Typo fix (#4)

- Typo fix (734229725e01c8246ee7ae491bb4ed390cc2e550)


Co-authored-by: Pierre Moulon <[email protected]>

app.py

@@ -1,288 +1,288 @@
-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). 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()
+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). 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 'Segment 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()