Spaces:
Runtime error
Runtime error
File size: 5,000 Bytes
2c50deb 1f28384 2c50deb 1f28384 3ed5219 1f28384 2c50deb 3ed5219 2c50deb 3ed5219 2c50deb 3ed5219 de52eea c46b2fc 3ed5219 1f28384 cd34820 1f28384 2c50deb 3ed5219 1f28384 2c50deb 3ed5219 2c50deb b10bb04 3ed5219 1f28384 2c50deb c46b2fc 1f28384 2c50deb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 |
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()
|