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"MLX90377软件库版本1.5详细规范"

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The document titled "PTC04_PSF_MLX90377_1_5.pdf" is a detailed guide for the MLX90377 Product Specific Function (PSF) software library. It provides information on the MLX Library Object Model and is version 1.5, with revision date May 19, 2021. The document contains 121 pages of comprehensive content, including the following sections: 1. Contents: The document begins with a table of contents, outlining the various sections and topics covered in the software library guide. 2. Introduction: The introduction provides an overview of the MLX90377 PSF software library, its purpose, and its relevance to users working with the MLX90377 sensor. 3. Object Model: The Object Model section delves into the structure and components of the MLX Library Object Model, outlining its various elements and their functionalities. 4. Software Functions: This section details the specific software functions provided by the MLX90377 PSF software library, including their usage, parameters, and integration with the MLX90377 sensor. 5. Revision History: The document includes a revision history section, which tracks the changes and updates made to the software library guide, ensuring users have access to the most current information. 6. Appendices: The appendices contain additional resources, references, and supplementary information related to the MLX90377 PSF software library, expanding on its usage and implementation. Overall, the "PTC04_PSF_MLX90377_1_5.pdf" serves as a comprehensive guide for users seeking to understand and utilize the MLX90377 Product Specific Function (PSF) software library. It offers detailed insights into the software library's structure, functions, and integration with the MLX90377 sensor, making it an essential resource for developers and engineers working with this technology.

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MLX90377 - PRODUCT SPECIFIC FUNCTION

{SOFTWARE LIBRARY}

PSF090377AAMLX Library Object Model Page 16 of 121

Ver.1.5

HRESULT ReadNvramCustomer();

5.8.3. Parameters

None

5.8.4. Return value

C++:

The return value obtained from the returned HRESULT is one of the following:

Return value Meaning

S_OK The operation completed successfully.

Any other error code The operation failed.

5.9. ProgramNvamCustomer Method

5.9.1. Description

Writes the customer part of NVRAM of MLX90377. Takes the values from the internal EEPROM cache.

Only the variables that are modified will be programmed.

5.9.2. Syntax

Visual Basic:

Sub ProgramNvamCustomer()

C++:

HRESULT ProgramNvamCustomer();

5.9.3. Parameters

None

5.9.4. Return value

C++:

The return value obtained from the returned HRESULT is one of the following:

Return value Meaning

S_OK The operation completed successfully.

MLX90377 - PRODUCT SPECIFIC FUNCTION

{SOFTWARE LIBRARY}

PSF090377AAMLX Library Object Model Page 17 of 121

Ver.1.5

Any other error code The operation failed.

5.10. ReadNvramMelexis Method

5.10.1. Description

Reads the Melexis part of NVRAM of MLX90377. Updates the internal EEPROM cache with values taken from

the chip. This method is provided to speed up the performance because it updates the whole cache with a

single command.

5.10.2. Syntax

Visual Basic:

Sub ReadNvramMelexis()

C++:

HRESULT ReadNvramMelexis();

5.10.3. Parameters

None

5.10.4. Return value

C++:

The return value obtained from the returned HRESULT is one of the following:

Return value Meaning

S_OK The operation completed successfully.

Any other error code The operation failed.

5.11. ReadNvramMelexisId Method

5.11.1. Description

Reads the Melexis ID part of NVRAM of MLX90377. Updates the internal EEPROM cache with values taken from

the chip. This method is provided to speed up the performance because it updates the whole cache with a

single command.

MLX90377 - PRODUCT SPECIFIC FUNCTION

{SOFTWARE LIBRARY}

PSF090377AAMLX Library Object Model Page 18 of 121

Ver.1.5

5.11.2. Syntax

Visual Basic:

Sub ReadNvramMelexisId()

C++:

HRESULT ReadNvramMelexisId();

5.11.3. Parameters

None

5.11.4. Return value

C++:

The return value obtained from the returned HRESULT is one of the following:

Return value Meaning

S_OK The operation completed successfully.

Any other error code The operation failed.

5.12. GetEEParameterCode Method

5.12.1. Description

Returns the code of a particular EEPROM parameter as it is represented in EEPROM. It is optimized because it

uses the EEPROM cache maintained by the library. ReadNvramCustomer method could be called before calling

GetEEParameterCode to update the whole cache. However GetEEParameterCode works correctly even if

ReadNvramCustomer is not called.

