CN117237440B - Image calibration method for printing control instrument - Google Patents
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Abstract
The invention discloses an image calibration method of a printing control instrument, which relates to the technical field of image calibration, and solves the technical problems that the position where printing paper is placed has deviation, parameters cannot be reasonably adjusted, and the integral quality of an image is affected.
Description
Technical Field
The invention relates to the technical field of image calibration, in particular to an image calibration method of a printing control instrument.
Background
The seal control instrument is an automatic seal stamping instrument, has the characteristics of scientificity and normalization compared with the traditional manual seal stamping, and realizes the separation of people and seal, so as to ensure the scientificity, safety and high efficiency of seal use. The seal is sealed in the seal control instrument, the instrument has the function of receiving the instruction to automatically seal, and the seal is completed in the seal control instrument.
When the existing printing control instrument is used, on one hand, deviation exists when the printing paper is placed due to the influence of human factors, further the problem of image quality exists, on the other hand, the influence of the image quality can be caused due to unreasonable parameter setting, but the parameter is automatically set by people, calculation of a system is not carried out, meanwhile, the factors affecting the parameter cannot be specifically analyzed, and the follow-up problem still exists.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an image calibration method of a printing control instrument, which solves the problems that deviation exists in the placement position of printing paper, parameters cannot be reasonably adjusted, and the integral quality of an image is affected.
In order to achieve the above purpose, the invention is realized by the following technical scheme: the image calibration method of the printing control instrument specifically comprises the following steps:
step one: acquiring an identification area of the printing control instrument, acquiring a placement position of printing paper, and analyzing and judging the placement position of the printing paper by combining the identification area to generate an analysis result, wherein the analysis result comprises abnormal placement and normal placement, and regulating the abnormal placement to obtain a regulating result;
step two: then, acquiring a normally placed printing paper image, recording the printing paper image as a target object, analyzing pixel values, bright noise points and dark noise points of the target object, judging the quality of the target object, classifying the quality into qualified and unqualified and generating an image quality classification result;
step three: then analyzing the object classified as the unqualified object, carrying out image segmentation processing on the unqualified object, and screening and marking the segmented image as an image to be analyzed;
step four: acquiring all areas to be analyzed, analyzing the distribution conditions of the areas to be analyzed, and generating distribution condition information, wherein the distribution condition information comprises centralized distribution and distributed distribution, and then analyzing the centralized distribution and the distributed distribution respectively to obtain influence results, wherein the influence results comprise ink thickness and printing speed;
step five: and obtaining an influence result of the unqualified target object, and displaying the influence result to a corresponding operator.
As a further aspect of the invention: the specific mode for generating the adjusting result in the first step is as follows:
s1: carrying out three-dimensional modeling processing on the identification area and the printing paper placement position, then respectively and longitudinally establishing a reference plane by four sides of the identification area, and simultaneously carrying out label processing on four vertexes of the printing paper and marking as Di, wherein i=1, 2, 3 and 4;
s2: then, obtaining distance references of four vertexes of the printing paper and four reference surfaces as LDi, and comparing the calculated distance LDi in the specific comparison mode: judging whether the distances between the two vertexes at the same side are the same, if so, indicating that the placement positions of the printing papers are normal placement, and if not, indicating that the placement positions of the printing papers are abnormal placement;
s3: and then analyzing the abnormally placed printing paper, obtaining the vertex with the smallest distance LDi at the same side, marking the vertex as an inclined point, simultaneously obtaining the angle between the printing paper edge where the inclined point is positioned and the reference plane, and generating an adjusting result. Specifically, the position of the printing paper and the position of the identification area are compared and analyzed to judge whether the printing paper is at a proper position or not, the distance between the vertex of the printing paper and the reference surface of the identification area is judged in a comparison mode, the placement positions with different distances are further recorded as abnormal placement, meanwhile, the abnormal placement positions are judged, and the inclination angle of the abnormal placement positions is obtained.
As a further aspect of the invention: the specific mode for generating the image classification result according to the pixel value in the second step is as follows:
p1: acquiring a pixel value of a target object, recording the pixel value as an XS, and then acquiring a pixel value of a standard image, recording the pixel value of the standard image as XSb, wherein the pixel value XSb of the standard image is represented as a pixel value of a finished product image, is an input parameter pixel value designated by a user, and comparing the pixel value with the pixel value of the standard image;
p2: when XS is equal to or greater than XSb, the pixel value of the target object exceeds the pixel value of the standard image, and the target object is marked as a qualified object, whereas when XS is less than XSb, the pixel value of the target object does not exceed the pixel value of the standard image, and the target object is marked as a disqualified object.
