Method and device for processing a three-dimensional image for inspecting a part and method for inspecting a part using the processing method

Abstract

One aspect of the invention relates to a method for processing a three-dimensional image associated with a plurality of cross-sectional images and comprising a part to be checked, the method comprising, for each associated cross-sectional image, the following steps: -binarising (E311) the relevant cross-sectional image to obtain a mask formed of a plurality of pixels corresponding to the pixels of the cross-sectional image relating to the cross-section of the part;- determining (E3121, E3122), from the mask, a surface representing this cross-section; - determining (E3123) a parametric function describing this surface; - defining a constant displacement field on each column of pixels, on the basis of the column co-ordinates of the pixels, so as to apply a flattening transformation to the surface represented by the parametric function; - rectifying (E314) the cross-section of the part in the relevant cross-sectional image by applying the determined displacement field to the pixels of the image.

Description

[0001]DESCRIPTION TITLE: METHOD AND DEVICE FOR PROCESSING A THREE-DIMENSIONAL IMAGE FOR INSPECTING A PART AND METHOD FOR INSPECTING A PART USING THE PROCESSING METHOD TECHNICAL FIELD OF THE INVENTION The technical field of the invention is that of non-destructive testing of industrial parts using three-dimensional tomographic images, and more particularly that of non-destructive testing of parts made of a woven composite material. The present invention relates to a method for processing a three-dimensional tomographic image within which a part is defined,and in particular a treatment method comprising a straightening. The invention also relates to an associated control method. The invention also relates to an associated device for processing a three-dimensional tomographic image within which a part is defined. Finally, it relates to an associated computer program. The present invention finds an advantageous application for the visual control of an aeronautical part made of three-dimensional woven composite material. TECHNOLOGICAL BACKGROUND OF THE INVENTION Aeronautical parts made of three-dimensional woven composite material, for example engine fan blades, are critical parts which must be systematically controlled in their entirety to guarantee their conformity and / or their material health. In particular, the weave must be systematically analyzed in the volume of the composite material,to ensure that it does not contain defects or anomalies. Weaving refers to the woven textile framework of the three-dimensional woven composite material, called reinforcement. The reinforcement is composed of threads, or strands, woven according to a defined weave topology over several weaving layers. In each weaving layer, the strands are woven in two main orthogonal directions, called the warp direction and the weft direction. The weaving layers are superimposed in a third direction corresponding to the thickness of the textile framework and called the reinforcement direction. These three directions of warp, weft and reinforcement preferably form an orthogonal reference frame, called the part reference frame. Currently,The analysis of the weave of the part is carried out non-destructively from a tomographic volume obtained by X-ray tomography imaging. The tomographic volume is a three-dimensional image representing the part. The analysis of the weave consists first of extracting from this three-dimensional image stacks (or sequences) of two-dimensional cuts (or sections). In each stack, each two-dimensional cut corresponds to a section of the three-dimensional image along a section plane perpendicular to the same axis of the part's reference frame. The section planes are distinct and successive along this axis of the part. This axis is different for each stack of two-dimensional cuts. For example,the two-dimensional sections of the first stack are perpendicular to the thickness direction while the two-dimensional sections of the second and third stacks are respectively perpendicular to the warp direction and the weft direction. The visual analysis then consists of visually analyzing the stacks of two-dimensional sections. The visual analysis of a given stack consists of manually and successively scrolling through each two-dimensional section of this stack to follow a given strand or a given set of strands in its entirety. This makes it possible to detect a difference in the position of the strands, which would be the sign of a potential anomaly. Some aeronautical parts have a very aerodynamic shape. Thus, fan blades include marked curvatures. The visual analysis then consists of scrolling through the two-dimensional sections of certain stacks several times. For example,the two-dimensional sections of the first stack (which, according to the example given previously, each represent a weaving layer) are displayed in the successive order of the sections and in the reverse order. This multiple scrolling makes it possible to compensate for the fact that certain two-dimensional sections do not give a view of the entire weaving layer due to the curvatures of the part. To avoid having to scroll through the two-dimensional sections several times, it is known from document FR3047339A1 to carry out a pre-processing of the three-dimensional image aimed at erasing, or flattening, the curvatures of the part. This pre-processing is carried out by straightening the part represented in the three-dimensional image corresponding to the tomographic volume. More precisely,a straightened three-dimensional image is reconstructed all around a median surface of the part. The straightening model then consists of unrolling the median surface of the part in order to make it flat. This straightening model has the effect of preserving the length of the median surface of the part. Such straightening is anamorphic: during reconstruction, the regions of the part are not interpolated in the same way depending on their distance from the surface. For example, the regions furthest from the surface, typically the regions located outside the part (or "extrados") and the regions located inside the part ("intrados") are undersampled or oversampled, respectively. As a result, they appear respectively spread or squashed on the straightened three-dimensional image. More generally, due to this unbalanced interpolation,regions of the part in the rectified three-dimensional image may appear blurred or exhibit artifacts. These regions cannot then be effectively inspected. SUMMARY OF THE INVENTION The present invention provides an alternative preprocessing method for improving the quality of the rectified three-dimensional image. More particularly, a first aspect of the invention relates to a method for processing a three-dimensional image for inspecting a part included in the three-dimensional image, the three-dimensional image being associated with a plurality of two-dimensional cross-sectional images, each cross-sectional image corresponding to a section of the three-dimensional image along a section plane perpendicular to a main axis of said part, the section planes being distinct, each cross-sectional image comprising a section of the part, each cross-sectional image being formed of a plurality of pixels,the method comprising a rectification processing comprising the following steps: - Processing each transverse image to obtain a plurality of rectified transverse images, each rectified transverse image comprising a rectified section of the part, said processing comprising, for each transverse image, the following steps: - Generating, from the transverse image concerned, a binary image to obtain a mask formed of a plurality of pixels corresponding to the pixels of the transverse image relating to the section of the part, the plurality of pixels of the mask being arranged according to rows of pixels and columns of pixels, each pixel being defined by a row coordinate and a column coordinate, - Determining, from the mask, a surface representing the section of the part, - Determining a parametric function describing said surface,- Defining a constant displacement field on each pixel column and dependent on the pixel column coordinates so as to apply a flattening transformation to the surface represented by the parametric function, - Straightening the part section in the relevant cross-sectional image by applying the determined displacement field to the pixels of the relevant cross-sectional image, to generate the straightened cross-sectional image associated with the relevant cross-sectional image, - Reconstructing a straightened three-dimensional image containing the straightened part from the straightened cross-sectional images obtained from the raw cross-sectional images. Thanks to the displacement field which defines relative displacement vectors, parallel to each other, the flattening model is based on a simple transformation,consisting of sliding portions of a curved section relative to each other. The portions of the straightened section are thus simply moved (translated). This displacement model has an advantage during the step of reconstructing the three-dimensional