CN112164072A

CN112164072A - Visible light imaging communication decoding method, device, equipment and medium

Info

Publication number: CN112164072A
Application number: CN202010987956.0A
Authority: CN
Inventors: 宋鸿展; 关伟鹏; 陈世桓; 伍文飞; 邓艾
Original assignee: Shenzhen Nanke Information Technology Co ltd
Current assignee: Shenzhen Nanke Information Technology Co ltd
Priority date: 2020-09-18
Filing date: 2020-09-18
Publication date: 2021-01-01

Abstract

The invention discloses a visible light imaging communication decoding method, a device, computer equipment and a storage medium, wherein an image acquisition device is adopted to acquire an image of a light source object at an emitting end to generate an image to be processed; performing feature extraction on an image to be processed to generate a feature set to be processed, wherein the feature set to be processed comprises a one-dimensional pixel matrix and light source object stripe features; performing binarization processing on the one-dimensional pixel matrix according to the stripe characteristics of the light source object to obtain a target pixel matrix; converting the target pixel matrix by adopting a preset decoding algorithm to generate a data bit matrix; receiving a byte head matrix sent by a transmitting terminal, and matching the byte head matrix with a data bit matrix to obtain a data section matrix; the problem of incorrect decoding caused by frame frequency change is solved, and therefore the accuracy of visible light imaging communication information decoding is guaranteed.

Description

Visible light imaging communication decoding method, device, equipment and medium

Technical Field

The present invention relates to the field of visible light communication, and in particular, to a method, an apparatus, a device, and a medium for decoding visible light imaging communication.

Background

Visible light communication is a communication mode in which light in the visible light band is used as an information carrier and an optical signal is directly transmitted in the air. The method is green, low-carbon, free of electromagnetic interference, low in cost, high in speed and high in confidentiality, and is beneficial to quickly constructing an anti-interference and anti-interception safety information space. Most cameras in the current visible light imaging communication process adopt a rolling curtain mechanism so as to convert binary information into stripe information, and the width of the stripe changes unpredictably because the cameras in the market inevitably generate frame frequency change influence. Most of the conventional information decoding methods decode the information by using a distance sampling scheme on the premise of unchanging the stripe width, so that the decoding accuracy is influenced by the frame frequency change, and the accuracy of the visible light communication in the imaging communication information decoding process is low in practical application.

Disclosure of Invention

The embodiment of the invention provides a visible light imaging communication decoding method, a visible light imaging communication decoding device, computer equipment and a storage medium, and aims to solve the problem that the accuracy of decoding visible light imaging communication information is not high.

A visible light imaging communication decoding method comprises the following steps:

adopting image acquisition equipment to acquire an image of a light source object at an emitting end to generate an image to be processed;

performing feature extraction on the image to be processed to generate a feature set to be processed, wherein the feature set to be processed comprises a one-dimensional pixel matrix and light source object stripe features;

performing binarization processing on the one-dimensional pixel matrix according to the stripe characteristics of the light source object to obtain a target pixel matrix;

converting the target pixel matrix by adopting a preset decoding algorithm to generate a data bit matrix;

and receiving the byte head matrix sent by the transmitting terminal, and matching the byte head matrix with the data bit matrix to obtain a data section matrix.

A visible light imaging communication decoding apparatus, comprising:

the image acquisition module is used for acquiring an image of a light source object at the transmitting end by adopting image acquisition equipment to generate an image to be processed;

the characteristic extraction module is used for extracting the characteristics of the image to be processed to generate a characteristic set to be processed, and the characteristic set to be processed comprises a one-dimensional pixel matrix and the stripe characteristics of a light source object;

the binarization processing module is used for carrying out binarization processing on the one-dimensional pixel matrix according to the stripe characteristics of the light source object to obtain a target pixel matrix;

the conversion module is used for converting the target pixel matrix by adopting a preset decoding algorithm to generate a data bit matrix;

and the matching module is used for receiving the byte head matrix sent by the transmitting terminal and matching the byte head matrix with the data bit matrix to obtain a data section matrix.

A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the above visible light imaging communication decoding method when executing the computer program.

A computer-readable storage medium, which stores a computer program that, when executed by a processor, implements the above-described visible light imaging communication decoding method.

According to the visible light imaging communication decoding method, the device, the computer equipment and the storage medium, the image acquisition equipment is adopted to acquire the image of the light source object at the transmitting end, and an image to be processed is generated; performing feature extraction on an image to be processed to generate a feature set to be processed, wherein the feature set to be processed comprises a one-dimensional pixel matrix and light source object stripe features; performing binarization processing on the one-dimensional pixel matrix according to the stripe characteristics of the light source object to obtain a target pixel matrix; converting the target pixel matrix by adopting a preset decoding algorithm to generate a data bit matrix; receiving a byte head matrix sent by a transmitting terminal, and matching the byte head matrix with a data bit matrix to obtain a data section matrix; the problem of incorrect decoding caused by frame frequency change is solved, and therefore the accuracy of visible light imaging communication information decoding is guaranteed.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.