5.12.2. Syntax

Visual Basic:

Function GetEEParameterCode(paramID as ParamCodesEEPROM)

C++:

HRESULT GetEEParameterCode(/*[in]*/ ParamCodesEEPROM paramID, /*[out,retval]*/ TVariant*

pVal);

MLX90377 - PRODUCT SPECIFIC FUNCTION

{SOFTWARE LIBRARY}

PSF090377AAMLX Library Object Model Page 19 of 121

Ver.1.5

5.12.3. Parameters

paramID

A ParamCodesEEPROM constant specifying the ID of the EEPROM parameter.

pVal

An address of VARIANT variable that will receive the return value of the method. The caller is

responsible to call VariantClear on that variable when it is no longer needed. The values are represented

as bytes or unsigned integers depending on the parameter.

5.12.4. Return value

Visual Basic:

A Variant containing the code of an EEPROM parameter.

C++:

The return value obtained from the returned HRESULT is one of the following:

Return value Meaning

S_OK The operation completed successfully. *pVal contains a valid value.

Any other error code The operation failed. *pVal is Empty.

5.13. SetEEParameterCode Method

5.13.1. Description

Changes the code of a particular EEPROM parameter. The method works with the EEPROM cache maintained

by the library.

ProgramNvamCustomer method must be called in order to update the EEPROM of the chip with the codes

from the cache.

5.13.2. Syntax

Visual Basic:

Sub SetEEParameterCode(paramID as ParamCodesEEPROM, Value)

C++:

HRESULT SetEEParameterCode(/*[in]*/ ParamCodesEEPROM paramID, /*[in]*/ TVariantInParam

Value);

5.13.3. Parameters

MLX90377 - PRODUCT SPECIFIC FUNCTION

{SOFTWARE LIBRARY}

PSF090377AAMLX Library Object Model Page 20 of 121

Ver.1.5

paramID

A ParamCodesEEPROM constant specifying the ID of the EEPROM parameter.

Value

A VARIANT containing new code for the parameter.

5.13.4. Return value

C++:

The return value obtained from the returned HRESULT is one of the following:

Return value Meaning

S_OK The operation completed successfully.

Any other error code The operation failed.

5.14. GetEEParameterValue Method

5.14.1. Description

Returns the translated value of a particular EEPROM parameter. It first calls GetEEParameterCode method and

then translates the code of the parameter into a suitable value. For example, the parameter ALPHAX is

translated into a floating point number which represents degrees.

Translation is not defined for all parameters and this method returns an error if it receives paramID which is

not supported.

5.14.2. Syntax

Visual Basic:

Function GetEEParameterValue(paramID as ParamCodesEEPROM)

C++:

HRESULT GetEEParameterValue(/*[in]*/ ParamCodesEEPROM paramID, /*[out,retval]*/ TVariant*

pVal);

5.14.3. Parameters

paramID

A ParamCodesEEPROM constant specifying the ID of the EEPROM parameter.

pVal

An address of VARIANT variable that will receive the return value of the method. The caller is

responsible to call VariantClear on that variable when it is no longer needed.

剩余120页未读，继续阅读

def focusPsf(psf, thresh): x = np.linspace(0, 1, psf.shape[0]) y = np.linspace(0, 1, psf.shape[1]) col, row = np.meshgrid(x, y) # Don't have to normalize as psf should already be normalized cen_row = np.uint8(np.sum(row * psf)) cen_col = np.uint8(np.sum(col * psf)) temp_sum = 0 radius = 0 psf /= np.sum(psf) while temp_sum < thresh: radius += 1 return_psf = psf[cen_row-radius:cen_row+radius+1, cen_col-radius:cen_col+radius+1] temp_sum = np.sum(return_psf) #print(temp_sum) #print(radius, temp_sum) return return_psf, cen_row, cen_col

它的输入是一个PSF和一个阈值，输出是一个在阈值条件下聚焦于PSF中心的PSF，以及PSF中心点的行和列坐标。该函数的实现过程如下： - 首先，通过np.linspace()函数生成了x和y轴的网格点col和row。 - 然后，通过np....

