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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, save_image_to_userfolder, read_image_from_userfolder | |
| import requests | |
| import json | |
| import torchvision | |
| import torch | |
| from tools.painter import mask_painter | |
| import psutil | |
| import time | |
| 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 = [] # save image path | |
| user_name = time.time() | |
| video_state["video_name"] = os.path.split(video_path)[-1] | |
| video_state["user_name"] = user_name | |
| os.makedirs(os.path.join("/tmp/{}/originimages/{}".format(video_state["user_name"], video_state["video_name"])), exist_ok=True) | |
| os.makedirs(os.path.join("/tmp/{}/paintedimages/{}".format(video_state["user_name"], video_state["video_name"])), exist_ok=True) | |
| operation_log = [("",""),("Upload video already. Try click the image for adding targets to track and inpaint.","Normal")] | |
| try: | |
| cap = cv2.VideoCapture(video_path) | |
| fps = cap.get(cv2.CAP_PROP_FPS) | |
| if not cap.isOpened(): | |
| operation_log = [("No frames extracted, please input video file with '.mp4.' '.mov'.", "Error")] | |
| print("No frames extracted, please input video file with '.mp4.' '.mov'.") | |
| return None, None, None, None, \ | |
| None, None, None, None, \ | |
| None, None, None, None, \ | |
| None, None, gr.update(visible=True, value=operation_log) | |
| image_index = 0 | |
| while cap.isOpened(): | |
| ret, frame = cap.read() | |
| if ret == True: | |
| current_memory_usage = psutil.virtual_memory().percent | |
| # try solve memory usage problem, save image to disk instead of memory | |
| frames.append(save_image_to_userfolder(video_state, image_index, frame, True)) | |
| image_index +=1 | |
| # frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) | |
| if current_memory_usage > 90: | |
| operation_log = [("Memory usage is too high (>90%). Stop the video extraction. Please reduce the video resolution or frame rate.", "Error")] | |
| print("Memory usage is too high (>90%). Please reduce the video resolution or frame rate.") | |
| break | |
| else: | |
| break | |
| except (OSError, TypeError, ValueError, KeyError, SyntaxError) as e: | |
| # except: | |
| operation_log = [("read_frame_source:{} error. {}\n".format(video_path, str(e)), "Error")] | |
| print("read_frame_source:{} error. {}\n".format(video_path, str(e))) | |
| return None, None, None, None, \ | |
| None, None, None, None, \ | |
| None, None, None, None, \ | |
| None, None, gr.update(visible=True, value=operation_log) | |
| first_image = read_image_from_userfolder(frames[0]) | |
| image_size = (first_image.shape[0], first_image.shape[1]) | |
| # initialize video_state | |
| video_state = { | |
| "user_name": user_name, | |
| "video_name": os.path.split(video_path)[-1], | |
| "origin_images": frames, | |
| "painted_images": frames.copy(), | |
| "masks": [np.zeros((image_size[0], image_size[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(first_image) | |
| return video_state, video_info, first_image, 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, value=operation_log), | |
| def run_example(example): | |
| return example | |
| # 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(read_image_from_userfolder(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]) | |
| operation_log = [("",""), ("Select frame {}. Try click image and add mask for tracking.".format(image_selection_slider),"Normal")] | |
| return read_image_from_userfolder(video_state["painted_images"][image_selection_slider]), video_state, interactive_state, operation_log | |
| # set the tracking end frame | |
| def get_end_number(track_pause_number_slider, video_state, interactive_state): | |
| track_pause_number_slider -= 1 | |
| interactive_state["track_end_number"] = track_pause_number_slider | |
| operation_log = [("",""),("Set the tracking finish at frame {}".format(track_pause_number_slider),"Normal")] | |
| return read_image_from_userfolder(video_state["painted_images"][track_pause_number_slider]),interactive_state, operation_log | |
| 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 | |
| model.samcontroler.sam_controler.reset_image() | |
| model.samcontroler.sam_controler.set_image(read_image_from_userfolder(video_state["origin_images"][video_state["select_frame_number"]])) | |
| prompt = get_prompt(click_state=click_state, click_input=coordinate) | |
| mask, logit, painted_image = model.first_frame_click( | |
