From 400e00418f1fdc81bea3b1d37593b475225a7b6e Mon Sep 17 00:00:00 2001 From: not-lain Date: Fri, 13 Mar 2026 00:31:56 +0100 Subject: [PATCH 1/5] cleanup --- evaluation/eval_lr.py | 2 +- inference_matanyone2.py | 2 +- matanyone2/utils/device.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/evaluation/eval_lr.py b/evaluation/eval_lr.py index 6531c0a..a11b68a 100644 --- a/evaluation/eval_lr.py +++ b/evaluation/eval_lr.py @@ -98,7 +98,7 @@ class Evaluator: # Write the header if row == 0: metricsheet.write(1, 0, 'Average') - metricsheet.write(1, 1, f'=AVERAGE(C2:ZZ2)') + metricsheet.write(1, 1, '=AVERAGE(C2:ZZ2)') for col in range(len(metric)): metricsheet.write(0, col + 2, col) colname = xlsxwriter.utility.xl_col_to_name(col + 2) diff --git a/inference_matanyone2.py b/inference_matanyone2.py index 66cc773..e9fbcff 100644 --- a/inference_matanyone2.py +++ b/inference_matanyone2.py @@ -8,7 +8,7 @@ from PIL import Image import torch import torch.nn.functional as F -from hugging_face.tools.download_util import load_file_from_url +from matanyone2.utils.download_util import load_file_from_url from matanyone2.utils.inference_utils import gen_dilate, gen_erosion, read_frame_from_videos from matanyone2.inference.inference_core import InferenceCore diff --git a/matanyone2/utils/device.py b/matanyone2/utils/device.py index 0bad062..5a0f573 100644 --- a/matanyone2/utils/device.py +++ b/matanyone2/utils/device.py @@ -1,3 +1,4 @@ +import contextlib import torch import functools @@ -22,7 +23,6 @@ def safe_autocast_decorator(enabled=True): return wrapper return decorator -import contextlib @contextlib.contextmanager def safe_autocast(enabled=True): device = get_default_device() From 9839d7233232d5445c5b10a916e7c8bc1f0024e2 Mon Sep 17 00:00:00 2001 From: not-lain Date: Fri, 13 Mar 2026 00:32:46 +0100 Subject: [PATCH 2/5] HF integration +uv + cli --- .gitignore | 4 +- README.md | 73 +- matanyone2/cli.py | 171 ++ matanyone2/utils/download_util.py | 109 + matanyone2/utils/inference_utils.py | 15 +- pyproject.toml | 92 +- uv.lock | 2881 +++++++++++++++++++++++++++ 7 files changed, 3295 insertions(+), 50 deletions(-) create mode 100644 matanyone2/cli.py create mode 100644 matanyone2/utils/download_util.py create mode 100644 uv.lock diff --git a/.gitignore b/.gitignore index b1de70c..16e71d4 100644 --- a/.gitignore +++ b/.gitignore @@ -5,4 +5,6 @@ hugging_face/assets/ results/ test_sample/ pretrained_models/ -data/ \ No newline at end of file +data/ +notebooks/ +.venv \ No newline at end of file diff --git a/README.md b/README.md index a948af2..e8ddbb1 100644 --- a/README.md +++ b/README.md @@ -49,6 +49,7 @@ ## 📮 Update +- [2026.03] Add uv and huggingface support for easy installation and usage. - [2026.03] Release inference codes, evaluation codes, and gradio demo. - [2025.12] This repo is created. @@ -65,23 +66,35 @@ ![overall_structure](assets/matanyone1vs2.jpg) ## 🔧 Installation -1. Clone Repo - ```bash - git clone https://github.com/pq-yang/MatAnyone2 - cd MatAnyone2 - ``` -2. Create Conda Environment and Install Dependencies - ```bash - # create new conda env - conda create -n matanyone2 python=3.10 -y - conda activate matanyone2 +### From PyPI / Source +```bash +# install from repo +pip install git+https://github.com/pq-yang/MatAnyone2.git#egg=matanyone2 - # install python dependencies - pip install -e . - # [optional] install python dependencies for gradio demo - pip3 install -r hugging_face/requirements.txt - ``` +# or install optional extras +pip install git+https://github.com/pq-yang/MatAnyone2.git#egg=matanyone2[gui] # Gradio demo + PySide6 +pip install git+https://github.com/pq-yang/MatAnyone2.git#egg=matanyone2[dev] # development / evaluation tools +pip install git+https://github.com/pq-yang/MatAnyone2.git#egg=matanyone2[all] # everything +``` + +### Conda +```bash +conda create -n matanyone2 python=3.10 -y +conda activate matanyone2 +pip install git+https://github.com/pq-yang/MatAnyone2.git#egg=matanyone2[all] +``` + +### uv (recommended) +```bash +# create a new project and add matanyone2 +uv init my-matting-project && cd my-matting-project +uv add matanyone2@git+https://github.com/pq-yang/MatAnyone2.git + +# or with optional extras +uv add matanyone2[gui]@git+https://github.com/pq-yang/MatAnyone2.git +uv add matanyone2[all]@git+https://github.com/pq-yang/MatAnyone2.git +``` ## 🔥 Inference @@ -109,15 +122,33 @@ Run the following command to try it out: ```shell # intput format: video folder -python inference_matanyone2.py -i inputs/video/test-sample1 -m inputs/mask/test-sample1.png +matanyone2 -i inputs/video/test-sample1 -m inputs/mask/test-sample1.png # intput format: mp4 -python inference_matanyone2.py -i inputs/video/test-sample2.mp4 -m inputs/mask/test-sample2.png +matanyone2 -i inputs/video/test-sample2.mp4 -m inputs/mask/test-sample2.png +# or via python +python inference_matanyone2.py -i inputs/video/test-sample1 -m inputs/mask/test-sample1.png ``` -The results will be saved in the `results` folder, including the foreground output video and the alpha output video. -- If you want to save the results as per-frame images, you can set `--save_image`. -- If you want to set a limit for the maximum input resolution, you can set `--max_size`, and the video will be downsampled if min(w, h) exceeds. By default, we don't set the limit. +The results will be saved in the `results` folder, including the foreground output video and the alpha output video. + +### Python API (recommended 🔥) +You can load the model directly from Hugging Face using `from_pretrained` and run inference programmatically: + +```python +from matanyone2 import MatAnyone2, InferenceCore + +model = MatAnyone2.from_pretrained("not-lain/matanyone2") +processor = InferenceCore(model, device="cuda:0") +processor.process_video( + input_path="inputs/video/test-sample2.mp4", + mask_path="inputs/mask/test-sample2.png", + output_path="results", +) +``` +- If you want to save the results as per-frame images, you can set `--save-image`. +- If you want to set a limit for the maximum input resolution, you can set `--max-size`, and the video will be downsampled if min(w, h) exceeds. By default, we don't set the limit. +- Run `matanyone2 --help` for a full list of options. ## 🎪 Interactive Demo To get rid of the preparation for first-frame segmentation mask, we prepare a gradio demo on [hugging face](https://huggingface.co/spaces/PeiqingYang/MatAnyone2) and could also **launch locally**. Just drop your video/image, assign the target masks with a few clicks, and get the the matting results! @@ -127,7 +158,7 @@ To get rid of the preparation for first-frame segmentation mask, we prepare a gr ```shell cd hugging_face -# install python dependencies +# install GUI dependencies (if not already installed via pip install -e ".[gui]") pip3 install -r requirements.txt # FFmpeg required # launch the demo diff --git a/matanyone2/cli.py b/matanyone2/cli.py new file mode 100644 index 0000000..ebe1a0f --- /dev/null +++ b/matanyone2/cli.py @@ -0,0 +1,171 @@ +import os +from typing import Annotated + +import cv2 +import tqdm +import typer +import imageio +import numpy as np +from PIL import Image + +import torch +import torch.nn.functional as F + +from matanyone2.utils.download_util import load_file_from_url +from matanyone2.utils.inference_utils import gen_dilate, gen_erosion, read_frame_from_videos +from