FIG. 1 is a schematic diagram of an application environment of a decoding method for visible light imaging communication according to an embodiment of the present invention;

FIG. 2 is a diagram of an exemplary visible light imaging communication decoding method according to an embodiment of the invention;

FIG. 3 is a diagram of another example of a visible light imaging communication decoding method according to an embodiment of the invention;

FIG. 4 is a diagram of another example of a visible light imaging communication decoding method according to an embodiment of the invention;

FIG. 5 is a diagram of another example of a visible light imaging communication decoding method according to an embodiment of the invention;

FIG. 6 is a diagram of another example of a visible light imaging communication decoding method according to an embodiment of the invention;

FIG. 7 is a schematic block diagram of a visible light imaging communication decoding apparatus according to an embodiment of the present invention;

FIG. 8 is another functional block diagram of a visible light imaging communication decoding apparatus according to an embodiment of the present invention;

FIG. 9 is another functional block diagram of a visible light imaging communication decoding apparatus according to an embodiment of the present invention;

FIG. 10 is a schematic diagram of a computer device according to an embodiment of the invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The visible light imaging communication decoding method provided by the embodiment of the invention can be applied to the application environment shown in fig. 1. Specifically, the visible light imaging communication decoding method is applied to a visible light imaging communication decoding system, and the visible light imaging communication decoding system comprises a transmitting end and a receiving end shown in fig. 1, wherein the transmitting end and the receiving end communicate through a network, so that the problem that the accuracy of decoding the visible light imaging communication information is not high is solved. The emitting end includes a computer, a controller, a driving circuit, a light source object, and a power module, wherein the emitting end may use an LED lamp, a candle, a bulb, etc. as the light source object. The receiving end can be a CMOS sensor camera carried by daily use equipment, wherein the daily use equipment includes a smart phone, a notebook computer, a tablet computer, a handheld internet device, multimedia equipment, wearable equipment or other types of terminal equipment.

In an embodiment, as shown in fig. 2, a method for decoding visible light imaging communication is provided, which is described by taking the method as an example applied to the receiving end in fig. 1, and includes the following steps:

and S10, acquiring the image of the light source object at the transmitting end by adopting an image acquisition device to generate an image to be processed.

Wherein, an LED lamp, a candle, a bulb and the like can be used as a light source object at the emitting end. The light source object is preferably an LED lamp that is illuminating. In one embodiment, the LED driving circuit is controlled by the controller of the transmitting terminal to drive the LED to emit the light signal.

The image acquisition device can be a camera, a scanner or an image acquisition card and other devices with an image acquisition function. In this embodiment, image acquisition is performed on a light source object at an emitting end, an optical signal is captured by using a CMOS sensor camera at a receiving end to record a video to an LED lamp, then an image is extracted frame by frame, a fringe distribution area is intercepted from the image, average frames are generated and normalized for an R channel, a G channel and a B channel, and then a picture is cut out by taking a limited number of fringes as a unit, thereby obtaining an image to be processed. Wherein the CMOS sensor camera has a rolling shutter effect and the high frequency flicker light source is recorded as a fringe image.

And S20, extracting the features of the image to be processed to generate a feature set to be processed, wherein the feature set to be processed comprises a one-dimensional pixel matrix and the stripe features of the light source object.

Specifically, masking is performed on an image to be processed to extract a light source object (LED) shape in the image to be processed, then each row of pixel values in the light source object (LED) shape is obtained, and a pixel average value of each row of pixels is calculated, thereby forming a one-dimensional pixel row matrix. It will be appreciated that a one-dimensional pixel row matrix is formed by the average of the pixels of each row of pixels within the light source object (LED) shape. Further, the one-dimensional pixel matrix is translated left and right, a new pixel matrix obtained after translation left and right is subtracted from the original one-dimensional pixel matrix, if the subtraction is positive, 1 is selected, otherwise 0 is selected, so that a left digit matrix after translation left and a right digit matrix after translation right are obtained, finally, the left digit matrix and the right digit matrix are subjected to exclusive-or processing, an exclusive-or matrix is generated, coordinates with the value of 1 in the exclusive-or matrix form a light source object stripe feature, wherein the light source object stripe feature is also called as a one-dimensional pixel matrix peak and valley coordinate.

And S30, performing binarization processing on the one-dimensional pixel matrix according to the stripe characteristics of the light source object to obtain a target pixel matrix.

