Spaces:
Running
on
T4
Running
on
T4
| from ultralytics import YOLO | |
| import gradio as gr | |
| import torch | |
| from tools import fast_process, format_results, box_prompt, point_prompt | |
| from PIL import ImageDraw | |
| import numpy as np | |
| # Load the pre-trained model | |
| model = YOLO('checkpoints/FastSAM.pt') | |
| device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| # Description | |
| title = "<center><strong><font size='8'>🏃 Fast Segment Anything 🤗</font></strong></center>" | |
| news = """ # 📖 News | |
| 🔥 2023/06/24: Add the 'Advanced options" in Everything mode to get a more detailed adjustment. | |
| 🔥 2023/06/26: Support the points mode. (Better and faster interaction will come soon!) | |
| """ | |
| description_e = """This is a demo on Github project 🏃 [Fast Segment Anything Model](https://github.com/CASIA-IVA-Lab/FastSAM). | |
| 🎯 Upload an Image, segment it with Fast Segment Anything (Everything mode). The other modes will come soon. | |
| ⌛️ It takes about 6~ seconds to generate segment results. The concurrency_count of queue is 1, please wait for a moment when it is crowded. | |
| 🚀 To get faster results, you can use a smaller input size and leave high_visual_quality unchecked. | |
| 📣 You can also obtain the segmentation results of any Image through this Colab: [](https://colab.research.google.com/drive/1oX14f6IneGGw612WgVlAiy91UHwFAvr9?usp=sharing) | |
| 😚 A huge thanks goes out to the @HuggingFace Team for supporting us with GPU grant. | |
| 🏠 Check out our [Model Card 🏃](https://huggingface.co/An-619/FastSAM) | |
| """ | |
| description_p = """This is a demo on Github project 🏃 [Fast Segment Anything Model](https://github.com/CASIA-IVA-Lab/FastSAM). | |
| 🎯 Upload an Image, add points and segment it with Fast Segment Anything (Points mode). | |
| ⌛️ It takes about 6~ seconds to generate segment results. The concurrency_count of queue is 1, please wait for a moment when it is crowded. | |
| 🚀 To get faster results, you can use a smaller input size and leave high_visual_quality unchecked. | |
| 📣 You can also obtain the segmentation results of any Image through this Colab: [](https://colab.research.google.com/drive/1oX14f6IneGGw612WgVlAiy91UHwFAvr9?usp=sharing) | |
| 😚 A huge thanks goes out to the @HuggingFace Team for supporting us with GPU grant. | |
| 🏠 Check out our [Model Card 🏃](https://huggingface.co/An-619/FastSAM) | |
| """ | |
| examples = [["assets/sa_8776.jpg"], ["assets/sa_414.jpg"], ["assets/sa_1309.jpg"], ["assets/sa_11025.jpg"], | |
| ["assets/sa_561.jpg"], ["assets/sa_192.jpg"], ["assets/sa_10039.jpg"], ["assets/sa_862.jpg"]] | |
| default_example = examples[0] | |
| css = "h1 { text-align: center } .about { text-align: justify; padding-left: 10%; padding-right: 10%; }" | |
| def segment_everything( | |
| input, | |
| input_size=1024, | |
| iou_threshold=0.7, | |
| conf_threshold=0.25, | |
| better_quality=False, | |
| withContours=True, | |
| mask_random_color=True, | |
| use_retina=True, | |
| ): | |
| input_size = int(input_size) # 确保 imgsz 是整数 | |
| # Thanks for the suggestion by hysts in HuggingFace. | |
| w, h = input.size | |
| scale = input_size / max(w, h) | |
| new_w = int(w * scale) | |
| new_h = int(h * scale) | |
| input = input.resize((new_w, new_h)) | |
| results = model(input, | |
| device=device, | |
| retina_masks=True, | |
| iou=iou_threshold, | |
| conf=conf_threshold, | |
| imgsz=input_size,) | |
| fig = fast_process(annotations=results[0].masks.data, | |
| image=input, | |
| device=device, | |
| scale=(1024 // input_size), | |
| better_quality=better_quality, | |
| mask_random_color=mask_random_color, | |
| bbox=None, | |
| use_retina=use_retina, | |
| withContours=withContours,) | |
| return fig | |
| def segment_with_points( | |
| input, | |
| input_size=1024, | |
| iou_threshold=0.7, | |
| conf_threshold=0.25, | |
| better_quality=False, | |
| withContours=True, | |
| mask_random_color=True, | |
| use_retina=True, | |
| ): | |
| global global_points | |
| global global_point_label | |
| input_size = int(input_size) # 确保 imgsz 是整数 | |
| # Thanks for the suggestion by hysts in HuggingFace. | |
| w, h = input.size | |
| scale = input_size / max(w, h) | |
| new_w = int(w * scale) | |
| new_h = int(h * scale) | |
| input = input.resize((new_w, new_h)) | |
| scaled_points = [[int(x * scale) for x in point] for point in global_points] | |
| results = model(input, | |
| device=device, | |
| retina_masks=True, | |
| iou=iou_threshold, | |
| conf=conf_threshold, | |
| imgsz=input_size,) | |
| results = format_results(results[0], 0) | |
| annotations, _ = point_prompt(results, scaled_points, global_point_label, new_h, new_w) | |
| annotations = np.array([annotations]) | |
| fig = fast_process(annotations=annotations, | |
| image=input, | |
| device=device, | |
| scale=(1024 // input_size), | |
| better_quality=better_quality, | |
| mask_random_color=mask_random_color, | |
| bbox=None, | |
