CN111292344A - Detection method of camera module - Google Patents

Abstract

The invention provides a detection method of a camera module, which is applied to the camera module with a camera lens and a photosensitive element, and comprises the following steps: (A) acquiring an original image by using a camera lens and a photosensitive element; (B) converting an original image into a gray image; (C) converting the gray image into a binary image according to a critical gray value; (D) obtaining boundary contours of a plurality of pixels in a binary image, wherein the gray value of the pixels is greater than or equal to a critical gray value; (E) obtaining a contour center of the boundary contour; and (F) judging whether the optical axis of the camera lens is opposite to the imaging center of the photosensitive element or not according to the imaging center and the outline center of the photosensitive element.

Description

Detection method of camera module

technical field

The invention relates to the field of optics, in particular to a detection method of a camera module.

Background technique

In recent years, with the evolution of the electronic industry and the vigorous development of industrial technology, the design and development of various electronic devices have gradually developed towards the direction of lightness and portability, so that users can use it in mobile commerce, entertainment or leisure anytime, anywhere. use. For example, various camera modules are widely used in various fields, such as in the field of portable electronic devices such as smart phones and wearable electronic devices. They have the advantages of small size and easy portability, and people can use them when they need it. It can be taken out at any time for image acquisition and storage, or further uploaded to the Internet through the mobile network, which not only has important commercial value, but also makes the daily life of the general public more colorful. Of course, camera modules are not only used in the field of portable electronic devices, but are also widely used in the field of vehicle electronics where safety is important.

Please refer to FIG. 1 , which is a conceptual diagram of a conventional camera module. The camera module 1 includes a camera lens 11 and a photosensitive element 12. The photosensitive element 12 senses the external light beam that passes through the camera lens 11 and is projected onto it to obtain an image. Whether the optical axis 111 of the camera lens 11 can be aligned with the photosensitive element 12? The imaging center 121 is an important key that affects the imaging quality of the camera module 1 . Therefore, in the production and assembly process of the camera module 1, how to effectively detect whether the optical axis 111 of the camera lens 11 is aligned with the imaging center 121 of the photosensitive element 12 has become an urgent topic to be studied.

SUMMARY OF THE INVENTION

The main purpose of the present invention is to provide a detection method of a camera module, especially a detection method that can detect whether the optical axis of the camera lens is aligned with the imaging center of the photosensitive element.

In a preferred embodiment, the present invention provides a method for detecting a camera module, which is applied to a camera module having a camera lens and a photosensitive element, including:

(A) using the camera lens and the photosensitive element to obtain an original image;

(B) converting the original image into a grayscale image (gray scale image);

(C) converting the grayscale image into a binary image according to a critical grayscale value;

(D) obtaining a boundary contour of a plurality of pixels that are greater than or equal to (≥) the critical gray value in the binary image;

(E) obtaining a contour center of the boundary contour; and

(F) According to an imaging center of the photosensitive element and the contour center, it is determined whether an optical axis of the camera lens is aligned with the imaging center of the photosensitive element.

Description of drawings

Figure 1: is a conceptual schematic diagram of an existing camera module.

FIG. 2 is a flowchart of a preferred method of the detection method of the camera module of the present invention.

FIG. 3 is a conceptual schematic diagram of the original image obtained through step S1 shown in FIG. 2 .

FIG. 4 is a conceptual schematic diagram of obtaining a grayscale image from the original image shown in FIG. 3 through step S2 shown in FIG. 2 .

FIG. 5 is a flow chart of a preferred execution of step S3 shown in FIG. 2 .

FIG. 6A is a conceptual schematic diagram of each gray value and the corresponding number of pixels in the gray image shown in FIG. 4 .

FIG. 6B is a graph of a preferred cumulative distribution function for the grayscale image shown in FIG. 4 .

FIG. 7 is a conceptual schematic diagram illustrating that the grayscale image shown in FIG. 4 is converted into a binary image through steps S31 and S32 shown in FIG. 5 .

FIG. 8 is a conceptual schematic diagram of obtaining the boundary contour of the binary image shown in FIG. 7 through step S4 shown in FIG. 2 .

FIG. 9 is a conceptual schematic diagram of obtaining the contour center for the boundary contour shown in FIG. 8 through step S5 shown in FIG. 2 .