image from the straightened transverse images. Indeed, the displacement model is isochoric, that is to say that the volume of the part is preserved during the straightening treatment process. This preservation of the volume makes it possible to limit interpolation errors of the straightened three-dimensional image. The straightened three-dimensional image thus advantageously presents a uniform quality across the entire straightened part. This allows for a reliable and complete inspection of the part. In addition to the characteristics just mentioned in the previous paragraph,the method according to the first aspect of the invention may have one or more complementary characteristics among the following, considered individually or according to all technically possible combinations: - The surface representing the section of the part is determined by carrying out the following steps: - Definition of a predetermined image having the same dimension as the binary image, the predetermined image comprising a plurality of pixels, each pixel having a predetermined value corresponding to the row coordinate of said associated pixel in the predetermined image, - Assignment of a predefined value to each pixel of the mask, said predefined value corresponding to the value of the pixel in the predetermined image having the same row and column coordinates as the pixel of the mask concerned, and - For each column of pixels of the mask,determining a center of said column from the predefined values ​​of the pixels and the number of pixels in said column, and assigning the determined center to each pixel in said column, the centers corresponding to a representation of said surface. - The centers are determined from the result of a matrix product between a matrix representing the predetermined image and a matrix representing the binary image. - The centers are barycenters, each barycenter being determined on the basis of the following expression:∑^ ^^^^,^^∗^^^,^^where:, - ^, ^ are the column and row coordinates of each pixel in the mask, respectively, - ^ ^ ^ ^, ^ ^ is the matrix representing the predetermined image, - ^ ^ ^, ^ ^is the matrix representing the binary image. - The parametric function is composed of a spline. - The application of the displacement field is broken down into a translation operation and / or an interpolation operation. - The processing method comprises a step of determining a projection frame, the steps of defining the parametric function and the displacement field being a function of the determined projection frame so that: - after the step of determining the surface and before the step of determining the parametric function, the surface is determined in the projection frame using a system of frame change equations defined on the basis of an angle parameter, - after the step of determining the displacement field and before the straightening step, the pixels of the transverse image concerned are interpolated in the projection frame to obtain a projected transverse image,the straightening step then being implemented on the projected transverse image. - The angle parameter has a non-zero predetermined value. - The angle parameter is determined between the step of generating the binary image and the step of determining the surface in the projection frame, by carrying out the following steps: - Extraction of the pixels relating to the centers or relating to the section of the part in the binary image, said pixels forming a point cloud, - Identification of a preferred direction of the point cloud in the frame of the binary image, - Determination of the angle parameter from the identified preferred direction. - The preferred direction is identified using a principal component analysis of the point cloud along two principal axes, the axes being perpendicular,the preferred direction being identified by selecting from the eigenvectors produced by the principal component analysis the eigenvector associated with the largest eigenvalue, the angle parameter then being determined from the components of the selected eigenvector and by limiting the value range of said parameter to the range [-45°, +45°]. - The step of determining the angle parameter is implemented in parallel with the step of determining the surface produced from the mask. - The plurality of rectified transverse images is obtained simultaneously, by parallel processing of each transverse image of the plurality of transverse images. - The step of generating the binary image comprises a step of selecting, in the binary image, a subset of pixels representing a region of interest comprising the section of the part,the mask being obtained from the subset of selected pixels. - The step of reconstructing a rectified three-dimensional image comprises a step of smoothing, along the main axis of the part, the rectified three-dimensional image, the smoothing step comprising the following steps: - Determination, from the rectified three-dimensional image, of a smoothing function of the rectified section of each rectified transverse image forming the rectified three-dimensional image, - Determination of a weighting function of the displacement field associated with each rectified transverse image, using the smoothing function and the displacement field used to obtain each rectified transverse image,- Application of the displacement field weighting function to each rectified transverse image to obtain a smoothed three-dimensional image. A second aspect of the invention relates to a method for inspecting a part from a three-dimensional image, comprising the steps of the processing method according to the method which is the subject of the first aspect of the invention to obtain a rectified three-dimensional image, and a step of inspecting the part from the rectified three-dimensional image. The part may be made of a three-dimensional woven composite material. A third aspect of the invention relates to a computer program comprising instructions which, when the program is executed by a computer,lead the latter to implement the steps of the processing method according to the first aspect of the invention. A fourth aspect of the invention relates to a device for processing a three-dimensional image for the inspection of a part included in the three-dimensional image, the three-dimensional image being associated with a plurality of two-dimensional transverse images, each transverse image corresponding to a section of the three-dimensional image along a section plane perpendicular to a main axis of said part, the section planes being distinct, each transverse image comprising a section of the part, each transverse image being formed of a plurality of pixels, the device comprising a processor configured to implement a rectification processing comprising: - Processing each transverse image to obtain a plurality of rectified transverse images,each straightened cross-sectional image comprising a straightened section of the part, said processing comprising, for each cross-sectional image, the following steps: - Generation, from the cross-sectional image concerned, of a binary image to obtain a mask formed of a plurality of pixels corresponding to the pixels of the cross-sectional image relating to the section of the part, the plurality of pixels of the mask being arranged according to rows of pixels and columns of pixels, each pixel being defined by a row coordinate and a column coordinate, - Determination, from the mask, of a surface representing the section of the part, - Determination of a parametric function describing said surface, - Definition of a constant displacement field on each column of pixels and dependent on the column coordinates of the pixels so as to apply a flattening transformation to the surface represented by the parametric function,- Straightening the section of the part in the cross-sectional image concerned by applying the determined displacement field to the pixels of the cross-sectional image concerned, to generate the straightened cross-sectional image associated with the cross-sectional image concerned, and - Reconstruction of a straightened three-dimensional image containing the straightened part from the straightened cross-sectional images. The invention and its various applications will be better understood upon reading the following description and examining the accompanying figures. BRIEF DESCRIPTION OF THE FIGURES The figures are presented for information purposes only and in no way limit the invention. - Figure 1 represents, in schematic form, a perspective view of an aeronautical part, - Figure 2 represents, in the form of a flowchart, an example of a method for processing a three-dimensional image in accordance with the invention, - Figure 3A represents, in schematic form,a first view of an example of a three-dimensional image of the part used by the treatment method, - Figure 3B represents, in schematic form, a second view of an example of a three-dimensional image of the part used by the treatment method, - Figure 3C represents, in schematic form, a third view of an example of a three-dimensional image of the part used by the treatment method, - Figure 4 represents, in schematic form, an example of a raw transverse image obtained from the raw three-dimensional image at the end of the first