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README.md

@@ -1,12 +1,13 @@
 ---
-title: Segment
-emoji: ⚡
-colorFrom: purple
-colorTo: green
+title: Segment-Anything-Video
+emoji: 🐨
+colorFrom: blue
+colorTo: yellow
 sdk: gradio
-sdk_version: 3.28.0
+sdk_version: 3.19.0
 app_file: app.py
 pinned: false
+license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,242 @@
+import gradio as gr
+from demo import automask_image_app, automask_video_app, sahi_autoseg_app
+
+
+def image_app():
+    with gr.Blocks():
+        with gr.Row():
+            with gr.Column():
+                seg_automask_image_file = gr.Image(type="filepath").style(height=260)
+                with gr.Row():
+                    with gr.Column():
+                        seg_automask_image_model_type = gr.Dropdown(
+                            choices=[
+                                "vit_h",
+                                "vit_l",
+                                "vit_b",
+                            ],
+                            value="vit_l",
+                            label="Model Type",
+                        )
+
+                        seg_automask_image_min_area = gr.Number(
+                            value=0,
+                            label="Min Area",
+                        )
+                    with gr.Row():
+                        with gr.Column():
+                            seg_automask_image_points_per_side = gr.Slider(
+                                minimum=0,
+                                maximum=32,
+                                step=2,
+                                value=16,
+                                label="Points per Side",
+                            )
+
+                            seg_automask_image_points_per_batch = gr.Slider(
+                                minimum=0,
+                                maximum=64,
+                                step=2,
+                                value=64,
+                                label="Points per Batch",
+                            )
+
+                seg_automask_image_predict = gr.Button(value="Generator")
+
+            with gr.Column():
+                output_image = gr.Image()
+
+        seg_automask_image_predict.click(
+            fn=automask_image_app,
+            inputs=[
+                seg_automask_image_file,
+                seg_automask_image_model_type,
+                seg_automask_image_points_per_side,
+                seg_automask_image_points_per_batch,
+                seg_automask_image_min_area,
+            ],
+            outputs=[output_image],
+        )
+
+
+def video_app():
+    with gr.Blocks():
+        with gr.Row():
+            with gr.Column():
+                seg_automask_video_file = gr.Video().style(height=260)
+                with gr.Row():
+                    with gr.Column():
+                        seg_automask_video_model_type = gr.Dropdown(
+                            choices=[
+                                "vit_h",
+                                "vit_l",
+                                "vit_b",
+                            ],
+                            value="vit_l",
+                            label="Model Type",
+                        )
+                        seg_automask_video_min_area = gr.Number(
+                            value=1000,
+                            label="Min Area",
+                        )
+
+                    with gr.Row():
+                        with gr.Column():
+                            seg_automask_video_points_per_side = gr.Slider(
+                                minimum=0,
+                                maximum=32,
+                                step=2,
+                                value=16,
+                                label="Points per Side",
+                            )
+                            
+                            seg_automask_video_points_per_batch = gr.Slider(
+                                minimum=0,
+                                maximum=64,
+                                step=2,
+                                value=64,
+                                label="Points per Batch",
+                            )
+
+                seg_automask_video_predict = gr.Button(value="Generator")
+            with gr.Column():
+                output_video = gr.Video()
+
+        seg_automask_video_predict.click(
+            fn=automask_video_app,
+            inputs=[
+                seg_automask_video_file,
+                seg_automask_video_model_type,
+                seg_automask_video_points_per_side,
+                seg_automask_video_points_per_batch,
+                seg_automask_video_min_area,
+            ],
+            outputs=[output_video],
+        )
+
+
+def sahi_app():
+    with gr.Blocks():
+        with gr.Row():
+            with gr.Column():
+                sahi_image_file = gr.Image(type="filepath").style(height=260)
+                sahi_autoseg_model_type = gr.Dropdown(
+                        choices=[
+                            "vit_h",
+                            "vit_l",
+                            "vit_b",
+                        ],
+                        value="vit_l",
+                        label="Sam Model Type",
+                    )
+
+                with gr.Row():
+                    with gr.Column():
+                        sahi_model_type = gr.Dropdown(
+                                choices=[
+                                    "yolov5",
+                                    "yolov8",
+                                ],
+                                value="yolov5",
+                                label="Detector Model Type",
+                            )
+                        sahi_image_size = gr.Slider(
+                            minimum=0,
+                            maximum=1600,
+                            step=32,
+                            value=640,
+                            label="Image Size",
+                        ) 
+                            
+                        sahi_overlap_width = gr.Slider(
+                            minimum=0,
+                            maximum=1,
+                            step=0.1,
+                            value=0.2,
+                            label="Overlap Width",
+                        )
+                        
+                        sahi_slice_width = gr.Slider(
+                            minimum=0,
+                            maximum=640,
+                            step=32,
+                            value=256,
+                            label="Slice Width",
+                        )
+
+                    with gr.Row():
+                        with gr.Column():                          
+                            sahi_model_path = gr.Dropdown(
+                                choices=[
+                                    "yolov5l.pt",
+                                    "yolov5l6.pt",
+                                    "yolov8l.pt",
+                                    "yolov8x.pt"
+                                ],
+                                value="yolov5l6.pt",
+                                label="Detector Model Path",
+                            )
+
+                            sahi_conf_th = gr.Slider(
+                                minimum=0,
+                                maximum=1,
+                                step=0.1,
+                                value=0.2,
+                                label="Confidence Threshold",
+                            )   
+                            sahi_overlap_height = gr.Slider(
+                                minimum=0,
+                                maximum=1,
+                                step=0.1,
+                                value=0.2,
+                                label="Overlap Height",
+                            )
+                            sahi_slice_height = gr.Slider(
+                                minimum=0,
+                                maximum=640,
+                                step=32,
+                                value=256,
+                                label="Slice Height",
+                            )
+                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:
+        with gr.Row():
+            with gr.Column():
+                with gr.Tab("Image"):
+                    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)
+
+
+if __name__ == "__main__":
+    metaseg_app()

