1. A phase unwrapping method for creating a countermeasure network based on a hopping connection scheme, comprising the steps of:

s1, establishing a generating countermeasure network, wherein the generating countermeasure network comprises a generating model and a countermeasure model; the generative model comprises an encoder and a decoder; the encoder is in jumping connection with the decoder;

s2, acquiring a training set to train the generated countermeasure network, and finishing the training when the result of the loss function of the generated countermeasure network is converged;

s3, inputting the truncated phase diagram to be analyzed into the generation countermeasure network trained in the step S2, and outputting the generated phase diagram.

2. A phase unwrapping method as recited in claim 1, wherein said encoder includes n down-sampling modules, said n down-sampling modules being sequentially connected; the decoder comprises n up-sampling modules which are sequentially connected; each down-sampling module in the encoder is skip-connected to a corresponding level of up-sampling modules in the decoder.

3. A phase unwrapping method as recited in claim 2, wherein BEF blocks are connected between the upsampling block of the decoder layer and the downsampling block of the encoder layer, and the BEF blocks transform the feature map output from the downsampling block to a frequency domain and extract high and low frequency portions of the feature map, and inject the high and low frequency portions into the upsampling blocks of the corresponding level.

4. A phase unwrapping method as recited in claim 3, wherein a first downsampling block of said encoder includes a convolutional layer; the 2 nd to the n-1 th down-sampling modules of the encoder comprise a linear rectifying layer, a convolution layer and a normalization layer which are connected in sequence; the nth down-sampling module of the encoder comprises a linear rectifying layer and a convolution layer;

the feature map in each up-sampling module of the decoder is in jump connection with the feature map with the same size in the corresponding layer of the encoder; each of the n upsampling modules comprises: the three up-sampling modules at the bottom layer further comprise a dropout layer for preventing overfitting, the up-sampling module at the top layer replaces the normalization layer with an activation function, and a generated phase diagram is output.

5. A phase unwrapping method as recited in claim 3, wherein the confrontation model includes m down-sampling modules, wherein the first m-1 down-sampling modules sequentially increase the number of feature channels while reducing the feature size, and the last down-sampling module decreases the number of channels to 1 while reducing the feature size, and outputs the probability matrix.

6. A phase unwrapping method as recited in claim 4, wherein said encoder includes 8 down-sampling modules, said 8 down-sampling modules being connected in series for changing the feature map; the decoder comprises 8 up-sampling modules, and the 8 up-sampling modules are connected in sequence; each down-sampling module in the encoder is skip-connected to a corresponding level of up-sampling modules in the decoder.

7. The phase unwrapping method according to claim 6, wherein a first downsampling module of the encoder includes a Conv 4 x 4 convolution layer for narrowing an image width and increasing a number of channels of the feature map, second to seventh downsampling modules include a LeakReLU layer, a Conv 4 x 4 layer, and a BatchNorm layer connected in sequence, the LeakReLU layer is for linear rectification, the BatchNorm layer is for normalization, and an eighth downsampling module includes a LeakReLU layer and a Conv 4 x 4 layer connected in sequence;

the first to third up-sampling modules of the decoder respectively comprise a Relu layer, a Conv Transpose 4 x 4 layer, a BatchNorm layer and a Dropout layer which are connected in sequence; wherein the Conv Transpose 4 x 4 layer is used for deconvolution, the Dropout layer is used to prevent overfitting, the Relu layer is used for linear rectification; the fourth to seventh up-sampling modules comprise a Relu layer, a Conv Transpose 4 x 4 layer and a BatchNorm layer which are connected in sequence; the eighth up-sampling module comprises a Relu layer, a ConvTranspose 4 x 4 layer and a Tanh layer which are connected in sequence, wherein the Tanh layer is an activation function and is used for outputting a generated phase diagram.

8. A three-dimensional reconstruction method, characterized in that the generated phase map is obtained by the phase unwrapping method according to any one of claims 1 to 7, and three-dimensional reconstruction is performed by the generated phase map.

9. A readable storage medium having stored thereon a computer program, wherein the program is executed by a processor to implement a phase unwrapping method for generating a countermeasure network based on a jump-connect form as recited in any one of claims 1 to 7.

10. A phase unwrapping device that generates a countermeasure network based on a jump-connect, comprising at least one processor, and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the phase unwrapping method of any one of claims 1-7.