step of the method shown in Figure 2, - Figure 5 represents, in flowchart form, the main steps of the second step of the method shown in Figure 2, - Figure 6 represents, in flowchart form, a first sub-step of the second step of the method shown in Figure 2, - Figure 7 represents, in schematic form,the first sub-step of the second step of the method shown in Figure 2, - Figure 8 represents, in the form of a flowchart, a variant of implementation of the step shown in Figure 5, - Figure 9A represents an example of a raw longitudinal image, - Figure 9B represents the rectified longitudinal image corresponding to the raw longitudinal image shown in Figure 9A, - Figure 10A represents a first example of a longitudinal image associated with a three-dimensional image rectified by the method shown in Figure 2, - Figure 10B represents a second example of a longitudinal image associated with a three-dimensional image rectified by the method shown in Figure 2, - Figure 10C represents a third example of a longitudinal image associated with a three-dimensional image rectified by the method shown in Figure 2,- Figure 10D represents a fourth example of a longitudinal image associated with a three-dimensional image rectified by the method represented in Figure 2, - Figure 11 represents, in the form of a flowchart, an alternative implementation of the method, - Figure 12 represents, in the form of a flowchart, an alternative implementation mode of the method represented in Figure 2, - Figure 13 represents, in schematic form, a step of the alternative implementation mode of the method represented in Figure 12, - Figure 14A represents, in the form of a flowchart, a first variant of the alternative implementation mode represented in Figure 12, - Figure 14B represents, in the form of a flowchart, a second variant of the alternative implementation mode represented in Figure 12, - Figure 15 represents, in the form of a flowchart, an alternative implementation mode of the method represented in Figure 12, - Figure 16 represents, in schematic form,a device for implementing the method for processing a three-dimensional image according to the invention. DETAILED DESCRIPTION The present invention is placed in the context of the non-destructive testing of an aeronautical part made of a woven composite material, using three-dimensional tomographic images. More particularly, the invention aims to allow rapid and easy visual inspection of the weaving topology of this part. In particular again, the invention aims to provide three-dimensional images, pre-processed by rectification, of better quality than the state of the art, thus making it possible to improve the reliability of the inspection. The invention finds in particular an advantageous application for the inspection of fan blades. For example,the invention finds an advantageous application for the control of the fan blades of LEAP engines for "Leading Edge Aviation Propulsion" according to the commonly used acronym of Anglo-Saxon origin. In the remainder of the description, and for the sake of simplicity, the term "part" will be used to designate both the physical part 10 and the representation (or model) of the part. The physical aeronautical part is intended for use in the field of aeronautics, more precisely in the context of turbomachines. The following description, as well as the figures, are illustrated for an engine fan blade. Naturally, other aeronautical parts may be concerned by the invention. Figure 1 represents a perspective view of an aeronautical part 10 such as a fan blade 10. The part 10 is represented in an orthogonal XYZ coordinate system defined by computer-aided design software, or CAD. In this coordinate system,the Z axis corresponds to the largest dimension of the part 10. This Z axis is associated with a longitudinal direction of the part 10 (it is called “longitudinal axis Z” in the following). The XY planes orthogonal to the longitudinal axis Z are transverse planes. In the present description, the term “length” is used to designate the dimension of the part 10 along the longitudinal axis. The term “thickness” is used to designate the dimension of the part 10 along the Y axis. In addition, the term “section” designates the shape of the part included in a plane perpendicular to one of the axes X, Y,Z of the part 10 and intersecting the volume of this part 10. The part 10 is made of a three-dimensional (or 3D) woven composite material. A 3D woven composite material is an assembly comprising at least one woven textile framework called a reinforcement and a binder called a matrix. The manufacture of a part in a woven composite material requires a first step of making the reinforcement by weaving, then a second step of assembly with the matrix, for example by injection after shaping in a mold. The reinforcement is composed of threads, or strands, woven in two main orthogonal directions, called the warp direction and the weft direction, and in a third direction corresponding to the thickness of the weave. In the XYZ coordinate system of the part 10, the warp direction is aligned along the longitudinal axis Z, the weft direction is aligned along the X axis, and the third direction is aligned along the third axis Y. In the remainder of the description,the term "warp strands" will refer to the strands aligned along the longitudinal axis Z of the XYZ reference frame of the part 10, and "weft strands" will refer to the strands aligned along the X axis. With reference to Figure 1, the part 10 has an aerodynamic shape. Thus, the part 10 has a high aspect ratio, i.e. a large dimension along the longitudinal axis Z compared to the transverse dimensions. It also has curvatures which extend along the longitudinal axis Z, on all intrados Int and extrados Ext surfaces. In particular, it has a crescent-shaped curvature linked to the intrados-extrados Int, Ext profiles and a helical shape. Finally, its thickness in the X direction varies greatly along the longitudinal axis Z. Figure 2 represents, in the form of a flowchart,an example of a processing method 1 according to the invention. This processing method 1 is also referred to as “method 1” in the following. The steps of this processing method 1 are typically carried out using a computing unit 100, illustrated in FIG. 16, comprising data processing means 110 (such as a processor) and a memory 120. A display monitor 130 may be provided, configured to display data in particular from the computing unit. More generally, the processing method 1 according to the invention is here implemented by computer. Prior to the implementation of the method 1,a method for acquiring a three-dimensional image I3D of the part 10 is implemented. This method for acquiring the three-dimensional image I3D of the part 10 is for example implemented in practice using an X-ray tomographic imaging system and calculation means. The reference frame of the three-dimensional image I3D corresponds to the global reference frame XYZ of the part 10. The three-dimensional image I3D is composed of voxels with coordinates (x,y,z), i.e. three-dimensional pixels. Each voxel is associated with a gray level value corresponding to a local attenuation coefficient for X-rays. In practice, the tomographic representation of the part 10 is defined, within the three-dimensional image I3D, on the basis of voxels having particular gray levels. These gray levels are a function, for example, of the local density of the material forming the part and of the atomic number of the elements constituting this material. In this example,in order to obtain the desired spatial resolution, the three-dimensional image I3D is obtained from three local three-dimensional images representing three consecutive regions of the part. The resulting three-dimensional image I3D is then called a global three-dimensional image. The number of regions of the part is not limited to three. Figure 3A, Figure 3B and Figure 3C represent different views of a three-dimensional image I3D of the part 10. In these figures 3A, 3B and 3C, the three local three-dimensional images are referenced I3D,1, I3D,2 and I3D,3. A local reference frame is associated with them. This local reference frame is distinct from the global reference frame XYZ of the part 10. This local reference frame is preferably defined on the basis of the part 10, so that the weaving directions (warp,of frame and thickness) are best aligned with the axes of the local reference frame. The processing method 1 is applied to the global three-dimensional image I3D or to each local three-dimensional image I3D,1, I3D,2 and I3D,3. In the remainder of the description, the three-dimensional image is called "raw ^, ^ ^ ^ », the overall three-dimensional image I3D or one of the intermediate three-dimensional images I3D,1, I3D,2 and I3D,3. The term “raw” refers to the three-dimensional image that has not yet undergone processing. The processing method 1 is now described in detail. As shown in Figure 2, the method 1 comprises a first step E2 aimed at providing a set of several raw transverse two-dimensional images ^ ^ ^ , ^^ , ^ ^ 1: ^ to which an E3 rectification treatment will be applied. These raw transverse images are associated with the raw three-dimensional image ^^ ^ ^ . They each correspond to a different transverse XY