Specifically, gradually reading interesting (ROI) regions between peaks and troughs in the stripe characteristics of the light source object, and acquiring a maximum value and a minimum value of the interesting (ROI) regions between each peak and each trough; and the minimum value in each interested Region (ROI) is compared with a preset background threshold value T_minA comparison is made if the minima in the region of interest (ROI) are less than a pre-set background threshold Tmin. The minimum value is determined as a new background threshold for the region of interest (ROI). Wherein, the background threshold value T_minCan be set and adjusted according to practical conditions (such as LED lamp intensity) in a self-defined way, and in the embodiment, the background threshold value T_minPreferably 20. Further, the difference between the maximum value and the minimum value in each region of interest (ROI) is compared with a predetermined specific threshold D_diffComparing if the difference between the maximum value and the minimum value in the region of interest (ROI) is greater than the predetermined specific threshold D_diffThen the mean of the maximum and minimum values in the region of interest (ROI) is binarized. Likewise, a specific threshold value D_diffThe LED lamp can be set and adjusted in a self-defined way according to the actual conditions (such as the intensity of the LED lamp); finally, the T is added_thresholdPerforming global binarization processing on each region of interest for a threshold value to process an area of interest (ROI) that is not binarized, so as to obtain a target pixel matrix, which is also a one-dimensional pixel matrix. Wherein, T_thresholdA threshold value T which can be self-defined according to actual conditions_thresholdThe threshold value is only required to be sufficient to distinguish the dark stripe from the bright stripe, T_thresholdThe threshold is typically twice the background threshold, T_thresholdThe threshold value is preferably 40.

And S40, converting the target pixel matrix by adopting a preset decoding algorithm to generate a data bit matrix.

The preset decoding algorithm is an algorithm which is set by a user to convert a target pixel matrix into a data bit matrix. In this embodiment, the target pixel matrix is a one-dimensional pixel matrix, and the predetermined decoding algorithm is an algorithm for decoding one-dimensional pixels into digits.

Specifically, the number of consecutive identical pixels in the target pixel matrix is extracted, and an initial integer string is generated. For example: if 2 consecutive pixels of the first pixel and the second pixel in the target pixel matrix are the same, 3 consecutive pixels of the sixth pixel, the seventh pixel and the eighth pixel are the same, 4 consecutive pixels of the tenth pixel, the eleventh pixel, the twelfth pixel and the thirteenth pixel are the same, 5 consecutive pixels of the twentieth pixel, the twenty-first pixel, the twenty-second pixel, the twenty-third pixel and the twenty-fourth pixel are the same, and 6 consecutive pixels of the thirty-first pixel, the thirty-second pixel, the thirty-third pixel, the thirty-fourth pixel, the thirty-fifth pixel and the thirty-sixth pixel are the same, the generated initial integer string is (2,3,4,5, 6).

Then, the initial integer string is sorted from small to large, and the value of the nth row (i.e., the value of the nth largest) in the initial integer string is taken as the pixel width. Wherein, N can be set by self according to actual conditions. In this embodiment, since there is interference in the collected to-be-processed graphics, there may be a small number of small interference values in the initial integer string, and it is found through multiple experiments that the fifth largest value has a good effect, and further adjustment can be performed for different to-be-processed graphics. Therefore, N is preferably 5, i.e., the value of the 5 th row in the initial integer string is taken as the pixel width. It should be noted that the pixel width in this embodiment is a pixel width represented by a binary bit.

Further, dividing each bit integer in the initial integer string by the pixel width to obtain a target integer string; finally, identifying the first pixel in the target integer string; if the first pixel is at a high level, recording odd-numbered integers in the target integer string as a first numerical value, and recording even-numbered integers in the target integer string as a second numerical value; and combining the target first numerical value and the target second numerical value to generate a data bit matrix. Additionally, if the first pixel is at a low level, the odd-bit integer in the target integer string is recorded as a third value, and the odd-bit integer in the target integer string is recorded as a fourth value; and combining the third numerical value and the fourth numerical value to generate a data bit matrix. Such as: suppose that five rows of pixels represent a binary bit, i.e. one binary bit represents a pixel width of 5, a white stripe is represented by 1, and a black stripe is represented by 0. If there is a white stripe with 15 rows of pixels, the stripe can represent 3 binary bits by rounding (i.e. the white stripe can be represented as 3 1(1,1, 1)); if the white stripe is 11 rows of pixels, the stripe can represent 2 binary bits by rounding (i.e. the white stripe can be represented by 2 1(1, 1)); if there is a black stripe that is 24 pixels wide, the black stripe can be obtained by rounding off the black stripe to represent 5 binary bits (i.e., the black stripe can represent 5 0's (0,0,0,0, 0)).

And S50, receiving the byte head matrix sent by the transmitting terminal, and matching the byte head matrix with the data bit matrix to obtain the data section matrix.

The byte head matrix is coded at a transmitting end in advance, the transmitting end endows each light source object (LED lamp) with an ID data code, and the light source objects (LED lamps) continuously transmit ID data information through high-speed light-dark conversion. In one embodiment, in order to obtain a complete ID data information from the receiving end, a header (header) is added to the ID data information to distinguish it from ID data encoding, and the header is a byte header matrix therein. Such as. If the received byte header matrix sent by the transmitting end is 10101, matching the byte header matrix with the data bit matrix according to the data bit matrix … 1100101010001110010101000 … obtained in step S40, so as to obtain two matching matrices 10101, where 00011100 between the two matching matrices is the data section matrix.

