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| import gradio as gr | |
| import argparse | |
| import gdown | |
| import cv2 | |
| 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 | |
| from tools.interact_tools import SamControler | |
| from tracker.base_tracker import BaseTracker | |
| from tools.painter import mask_painter | |
| try: | |
| from mmcv.cnn import ConvModule | |
| except: | |
| os.system("mim install mmcv") | |
| # 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 | |
| def download_checkpoint_from_google_drive(file_id, folder, filename): | |
| os.makedirs(folder, exist_ok=True) | |
| filepath = os.path.join(folder, filename) | |
| if not os.path.exists(filepath): | |
| print("Downloading checkpoints from Google Drive... tips: If you cannot see the progress bar, please try to download it manuall \ | |
| and put it in the checkpointes directory. E2FGVI-HQ-CVPR22.pth: https://github.com/MCG-NKU/E2FGVI(E2FGVI-HQ model)") | |
| url = f"https://drive.google.com/uc?id={file_id}" | |
| gdown.download(url, filepath, quiet=False) | |
| print("Downloaded 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))) | |
| image_size = (frames[0].shape[0],frames[0].shape[1]) | |
| # initialize video_state | |
| video_state = { | |
| "video_name": os.path.split(video_path)[-1], | |
| "origin_images": frames, | |
| "painted_images": frames.copy(), | |
| "masks": [np.zeros((frames[0].shape[0],frames[0].shape[1]), np.uint8)]*len(frames), | |
| "logits": [None]*len(frames), | |
| "select_frame_number": 0, | |
| "fps": fps | |
| } | |
| video_info = "Video Name: {}, FPS: {}, Total Frames: {}, Image Size:{}".format(video_state["video_name"], video_state["fps"], len(frames), image_size) | |
| model.samcontroler.sam_controler.reset_image() | |
| model.samcontroler.sam_controler.set_image(video_state["origin_images"][0]) | |
| 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)), \ | |
| gr.update(visible=True), gr.update(visible=True), \ | |
| gr.update(visible=True), gr.update(visible=True), \ | |
| gr.update(visible=True), gr.update(visible=True), \ | |
| gr.update(visible=True), gr.update(visible=True), \ | |
| gr.update(visible=True), gr.update(visible=True) | |
| def run_example(example): | |
| return video_input | |
| # get the select frame from gradio slider | |
| def select_template(image_selection_slider, video_state, interactive_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]) | |
| # update the masks when select a new template frame | |
| # if video_state["masks"][image_selection_slider] is not None: | |
| # video_state["painted_images"][image_selection_slider] = mask_painter(video_state["origin_images"][image_selection_slider], video_state["masks"][image_selection_slider]) | |
| return video_state["painted_images"][image_selection_slider], video_state, interactive_state | |
| # set the tracking end frame | |
| def get_end_number(track_pause_number_slider, video_state, interactive_state): | |
| interactive_state["track_end_number"] = track_pause_number_slider | |
| return video_state["painted_images"][track_pause_number_slider],interactive_state | |
| def get_resize_ratio(resize_ratio_slider, interactive_state): | |
| interactive_state["resize_ratio"] = resize_ratio_slider | |
| return interactive_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 | |
| def add_multi_mask(video_state, interactive_state, mask_dropdown): | |
| mask = video_state["masks"][video_state["select_frame_number"]] | |
| interactive_state["multi_mask"]["masks"].append(mask) | |
| interactive_state["multi_mask"]["mask_names"].append("mask_{:03d}".format(len(interactive_state["multi_mask"]["masks"]))) | |
| mask_dropdown.append("mask_{:03d}".format(len(interactive_state["multi_mask"]["masks"]))) | |
| select_frame = show_mask(video_state, interactive_state, mask_dropdown) | |
| return interactive_state, gr.update(choices=interactive_state["multi_mask"]["mask_names"], value=mask_dropdown), select_frame, [[],[]] | |
| def clear_click(video_state, click_state): | |
| click_state = [[],[]] | |
| template_frame = video_state["origin_images"][video_state["select_frame_number"]] | |