section Si of the part 10, each XY section corresponding to a coordinate on the longitudinal axis Z distinct from that of the other XY sections. As previously described, the term "transverse" here refers to an XY transverse section plane orthogonal to the longitudinal axis Z. As shown in Figure 4, each raw transverse image corresponds to an image formed from a plurality of pixels ^ ^ ^^, ^^ distributed in rows and columns. For readability, the raw cross-sectional image ^ ^ ^ , ^^ here represented in figure 4 is not a real image but a schematic representation of this real image. Classically, the raw transverse image ^ ^ ^ , ^^ is defined on the basis of a "grid" formed of pixels ^ ^ ^ ^, ^ ^ . Each pixel ^ ^ ^^, ^ ^ has a column coordinate ^ and a row coordinate ^. These row and column coordinates are defined relative to an origin O with coordinates (0,0). In Figure 4, the origin O is here defined at the top left corner of the raw cross-sectional image ^ ^ ^ ,^^ . Here, as seen in Figure 4, each pixel ^ ^ ^^, ^^ is associated with a gray level. The Si section of part 10 is defined in the raw cross-sectional image ^ ^ ^ , ^^ by pixels having particular gray levels. These gray levels are in fact different from the gray levels of the pixels not relating to the part 10. Moreover, they extend over a greater dynamic range of gray levels. In Figure 4, it is also visible that the section Si has a crescent shape oriented along the pixel lines and delimited by a lower curved line ^ ^^^^ and an upper curved line ^ ^^^. The terms "lower" and "upper" are here chosen with respect to the reference mark ^^, ^^ illustrated in Figure 4, and defined for the raw cross-sectional image. An objective of the rectification processing E3 which will be described below is to straighten the section Si of the part 10 by first determining a mean curved line between the lower and upper curved lines ^ ^ ^^^ and ^ ^^^, then making this average curved line substantially straight. As will also be described later, this E3 straightening treatment will be applied identically to each raw transverse image ^ ^ ^ , ^^ . The set of straightened sections will then compose a straightened piece on which the weave analysis can be carried out. The raw transverse images ^ ^ ^ , ^^are obtained in the following manner. In a first sub-step E21, we determine in the raw three-dimensional image ^ ^ ^ ^ , a set of different coordinates " ^ , ^ ^ 1: ^, along the longitudinal axis Z. These coordinates are preferably chosen to increase along the longitudinal axis Z in a range of values ​​extending over the length of the part 10 represented in the raw three-dimensional image ^ ^ ^ ^ . Thus, the coordinates zi are distinct from each other. Then, we determine, for each of these coordinates " ^ , ^ ^ 1: ^, the corresponding section along a cutting plane XY orthogonal to the longitudinal axis Z. As the coordinates zi are distinct from each other, the sections are also distinct. Finally, in a step E22, the transverse images ^ are generated ^ ^ , ^^, ^ ^ 1: ^ corresponding to the determined sections. In the remainder of the description, the transverse images obtained at the end of step E2 are called “raw transverse images” because they have not yet undergone the rectification processing of step E3. At the end of step E2, a plurality of raw transverse images are thus available ^ ^ ^ , ^^ , ^ ^ 1: ^, to which the rectification treatment described below will be applied. In the following, the rectification treatment is described for a transverse image ^ ^ ^ , ^^ but applies equally to all raw cross-sectional images ^ ^ ^ , ^^, ^ ^ 1: ^. The rectification processing of step E3 aims to obtain a rectified three-dimensional image from a plurality of rectified sections Si of the part 10. Figure 5 represents, in the form of a flowchart, the main steps of step E3. The first step E31 is described below in relation to Figures 6 and 7. As indicated previously, this first step E31 is carried out on each transverse image obtained in step E2. It specifically aims to implement the straightening of the section Si of the part 10 represented in the raw transverse image ^ ^ ^ , ^^ concerned. To do this, we first determine a curve # ^ ^^^ which allows us to represent the average curved line of the section Si of part 10, then we determine a displacement field $ ^ ^^^ which allows this curve to be flattened Finally, we carry out the straightening of the section Si of part 10 with the displacement field $ ^ ^^^ determined. These different operations are represented respectively by steps E311, E312 and E313 described below in relation to figure 6. The determination of the curve # ^ ^ ^ ^ representing the average curved line of the part section comprises the successive steps E311 and E312. First, in the first step E311 illustrated in Figure 6, a binarization (i.e., a conversion into binary form) of the raw cross-sectional image ^ ^ ^ , ^^ concerned is carried out to identify all the pixels relating to the section of the part 10. We then obtain a binary image ^ ^ formed from a plurality of pixels and having the same dimensions as the raw transverse image ^ ^ ^ , ^^ concerned. In this binary image ^ ^, all pixels relating to section Si of part 10 represented in this raw cross-sectional image have the value 1, and all other pixels have the value 0. Thus, the binary image ^ ^ provides a % mask ^,^^ relating to the section of the part in the raw cross-sectional image ^ ^ ^ , ^^ concerned. The mask % ^,^^ obtained is an image as illustrated in Figure 7 (in connection with step E311). For the sake of readability, this image is represented here not in the form of a real image but as a schematic representation of this real image. Similarly, the other images in Figure 7 (in connection with steps E312 and E314) are schematic representations of real images. It is formed of pixels and has the same dimensions as the raw transverse image ^ ^ ^ , ^^ . Furthermore, each pixel of the mask % ^,^^is then associated, by definition, with a value 0 or 1, in the manner described in the previous paragraph. On the image of the % mask ^,^^ shown in Figure 7, pixels with a value of 0 (not relating to the part section) are shown in black, while pixels with a value of 1 (relating to the part section) are shown in white. For readability, the black color representing pixels not relating to the part section will be replaced by white in the other images of Figure 7. Binarization can be performed using gray levels, by choosing a threshold. The choice of the threshold can advantageously be obtained via the Otsu method. More details concerning this method are for example given in the document "A threshold selection method from gray-level histograms", IEEE Trans. Sys. Man. Cyber., vol.9, 1979, p.62-66, by Nobuyuki Otsu. Advantageously, the mask % ^,^^obtained can be smoothed. This smoothing can be done by performing morphological operations such as erosion and / or dilation on the mask % ^,^^ . Once the mask % ^,^^ obtained from step E311, method 1 comprises a step E312 of determining curve # ^ ^ ^ ^ . This step is performed from the % mask ^,^^ . In practice, this curve # ^ ^ ^ ^ corresponds to an average curve that is, a curve that is equidistant from the lower and upper curved lines ^ ^ ^ ^ ^ , ^ ^ ^ ^ (in the Y direction of the mask pixel columns % ^,^^ , see figure 4). The average curve # &^ ^^, ^^ is preferably determined in the following manner. First, in step E3121 illustrated in FIG. 7, an image called a "predetermined image" ^» is defined. This has the same dimension as the binary image ^ ^ . Each pixel ^ ^ ^ ^, ^ ^ which composes the predetermined image' ^ has a value that corresponds to the row coordinate ^ of that pixel. In other words, the predetermined image ' ^ is an image of the coordinates of the mask lines: ' ^ ^ ^, ^ ^ ^ ^. On the predetermined image ' ^ illustrated in Figure 7, these values ​​of the coordinates of the lines of the mask are represented schematically by different densities of points. Then, in step E3122 also illustrated in Figure 7, each pixel is assigned ^ & ^ ^, ^ ^ of the mask the value of the corresponding pixel ^ ^ ^ ^, ^ ^ of the predetermined image' ^ . By definition, a pixel ^ & ^ ^, ^ ^ of the mask which corresponds to a pixel ^ ^^ ^, ^ ^ of the predetermined image in the mask the same row coordinate ^ and the same column coordinate ^ as this pixel ^ ^ ^ ^, ^ ^ of the predetermined image in the predetermined image. The image ( ^ obtained at the end of this step E3122 differs from the mask only in that the pixels which had the value 1 in the mask now have, as their value, the value of their line coordinate ^. The image ( ^ obtained is in other words an image of the line coordinates ( ^ ^ ^, ^ ^ ^ ^. This image is illustrated in Figure 7 in relation to step E3122. Note that each pixel of the image ( ^ line coordinates relating to section Si of part 10 have been weighted by its line coordinate ^. This weighting is now used to determine a center ^ ) of each column of pixels in the mask % ^. In practice, we determine a barycenter ^ ) of each column of pixels in the mask. To do this, the following processing is performed on each column of the image ( ^ . We first determine a ratio between the sum of the values ​​of the pixels in this column and the number