add beit-base-finetuned-ade-640-640

beit-base-finetuned-ade-640-640/Dockerfile

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+FROM python:3.7.4-slim
+
+WORKDIR /app
+
+COPY requirements.txt /app
+
+RUN pip config set global.index-url https://pypi.mirrors.ustc.edu.cn/simple/ 
+
+RUN pip3 install --trusted-host pypi.python.org -r requirements.txt 
+
+COPY . /app
+
+CMD ["python", "beit.py"]

beit-base-finetuned-ade-640-640/beit.py

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+#语义分割
+from transformers import BeitFeatureExtractor, BeitForSemanticSegmentation
+import torch
+from PIL import Image
+from torch import nn
+import numpy as np
+import matplotlib.pyplot as plt
+import io
+import gradio as gr
+
+
+def ade_palette():
+    """ADE20K palette that maps each class to RGB values."""
+    return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
+            [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
+            [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
+            [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
+            [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
+            [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
+            [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
+            [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
+            [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
+            [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
+            [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
+            [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
+            [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
+            [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
+            [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
+            [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
+            [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
+            [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
+            [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
+            [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
+            [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
+            [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
+            [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
+            [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
+            [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
+            [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
+            [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
+            [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
+            [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
+            [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
+            [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
+            [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
+            [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
+            [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
+            [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
+            [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
+            [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
+            [102, 255, 0], [92, 0, 255]]
+
+
+# Draw the bounding boxes on image.
+def fig2img(fig):
+    buf = io.BytesIO()
+    fig.savefig(buf)
+    buf.seek(0)
+    img = Image.open(buf)
+    return img
+
+
+# Draw the bounding boxes.
+def visualize_prediction(outputs, image):
+    # First, rescale logits to original image size
+    logits = nn.functional.interpolate(outputs.logits,
+                size=image.size[::-1], # (height, width)
+                mode='bilinear',
+                align_corners=False)
+
+    # Second, apply argmax on the class dimension
+    seg = logits.argmax(dim=1)[0].cpu()
+    color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3
+    palette = np.array(ade_palette())
+    for label, color in enumerate(palette):
+        color_seg[seg == label, :] = color
+    # Convert to BGR
+    color_seg = color_seg[..., ::-1]
+
+    # Show image + mask
+    img = np.array(image) * 0.5 + color_seg * 0.5
+    img = img.astype(np.uint8)
+
+    plt.figure(figsize=(15, 10))
+    plt.axis('off')
+    plt.imshow(img)
+    return fig2img(plt.gcf())
+
+
+def detect_objects(image_input):
+    model_name = "microsoft/beit-base-finetuned-ade-640-640"
+    feature_extractor = BeitFeatureExtractor(do_resize=True, size=640, do_center_crop=False)
+    model = BeitForSemanticSegmentation.from_pretrained(model_name)
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+
+    # if image comes from upload
+    if image_input:
+        image = image_input
+
+    pixel_values = feature_extractor(image, return_tensors="pt").pixel_values.to(device)
+    outputs = model(pixel_values)
+
+    #Visualize prediction
+    viz_img = visualize_prediction(outputs, image)
+    return viz_img
+
+
+demo = gr.Interface(fn = detect_objects, 
+                    inputs = gr.Image(type = 'pil'), 
+                    outputs = gr.Image(shape = (650,650)),
+                    title = "语义分割",
+                    allow_flagging="never",
+                    examples = ['1.png', '2.png'])
+
+
+if __name__ == '__main__':
+    demo.queue().launch(server_name = "0.0.0.0", server_port = 7001, max_threads=40)

beit-base-finetuned-ade-640-640/requirements.txt

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+gradio
+torch
+transformers
+datasets
+matplotlib
+huggingface_hub
+pillow
+numpy