add

Dockerfile

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+FROM python:3.8-slim-buster
+
+WORKDIR /app
+
+COPY . /app
+
+#RUN pip config set global.index-url https://pypi.mirrors.ustc.edu.cn/simple
+#RUN pip config set global.index-url https://mirrors.aliyun.com/pypi/simple
+RUN sed -i "s@http://deb.debian.org@http://mirrors.tuna.tsinghua.edu.cn@g" /etc/apt/sources.list
+RUN apt-get clean
+
+RUN apt-get update && apt-get install ffmpeg libsm6 libxext6  -y
+RUN pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
+RUN pip install -r requirements.txt
+
+CMD ["python", "app.py"]

app.py

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+from ultralytics import YOLO
+import numpy as np
+import matplotlib.pyplot as plt
+import gradio as gr
+import cv2
+import torch
+from PIL import Image
+
+# Load the pre-trained model
+model = YOLO('checkpoints/FastSAM.pt')
+
+# Description
+title = "<center><strong><font size='8'>快速分割一切</font></strong></center>"
+
+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%; } footer {visibility: hidden}"
+
+def fast_process(annotations, image, high_quality, device, scale):
+    if isinstance(annotations[0],dict):
+        annotations = [annotation['segmentation'] for annotation in annotations]
+
+
+    original_h = image.height
+    original_w = image.width
+    if high_quality == True:
+        if isinstance(annotations[0],torch.Tensor):
+            annotations = np.array(annotations.cpu())
+        for i, mask in enumerate(annotations):
+            mask = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8))
+            annotations[i] = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((8, 8), np.uint8))
+    if device == 'cpu':
+        annotations = np.array(annotations)
+        inner_mask = fast_show_mask(annotations,
+                       plt.gca(),
+                       bbox=None,
+                       points=None,
+                       pointlabel=None,
+                       retinamask=True,
+                       target_height=original_h,
+                       target_width=original_w)
+    else:
+        if isinstance(annotations[0],np.ndarray):
+            annotations = torch.from_numpy(annotations)
+        inner_mask = fast_show_mask_gpu(annotations,
+                           plt.gca(),
+                           bbox=None,
+                           points=None,
+                           pointlabel=None)
+    if isinstance(annotations, torch.Tensor):
+        annotations = annotations.cpu().numpy()
+    
+    if high_quality:
+        contour_all = []
+        temp = np.zeros((original_h, original_w,1))
+        for i, mask in enumerate(annotations):
+            if type(mask) == dict:
+                mask = mask['segmentation']
+            annotation = mask.astype(np.uint8)
+            contours, _ = cv2.findContours(annotation, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+            for contour in contours:
+                contour_all.append(contour)
+        cv2.drawContours(temp, contour_all, -1, (255, 255, 255), 2 // scale)
+        color = np.array([0 / 255, 0 / 255, 255 / 255, 0.9])
+        contour_mask = temp / 255 * color.reshape(1, 1, -1)
+    image = image.convert('RGBA')
+    
+    overlay_inner = Image.fromarray((inner_mask * 255).astype(np.uint8), 'RGBA')
+    image.paste(overlay_inner, (0, 0), overlay_inner)
+    
+    if high_quality:
+        overlay_contour = Image.fromarray((contour_mask * 255).astype(np.uint8), 'RGBA')
+        image.paste(overlay_contour, (0, 0), overlay_contour)
+        
+    return image
+
+#   CPU post process
+def fast_show_mask(annotation, ax, bbox=None, 
+                   points=None, pointlabel=None,
+                   retinamask=True, target_height=960,
+                   target_width=960):
+    msak_sum = annotation.shape[0]
+    height = annotation.shape[1]
+    weight = annotation.shape[2]
+    # 将annotation 按照面积 排序
+    areas = np.sum(annotation, axis=(1, 2))
+    sorted_indices = np.argsort(areas)[::1]
+    annotation = annotation[sorted_indices]
+
+    index = (annotation != 0).argmax(axis=0)
+    color = np.random.random((msak_sum,1,1,3))
+    transparency = np.ones((msak_sum,1,1,1)) * 0.6
+    visual = np.concatenate([color,transparency],axis=-1)
+    mask_image = np.expand_dims(annotation,-1) * visual
+
+    mask = np.zeros((height,weight,4))
+
+    h_indices, w_indices = np.meshgrid(np.arange(height), np.arange(weight), indexing='ij')
+    indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))
+    # 使用向量化索引更新show的值
+    mask[h_indices, w_indices, :] = mask_image[indices]
+    if bbox is not None:
+        x1, y1, x2, y2 = bbox