In one embodiment, the transmitting end transmits ID data information at a fixed frequency, including a byte header matrix (header) and payload data (data section matrix), and the receiving end needs to decode at a synchronous rate to improve the decoding accuracy, the rate of the transmitting end is represented by a pixel width represented by a binary bit at the receiving end, and there are problems of interference and camera frame rate variation during the transmission process, so that the pixel width represented by a binary bit varies, and the existing sampling decoding will generate interference accumulation to reduce the decoding rate. In this embodiment, the receiving end decoding first needs to correctly decode the binary bits, and then obtains the data byte matrix, i.e., the load data, by matching the byte header matrix. The present embodiment will better synchronize the transmitting end rate and decode it by determining the pixel width represented by a binary bit and further determining the binary number represented by each stripe by rounding.

In this embodiment, an image acquisition device is used to acquire an image of a light source object at an emission end to generate an image to be processed; performing feature extraction on an image to be processed to generate a feature set to be processed, wherein the feature set to be processed comprises a one-dimensional pixel matrix and light source object stripe features; performing binarization processing on the one-dimensional pixel matrix according to the stripe characteristics of the light source object to obtain a target pixel matrix; converting the target pixel matrix by adopting a preset decoding algorithm to generate a data bit matrix; receiving a byte head matrix sent by a transmitting terminal, and matching the byte head matrix with a data bit matrix to obtain a data section matrix; the problem of incorrect decoding caused by frame frequency change is solved, and therefore the accuracy of visible light imaging communication information decoding is guaranteed.

In an embodiment, as shown in fig. 3, the feature extraction is performed on the image to be processed to generate a feature set to be processed, which specifically includes the following steps:

and S201, converting the image to be processed into a binary image.

Specifically, the step of converting the image to be processed into the binarized image is to set the gray value of the pixel point on the image to be processed to be 0 or 255, that is, the whole image to be processed shows an obvious black-and-white effect, that is, the 256 gray level images are selected through a proper threshold value to obtain the binarized image which can still reflect the whole and local features of the image. In a specific embodiment, a cvThreshold function and a cvaddictevthreshold function in OpenCV may be used to implement binarization of an image to be processed.

And S202, removing small connected regions in the binary image by adopting a morphological closed algorithm to obtain a target image.

The morphological closed-loop algorithm is an algorithm for image basic transformation, and is only performed on a binary image. In mathematical morphology, the closed operation is defined as dilation before erosion. Where the sum indicates corrosion and swelling, respectively. The morphological closed algorithm has the functions of filling a region of a fine black cavity in a white object, connecting adjacent objects, connecting the same structural element, performing repeated iterative processing, smoothing the boundary of the white object without obviously changing the area of the white object, and the like.

In a binarized image, the smallest unit is a pixel, 8 adjacent pixels are around each pixel, and 2 kinds of adjacent relations are common: 4 contiguous with 8 contiguous. 4 are adjacent to a total of 4 points, i.e. up, down, left and right. 8 contiguous points-8 in total-include diagonally located points. If the pixel points A and B are adjacent, we call A and B to be communicated, and if A and B are communicated and B and C is communicated, A and C are communicated. Thus, dots that are in communication with each other form one region, while dots that are not in communication form a different region. Such a set of points where all points are connected to each other is called a connected region.

Specifically, the removal of the small connected region in the binarized image by using the morphological closed algorithm means that the noise in the binarized image is removed by using the morphological closed algorithm, for example, the connected region with a smaller black area in the binarized image is removed by using the morphological closed algorithm.

And S203, carrying out masking processing on the target image to obtain the shape of the light source object.

Specifically, the target image may be masked by image processing software, so as to extract a light source object shape from the target image, where the light source object shape is an LED lamp shape in this embodiment.

And S204, processing the pixel value of each row in the shape of the light source object to obtain a one-dimensional pixel matrix.

Specifically, after the light source object shape is obtained according to step S203, a one-dimensional row pixel matrix can be formed by summing and averaging values of each row of pixel points in the light source object shape. Optionally, opencv and C + + code operations may be adopted to implement processing on each row of pixel values in the shape of the light source object, so as to obtain a one-dimensional pixel matrix. Illustratively, if the first row of pixels in the light source object shape has

values

30,36,30,36,36,30 in sequence, the average value of the first row is 33, and so on, the average value of the pixels in each row can be 33,56,89,145,232,123,67,34,0,0,35,76 in sequence, and thus a one-dimensional matrix of pixels [33,56,89,145,232,123,67,34,0,0,35,76] of 1 × 12 is formed

S205, smoothing the one-dimensional pixel matrix to obtain a left shift matrix and a right shift matrix.

