update

MANIFEST.in

@@ -1 +0,0 @@
-include requirements.txt

app.py

@@ -1,5 +1,5 @@
 import gradio as gr
-from demo import automask_image_app, automask_video_app
+from demo import automask_image_app, automask_video_app, manual_app, sahi_autoseg_app
 
 
 def image_app():
@@ -115,6 +115,114 @@ def video_app():
         )
 
 
+def sahi_app():
+    with gr.Blocks():
+        with gr.Row():
+            with gr.Column():
+                sahi_image_file = gr.Image(type="filepath").style(height=260)
+
+                with gr.Row():
+                    with gr.Column():
+                        sahi_autoseg_model_type = gr.Dropdown(
+                            choices=[
+                                "vit_h",
+                                "vit_l",
+                                "vit_b",
+                            ],
+                            value="vit_l",
+                            label="Sam Model Type",
+                        )
+                        sahi_model_type = gr.Dropdown(
+                            choices=[
+                                "yolov5",
+                                "yolov8",
+                            ],
+                            value="yolov5",
+                            label="Detector Model Type",
+                        )
+
+                        sahi_model_path = gr.Dropdown(
+                            choices=[
+                                "yolov5m",
+                                "yolov5l",
+                                "yolov5m6",
+                                "yolov5l6",
+                            ],
+                            value="yolov5m",
+                            label="Detector Model Path",
+                        )
+                        sahi_conf_th = gr.Slider(
+                            minimum=0,
+                            maximum=1,
+                            step=0.1,
+                            value=0.2,
+                            label="Confidence Threshold",
+                        )    
+                        
+                        sahi_image_size = gr.Slider(
+                            minimum=0,
+                            maximum=1600,
+                            step=32,
+                            value=640,
+                            label="Image Size",
+                        )
+                        
+                        sahi_slice_height = gr.Slider(
+                            minimum=0,
+                            maximum=640,
+                            step=32,
+                            value=256,
+                            label="Slice Height",
+                        )
+
+                        sahi_slice_width = gr.Slider(
+                            minimum=0,
+                            maximum=640,
+                            step=32,
+                            value=256,
+                            label="Slice Width",
+                        )
+                        
+                        sahi_overlap_height = gr.Slider(
+                            minimum=0,
+                            maximum=1,
+                            step=0.1,
+                            value=0.2,
+                            label="Overlap Height",
+                        )
+                        
+                        sahi_overlap_width = gr.Slider(
+                            minimum=0,
+                            maximum=1,
+                            step=0.1,
+                            value=0.2,
+                            label="Overlap Width",
+                        )
+
+
+                sahi_image_predict = gr.Button(value="Generator")
+
+            with gr.Column():
+                output_image = gr.Image()
+
+        sahi_image_predict.click(
+            fn=sahi_autoseg_app,
+            inputs=[
+                sahi_image_file,
+                sahi_autoseg_model_type,
+                sahi_model_type,
+                sahi_model_path,
+                sahi_conf_th,
+                sahi_image_size,
+                sahi_slice_height,
+                sahi_slice_width,
+                sahi_overlap_height,
+                sahi_overlap_width,
+
+            ],
+            outputs=[output_image],
+        )
+
 def metaseg_app():
     app = gr.Blocks()
     with app:
@@ -134,6 +242,9 @@ def metaseg_app():
                     image_app()
                 with gr.Tab("Video"):
                     video_app()
+                with gr.Tab("SAHI"):
+                    sahi_app()
+                
 
     app.queue(concurrency_count=1)
     app.launch(debug=True, enable_queue=True)

demo.py

@@ -2,7 +2,6 @@ from metaseg import SegAutoMaskPredictor, SegManualMaskPredictor, SahiAutoSegmen
 
 # For image
 
-
 def automask_image_app(image_path, model_type, points_per_side, points_per_batch, min_area):
     SegAutoMaskPredictor().image_predict(
         source=image_path,
@@ -19,7 +18,6 @@ def automask_image_app(image_path, model_type, points_per_side, points_per_batch
 
 # For video
 
-
 def automask_video_app(video_path, model_type, points_per_side, points_per_batch, min_area):
     SegAutoMaskPredictor().video_predict(
         source=video_path,
@@ -36,7 +34,6 @@ def automask_video_app(video_path, model_type, points_per_side, points_per_batch
 
 # For manuel box and point selection
 
-
 def manual_app(image_path, model_type, input_point, input_label, input_box, multimask_output, random_color):
     SegManualMaskPredictor().image_predict(
         source=image_path,
@@ -55,11 +52,9 @@ def manual_app(image_path, model_type, input_point, input_label, input_box, mult
 
 # For sahi sliced prediction
 
-from metaseg import SahiAutoSegmentation, sahi_sliced_predict
-
-
-def sahi_app(
+def sahi_autoseg_app(
     image_path,
+    sam_model_type,
     detection_model_type,
     detection_model_path,
     conf_th,
@@ -81,9 +76,9 @@ def sahi_app(
         overlap_width_ratio=overlap_width_ratio,
     )
 
-    autoseg = SahiAutoSegmentation().predict(
+    SahiAutoSegmentation().predict(
         source=image_path,
-        model_type="vit_b",
+        model_type=sam_model_type,
         input_box=boxes,
         multimask_output=False,
         random_color=False,

pyproject.toml

@@ -1,6 +0,0 @@
-[tool.black]
-line-length = 120
-
-[tool.isort]
-line_length = 120
-profile = "black"

setup.cfg

@@ -1,11 +0,0 @@
-[isort]
-line_length=100
-multi_line_output=3
-include_trailing_comma=True
-known_standard_library=numpy,setuptools
-skip_glob=*/__init__.py
-known_myself=segment_anything
-known_third_party=matplotlib,cv2,torch,torchvision,pycocotools,onnx,black,isort
-no_lines_before=STDLIB,THIRDPARTY
-sections=FUTURE,STDLIB,THIRDPARTY,MYSELF,FIRSTPARTY,LOCALFOLDER
-default_section=FIRSTPARTY

setup.py

@@ -1,58 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import io
-import os
-import re
-
-from setuptools import find_packages, setup
-
-
-def get_long_description():
-    base_dir = os.path.abspath(os.path.dirname(__file__))
-    with io.open(os.path.join(base_dir, "README.md"), encoding="utf-8") as f:
-        return f.read()
-
-
-def get_requirements():
-    with open("requirements.txt") as f:
-        return f.read().splitlines()
-
-
-def get_version():
-    current_dir = os.path.abspath(os.path.dirname(__file__))
-    version_file = os.path.join(current_dir, "metaseg", "__init__.py")
-    with io.open(version_file, encoding="utf-8") as f:
-        return re.search(r'^__version__ = [\'"]([^\'"]*)[\'"]', f.read(), re.M).group(1)
-
-
-_ALL_REQUIREMENTS = ["matplotlib", "pycocotools", "opencv-python", "onnx", "onnxruntime"]
-
-_DEV_REQUIREMENTS = [
-    "black==23.*",
-    "isort==5.12.0",
-    "flake8",
-    "mypy",
-]
-
-extras = {
-    "all": _ALL_REQUIREMENTS,
-    "dev": _DEV_REQUIREMENTS,
-}
-
-setup(
-    name="metaseg",
-    license="Apache-2.0",
-    author="kadirnar",
-    long_description=get_long_description(),
-    long_description_content_type="text/markdown",
-    url="https://github.com/kadirnar/segment-anything-pip",
-    version=get_version(),
-    install_requires=get_requirements(),
-    packages=find_packages(exclude=("notebook")),
-    extras_require=extras,
-    python_requires=">=3.8",
-)