JP5945188B2 - Shape measuring device - Google Patents

Description

  The technology disclosed in the present specification relates to a shape measuring apparatus that measures a workpiece shape from a photographed workpiece image.

  2. Description of the Related Art A shape measuring device that photographs a workpiece such as an industrial part and measures the shape of the workpiece from the photographed workpiece image has been developed. In this shape measuring apparatus, calibration is required for associating position information (coordinate values) in the workpiece image with the actual position. Patent Document 1 discloses a conventional technique related to calibration.

JP 2005-250628 A

  When calibration is performed in the shape measuring apparatus, first, a calibration board provided with a pattern having a known dimension (for example, a lattice pattern, a dot pattern, etc.) is photographed by the photographing apparatus. Next, position coordinates of a reference point (for example, a grid point) are calculated from the captured image (calibration image), and the position coordinates are stored as calibration data. Therefore, in order to perform calibration accurately, the calibration board must be properly photographed.

  However, the operator's skill is required for photographing the calibration board. That is, if the exposure time of the photographing apparatus and the illumination intensity when illuminating the work are not appropriate, the pattern (for example, a lattice pattern) in the calibration board image is distorted. If the pattern is distorted, the position coordinates of the reference point cannot be calculated accurately, and highly accurate calibration data cannot be obtained. In the present specification, a technique is disclosed that makes it possible to appropriately photograph a calibration board without requiring the skill of an operator.

  The shape measuring device disclosed in this specification is a measuring device that measures the shape of a workpiece, and includes a photographing device, a light amount adjusting unit that adjusts a light amount of an image photographed by the photographing device, and a calibration photographed by the photographing device. Determined by the light amount determination means, the light amount determination means for determining the light amount adjusted by the light amount adjustment means, and the light amount determination means based on the distortion amount calculated by the distortion amount calculation means. Calibration data calculating means for calculating calibration data from a calibration image captured by the imaging device when the amount of light is low.

  In this shape measuring apparatus, when a calibration image is captured by the imaging apparatus, a distortion amount is calculated from the captured calibration image. Then, based on the calculated distortion amount, the light amount (for example, exposure time, illumination intensity, etc.) of the image photographed by the photographing apparatus is determined. Therefore, the light quantity can be adjusted to an appropriate amount without requiring the skill of the operator, and the calibration image can be appropriately captured.

The figure which shows the structure of a shape measuring device. The flowchart which shows the procedure of a calibration. The flowchart which shows the procedure of exposure time determination processing. The flowchart which shows the procedure of distortion amount calculation processing. The figure which shows an example of the image | photographed calibration image. The figure which shows an example of the vertical direction edge emphasis image obtained from the image | photographed image. The figure which shows an example of the horizontal direction edge emphasis image obtained from the image | photographed image. The figure for demonstrating the process on (rho) -theta space. The figure which shows an example of the process target area | region of a perpendicular direction. The figure which shows an example of the process target area | region of a horizontal direction. The figure which shows the state which divided | segmented the vertical direction edge emphasis image to the vertical direction. The figure for demonstrating the process which calculates the distortion amount of a vertical direction edge emphasis image. The figure for demonstrating the process which calculates the distortion amount of a horizontal direction edge emphasis image. The flowchart which shows the procedure of a calibration data creation process. The figure which shows the calibration image and the reference point obtained from the calibration image. The flowchart which shows the procedure of the process which measures the shape of a workpiece | work. The figure for demonstrating the idea of thickness correction of a workpiece | work. The figure for demonstrating the distortion correction process of an image (the 1). The figure for demonstrating the distortion correction process of an image (the 2). The figure which shows the calibration image image | photographed changing exposure time. The flowchart which shows the other procedure of exposure time determination processing. The flowchart which shows the other procedure of exposure time determination processing. Another example of the pattern printed on the photographing surface of the calibration board. Another example of the pattern printed on the photographing surface of the calibration board. The figure for demonstrating the method to calculate distortion amount from the image at the time of image | photographing the calibration board shown in FIG. The figure for demonstrating the other method of calculating distortion amount from the image at the time of image | photographing the calibration board shown in FIG. The figure for demonstrating the other method of calculating distortion amount from the image at the time of image | photographing the calibration board shown in FIG. The figure for demonstrating the method to calculate the distortion amount of each dot contained in the image when the calibration board shown in FIG. 24 is image | photographed.