Background

In recent years, three-dimensional reconstruction has been a hot topic in machine vision, and is widely applied to the fields of machine intelligence, automatic driving, virtual reality, three-dimensional manufacturing, 3D printing and the like. In a target comprising a large-area weak texture region and a scene with high modeling precision requirement, the effect of three-dimensional reconstruction by using the traditional passive stereo matching is not ideal. For this reason, adding projection "feature information" to the object to be measured is generally used in order to improve the three-dimensional modeling accuracy of the weak texture region. The currently common projection method mainly includes fringe projection and speckle projection, wherein fringe projection three-dimensional imaging has the advantages of non-contact type, high universality, high precision, high speed and the like, and is a mainstream three-dimensional sensing technology at present. The fringe projection imaging is to project the fringe structure light to the surface of a target to be measured, synchronously adopt a camera to collect a deformation image modulated by the surface of a curved surface to be measured, calculate phase information from the deformation image, and finally restore three-dimensional information. However, the spatial distribution of the phase calculated from the fringes of the deformed image is truncated, and there are phase jumps somewhere. The real continuous phase can be calculated only by unfolding the phase and determining the correct corresponding level of the stripes, and the surface three-dimensional information of the target to be measured can be obtained through the matched three-dimensional coordinate conversion. The problem of phase unwrapping is complex, and the accuracy of phase unwrapping has a great influence on the measurement accuracy of the whole measurement system.

The traditional phase unwrapping mainly adopts two modes of spatial phase unwrapping and temporal phase unwrapping, wherein the three-dimensional measurement method based on the spatial phase unwrapping has complex steps and more time consumption, and particularly, when an unsmooth continuous surface, a complex surface, an undersampled area or interference noise exists, the phase unwrapping is difficult and large errors are easily caused. A three-dimensional measurement system based on time phase expansion needs to project and shoot a plurality of fringe patterns of an object to be measured under a static condition for cooperative processing, and then a continuous phase can be calculated. In recent years, a few scholars begin to pay attention to a phase unwrapping method based on a neural network, but a classification method is mostly used, a large amount of phase level information needs to be labeled, and particularly when the number of stripe classifications is large, labeling work is cumbersome, the classification time is long, and a good effect is difficult to achieve. Meanwhile, because the face shape of the face is complex, and areas such as space isolation, shadow, phase jump and the like exist, the phase expansion research corresponding to the face shape is less, a phase expansion method which gives consideration to precision and efficiency is lacked, important information is lost when the face is processed, and the phase expansion effect is poor. For example, publication No. CN 109448083a discloses a method for generating a face animation from a single image, but the neural network used in the method has a problem that the gradient disappears when the number of network layers is large, and is not favorable for the back propagation of the gradient.

Disclosure of Invention

The present invention is directed to overcome the above-mentioned deficiencies in the prior art, and to provide a generating countermeasure network with good gradient back propagation characteristics while maintaining accuracy and efficiency, and a generated phase diagram is obtained through the network.

In order to achieve the above purpose, the invention provides the following technical scheme:

the invention provides a phase unwrapping method for generating a countermeasure network based on a jump connection mode, which comprises the following steps

S1, establishing a generating countermeasure network, wherein the generating countermeasure network comprises a generating model and a countermeasure model; the generative model comprises an encoder and a decoder; the encoder is in jumping connection with the decoder;

s2, acquiring a training set to train the generated countermeasure network, and finishing the training when the result of the loss function of the generated countermeasure network is converged;

s3, inputting the truncated phase diagram to be analyzed into the generation countermeasure network trained in the step S2, and outputting the generated phase diagram.

Further, the encoder comprises n down-sampling modules, which are connected in sequence; the decoder comprises n upsampling modules, which are connected in sequence; each down-sampling module in the encoder is skip-connected to a corresponding level of up-sampling modules in the decoder.

Preferably, a BEF module is connected between the upsampling module and the downsampling module of the encoder surface layer, and the BEF module transforms the feature map output by the downsampling module to the frequency domain, extracts the high-frequency and low-frequency parts, and injects the high-frequency and low-frequency parts into the upsampling module of the corresponding level.

