(value="Get video info", interactive=True, variant="primary") # click points settins, negative or positive, mode continuous or single with gr.Row(): with gr.Row(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) image_selection_slider = gr.Slider(minimum=1, maximum=100, step=1, value=1, label="Image Selection", invisible=False) with gr.Column(scale=0.5): video_output = gr.Video().style(height=360) tracking_video_predict_button = gr.Button(value="Tracking") # first step: get the video information extract_frames_button.click( fn=get_frames_from_video, inputs=[ video_input, video_state ], outputs=[video_state, image_selection_slider], ) # second step: select images from slider image_selection_slider.release(fn=select_template, inputs=[image_selection_slider, video_state], outputs=[template_frame, video_state], api_name="select_image") template_frame.select( fn=sam_refine, inputs=[video_state, point_prompt, click_state, interactive_state], outputs=[template_frame, video_state, interactive_state] ) tracking_video_predict_button.click( fn=vos_tracking_video, inputs=[video_state, interactive_state], outputs=[video_output, video_state, interactive_state] ) # clear input video_input.clear( lambda: ( { "origin_images": None, "painted_images": None, "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 }, [[],[]] ), [], [ video_state, interactive_state, click_state, ], queue=False, show_progress=False ) clear_button_image.click( lambda: ( { "origin_images": None, "painted_images": None, "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 }, [[],[]] ), [], [ video_state, interactive_state, click_state, ], queue=False, show_progress=False ) clear_button_clike.click( lambda: ([[],[]]), [], [click_state], queue=False, show_progress=False ) iface.queue(concurrency_count=1) iface.launch(enable_queue=True)