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 = "
🏃 Fast Segment Anything 🤗
" 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). Welcome to give a star ⭐️ to it. 🎯 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: [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](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 = """ # 🎯 Instructions for points mode This is a demo on Github project 🏃 [Fast Segment Anything Model](https://github.com/CASIA-IVA-Lab/FastSAM). Welcome to give a star ⭐️ to it. 1. Upload an image or choose an example. 2. Choose the point label ('Add mask' means a positive point. 'Remove' Area means a negative point that is not segmented). 3. Add points one by one on the image. 4. Click the 'Segemnt with points prompt' button to get the segmentation results. **5. If you get Error, click the 'Clear points' button and try again may help.** """ 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, use_retina=True, mask_random_color=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()