81 lines
2.6 KiB
Python
81 lines
2.6 KiB
Python
#目标检测
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import io
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import gradio as gr
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import matplotlib.pyplot as plt
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import torch
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from PIL import Image
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from transformers import AutoFeatureExtractor, DetrForObjectDetection
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def make_prediction(img, feature_extractor, model):
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inputs = feature_extractor(img, return_tensors="pt")
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outputs = model(**inputs)
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img_size = torch.tensor([tuple(reversed(img.size))])
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processed_outputs = feature_extractor.post_process(outputs, img_size)
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return processed_outputs[0]
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def detect_objects(image_input):
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#Extract model and feature extractor
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feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/detr-resnet-50")
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model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
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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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#Make prediction
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processed_outputs = make_prediction(image, feature_extractor, model)
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#Visualize prediction
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viz_img = visualize_prediction(image, processed_outputs, 0.7, model.config.id2label)
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return viz_img
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# visualization
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COLORS = [
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[0.000, 0.447, 0.741],
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[0.850, 0.325, 0.098],
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[0.929, 0.694, 0.125],
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[0.494, 0.184, 0.556],
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[0.466, 0.674, 0.188],
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[0.301, 0.745, 0.933]
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]
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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(pil_img, output_dict, threshold=0.7, id2label=None):
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keep = output_dict["scores"] > threshold
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boxes = output_dict["boxes"][keep].tolist()
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scores = output_dict["scores"][keep].tolist()
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labels = output_dict["labels"][keep].tolist()
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if id2label is not None:
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labels = [id2label[x] for x in labels]
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plt.figure(figsize=(16, 10))
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plt.imshow(pil_img)
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ax = plt.gca()
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colors = COLORS * 100
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for score, (xmin, ymin, xmax, ymax), label, color in zip(scores, boxes, labels, colors):
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ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, fill=False, color=color, linewidth=3))
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ax.text(xmin, ymin, f"{label}: {score:0.2f}", fontsize=15, bbox=dict(facecolor="yellow", alpha=0.5))
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plt.axis("off")
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return fig2img(plt.gcf())
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demo = gr.Interface(detect_objects,
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inputs = gr.Image(type = 'pil'),
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outputs = gr.Image(shape = (650,650)),
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title = "目标检测",
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allow_flagging="never",
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examples = ['1.jpg', '2.jpg', '3.jpg'])
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if __name__ == '__main__':
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demo.queue().launch(server_name="0.0.0.0", server_port=7002, max_threads=40)
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