Further, the first downsampling module of the encoder includes a convolutional layer; the 2 nd to the n-1 th down-sampling modules of the encoder comprise a linear rectifying layer, a convolution layer and a normalization layer which are connected in sequence; the nth down-sampling module of the encoder comprises a linear rectifying layer and a convolution layer;

the feature diagram in each up-sampling module of the decoder is in jump connection with the feature diagram with the same size in the corresponding layer of the encoder, the feature diagram passes through a linear rectification layer, an anti-convolution layer and a normalization layer, the three up-sampling modules at the bottom layer further comprise a dropout layer to prevent overfitting, the up-sampling module at the top layer replaces the normalization layer with an activation function, and a phase diagram is output and generated.

Furthermore, the confrontation model comprises m down-sampling modules, wherein the first m-1 down-sampling modules sequentially increase the number of characteristic channels and reduce the size of the characteristic diagram, and the last down-sampling module reduces the number of the channels to 1 and reduces the size of the characteristic diagram and outputs a probability matrix.

Further, the encoder comprises 8 down-sampling modules, and the 8 down-sampling modules are connected in sequence and used for changing the feature map; the decoder comprises 8 up-sampling modules, and the 8 up-sampling modules are connected in sequence; each down-sampling module in the encoder is skip-connected to a corresponding level of up-sampling modules in the decoder.

Further, the first down-sampling module of the encoder comprises a Conv 4 × 4 convolution layer for reducing the width and height of the image and increasing the number of channels of the feature map, the second to seventh down-sampling modules comprise a LeakReLU layer, a Conv 4 × 4 layer and a BatchNorm layer which are connected in sequence, the LeakReLU layer is used for linear rectification, the BatchNorm layer is used for standardization, and the eighth down-sampling module comprises a LeakReLU layer and a Conv 4 × 4 layer which are connected in sequence.

The first to third up-sampling modules of the decoder respectively comprise a Relu layer, a Conv Transpose 4 x 4 layer, a BatchNorm layer and a Dropout layer which are connected in sequence; wherein the Conv Transpose 4 x 4 layer is used for deconvolution, the Dropout layer is used to prevent overfitting, the Relu layer is used for linear rectification; the fourth to seventh up-sampling modules comprise a Relu layer, a Conv Transpose 4 x 4 layer and a BatchNorm layer which are connected in sequence; the eighth up-sampling module comprises a Relu layer, a ConvTranspose 4 x 4 layer and a Tanh layer which are connected in sequence, wherein the Tanh layer is an activation function and is used for outputting a generated phase diagram.

Further, the countermeasure model comprises 5 down-sampling modules, wherein the first down-sampling module comprises a Conv 4 layer and a LeakyReLU layer which are connected in sequence; the second to the fourth down-sampling modules respectively comprise a Conv 4 layer, a Batch Norm layer and a LeakyReLU layer which are connected in sequence; the fifth down-sampling module comprises a Conv 4 x 4 layer and a Sigmoid layer which are connected in sequence, wherein the Sigmoid layer is an activation function and is used for outputting a 30 x 30 probability matrix.

Meanwhile, the invention also provides a three-dimensional reconstruction method, which comprises the following steps: and obtaining a generated phase diagram by adopting the phase unwrapping method, and performing three-dimensional reconstruction through the generated phase diagram.

The invention also provides a readable storage medium, which stores a computer program, and the program is executed by a processor to realize the phase unwrapping method based on the jump-connection type generation countermeasure network.

Meanwhile, the invention also provides a phase unwrapping device for generating a countermeasure network based on a jump connection mode, which comprises at least one processor and a memory, wherein the memory is in communication connection with the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the phase unwrapping method described above.

Compared with the prior art, the invention has the beneficial effects that:

1. the neural network learns a mapping relation, and when the space is isolated, errors are not accumulated and transferred, namely the error of one point does not influence other points; the traditional phase expansion method is sequentially expanded, errors, particularly errors of shadow and phase jump areas, are accumulated and transmitted in the calculation process, and errors of one point can affect each peripheral point to cause error transmission and diffusion; meanwhile, the invention adopts a jump connection type to generate the confrontation network, and connects the encoder and the decoder in a jump way, thereby being beneficial to the reverse propagation of gradient, accelerating the training process of generating the confrontation network, reducing the spatial information loss brought in the down-sampling process, simultaneously leading the characteristic diagram of the up-sampling recovery to contain more low-level semantic information, improving the fineness of the result, leading the decoder to have more image detail information and further recovering better images.