matanyone2.inference.inference_core import InferenceCore +from matanyone2.utils.get_default_model import get_matanyone2_model +from matanyone2.utils.device import get_default_device, safe_autocast_decorator + +import warnings +warnings.filterwarnings("ignore") + +app = typer.Typer(help="MatAnyone2 video matting inference") + + +@torch.inference_mode() +@safe_autocast_decorator() +def run_inference(input_path, mask_path, output_path, ckpt_path, n_warmup=10, r_erode=10, + r_dilate=10, suffix="", save_image=False, max_size=-1): + device = get_default_device() + + # download ckpt for the first inference + pretrain_model_url = "https://github.com/pq-yang/MatAnyone2/releases/download/v1.0.0/matanyone2.pth" + ckpt_path = load_file_from_url(pretrain_model_url, 'pretrained_models') + + # load MatAnyone model + matanyone2 = get_matanyone2_model(ckpt_path, device) + + # init inference processor + processor = InferenceCore(matanyone2, cfg=matanyone2.cfg) + + # inference parameters + r_erode = int(r_erode) + r_dilate = int(r_dilate) + n_warmup = int(n_warmup) + max_size = int(max_size) + + # load input frames + vframes, fps, length, video_name = read_frame_from_videos(input_path) + repeated_frames = vframes[0].unsqueeze(0).repeat(n_warmup, 1, 1, 1) + vframes = torch.cat([repeated_frames, vframes], dim=0).float() + length += n_warmup + + # resize if needed + if max_size > 0: + h, w = vframes.shape[-2:] + min_side = min(h, w) + if min_side > max_size: + new_h = int(h / min_side * max_size) + new_w = int(w / min_side * max_size) + vframes = F.interpolate(vframes, size=(new_h, new_w), mode="area") + print(f'Resize to {new_h}x{new_w} for processing...') + + # set output paths + os.makedirs(output_path, exist_ok=True) + if suffix != "": + video_name = f'{video_name}_{suffix}' + if save_image: + os.makedirs(f'{output_path}/{video_name}', exist_ok=True) + os.makedirs(f'{output_path}/{video_name}/pha', exist_ok=True) + os.makedirs(f'{output_path}/{video_name}/fgr', exist_ok=True) + + # load the first-frame mask + mask = Image.open(mask_path).convert('L') + mask = np.array(mask) + + bgr = (np.array([120, 255, 155], dtype=np.float32) / 255).reshape((1, 1, 3)) + objects = [1] + + # [optional] erode & dilate + if r_dilate > 0: + mask = gen_dilate(mask, r_dilate, r_dilate) + if r_erode > 0: + mask = gen_erosion(mask, r_erode, r_erode) + + mask = torch.from_numpy(mask).float().to(device) + + if max_size > 0: + mask = F.interpolate(mask.unsqueeze(0).unsqueeze(0), size=(new_h, new_w), mode="nearest") + mask = mask[0, 0] + + # inference start + phas = [] + fgrs = [] + for ti in tqdm.tqdm(range(length)): + image = vframes[ti] + image_np = np.array(image.permute(1, 2, 0)) + image = (image / 255.).float().to(device) + + if ti == 0: + output_prob = processor.step(image, mask, objects=objects) + output_prob = processor.step(image, first_frame_pred=True) + else: + if ti <= n_warmup: + output_prob = processor.step(image, first_frame_pred=True) + else: + output_prob = processor.step(image) + + mask = processor.output_prob_to_mask(output_prob) + + pha = mask.unsqueeze(2).cpu().numpy() + com_np = image_np / 255. * pha + bgr * (1 - pha) + + if ti > (n_warmup - 1): + com_np = np.round(np.clip(com_np * 255.0, 0, 255)).astype(np.uint8) + pha = np.round(np.clip(pha * 255.0, 0, 255)).astype(np.uint8) + fgrs.append(com_np) + phas.append(pha) + if save_image: + cv2.imwrite(f'{output_path}/{video_name}/fgr/{str(ti - n_warmup).zfill(4)}.png', + com_np[..., [2, 1, 0]]) + cv2.imwrite(f'{output_path}/{video_name}/pha/{str(ti - n_warmup).zfill(4)}.png', + pha) + + phas = np.array(phas) + fgrs = np.array(fgrs) + + imageio.mimwrite(f'{output_path}/{video_name}_fgr.mp4', fgrs, fps=fps, quality=7) + imageio.mimwrite(f'{output_path}/{video_name}_pha.mp4', phas, fps=fps, quality=7) + + +@app.command() +def main( + input_path: Annotated[str, typer.Option("-i", "--input-path", + help="Path of the input video or frame folder.")], + mask_path: Annotated[str, typer.Option("-m", "--mask-path", + help="Path of the first-frame segmentation mask.")], + output_path: Annotated[str, typer.Option("-o", "--output-path", + help="Output folder.")] = "results/", + ckpt_path: Annotated[str, typer.Option("-c", "--ckpt-path", + help="Path of the MatAnyone2 model.")] = "pretrained_models/matanyone2.pth", + warmup: Annotated[int, typer.Option("-w", "--warmup", + help="Number of warmup iterations for the first frame alpha prediction.")] = 10, + erode_kernel: Annotated[int, typer.Option("-e", "--erode-kernel", + help="Erosion kernel size on the input mask.")] = 10, + dilate_kernel: Annotated[int, typer.Option("-d", "--dilate-kernel", + help="Dilation kernel size on the input mask.")] = 10, + suffix: Annotated[str, typer.Option( + help="Suffix to specify different target when saving.")] = "", + save_image: Annotated[bool, typer.Option("--save-image", + help="Save output frames.")] = False, + max_size: Annotated[int, typer.Option( + help="Downsamples if min(w, h) exceeds this value. -1 means no limit.")] = -1, +): + """Run MatAnyone2 video matting inference.""" + run_inference( + input_path=input_path, + mask_path=mask_path, + output_path=output_path, + ckpt_path=ckpt_path, + n_warmup=warmup, + r_erode=erode_kernel, + r_dilate=dilate_kernel, + suffix=suffix, + save_image=save_image, + max_size=max_size, + ) + + +if __name__ == '__main__': + app() diff --git a/matanyone2/utils/download_util.py b/matanyone2/utils/download_util.py new file mode 100644 index 0000000..a880b1f --- /dev/null +++ b/matanyone2/utils/download_util.py @@ -0,0 +1,109 @@ +import math +import os +import requests +from torch.hub import download_url_to_file, get_dir +from tqdm import tqdm +from urllib.parse import urlparse + +def sizeof_fmt(size, suffix='B'): + """Get human readable file size. + + Args: + size (int): File size. + suffix (str): Suffix. Default: 'B'. + + Return: + str: Formated file siz. + """ + for unit in ['', 'K', 'M', 'G', 'T', 'P', 'E', 'Z']: + if abs(size) < 1024.0: + return f'{size:3.1f} {unit}{suffix}' + size /= 1024.0 + return f'{size:3.1f} Y{suffix}' + + +def download_file_from_google_drive(file_id, save_path): + """Download files from google drive. + Ref: + https://stackoverflow.com/questions/25010369/wget-curl-large-file-from-google-drive # noqa E501 + Args: + file_id (str): File id. + save_path (str): Save path. + """ + + session = requests.Session() + URL = 'https://docs.google.com/uc?export=download' + params = {'id': file_id} + + response = session.get(URL, params=params, stream=True) + token = get_confirm_token(response) + if token: + params['confirm'] = token + response = session.get(URL, params=params, stream=True) + + # get file size + response_file_size = session.get(URL, params=params, stream=True, headers={'Range': 'bytes=0-2'}) + print(response_file_size) + if 'Content-Range' in response_file_size.headers: + file_size = int(response_file_size.headers['Content-Range'].split('/')[1]) + else: + file_size = None + + save_response_content(response, save_path, file_size) + + +def get_confirm_token(response): + for key, value in response.cookies.items(): + if key.startswith('download_warning'): + return value + return None + + +def save_response_content(response, destination, file_size=None, chunk_size=32768): + if file_size is not None: + pbar = tqdm(total=math.ceil(file_size / chunk_size), unit='chunk') + + readable_file_size = sizeof_fmt(file_size) + else: + pbar = None + + with open(destination, 'wb') as f: + downloaded_size = 0 + for chunk in response.iter_content(chunk_size): + downloaded_size += chunk_size + if pbar is not None: + pbar.update(1) + pbar.set_description(f'Download {sizeof_fmt(downloaded_size)} / {readable_file_size}') + if chunk: # filter out keep-alive new chunks + f.write(chunk) + if pbar is not None: + pbar.close() + + +def load_file_from_url(url, model_dir=None, progress=True, file_name=None): + """Load file form http url, will download models if necessary. + Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py + Args: + url (str): URL to be downloaded. + model_dir (str): The path to save the downloaded model. Should be a full path. If None, use pytorch hub_dir. + Default: None. + progress (bool): Whether to show the download progress. Default: True. + file_name (str): The downloaded file name. If None, use the file name in the url. Default: None. + Returns: + str: The path to the downloaded file. + """ + if model_dir is None: # use the pytorch hub_dir + hub_dir = get_dir() + model_dir = os.path.join(hub_dir, 'checkpoints') + + os.makedirs(model_dir, exist_ok=True) + + parts = urlparse(url) + filename = os.path.basename(parts.path) + if file_name is not None: + filename = file_name + cached_file = os.path.abspath(os.path.join(model_dir, filename)) + if not os.path.exists(cached_file): + print(f'Downloading: "{url}" to {cached_file}\n') + download_url_to_file(url, cached_file, hash_prefix=None, progress=progress) + return cached_file diff --git a/matanyone2/utils/inference_utils.py b/matanyone2/utils/inference_utils.py index c5bb6a0..a356422 100644 --- a/matanyone2/utils/inference_utils.py +++ b/matanyone2/utils/inference_utils.py @@ -4,7 +4,6 @@ import random import numpy as np import torch -import torchvision IMAGE_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.JPG', '.JPEG', '.PNG') VIDEO_EXTENSIONS = ('.mp4', '.mov', '.avi', '.MP4', '.MOV', '.AVI') @@ -12,8 +11,18 @@ VIDEO_EXTENSIONS = ('.mp4', '.mov', '.avi', '.MP4', '.MOV', '.AVI') def read_frame_from_videos(frame_root): if frame_root.endswith(VIDEO_EXTENSIONS): # Video file path video_name = os.path.basename(frame_root)[:-4] - frames, _, info = torchvision.io.read_video(filename=frame_root, pts_unit='sec', output_format='TCHW') # RGB - fps = info['video_fps'] + cap = cv2.VideoCapture(frame_root) + fps = cap.get(cv2.CAP_PROP_FPS) + if fps <= 0: + fps = 24 + frames = [] + while True: + ret, frame = cap.read() + if not ret: + break + frames.append(frame[..., [2, 1, 0]]) # BGR to RGB, HWC + cap.release() + frames = torch.Tensor(np.array(frames)).permute(0, 3, 1, 2).contiguous() # TCHW else: video_name = os.path.basename(frame_root) frames = [] diff --git a/pyproject.toml b/pyproject.toml index 226223e..015dfe6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,7 +14,7 @@ column_limit = 100 name = "matanyone2" version = "1.0.0" authors = [{ name = "Peiqing Yang", email = "peiqingyang99@outlook.com" }] -description = "" +description = "Robust and Real-Time Video Matting with Temporal Guidance" readme = "README.md" requires-python = ">=3.10" classifiers = [ @@ -22,43 +22,85 @@ classifiers = [ "Operating System :: OS Independent", ] dependencies = [ + 'numpy >= 1.21', + 'Pillow >= 9.5', + 'opencv-python >= 4.8', + 'scipy >= 1.7', + 'tqdm >= 4.66.1', + 'einops >= 0.6', + 'hydra-core >= 1.3.2', + 'requests', + 'imageio >= 2.25.0', + 'imageio[ffmpeg]', + 'huggingface_hub >= 0.25.0', + 'safetensors', + 'kornia', + 'easydict', + 'torch', + 'torchvision', + 'typer >= 0.9.0', + "av>=16.1.0", +] + +[project.optional-dependencies] +dev = [ 'cython', 'gitpython >= 3.1', 