CN111487036A - Python-based optical filter offset detection method - Google Patents
Python-based optical filter offset detection method Download PDFInfo
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- CN111487036A CN111487036A CN202010277106.1A CN202010277106A CN111487036A CN 111487036 A CN111487036 A CN 111487036A CN 202010277106 A CN202010277106 A CN 202010277106A CN 111487036 A CN111487036 A CN 111487036A
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Abstract
The invention relates to a Python-based optical filter shift detection method, which comprises the following steps of ① obtaining a thermal image, ② dividing the thermal image into an area 1, an area 2, an area 3 and an area 4, ③ analyzing to obtain arrays, ④ obtaining a pixel array [ A, B, C ] corresponding to the pixel value of each coordinate, ⑤ judging the pixel array [ A, B, C ] corresponding to each coordinate according to the following unqualified preset value standard, carrying out area division on the thermal image into the area 1, the area 2, the area 3 and the area 4, wherein the areas are common bad stain positions, analyzing to obtain an array coordinate list of the array 1, the array 2, the array 3 and the array 4 corresponding to the corresponding area, then judging the values of red, green and blue pixels of each coordinate according to preset unqualified parameter values, judging accuracy is good, in addition, in order to avoid the influence of the pixel values caused by the interference of dust, stains and the like, the initial product is subjected to adjacency analysis, the accuracy is further improved, and the production requirements are met.
Description
Technical Field
The invention relates to the field of chip and optical filter offset detection of a camera, in particular to an optical filter offset detection method based on Python.
Background
In the field of camera module production, a chip is firstly packaged on the surface of a ceramic substrate, then a filter is covered on the surface of the chip, a filter black film (the filter and the filter black film are integrated) on the peripheral side of the filter is directly pasted on the ceramic substrate, the filter mainly plays roles of protection, filtering and the like, the installation position of the filter is very important, if the filter is misaligned, the filter black film can shield the chip to influence the work of the chip in the later period, therefore, in the prior art, the product formed by combining the chip and the filter needs to be subjected to filter position detection, the detection result shows that the product has two conditions of qualification and disqualification as shown in figure 1, the qualified image is the whole chip, the unqualified image is the image formed by blocking the black filter black film, and the existing method for detecting whether the filter has position misalignment is the traditional area summation method, the area summation method is insufficient in accuracy and efficiency.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a Python-based optical filter offset detection method, which has the advantages of high accuracy and capability of rapidly finishing defective judgment, and the specific technical scheme is as follows:
a filter shift detection method based on Python comprises the following steps,
① acquiring images, wherein the camera shoots and acquires thermal images of the chip and the chip of the optical filter combination product;
② dividing the thermal image into region 1, region 2, region 3 and region 4, wherein region 1 is at the upper left corner, region 2 is at the upper right corner, region 3 is at the lower left corner, and region 4 is at the lower right corner;
③, analyzing the pixel coordinates of the chips in the area 1, the area 2, the area 3 and the area 4 to obtain an array 1, an array 2, an array 3 and an array 4, wherein the array 1 corresponds to the area 1, the array 2 corresponds to the area 2, the array 3 corresponds to the area 3, the array 4 corresponds to the area 4, each array comprises a plurality of pixel coordinate points according to the pixel length, and the pixel coordinate points are recorded as
Array 1: [ (x1, y1), (x2, y2) … … ],
array 2: [ (x1, y1), (x2, y2) … … ],
array 3: [ (x1, y1), (x2, y2) … … ],
array 4: [ (x1, y1), (x2, y2) … … ],
④ obtaining pixel arrays of each coordinate, analyzing all coordinates in array 1, array 2, array 3 and array 4 to obtain pixel arrays [ A, B, C ] corresponding to the pixel value of each coordinate, wherein A represents the red pixel value, B represents the green pixel value, and C represents the blue pixel value;
⑤, judging the pixel array [ A, B, C ] corresponding to each coordinate according to the following unqualified preset value standard,
red: a is more than or equal to 240 and less than or equal to 250,
green: b is more than or equal to 200 and less than or equal to 260,
blue: c is more than or equal to 10 and less than or equal to 70,
if the pixel arrays [ A, B and C ] with coordinates meet the numerical value standard, the coordinates are sequentially classified into unqualified arrays W, and if the length of the unqualified arrays W is larger than a self-defined standard parameter Z, the product is preliminarily judged to be an unqualified product;
if the data of the pixel arrays [ A, B and C ] of all the coordinates do not meet the numerical value standard or if the length of the unqualified array W is less than or equal to the self-defined standard parameter Z, judging the product as a qualified product.
2. The Python-based filter shift detection method according to claim 1, wherein: the method also comprises the following steps of,
⑥, carrying out adjacency analysis on the coordinates in the array W, namely taking any coordinate W1(X1, Y1) in the array W as a judgment basis, if the parameters of the coordinates W2(X2, Y2) accord with X1+1= X2 or X1+0= X2, obtaining the result which preliminarily accords with the adjacent characteristics, sequentially carrying out four cycles, if the result which preliminarily accords with the adjacent characteristics can be obtained, finishing screening, and finally judging that the product is a non-qualified product.