2. The BEF module is connected between the surface layer up-sampling module and the surface layer down-sampling module in the generated model, so that the low-frequency part and the high-frequency part in the picture of the up-sampling module can be transmitted to the up-sampling module, and the quality of the picture generated by the generated model is further enhanced; particularly, when the pictures with more glasses and shadows are processed, the pictures generated by the generated model have more detail information, and the generated phase expansion pictures are more beneficial to face recognition in the later period.

3. Compared with the traditional three-frequency four-step phase shift method (requiring three sets of 12 fringe images projected), the method introduces the idea of image domain conversion by a neural network, directly obtains a continuously generated phase image from a truncated phase image, greatly reduces the number of required projected fringe images (only 3 fringe images need to be projected), reduces the complexity of the system and improves the running speed of the system; meanwhile, the error generated by the movement of the object to be detected is reduced, and the high-speed high-precision three-dimensional imaging of a dynamic or static target can be met; compared with the traditional space phase unwrapping method, the method can reduce error diffusion and error accumulation, and is less influenced by areas such as phase jump.

Description of the drawings:

fig. 1 is a flowchart of a phase unwrapping method for generating a countermeasure network based on a hopping connection scheme according to an exemplary embodiment of the present invention;

fig. 2 is an overall structural view of a hopping connection type generation countermeasure network proposed by an exemplary embodiment of the present invention;

FIG. 3 is a diagram of a generative model architecture for a hopping-connected generative countermeasure network as set forth in an exemplary embodiment of the invention;

FIG. 4 is a diagram of a countermeasure model architecture for a jump-to-generate countermeasure network in accordance with an exemplary embodiment of the present invention;

FIG. 5 is a SmoothL1Loss drop curve for a 100 round of training of a hopping-connected generation countermeasure network as proposed by an exemplary embodiment of the present invention;

fig. 6 is a diagram comparing a phase generation diagram generated for a jump-connection type generation countermeasure network proposed by an exemplary embodiment of the present invention with a truncated phase diagram and a phase diagram true value.

Fig. 7 is a comparison of generated phase maps using BEF modules versus unused BEF modules in an exemplary embodiment of the invention.

Detailed Description

The present invention will be described in further detail with reference to test examples and specific embodiments. It should be understood that the scope of the above-described subject matter is not limited to the following examples, and any techniques implemented based on the disclosure of the present invention are within the scope of the present invention.

Example 1

FIG. 1 is a flow chart of a method for generating a phase unwrapping based on a hopping connection type countermeasure network, which is proposed by an exemplary embodiment of the present invention, and includes

S1, establishing a generating countermeasure network, wherein the generating countermeasure network comprises a generating model and a countermeasure model; the generative model comprises an encoder and a decoder; the encoder is in jumping connection with the decoder;

preferably, the encoder may include n down-sampling modules, which are sequentially connected; the decoder comprises n upsampling modules, which are connected in sequence; each down-sampling module in the encoder is skip-connected to a corresponding level of up-sampling modules in the decoder.

Preferably, the first down-sampling module of the encoder comprises a convolutional layer; the 2 nd to the n-1 th down-sampling modules of the encoder comprise a linear rectifying layer, a convolution layer and a normalization layer which are connected in sequence; the nth down-sampling module of the encoder comprises a linear rectifying layer and a convolution layer; the feature diagram in each up-sampling module of the decoder is in jump connection with the feature diagram with the same size in the corresponding layer of the encoder, the feature diagram passes through a linear rectification layer, an anti-convolution layer and a normalization layer, the three up-sampling modules at the bottom layer further comprise a dropout layer to prevent overfitting, the up-sampling module at the top layer replaces the normalization layer with an activation function, and a phase diagram is output and generated.

In order to make the generated phase unwrapped map have more effective information and enhance the information characteristics (such as phase discontinuity, shadow, jumping point, etc.) at the key region, a BEF module may be connected between the up-sampling module and the down-sampling module of the encoder surface layer. The BEF module can extract the low-frequency part containing the picture basic information and the high-frequency information containing the information at the key area of the picture from the up-sampling module and then inject the low-frequency part and the high-frequency information into the down-sampling module. The BEF module can effectively enhance the effective content in the generated phase diagram generated by the generated model and weaken the noise which interferes the image.

Preferably, the confrontation model comprises m down-sampling modules, wherein the first m-1 down-sampling modules sequentially increase the number of characteristic channels and reduce the size of the characteristic diagram, and the last down-sampling module reduces the number of channels to 1 and reduces the size of the characteristic diagram and outputs a probability matrix.

