CN113689366B - Dynamic temperature width adjusting method and device - Google Patents

Abstract

The invention relates to a temperature width dynamic adjustment method and a temperature width dynamic adjustment device. The method mainly comprises the following steps: selecting a proper image preprocessing method according to the temperature distribution condition of a scene to be observed, and carrying out mapping processing on gray values so as to enhance the quality of images; starting a dynamic regulation mode, and mapping the maximum temperature in the actual scene to the maximum temperature grade in the dynamic regulation mode; determining a target temperature range needing to be emphasized by repeatedly changing the temperature grade range; and obtaining the finally adjusted observation picture. According to the invention, the concerned region can be flexibly reserved according to the requirement of a user, the non-concerned region is weakened, the influence of the temperature change of the non-concerned region on the temperature of the concerned region is avoided, and the image detail degree and the image fidelity degree of the concerned region are ensured.

Description

A method and device for dynamically adjusting temperature width

[Technical field]

The invention relates to the field of temperature measurement with infrared thermal imagers, and in particular to a method and device for dynamically adjusting temperature width.

【Background technique】

Infrared thermal imagers use infrared detectors and optical imaging lenses to receive the infrared radiation energy distribution pattern of the target to be measured and reflect it on the photosensitive element of the infrared detector, thereby obtaining an infrared thermal image. This thermal image corresponds to the heat distribution field on the surface of the object. In layman's terms, infrared thermal imagers convert the invisible infrared energy emitted by an object into a visible thermal image. The different colors on the thermal image represent the different temperatures of the object being measured.

In the existing field of temperature measurement using infrared thermal imagers, temperature data can be processed into visual infrared images. However, the highlighting of the focus area in the existing infrared images destroys the original infrared imaging effect of the image. A single method of highlighting or marking the temperature area cannot guarantee the authenticity and detail of the focus area image.

In view of this, how to overcome the defects of the prior art, solve the above technical problems, and provide a method that can dynamically adjust the temperature range and obtain a distortion-free and high-detail temperature image of the area of interest is a difficult problem to be solved in this technical field.

[Summary of the invention]

In view of the above defects or improvement needs of the prior art, the present invention provides a method and device for dynamically adjusting the temperature width, which can dynamically adjust the target temperature range and only retain the infrared image of the observed object within the target temperature range, and perform monochrome overlay processing on the image outside the target temperature range, thereby highlighting the area of interest and avoiding the influence of the non-area of interest on the color of the image of the area of interest.

The embodiment of the present invention adopts the following technical solution:

In a first aspect, the present invention provides a method for dynamically adjusting temperature width, comprising: selecting a suitable image preprocessing method according to the temperature distribution of a scene to be observed, and mapping the grayscale values to enhance the quality of the image;

Enable the dynamic adjustment mode and map the maximum temperature in the actual scene to the maximum temperature level in the dynamic adjustment mode;

By repeatedly changing the temperature level range, determine the target temperature range that needs to be focused on;

Get the final adjusted observation picture.

Furthermore, the method of selecting a suitable image preprocessing method according to the temperature distribution of the scene to be observed and mapping the grayscale values to enhance the image quality specifically includes:

Turn on the product in the scene to be observed and observe the temperature distribution of the scene to be observed;

If the temperature distribution in the scene to be observed is relatively uniform, the linear grayscale mapping method is used for image preprocessing;

If the temperature distribution in the scene to be observed is uneven, the histogram equalization method is used for image preprocessing.

Furthermore, the linear grayscale mapping method specifically includes: modifying the grayscale value of each pixel of the original image according to the linear mapping rule, thereby changing the dynamic range of the image grayscale.

Furthermore, the histogram equalization method specifically includes: performing histogram equalization on the image so that the grayscale distribution probability of the image is the same.

Furthermore, before performing histogram equalization, the grayscale range of the image is normalized to [0,1].

Furthermore, the image grayscale is quantized to 8 bits, and the image grayscale range is [0, 255].

Furthermore, the enabling of the dynamic adjustment mode to map the maximum temperature in the actual scene to the maximum temperature level in the dynamic adjustment mode specifically includes:

The dynamic adjustment function is enabled to obtain the highest temperature and the lowest temperature in the actual scene, and the highest temperature level value _TH and the lowest temperature level value _TL are obtained after the grayscale is remapped according to the selected algorithm.