| return template_frame, click_state | |
| def remove_multi_mask(interactive_state): | |
| interactive_state["multi_mask"]["mask_names"]= [] | |
| interactive_state["multi_mask"]["masks"] = [] | |
| return interactive_state | |
| def show_mask(video_state, interactive_state, mask_dropdown): | |
| mask_dropdown.sort() | |
| select_frame = video_state["origin_images"][video_state["select_frame_number"]] | |
| for i in range(len(mask_dropdown)): | |
| mask_number = int(mask_dropdown[i].split("_")[1]) - 1 | |
| mask = interactive_state["multi_mask"]["masks"][mask_number] | |
| select_frame = mask_painter(select_frame, mask.astype('uint8'), mask_color=mask_number+2) | |
| return select_frame | |
| # tracking vos | |
| def vos_tracking_video(video_state, interactive_state, mask_dropdown): | |
| model.xmem.clear_memory() | |
| if interactive_state["track_end_number"]: | |
| following_frames = video_state["origin_images"][video_state["select_frame_number"]:interactive_state["track_end_number"]] | |
| else: | |
| following_frames = video_state["origin_images"][video_state["select_frame_number"]:] | |
| if interactive_state["multi_mask"]["masks"]: | |
| if len(mask_dropdown) == 0: | |
| mask_dropdown = ["mask_001"] | |
| mask_dropdown.sort() | |
| template_mask = interactive_state["multi_mask"]["masks"][int(mask_dropdown[0].split("_")[1]) - 1] * (int(mask_dropdown[0].split("_")[1])) | |
| for i in range(1,len(mask_dropdown)): | |
| mask_number = int(mask_dropdown[i].split("_")[1]) - 1 | |
| template_mask = np.clip(template_mask+interactive_state["multi_mask"]["masks"][mask_number]*(mask_number+1), 0, mask_number+1) | |
| video_state["masks"][video_state["select_frame_number"]]= template_mask | |
| else: | |
| 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) | |
| # clear GPU memory | |
| model.xmem.clear_memory() | |
| if interactive_state["track_end_number"]: | |
| video_state["masks"][video_state["select_frame_number"]:interactive_state["track_end_number"]] = masks | |
| video_state["logits"][video_state["select_frame_number"]:interactive_state["track_end_number"]] = logits | |
| video_state["painted_images"][video_state["select_frame_number"]:interactive_state["track_end_number"]] = painted_images | |
| else: | |
| 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/track/{}".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 | |
| # extracting masks from mask_dropdown | |
| # def extract_sole_mask(video_state, mask_dropdown): | |
| # combined_masks = | |
| # unique_masks = np.unique(combined_masks) | |
| # return 0 | |
| # inpaint | |
| def inpaint_video(video_state, interactive_state, mask_dropdown): | |
| frames = np.asarray(video_state["origin_images"]) | |
| fps = video_state["fps"] | |
| inpaint_masks = np.asarray(video_state["masks"]) | |
| if len(mask_dropdown) == 0: | |
| mask_dropdown = ["mask_001"] | |
| mask_dropdown.sort() | |
| # convert mask_dropdown to mask numbers | |
| inpaint_mask_numbers = [int(mask_dropdown[i].split("_")[1]) for i in range(len(mask_dropdown))] | |
| # interate through all masks and remove the masks that are not in mask_dropdown | |
| unique_masks = np.unique(inpaint_masks) | |
| num_masks = len(unique_masks) - 1 | |
| for i in range(1, num_masks + 1): | |
| if i in inpaint_mask_numbers: | |
| continue | |
| inpaint_masks[inpaint_masks==i] = 0 | |
| # inpaint for videos | |
| inpainted_frames = model.baseinpainter.inpaint(frames, inpaint_masks, ratio=interactive_state["resize_ratio"]) # numpy array, T, H, W, 3 | |
| 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 | |
| return video_output | |
| # 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. | |
| """ | |
| # height, width, layers = frames[0].shape | |
| # fourcc = cv2.VideoWriter_fourcc(*"mp4v") | |
| # video = cv2.VideoWriter(output_path, fourcc, fps, (width, height)) | |
| # print(output_path) | |
| # for frame in frames: | |
| # video.write(frame) | |
| # video.release() | |
| 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 | |
| # args, defined in track_anything.py | |
| args = parse_augment() | |
| # check and download checkpoints if needed | |