As a further aspect of the invention: in the second step, the specific mode of generating the image classification result by judging the bright noise point is as follows:
m1: obtaining noise points of a standard image, wherein the noise points comprise bright noise points and dark noise points, analyzing and judging the influence of the bright noise points and the dark noise points on the definition of the standard image respectively, and obtaining the number of the bright noise points and recording the number as LDb when the bright noise points influence the definition of the standard image;
m2: and then obtaining the target object bright noise number as LD, comparing the LD with LDb, if the LD is more than or equal to LDb, indicating that the target object bright noise number exceeds the standard image bright noise number, classifying the target object as an unqualified object, otherwise, if the LD is less than LDb, indicating that the target object bright noise number does not exceed the standard image noise number, and classifying the target object as a qualified object.
As a further aspect of the invention: in the second step, the specific mode of judging the dark noise point to generate the image classification result is as follows:
n1: acquiring the number of dark noise points of the standard image as ADb, judging the change of the definition of the standard image when the number of dark noise points ADb is reduced, and if the number of dark noise points ADb is reduced and the definition of the standard image is increased, inversely proportional relation between the number of table dark noise points and the definition of the non-standard image is obtained;
n2: and then obtaining the dark noise count of the target object, recording as AD, comparing the AD with ADb, when AD is more than or equal to ADb, indicating that the definition of the target object is unqualified, classifying the target object as an unqualified object, and otherwise, classifying the target object as a qualified object when AD is less than ADb.
As a further aspect of the invention: the specific mode of acquiring the area to be analyzed in the third step is as follows:
a1: acquiring the length and the width of an unqualified target object, respectively marking the length and the width as CD and KD, enabling the unqualified target object to be rectangular, simultaneously acquiring the minimum convention number of the length CD and the width KD as G, and then carrying out G segmentation on the length CD and the width KD of the target object to obtain G segmented images; specifically, the default g+.1 is found when the least common divisor G is applied to the length CD and the width KD.
A2: then, the G divided images are marked as k, and k=1, 2, …, G, and the color values of the divided images k are obtained and marked as SCk, and the average value mark of the color values of the G divided images is calculated and marked as SCp, and the color values SCk of the different divided images k are compared with the average value SCp, specifically in the following comparison manner:
a21: if SCk is more than or equal to SCp, marking the segmented image k as an over-average image, otherwise, marking the segmented image k as a low-average image if SCk is less than SCp, and then respectively analyzing the over-average image and the low-average image;
a22: and then acquiring a color value record of the standard image as SCb, acquiring a maximum value record of the color value of the low-average image as SCmax, comparing SCmax with SCb, and judging that the color value of the low-average image is abnormal when SCmax is smaller than SCb. Specifically, when the maximum value of the color values of the low-average image does not exceed the color value SCb of the standard image, that is, the color values of all the low-average images do not exceed the color value SCb of the standard image, the low-average image is marked as an area to be analyzed, and analysis is not needed for those high-average images.
As a further aspect of the invention: the specific mode for obtaining the influence result in the fourth step is as follows:
analysis of the centralized distribution:
b1: acquiring all the areas to be analyzed, marking the areas to be analyzed as m, acquiring the corresponding ink thickness marks as YMm and the exposure time marks as Tm, and simultaneously acquiring the ink thickness YMb and the exposure time Tb of the standard image;
b2: comparing YMm with YMb, when YMm is larger than YMb, judging that the ink thickness of the area m to be analyzed exceeds a standard value, then calculating the difference between YMm and YMb to be | YMm-YMb |, and generating an ink thickness influence result, otherwise, when YMm is smaller than or equal to YMb, judging that the ink thickness of the area m to be analyzed does not exceed the standard value, and not performing any treatment;
b3: and comparing the exposure time length, when Tm is larger than Tb, judging that the exposure time length of the area m to be analyzed is too long, otherwise, when Tm is smaller than or equal to Tb, judging that the exposure time length of the area m to be analyzed is normal, and calculating the exposure speed of the printing control instrument according to the acquired exposure time length, wherein the specific calculation mode is as follows:
b31: acquiring all the areas m to be analyzed positioned on a straight line, taking the number as H, acquiring the total length mark of the areas m to be analyzed on the straight line as CDz, and simultaneously acquiring the exposure time mark as Tz;
b32: CDz and Tz are then substituted into the formulaThen the exposure speed of the individual areas m to be analyzed is calculated as +.>And difference calculation is performed between it and the current exposure speed SD1 +.>Simultaneously generating an exposure speed influence result;
the analysis of the distributed distribution is the same as the analysis of the centralized distribution described above.