of pixels in the column belonging to section Si of part 10. This ratio gives the barycenter ^ ) of the column concerned. Then, the determined barycenter is assigned to the pixels of the column concerned of the mask % ^ The mask has thus been modified: the pixels which previously had the value 1 now have the value of the barycenter of their column ^ ) . The modified mask is, in other words, an image * ^ ^^, ^^ ^ ^ ) of the mask column centers % ^ . Preferably, each column center ^ ) is determined from a matrix product between a matrix ^ ^ ^ ^, ^^ representing the binary image ^ ^ and a matrix ' ^ ^ ^, ^ ^ representing the predetermined image' ^ . More precisely, each column center ^ ) is the result of the following report: - Where ^, ^ are the column and row coordinates of each pixel in the mask respectively % ^,^^ , - ' ^ ^ ^, ^ ^ is the matrix representing the predetermined image ' ^ ^ ^, ^ ^ , and - ^ ^ ^^, ^^ is the matrix representing the binary image ^ ^ ^^, ^^. We note, moreover, that ∑ ^ ' ^ ^^, ^^ ∗ ^ ^ ^^, ^^ is the sum of the pixel values ​​in column ^, and that ∑ ^ ^ ^ ^ ^, ^ ^ is the number of pixels in column ^ belonging to section , ^ of part 10. Finally, in step E3123, we determine a parametric function # ^^^^ which describes the centers ^ ) columns of the image * ^ centers. The parametric function # ^ ^ ^ ^ thus determined forms the average curve # &^ ^ ^ ^ sought. This average curve # &^ ^ ^ ^ is illustrated in Figure 6, in relation to step E3123. It is represented in an image of the same dimension as the binary image ^ ^ . The parametric function # ^ ^ ^ ^ is defined to be constant over each column ^ of pixels in the image of the centers * ^ , and to depend on the column coordinates ^. For this, we implement an interpolation on the image * ^ column centers ^ ) to find the parameters of the parametric function # ^ ^ ^ ^ . These parameters are then gradually adjusted on pairs {column coordinate, column center ^) associated with this column coordinate} such that the parametric function tends towards the column centers: # ^ ^^^ - ^ ) . This adjustment is for example implemented by minimizing an objective function such as the norm . of the error between the value of the center and a value of the parametric function: | | ^ ) 0 # ^ ^ ^ ^| | . The optimization ends when a convergence criterion is reached, or when a certain number of iterations is reached. Preferably, the parametric function # ^ ^^^ is composed of a spline. The spline is constructed to correspond to the best approximation of the pairs of points {column coordinate, column center ^ )associated with this column coordinate} obtained from the centers (or barycenters). Each pair of points corresponds to a center (or barycenter). It is possible to use other parametric functions, for example polynomial functions. Note, however, that using a parametric function composed of a spline offers the advantage of avoiding unwanted oscillations at the ends (i.e., at the edges, along the upper and lower curved lines ^ ^ ^^^, ^ ^^^ of the mask % ^ ^. Advantageously, the use of a spline is associated with a smoothing effect which makes it easier to converge the adjustment of the function's parameters. We also obtain a function # ^ ^ ^ ^smoother and more regular which facilitates the next step E313 of determining a displacement field. Alternatively, it is possible to determine a median curve instead of a mean curve to represent the surface of the section of the part. In this case, median centers will be determined instead of barycenters. It should be noted that this way of determining the mean curve # &^ ^^^ does not rely on detection of the external edges of the mask % ^,^^. This contour detection generally includes a step of detecting the upper and lower contours and a step of determining the average resulting from these two extracted contours. This contour detection has the disadvantage of being particularly sensitive to the presence of imperfections in the binary image, for example the presence of noise on the contours (outliers) or discontinuities (missing points). However, these imperfections are common in practice. Thus, the presence of parts external to the part to be controlled, for example support parts allowing the part to be held, can interfere and make the contours discontinuous. Advantageously, the way of determining the average curve # &^ ^ ^ ^ at step E312 makes it possible to overcome these drawbacks and, thus, to be much more robust than contour detection. Indeed, by calculating the column centers ^ )from the predetermined image' ^ , we have many more points than when we use only the two points belonging to the internal and external contours. The determination of the centers is thus possible even if one of the contours has a missing point. It is also less sensitive to the presence of an aberrant point. In addition, determining the centers by matrix calculation is much more advantageous from a calculation time point of view since computer languages ​​and computers are optimized for this type of operation. The method continues with a step E313 of determining the displacement field $ ^ ^^^ performed from the average curve # &^ ^^^ determined previously. The displacement field $ ^ ^ ^ ^ thus determined is illustrated in Figure 7 in relation to step E314. The displacement field $ ^ ^^^ gives the relationship between the average curve # &^^^^ and a flattened average curve # &1^ ^^^. Such a flattened mean curve is for example illustrated in figure 7 in connection with step E314. The flattened mean curve # &1^ ^ ^ ^ is for example obtained by projection of the average curve # &^ ^ ^ ^ on the abscissa axis ^. To determine this displacement field $ ^ ^ ^ ^ , the inventors have identified that an important parameter to take into account is the way in which the part 10 deforms. Observations carried out within the framework of the invention have thus made it possible to show that the part 10 deforms like a structure where essentially the weft strands (directed in the direction of the pixel lines in the mask % ^) move in simple shear parallel to the thickness Y of the part 10 (or perpendicular to the average directions of warp Z and weft X). Thus, the inventors sought a displacement field $ ^ ^^^ which makes it possible to reproduce these observed deformations. In particular, the inventors sought a displacement field which makes it possible to preserve the volume of the part 10 during the straightening treatment. In other words, the inventors sought a displacement field such that the straightening treatment is an isochoric transformation. Such a displacement field $ ^ ^ ^ ^defines relative displacement vectors, parallel to each other and directed according to the direction of the pixel columns of the mask (which corresponds to the direction orthogonal to the weft strands). In Figure 7, three displacement vectors (among all the possible displacement vectors) are represented as an example in schematic form. They are noted Each relative displacement vector has the action of sliding, relative to each other, columns of pixels so that the average curve # &^ ^ ^ ^ merges with a pixel line (or the ^ axis of the image shown in Figure 7 in relation to step E314). Each portion of the flattened average curve # &1^ ^^^ spans a column width of mask pixels (which corresponds to the dimension of a mask pixel in the direction of the pixel lines). In other words, the displacement field $ ^ ^ ^ ^is constant on each column of pixels. It is directed along the Y axis of the columns of pixels and depends on a position on the ^ axis: $ ^ ^^^ ^ 0# &^ ^^^. Thus defined, the displacement field $ ^ ^^^ corresponds, in practice, to a simple linear transformation: the columns of pixels are simply moved (translated) without variation of surface (in the XY plane) or volume when considering the direction along the main Z axis. Moreover, as the displacement field $ ^ ^ ^ ^ is directed only in the direction of the mask's pixel columns, it only has an effect in the relevant XY transverse plane. This ensures that each raw transverse image ^ ^ ^ , ^^ of the raw cross-sectional image set processed in step E31, independently. This then makes it possible to process each raw cross-sectional image simultaneously, in parallel. This variant embodiment is illustrated in Figure 8. It offers the advantage of being very quick to execute. As shown in Figures 6 and 7, the method continues in step E314. During this step, the section Si of the part 10 is subjected to the displacement field $ ^ ^ ^ ^ to perform the rectification. Thus, in step E314 illustrated in Figure 7, we first apply the displacement field $ ^ ^^^ determined previously at the raw cross-sectional image pixels ^ ^ ^ , ^^ to obtain a straightened section, ^ 3 of part 10. The application of the displacement field preferably corresponds to a translation (or an offset) and a linear interpolation. For this, the displacement field $ ^^^^ is decomposed into an integer part $ ^,^ ^x^ and a real part $ 5,^ : $ ^ ^^^ ^ $ ^,^ ^x^ $6 5,^ ^x^ The integer part is equal to the rounding down of the displacement field $ ^ ^^^: $ ^,^ ^^^ ^ ⌊$ ^ ^^^⌋. Then, based on this displacement field, the straightened transverse image ^ ^ 3 , ^^ is obtained in two steps. In a first step, a translation (of whole pixels) is