62 lines
1.7 KiB
Python
62 lines
1.7 KiB
Python
from rknn.api import RKNN
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import cv2
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import numpy as np
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# 初始化 RKNN
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rknn = RKNN()
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# 配置参数（关键！）
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rknn.config(
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target_platform="rk3588", # 根据实际芯片型号修改
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mean_values=[[123.675, 116.28, 103.53]], # 文本检测模型, 默认输入是255, 使用这些值归一化到0-1
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std_values=[[58.395, 57.12, 57.375]],
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# mean_values=[[127.5, 127.5, 127.5]], # 文本识别模型
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# std_values=[[127.5, 127.5, 127.5]],
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quant_img_RGB2BGR=True,
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# optimization_level=3, # 最高优化级别
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)
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# 加载 ONNX, 只支持固定输入的onnx模型
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ret = rknn.load_onnx(model="/home/admin-root/haotian/康达瑞贝斯机器狗/det_shape_20250814.onnx")
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assert ret == 0, "加载 ONNX 失败！"
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# 转换模型
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ret = rknn.build(
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do_quantization=False, # 启用量化
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# dataset="dataset.txt", # 校准数据路径
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)
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assert ret == 0, "转换 RKNN 失败！"
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# 导出 RKNN
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ret = rknn.export_rknn("/home/admin-root/haotian/康达瑞贝斯机器狗/det_shape_20250814_0.rknn")
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assert ret == 0, "导出 RKNN 失败！"
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# # Set inputs
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# img = cv2.imread('/home/admin-root/haotian/rk3588/pytorch模型转rknn/images/bus.jpg')
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# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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# # img.resize((3, 640, 640))
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# img.resize((3, 48, 320))
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# img = np.expand_dims(img, 0)
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# # Init runtime environment
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# print('--> Init runtime environment')
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# ret = rknn.init_runtime()
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# if ret != 0:
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# print('Init runtime environment failed!')
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# exit(ret)
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# print('done')
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# # Inference
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# print('--> Running model')
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# outputs = rknn.inference(inputs=[img], data_format=['nchw'])
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# np.save('./tflite_mobilenet_v1_0.npy', outputs[0])
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# print(len(outputs))
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# print('done')
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rknn.release()
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