Specifically, the one-dimensional pixel matrix is first smoothed, and the purpose of smoothing the one-dimensional pixel matrix is to eliminate noise components in the one-dimensional pixel matrix. Optionally, the smoothing of the one-dimensional pixel matrix may be implemented by using a mean filtering method, a median filtering method, a gaussian filtering method, or a bilateral filtering method. Further, the one-dimensional pixel matrix after smoothing is respectively subjected to left shift processing and right shift processing, the matrix obtained after the left shift processing and the right shift processing is compared with the original one-dimensional pixel matrix (subtraction processing), 1 is taken if the matrix is a positive digit after the subtraction, 0 is taken if the matrix is a negative digit, 0 is directly taken by an overflowing part, and finally a left shift matrix and a right shift matrix are respectively obtained.

And S206, carrying out XOR processing on the left shift matrix and the right shift matrix to obtain the stripe characteristics of the light source object.

Specifically, the exclusive or processing is performed on the left shift matrix and the right shift matrix in step S205, and the coordinate of 1 in the matrix obtained after the exclusive or processing is used as the peak and trough coordinates of the one-dimensional pixel matrix, which is also referred to as the light source object stripe feature.

In the embodiment, an image to be processed is converted into a binary image; removing small connected regions in the binary image by adopting a morphological closed algorithm to obtain a target image; performing masking processing on the target image to obtain the shape of a light source object; processing pixel values of each row in the shape of the light source object to obtain a one-dimensional pixel matrix; smoothing the one-dimensional pixel matrix to obtain a left shift matrix and a right shift matrix; performing exclusive or processing on the left shift matrix and the right shift matrix to obtain the stripe characteristics of the light source object; therefore, the reliability and the accuracy of the acquired fringe characteristics of the light source object are improved.

In an embodiment, as shown in fig. 4, a binarization process is performed on a one-dimensional pixel matrix according to a fringe characteristic of a light source object to obtain a target pixel matrix, which specifically includes the following steps:

s301, reading the interested areas between the wave crests and the wave troughs in the stripe characteristics of the light source object one by one, and acquiring the maximum value and the minimum value in each interested area.

The region of interest is also referred to as an ROI (region of interest) region. In machine vision, image processing, a region to be processed, called a region of interest, is delineated from a processed image in the form of a box, a circle, an ellipse, an irregular polygon, or the like. In the present embodiment, the region between the peak and the valley is determined as the region of interest. The coordinate values of the peak and the trough are determined according to step S206, so that the regions of interest between the peak and the trough can be directly read one by one according to the coordinate values of the peak and the trough. Further, after the regions of interest are determined, a maximum value and a minimum value in each region of interest are acquired.

And S302, if the minimum value in the region of interest is smaller than a preset background threshold value, determining the minimum value as a target background threshold value.

The preset background threshold Tmin is a threshold set in advance according to practical situations in a self-defined manner. The preset background threshold value can be set and adjusted according to practical conditions (such as LED lamp intensity) in a self-defined mode. It should be noted that the preset background thresholds set correspondingly in different regions of interest may be the same or different. In the present embodiment, the background threshold Tmin is preferably set to 20. Specifically, after the minimum value in each region of interest is determined according to step S301, the minimum value in each region of interest is compared with the corresponding preset background threshold, and if the minimum value in the region of interest is smaller than the preset background threshold, the minimum value is determined as the target background threshold. And if the minimum value in the region of interest is smaller than a preset background threshold value, directly determining the preset background threshold value as a target background threshold value.

And S303, if the difference value between the maximum value and the minimum value in the region of interest is larger than a first threshold value, acquiring a mean value between the maximum value and the minimum value in the region of interest, and determining the region of interest as a target region of interest.

Wherein the first threshold is a preset specific threshold D_diff. Specific threshold value D_diffThe LED lamp can be set and adjusted in a customized way according to actual conditions (such as the intensity of the LED lamp). Specifically, a subtraction processing is performed on the maximum value and the minimum value in each region of interest to obtain a difference value between the maximum value and the minimum value in each region of interest; then, comparing the difference between the maximum value and the minimum value in each region of interest with a first threshold, if the difference between the maximum value and the minimum value in the region of interest is greater than the first threshold, obtaining the mean value between the maximum value and the minimum value in the region of interest, namely, after adding the maximum value and the minimum value, taking the mean value as the mean value between the maximum value and the minimum value in the region of interest, and determining the region of interest as a target region of interest. If the difference value between the maximum value and the minimum value in the region of interest is less than or equal to the first threshold value, continuing to read the next region of interest.

And S304, performing binarization processing on the target region of interest.

Specifically, binarization processing is performed on the target region of interest. The binarization processing is carried out on the target region of interest, namely, the gray value of a pixel point on the target region of interest is set to be 0 or 255, namely, the whole target region of interest is obviously black and white, namely, a binary image which can still reflect the whole and local characteristics of the image is obtained by selecting the gray images with 256 brightness levels through proper threshold values. In a specific embodiment, cvThreshold function and cvaddtivethreshold function in OpenCV may be adopted to implement the binarization processing of the target region of interest.

