Create app.py

app.py

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+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 = "<center><strong><font size='10'> Fast Segment Anything </font></strong></center>"
+
+
+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()