from ultralytics import YOLO import gradio as gr import torch from utils.tools_gradio import fast_process from utils.tools import format_results, box_prompt, point_prompt, text_prompt from PIL import ImageDraw import numpy as np # Load the pre-trained model model = YOLO('./weights/FastSAM.pt') device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Description title = "
Fast Segment Anything
" examples = [["examples/sa_8776.jpg"], ["examples/sa_414.jpg"], ["examples/sa_1309.jpg"], ["examples/sa_11025.jpg"], ["examples/sa_561.jpg"], ["examples/sa_192.jpg"], ["examples/sa_10039.jpg"], ["examples/sa_862.jpg"]] default_example = examples[0] def segment_everything( input, input_size=1024, iou_threshold=0.7, conf_threshold=0.25, better_quality=False, withContours=True, use_retina=True, text="", wider=False, mask_random_color=True, ): input_size = int(input_size) 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,) if len(text) > 0: results = format_results(results[0], 0) annotations, _ = text_prompt(results, text, input, device=device, wider=wider) annotations = np.array([annotations]) else: annotations = results[0].masks.data 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,) return fig def segment_with_points( input, input_size=1024, iou_threshold=0.7, conf_threshold=0.25, better_quality=False, withContours=True, use_retina=True, mask_random_color=True, ): global global_points global global_point_label input_size = int(input_size) 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): global global_points global global_point_label 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_points.append([x, y]) global_point_label.append(1 if label == 'Add Mask' else 0) print(x, y, label == 'Add Mask') draw = ImageDraw.Draw(image) draw.ellipse([(x - point_radius, y - point_radius), (x + point_radius, y + point_radius)], fill=point_color) return image 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') cond_img_t = gr.Image(label="Input with text", value="examples/dogs.jpg", 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') segm_img_t = gr.Image(label="Segmented Image with text", interactive=False, type='pil') global_points = [] global_point_label = [] 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.Tab("Text mode"): # Images with gr.Row(variant="panel"): with gr.Column(scale=1): cond_img_t.render() with gr.Column(scale=1): segm_img_t.render() # Submit & Clear with gr.Row(): with gr.Column(): input_size_slider_t = 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.Row(): with gr.Column(): contour_check = gr.Checkbox(value=True, label='withContours', info='draw the edges of the masks') text_box = gr.Textbox(label="text prompt", value="a black dog") with gr.Column(): segment_btn_t = gr.Button("Segment with text", variant='primary') clear_btn_t = gr.Button("Clear", variant="secondary") gr.Markdown("Try some of the examples below ⬇️") gr.Examples(examples=[["examples/dogs.jpg"], ["examples/fruits.jpg"], ["examples/flowers.jpg"]], inputs=[cond_img_t], 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') retina_check = gr.Checkbox(value=True, label='use_retina', info='draw high-resolution segmentation masks') wider_check = gr.Checkbox(value=False, label='wider', info='wider result') segment_btn_t.click(segment_everything, inputs=[ cond_img_t, input_size_slider_t, iou_threshold, conf_threshold, mor_check, contour_check, retina_check, text_box, wider_check, ], outputs=segm_img_t) def clear(): return None, None def clear_text(): return None, 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]) clear_btn_t.click(clear_text, outputs=[cond_img_p, segm_img_p, text_box]) demo.queue() demo.launch()