It should be noted that the number n of the up-sampling modules and the down-sampling modules of the generation model and the number m of the counter model down-sampling modules can be flexibly set according to the size of the actual training picture and/or the size of the band resolution picture, for example, when a picture with 256 pixels by three channels (i.e. a picture with 3 pixels by 256 pixels) is processed, n-8, m-5, or n-7, m-5 can be selected; when processing the pictures of 3 × 512, n may be 8, m may be 5, or n may be 9, m may be 6, etc.; the specific setting condition can be flexibly set according to the calculation power, the precision requirement and the processing time requirement of an actual computer.

S2, acquiring a training set to train the generated countermeasure network, and finishing the training when the result of the loss function of the generated countermeasure network is converged;

s3, inputting the truncated phase diagram to be analyzed into the generation countermeasure network trained in the step S2, and outputting the generated phase diagram.

Meanwhile, the embodiment also provides a three-dimensional reconstruction method, which comprises the following steps: and obtaining a generated phase diagram by adopting the phase unwrapping method, and performing three-dimensional reconstruction through the generated phase diagram.

Meanwhile, the embodiment also provides a readable storage medium, on which a computer program is stored, the program being executed by a processor to implement the above-mentioned phase unwrapping method based on the jump-connection type generation countermeasure network.

Meanwhile, the embodiment also provides a phase unwrapping device for generating a countermeasure network based on a jump connection mode, which comprises at least one processor and a memory, wherein the memory is in communication connection with the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the phase unwrapping method described above.

Example 2

On the basis of embodiment 1, the present embodiment employs a generative model as shown in fig. 2, and an antagonistic model as shown in fig. 3.

Fig. 2 is a diagram showing a generation model structure of a hopping connection type generation countermeasure network proposed in the present embodiment;

eight down-sampling modules of an encoder in the generated model are sequentially connected, each down-sampling module increases the number of channels of the feature map by utilizing the convolution layer, simultaneously reduces the width and the height of the feature map to one half respectively, and the width and the height of the feature map sequentially output by the eight down-sampling modules in the encoder of the generated model are respectively as follows: 1/2H 1/2W, 1/4H 1/4W, 1/8H 1/8W, 1/16H 1/16W, 1/32H 1/32W, 1/64H 1/64W, 1/128H 1/128W, 1/256H 1/256W, the first down-sampling module includes 1 convolution layer of 4 x 4, the width and height of the image is reduced and the channel number of the characteristic diagram is increased, the second to seventh down-sampling modules are composed of the following layers which are connected in sequence, firstly, the linear rectification layer inputs the value amplitude smaller than 0 to zero, the value larger than 0 is not changed, the network training can be faster, the nonlinearity of the network is increased, meanwhile, the gradient is prevented from disappearing, and the operation memory can be saved because the original value is calculated; secondly, 1 convolution layer of 4 x 4 is used for reducing the width and the height of an image and increasing the channel number of a characteristic image; finally, the data were normalized using the BatchNorm2d layer to prevent the disappearance of the gradient and the explosion of the gradient. In the eighth downsampling block, only the linear rectifying layer and the convolutional layer are used, and the BatchNorm2d layer is removed.

Eight up-sampling modules of a decoder in the generated model are connected in sequence, each up-sampling module utilizes the deconvolution layer to recover the channel number of the feature map and the size of the feature map from bottom to top in sequence, the recovered feature map is output, and after up-sampling of each up-sampling module, the resolution of the output feature map is recovered to 1/256H 1/256W, 1/128H 1/128W, 1/64H 1/64W, 1/32H 1/32W, 1/16H 1/16W, 1/8H 1/8W, 1/4H 1/4W, 1/2H 1/2W and H W in sequence; the feature map in each up-sampling module is firstly connected with the feature map with the same size in the corresponding layer of the encoder in a jumping mode, the feature map passes through a linear rectification layer and a 4 x 4 deconvolution layer and is standardized again, a dropout is added to the three up-sampling modules at the bottom to prevent overfitting, the normalization is replaced by an activation function Tanh through the up-sampling module at the top, and finally the phase map is output and generated.