  Hereinafter, some technical features of the embodiments disclosed in this specification will be described. In addition, each matter described below has technical usefulness independently.

(Characteristic 1) In the embodiment disclosed in the present specification, the light amount adjusting means may adjust the light amount of an image photographed by the photographing apparatus in a plurality of stages. The photographing apparatus may photograph a calibration image for each light amount adjusted in a plurality of stages. The distortion amount calculation means may calculate the distortion amount for each of the calibration images photographed for each light amount adjusted in a plurality of stages. The calibration data calculation unit may calculate calibration data from a calibration image that is captured with a light amount that minimizes the distortion amount among the calculated distortion amounts. With such a configuration, it is possible to suitably determine the amount of light with which the amount of distortion becomes small.

(Characteristic 2) In the embodiment disclosed in this specification, the calibration image may be a flat image on which a known pattern is printed. The distortion amount calculation means may calculate the distortion amount of the pattern. According to such a configuration, since the difference (distortion) between the known pattern and the pattern in the captured image is used, the distortion amount of the image can be suitably evaluated.

(Feature 3) In the embodiment disclosed in this specification, the pattern may be a checkered pattern. The distortion amount calculation means may extract a straight line group from the checkered calibration image and calculate a distortion amount from the distortion of each straight line constituting the extracted straight line group. According to such a configuration, since the amount of distortion is calculated from the distortion of each straight line extracted from the checkerboard pattern, the entire image photographed by the photographing apparatus can be suitably evaluated.

  The shape measuring apparatus 10 according to the present embodiment images a workpiece (for example, a spiral spring) and measures the shape of the workpiece from the captured image. The shape measuring apparatus 10 is used, for example, for shape inspection when shipping industrial products. As shown in FIG. 1, the shape measuring apparatus 10 includes a stage 12, a CCD camera 16 fixed to the stage 12, a computer 22 connected to the CCD camera 16 via a communication line 18, and a computer 22. The display 20 and the input device 24 (for example, a keyboard etc.) connected are provided.

  A calibration board 14 is placed on the stage 12 during calibration, and a workpiece (not shown) is placed during shape measurement. The CCD camera 16 is disposed on the stage 12 and photographs the calibration board 14 or work placed on the stage 12. The CCD camera 16 can adjust the exposure time during photographing. The calibration board 14 or work placed on the stage 12 is illuminated by an illuminator (not shown).

  Image data captured by the CCD camera 16 is input to the computer 22 via the communication line 18. In addition, the thickness of the workpiece is input to the computer 22 from the input device 24. The computer 22 stores a program for executing various processes described later. The computer 22 processes the image data of the calibration board 14 photographed by the CCD camera 16 to create calibration data, and processes the image data of the workpiece photographed by the CCD camera 16 to measure the shape of the workpiece. . The computer 22 displays the measured workpiece shape data on the display 20.

  FIG. 2 is a flowchart showing a flow of processing for creating calibration data by the shape measuring apparatus 10. The shape measuring apparatus 10 creates calibration data through each process shown in FIG. Hereinafter, the flow of processing by the shape measuring apparatus 10 will be described with reference to the flowchart shown in FIG.

  First, in step S <b> 10 of FIG. 2, the operator sets the calibration board 14 at the photographing position on the stage 12. A pattern having a known dimension (for example, a lattice pattern, a dot pattern, etc.) is formed on the imaging surface of the calibration board 14. In this embodiment, as shown in FIG. 5, a checkered pattern is printed on the imaging surface of the calibration board 14. In step S <b> 10, the calibration board 14 is set on the stage 12 so that the photographing surface (pattern) of the calibration board 14 is photographed by the CCD camera 16.

  Next, in step S20 of FIG. 2, a process for determining an exposure time when the calibration board 14 is photographed by the CCD camera 16 is performed. That is, as shown in FIG. 20, if the exposure time is too short, the pattern of the calibration board 14 in the image taken by the CCD camera 16 becomes unclear. Even if the exposure time is too long, the pattern of the calibration board 14 in the image taken by the CCD camera 16 becomes unclear. If the pattern in the image is unclear, the obtained calibration data also has an incorrect value. Therefore, by executing step S20, the exposure time at the time of shooting is determined.