carried out to obtain a first rectified image In a second step, we determine the rectified image ^ ^ 3^ ^, y ^ from ^^, y^ of the first straightened image: ^9 3 , ^ ^^, y^ ^ Determination of the vertical gradient ; 3 ; < ^ 9,^ ^ ^, y ^ can be obtained by finite difference approximation or by any other numerical means. For example, the gradient determination can be achieved by convolution with Sobel-type operators. The rectified transverse image is then calculated as follows: ⋅ $ 5,^ ^x^. @ B is a Sobel filter whose kernel is for example of type C 01, 0, 61 E . The displacement is thus composed of a translation (or a shift) and a one-dimensional interpolation of the gray levels which remains limited to half a pixel or less and is constant on each column of pixels. This then constitutes a very simple interpolation. This division of the displacement between an integer part and a non-integer displacement remainder is thus inexpensive in terms of computing resources. Alternatively, this deformation can consist of a simple translation, or a simple interpolation. As previously described in figures 6 and 8, step E31 is repeated on each raw transverse image ^^ ^ , ^^ . We thus have, at this stage of process 1, a set of rectified transverse images ^ ^ 3 , ^^ , ^ ^ 1: ^ in which each straightened transverse image ^ ^ 3 , ^^ represents a straightened section, ^ 3 of part 10 (as illustrated by Figure 7). As shown in Figure 5, the method continues with a step E32 of reconstructing a rectified three-dimensional image ^ ^ 3 ^ . This step E32 is performed from the rectified transverse images. Specifically, this step E32 consists of stacking the rectified transverse images ^ ^ 3 , ^^ one after the other along the main Z axis of the part. For this, each straightened transverse image ^ ^ 3 , ^^ is identified by an axial coordinate " ^which corresponds to the coordinate ^ of the corresponding section plane. The rectified three-dimensional image ^ ^ 3 ^ is then the volume formed by these assembled rectified transverse images. Similarly, in this rectified three-dimensional image ^ ^ 3 ^ , the straightened part is the volume formed by the straightened sections, ^ 3 represented in the straightened transverse images ^ ^ 3 , ^^ of the assembly. The contribution and the interest of the method 1 are illustrated in particular with the support of a rectified two-dimensional image corresponding to a longitudinal section XZ of this rectified three-dimensional image. This longitudinal section is perpendicular to the Y axis of the XYZ reference of the part 10. Figures 9A and 9B respectively represent an example of a longitudinal image ^ F ^ , ^G obtained from the raw three-dimensional image ^ ^^ ^ and the straightened longitudinal image ^ F 3 , ^G corresponding obtained by applying the method according to the invention. It is noted that the missing information in the longitudinal image ^ F ^ , ^G obtained from the raw three-dimensional image ^ ^ ^ ^ is restored in the straightened longitudinal image . It is thus possible to detect, by analyzing a single image, a weaving anomaly, where previously it was necessary to scroll through several images. Thanks to the simple displacement produced by the displacement field, the texture of the piece (i.e. the strands) are reconstructed without volume variations. We therefore obtain a rectified representation of homogeneous quality over its entirety. In addition, the spatial resolution of the rectified longitudinal image is sufficient to exploit sub-images of the rectified longitudinal image by enlarging them. At the end of the rectification processing step E3, we thus have a rectified three-dimensional image ^ ^ 3 ^ which allows for easy and reliable weaving analysis. When three local three-dimensional images ^ ^^,^ , ^ ^^, , ^ ^^,^ (see figure 3) are used to represent part 10 in its entirety, method 1 is implemented for each of these images ^ ^^,^ , ^ ^^, , ^ ^^,^three-dimensional local images. We then obtain three rectified local images which, once concatenated, make it possible to obtain a global rectified three-dimensional image. As previously, we choose a longitudinal section XZ associated with the global rectified three-dimensional image to illustrate the results obtained by method 1. Thus, figures 10A, 10B, 10C and 10D each represent a rectified longitudinal image ^ F , , 3 ^ G obtained from a global rectified three-dimensional image. In addition, each image represents a different part 10. As can be seen in these figures 10A to 10D, each rectified longitudinal image ^ F , , 3 ^ G is formed from three straightened longitudinal images each of these images being obtained from the rectified local three-dimensional image. We observe in all the figures a good continuity between the rectified longitudinal images ^ F , , 3 ^ G,^ , ^ F , , 3 ^ G, , ^ F , , 3 ^ G,^ This is an advantage for viewing the entire straightened part in a single image. This advantage is linked to the definition of the displacement field which provides an effect only in the transverse section planes and thus guarantees the independence of processing of local three-dimensional images ^ ^^,^ , ^ ^^, , ^ ^^,^ . We also note the good repeatability of process 1 with respect to inter-blade differences. This is advantageous for a production application. In an optimization mode, step E32 of reconstruction of the rectified three-dimensional image may comprise an optional step of smoothing, along the main axis Z, the straightened part 10. This smoothing step is optional. This smoothing step may comprise the determination of a smoothing function for each XY plane of the straightened part 10. This smoothing function is for example determined from a plurality of straightened transverse images of the straightened three-dimensional image . The determination of the smoothing function can for example be based on the determination of a weighting function of the displacement field $ ^ ^^^ associated with each straightened transverse image. This displacement field $ ^ ^^^ has therefore been stored using a storage means during step E313 of method 1. This weighting function aims to adapt the amplitude of each displacement field $ ^ ^ ^ ^so as to avoid discontinuities, along the main Z axis, between the different straightened sections, ^ 3 forming the rectified part 10. The weighting function can then be applied to each rectified transverse image ^ ^ 3 , ^^ . Thus, the displacement field $ ^ ^ ^ ^ is updated. The smoothing step is advantageously used to speed up the calculation time of processing method 1. Indeed, it allows the processing to be limited to a subset of raw transverse images ^ ^ 3 , ^^ of the plurality of raw transverse images ^ ^ 3 , ^^. For example, this subset may contain one raw cross-sectional image out of four of the plurality of raw cross-sectional images. In this case, the smoothing step makes it possible to reduce the calculation time by four. Figure 11 represents, in the form of a flowchart, an alternative implementation of method 1. According to this alternative, method 1 begins with a step E1. This step E1 consists of resizing the raw three-dimensional image ^ ^ ^ ^ from a predetermined three-dimensional region. Resizing is performed first by extracting from the raw three-dimensional image ^ ^ ^^ the subset of voxels corresponding to the predetermined three-dimensional region. Then, the resizing continues by generating a raw three-dimensional image from this selected subset. This generated raw three-dimensional image is called the "resized raw three-dimensional image". The predetermined three-dimensional region is preferably an input parameter of method 1. It is defined by dimension and position parameters in the raw three-dimensional image ^ ^ ^^ . These parameters are for example chosen to include the voxels relating to the part 10 so as to limit the number of voxels not belonging to the part. For example, it is possible to limit them to the voxels complementary to the volume formed by the voxels relating to the part 10. The resized raw three-dimensional image thus corresponds to a file of reduced size since the number of voxels not relating to the part is reduced. It is easier to store and is processed more quickly by the processing method 1 described previously. In another variant, the resizing step is not carried out on the raw three-dimensional image, but on the transverse images. This step is then carried out after step E2 and before step E3.Similar to step E1, this step consists of extracting a subset of pixels in the initial raw cross-sectional image from a predetermined two-dimensional region of interest and then generating an image corresponding to the subset of extracted pixels. The predetermined two-dimensional region of interest may be an input parameter of the method 1 and preferably includes the pixels relating to the section of the part and the pixels complementary to the surface formed by these pixels relating to the section of the part. Now, a second embodiment of the processing method is described. A first implementation variant is shown in FIG. 12, in the form of a flowchart. According to this second implementation mode, the processing method 2 aims to take into account the fact that the section Si of the part 10 is not elongated along the lines of pixels of