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Update app.py
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app.py
CHANGED
@@ -12,19 +12,39 @@ model = YOLO('NailongKiller.yolo11n.pt').to(device)
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@spaces.GPU
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def predict(img):
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#
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img_tensor = torch.tensor(img_resized, dtype=torch.float32).permute(2, 0, 1).unsqueeze(0).div(255.0).to(device)
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#
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results = model.predict(
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conf=0.50, #
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iou=0.45, #
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max_det=100 #
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demo = gr.Interface(
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fn=predict,
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@spaces.GPU
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def predict(img):
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# 将输入图像转换为PIL Image对象
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input_image = Image.fromarray(img)
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original_size = input_image.size
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# 计算填充尺寸
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max_size = max(original_size)
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pad_w = max_size - original_size[0]
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pad_h = max_size - original_size[1]
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# 创建方形画布并保持宽高比
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padded_img = Image.new('RGB', (max_size, max_size), (114, 114, 114)) # 使用灰色填充
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padded_img.paste(input_image, (pad_w//2, pad_h//2))
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# 转换为numpy数组并进行预测
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img_array = np.array(padded_img)
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# 进行预测
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results = model.predict(
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img_array,
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conf=0.50, # 置信度阈值
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iou=0.45, # IOU阈值
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max_det=100 # 最大检测数量
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)
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# 获取预测结果
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result_img = results[0].plot()
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# 裁剪回原始尺寸
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if pad_w > 0 or pad_h > 0:
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result_img = result_img[pad_h//2:pad_h//2 + original_size[1],
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pad_w//2:pad_w//2 + original_size[0]]
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return result_img
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demo = gr.Interface(
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fn=predict,
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