对以下代码进行注解 def tiled_conv_layer(input_img, tiling_factor, tile_size, kernel_size, name='tiling_conv', regularizer=None, nonneg=False): dims = input_img.get_shape().as_list() with tf.variable_scope(name): kernel_lists = [[tf.get_variable('kernel_%d%d'%(i,j), shape=(kernel_size, kernel_size, 1, 1), initializer=tf.contrib.layers.xavier_initializer()) for i in range(tiling_factor)] for j in range(tiling_factor)] pad_one, pad_two = np.ceil((tile_size - kernel_size)/2).astype(np.uint32), np.floor((tile_size - kernel_size)//2).astype(np.uint32) kernels_pad = [[tf.pad(kernel, [[pad_one, pad_two], [pad_one, pad_two], [0,0], [0,0]]) for kernel in kernels] for kernels in kernel_lists] #[tf.summary.image('kernel_%d%d'%(i,j), tf.transpose(kernel, [2,0,1,3])) for j, kernel_list in enumerate(kernels_pad) for i, kernel in enumerate(kernel_list) ] psf = tf.concat([tf.concat(kernel_list, axis=0) for kernel_list in kernels_pad], axis=1) if regularizer is not None: tf.contrib.layers.apply_regularization(regularizer, weights_list=[tf.transpose(psf, [2,0,1,3])]) if nonneg: psf = tf.abs(psf) tf.summary.image("tiled_psf", tf.expand_dims(tf.squeeze(psf, -1), 0)) img_pad = np.ceil(tile_size * tiling_factor / 2).astype(np.uint32) input_img_pad = tf.pad(input_img, [[0,0],[img_pad,img_pad],[img_pad,img_pad],[0,0]]) output_img = fft_conv2d(input_img, psf) #output_img = tf.slice(output_img, [0,img_pad,img_pad,0], [-1,dims[1],dims[2],-1]) return output_img

- 创建一个包含tiling_factor x tiling_factor个卷积核的列表，每个卷积核大小为kernel_size x kernel_size x 1 x 1。这些卷积核的初始化使用了Xavier初始化器。 - 计算pad_one和pad_two两个值，用于对卷积核进行...

解释代码def genBlurImage(p_obj, img): smax = p_obj['delta0'] / p_obj['D'] * p_obj['N'] temp = np.arange(1,101) patchN = temp[np.argmin((smax*np.ones(100)/temp - 2)**2)] patch_size = round(p_obj['N'] / patchN) xtemp = np.round_(p_obj['N']/(2*patchN) + np.linspace(0, p_obj['N'] - p_obj['N']/patchN + 0.001, patchN)) xx, yy = np.meshgrid(xtemp, xtemp) xx_flat, yy_flat = xx.flatten(), yy.flatten() NN = 32 # For extreme scenarios, this may need to be increased img_patches = np.zeros((p_obj['N'], p_obj['N'], int(patchN**2))) den = np.zeros((p_obj['N'], p_obj['N'])) patch_indx, patch_indy = np.meshgrid(np.linspace(-patch_size, patch_size+0.001, num=2patch_size+1), np.linspace(-patch_size, patch_size+0.001, num=2patch_size+1)) for i in range(int(patchN2)): aa = genZernikeCoeff(36, p_obj['Dr0']) temp, x, y, nothing, nothing2 = psfGen(NN, coeff=aa, L=p_obj['L'], D=p_obj['D'], z_i=1.2, wavelength=p_obj['wvl']) psf = np.abs(temp) 2 psf = psf / np.sum(psf.ravel()) focus_psf, _, _ = centroidPsf(psf, 0.85) #: Depending on the size of your PSFs, you may want to use this psf = resize(psf, (round(NN/p_obj['scaling']), round(NN/p_obj['scaling']))) patch_mask = np.zeros((p_obj['N'], p_obj['N'])) patch_mask[round(xx_flat[i]), round(yy_flat[i])] = 1 patch_mask = scipy.signal.fftconvolve(patch_mask, np.exp(-patch_indx2/patch_size2)np.exp(-patch_indy2/patch_size2)np.ones((patch_size2+1, patch_size2+1)), mode='same') den += scipy.signal.fftconvolve(patch_mask, psf, mode='same') img_patches[:,:,i] = scipy.signal.fftconvolve(img * patch_mask, psf, mode='same') out_img = np.sum(img_patches, axis=2) / (den + 0.000001) return out_img

1. 首先计算出图像的最大模糊程度smax，以及每个小块的大小patchN。 2. 然后使用np.linspace函数创建一维数组xtemp，并使用np.meshgrid函数将其扩展为二维数组xx和yy。 3. 将xx和yy展平为一维数组xx_flat和yy_flat...

