start app develop

demo.py

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+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"