app.py

import sys
import os
import cv2
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import torch
import torchvision
import glob
import gradio as gr
from PIL import Image
from segment_anything import SamAutomaticMaskGenerator, SamPredictor, sam_model_registry
import logging
from huggingface_hub import login
from huggingface_hub import Repository
from huggingface_hub import hf_hub_download

token = os.environ['HUB_TOKEN']
loc =hf_hub_download(repo_id="JunchuanYu/files_for_segmentRS", filename="utils.py",repo_type="dataset",local_dir='.',token=token)
sys.path.append(loc)
from utils import *

with gr.Blocks(theme='gradio/soft') as demo:
    gr.Markdown(title)
    with gr.Accordion("Instructions For User 👉", open=False):
        gr.Markdown(description)
    x=gr.State(value=[])
    y=gr.State(value=[])
    label=gr.State(value=[])

    with gr.Row():
        with gr.Column():  
            mode=gr.inputs.Radio(['Positive','Negative'], type="value",default='Positive',label='Types of sampling methods')
        with gr.Column():
                clear_bn=gr.Button("Clear Selection")
                interseg_button = gr.Button("Interactive Segment",variant='primary')
    with gr.Row():
        input_img = gr.Image(label="Input")
        gallery = gr.Image(label="Selected Sample Points")
        
    input_img.select(get_select_coords, [input_img, mode,x,y,label], [gallery,x,y,label])
    
    with gr.Row():
        output_img = gr.Image(label="Result")           
        mask_img = gr.Image(label="Mask")      
    with gr.Row():
        with gr.Column():
            pred_iou_thresh = gr.Slider(minimum=0.8, maximum=1, value=0.90, step=0.01, interactive=True, label="Prediction Thresh")
        with gr.Column():
            points_per_side = gr.Slider(minimum=16, maximum=96, value=32, step=16, interactive=True, label="Points Per Side")
    autoseg_button = gr.Button("Auto Segment",variant="primary")
    emptyBtn = gr.Button("Restart",variant="secondary")

    interseg_button.click(interactive_seg, inputs=[input_img,x,y,label], outputs=[output_img,mask_img])
    autoseg_button.click(auto_seg, inputs=[input_img,pred_iou_thresh,points_per_side], outputs=[mask_img])

    clear_bn.click(clear_point,outputs=[gallery,x,y,label],show_progress=True)
    emptyBtn.click(reset_state,outputs=[input_img,gallery,output_img,mask_img,x,y,label],show_progress=True,)   
    
    example = gr.Examples(
        examples=[[s,0.88,32] for s in glob.glob('./images/*')],
        fn=auto_seg,
        inputs=[input_img,pred_iou_thresh,points_per_side],
        outputs=[output_img],
        cache_examples=False,examples_per_page=5)
    
    gr.Markdown(descriptionend)
if __name__ == "__main__":
    demo.launch(debug=False,show_api=False)

# 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 Model (SAM) 
#     ### A test on remote sensing data
#     - Paper:[(https://arxiv.org/abs/2304.02643](https://arxiv.org/abs/2304.02643)
#     - Github:[https://github.com/facebookresearch/segment-anything](https://github.com/facebookresearch/segment-anything)
#     - Dataset:https://ai.facebook.com/datasets/segment-anything-downloads/(https://ai.facebook.com/datasets/segment-anything-downloads/)
#     - Official Demo:[https://segment-anything.com/demo](https://segment-anything.com/demo)
#     """
#     )
#     with gr.Row():
#         image = gr.Image()
#         image_output = gr.Image()
#     # print(image.shape)
#     segment_image_button = gr.Button("Segment")
#     segment_image_button.click(segment_image, inputs=[image], outputs=image_output)
#     gr.Examples(glob.glob('./images/*'),image,image_output,segment_image)
#     gr.Markdown("""
# ###  <div align=center>you can follow the WeChat public account [45度科研人] and leave me a message!  </div>
# <br />
# <br />
# <div style="display:flex; justify-content:center;">
#     <img src="https://dunazo.oss-cn-beijing.aliyuncs.com/blog/wechat-simple.png" style="margin-right:25px;width:200px;height:200px;">
#     <div style="width:25px;"></div>
#     <img src="https://dunazo.oss-cn-beijing.aliyuncs.com/blog/shoukuanma222.png" style="margin-left:25px;width:170px;height:190px;">
# </div>
# """)
# demo.launch(debug=True)