def convert_otf2psf(otf, size): ker = torch.zeros(size).cuda() #psf = torch.fft.ifftn(otf, dim=(-3,-2,-1)) #psf = torch.stack((psf.real,psf.imag),-1) psf = torch.fft.ifft2(torch.complex(otf[...,0],otf[...,1]),dim=(-3,-2,-1)) psf = psf.real # circularly shift ksize = size[-1] centre = ksize//2 + 1 print(psf.size()) print(psf.type()) ker[:, :, (centre-1):, (centre-1):] = psf[:, :, :centre, :centre]#.mean(dim=1, keepdim=True) ker[:, :, (centre-1):, :(centre-1)] = psf[:, :, :centre, -(centre-1):]#.mean(dim=1, keepdim=True) ker[:, :, :(centre-1), (centre-1):] = psf[:, :, -(centre-1):, :centre]#.mean(dim=1, keepdim=True) ker[:, :, :(centre-1), :(centre-1)] = psf[:, :, -(centre-1):, -(centre-1):]#.mean(dim=1, keepdim=True) return ker这段代码报错， Warning: Casting complex values to real discards the imaginary part (function operator())，究竟是哪行代码的问题，该如何修改呢

psf = psf.real ``` 这行代码会将复数张量 `psf` 的实部提取出来，舍弃掉虚部。因此，如果你要保留复数张量的信息，可以不使用这行代码。如果你需要将复数张量转换为实数张量，可以使用以下代码： ``` psf = ...

% 读取彩色图像并转换为灰度图像 img = imread('peppers.jpg'); gray_img = rgb2gray(img); % 对灰度图像进行高斯模糊处理 h = fspecial('gaussian', [13, 13], 15); blur_img = imfilter(gray_img, h, 'symmetric'); % 构造退化模型，得到退化图像 psf = fspecial('gaussian', [13, 13], 15); degraded_img = imfilter(gray_img, psf, 'symmetric'); % 使用盲去卷积算法进行图像复原 num_iters = [10, 50]; for i = 1:length(num_iters) % 设置算法参数 num_iter = num_iters(i); lambda = 1; beta = 2; tol = 1e-4; % 调用盲去卷积函数进行迭代复原 [restored_img, psf_est] = deconvblind(degraded_img, psf, num_iter, lambda, beta, tol); % 显示复原结果和重建的 PSF figure; subplot(1, 2, 1); imshow(restored_img); title(sprintf('Restored Image (Iter: %d)', num_iter)); subplot(1, 2, 2); imshow(psf_est, []); title(sprintf('Estimated PSF (Iter: %d)', num_iter)); end 此代码在matlab中出现如下报错错误使用 deconvblind>parse_inputs (line 362) DAMPAR has to be of the same class as the input image. 出错 deconvblind (line 123) parse_inputs(varargin{:}); 出错 untitled4 (line 23) [restored_img, psf_est] = deconvblind(degraded_img, psf, num_iter, lambda, beta, tol);

这个错误提示是因为在调用 `deconvblind` 函数时，输入的 `psf` 参数和 `degraded_img` 参数的数据类型不一致导致的。具体来说，`psf` 参数是通过 `fspecial` 函数生成的，数据类型为 `double`，而 `degraded_img` ...

def convert_otf2psf(otf, size): ker = torch.zeros(size).cuda() psf = torch.fft.ifft2(torch.complex(otf[...,0],otf[...,1]), dim=(-3,-2,-1)) psf = psf.real # circularly shift ksize = size[-1] centre = ksize//2 + 1 #print(psf.type()) ker[:, :, (centre-1):, (centre-1):] = psf[:, :, :centre, :centre]#.mean(dim=1, keepdim=True) ker[:, :, (centre-1):, :(centre-1)] = psf[:, :, :centre, -(centre-1):]#.mean(dim=1, keepdim=True) ker[:, :, :(centre-1), (centre-1):] = psf[:, :, -(centre-1):, :centre]#.mean(dim=1, keepdim=True) ker[:, :, :(centre-1), :(centre-1)] = psf[:, :, -(centre-1):, -(centre-1):]#.mean(dim=1, keepdim=True) return ker假设输入的ker是维度为5的张量，这段代码中哪句话会出现UserWarning: Casting complex values to real discards the imaginary part这种数据不匹配的情况

这段代码中，第3行的 `psf = torch.fft.ifft2(torch.complex(otf[...,0],otf[...,1]), dim=(-3,-2,-1))` 会出现 `UserWarning: Casting complex values to real discards the imaginary part` 这种数据不匹配的情况...