Advantageous effects
The invention provides an image calibration method of a printing control instrument. Compared with the prior art, the method has the following beneficial effects:
the invention can reduce the influence caused by the deviation of the subsequent position by identifying and analyzing the placement position of the printing paper and reasonably adjusting the placement position according to the identification result, then analyzes the image quality, judges whether the image quality reaches the standard according to the pixel value and the noise number of the image, analyzes the image which does not reach the standard, determines specific reasons by analyzing the thickness of the printing ink and the exposure time length, and reasonably adjusts the printing ink according to the specific reasons.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a process diagram of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present application provides a method for calibrating an image of a print controller, which specifically includes the following steps:
step one: the method comprises the steps of obtaining an identification area of a printing control instrument, obtaining a placement position of printing paper, and carrying out analysis and judgment on the placement position of the printing paper by combining the identification area to generate an analysis result, wherein the analysis result comprises abnormal placement and normal placement, and adjusting the abnormal placement to obtain an adjustment result, and the obtaining mode of the adjustment result is as follows:
s1: carrying out three-dimensional modeling processing on the identification area and the printing paper placement position, then respectively and longitudinally establishing a reference plane by four sides of the identification area, and simultaneously carrying out label processing on four vertexes of the printing paper and marking as Di, wherein i=1, 2, 3 and 4;
s2: then, obtaining distance references of four vertexes of the printing paper and four reference surfaces as LDi, and comparing the calculated distance LDi in the specific comparison mode: judging whether the distances between the two vertexes at the same side are the same, if so, indicating that the placement positions of the printing papers are normal placement, and if not, indicating that the placement positions of the printing papers are abnormal placement;
s3: and then analyzing the abnormally placed printing paper, obtaining the vertex with the smallest distance LDi at the same side, marking the vertex as an inclined point, simultaneously obtaining the angle between the printing paper edge where the inclined point is positioned and the reference plane, and generating an adjusting result. Specifically, the position of the printing paper and the position of the identification area are compared and analyzed to judge whether the printing paper is at a proper position or not, the distance between the vertex of the printing paper and the reference surface of the identification area is judged in a comparison mode, the placement positions with different distances are further recorded as abnormal placement, meanwhile, the abnormal placement positions are judged, and the inclination angle of the abnormal placement positions is obtained.
Step two: then, a normally placed printing paper image is obtained and recorded as a target object, then, the pixel value of the target object is analyzed to judge the quality of the target object, meanwhile, the quality is classified into qualified and unqualified and an image quality classification result is generated, and the specific obtaining mode of the image classification result is as follows:
p1: acquiring a pixel value of a target object, recording the pixel value as an XS, and then acquiring a pixel value of a standard image, recording the pixel value of the standard image as XSb, wherein the pixel value XSb of the standard image is represented as a pixel value of a finished product image, is an input parameter pixel value designated by a user, and comparing the pixel value with the pixel value of the standard image;
p2: when XS is equal to or greater than XSb, the pixel value of the target object exceeds the pixel value of the standard image, and the target object is marked as a qualified object, whereas when XS is less than XSb, the pixel value of the target object does not exceed the pixel value of the standard image, and the target object is marked as a disqualified object. Specifically, the image pixels are the basic requirement for judging the image quality, the object is reasonably judged and classified according to the printed image pixel values, and the image pixel values can be directly obtained through software, such as Photoshop, GIMP.
Step three: then analyzing the object classified as the unqualified object, carrying out image segmentation processing on the unqualified object, screening the segmented image and marking the segmented image as an image to be analyzed, wherein the acquisition mode of the area to be analyzed is as follows:
a1: acquiring the length and the width of an unqualified target object, respectively marking the length and the width as CD and KD, enabling the unqualified target object to be rectangular, simultaneously acquiring the minimum convention number of the length CD and the width KD as G, and then carrying out G segmentation on the length CD and the width KD of the target object to obtain G segmented images; specifically, the default g+.1 is found when the least common divisor G is applied to the length CD and the width KD.