Among them, the reference numerals are described as follows:

1 camera module

2 original images

3 Grayscale images

4 binary images

11 camera lens

12 photosensitive elements

41 Border Outlines

42 Optical Circle

43 Contour Center

44 center of binary image

111 optical axis

121 Imaging Center

S1 step

S2 step

S3 step

Step S4

Step S5

Step S6

Step S31

Step S32

Detailed ways

First of all, it should be noted that the detection method of the camera module of the present disclosure can be applied to the camera module 1 shown in FIG. 1 and is applicable to the production line of the camera module 1. Generally speaking, the density of the light source on the photosensitive element 12 will be closer to the camera module. The optical axis 111 of the lens 11 is higher, so the detection method of the camera module of the present disclosure is to use the brightness values (Intensity) of multiple pixels on the photosensitive element 12 to find the optical axis 111 on the photosensitive element 12 corresponding to the camera lens 11 the location (optical center), and then compare the distance between the location and the imaging center 121 of the photosensitive element 12 to determine whether the optical axis 111 of the camera lens 11 is aligned with the imaging center 121 of the photosensitive element 12 . In a preferred embodiment, the photosensitive element 12 may be a Complementary Metal-Oxide-Semiconductor (CMOS) or a Charge Coupled Device (CCD), and the imaging center 121 is located on the entire photosensitive element 12 . at the center, but not limited to the above.

Please refer to FIG. 2 , which is a flowchart of a preferred method of the detection method of the camera module of the present invention. The detection method of the camera module includes: step S1, using a camera lens and a photosensitive element to obtain an original image; step S2, converting the original image into a gray scale image; step S3, converting the gray scale image according to a critical gray value is a binary image (binary image); step S4, obtains a boundary contour of a plurality of pixels that is greater than or equal to (≥) critical gray value in the binary image; step S5, obtains the contour center of the boundary contour; step S6, according to the photosensitive The imaging center of the element and the contour center of the boundary outline determine whether the optical axis of the camera lens is aligned with the imaging center of the photosensitive element.

The above steps S1 to S6 are described below with a preferred embodiment, and the content shown in FIG. 3 to FIG. 9 is used to assist the description. FIG. 3 shows the original image 2 obtained through step S1 shown in FIG. 2 , which can be a color image of RGB type or a color image of CMYK type. FIG. 4 is a grayscale image 3 obtained from the original image shown in FIG. 3 through step S2 shown in FIG. 2 , wherein each pixel in the grayscale image 3 is represented by a grayscale value of 0 to 255, and different grayscales The degree values represent different brightness respectively.

Furthermore, FIG. 5 illustrates a preferred execution flow chart of step S3 shown in FIG. 2 , which includes: step S31 , using a cumulative distribution function (Cumulative Distribution Function, CDF) to obtain a critical gray value corresponding to a specific coverage ratio. ; Step S32, is greater than or equal to (≥) each pixel of the critical gray value in the grayscale image is classified as a high-brightness pixel, and in the grayscale image is less than (<) each pixel of the critical gray value is classified as Low luminance pixels to binarize grayscale images.

Further, please refer to FIG. 6A and FIG. 6B , FIG. 6A illustrates the number of pixels (vertical axis) corresponding to each gray value (horizontal axis) in the grayscale image, and by performing step S31, the result shown in FIG. 6B can be obtained. The cumulative distribution function diagram shown in the figure, the cumulative distribution function is defined as: F _X (x)=P (X≤x), P is the coverage rate, x is the gray value, and X is a random variable. In this preferred embodiment, the specific coverage ratio is set to 0.4, but the practical application is not limited to this. As shown in FIG. 6B , the critical gray value corresponding to the specific coverage ratio of 0.4 is 120, that is, , in this preferred embodiment, the critical gray value 120 can be obtained through step S31 shown in FIG. 5 .

In addition, the cumulative distribution function is the integral of the probability density function, which can completely describe the probability distribution of a random variable X, which is known to those skilled in the art, so it will not be repeated here, and the present disclosure does not limit the use of the cumulative distribution The function obtains the critical gray value, and those skilled in the art can make any equivalent design changes according to actual application requirements.

In addition, in this preferred embodiment, by executing step S32, each pixel in the grayscale image 3 that is greater than or equal to (≥) the critical grayscale value of 120 can be set to a grayscale maximum value (such as 255) and the grayscale Each pixel in the image 3 that is less than (<) the critical gray value 120 is set to a gray minimum value (such as 0), so that the gray image 3 shown in Figure 4 is converted into the binary image 4 shown in Figure 7, where , for the sake of clarity, the black dots in the binary image 4 shown in FIG. 7 represent the pixels set to the grayscale maximum value (eg, 255).

Please refer to FIG. 8 , which is the boundary contour of the binary image shown in FIG. 7 obtained through step S4 shown in FIG. 2 . In this preferred embodiment, step S4 is to use an active contour model (Active Contour Model) to obtain the boundary contour 41 of a plurality of pixels set as a grayscale maximum value (such as 255) in the binary image 4; wherein, the active contour model Also known as "Snakes", it is a framework for extracting object contour lines from two-dimensional images that may contain noise. Active contour models are also known to those skilled in the art, so they will not be repeated here. Of course, the present disclosure does not limit the use of the active contour model to obtain the boundary contour, and those skilled in the art can make any equivalent design changes according to practical application requirements.