S305, carrying out global binarization processing on each region of interest by adopting a target threshold value to obtain a target pixel matrix.

Wherein, the target threshold is T which can be self-defined according to actual conditions_thresholdThe threshold target threshold value is only required to be capable of distinguishing the dark stripes from the bright stripes; the target threshold is typically twice the background threshold, and preferably 40. Specifically, a target threshold is adopted to perform global binarization processing on each region of interest, so as to process the regions of interest that are not subjected to binarization in step S304, and obtain a target pixel matrix.

In the embodiment, the interested areas between the wave crests and the wave troughs in the stripe characteristics of the light source object are read one by one, and the maximum value and the minimum value in each interested area are obtained; if the minimum value in the region of interest is smaller than a preset background threshold value, determining the minimum value as a target background threshold value; if the difference value between the maximum value and the minimum value in the region of interest is larger than a first threshold value, acquiring a mean value between the maximum value and the minimum value in the region of interest, and determining the region of interest as a target region of interest; carrying out binarization processing on the target region of interest; carrying out global binarization processing on each region of interest by adopting a target threshold value to obtain a target pixel matrix; thereby ensuring the accuracy of the obtained target pixel matrix.

In an embodiment, as shown in fig. 5, the converting the target pixel matrix by using a preset decoding algorithm to generate the data bit matrix specifically includes the following steps:

s401, extracting the number of continuous same pixels in the target pixel matrix and generating an initial integer string.

Specifically, the number of consecutive identical pixels in the target pixel matrix is extracted, and an initial integer string is generated. Such as: the one-dimensional matrix is 1111110001111111111111000000; that 6 consecutive 1 s are denoted as 6, and so on, then the one-dimensional matrix can be transformed into an integer string (63136).

S402, taking the Nth value in the initial integer string as the pixel width.

Specifically, after the initial integer string is generated, the initial integer string is sorted from small to large, and then the nth value in the initial integer string is used as the pixel width. Preferably, in this embodiment, since the collected to-be-processed graphics have interference, a small number of small interference values may exist in the initial integer string, and it is found through multiple experiments that the effect of taking the fifth largest value in the initial integer string is good, and further adjustment can be performed on different to-be-processed graphics. Therefore, N is preferably 5, i.e., the value of the 5 th row in the initial integer string is taken as the pixel width.

Further, since each integer in the initial integer string generated according to S401 belongs to a decimal number, after the pixel width is determined, it is necessary to convert the pixel width into a binary number, that is, the pixel width in the present embodiment is the pixel width represented by a binary bit.

And S403, dividing each bit integer in the initial integer string by the pixel width to obtain a target integer string.

Specifically, each bit integer in the initial integer string is divided by the pixel width and rounded to obtain the target integer string. Such as: if the pixel width is 5 and the initial integer string is (5,4,6,13,16,11,6,5,4), the target integer string obtained by dividing each bit integer in the initial integer string by the pixel width 5 and rounding is (1,1,1,3,3,2,1,1, 1).

S404, identifying the first pixel in the target integer string.

S405, if the first pixel is in a high level, the odd-numbered integers in the integer string are recorded as a first numerical value, and the even-numbered integers in the integer string are recorded as a second numerical value.

And S406, combining the first numerical value and the second numerical value to generate a data bit matrix.

Specifically, after the target integer string is determined, a first pixel in the target integer string is identified, if the first pixel is at a high level, odd-bit integers in the integer string are recorded as a first numerical value (for example, integers located at odd bits in the integer string are recorded as p 1 s (p is a value of an odd-bit integer), even-bit integers in the integer string are recorded as a second numerical value (for example, integers located at even bits in the integer string are recorded as q 1 s (q is a value of an even-bit integer)), and finally the first numerical value and the second numerical value are combined to generate the data bit matrix.

Illustratively, if the target integer string is (1,1,1,3,3,2,1, 1), and the first pixel in the target integer string is high, the data bit matrix generated after the odd-bit integer in the integer string is denoted as p 1(p is the value of the odd-bit integer) and the integer in the even-bit string is denoted as q 1(q is the value of the even-bit integer), is (1,0,1,0,0,0,1,1,1,0,0,0,1, 1).

In this embodiment, the number of consecutive identical pixels in the target pixel matrix is extracted to generate an initial integer string; taking the Nth value in the initial integer string as the pixel width; dividing each bit integer in the initial integer string by the pixel width to obtain a target integer string; identifying a first pixel in the target integer string; if the first pixel is at a high level, recording odd-numbered integers in the integer string as a first numerical value, and recording even-numbered integers in the integer string as a second numerical value; combining the first numerical value and the second numerical value to generate a data bit matrix; thereby ensuring the accuracy of the generated data bit matrix.

In an embodiment, as shown in fig. 6, after identifying the first pixel in the target pixel matrix, the visible light imaging communication decoding method further includes the following steps:

s407, if the first pixel is at low level, the odd-bit integer in the integer string is recorded as a third value, and the odd-bit integer in the integer string is recorded as a fourth value.