Furthermore, the step of repeatedly changing the temperature level range to determine the target temperature range that needs to be focused on includes:

Select a temperature value T _l that is higher than the current minimum temperature level. If the temperature value t ₁ of a certain pixel satisfies T _L ≤ _{t 1} ≤ T _l , then assign all grayscale values of the pixel to the minimum grayscale value, and the grayscale values of other pixels remain unchanged.

Select a temperature value _Th that is lower than the current highest temperature level. If the temperature value _t2 of a certain pixel satisfies _Th ≤ _t2 ≤ _TH , assign all grayscale values of the pixel to the highest grayscale value, and the grayscale values of other pixels remain unchanged.

Repeat the steps of selecting T _l and _Th and observe the temperature image until t ₁ and t ₂ are determined to be the target temperature ranges that need to be observed.

Furthermore, the obtaining of the finally adjusted observation picture specifically includes:

The imaging result is processed by the acquired target temperature range, so as to obtain the infrared temperature picture of the observed target corresponding to the target temperature range.

On the other hand, the present invention provides a temperature width dynamic adjustment device, specifically: including at least one processor and a memory, at least one processor and the memory are connected through a data bus, the memory stores instructions that can be executed by at least one processor, and after the instructions are executed by the processor, they are used to complete the temperature width dynamic adjustment method in the first aspect.

Compared with the prior art, the beneficial effect of the embodiments of the present invention is that the temperature width dynamic adjustment method provided by the present invention flexibly retains the area of interest and weakens the non-area of interest according to the needs of the user, thereby avoiding the influence of temperature changes in the non-area of interest on the temperature of the area of interest, and ensuring the image detail and authenticity of the area of interest.

【Brief Description of the Drawings】

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

FIG1 is a flow chart of a method for dynamically adjusting temperature width provided in Example 1 of the present invention;

FIG2 is a specific flow chart of step 100 provided in Example 1 of the present invention;

FIG3 is a specific flow chart of step 300 provided in Embodiment 1 of the present invention;

FIG4 is a block diagram of a temperature width dynamic adjustment system module provided by Embodiment 2 of the present invention;

FIG. 5 is a schematic diagram of the structure of a temperature width dynamic adjustment device provided in Example 3 of the present invention.

【Detailed ways】

In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other. The present invention will be described in detail below with reference to the accompanying drawings and embodiments.

Embodiment 1:

As shown in FIG. 1 , an embodiment of the present invention provides a method for dynamically adjusting a temperature width, and the specific steps are as follows.

Step 100: Select a suitable image preprocessing method according to the temperature distribution of the scene to be observed, and perform mapping processing on the grayscale values to enhance the quality of the image.

Step 200: Enable the dynamic adjustment mode and map the maximum temperature in the actual scene to the maximum temperature level in the dynamic adjustment mode.

Step 300: Determine the target temperature range that needs to be focused on by repeatedly changing the temperature level range.

Step 400: Obtain the final adjusted observation picture.

In this preferred embodiment, a step of preparation is required before the above steps are implemented: select a suitable infrared observation scene as needed. Specifically, it is necessary to first select a suitable product and adjust the product to a suitable observation angle and observation distance to ensure the availability of the observation scene.

After the preparation work is completed, step 100 is performed. Specifically, as shown in FIG. 2 , in this preferred embodiment, step 100 can be expanded to:

Step 101: Turn on the product in the scene to be observed and observe the temperature distribution of the scene to be observed.

Step 102: If the temperature distribution in the scene to be observed is relatively uniform, a linear grayscale mapping method is used to perform image preprocessing.

Step 103: If the temperature distribution in the scene to be observed is uneven, a histogram equalization method is used to perform image preprocessing.

The above-mentioned step 102 and step 103 are in parallel relationship, and which step to execute is selected according to the temperature distribution in the scene to be observed.

The method of step 102 is to modify the gray value of each pixel of the original image according to the rule of linear mapping, thereby changing the dynamic range of the image grayscale, which can expand the grayscale dynamic range and map the image grayscale to the entire grayscale range as needed.

The principle of linear grayscale enhancement in step 102 is: in the same image, for each pixel point of the input image, its grayscale value is mapped according to the linear function of the following formula (1), and the grayscale adjustment of the entire image does not need to consider the position of the pixel in the image, and the grayscale value of the image is directly adjusted.