  FIG. 3 is a flowchart showing the flow of the exposure time determination process (the process of step S20 in FIG. 2). First, in step S22 of FIG. 3, the computer 22 sets the exposure time of the CCD camera 16 to an initial value. Next, in step S <b> 24 of FIG. 3, the calibration board 14 is photographed by the CCD camera 16. Thereby, the calibration image 30 shown in FIG. 5 is acquired. In the calibration image 30, white square-shaped white areas 32 and black square-shaped black areas 34 are alternately arranged in the xy direction. An image (calibration image) captured by the CCD camera 16 is input to the computer 22.

  Next, in step S26 of FIG. 3, the computer 22 calculates the distortion amount of the calibration image 30 photographed in step S24. The distortion amount is a parameter used for evaluating whether or not the exposure time is appropriate. That is, as illustrated in FIG. 20, when the exposure time is short, the black region 34 in the calibration image 30 expands. On the other hand, when the exposure time is long, the white area 32 in the calibration image 30 swells. For this reason, when the exposure time is not appropriate, the rectangular distortion of the white region 32 and the black region 34 increases. Therefore, by calculating the distortion amount of the calibration image 30, it is evaluated whether or not the exposure time is appropriate.

  FIG. 4 is a flowchart showing a flow of processing for calculating the distortion amount. First, in step S38 in FIG. 4, the computer 22 creates edge-enhanced images in the horizontal direction (x direction) and the vertical direction (y direction) from the calibration image photographed in step S24. That is, the computer 22 selects the image 36 in which the boundary line 38 in the vertical direction (y direction) is emphasized among the boundary lines of the white region 32 and the black region 34 of the calibration image 30 illustrated in FIG. As shown in FIG. 7, an image 40 is created in which the boundary line 42 in the horizontal direction (x direction) is emphasized. As shown in FIG. 6, the vertical edge enhanced image 36 includes a plurality of vertical boundary lines 38, and as shown in FIG. 7, the horizontal edge enhanced image 40 includes a plurality of horizontal boundaries. Line 42 is included. The edge-enhanced images 36 and 40 can be created, for example, by applying a Sobel filter to the calibration image 30.

  Next, in step S40 of FIG. 4, the computer 22 calculates a representative straight line from the boundary lines 42 and 38 of the horizontal and vertical edge enhanced images 40 and 36 created in step S38. That is, the boundary line 38 of the vertical edge enhanced image 36 is curved due to distortion of the lens of the CCD camera 16 and the like, and the boundary line 42 of the horizontal edge enhanced image 40 is similarly curved. In step S40, a representative straight line is calculated by linearly approximating the boundary lines 38 and 42 of these edge enhanced images 36 and 40.

  Here, as a method of calculating the representative straight line from the boundary lines 38 and 42 of the edge-enhanced images 36 and 40, for example, Hough transformation can be used. Note that the method for calculating the representative straight line from the boundary line 38 of the vertical edge-enhanced image 36 and the method for calculating the representative straight line from the boundary line 42 of the horizontal edge-enhanced image 40 are the same. A method of calculating the representative straight line from the boundary line 38 of the direction edge enhanced image 36 will be described.

First, the vertical edge-enhanced image 36 is binarized and subjected to Hough conversion. By performing the Hough transform, the data on the xy plane is developed on the ρ-θ plane as shown in FIG. As shown in FIG. 6, the vertical edge-enhanced image 36 includes eight boundary lines 38. For this reason, there are eight data groups in FIG. Next, the data group of FIG. 8A is smoothed by applying a Gaussian window (FIG. 8B), and then the smoothed data group (FIG. 8B) is binarized (FIG. 8). (C)) Then, the binarized data group (FIG. 8C) is labeled (FIG. 8D). Then, a label whose area is equal to or greater than a predetermined threshold is extracted. Next, the center of gravity of each label extracted on the ρ-θ plane is calculated. For the calculation of the center of gravity, an expression obtained by weighting the expression for calculating the center of gravity by arithmetic mean by the nth power of the density value h (θ, ρ) is used. The weighted centroid (θ _c (i), ρ _c (i)) corresponding to the i-th label (LABEL (i)) is calculated by the following equation (1). Here, N _i is the number of pixels belonging to LABEL (i).