As a preferred scheme of the invention, the chip and optical filter combination product comprises a chip, a ceramic substrate, an optical filter and an optical filter black film, wherein the chip is arranged in the ceramic substrate, the optical filter is arranged in the optical filter black film, the chip, the ceramic substrate, the optical filter and the optical filter black film are in a square frame shape, and the optical filter black film is attached to the ceramic substrate; the filter covers the chip.
As a preferred scheme of the invention, the custom standard parameter Z is 5.
The invention has the beneficial effects that: the thermal image is divided into an area 1, an area 2, an area 3 and an area 4, the areas are common bad stain positions, array coordinates of an array 1, an array 2, an array 3 and an array 4 corresponding to the corresponding areas are obtained through analysis, then the numerical values of red, green and blue pixels of each coordinate are judged according to preset unqualified parameter numerical values, the judgment accuracy is good, in addition, in order to avoid the influence of interference of dust, stains and the like on the pixel value, the primary unqualified products are subjected to adjacency analysis, the accuracy detection is further improved, no detection error is realized, and the production requirements are met.
Drawings
FIG. 1 is a schematic diagram showing whether a chip is blocked in both pass and fail cases;
FIG. 2 is an overall flow block diagram;
FIG. 3 is a thermal image of a chip;
FIG. 4 is a thermal image area division diagram;
FIG. 5 is a schematic diagram of a pixel array of coordinates;
fig. 6 is a perspective view of a chip and filter combination product.
Detailed Description
The following description of the embodiments of the present invention will be made with reference to the accompanying drawings:
in the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or regional relationships based on the orientations or regional relationships shown in the drawings, and are only used for convenience in describing the present invention and simplifying the description, but do not indicate or imply that the regions or elements referred to must have specific orientations, be constructed in specific orientations, and be operated, and thus, should not be construed as limiting the present invention.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood as appropriate by those of ordinary skill in the art.
The overall process flow is shown in fig. 2, and a Python-based filter shift detection method includes the following steps,
① acquiring images, wherein the thermal images of the chip and the optical filter combination product are acquired by the camera at the initial position, and the thermal images of the chip are as shown in FIG. 3;
② dividing the thermal image into area 1, area 2, area 3, and area 4, wherein area 1 is at the top left, area 2 is at the top right, area 3 is at the bottom left, area 4 is at the bottom right, and the division is shown in FIG. 4;
③, analyzing the pixel coordinates of the chips in the area 1, the area 2, the area 3 and the area 4 to obtain an array 1, an array 2, an array 3 and an array 4, wherein the array 1 corresponds to the area 1, the array 2 corresponds to the area 2, the array 3 corresponds to the area 3, the array 4 corresponds to the area 4, each array comprises a plurality of pixel coordinate points according to the pixel length (namely, a single pixel), and the pixel coordinate points are recorded as
Array 1: [ (x1, y1), (x2, y2) … … ],
array 2: [ (x1, y1), (x2, y2) … … ],
array 3: [ (x1, y1), (x2, y2) … … ],
array 4: [ (x1, y1), (x2, y2) … … ],
the coordinate values are increased progressively according to 1 unit pixel value, as shown in fig. 5, the number of pixel coordinate points in each array is related to the area of the selected area, and the larger the selected area is, the larger the number of pixel coordinate points is;
④ obtaining pixel arrays of each coordinate, analyzing all coordinates in array 1, array 2, array 3 and array 4 to obtain pixel arrays [ A, B, C ] corresponding to the pixel value of each coordinate, where A represents the red pixel value, B represents the green pixel value, and C represents the blue pixel value, as shown in FIG. 5;
⑤, judging the pixel array [ A, B, C ] corresponding to each coordinate according to the following unqualified preset value standard,
red: a is more than or equal to 240 and less than or equal to 250,
green: b is more than or equal to 200 and less than or equal to 260,
blue: c is more than or equal to 10 and less than or equal to 70,
if the pixel arrays [ A, B and C ] with coordinates meet the numerical standard, the coordinates are sequentially classified into unqualified arrays W, if the length of the unqualified arrays W is larger than a self-defined standard parameter Z, the length of the unqualified arrays W refers to the number of the coordinates classified into the arrays W, and the product is preliminarily judged to be an unqualified product;
if the data of the pixel arrays [ A, B and C ] of all coordinates do not meet the numerical standard or if the length of the unqualified array W is less than or equal to a self-defined standard parameter Z, judging the product as a qualified product, wherein the self-defined standard parameter Z is an adjustable integer value, the self-defined standard parameter Z is preferably 5, for example, 3 values of [ A, B, C ] corresponding to coordinates (x1, y1) all meet the numerical standard, and the coordinates are classified into the unqualified array W; if the A, B value of [ A, B, C ] for coordinates (x2, y2) meets the above numerical criteria but C does not, then coordinates (x2, y2) cannot be assigned to failing array W, as long as at least one value of A, B, C does not meet the criteria, i.e., the coordinates cannot be assigned to failing array W.