the raw cross-sectional image ^. ^ ^ , ^^. To do this, according to the first variant illustrated in figure 12, we first determine a projection reference frame ^^ H , ^ H ^ such that, in this projection frame, the section Si of the raw transverse image ^ ^ ^ , ^^ be transformed into an elongated section along a line of pixels of this raw transverse image ^ ^ ^ , ^^ . Then we define the average curve # ^ ^^′^ and the straightening of the raw cross-sectional part according to this projection reference frame. Method 2 is identical to method 1 described previously except for steps E4, E5 and E6 which are new. Step E4 aims to determine the projection reference frame defined previously. It is carried out between step E311 of generating the binary image and step E3123 of defining the parametric function. This projection reference frame is preferably determined from the binary image ^ ^, but can also be determined from the image * ^ column centers ^ ) . We consider here the case where it is determined from the binary image ^ ^ . We are looking for a rotation transformation relative to the center of the binary image ^ ^ which allows you to find the alignment of the Si section with one of the lines of pixels of the binary image ^ ^. The rotation angle associated with the desired rotation transformation is the angle J referenced in Figure 13. Specifically, this angle J is defined between a line of pixels of the binary image and a preferred direction d of the section Si of the part. The expression “preferred direction” designates the direction corresponding to the largest dimension of the section Si of the part 10 in the image considered. According to a first variant of implementation of step E4, illustrated in Figure 14A, the angle J is predetermined and non-zero. Step E4 includes a step E43 which consists of defining a system of equations for changing the reference frame. This system of equations for changing the reference frame is written for example in the following manner: K ^H L ^ Kcos J ​​0 sin J ^L 0 K ^̅L? 6 K ^̅L where K ^̅ ^H sin J cos J L >K^ ^S ^S ^SL is the position of the center of the binary image. In this alternative embodiment, the angle J is constant for all the processed raw transverse images. Method 2 according to the second embodiment is advantageous for taking into account the relative orientation of the local three-dimensional images. According to another alternative implementation of step E4, illustrated in Figure 14B, the angle J is determined from the binary image, or from the image of the centers. As previously, we will consider the case where it is the binary image that is used. First of all, in a first step E41, the pixels relating to the section of the part are extracted from the binary image. These pixels form a point cloud. The geometric characteristics of this point cloud are then analyzed in a step E42 to identify its preferred direction d. There are several ways of carrying out this step E42.A first method is based on the use of a polynomial regression performed from the point cloud. According to a second method, a principal component analysis (or PCA) can be performed along two perpendicular principal axes. This analysis then provides eigenvalues ​​and associated eigenvectors allowing the point cloud to be modeled. Then, according to this second method, it is a question of selecting, among the eigenvectors produced by the principal component analysis, the eigenvector associated with the largest eigenvalue. Then, the angle J is determined from the components of the selected eigenvector. Preferably, the determination of the angle J is implemented by limiting the value range of this angle J to the range [-45°, +45°].Alternatively, a third method, consisting of calculating the geometric moment of inertia of the point cloud, then choosing as the preferred direction the direction which corresponds to the minimum moment of inertia can be used. As a further variant, a fourth method consists of calculating the moment of inertia of the point cloud. Among these methods, principal component analysis is preferred. This is indeed, in principle, close to the action sought for the displacement field. Step E4 then continues with step E43 described previously, which consists of defining the system of equations for changing the reference frame by taking into account the angle parameter J determined in steps E41 and E42. This variant embodiment is suitable for a very general production mode, in which the orientation of the part to be checked varies from one part to another: the treatment method 2 has the capacity to adapt to each of the parts by taking into account its orientation.Once the projection reference has been identified in step E4, the average curve is determined in step E5. in the projection frame. Step E5 is carried out between step E3122 of determining the image * ^ column centers and step E3123 of determining the parametric function # ^ ^^^. To do this, we apply the equation system described previously to all the pixels of the image of the column centers * ^ ^^, ^^. We then obtain an image of the centers * ^ ^^′, ^′^ in the projection frame. The parametric function # ^ ^^′^ is then defined from this image of the centers, in the projection frame. Finally, after step E313 of determining the displacement field $ ^ ^ ^′ ^ and before the rectification step E314, the same equation system is applied to the pixels of the raw transverse image ^ ^ ^ , ^^. We thus obtain a raw transverse image ^ ^ 3 , ^^ ^^′, ^′^ in the same frame of reference as that used to determine the displacement field $ ^ ^ ^′ ^ . A second implementation variant is shown in Figure 15. According to a second variant of this second embodiment, illustrated in Figure 15 in the form of a flowchart, the processing method 2 comprises, during step E31 of processing each raw transverse image an image registration step E310. This image registration step is optional. This step E310 is then implemented before the step E311 of generating the binary image. It makes it possible to obtain from the raw transverse image ^ ^ ^ , ^^ , a raw cross-sectional image re-aligned ^ ^ ^ , ^ 3 ^ . This raw cross-sectional image has been realigned ^ ^ ^ , ^ 3^ is of the same dimension as the raw transverse image It presents, like the raw transverse image, pixels arranged according to rows and columns of pixels, and comprises the section Si of the part 10. The objective of step E310 is to align, in the registered raw transverse image, the columns of pixels according to the Y direction corresponding to the thickness of the part 10. Thus, the rows of pixels of the registered raw transverse image ^ ^ ^ , ^ 3 ^ are oriented along the X direction corresponding to the X frame direction of the reference frame of part 10. As the section Si of part 10 is elongated along this X frame direction, this section Si is intrinsically represented in the recalibrated raw transverse image ^ ^ ^ , ^ 3 ^ in an elongated manner along the pixel lines of this re-aligned raw transverse image ^ ^ ^ , ^ 3^ . The registration step E310 preferably begins with a step of determining the section Si of the part 10 in the raw transverse image ^ ^ ^ , ^^ . Step E310 can then continue with a step of determining, from the determined section Si, the orientation of the columns of pixels of the raw transverse image ^ ^ ^ , ^^relative to the orientation of the reinforcement (or thickness) Y axis of the part 10. Step E310 then comprises a step of resetting the determined section Si according to the determined orientation. Finally, the resetting step E310 makes it possible to position all the 3D volumes, i.e. all the raw three-dimensional images, in a common reference frame, that of the part 1. Advantageously, this step makes it possible to make the image analyses invariant to the positioning of the part 1 during acquisition. Step E311 of generating the binary image is then carried out from the recalibrated raw transverse image . The other steps shown in Figure 15 are unchanged from the embodiment illustrated in Figures 6 and 7, and are not described again here. A second aspect of the invention is a method 3 for inspecting an aeronautical part. This method comprises the steps of the method 1, 2 which has just been described and a step of inspecting the part from the rectified three-dimensional image. The inspection step comprises, for example, a step of visual analysis of three sets of images associated with the rectified three-dimensional image ^ ^ 3 ^ . Each set of images includes images each corresponding to a section of this rectified three-dimensional image ^ ^ 3^ according to a section plane perpendicular to an axis of the XYZ reference frame of the part 10. The axis is different for each set of rectified images: for example, the axis is the Z axis for the first set, the Y axis for the second set and the X axis for the third set. This control step is for example implemented manually or automatically. A third aspect of the invention is the device 100 shown in FIG. 16 and described previously. This device 100 is configured to execute the steps of the method 1, 2. Finally, a fourth aspect of the invention is a computer program comprising instructions which, when the program is executed by the device, cause it to implement the steps of the method 1, 2.