A2: then, the G divided images are marked as k, and k=1, 2, …, G, and the color values of the divided images k are obtained and marked as SCk, and the average value mark of the color values of the G divided images is calculated and marked as SCp, and the color values SCk of the different divided images k are compared with the average value SCp, specifically in the following comparison manner:
a21: if SCk is more than or equal to SCp, marking the segmented image k as an over-average image, otherwise, marking the segmented image k as a low-average image if SCk is less than SCp, and then respectively analyzing the over-average image and the low-average image;
a22: and then acquiring a color value record of the standard image as SCb, acquiring a maximum value record of the color value of the low-average image as SCmax, comparing SCmax with SCb, and judging that the color value of the low-average image is abnormal when SCmax is smaller than SCb. Specifically, when the maximum value of the color values of the low-average image does not exceed the color value SCb of the standard image, that is, the color values of all the low-average images do not exceed the color value SCb of the standard image, the low-average image is marked as an area to be analyzed, and analysis is not needed for those high-average images.
Step four: obtaining all areas to be analyzed, analyzing the distribution conditions of the areas to be analyzed, generating distribution condition information, wherein the distribution condition information comprises centralized distribution and distributed distribution, and then analyzing the centralized distribution and the distributed distribution respectively to obtain an influence result, wherein the influence result comprises the thickness of ink and the printing speed, and the mode of obtaining the influence result is as follows:
analysis of the centralized distribution:
b1: acquiring all the areas to be analyzed, marking the areas to be analyzed as m, acquiring the corresponding ink thickness marks as YMm and the exposure time marks as Tm, and simultaneously acquiring the ink thickness YMb and the exposure time Tb of the standard image;
b2: comparing YMm with YMb, when YMm is larger than YMb, judging that the ink thickness of the area m to be analyzed exceeds a standard value, then calculating the difference between YMm and YMb to be | YMm-YMb |, and generating an ink thickness influence result, otherwise, when YMm is smaller than or equal to YMb, judging that the ink thickness of the area m to be analyzed does not exceed the standard value, and not performing any treatment;
b3: and comparing the exposure time length, when Tm is larger than Tb, judging that the exposure time length of the area m to be analyzed is too long, otherwise, when Tm is smaller than or equal to Tb, judging that the exposure time length of the area m to be analyzed is normal, and calculating the exposure speed of the printing control instrument according to the acquired exposure time length, wherein the specific calculation mode is as follows:
b31: acquiring all the areas m to be analyzed positioned on a straight line, taking the number as H, acquiring the total length mark of the areas m to be analyzed on the straight line as CDz, and simultaneously acquiring the exposure time mark as Tz;
b32: CDz and Tz are then substituted into the formulaThen the exposure speed of the individual areas m to be analyzed is calculated as +.>And difference calculation is performed between it and the current exposure speed SD1 +.>And simultaneously generating an exposure speed influence result.
The analysis of the distributed distribution is the same as the analysis of the centralized distribution described above.
Specifically, the ink thickness and the exposure speed of the area to be analyzed are analyzed, wherein the exposure speed is equal to the printing speed of a printing controller, and meanwhile, for the case of centralized distribution and distributed distribution, the reason for occurrence of the problem is one of the ink thickness and the exposure time, and if the reason for occurrence of the centralized distribution is the ink thickness, the reason for occurrence of the distributed distribution is the exposure time.
Step five: and obtaining an influence result of the unqualified target object, and displaying the influence result to a corresponding operator.
The second embodiment of the present invention is different from the first embodiment in that the method for determining the target object in the second embodiment is different, in that the target object is classified by analyzing the noise point of the image in the second embodiment, and the specific acquisition method of the image classification result is as follows:
m1: obtaining noise points of a standard image, wherein the noise points comprise bright noise points and dark noise points, analyzing and judging the influence of the bright noise points and the dark noise points on the definition of the standard image respectively, and obtaining the number of the bright noise points and recording the number as LDb when the bright noise points influence the definition of the standard image;
m2: and then obtaining the target object bright noise number as LD, comparing the LD with LDb, if the LD is more than or equal to LDb, indicating that the target object bright noise number exceeds the standard image bright noise number, classifying the target object as an unqualified object, otherwise, if the LD is less than LDb, indicating that the target object bright noise number does not exceed the standard image noise number, and classifying the target object as a qualified object.