Meanwhile, in order to avoid the influence of dust, dirt and the like on the filter to judge the filter shift, the filter shift detection method based on Python further comprises a step ⑥, wherein the step ⑥ is performed after the step ⑤, any coordinate W1(X1, Y1) in the array W is taken as a judgment basis, if a parameter of a coordinate W2(X2, Y2) (W2 is a coordinate already in the array W) meets X1+1= X2 or X1+0= X2, a result primarily meeting the adjacent characteristic is obtained, four cycles are sequentially performed (the four cycles refer to the step of extracting W1 for judgment), if the result primarily meeting the adjacent characteristic can be obtained, screening is completed, and finally, the product is judged to be a non-qualified product, and if the four cycle periods are not met, the product is judged to be a qualified product.
Specifically, as shown in fig. 6, the chip and filter combination product is composed of a chip 101, a ceramic substrate 102, a filter 103, and a filter black film 104, where the chip 101 is disposed in the ceramic substrate 102, the filter 103 is disposed in the filter black film 104, the chip 101, the ceramic substrate 102, the filter 103, and the filter black film 104 are in a square frame shape, and the area of the filter 103 is larger than that of the filter 103, so as to ensure that the filter black film 104 is attached to the ceramic substrate 102; the filter 103 covers the chip 101.
The above description is for the purpose of describing the invention in more detail with reference to specific preferred embodiments, and it should not be construed that the embodiments of the invention are limited to those described herein, and it will be apparent to those skilled in the art that various changes and modifications can be made without departing from the spirit and scope of the invention.
Claims (4)
1. A filter shift detection method based on Python is characterized by comprising the following steps,
① acquiring images, wherein the camera shoots and acquires thermal images of the chip and the chip of the optical filter combination product;
② dividing the thermal image into region 1, region 2, region 3 and region 4, wherein region 1 is at the upper left corner, region 2 is at the upper right corner, region 3 is at the lower left corner, and region 4 is at the lower right corner;
③, analyzing the pixel coordinates of the chips in the area 1, the area 2, the area 3 and the area 4 to obtain an array 1, an array 2, an array 3 and an array 4, wherein the array 1 corresponds to the area 1, the array 2 corresponds to the area 2, the array 3 corresponds to the area 3, the array 4 corresponds to the area 4, each array comprises a plurality of pixel coordinate points according to the pixel length, and the pixel coordinate points are recorded as
Array 1: [ (x1, y1), (x2, y2) … … ],
array 2: [ (x1, y1), (x2, y2) … … ],
array 3: [ (x1, y1), (x2, y2) … … ],
array 4: [ (x1, y1), (x2, y2) … … ],
④ obtaining pixel arrays of each coordinate, analyzing all coordinates in array 1, array 2, array 3 and array 4 to obtain pixel arrays [ A, B, C ] corresponding to the pixel value of each coordinate, wherein A represents the red pixel value, B represents the green pixel value, and C represents the blue pixel value;
⑤, judging the pixel array [ A, B, C ] corresponding to each coordinate according to the following unqualified preset value standard,
red: a is more than or equal to 240 and less than or equal to 250,
green: b is more than or equal to 200 and less than or equal to 260,
blue: c is more than or equal to 10 and less than or equal to 70,
if the pixel arrays [ A, B and C ] with coordinates meet the numerical value standard, the coordinates are sequentially classified into unqualified arrays W, and if the length of the unqualified arrays W is larger than a self-defined standard parameter Z, the product is preliminarily judged to be an unqualified product;
if the data of the pixel array [ A, B, C ] of all coordinates do not accord with the numerical value standard or if the length of the unqualified array W is less than or equal to the self-defined standard parameter Z, judging the product as a qualified product.
2. The Python-based filter shift detection method according to claim 1, wherein: the method also comprises the following steps of,
⑥, carrying out adjacency analysis on the coordinates in the array W, namely taking any coordinate W1(X1, Y1) in the array W as a judgment basis, if the parameters of the coordinates W2(X2, Y2) accord with X1+1= X2 or X1+0= X2, obtaining the result which preliminarily accords with the adjacent characteristics, sequentially carrying out four cycles, if the result which preliminarily accords with the adjacent characteristics can be obtained, finishing screening, and finally judging that the product is a non-qualified product.
3. The Python-based filter shift detection method according to claim 1 or 2, wherein: the chip and the optical filter combined product consists of a chip, a ceramic substrate, an optical filter and an optical filter black film, wherein the chip is arranged in the ceramic substrate, the optical filter is arranged in the optical filter black film, the chip, the ceramic substrate, the optical filter and the optical filter black film are in a square frame shape, and the optical filter black film is attached to the ceramic substrate; the filter covers the chip.
4. The Python-based filter shift detection method according to claim 1, wherein: the custom criteria parameter Z is 5.
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