| image=read_image_from_userfolder(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"]] = save_image_to_userfolder(video_state, index=video_state["select_frame_number"], image=cv2.cvtColor(np.asarray(painted_image),cv2.COLOR_BGR2RGB),type=False) | |
| operation_log = [("",""), ("Use SAM for segment. You can try add positive and negative points by clicking. Or press Clear clicks button to refresh the image. Press Add mask button when you are satisfied with the segment","Normal")] | |
| return painted_image, video_state, interactive_state, operation_log | |
| def add_multi_mask(video_state, interactive_state, mask_dropdown): | |
| try: | |
| 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, run_status = show_mask(video_state, interactive_state, mask_dropdown) | |
| operation_log = [("",""),("Added a mask, use the mask select for target tracking or inpainting.","Normal")] | |
| except: | |
| operation_log = [("Please click the left image to generate mask.", "Error"), ("","")] | |
| return interactive_state, gr.update(choices=interactive_state["multi_mask"]["mask_names"], value=mask_dropdown), select_frame, [[],[]], operation_log | |
| def clear_click(video_state, click_state): | |
| click_state = [[],[]] | |
| template_frame = read_image_from_userfolder(video_state["origin_images"][video_state["select_frame_number"]]) | |
| operation_log = [("",""), ("Clear points history and refresh the image.","Normal")] | |
| return template_frame, click_state, operation_log | |
| def remove_multi_mask(interactive_state, mask_dropdown): | |
| interactive_state["multi_mask"]["mask_names"]= [] | |
| interactive_state["multi_mask"]["masks"] = [] | |
| operation_log = [("",""), ("Remove all mask, please add new masks","Normal")] | |
| return interactive_state, gr.update(choices=[],value=[]), operation_log | |
| def show_mask(video_state, interactive_state, mask_dropdown): | |
| mask_dropdown.sort() | |
| select_frame = read_image_from_userfolder(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) | |
| operation_log = [("",""), ("Select {} for tracking or inpainting".format(mask_dropdown),"Normal")] | |
| return select_frame, operation_log | |
| # tracking vos | |
| def vos_tracking_video(video_state, interactive_state, mask_dropdown): | |
| operation_log = [("",""), ("Track the selected masks, and then you can select the masks for inpainting.","Normal")] | |
| 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"] | |
| # operation error | |
| if len(np.unique(template_mask))==1: | |
| template_mask[0][0]=1 | |
| operation_log = [("Error! Please add at least one mask to track by clicking the left image.","Error"), ("","")] | |
| # return video_output, video_state, interactive_state, operation_error | |
| masks, logits, painted_images_path = model.generator(images=following_frames, template_mask=template_mask, video_state=video_state) | |
| # 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_path | |
| 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_path | |
| 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 | |
| #### shanggao code for mask save | |
| return video_output, video_state, interactive_state, operation_log | |
| # inpaint | |
| def inpaint_video(video_state, interactive_state, mask_dropdown): | |
| operation_log = [("",""), ("Removed the selected masks.","Normal")] | |
| # solve memory | |
| 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 | |
| try: | |
| inpainted_frames = model.baseinpainter.inpaint(frames, inpaint_masks, ratio=interactive_state["resize_ratio"]) # numpy array, T, H, W, 3 | |
| video_output = generate_video_from_paintedframes(inpainted_frames, output_path="./result/inpaint/{}".format(video_state["video_name"]), fps=fps) | |
| except: | |
| operation_log = [("Error! You are trying to inpaint without masks input. Please track the selected mask first, and then press inpaint. If VRAM exceeded, please use the resize ratio to scaling down the image size.","Error"), ("","")] | |
| inpainted_frames = video_state["origin_images"] | |
| 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, operation_log | |
| # generate video after vos inference | |
| def generate_video_from_frames(frames_path, 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 = [] | |
| for file in frames_path: | |
| frames.append(read_image_from_userfolder(file)) | |
| 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 | |
| def generate_video_from_paintedframes(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 = 12213 | |