The countermeasure model and the generation model are opposite in target, the countermeasure model distinguishes true values of the phase diagram and generates the phase diagram as much as possible, the generation model generates continuous two-bit generation diagrams similar to the continuous phase development diagram as much as possible, and the two diagrams are in mutual countermeasure in the training process so as to urge the optimization of the two parties. The confrontation model comprises five down-sampling modules which are connected in sequence, the first four down-sampling modules sequentially increase the number of channels of the feature map and reduce the size of the feature map, the last down-sampling module reduces the number of channels to one and reduces the size of the feature map, and finally a 30 x 30 probability matrix is output. The widths and heights of the feature maps sequentially output by the 5 down-sampling modules in the confrontation model are respectively as follows: 1/2H 1/2W, 1/4H 1/4W, 1/8H 1/8W, (1/8H-1) 1/8W-1 and (1/8H-2) 1/8W-2), the first down-sampling module is composed of a 4 x 4 convolution layer and a linear rectification layer, the second to the fourth down-sampling modules are composed of a convolution layer, a normalization layer and a linear rectification layer which are connected in sequence, wherein, a convolution function conv2d of 4 x 4 is used for changing the number of channels and the size of the feature map; and normalizing the data with an nn.batchnorm2d layer to prevent gradient disappearance and gradient explosion; the linear rectification unit enables the network to train faster, increases the nonlinearity of the network, prevents the gradient from disappearing, saves the operation memory and improves the operation speed because the linear rectification unit calculates the original value. And after the last downsampling module passes through the convolution layer, the final result is directly output through an activation function.

Preferably, a BEF module may be further connected between the upsampling module and the downsampling module of the encoder surface layer, and the BEF module may be a band-stop filter, and transforms the feature map output by the downsampling module to the frequency domain, extracts the high-frequency and low-frequency portions, and injects the high-frequency and low-frequency portions into the upsampling module of the corresponding level. The low-frequency part of the feature picture after the convolution layer of the downsampling module carries basic information of the picture, the high-frequency part carries key point information (shadow and jumping points) of the picture, and the generated phase diagram is convenient to process at a later stage in order to strengthen the basic information and the key point information in the generated phase diagram. The BEF module has a stopband range of 1/8-7/8 of the whole frequency interval, the stopband internal gain is 0, the stopband external gain is 0.1, and the stopband external gain can be continuously reduced along with the continuous depth of the encoder layers in which the BEF module is located.

The generation of the countermeasure network can be trained by adopting the following method:

1) the phase shift fringe image is projected to a detected human face object, the deformation fringe is modulated by the surface type of the detected human face object, the phase shift fringe is synchronously shot by a camera, then three fringe images with different phase shifts are obtained by moving the phase shift fringe, a cut-off phase diagram is calculated, a traditional three-frequency four-step phase shift method is adopted to obtain a true value (regarded as a ground route) of the phase diagram of the same detected human face object, the true value and the cut-off phase diagram are used as a pair of paired images, and the two paired images form a training set.

Projecting the phase shift stripe to the surface of the detected target face, wherein the phase shift stripe uniformly moves for N times within a 2 pi period, N is more than or equal to 3, and the phase shift amount of each time is 2 pi/N; the pattern of any set of N-step phase-shifted stripes is represented as:

in the formula I_i(x, y) represents the light intensity in the fringe pattern as a known quantity, a (x, y) represents the background light intensity in the fringe pattern, b (x, y) represents the modulated light intensity in the fringe pattern, i is 1, …, N represents the serial number of the ith phase-shift fringe,for phase shifting, f₀The frequency of the sinusoidal fringes. The above formula has three unknowns, and at least three independent equations are needed, so that at least three phase-shifted images are needed to solve

Based on a phase shift method, when the fringe image is converted into a truncated phase image, the truncated phase calculation formula is as follows:

wherein, i is 1, …, N represents the phase shift of the ith step (N ≧ 3). I is_i(x, y) represents the light intensity of the photographed ith deformed fringe pattern.

2) And (3) repeating the step 1) aiming at a plurality of different human face types to generate a plurality of pairs of truncated phase diagrams and phase diagram truth values.

3) And inputting the truncated phase map in the first pair of paired images into a generation model of the generation countermeasure network, and outputting a corresponding generated phase map.

4) Combining the truncated phase diagram in the first pair of paired images with the phase diagram truth value in the first pair of paired images, inputting the combined result into a countermeasure model, and calculating BCELoss of the output and a true label (all 1) of the combined result, so that a discriminator can discriminate the phase diagram truth value as true as possible; the truncated phase diagram of the first paired image and the corresponding generated phase diagram are merged and input into the countermeasure model, and BCELoss of the output and false labels (all 0) are calculated, so that a discriminator can discriminate the generated phase diagram as false as possible, and the countermeasure model is trained by taking the average value of the two BCELoss as a loss function errD of a discrimination network.

