将Float32数组转换为coreml中的图像

import CoreML
import CoreVideo
import numpy as np

# 创建PixelBuffer
width = 224
height = 224
pixel_format = CoreVideo.kCVPixelFormatType_32BGRA
pixel_buffer = CoreVideo.CVPixelBufferCreate(None, width, height, pixel_format, None, None)

# 获取PixelBuffer的基地址
CVPixelBufferLockBaseAddress(pixel_buffer, CVPixelBufferLockFlags(rawValue: 0))
base_address = CVPixelBufferGetBaseAddress(pixel_buffer)

# 将Float32数组填充到PixelBuffer的基地址
float32_array = np.random.rand(height, width, 4).astype(np.float32)  # 示例中为一个随机生成的数组，具体数据应根据实际情况获取
np.copyto(base_address, float32_array.flatten())

# 创建CoreML模型
model = CoreML.models.MLModel("YourModel.mlmodel")  # 替换为您自己的模型

# 运行模型进行预测
prediction = model.predict({"input": pixel_buffer})  # 输入的名称根据模型的输入要求而定