demo.py

@@ -0,0 +1,87 @@
+from metaseg import SegAutoMaskPredictor, SegManualMaskPredictor, SahiAutoSegmentation, sahi_sliced_predict
+
+# For image
+
+def automask_image_app(image_path, model_type, points_per_side, points_per_batch, min_area):
+    SegAutoMaskPredictor().image_predict(
+        source=image_path,
+        model_type=model_type,  # vit_l, vit_h, vit_b
+        points_per_side=points_per_side,
+        points_per_batch=points_per_batch,
+        min_area=min_area,
+        output_path="output.png",
+        show=False,
+        save=True,
+    )
+    return "output.png"
+
+
+# For video
+
+def automask_video_app(video_path, model_type, points_per_side, points_per_batch, min_area):
+    SegAutoMaskPredictor().video_predict(
+        source=video_path,
+        model_type=model_type,  # vit_l, vit_h, vit_b
+        points_per_side=points_per_side,
+        points_per_batch=points_per_batch,
+        min_area=min_area,
+        output_path="output.mp4",
+    )
+    return "output.mp4"
+
+
+# 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,
+        model_type=model_type,  # vit_l, vit_h, vit_b
+        input_point=input_point,
+        input_label=input_label,
+        input_box=input_box,
+        multimask_output=multimask_output,
+        random_color=random_color,
+        output_path="output.png",
+        show=False,
+        save=True,
+    )
+    return "output.png"
+
+
+# For sahi sliced prediction
+
+def sahi_autoseg_app(
+    image_path,
+    sam_model_type,
+    detection_model_type,
+    detection_model_path,
+    conf_th,
+    image_size,
+    slice_height,
+    slice_width,
+    overlap_height_ratio,
+    overlap_width_ratio,
+):
+    boxes = sahi_sliced_predict(
+        image_path=image_path,
+        detection_model_type=detection_model_type,  # yolov8, detectron2, mmdetection, torchvision
+        detection_model_path=detection_model_path,
+        conf_th=conf_th,
+        image_size=image_size,
+        slice_height=slice_height,
+        slice_width=slice_width,
+        overlap_height_ratio=overlap_height_ratio,
+        overlap_width_ratio=overlap_width_ratio,
+    )
+
+    SahiAutoSegmentation().predict(
+        source=image_path,
+        model_type=sam_model_type,
+        input_box=boxes,
+        multimask_output=False,
+        random_color=False,
+        show=False,
+        save=True,
+    )
+    
+    return "output.png"

requirements.txt

@@ -0,0 +1,3 @@
+metaseg==0.5.8
+sahi
+yolov5