When the center of gravity (θ _c (i), ρ _c (i)) of the label is calculated by the above formula, it is converted into a representative straight line by the following formula (2) using this. Here, a _i = cos (θ _c (i)), b _i = sin (θ _c (i)), and c _i = ρ _c (i).

  As described above, the data on the xy plane is developed on the ρ-θ plane by the Hough transform, so that the representative straight line can be suitably calculated from the image including noise. Further, since the representative straight line is calculated from the center of gravity obtained by weighting the density value of each point developed on the ρ-θ plane by the nth power, the representative straight line can be calculated stably.

  Next, in step S42 of FIG. 4, the computer 22 determines a processing target area using the representative straight line obtained in step S40. That is, as shown in FIG. 9, a vertical boundary line 38 is included in a region 46 sandwiched between straight lines 46a and 46b obtained by shifting the representative straight line in the horizontal direction. Then, a region 46 sandwiched between the shifted straight lines 46a and 46b is set as a processing target region. Similarly, with respect to the horizontal boundary line 42, as shown in FIG. 10, the horizontal boundary line 42 is included in a region 48 sandwiched between straight lines 48a and 48b obtained by shifting the representative straight line in the vertical direction. A region 48 sandwiched between the shifted straight lines 48a and 48b is set as a processing target region.

Next, in step S44 of FIG. 4, the computer 22 divides the edge-enhanced images 36 and 40 into a plurality of regions. Specifically, as shown in FIG. 11, the vertical edge-enhanced image 36 is equally divided into J in the vertical direction by a cutting line 50 (l _{0 to} l _J ) extending in the horizontal direction (J = 7 in FIG. 11). . As a result, the boundary line 38 extending in the vertical direction is equally divided into J in the vertical direction. Similarly, as shown in FIG. 13, the horizontal edge-enhanced image 40 is equally divided in the horizontal direction by cutting lines 52 (m _{0 to} m _I ) extending in the vertical direction. Thus, the boundary line 42 extending in the horizontal direction is equally divided into I in the horizontal direction.

Next, in step S46 of FIG. 4, the computer 22 linearly approximates the boundary lines 38 and 42 in each region divided in step S44. That is, as shown in FIG. 12, the vertical edge-enhanced image 36 is divided in the vertical direction by step S44. Accordingly, the boundary line 38 and the processing target area 46 are also divided in the vertical direction. Therefore, the divided boundary lines 38 _{i, j} are linearly approximated in each of the divided processing target areas 46 _{i, j} . As a result, a division boundary approximation line (x = α _{i, j} y + β _{i, j} (i = 0 to 7, j = 0 to 6)) obtained by linear approximation of the plurality of division boundary lines 38 _{i, j} is obtained. As a method for linearly approximating the divided boundary lines 38 _{i, j} , the least square method or the Hough transform described above can be used. As for the horizontal edge-enhanced image 40, as shown in FIG. 13, in each processing target area 48 _{i, j} divided in the horizontal direction, the divided boundary lines 42 _{i, j} are linearly approximated. As a result, a division boundary approximation line (y = A _{i, j} x + B _{i, j} (i = 0-12, j = 0-5)) obtained by linear approximation of the plurality of division boundary lines 42 _{i, j} is obtained. Note that, when calculating the divided boundary approximate straight line, the calculation target area is limited to the processing target areas 46 _{i, j} , 48 _{i, j} , so that the calculation processing amount can be greatly reduced.

Next, in step S48 in FIG. 4, the computer 22 calculates the distortion amount of the image captured by the CCD camera 16 in step S24 in FIG. Specifically, first, as shown in FIG. 12, each vertical boundary line 38 _i (ie, the i-th (i = 0 to 7) boundary line 38 extending in the vertical direction) is calculated in step S46. The variance σ _β ² (i) of the x-intercept of the vertical division boundary approximation straight line (x = α _{i, j} y + β _{i, j} (i = 0-7, j = 0-6)) is calculated. The variance σ _β ² (i) of the x intercept is calculated by the following equation (3). Note that β _ave (i) is an average of x-intercepts. N _y is the number of sections after division.

After calculating the variance sigma _beta ² (i) for each of the vertical boundary line 38 _i, the equation (4) below, calculates the average value sigma _beta ² _ave of these variance σ β ² _(i). N _x is the number of sections after division.