Specifically, in the second embodiment, the influence on the overall definition of the image is caused when the bright noise is determined to be increased, and the analysis on the influence on the definition of the image when the bright noise is reduced is opposite to the bright noise analysis, and will not be described in detail here.
The third embodiment of the present invention is different from the first and second embodiments in that the present embodiment is an analysis that affects the overall sharpness of an image when dark noise exists, and a specific acquisition manner of an image classification result is as follows:
n1: acquiring the number of dark noise points of the standard image as ADb, judging the change of the definition of the standard image when the number of dark noise points ADb is reduced, and if the number of dark noise points ADb is reduced and the definition of the standard image is increased, inversely proportional relation between the number of table dark noise points and the definition of the non-standard image is obtained;
n2: and then obtaining the dark noise count of the target object, recording as AD, comparing the AD with ADb, when AD is more than or equal to ADb, indicating that the definition of the target object is unqualified, classifying the target object as an unqualified object, and otherwise, classifying the target object as a qualified object when AD is less than ADb.
Specifically, in the third embodiment, the influence on the overall definition when the number of the dark noise points is increased or reduced is considered that the image definition is increased when the number of the dark noise points is reduced, and meanwhile, when the number of the dark noise points is increased and the image definition is increased, too much detailed description is not made here, and the analysis process is the same as that of the dark noise points.
In the fourth embodiment, as the fourth embodiment of the present invention, the emphasis is placed on the implementation of the first, second and third embodiments in combination.
Some of the data in the above formulas are numerical calculated by removing their dimensionality, and the contents not described in detail in the present specification are all well known in the prior art.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (2)
1. The image calibration method of the printing control instrument is characterized by comprising the following steps of:
step one: the method comprises the steps of obtaining an identification area of a printing control instrument, obtaining a placement position of printing paper, and carrying out analysis and judgment on the placement position of the printing paper by combining the identification area to generate an analysis result, wherein the analysis result comprises abnormal placement and normal placement, and adjusting the abnormal placement to obtain an adjustment result, and the specific mode of generating the adjustment result is as follows:
s1: carrying out three-dimensional modeling processing on the identification area and the printing paper placement position, then respectively and longitudinally establishing a reference plane by four sides of the identification area, and simultaneously carrying out label processing on four vertexes of the printing paper and marking as Di, wherein i=1, 2, 3 and 4;
s2: then, obtaining distance references of four vertexes of the printing paper and four reference surfaces as LDi, and comparing the calculated distance LDi in the specific comparison mode: judging whether the distances between the two vertexes at the same side are the same, if so, indicating that the placement positions of the printing papers are normal placement, and if not, indicating that the placement positions of the printing papers are abnormal placement;
s3: then analyzing the abnormally placed printing paper, obtaining the vertex with the smallest distance LDi on the same side, marking the vertex as an inclined point, simultaneously obtaining the angle between the printing paper edge where the inclined point is positioned and a reference surface, and generating an adjusting result;
step two: then, a normally placed printing paper image is obtained and recorded as a target object, then, the pixel value, the bright noise point and the dark noise point of the target object are analyzed, the quality of the target object is judged, meanwhile, the quality is classified into qualified and unqualified and an image quality classification result is generated, and the specific mode for generating the image classification result according to the pixel value is as follows:
p1: acquiring a pixel value of a target object, recording the pixel value as an XS, and then acquiring a pixel value of a standard image, recording the pixel value of the standard image as XSb, wherein the pixel value XSb of the standard image is represented as a pixel value of a finished product image, is an input parameter pixel value designated by a user, and comparing the pixel value with the pixel value of the standard image;
p2: when XS is larger than or equal to XSb, the pixel value of the target object exceeds the pixel value of the standard image, and the target object is marked as a qualified object, otherwise when XS is smaller than XSb, the pixel value of the target object does not exceed the pixel value of the standard image, and the target object is marked as a disqualified object, and the specific mode for generating the image classification result by judging the bright noise point is as follows:
m1: obtaining noise points of a standard image, wherein the noise points comprise bright noise points and dark noise points, analyzing and judging the influence of the bright noise points and the dark noise points on the definition of the standard image respectively, and obtaining the number of the bright noise points and recording the number as LDb when the bright noise points influence the definition of the standard image;
m2: then obtaining the target object bright noise number as LD and comparing the LD with LDb, if LD is more than or equal to LDb, indicating that the target object bright noise number exceeds the standard image bright noise number, classifying the target object as an unqualified object, otherwise, if LD is less than LDb, indicating that the target object bright noise number does not exceed the standard image noise number, and classifying the target object as a qualified object;
the specific mode for judging the dark noise point to generate the image classification result is as follows:
n1: acquiring the number of dark noise points of the standard image as ADb, judging the change of the definition of the standard image when the number of dark noise points ADb is reduced, and if the number of dark noise points ADb is reduced and the definition of the standard image is increased, inversely proportional relation between the number of table dark noise points and the definition of the non-standard image is obtained;
n2: then, the number of dark noise of the target object is obtained and recorded as AD, and compared with ADb, when AD is more than or equal to ADb, the definition of the target object is unqualified, the target object is classified as an unqualified object, and otherwise, when AD is less than ADb, the target object is classified as a qualified object;
step three: then analyzing the object classified as the unqualified object, carrying out image segmentation processing on the unqualified object, and screening and marking the segmented image as an image to be analyzed;
step four: obtaining all areas to be analyzed, analyzing the distribution conditions of the areas to be analyzed, generating distribution condition information, wherein the distribution condition information comprises centralized distribution and distributed distribution, then analyzing the centralized distribution and the distributed distribution respectively to obtain an influence result, wherein the influence result comprises ink thickness and printing speed, and the specific mode of obtaining the areas to be analyzed is as follows:
a1: acquiring the length and the width of an unqualified target object, respectively marking the length and the width as CD and KD, simultaneously acquiring the minimum convention number of the length CD and the width KD as G, and then carrying out G segmentation on the length CD and the width KD of the target object to obtain G segmented images;
a2: then, the G divided images are marked as k, and k=1, 2, …, G, and the color values of the divided images k are obtained and marked as SCk, and the average value mark of the color values of the G divided images is calculated and marked as SCp, and the color values SCk of the different divided images k are compared with the average value SCp, specifically in the following comparison manner:
a21: if SCk is more than or equal to SCp, marking the segmented image k as an over-average image, otherwise, marking the segmented image k as a low-average image if SCk is less than SCp, and then respectively analyzing the over-average image and the low-average image;
a22: then, acquiring a color value record of the standard image as SCb, acquiring a maximum value record of the color value of the low-average image as SCmax, comparing SCmax with SCb, and judging that the color value of the low-average image is abnormal when SCmax is smaller than SCb;
analysis of the centralized distribution:
b1: acquiring all areas to be analyzed, marking the areas to be analyzed as m, acquiring corresponding ink thickness marks as YMm and exposure time length as Tm, acquiring ink thickness YMb and exposure time length Tb of a standard image, and then acquiring current exposure speed SD1;
b2: comparing YMm with YMb, when YMm is larger than YMb, judging that the ink thickness of the area m to be analyzed exceeds a standard value, then calculating the difference between YMm and YMb to be | YMm-YMb |, and generating an ink thickness influence result, otherwise, when YMm is smaller than or equal to YMb, judging that the ink thickness of the area m to be analyzed does not exceed the standard value, and not performing any treatment;
b3: comparing the exposure time length, when Tm is larger than Tb, judging that the exposure time length of the area m to be analyzed is too long, otherwise, when Tm is smaller than or equal to Tb, judging that the exposure time length of the area m to be analyzed is normal, calculating the exposure speed of the printer according to the acquired exposure time length, and the analysis mode of the dispersive distribution is the same as the centralized analysis mode;
step five: and obtaining an influence result of the unqualified target object, and displaying the influence result to a corresponding operator.
2. The method for calibrating an image of a printer according to claim 1, wherein the exposure speed in B3 is calculated as follows:
b31: acquiring all the areas m to be analyzed positioned on a straight line, taking the number as H, acquiring the total length mark of the areas m to be analyzed on the straight line as CDz, and simultaneously acquiring the exposure time mark as Tz;
b32: CDz and Tz are then substituted into the formulaThen the exposure speed of the individual areas m to be analyzed is calculated as +.>And difference calculation is performed between it and the current exposure speed SD1 +.>And simultaneously generating an exposure speed influence result.
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