And S408, combining the third numerical value and the fourth numerical value to generate a data bit matrix.

Specifically, if the first pixel is at a low level, the data bit matrix can be generated by recording odd-bit integers in the integer string as a third value (for example, recording integers in odd bits in the integer string as p 0 s (p is a value of an odd-bit integer), and recording even-bit integers in the integer string as a fourth value (for example, recording integers in even bits in the integer string as q 0 s (q is a value of an even-bit integer)), and finally combining the third value and the fourth value.

Illustratively, if the target integer string is (1,1,1,3,3,2,1, 1), and the first pixel in the target integer string is low, then the data bit matrix generated after the even-numbered integers in the integer string are denoted as p 1 s (p is the value of the odd-numbered integer), and the odd-numbered integers in the integer string are denoted as q 1 s (q is the value of the even-numbered integer), is (0,1,0,1,1,1,0,0,0,1,1,1,0,1, 0).

In this embodiment, if the first pixel is at a low level, the odd-numbered integer in the integer string is recorded as a third value, and the odd-numbered integer in the integer string is recorded as a fourth value; combining the third numerical value and the fourth numerical value to generate a data bit matrix; thereby further ensuring the accuracy of the generated data bit matrix.

It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

In an embodiment, a visible light imaging communication decoding device is provided, and the visible light imaging communication decoding device corresponds to the visible light imaging communication decoding method in the above embodiments one to one. As shown in fig. 7, the visible light imaging communication decoding apparatus includes: the image acquisition module 10, the feature extraction module 20, the binarization processing module 30, the conversion module 40 and the matching module 50. The functional modules are explained in detail as follows:

the image acquisition module 10 is used for acquiring an image of a light source object at an emitting end by adopting image acquisition equipment to generate an image to be processed;

the feature extraction module 20 is configured to perform feature extraction on the image to be processed to generate a feature set to be processed, where the feature set to be processed includes a one-dimensional pixel matrix and a light source object stripe feature;

a binarization processing module 30, configured to perform binarization processing on the one-dimensional pixel matrix according to the light source object stripe characteristics to obtain a target pixel matrix;

the conversion module 40 is configured to convert the target pixel matrix by using a preset decoding algorithm to generate a data bit matrix;

and the matching module 50 is configured to receive the byte header matrix sent by the transmitting end, and match the byte header matrix with the data bit matrix to obtain a data section matrix.

Preferably, as shown in fig. 8, the feature extraction module 20 includes:

a first conversion unit 201 for converting the image to be processed into a binarized image,

a removing unit 202, configured to remove a small connected region in the binarized image by using a morphological close algorithm to obtain a target image;

a mask processing unit 203, configured to perform mask processing on the target image to obtain a light source object shape;

a first processing unit 204, configured to process pixel values in each row of the light source object shape to obtain a one-dimensional pixel matrix;

a smoothing unit 205, configured to perform smoothing on the one-dimensional pixel matrix to obtain a left shift matrix and a right shift matrix;

and the exclusive or processing unit 206 is configured to perform exclusive or processing on the left shift matrix and the right shift matrix to obtain a light source object fringe characteristic.

Preferably, as shown in fig. 9, the binarization processing module 30 includes:

the reading unit 301 is configured to read regions of interest between peaks and troughs in the fringe characteristics of the light source object one by one, and obtain a maximum value and a minimum value in each region of interest;

a first determining unit 302, configured to determine a minimum value in the region of interest as a target background threshold when the minimum value is smaller than a preset background threshold;

a second determining unit 303, configured to, when a difference between a maximum value and a minimum value in the region of interest is greater than a first threshold, obtain a mean value between the maximum value and the minimum value in the region of interest, and determine the region of interest as a target region of interest;

a first binarization processing unit 304, configured to perform binarization processing on the target region of interest;

a global binarization processing unit 305, configured to perform global binarization processing on each of the regions of interest by using a target threshold value, so as to obtain a target pixel matrix.

Preferably, the conversion module 40 comprises:

the extraction unit is used for extracting the number of continuous same pixels in the target pixel matrix and generating an initial integer string;

a pixel width determination unit for taking an nth value in the initial integer string as a pixel width;

a target integer string determining unit, configured to divide each bit integer in the initial integer string by the pixel width to obtain a target integer string;

an identifying unit for identifying a first pixel in the target integer string;

a first recording unit, configured to record odd-numbered integers in the integer string as a first numerical value and even-numbered integers in the integer string as a second numerical value when the first pixel is at a high level;

and the first combination unit is used for combining the first numerical value and the second numerical value to generate a data bit matrix.

Preferably, the conversion module 40 further comprises:

a second recording unit, configured to record an odd-numbered integer in the integer string as a third value and record an odd-numbered integer in the integer string as a fourth value when the first pixel is at a low level;

and the second combination unit is used for combining the third numerical value and the fourth numerical value to generate a data bit matrix.