Claims

CLAIMS

1. Method (1) for processing a three-dimensional image (^ ^ ^ ^ ) for the control of a part (10) included in the three-dimensional image (^ ^ ^ ^ ), the three-dimensional image (^ ^ ^ ^ ) being associated with a plurality of transverse images (^ ^ ^ , ^^ , ^ ^ 1: ^) two-dimensional, each transverse image corresponding to a section (XY) of the three-dimensional image (^ ^ ^ ^ ) according to a section plane perpendicular to a main axis (Z) of said part (10), the section planes being distinct, each transverse image (^ ^ ^ , ^^ ) comprising a section (Si) of the part (10), each transverse image (^ ^ ^ , ^^) being formed from a plurality of pixels (^^^, ^^), the method (1) comprising a rectification processing (E3) comprising the following steps: - Processing (E31) of each transverse image (^ ^ ^ , ^^ ) to obtain a plurality of rectified transverse images (^ ^ 3 , ^^ , ^ ^ 1: ^), each straightened transverse image (^ ^ 3 , ^^ ) comprising a straightened section (, ^ 3 ) of the part, said processing (E31) comprising, for each transverse image (^ ^ ^ , ^^ ), the following steps: o Generation (E311), from the transverse image (^ ^ ^ , ^^ ) concerned, of a binary image (^ ^ ) to obtain a mask (% ^ ) formed from a plurality of pixels corresponding to the pixels of the transverse image (^ ^ ^ , ^^) relating to the section (Si) of the part (10), the plurality of pixels of the mask (% ^ ) being arranged in rows of pixels and columns of pixels, each pixel being defined by a row coordinate (^) and a column coordinate (^), o Determination (E312, E3121, E3122), from the mask (% ^ ), of a surface representing the section (Si) of the part (10), said surface representing the section (Si) of the part (10) corresponding to an average curve or a median curve of the section (Si) of the part (10), o Determination (E312, E3123) of a parametric function (# ^ ^^^, # &^ ^^^) describing said surface, o Definition (E313) of a displacement field ($ ^ ^^^) constant on each column of pixels and dependent on the column coordinates of the pixels so as to apply a flattening transformation to the surface represented by the parametric function (# ^^^^), o Straightening (E314) of the section (Si) of the part (10) in the transverse image concerned by applying the determined displacement field ($ ^ ^ ^ ^ ) to the pixels of the transverse image concerned, to generate the rectified transverse image (^ ^ 3 , ^^ ) associated with the transverse image (^ ^ ^ , ^^ ) concerned, and - Reconstruction (E32) of a rectified three-dimensional image (^ ^ ^ ^ ) containing the part (10) rectified from the rectified transverse images (^ ^ 3 , ^^ ) obtained from raw cross-sectional images

2. Processing method (1) according to claim 1, in which the surface representing the section (Si) of the part (10) is determined by carrying out the following steps: - Definition (E3121) of a predetermined image ('^ ) having the same dimension as the binary image (^ ^ ), the predetermined image (' ^ ) comprising a plurality of pixels (^ ^ ^ ^, ^ ^ ), each pixel (^ ^ ^ ^, ^ ^ ) having a predetermined value corresponding to the row coordinate (^) of said associated pixel in the predetermined image (' ^ ), - Assignment (E3122) of a predefined value to each pixel of the mask (% ^ ), said predefined value corresponding to the value of the pixel in the predetermined image (' ^ ) having the same row and column coordinates as the mask pixel (% ^ ) concerned, and - For each column of pixels of the mask (% ^), determining a center of said column from the predefined values ​​of the pixels and the number of pixels of said column, and assigning the determined center to each pixel of said column, the centers corresponding to a representation of said surface.

3. Processing method (1) according to claim 2, wherein the centers are determined from the result of a matrix product between a matrix representing the predetermined image and a matrix representing the binary image.

4. Processing method (1) according to one of claims 2 to 3, wherein the centers are barycenters, each barycenter being determined on the basis of the following expression: ∑^ ^^^^,^^∗^^^,^^ Or : - ^, ^ are the column and row coordinates of each pixel in the mask, respectively, - ^ ^ ^ ^, ^ ^ is the matrix representing the predetermined image (' ^ ), and - ^ ^^, ^ ^ is the matrix representing the binary image (^ ^ ).

5. Processing method (1) according to one of claims 1 to 4, in which the parametric function is composed of a spline.

6. Processing method (1) according to one of claims 1 to 5, in which the application of the displacement field is broken down into a translation operation and / or an interpolation operation.

7. Processing method (1) according to one of claims 1 to 6, in which the plurality of rectified transverse images is obtained simultaneously, by parallel processing of each transverse image of the plurality of transverse images.

8. Method (3) for controlling a part from a three-dimensional image (^ ^ ^^ ), comprising the steps of the processing method (1) according to one of claims 1 to 7 to obtain a rectified three-dimensional image ^^ ^ 3 ^ ), and a step of checking the part from the rectified three-dimensional image ^^ ^ 3 ^).

9. Control method (3) according to claim 8, in which the part (10) is made of three-dimensional woven composite material.

10. Device (100) for processing a three-dimensional image (^ ^ ^ ^ ) for the control of a part (10) included in the three-dimensional image (^ ^ ^ ^ ), the three-dimensional image (^ ^ ^ ^ ), being associated with a plurality of transverse images ^ 1: ^) two-dimensional, each transverse image (^ ^ ^ , ^^ ) corresponding to a section of the three-dimensional image (^ ^ ^^ ) according to a section plane (XY) perpendicular to a main axis (Z) of said part (10), the section planes (XY) being distinct, each transverse image (^ ^ ^ , ^^ ) comprising a section (, ^ ) of the part (10), each transverse image (^ ^ ^ , ^^ ) being formed of a plurality of pixels, the device comprising a processor (110) configured to implement a rectification processing (E3) comprising: - Processing (E31) of each transverse image (^ ^ ^ , ^^ ) to obtain a plurality of rectified transverse images (^ ^ 3 , ^^ , ^ ^ 1: ^)), each straightened transverse image (^ ^ 3 , ^^ ) comprising a straightened section (, ^ 3 ) of the part, said processing (E31) comprising, for each transverse image (^ ^ ^ , ^^), the following steps: o Generation (E311), from the transverse image concerned, of a binary image (^ ^ ) to obtain a mask (% ^ ) formed from a plurality of pixels corresponding to the pixels of the transverse image (^ ^ ^ , ^^ ) relating to the section (Si) of the part (10), the plurality of pixels of the mask (% ^ ) being arranged in rows of pixels and columns of pixels, each pixel being defined by a row coordinate (^) and a column coordinate (^), o Determination (E312, E3121, E3122), from the mask (% ^ ), of a surface representing the section (Si) of the part (10), said surface representing the section (Si) of the part (10) corresponding to an average curve or a median curve of the section (Si) of the part (10), o Determination (E312, E3123) of a parametric function (# ^ ^ ^ ^ , # &^ ^ ^^ ) describing said surface, o Definition (E313) of a displacement field constant on each column of pixels and dependent on the column coordinates of the pixels so as to apply a flattening transformation to the surface represented by the parametric function (# ^ ^ ^ ^ ), o Straightening (E314) of the section (Si) of the part (10) in the relevant transverse image by applying the determined displacement field ($ ^ ^^^) to the pixels of the transverse image concerned, to generate the rectified transverse image (^ ^ 3 , ^^ ) associated with the transverse image (^ ^ ^ , ^^ ) concerned, and - Reconstruction (E32) of a rectified three-dimensional image (^ ^ ^ ^ ) containing the part (10) rectified from the rectified transverse images (^ ^ 3 , ^^ ).

Images