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| import os | |
| import time | |
| import torch | |
| import numpy as np | |
| import gradio as gr | |
| from segment_anything import build_sam, SamAutomaticMaskGenerator | |
| from segment_anything.utils.amg import ( | |
| build_all_layer_point_grids | |
| ) | |
| os.system(r'python -m wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth') | |
| hourglass_args = { | |
| "baseline": { | |
| "use_hourglass": False, | |
| "hourglass_clustering_location": -1, | |
| }, | |
| "1.2x faster": { | |
| "use_hourglass": True, | |
| "hourglass_clustering_location": 16, | |
| "hourglass_num_cluster": 81, | |
| }, | |
| "1.5x faster": { | |
| "use_hourglass": True, | |
| "hourglass_clustering_location": 6, | |
| "hourglass_num_cluster": 81, | |
| }, | |
| } | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| mask_generator = SamAutomaticMaskGenerator( | |
| build_sam(checkpoint="sam_vit_h_4b8939.pth", use_hourglass=True), | |
| ) | |
| mask_generator.predictor.model.to(device=device) | |
| def predict(image, speed_mode, points_per_side): | |
| points_per_side = int(points_per_side) | |
| mask_generator.predictor.model.image_encoder.load_hourglass_args(**hourglass_args[speed_mode]) | |
| if points_per_side is not None: | |
| mask_generator.point_grids = build_all_layer_point_grids( | |
| points_per_side, | |
| mask_generator.crop_n_layers, | |
| mask_generator.crop_n_points_downscale_factor, | |
| ) | |
| mask_generator.points_per_batch = 64 if points_per_side > 12 else points_per_side * points_per_side | |
| start = time.perf_counter() | |
| with torch.no_grad(): | |
| masks = mask_generator.generate(image) | |
| eta = time.perf_counter() - start | |
| eta_text = f"Time of generation: {eta:.2f} seconds" | |
| if len(masks) == 0: | |
| return image | |
| sorted_masks = sorted(masks, key=(lambda x: x['area']), reverse=True) | |
| img = np.ones(image.shape) | |
| for mask in sorted_masks: | |
| m = mask['segmentation'] | |
| color_mask = np.random.random((1, 1, 3)) | |
| img = img * (1 - m[..., None]) + color_mask * m[..., None] | |
| image = (image * 0.65 + img * 255 * 0.35).astype(np.uint8) | |
| return image, eta_text | |
| description = """ | |
| # <center>Expedit-SAM (Expedite Segment Anything Model without any training)</center> | |
| Github link: [Link](https://github.com/Expedit-LargeScale-Vision-Transformer/Expedit-SAM) | |
| You can select the speed mode you want to use from the "Speed Mode" dropdown menu and click "Run" to segment the image you uploaded to the "Input Image" box. | |
| Points per side is a hyper-parameter that controls the number of points used to generate the segmentation masks. The higher the number, the more accurate the segmentation masks will be, but the slower the inference speed will be. The default value is 12. | |
| """ | |
| if (SPACE_ID := os.getenv('SPACE_ID')) is not None: | |
| description += f'\n<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. <a href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>' | |
| def main(): | |
| with gr.Blocks() as demo: | |
| gr.Markdown(description) | |
| with gr.Column(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_image = gr.Image(label="Input Image") | |
| with gr.Row(): | |
| points_per_side = gr.Dropdown( | |
| choices=[4, 6, 8, 12, 16, 32], | |
| value=12, | |
| label="Points per Side", | |
| ) | |
| speed_mode = gr.Dropdown( | |
| choices=list(hourglass_args.keys()), | |
| value="baseline", | |
| label="Speed Mode", | |
| multiselect=False, | |
| ) | |
| with gr.Row(): | |
| run_btn = gr.Button(label="Run", value="Run") | |
| clear_btn = gr.Button(label="Clear", value="Clear") | |
| with gr.Column(): | |
| output_image = gr.Image(label="Output Image") | |
| eta_label = gr.Label(label="ETA") | |
| gr.Examples( | |
| examples=[ | |
| ["./notebooks/images/dog.jpg"], | |
| ["notebooks/images/groceries.jpg"], | |
| ["notebooks/images/truck.jpg"], | |
| ], | |
| inputs=[input_image], | |
| outputs=[output_image], | |
| fn=predict, | |
| ) | |
| run_btn.click( | |
| fn=predict, | |
| inputs=[input_image, speed_mode, points_per_side], | |
| outputs=[output_image, eta_label] | |
| ) | |
| clear_btn.click( | |
| fn=lambda: [None, None], | |
| inputs=None, | |
| outputs=[input_image, output_image], | |
| queue=False, | |
| ) | |
| demo.queue() | |
| demo.launch() | |
| if __name__ == "__main__": | |
| main() | |