For specific limitations of the visible light imaging communication decoding apparatus, reference may be made to the above limitations on the visible light imaging communication decoding method, which is not described herein again. The modules in the visible light imaging communication decoding device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 10. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing the data used in the visible light imaging communication decoding method in the above embodiment. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a visible light imaging communication decoding method.

In one embodiment, a computer device is provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the visible light imaging communication decoding method in the above embodiments is implemented. In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which when executed by a processor implements the visible light imaging communication decoding method in the above-described embodiments.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions.

The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.

Claims

1. A visible light imaging communication decoding method is characterized by comprising the following steps:

2. The decoding method of visible light imaging communication according to claim 1, wherein the performing feature extraction on the image to be processed to generate a feature set to be processed comprises:

converting the image to be processed into a binary image,

removing small connected regions in the binary image by adopting a morphological closed algorithm to obtain a target image;

performing masking processing on the target image to obtain the shape of a light source object;

processing the pixel value of each row in the shape of the light source object to obtain a one-dimensional pixel matrix;

performing smoothing processing on the one-dimensional pixel matrix to obtain a left shift matrix and a right shift matrix;

and carrying out exclusive OR processing on the left shift matrix and the right shift matrix to obtain the stripe characteristics of the light source object.

3. The decoding method for visible light imaging communication according to claim 1, wherein the binarizing the one-dimensional pixel matrix according to the light source object stripe feature to obtain a target pixel matrix comprises:

reading the interested areas between the wave crests and the wave troughs in the stripe characteristics of the light source object one by one, and acquiring a maximum value and a minimum value in each interested area;

if the minimum value in the region of interest is smaller than a preset background threshold value, determining the minimum value as a target background threshold value;

if the difference value between the maximum value and the minimum value in the region of interest is larger than a first threshold value, acquiring a mean value between the maximum value and the minimum value in the region of interest, and determining the region of interest as a target region of interest;

carrying out binarization processing on the target region of interest;

and carrying out global binarization processing on each region of interest by adopting a target threshold value to obtain a target pixel matrix.

4. The visible light imaging communication decoding method of claim 1, wherein the converting the target pixel matrix by using a preset decoding algorithm to generate a data bit matrix comprises:

extracting the number of continuous same pixels in the target pixel matrix to generate an initial integer string;

taking the nth value in the initial integer string as the pixel width;

dividing each bit integer in the initial integer string by the pixel width to obtain a target integer string;

identifying a first pixel in the target integer string;

if the first pixel is at a high level, recording odd-numbered integers in the integer string as a first numerical value, and recording even-numbered integers in the integer string as a second numerical value;

and combining the first numerical value and the second numerical value to generate a data bit matrix.

5. The visible light imaging communication decoding method of claim 4, wherein the visible light imaging communication decoding, after identifying a first pixel in the target pixel matrix, further comprises:

if the first pixel is at a low level, recording an odd-bit integer in the integer string as a third numerical value, and recording an odd-bit integer in the integer string as a fourth numerical value;

and combining the third numerical value and the fourth numerical value to generate a data bit matrix.

6. A visible light imaging communication decoding apparatus, comprising:

7. The visible-light imaging communication decoding apparatus of claim 6, wherein the feature extraction module comprises:

a first conversion unit for converting the image to be processed into a binarized image,

the removing unit is used for removing the small connected region in the binary image by adopting a morphological closed algorithm to obtain a target image;

the mask processing unit is used for performing mask processing on the target image to obtain the shape of the light source object;

the first processing unit is used for processing the pixel values of each row in the shape of the light source object to obtain a one-dimensional pixel matrix;

the smoothing unit is used for smoothing the one-dimensional pixel matrix to obtain a left shift matrix and a right shift matrix;

and the exclusive OR processing unit is used for carrying out exclusive OR processing on the left shift matrix and the right shift matrix to obtain the stripe characteristics of the light source object.

8. The visible-light imaging communication decoding apparatus according to claim 6, wherein the binarization processing module includes:

the reading unit is used for reading the interested areas between the wave crests and the wave troughs in the stripe characteristics of the light source object one by one and acquiring the maximum value and the minimum value in each interested area;

the first determining unit is used for determining the minimum value in the region of interest as a target background threshold value when the minimum value is smaller than a preset background threshold value;

the second determining unit is used for acquiring a mean value between the maximum value and the minimum value in the region of interest when the difference value between the maximum value and the minimum value in the region of interest is larger than a first threshold value, and determining the region of interest as a target region of interest;

the first binarization processing unit is used for carrying out binarization processing on the target region of interest;

and the global binarization processing unit is used for carrying out global binarization processing on each region of interest by adopting a target threshold value to obtain a target pixel matrix.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the visible light imaging communication decoding method according to any one of claims 1 to 5 when executing the computer program.

10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, implements the visible light imaging communication decoding method according to any one of claims 1 to 5.