MetaSeg: Packaged version of the Segment Anything repository

This repo is a packaged version of the segment-anything model.

Installation

pip install metaseg

Usage

from metaseg import SegAutoMaskPredictor, SegManualMaskPredictor

# If gpu memory is not enough, reduce the points_per_side and points_per_batch.

# For image
results = SegAutoMaskPredictor().image_predict(
    source="image.jpg",
    model_type="vit_l", # vit_l, vit_h, vit_b
    points_per_side=16,
    points_per_batch=64,
    min_area=0,
    output_path="output.jpg",
    show=True,
    save=False,
)

# For video
results = SegAutoMaskPredictor().video_predict(
    source="video.mp4",
    model_type="vit_l", # vit_l, vit_h, vit_b
    points_per_side=16,
    points_per_batch=64,
    min_area=1000,
    output_path="output.mp4",
)

# For manuel box and point selection

# For image
results = SegManualMaskPredictor().image_predict(
    source="image.jpg",
    model_type="vit_l", # vit_l, vit_h, vit_b
    input_point=[[100, 100], [200, 200]],
    input_label=[0, 1],
    input_box=[100, 100, 200, 200], # or [[100, 100, 200, 200], [100, 100, 200, 200]]
    multimask_output=False,
    random_color=False,
    show=True,
    save=False,
)

# For video

results = SegManualMaskPredictor().video_predict(
    source="video.mp4",
    model_type="vit_l", # vit_l, vit_h, vit_b
    input_point=[0, 0, 100, 100],
    input_label=[0, 1],
    input_box=None,
    multimask_output=False,
    random_color=False,
    output_path="output.mp4",
)

SAHI + Segment Anything

pip install sahi metaseg

from metaseg.sahi_predict import SahiAutoSegmentation, sahi_sliced_predict

image_path = "image.jpg"
boxes = sahi_sliced_predict(
    image_path=image_path,
    detection_model_type="yolov5",  # yolov8, detectron2, mmdetection, torchvision
    detection_model_path="yolov5l6.pt",
    conf_th=0.25,
    image_size=1280,
    slice_height=256,
    slice_width=256,
    overlap_height_ratio=0.2,
    overlap_width_ratio=0.2,
)

SahiAutoSegmentation().image_predict(
    source=image_path,
    model_type="vit_b",
    input_box=boxes,
    multimask_output=False,
    random_color=False,
    show=True,
    save=False,
)

FalAI(Cloud GPU) + Segment Anything

pip install metaseg fal_serverless
fal-serverless auth login

# For Auto Mask
from metaseg import falai_automask_image

image = falai_automask_image(
    image_path="image.jpg",
    model_type="vit_b",
    points_per_side=16,
    points_per_batch=32,
    min_area=0,
)
image.show() # Show image
image.save("output.jpg") # Save image

# For Manual Mask
from metaseg import falai_manuelmask_image

image = falai_manualmask_image(
    image_path="image.jpg",
    model_type="vit_b",
    input_point=[[100, 100], [200, 200]],
    input_label=[0, 1],
    input_box=[100, 100, 200, 200], # or [[100, 100, 200, 200], [100, 100, 200, 200]],
    multimask_output=False,
    random_color=False,
)
image.show() # Show image
image.save("output.jpg") # Save image

Extra Features

Support for Yolov5/8, Detectron2, Mmdetection, Torchvision models
Support for video and web application(Huggingface Spaces)
Support for manual single multi box and point selection
Support for pip installation
Support for SAHI library
Support for FalAI

Metadata

Release files for metaseg 0.7.8

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for metaseg 0.7.8
File	Size	Uploaded
metaseg-0.7.8.tar.gz	39.2 kB	Jun 29, 2023	Details

Built distribution (wheel)

Table of built distributions (wheels) for metaseg 0.7.8
File	Interpreter	ABI	Platform	Reset
metaseg-0.7.8-py3-none-any.whl 47.9 kB Jun 29, 2023	Python 3	none	any	Details

Total release size: 87.1 kB

Release files / metaseg-0.7.8.tar.gz

Download URL	metaseg-0.7.8.tar.gz
Size	39.2 kB
Tags	Source
SHA-256 checksum How to use checksums	`d36c7638439cbfd92fafa0649cc77735be1799e1fa1c74497e23e9e3d7011ad2`
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Upload date	Jun 29, 2023
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Uploaded via	`twine/4.0.1 CPython/3.11.4`

Release files / metaseg-0.7.8-py3-none-any.whl

Download URL	metaseg-0.7.8-py3-none-any.whl
Size	47.9 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`d3706d5936952a64a144baaf900c275f630b9c6f05222fa50087f7ede32a2989`
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Upload date	Jun 29, 2023
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.1 CPython/3.11.4`