Similarly, as shown in FIG. 13, the horizontal direction calculated in step S46 for each of the horizontal boundary lines 42 _j (that is, the jth (j = 0 to 5) boundary lines 42 extending in the horizontal direction). The variance σ _B ² (j) of the y intercept of the divided boundary approximation straight line (y = A _{i, j} x + B _{i, j} (i = 0-12, j = 0-5)) is calculated. The variance σ _B ² (j) of the y intercept is calculated by the following equation (5). B _ave (j) is an average of y-intercepts.

After calculating the horizontal boundary line 42 variance sigma _B ² for each of the _j (j), the following equation (6), calculates the average value sigma _B ² _ave of these variance σ _B ² (j).

Finally, the sum of the average variance σ _β ² _ave for the vertical boundary 38 calculated as described above and the average variance σ _B ² _ave for the horizontal boundary 42 is shown in FIG. 3, the distortion amount of the image taken by the CCD camera 16 in step S24.

Here, the meaning of the distortion amount σ ² calculated in step S48 will be briefly described. As shown in FIG. 20, in the calibration image 30 where the exposure time is not appropriate, the black region 34 or the white region 32 swells. On the other hand, in the calibration image 30, white areas 32 and black areas 34 are alternately arranged in the xy direction. For this reason, in the calibration image 30 in which the exposure time is not appropriate, the boundary lines 38 and 42 of the edge enhanced images 36 and 40 are alternately shifted left and right or up and down. Accordingly, the exposure time of the calibration image is obtained by taking the sum of the average value σ _β ² _ave of the dispersion for the vertical boundary line 38 and the average value σ _B ² _ave of the dispersion for the horizontal boundary line 42. It is possible to evaluate whether or not is appropriate.

When the distortion amount σ ² is calculated in step S48, the process returns to step S28 in FIG. 3, and the computer 22 determines whether or not the calculated distortion amount σ ² is smaller than the minimum distortion amount σ _min ² . When the exposure time is the initial value, the minimum distortion amount σ _min ² is not stored in the memory of the computer 22, and therefore it is determined that the distortion amount σ ² calculated in step S 26 is smaller than the minimum distortion amount σ _min ^2. . On the other hand, when the exposure time is not the initial value, the minimum amount of distortion sigma _min ² in the memory of the computer 22 has already been stored, the minimum amount of distortion the distortion amount calculated in step S26 sigma ² is stored sigma _It is determined whether or not it is smaller than _min ² .

When the calculated strain amount σ ² is smaller than the minimum strain amount σ _min ² , the process proceeds to step S30 in FIG. 3, and the computer 22 stores the calculated strain amount σ ² as the minimum strain amount σ _min ² . As a result, the minimum distortion amount σ _min ² is updated. On the other hand, when the calculated distortion amount σ ² is larger than the minimum distortion amount σ _min ² , the process skips step S30 in FIG. 3 and proceeds to step S32, and the computer 22 determines whether or not the exposure time has reached the final value. To do.

If the exposure time is not the final price, the process proceeds to step S36 in FIG. 3, and the computer 22 changes the exposure time and repeats the processing from step S24. Thus, the calibration board 14 is photographed while changing the exposure time, the distortion amount σ ² is calculated for the photographed calibration image, and the minimum distortion amount σ _min ² is updated. On the other hand, if the exposure time is the final value, the process proceeds to step S34 in FIG. 3, and the computer 22 determines the exposure time when the minimum distortion amount σ _min ² is calculated as the final exposure time. Thus, an appropriate exposure time is determined from the exposure time within a predetermined range (that is, the initial value to the final value).

  When the exposure time is determined, the process proceeds to step S50 in FIG. 2, and the computer 22 executes calibration data creation processing. The calibration data creation process will be described with reference to the flowchart of FIG.

First, in step S52 of FIG. 14, the computer 22 specifies a calibration image photographed with the exposure time determined in step S34 of FIG. And the process of step S54-S62 of FIG. 14 is performed with respect to the specified calibration image. Here, the processes of steps S54 to S62 are the same as the processes of steps S38 to S46 of FIG. Therefore, by executing the processing of steps S54 to S62, the divided boundary approximate straight line (x = α _{i, j} y + β _{i, j} (i = 0 to 7, j = 0-6)) are obtained. Further, a divided boundary approximate straight line (y = A _{i, j} x + B _{i, j} (j = 0 to 5, i = 0 to 12)) is obtained for a plurality of boundary lines 42 in the horizontal direction. In addition, if the division | segmentation boundary approximation straight line obtained by performing the process of step S40-S46 of FIG. 4 is memorize | stored previously, the process of step S54-S62 of FIG. 14 does not need to be performed.