| # args.device = "cuda:8" | |
| # 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. 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">Track-Anything</a> <a href="https://huggingface.co/spaces/VIPLab/Track-Anything?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a> If you stuck in unknown errors, please feel free to watch the Tutorial video.</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": 0.6 | |
| } | |
| ) | |
| video_state = gr.State( | |
| { | |
| "user_name": "", | |
| "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(): | |
| with gr.Column(): | |
| with gr.Tab("Test"): | |
| # 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(label="Video Info") | |
| resize_info = gr.Textbox(value="If you want to use the inpaint function, it is best to git clone the repo 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.", label="Tips for running this demo.") | |
| resize_ratio_slider = gr.Slider(minimum=0.02, maximum=1, step=0.02, value=0.6, 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) | |
| remove_mask_button = gr.Button(value="Remove mask", interactive=True, visible=False) | |
| 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="Track start frame", visible=False) | |
| track_pause_number_slider = gr.Slider(minimum=1, maximum=100, step=1, value=1, label="Track end frame", visible=False) | |
| with gr.Column(): | |
| run_status = gr.HighlightedText(value=[("Run","Error"),("Status","Normal")], visible=True) | |
| mask_dropdown = gr.Dropdown(multiselect=True, value=[], label="Mask selection", info=".", 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) | |
| # 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, | |
| ) | |
| with gr.Tab("Tutorial"): | |
| with gr.Column(): | |
| with gr.Row(scale=0.4): | |
| video_demo_operation = gr.Video(autosize=True) | |
| # set example | |
| gr.Markdown("## Operation tutorial video") | |
| gr.Examples( | |
| examples=[os.path.join(os.path.dirname(__file__), "./test_sample/", test_sample) for test_sample in ["huggingface_demo_operation.mp4"]], | |
| fn=run_example, | |
| inputs=[ | |
| video_demo_operation | |
| ], | |
| outputs=[video_demo_operation], | |
| # cache_examples=True, | |
| ) | |
| # 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, clear_button_click, Add_mask_button, | |
| template_frame, tracking_video_predict_button, video_output, mask_dropdown, | |
| remove_mask_button, inpaint_video_predict_button, run_status] | |
| ) | |
| # 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, run_status], 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, run_status], 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, run_status] | |
| ) | |
| # 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, run_status] | |
| ) | |
| remove_mask_button.click( | |
| fn=remove_multi_mask, | |
| inputs=[interactive_state, mask_dropdown], | |
| outputs=[interactive_state, mask_dropdown, run_status] | |
| ) | |
| # 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, run_status] | |
| ) | |
| # 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, run_status] | |
| ) | |
| # click to get mask | |
| mask_dropdown.change( | |
| fn=show_mask, | |
| inputs=[video_state, interactive_state, mask_dropdown], | |
| outputs=[template_frame, run_status] | |
| ) | |
| # clear input | |
| video_input.clear( | |
| lambda: ( | |
| { | |
| "user_name": "", | |
| "video_name": "", | |
| "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": 0.6 | |
| }, | |
| [[],[]], | |
| 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, value=[]), gr.update(visible=False), \ | |
| gr.update(visible=False), gr.update(visible=True) | |
| ), | |
| [], | |
| [ | |
| video_state, | |
| interactive_state, | |
| click_state, | |
| video_output, | |
| template_frame, | |
| tracking_video_predict_button, image_selection_slider , track_pause_number_slider,point_prompt, clear_button_click, | |
| Add_mask_button, template_frame, tracking_video_predict_button, video_output, mask_dropdown, remove_mask_button,inpaint_video_predict_button, run_status | |
| ], | |
| queue=False, | |
| show_progress=False) | |
| # points clear | |
| clear_button_click.click( | |
| fn = clear_click, | |
| inputs = [video_state, click_state,], | |
| outputs = [template_frame,click_state, run_status], | |
| ) | |
| iface.queue(concurrency_count=1) | |
| # iface.launch(debug=True, enable_queue=True, server_port=args.port, server_name="0.0.0.0") | |
| iface.launch(debug=True, enable_queue=True) |