'thinplate@git+https://github.com/cheind/py-thin-plate-spline', 'hickle >= 5.0', 'tensorboard >= 2.11', - 'numpy >= 1.21', - 'Pillow >= 9.5', - 'opencv-python >= 4.8', - 'scipy >= 1.7', 'pycocotools >= 2.0.7', - 'tqdm >= 4.66.1', - 'gradio >= 3.34', 'gdown >= 4.7.1', - 'einops >= 0.6', - 'hydra-core >= 1.3.2', - 'PySide6 >= 6.2.0', - 'charset-normalizer >= 3.1.0', - 'netifaces >= 0.11.0', - 'cchardet >= 2.1.7', - 'easydict', - 'av >= 0.5.2', - 'requests', - 'pyqtdarktheme', - 'imageio == 2.25.0', - 'imageio[ffmpeg]', - 'huggingface_hub == 0.36.2', - 'safetensors', 'xlsxwriter', - 'kornia', ] +gui = [ + 'gradio >= 6.9.0', + 'PySide6 >= 6.2.0', + 'pyqtdarktheme', +] +all = [ + 'cython', + 'gitpython >= 3.1', + 'thinplate@git+https://github.com/cheind/py-thin-plate-spline', + 'hickle >= 5.0', + 'tensorboard >= 2.11', + 'pycocotools >= 2.0.7', + 'gdown >= 4.7.1', + 'xlsxwriter', + 'gradio >= 6.9.0', + 'PySide6 >= 6.2.0', + 'pyqtdarktheme', +] + +[project.scripts] +matanyone2 = "matanyone2.cli:app" [tool.hatch.build.targets.wheel] packages = ["matanyone2"] +[tool.hatch.build.targets.wheel.force-include] +"matanyone2/config" = "matanyone2/config" + +[tool.hatch.build.targets.sdist] +include = [ + "matanyone2/", + "inference_matanyone2.py", + "README.md", + 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upload-time = "2025-09-16T00:16:20.108Z" }, +] From 12eeaa10fc0006a1a16aa6360948e664e5e3f30b Mon Sep 17 00:00:00 2001 From: Not Lain <70411813+not-lain@users.noreply.github.com> Date: Sun, 15 Mar 2026 19:53:14 +0100 Subject: [PATCH 3/5] Update pyproject.toml Co-authored-by: Peiqing Yang <142089663+pq-yang@users.noreply.github.com> --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 015dfe6..7995afb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,7 +14,7 @@ column_limit = 100 name = "matanyone2" version = "1.0.0" authors = [{ name = "Peiqing Yang", email = "peiqingyang99@outlook.com" }] -description = "Robust and Real-Time Video Matting with Temporal Guidance" +description = "Scaling Video Matting via a Learned Quality Evaluator" readme = "README.md" requires-python = ">=3.10" classifiers = [ From 49a4b36032f86e91726d9090289133629f560e05 Mon Sep 17 00:00:00 2001 From: pq-yang Date: Mon, 16 Mar 2026 06:45:38 +0000 Subject: [PATCH 4/5] cleanup --- README.md | 67 +++++++++++++++-------------- matanyone2/utils/inference_utils.py | 15 ++----- pyproject.toml | 60 ++++++++++---------------- 3 files changed, 59 insertions(+), 83 deletions(-) diff --git a/README.md b/README.md index e8ddbb1..48b9ad2 100644 --- a/README.md +++ b/README.md @@ -67,33 +67,31 @@ ## 🔧 Installation -### From PyPI / Source -```bash -# install from repo -pip install git+https://github.com/pq-yang/MatAnyone2.git#egg=matanyone2 - -# or install optional extras -pip install git+https://github.com/pq-yang/MatAnyone2.git#egg=matanyone2[gui] # Gradio demo + PySide6 -pip install git+https://github.com/pq-yang/MatAnyone2.git#egg=matanyone2[dev] # development / evaluation tools -pip install git+https://github.com/pq-yang/MatAnyone2.git#egg=matanyone2[all] # everything -``` - ### Conda -```bash -conda create -n matanyone2 python=3.10 -y -conda activate matanyone2 -pip install git+https://github.com/pq-yang/MatAnyone2.git#egg=matanyone2[all] -``` +1. Clone Repo + ```bash + git clone https://github.com/pq-yang/MatAnyone2 + cd MatAnyone2 + ``` -### uv (recommended) +2. Create Conda Environment and Install Dependencies + ```bash + # create new conda env + conda create -n matanyone2 python=3.10 -y + conda activate matanyone2 + + # install python dependencies + pip install -e . + # [optional] install python dependencies for gradio demo + pip3 install -r hugging_face/requirements.txt + ``` + +### uv +You may also install via [uv](https://docs.astral.sh/uv/): ```bash # create a new project and add matanyone2 uv init my-matting-project && cd my-matting-project uv add matanyone2@git+https://github.com/pq-yang/MatAnyone2.git - -# or with optional extras -uv add matanyone2[gui]@git+https://github.com/pq-yang/MatAnyone2.git -uv add matanyone2[all]@git+https://github.com/pq-yang/MatAnyone2.git ``` ## 🔥 Inference @@ -122,23 +120,29 @@ Run the following command to try it out: ```shell # intput format: video folder -matanyone2 -i inputs/video/test-sample1 -m inputs/mask/test-sample1.png +python inference_matanyone2.py -i inputs/video/test-sample1 -m inputs/mask/test-sample1.png # intput format: mp4 -matanyone2 -i inputs/video/test-sample2.mp4 -m inputs/mask/test-sample2.png - -# or via python -python inference_matanyone2.py -i inputs/video/test-sample1 -m inputs/mask/test-sample1.png +python inference_matanyone2.py -i inputs/video/test-sample2.mp4 -m inputs/mask/test-sample2.png ``` -The results will be saved in the `results` folder, including the foreground output video and the alpha output video. +- The results will be saved in the `results` folder, including the foreground output video and the alpha output video. +- If you want to save the results as per-frame images, you can set `--save-image`. +- If you want to set a limit for the maximum input resolution, you can set `--max-size`, and the video will be downsampled if min(w, h) exceeds. By default, we don't set the limit. -### Python API (recommended 🔥) +### uv +If you install via uv, you may try the following command: +```shell +matanyone2 -i inputs/video/test-sample1 -m inputs/mask/test-sample1.png +``` +- Run `matanyone2 --help` for a full list of options. + +### Python API 🤗 You can load the model directly from Hugging Face using `from_pretrained` and run inference programmatically: ```python from matanyone2 import MatAnyone2, InferenceCore -model = MatAnyone2.from_pretrained("not-lain/matanyone2") +model = MatAnyone2.from_pretrained("PeiqingYang/MatAnyone2") processor = InferenceCore(model, device="cuda:0") processor.process_video( input_path="inputs/video/test-sample2.mp4", @@ -146,9 +150,6 @@ processor.process_video( output_path="results", ) ``` -- If you want to save the results as per-frame images, you can set `--save-image`. -- If you want to set a limit for the maximum input resolution, you can set `--max-size`, and the video will be downsampled if min(w, h) exceeds. By default, we don't set the limit. -- Run `matanyone2 --help` for a full list of options. ## 🎪 Interactive Demo To get rid of the preparation for first-frame segmentation mask, we prepare a gradio demo on [hugging face](https://huggingface.co/spaces/PeiqingYang/MatAnyone2) and could also **launch locally**. Just drop your video/image, assign the target masks with a few clicks, and get the the matting results! @@ -158,7 +159,7 @@ To get rid of the preparation for first-frame segmentation mask, we prepare a gr ```shell cd hugging_face -# install GUI dependencies (if not already installed via pip install -e ".[gui]") +# install GUI dependencies pip3 install -r requirements.txt # FFmpeg required # launch the demo diff --git a/matanyone2/utils/inference_utils.py b/matanyone2/utils/inference_utils.py index a356422..c5bb6a0 100644 --- a/matanyone2/utils/inference_utils.py +++ b/matanyone2/utils/inference_utils.py @@ -4,6 +4,7 @@ import random import numpy as np import torch +import torchvision IMAGE_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.JPG', '.JPEG', '.PNG') VIDEO_EXTENSIONS = ('.mp4', '.mov', '.avi', '.MP4', '.MOV', '.AVI') @@ -11,18 +12,8 @@ VIDEO_EXTENSIONS = ('.mp4', '.mov', '.avi', '.MP4', '.MOV', '.AVI') def read_frame_from_videos(frame_root): if frame_root.endswith(VIDEO_EXTENSIONS): # Video file path video_name = os.path.basename(frame_root)[:-4] - cap = cv2.VideoCapture(frame_root) - fps = cap.get(cv2.CAP_PROP_FPS) - if fps <= 0: - fps = 24 - frames = [] - while True: - ret, frame = cap.read() - if not ret: - break - frames.append(frame[..., [2, 1, 0]]) # BGR to RGB, HWC - cap.release() - frames = torch.Tensor(np.array(frames)).permute(0, 3, 1, 2).contiguous() # TCHW + frames, _, info = torchvision.io.read_video(filename=frame_root, pts_unit='sec', output_format='TCHW') # RGB + fps = info['video_fps'] else: video_name = os.path.basename(frame_root) frames = [] diff --git a/pyproject.toml b/pyproject.toml index 7995afb..cbdaef5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,54 +22,35 @@ classifiers = [ "Operating System :: OS Independent", ] dependencies = [ + 'cython', + 'gitpython >= 3.1', + 'thinplate@git+https://github.com/cheind/py-thin-plate-spline', + 'hickle >= 5.0', + 'tensorboard >= 2.11', 'numpy >= 1.21', 'Pillow >= 9.5', 'opencv-python >= 4.8', 'scipy >= 1.7', + 'pycocotools >= 2.0.7', 'tqdm >= 4.66.1', + 'gradio >= 3.34', + 'gdown >= 4.7.1', 'einops >= 0.6', 'hydra-core >= 1.3.2', - 'requests', - 'imageio >= 2.25.0', - 'imageio[ffmpeg]', - 'huggingface_hub >= 0.25.0', - 'safetensors', - 'kornia', + 'PySide6 >= 6.2.0', + 'charset-normalizer >= 3.1.0', + 'netifaces >= 0.11.0', + 'cchardet >= 2.1.7', 'easydict', - 'torch', - 'torchvision', - 'typer >= 0.9.0', - "av>=16.1.0", -] - -[project.optional-dependencies] -dev = [ - 'cython', - 'gitpython >= 3.1', - 'thinplate@git+https://github.com/cheind/py-thin-plate-spline', - 'hickle >= 5.0', - 'tensorboard >= 2.11', - 'pycocotools >= 2.0.7', - 'gdown >= 4.7.1', - 'xlsxwriter', -] -gui = [ - 'gradio >= 6.9.0', - 'PySide6 >= 6.2.0', + 'av >= 0.5.2', + 'requests', 'pyqtdarktheme', -] -all = [ - 'cython', - 'gitpython >= 3.1', - 'thinplate@git+https://github.com/cheind/py-thin-plate-spline', - 'hickle >= 5.0', - 'tensorboard >= 2.11', - 'pycocotools >= 2.0.7', - 'gdown >= 4.7.1', + 'imageio == 2.25.0', + 'imageio[ffmpeg]', + 'huggingface_hub == 0.36.2', + 'safetensors', 'xlsxwriter', - 'gradio >= 6.9.0', - 'PySide6 >= 6.2.0', - 'pyqtdarktheme', + 'kornia', ] [project.scripts] @@ -104,3 +85,6 @@ torchvision = { index = "pytorch-cu128" } [project.urls] "Homepage" = "https://github.com/pq-yang/MatAnyone2" "Bug Tracker" = "https://github.com/pq-yang/MatAnyone2/issues" + +[tool.setuptools] +package-dir = {"" = "."} From aef8ef11a2ce1dde6d05e89e0792790aa10b3530 Mon Sep 17 00:00:00 2001 From: pq-yang Date: Mon, 16 Mar 2026 11:08:48 +0000 Subject: [PATCH 5/5] cleanup --- README.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 48b9ad2..4567b54 100644 --- a/README.md +++ b/README.md @@ -49,7 +49,7 @@ ## 📮 Update -- [2026.03] Add uv and huggingface support for easy installation and usage. +- [2026.03] Add uv, CLI, and huggingface support for easy installation and usage. - [2026.03] Release inference codes, evaluation codes, and gradio demo. - [2025.12] This repo is created. @@ -129,8 +129,7 @@ python inference_matanyone2.py -i inputs/video/test-sample2.mp4 -m inputs/mask/t - If you want to save the results as per-frame images, you can set `--save-image`. - If you want to set a limit for the maximum input resolution, you can set `--max-size`, and the video will be downsampled if min(w, h) exceeds. By default, we don't set the limit. -### uv -If you install via uv, you may try the following command: +Or you may directly run via CLI command: ```shell matanyone2 -i inputs/video/test-sample1 -m inputs/mask/test-sample1.png ```