Next, in step S64 of FIG. 14, the computer 22 determines that the division boundary approximate line (x = α _{i, j} y + β _{i, j} (i = 0-7, j = 0-6)) and the division boundary approximation line (y = A _{i, j} x + B _{i, j} (j = 0 to 5, i = 0 to 12)) and the position data of the intersection (reference point) are calculated. Next, in step S66 of FIG. 14, the computer 22 stores the coordinate data of the intersection obtained in step S64 as calibration data. Thus, the calibration data creation process in FIG. 2 is completed. When the calibration data creation processing is completed, as shown in FIG. 15, the position (pxi _{, j} , pyi _{, j} ) of the vertex of the white area 32 (that is, the vertex of the black area 34) in the calibration image 30. Is stored in the memory as calibration data.

  Next, processing for measuring the shape of the workpiece by the shape measuring apparatus 10 will be described. FIG. 16 is a flowchart showing the flow of processing for measuring the shape of a workpiece. The shape measuring apparatus 10 creates data obtained by measuring the shape of the workpiece through each process shown in FIG. Hereinafter, the flow of processing by the shape measuring apparatus 10 will be described along the flowchart shown in FIG.

  First, in step S <b> 70 of FIG. 16, the worker inputs the workpiece thickness into the computer 22 using the input device 24. That is, as shown in FIG. 17, when the thickness of the calibration board 14 and the thickness of the work W are different, the actual size is different even if the positions in the photographed image are the same. Therefore, the thickness of the workpiece W is input to the computer 22 in order to take into account the difference between the thickness of the calibration board 14 and the thickness of the workpiece W.

  Next, in step S <b> 72 of FIG. 16, the computer 22 sets an exposure time when photographing the workpiece W. For example, the computer 22 may set the exposure time determined in step S34 in FIG. Alternatively, an exposure time different from this may be set depending on illumination conditions. Next, in step S <b> 74 of FIG. 16, the worker installs the workpiece W on the stage 12. Next, in step S <b> 76 of FIG. 16, the computer 22 photographs the work W by the CCD camera 16. An image of the workpiece W photographed by the CCD camera 16 is input to the computer 22 via the communication line 18.

  Next, in step S78 of FIG. 16, the computer 22 reads the calibration data created in step S50 of FIG. Next, the process proceeds to step S80 in FIG. 16, and the computer 22 corrects the calibration data read out in step S78 with the thickness of the workpiece W input in step S70. That is, the calibration data is corrected in consideration of the difference between the thickness of the calibration board 14 and the thickness of the workpiece W. The thickness correction of the workpiece W in step S80 can be performed by a conventionally known method.

Next, in step S82 of FIG. 16, the computer 22 corrects the image of the workpiece W photographed in step S76 using the calibration data corrected in step S80. That is, as described above, the calibration data stores the position coordinates of the reference point in the calibration image. Therefore, if the captured image is distorted, as shown in FIG. 18, four adjacent reference points (pxi _{, j} , pyi _{, j} ), (pxi _{i + 1, j} , py _{i + 1, j} ), (px A square 54 formed by connecting _{i, j + 1} , py _{i, j + 1} ), (pxi _{+ 1, j + 1} , py _{i + 1, j + 1} ) is also distorted. On the other hand, the pattern (rectangle) printed on the calibration board 14 is not distorted, and the size of this pattern is known. Therefore, as shown in FIG. 19, by converting a square 54 formed by four adjacent reference points in the image into a correct shape 56 printed on the calibration board 14, distortion of the captured image can be reduced. Correct it. In step S83, the computer 22 measures the dimension of the workpiece W from the image whose distortion has been corrected. The measured result (the dimension of the workpiece W) is displayed on the monitor 20.

  As described above, in the shape measuring apparatus 10 of this embodiment, the calibration board 14 is photographed by the CCD camera 16 while changing the exposure time, and the distortion amount of each calibration image 30 obtained by the photographing is calculated. . Then, the exposure time at which the calculated amount of distortion becomes the smallest is determined as the exposure time when photographing the calibration board 14 and the workpiece W. Therefore, the light amount is automatically adjusted to an appropriate light amount without requiring the skill of the operator, and the calibration board 14 and the work W can be photographed with an appropriate exposure time.

