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import gradio as gr |
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import numpy as np |
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import cv2 |
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from PIL import Image |
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from src.plot_utils import show_masks |
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from gradio_image_annotation import image_annotator |
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from sam2.build_sam import build_sam2 |
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from sam2.sam2_image_predictor import SAM2ImagePredictor |
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choice_mapping = { |
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"tiny": ["sam2_hiera_t.yaml", "assets/checkpoints/sam2_hiera_tiny.pt"], |
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"small": ["sam2_hiera_s.yaml", "assets/checkpoints/sam2_hiera_small.pt"], |
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"base_plus": ["sam2_hiera_b+.yaml", "assets/checkpoints/sam2_hiera_base_plus.pt"], |
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"large": ["sam2_hiera_l.yaml", "assets/checkpoints/sam2_hiera_large.pt"], |
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} |
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def predict(model_choice: str, annotations, image): |
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config_file, ckpt_path = choice_mapping[str(model_choice)] |
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sam2_model = build_sam2(config_file, ckpt_path, device="cpu") |
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predictor = SAM2ImagePredictor(sam2_model) |
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predictor.set_image(image) |
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coordinates = np.array( |
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[ |
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int(annotations["boxes"][0]["xmin"]), |
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int(annotations["boxes"][0]["ymin"]), |
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int(annotations["boxes"][0]["xmax"]), |
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int(annotations["boxes"][0]["ymax"]), |
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] |
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) |
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masks, scores, _ = predictor.predict( |
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point_coords=None, |
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point_labels=None, |
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box=coordinates[None, :], |
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multimask_output=False, |
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) |
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mask = masks.transpose(1, 2, 0) |
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mask_image = (mask * 255).astype(np.uint8) |
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cv2.imwrite("mask.png", mask_image) |
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return [ |
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show_masks(image, masks, scores, box_coords=coordinates), |
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gr.DownloadButton("Download Mask", value="mask.png", visible=True), |
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] |
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with gr.Blocks(delete_cache=(30, 30)) as demo: |
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gr.Markdown( |
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""" |
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# 1. Choose Model Checkpoint |
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""" |
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) |
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with gr.Row(): |
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model = gr.Dropdown( |
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choices=["tiny", "small", "base_plus", "large"], |
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value="tiny", |
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label="Model Checkpoint", |
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info="Which model checkpoint to load?", |
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) |
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gr.Markdown( |
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""" |
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# 2. Upload an Image |
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""" |
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) |
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with gr.Row(): |
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img = gr.Image(value="./assets/img.png", type="numpy", label="Input Image") |
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gr.Markdown( |
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""" |
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# 3. Draw Bounding Box |
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""" |
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) |
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annotator = image_annotator( |
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value={"image": img.value["path"]}, |
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disable_edit_boxes=True, |
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single_box=True, |
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label="Draw a bounding box", |
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) |
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btn = gr.Button("Get Segmentation Mask") |
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download_btn = gr.DownloadButton("Download Mask", value="mask.png", visible=False) |
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btn.click( |
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fn=predict, inputs=[model, annotator, img], outputs=[gr.Plot(), download_btn] |
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) |
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demo.launch() |
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