5) The truncated phase map of the first pair of paired images and the corresponding generated phase map output by the generator are combined and input to the countermeasure model, and the output and the BCELoss of the same-size true labels (all 1) are calculated as errG1 in order for the generator to make the generated phase map be determined as true as possible, so as to make the phase map "false-true" as possible.

The SmoothL1Loss and the simLoss between the phase diagram truth value and the corresponding generated phase diagram are calculated, the latter is the difference value between the structural similarity index of the two images and 1, and the errG1, the SmoothL1Loss and the simLoss are weighted and then summed to be used as the Loss function of the generator.

This example trains the generation model using 0.5 × errG1+20 × SmoothL1Loss +5 × Simm _ Loss as the Loss function errG of the generator.

6) After the training of the first pair of paired images is completed, the next pair of paired images continues to be loaded for training, and the training of the confrontation model and the generation model are performed alternately as above until the results of the Loss functions converge (errD and errG1 are approximately equal, and SmoothL1Loss and simmLoss are approximately 0).

7) And (3) training the generation model and the countermeasure model of the countermeasure network once each pair of the truncated phase diagram and the phase diagram true value is loaded, wherein the training is called a round, and the training is finished when the result of the loss function is converged. Referring to fig. 5, the proposed hopping-connected generation for the exemplary embodiment of the present invention generates a droop curve of SmoothL1Loss against the network training 100 rounds, and it can be seen that the value of SmoothL1Loss is continuously reduced to near zero as the training process, i.e. the difference between the generated phase diagram generated by the generation network and the true value of the phase diagram is continuously reduced. In training the generated countermeasure network proposed in the present exemplary embodiment, in order to make the structure of the generated countermeasure network more stable, the present embodiment additionally performs about 900 rounds of training after the SmoothL1Loss converges, and a total of about 1000 rounds of training.

After the training of the generated countermeasure network is finished, the truncated phase diagram can be input into a trained generation model of the jump connection type generated countermeasure network to obtain a generated phase diagram; and calculating the three-dimensional surface shape depth by the generated phase diagram to obtain the three-dimensional point cloud data of the detected human face object.

The truncated phase diagram is input into the trained skip-connect type generated countermeasure network, and the generated phase diagram is shown in fig. 6, where (a) in fig. 6 is the truncated phase diagram of the input of the generated countermeasure network proposed in this embodiment, (b) in fig. 6 is the generated phase diagram of the output of the generated countermeasure network proposed in this embodiment, and (c) in fig. 6 is the true value of the phase diagram obtained by the conventional method. It can be seen from the figure that the generated phase diagram has high Structural Similarity (SSIM) with the true value of the phase diagram, and the generated phase diagram has higher precision and better detail display.

Example 3

On the basis of the embodiment 2, in order to verify the function of the BEF module in generating the countermeasure network, the embodiment respectively adopts the same training set to train two kinds of generated countermeasure networks with the BEF module and without the BEF module, which have the same parameter configuration; the same truncated phase map is input after the training is finished, and the comparison of the generated phase maps is shown in fig. 7. It can be seen that the generated phase map using the BEF module has more details in the phase jump region (e.g. nostril, shadow region due to eye occlusion) of the image, while the generated phase map without the BEF module has poor phase unwrapping effect due to the lack of details in the phase jump region; and analyzing the phase map errors of the generated phase map obtained by using the BEF module and the generated phase map obtained by not using the BEF module through software, wherein the phase map errors of the generated phase map obtained by using the BEF module are greatly reduced compared with the phase map errors of the generated phase map obtained by not using the BEF module.

Those skilled in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media that can store program codes, such as a removable Memory device, a Read Only Memory (ROM), a magnetic disk, or an optical disk.

When the integrated unit of the present invention is implemented in the form of a software functional unit and sold or used as a separate product, it may also be stored in a computer-readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a magnetic or optical disk, or other various media that can store program code.

The foregoing is merely a detailed description of specific embodiments of the invention and is not intended to limit the invention. Various alterations, modifications and improvements will occur to those skilled in the art without departing from the spirit and scope of the invention.