| SAM_checkpoint_dict = { | |
| 'vit_h': "sam_vit_h_4b8939.pth", | |
| 'vit_l': "sam_vit_l_0b3195.pth", | |
| "vit_b": "sam_vit_b_01ec64.pth" | |
| } | |
| SAM_checkpoint_url_dict = { | |
| 'vit_h': "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth", | |
| 'vit_l': "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth", | |
| 'vit_b': "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth" | |
| } | |
| sam_checkpoint = SAM_checkpoint_dict[args.sam_model_type] | |
| sam_checkpoint_url = SAM_checkpoint_url_dict[args.sam_model_type] | |
| xmem_checkpoint = "XMem-s012.pth" | |
| xmem_checkpoint_url = "https://github.com/hkchengrex/XMem/releases/download/v1.0/XMem-s012.pth" | |
| e2fgvi_checkpoint = "E2FGVI-HQ-CVPR22.pth" | |
| e2fgvi_checkpoint_id = "10wGdKSUOie0XmCr8SQ2A2FeDe-mfn5w3" | |
| folder ="./checkpoints" | |
| SAM_checkpoint = download_checkpoint(sam_checkpoint_url, folder, sam_checkpoint) | |
| xmem_checkpoint = download_checkpoint(xmem_checkpoint_url, folder, xmem_checkpoint) | |
| e2fgvi_checkpoint = download_checkpoint_from_google_drive(e2fgvi_checkpoint_id, folder, e2fgvi_checkpoint) | |
| # args.port = 12315 | |
| # args.device = "cuda:2" | |
| # args.mask_save = True | |
| # initialize sam, xmem, e2fgvi models | |
| model = TrackingAnything(SAM_checkpoint, xmem_checkpoint, e2fgvi_checkpoint,args) | |
| title = """<p><h1 align="center">Track-Anything</h1></p> | |
| """ | |
| description = """<p>Gradio demo for Track Anything, a flexible and interactive tool for video object tracking, segmentation, and inpainting. I To use it, simply upload your video, or click one of the examples to load them. Code: <a href="https://github.com/gaomingqi/Track-Anything">https://github.com/gaomingqi/Track-Anything</a> <a href="https://huggingface.co/spaces/watchtowerss/Track-Anything?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>""" | |
| 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, | |
| "multi_mask": { | |
| "mask_names": [], | |
| "masks": [] | |
| }, | |
| "track_end_number": None, | |
| "resize_ratio": 1 | |
| } | |
| ) | |
| video_state = gr.State( | |
| { | |
| "video_name": "", | |
| "origin_images": None, | |
| "painted_images": None, | |
| "masks": None, | |
| "inpaint_masks": None, | |
| "logits": None, | |
| "select_frame_number": 0, | |
| "fps": 30 | |
| } | |
| ) | |
| gr.Markdown(title) | |
| gr.Markdown(description) | |
| with gr.Row(): | |
| # for user video input | |
| with gr.Column(): | |
| with gr.Row(scale=0.4): | |
| video_input = gr.Video(autosize=True) | |
| with gr.Column(): | |
| video_info = gr.Textbox() | |
| resize_info = gr.Textbox(value="Due to server restrictions, please upload a video that is no longer than 2 minutes. If you want to use the inpaint function, it is best to download and use a machine with more VRAM locally. \ | |
| Alternatively, you can use the resize ratio slider to scale down the original image to around 360P resolution for faster processing.") | |
| resize_ratio_slider = gr.Slider(minimum=0.02, maximum=1, step=0.02, value=1, label="Resize ratio", visible=True) | |
| with gr.Row(): | |
| # put the template frame under the radio button | |
| with gr.Column(): | |
| # 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(): | |
| point_prompt = gr.Radio( | |
| choices=["Positive", "Negative"], | |
| value="Positive", | |
| label="Point Prompt", | |
| interactive=True, | |
| visible=False) | |
| click_mode = gr.Radio( | |
| choices=["Continuous", "Single"], | |
| value="Continuous", | |
| label="Clicking Mode", | |
| interactive=True, | |
| visible=False) | |
| with gr.Row(): | |
| clear_button_click = gr.Button(value="Clear Clicks", interactive=True, visible=False).style(height=160) | |
| Add_mask_button = gr.Button(value="Add mask", interactive=True, visible=False) | |
| template_frame = gr.Image(type="pil",interactive=True, elem_id="template_frame", visible=False).style(height=360) | |
| image_selection_slider = gr.Slider(minimum=1, maximum=100, step=1, value=1, label="Image Selection", visible=False) | |
| 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) | |