Spaces:
Runtime error
Runtime error
| 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) |