  Further, in the shape measuring apparatus 10 of the present embodiment, the boundary lines 38 and 42 in the vertical direction and the horizontal direction are extracted from the image of the calibration board 14 on which the checkered pattern is printed, and the boundary lines 38 and 42 are straight lines. Approximate. Then, the approximated straight line (representative straight line) is shifted in the horizontal direction or the vertical direction, and the region 46 or 48 including the corresponding boundary line 38 or 42 is extracted. Then, the position data of the reference point is specified within the extracted areas 46 and 48. Therefore, relatively narrow areas 46 and 48 including the reference point are extracted from the calibration image 30, and the coordinates of the reference point are specified in the extracted areas 46 and 48. For this reason, it is possible to reduce the amount of calculation required for the process of specifying the coordinates of the reference point.

  The correspondence relationship between the present embodiment and the claims will be described. The CCD camera 16 is an example of a “photographing device”. The adjustment process of the exposure time of the CCD camera 16 by the computer 22 is an example of the “light quantity adjusting unit”. The distortion amount calculation processing by the computer 22 (step S26 in FIG. 3 and steps S38 to S48 in FIG. 4) is an example of “distortion amount calculation means”. The calibration data creation processing (step S50 in FIG. 2 and steps S52 to S66 in FIG. 14) by the computer 22 is an example of “calibration data creation means”.

  Specific examples of the present invention have been described in detail above, but these are merely examples and do not limit the scope of the claims. The technology described in the claims includes various modifications and changes of the specific examples illustrated above.

For example, in the above-described embodiment, the exposure time with the smallest distortion amount is searched for within the predetermined exposure time range, and the exposure time with the smallest distortion amount is set as the exposure time at the time of shooting. The technology disclosed in the specification is not limited to such a form. For example, as shown in FIG. 21, the computer 22 sets the exposure time to an initial value (step S84), images the calibration board 14 (step S86), and obtains a calibration image 30 obtained by the imaging. A distortion amount σ ² is calculated (step S88). Then, the computer 22 determines whether or not the calculated distortion amount σ ² has become smaller than the threshold value (step S90). If the calculated distortion amount σ ² is not smaller than the threshold value (NO in step S90), the computer 22 changes the exposure time (step S94) and repeats the processing from step S86. On the other hand, if the calculated distortion amount σ ² is smaller than the threshold value (YES in step S90), the computer 22 determines the exposure time for making the distortion amount σ ² smaller than the threshold value as the exposure time at the time of shooting (step S92). ). Also by such a method, the exposure time for sufficiently reducing the distortion amount σ ² can be determined, and the calibration board 14 and the workpiece W can be suitably photographed.

Alternatively, as shown in FIG. 22, the computer 22 sets the exposure time to an initial value (step S96), images the calibration board 14 (step S98), and obtains a calibration image 30 obtained by the imaging. A distortion amount σ ² is calculated (step S100). Then, the computer 22 determines whether or not the calculated strain amount σ ² is smaller than the first set value, and the absolute value of the calculated change amount Δσ ² of the strain amount σ ² is smaller than the second set value. It is determined whether or not (step S102). Then, when the calculated strain amount σ ² is not smaller than the first set value or the absolute value of the calculated change amount Δσ ² of the strain amount σ ² is not smaller than the second set value (NO in step S102). The computer 22 changes the exposure time (step S106) and repeats the processing from step S98. On the other hand, when the calculated strain amount σ ² is smaller than the first set value and the absolute value of the calculated change amount Δσ ² of the strain amount σ ² is smaller than the second set value (YES in step S102), The computer 22 determines the exposure time at that time as the exposure time at the time of photographing (step S104). Also by such a method, the exposure time can be determined in the vicinity where the distortion amount σ ² becomes the minimum value, and the calibration board 14 and the workpiece W can be suitably photographed.