用维纳滤波复原运动模糊图像代码

blurred = cv2.filter2D(img, -1, k) var_n = np.mean((img - blurred) ** 2) var_k = k_size ** 2 * var_n / np.var(k) # 维纳滤波 psf = np.ones((k_size, k_size), dtype=np.float32) / k_size ** 2 fft_...

import numpy as npimport cv2# 读取图像img = cv2.imread('lena.png', 0)# 添加高斯噪声mean = 0var = 0.1sigma = var 0.5noise = np.random.normal(mean, sigma, img.shape)noisy_img = img + noise# 定义维纳滤波器函数def wiener_filter(img, psf, K=0.01): # 计算傅里叶变换 img_fft = np.fft.fft2(img) psf_fft = np.fft.fft2(psf) # 计算功率谱 img_power = np.abs(img_fft) 2 psf_power = np.abs(psf_fft) ** 2 # 计算信噪比 snr = img_power / (psf_power + K) # 计算滤波器 result_fft = img_fft * snr / psf_fft result = np.fft.ifft2(result_fft) # 返回滤波结果 return np.abs(result)# 定义维纳滤波器的卷积核kernel_size = 3kernel = np.ones((kernel_size, kernel_size)) / kernel_size ** 2# 计算图像的自相关函数acf = cv2.calcHist([img], [0], None, [256], [0, 256])# 计算维纳滤波器的卷积核gamma = 0.1alpha = 0.5beta = 1 - alpha - gammapsf = np.zeros((kernel_size, kernel_size))for i in range(kernel_size): for j in range(kernel_size): i_shift = i - kernel_size // 2 j_shift = j - kernel_size // 2 psf[i, j] = np.exp(-np.pi * ((i_shift 2 + j_shift 2) / (2 * alpha ** 2))) * np.cos(2 * np.pi * (i_shift + j_shift) / (2 * beta))psf = psf / np.sum(psf)# 对带噪声图像进行维纳滤波filtered_img = wiener_filter(noisy_img, psf)# 显示结果cv2.imshow('Original Image', img)cv2.imshow('Noisy Image', noisy_img)cv2.imshow('Filtered Image', filtered_img)cv2.waitKey(0)cv2.destroyAllWindows()这段代码报错为Traceback (most recent call last): File "<input>", line 1, in <module> File "D:\Pycharm\PyCharm 2020.3.5\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 197, in runfile pydev_imports.execfile(filename, global_vars, local_vars) # execute the script File "D:\Pycharm\PyCharm 2020.3.5\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile exec(compile(contents+"\n", file, 'exec'), glob, loc) File "E:/Python_project/class_/weinalvboqi.py", line 54, in <module> filtered_img = wiener_filter(noisy_img, psf) File "E:/Python_project/class_/weinalvboqi.py", line 25, in wiener_filter snr = img_power / (psf_power + K) ValueError: operands could not be broadcast together with shapes (1024,2800) (3,3)什么意思，如何修改

这个错误的意思是图像的噪声卷积核和维纳滤波器的卷积核形状不匹配，导致无法进行计算。可以尝试将维纳滤波器的卷积核的大小调整为与图像的噪声卷积核相同的大小，或者将图像的噪声卷积核的大小调整为与维纳滤波器的...

def UNet_wiener(height, width, initial_psf, initial_K, encoding_cs=[24, 64, 128, 256, 512, 1024,], center_cs=1024, decoding_cs=[512, 256, 128, 64, 24, 24], skip_connections=[True, True, True, True, True, True]):

编码路径包含多个卷积层，每个卷积层都包含卷积、批归一化和激活函数。...此外，该网络还包含一个Wiener滤波层，用于估计图像的PSF和噪声水平。整个网络的输出是重建的图像。该网络可用于图像去模糊等任务中。

FCYFCY12

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用维纳滤波复原运动模糊图像代码

def UNet_wiener(height, width, initial_psf, initial_K, encoding_cs=[24, 64, 128, 256, 512, 1024,], center_cs=1024, decoding_cs=[512, 256, 128, 64, 24, 24], skip_connections=[True, True, True, True, True, True]):

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