| use_retina=use_retina, | |
| withContours=withContours,) | |
| global_points = [] | |
| global_point_label = [] | |
| return fig, None | |
| def get_points_with_draw(image, label, evt: gr.SelectData): | |
| x, y = evt.index[0], evt.index[1] | |
| point_radius, point_color = 15, (255, 255, 0) if label == 'Add Mask' else (255, 0, 255) | |
| global global_points | |
| global global_point_label | |
| print((x, y)) | |
| global_points.append([x, y]) | |
| global_point_label.append(1 if label == 'Add Mask' else 0) | |
| # 创建一个可以在图像上绘图的对象 | |
| draw = ImageDraw.Draw(image) | |
| draw.ellipse([(x - point_radius, y - point_radius), (x + point_radius, y + point_radius)], fill=point_color) | |
| return image | |
| # input_size=1024 | |
| # high_quality_visual=True | |
| # inp = 'assets/sa_192.jpg' | |
| # input = Image.open(inp) | |
| # device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| # input_size = int(input_size) # 确保 imgsz 是整数 | |
| # results = model(input, device=device, retina_masks=True, iou=0.7, conf=0.25, imgsz=input_size) | |
| # pil_image = fast_process(annotations=results[0].masks.data, | |
| # image=input, high_quality=high_quality_visual, device=device) | |
| cond_img_e = gr.Image(label="Input", value=default_example[0], type='pil') | |
| cond_img_p = gr.Image(label="Input with points", value=default_example[0], type='pil') | |
| segm_img_e = gr.Image(label="Segmented Image", interactive=False, type='pil') | |
| segm_img_p = gr.Image(label="Segmented Image with points", interactive=False, type='pil') | |
| global_points = [] | |
| global_point_label = [] # TODO:Clear points each image | |
| input_size_slider = gr.components.Slider(minimum=512, | |
| maximum=1024, | |
| value=1024, | |
| step=64, | |
| label='Input_size', | |
| info='Our model was trained on a size of 1024') | |
| with gr.Blocks(css=css, title='Fast Segment Anything') as demo: | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| # Title | |
| gr.Markdown(title) | |
| with gr.Column(scale=1): | |
| # News | |
| gr.Markdown(news) | |
| with gr.Tab("Everything mode"): | |
| # Images | |
| with gr.Row(variant="panel"): | |
| with gr.Column(scale=1): | |
| cond_img_e.render() | |
| with gr.Column(scale=1): | |
| segm_img_e.render() | |
| # Submit & Clear | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_size_slider.render() | |
| with gr.Row(): | |
| contour_check = gr.Checkbox(value=True, label='withContours', info='draw the edges of the masks') | |
| with gr.Column(): | |
| segment_btn_e = gr.Button("Segment Everything", variant='primary') | |
| clear_btn_e = gr.Button("Clear", variant="secondary") | |
| gr.Markdown("Try some of the examples below ⬇️") | |
| gr.Examples(examples=examples, | |
| inputs=[cond_img_e], | |
| outputs=segm_img_e, | |
| fn=segment_everything, | |
| cache_examples=True, | |
| examples_per_page=4) | |
| with gr.Column(): | |
| with gr.Accordion("Advanced options", open=False): | |
| iou_threshold = gr.Slider(0.1, 0.9, 0.7, step=0.1, label='iou', info='iou threshold for filtering the annotations') | |
| conf_threshold = gr.Slider(0.1, 0.9, 0.25, step=0.05, label='conf', info='object confidence threshold') | |
| with gr.Row(): | |
| mor_check = gr.Checkbox(value=False, label='better_visual_quality', info='better quality using morphologyEx') | |
| with gr.Column(): | |
| retina_check = gr.Checkbox(value=True, label='use_retina', info='draw high-resolution segmentation masks') | |
| # Description | |
| gr.Markdown(description_e) | |
| with gr.Tab("Points mode"): | |
| # Images | |
| with gr.Row(variant="panel"): | |
| with gr.Column(scale=1): | |
| cond_img_p.render() | |
| with gr.Column(scale=1): | |
| segm_img_p.render() | |
| # Submit & Clear | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| add_or_remove = gr.Radio(["Add Mask", "Remove Area"], value="Add Mask", label="Point_label (foreground/background)") | |
| with gr.Column(): | |
| segment_btn_p = gr.Button("Segment with points prompt", variant='primary') | |
| clear_btn_p = gr.Button("Clear points", variant='secondary') | |
| gr.Markdown("Try some of the examples below ⬇️") | |
| gr.Examples(examples=examples, | |
| inputs=[cond_img_p], | |
| outputs=segm_img_p, | |
| fn=segment_with_points, | |
| # cache_examples=True, | |
| examples_per_page=4) | |
| with gr.Column(): | |
| # Description | |
| gr.Markdown(description_p) | |
| cond_img_p.select(get_points_with_draw, [cond_img_p, add_or_remove], cond_img_p) | |
| segment_btn_e.click(segment_everything, | |
| inputs=[cond_img_e, input_size_slider, iou_threshold, conf_threshold, mor_check, contour_check, retina_check], | |
| outputs=segm_img_e) | |
| segment_btn_p.click(segment_with_points, | |
| inputs=[cond_img_p], | |
| outputs=[segm_img_p, cond_img_p]) | |
| def clear(): | |
| return None, None | |
| clear_btn_e.click(clear, outputs=[cond_img_e, segm_img_e]) | |
| clear_btn_p.click(clear, outputs=[cond_img_p, segm_img_p]) | |
| demo.queue() | |
| demo.launch() | |