In the above-described embodiment, the process for determining the exposure time (FIG. 3) may be performed in two stages, that is, the coarse search and the detailed search, so that the exposure time determination process can be speeded up. Further, the exposure time at which the distortion amount σ ² becomes the minimum value may be searched using the binary search method or Newton method with the distortion amount σ ² of the calibration image 30 as an evaluation function. The light amount of the captured image may be adjusted by adjusting the illumination intensity of the illumination device, the aperture of the CCD camera 16, and the gain of the CCD camera 16, instead of adjusting the exposure time (exposure time) of the CCD camera 16. Good. Moreover, you may adjust combining these.

  Furthermore, in the above-described embodiment, the checkered pattern calibration board 14 is used, but a lattice pattern calibration board 58 as shown in FIG. 23 may be used, or a dot pattern as shown in FIG. A calibration board 60 may be used. When the lattice-pattern calibration board 58 shown in FIG. 23 is used, the distortion amount of the captured calibration image can be calculated by the same method as in the above-described embodiment. In other words, the distortion amount of the captured image can be calculated from the distortion amount of the straight line in the vertical direction and the distortion amount of the straight line in the horizontal direction.

On the other hand, when the dot pattern calibration board 60 shown in FIG. 24 is used, the image distortion amount can be calculated by the following method. For example, as shown in FIG. 25, the y distortion of the image using the y-intercept of the straight line 64 connecting the adjacent dots 62 in the horizontal direction and the x-intercept of the straight line connecting the adjacent dots 62 in the vertical direction. Can be calculated. Alternatively, as shown in FIG. 26, the amount of distortion may be calculated from the degree of distortion of a rectangular shape 66 formed by connecting adjacent dots 62 in the horizontal direction and the vertical direction. Furthermore, as shown in FIG. 27, the amount of distortion may be calculated from the degree of collapse of each dot 62. Specifically, as shown in FIG. 28, first, the dot 62 is binarized to calculate the contour line 70 and the center of gravity G (c _x , c _y ). Then, the center of gravity _{G (c} x, c _y) each point on the contour line 70 from _(x i, _{y i)} calculates the distance _{l i} to, calculates the average radius _{r ave} from the calculated distance _{l i} (72 in the figure is a circle with an average radius r _ave ). Next, using the distance l _i from the center of gravity G (c _x , c _y ) to each point (x _i , y _i ) on the contour line 70 and the average radius r _ave , the distortion amount D by the following equation (7) Is calculated.

  The distortion amount D calculated by the above equation (7) is calculated for each dot 62 and averaged to obtain the distortion amount of the image. Also by such a method, the distortion amount of the calibration image 30 can be calculated.

Alternatively, to calculate the area of each dot 62 may be the amount of strain dispersion D _m of the area.

  The technical elements described in this specification or the drawings exhibit technical usefulness alone or in various combinations, and are not limited to the combinations described in the claims at the time of filing. In addition, the technology exemplified in this specification or the drawings achieves a plurality of objects at the same time, and has technical utility by achieving one of the objects.

10: shape measuring device 12: stage 14: calibration board 16: CCD camera 18: communication line 20: display 22: computer 24: input device

Claims (4)

A measuring device that measures the shape of a workpiece,
A photographing device;
A light amount adjusting means for adjusting a light amount of an image photographed by the photographing device;
A distortion amount calculating means for calculating a distortion amount of a calibration image photographed by the photographing apparatus;
A light amount determination means for determining a light amount to be adjusted by the light amount adjustment means based on the distortion amount calculated by the distortion amount calculation means;
Calibration data calculating means for calculating calibration data from a calibration image photographed by the photographing device when the light quantity is determined by the light quantity determining means;
A shape measuring device. The light amount adjusting means adjusts the light amount of the image photographed by the photographing apparatus in a plurality of stages,
The imaging device captures a calibration image for each light amount adjusted in multiple stages,
The distortion amount calculation means calculates the distortion amount for each of the calibration images taken for each light amount adjusted in a plurality of stages,
The calibration data calculation means calculates calibration data from a calibration image taken with a light amount with the smallest distortion amount among the calculated distortion amounts.
The shape measuring apparatus according to claim 1. The calibration image is a flat image on which a known pattern is printed,
The shape measuring apparatus according to claim 1, wherein the distortion amount calculating unit calculates a distortion amount of the pattern. The pattern is a checkered pattern,
The shape measuring apparatus according to claim 3, wherein the distortion amount calculating unit extracts a straight line group from the checkered pattern calibration image, and calculates a distortion amount from the distortion of each straight line constituting the extracted straight line group.