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import os
import cv2
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import torch
import gradio as gr
from PIL import Image
from segment_anything import SamAutomaticMaskGenerator, SamPredictor, sam_model_registry
matplotlib.pyplot.switch_backend('Agg') # for matplotlib to work in gradio
#setup model
sam_checkpoint = "sam_vit_h_4b8939.pth"
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # use GPU if available
model_type = "default"
sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
sam.to(device=device)
mask_generator = SamAutomaticMaskGenerator(sam)
predictor = SamPredictor(sam)
def show_anns(anns):
if len(anns) == 0:
return
sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
ax = plt.gca()
ax.set_autoscale_on(False)
polygons = []
color = []
for ann in sorted_anns:
m = ann['segmentation']
img = np.ones((m.shape[0], m.shape[1], 3))
color_mask = np.random.random((1, 3)).tolist()[0]
for i in range(3):
img[:,:,i] = color_mask[i]
ax.imshow(np.dstack((img, m*0.35)))
def segment_image(image):
masks = mask_generator.generate(image)
plt.clf()
ppi = 100
height, width, _ = image.shape
plt.figure(figsize=(width / ppi, height / ppi), dpi=ppi)
plt.imshow(image)
show_anns(masks)
plt.axis('off')
plt.savefig('output.png', bbox_inches='tight', pad_inches=0)
output = cv2.imread('output.png')
return Image.fromarray(output)
with gr.Blocks() as demo:
gr.Markdown("## Segment-anything Demo")
with gr.Row():
image = gr.Image()
image_output = gr.Image()
segment_image_button = gr.Button("Segment Image")
segment_image_button.click(segment_image, inputs=[image], outputs=image_output)
demo.launch()
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