+        ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor='b', linewidth=1))
+    # draw point
+    if points is not None:
+        plt.scatter([point[0] for i, point in enumerate(points) if pointlabel[i]==1], [point[1] for i, point in enumerate(points) if pointlabel[i]==1], s=20, c='y')
+        plt.scatter([point[0] for i, point in enumerate(points) if pointlabel[i]==0], [point[1] for i, point in enumerate(points) if pointlabel[i]==0], s=20, c='m')
+    
+    if retinamask==False:
+        mask = cv2.resize(mask, (target_width, target_height), interpolation=cv2.INTER_NEAREST)
+    
+    return mask
+
+
+def fast_show_mask_gpu(annotation, ax,
+                       bbox=None, points=None, 
+                       pointlabel=None):
+    msak_sum = annotation.shape[0]
+    height = annotation.shape[1]
+    weight = annotation.shape[2]
+    areas = torch.sum(annotation, dim=(1, 2))
+    sorted_indices = torch.argsort(areas, descending=False)
+    annotation = annotation[sorted_indices]
+    # 找每个位置第一个非零值下标
+    index = (annotation != 0).to(torch.long).argmax(dim=0)
+    color = torch.rand((msak_sum,1,1,3)).to(annotation.device)
+    transparency = torch.ones((msak_sum,1,1,1)).to(annotation.device) * 0.6
+    visual = torch.cat([color,transparency],dim=-1)
+    mask_image = torch.unsqueeze(annotation,-1) * visual
+    # 按index取数，index指每个位置选哪个batch的数，把mask_image转成一个batch的形式
+    mask = torch.zeros((height,weight,4)).to(annotation.device)
+    h_indices, w_indices = torch.meshgrid(torch.arange(height), torch.arange(weight))
+    indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))
+    # 使用向量化索引更新show的值
+    mask[h_indices, w_indices, :] = mask_image[indices]
+    mask_cpu = mask.cpu().numpy()
+    if bbox is not None:
+        x1, y1, x2, y2 = bbox
+        ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor='b', linewidth=1))
+    # draw point
+    if points is not None:
+        plt.scatter([point[0] for i, point in enumerate(points) if pointlabel[i]==1], [point[1] for i, point in enumerate(points) if pointlabel[i]==1], s=20, c='y')
+        plt.scatter([point[0] for i, point in enumerate(points) if pointlabel[i]==0], [point[1] for i, point in enumerate(points) if pointlabel[i]==0], s=20, c='m')
+    return mask_cpu
+
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+def segment_image(input, input_size=1024, high_visual_quality=True, iou_threshold=0.7, conf_threshold=0.25):
+    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, high_quality=high_visual_quality,
+                        device=device, scale=(1024 // input_size))
+    return fig
+
+
+cond_img = gr.Image(label="输入", value=default_example[0], type='pil')
+
+segm_img = gr.Image(label="分割后的图片", interactive=False, type='pil')
+
+input_size_slider = gr.components.Slider(minimum=512, maximum=1024, value=1024, step=64, label='输入尺寸')
+
+with gr.Blocks(css=css, title='快速分割一切') as demo:
+    with gr.Row():
+        gr.Markdown(title)
+        
+    # Images
+    with gr.Row(variant="panel"):
+        with gr.Column(scale=1):
+            cond_img.render()
+            
+        with gr.Column(scale=1):
+            segm_img.render()
+    
+    # Submit & Clear
+    with gr.Row():
+        with gr.Column():
+            input_size_slider.render()
+            
+            with gr.Row():
+                vis_check = gr.Checkbox(value=True, label='高质量')
+                
+                with gr.Column():
+                    segment_btn = gr.Button("分割一切", variant='primary')
+                    
+            
+            gr.Examples(examples=examples,
+                        inputs=[cond_img],
+                        outputs=segm_img,
+                        fn=segment_image,
+                        cache_examples=True,
+                        examples_per_page=4, label="示例图片")
+
+        with gr.Column():
+            with gr.Accordion("高级选项", open=False):
+                iou_threshold = gr.Slider(0.1, 0.9, 0.7, step=0.1, label='iou_threshold')
+                conf_threshold = gr.Slider(0.1, 0.9, 0.25, step=0.05, label='conf_threshold')
+                
+    segment_btn.click(segment_image,
+                     inputs=[cond_img, input_size_slider, vis_check, iou_threshold, conf_threshold],
+                     outputs=segm_img)  
+    
+
+demo.queue().launch(server_name="0.0.0.0")
+

requirements.txt

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+# Base-----------------------------------
+matplotlib==3.2.2
+numpy
+opencv-python
+# Pillow>=7.1.2
+# PyYAML>=5.3.1
+# requests>=2.23.0
+# scipy>=1.4.1
+# torch
+# torchvision
+# tqdm>=4.64.0
+
+# pandas>=1.1.4
+# seaborn>=0.11.0
+
+# Ultralytics-----------------------------------
+ultralytics==8.0.121
+gradio
+