137 lines
5.6 KiB
Python
137 lines
5.6 KiB
Python
#语义分割
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from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation
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import torch
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from PIL import Image
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from torch import nn
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import numpy as np
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import matplotlib.pyplot as plt
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import io
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import gradio as gr
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from gradio.themes.utils import sizes
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theme = gr.themes.Default(radius_size=sizes.radius_none).set(
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block_label_text_color = '#4D63FF',
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block_title_text_color = '#4D63FF',
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button_primary_text_color = '#4D63FF',
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button_primary_background_fill='#FFFFFF',
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button_primary_border_color='#4D63FF',
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button_primary_background_fill_hover='#EDEFFF',
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)
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def ade_palette():
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"""ADE20K palette that maps each class to RGB values."""
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return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
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[4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
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[230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
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[150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
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[143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
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[0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
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[255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
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[255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
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[255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
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[224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
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[255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
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[6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
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[140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
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[255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
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[255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
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[11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
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[0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
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[255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
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[0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
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[173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
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[255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
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[255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
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[255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
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[0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
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[0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
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[143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
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[8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
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[255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
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[92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
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[163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
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[255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
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[255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
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[10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
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[255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
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[41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
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[71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
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[184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
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[102, 255, 0], [92, 0, 255]]
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# Draw the bounding boxes on image.
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def fig2img(fig):
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buf = io.BytesIO()
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fig.savefig(buf)
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buf.seek(0)
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img = Image.open(buf)
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return img
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# Draw the bounding boxes.
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def visualize_prediction(outputs, image):
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# First, rescale logits to original image size
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logits = nn.functional.interpolate(outputs.logits,
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size=image.size[::-1], # (height, width)
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mode='bilinear',
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align_corners=False)
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# Second, apply argmax on the class dimension
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seg = logits.argmax(dim=1)[0].cpu()
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color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3
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palette = np.array(ade_palette())
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for label, color in enumerate(palette):
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color_seg[seg == label, :] = color
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# Convert to BGR
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color_seg = color_seg[..., ::-1]
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# Show image + mask
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img = np.array(image) * 0.5 + color_seg * 0.5
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img = img.astype(np.uint8)
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plt.figure(figsize=(15, 10))
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plt.axis('off')
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plt.imshow(img)
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return fig2img(plt.gcf())
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def detect_objects(image_input):
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feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b5-finetuned-ade-640-640")
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model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b5-finetuned-ade-640-640")
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model.to(device)
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# if image comes from upload
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if image_input:
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image = image_input
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pixel_values = feature_extractor(image, return_tensors="pt").pixel_values.to(device)
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outputs = model(pixel_values)
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#Visualize prediction
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viz_img = visualize_prediction(outputs, image)
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return viz_img
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with gr.Blocks(theme=theme, css="footer {visibility: hidden}") as demo:
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gr.Markdown("""
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<div align='center' ><font size='60'>语义分割</font></div>
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""")
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with gr.Row():
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with gr.Column():
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image = gr.Image(label="图片", type="pil")
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with gr.Row():
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button = gr.Button("提交", variant="primary")
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box2 = gr.Image(label="图片", shape=(650, 650))
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button.click(fn=detect_objects, inputs=[image], outputs=box2)
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examples = gr.Examples(examples=[['2.png']], inputs=[image], label="例子")
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if __name__ == '__main__':
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demo.queue().launch(server_name = "0.0.0.0")
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