Please refer to FIG. 9 , which is the contour center of the boundary contour shown in FIG. 8 obtained through step S5 shown in FIG. 2 . In this preferred embodiment, step S5 is to use an ellipse fitting algorithm (Ellipse Fitting Algorithm) to obtain the optical circle 42 fitted with the boundary contour 41 and use the center of the optical circle 42 as the contour center 43, and because of the binary value The size of the image 4 corresponds to the size of the photosensitive element 12, so the contour center 43 obtained in step S5 can represent the position (optical center) on the photosensitive element 12 corresponding to the optical axis 111 of the camera lens 11; The co-algorithm is also known to those skilled in the art, so it will not be repeated here. Of course, the present disclosure does not limit the use of an ellipse fitting algorithm to obtain the contour center of the boundary contour, and those skilled in the art can make any equivalent design changes according to practical application requirements.

Likewise, since the size of the binary image 4 corresponds to the size of the photosensitive element 12 , the center 44 of the binary image 4 may represent the imaging center 121 of the photosensitive element 12 . In this preferred embodiment, when the center 44 of the binary image 4 (representing the imaging center 121 of the photosensitive element 12 ) and the contour center 43 of the boundary contour 41 (representing the position on the photosensitive element 12 corresponding to the optical axis 111 of the camera lens 11 ) When the distance between the location) is within a predetermined distance, it is considered that the imaging center 121 of the photosensitive element 12 overlaps or is adjacent to the position on the photosensitive element 12 corresponding to the optical axis 111 of the camera lens 11, and it is judged that the imaging center 121 of the photosensitive element 12 The optical axis 111 of the lens 11 has been aligned with the imaging center 121 of the photosensitive element 12 . On the contrary, when the center 44 of the binary image 4 (representing the imaging center 121 of the photosensitive element 12 ) and the contour center 43 of the boundary contour 41 (that is, the photosensitive element 12 ) When the distance corresponding to the optical axis 111 of the camera lens 11 is greater than a predetermined distance, it is determined that the optical axis 111 of the camera lens 11 is not aligned with the imaging center 121 of the photosensitive element 12, and the camera lens 11 The photosensitive element 12 must be reassembled or calibrated.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the claims of the present invention. Therefore, all other equivalent changes or modifications made without departing from the spirit of the present disclosure shall be included in the scope of the present disclosure. within the claims.

Claims (9)

1. A detection method for a camera module, applied to a camera module with a camera lens and a photosensitive element, comprising: (A) using the camera lens and the photosensitive element to obtain an original image; (B) converting the original image into a grayscale image (gray scale image); (C) converting the grayscale image into a binary image according to a critical grayscale value; (D) obtaining a boundary contour of a plurality of pixels that are greater than or equal to (≥) the critical gray value in the binary image; (E) obtaining a contour center of the boundary contour; and (F) According to an imaging center of the photosensitive element and the contour center, it is determined whether an optical axis of the camera lens is aligned with the imaging center of the photosensitive element. 2. The detection method of a camera module as claimed in claim 1, wherein the step (C) comprises: (C1) Using a Cumulative Distribution Function (CDF) to obtain the critical gray value corresponding to a specific coverage ratio. 3 . The detection method of a camera module according to claim 2 , wherein the specific coverage ratio is 0.4. 4 . 4. The detection method of a camera module as claimed in claim 2, wherein the step (C) further comprises: (C2) Classify each pixel in the grayscale image that is greater than or equal to (≥) the critical grayscale value as a high-brightness pixel, and classify each pixel in the grayscale image that is less than (<) the critical grayscale value The pixel is classified as a low luminance pixel. 5. The detection method of a camera module according to claim 1, wherein the step (D) comprises: The boundary contour is obtained using an Active Contour Model. 6. The detection method of a camera module as claimed in claim 1, wherein the step (E) comprises: An ellipse fitting algorithm (Ellipse Fitting Algorithm) is used to obtain an optical circle fitted with the boundary contour, and a center of the optical circle is used as the contour center. 7 . The detection method of a camera module according to claim 1 , wherein, in the step (F), when the imaging center overlaps or is adjacent to the contour center, it is determined that the optical axis is aligned with the imaging center. 8 . 8. The detection method of a camera module as claimed in claim 1, which is applied to a production line of the camera module. 9 . The detection method of a camera module as claimed in claim 1 , wherein the photosensitive element is a Complementary Metal-Oxide-Semiconductor (CMOS) or a Photocoupler (ChargeCoupled Device, CCD) 9 . .

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