Suppose the input image is f(x,y) and the processed image is g(x,y). If the grayscale range of the original image f(x,y) is [a,b] and the grayscale range of the linearly adjusted image g(x,y) is [c,d], then the linear grayscale enhancement can be expressed as the following mathematical transformation:

The histogram equalization method in step 103 specifically includes: performing histogram equalization on the image so that the grayscale distribution probability of the image is the same. Specifically, in the histogram, if the grayscale is concentrated in the high grayscale area, the low grayscale of the image is not easy to distinguish, and if the grayscale is concentrated in the low grayscale area, the high grayscale is not easy to distinguish. In order to make it easy to distinguish high and low grayscales, the best way is to convert the image so that the grayscale distribution probability is the same. This is the purpose of histogram equalization.

Generally, for the convenience of discussion, before performing the histogram equalization operation, the grayscale range of the image should be normalized, that is, the grayscale range should be [0,1]. First, make the following assumptions: the grayscale value of the original image to be processed is represented by r (0≤r≤1), its probability density is represented by p _r (r), the pixel grayscale value of the transformed image is represented by s, and the probability density is represented by p _s (s). Assume that the image is transformed as follows:

s＝T(r), 0≤r≤L-1 (2)

Its transformation function s=T(r) satisfies the following conditions:

(a): The function T(r) is unidirectional and monotonically increasing. This condition ensures the unidirectional mapping during the conversion process and the grayscale of the converted image does not reverse.

(b): When 0≤r≤1, 0≤T(r)≤L-1. This condition limits the grayscale level to have the same grayscale range as the input grayscale level.

In the above formula, L is the gray level. The purpose is to make the gray level probability distribution equal:

The relationship between the grayscale distribution before and after the transformation can be expressed as:

So we have:

T′(r)＝(L-1)pr( _r ) (5)

Points include:

Discretization representation:

Assume that the image grayscale is quantized to 8 bits, that is, the grayscale range of the image is [0, 255], and N represents the total number of pixels.

In this preferred embodiment, step 200 (enabling the dynamic adjustment mode and mapping the maximum temperature in the actual scene to the maximum temperature level in the dynamic adjustment mode) can be expanded to: enabling the dynamic adjustment function, obtaining the highest temperature and the lowest temperature in the actual scene, and remapping the grayscale according to the selected algorithm to obtain the highest temperature level value _TH and the lowest temperature level value _TL .

As shown in FIG. 3 , in this preferred embodiment, step 300 (determining the target temperature range that needs to be focused on by repeatedly changing the temperature level range) can be expanded to:

Step 301: Select a temperature value (T _l ) higher than the current minimum temperature level value. If the temperature value of a pixel point (t ₁ ) satisfies the condition (T _L ≤ _{t 1} ≤ T _l ), all grayscale values of the pixel point are assigned to the minimum grayscale value, and the grayscale values of other pixels remain unchanged. When the grayscale range of the above-mentioned image is set to [0, 255], the minimum grayscale value assigned in this step is 0.

Step 302: Select a temperature value ( _Th ) lower than the current maximum temperature level. If the temperature value of a pixel ( _t2 ) satisfies the condition ( _{Th≤t2≤TH )} , all grayscale values of the pixel are assigned the maximum grayscale value, and the grayscale values of other pixels remain unchanged. When the grayscale range of the above-mentioned image is set to [ _0,255 ], the maximum grayscale value assigned in this step is 255.

Step 303: Repeat steps 301 and 302 to observe the temperature image until the target temperature ranges that need to be focused on are determined: _t1 and _t2 .

In this preferred embodiment, step 400 (obtaining the observation picture after the final adjustment) specifically includes: processing the imaging result by using the acquired target temperature range, thereby obtaining the infrared temperature picture of the observation target corresponding to the target temperature range.

The temperature width dynamic adjustment method provided by the present invention flexibly retains the focus area and weakens the non-focus area according to the needs of the user, thereby avoiding the influence of the temperature change of the non-focus area on the temperature of the focus area and ensuring the image detail and authenticity of the focus area.

Embodiment 2:

Based on the temperature-width dynamic adjustment method provided in Example 1, this Example 2 provides a temperature-width dynamic adjustment system corresponding to Example 1. As shown in FIG. 4 , the system includes an image preprocessing module, a dynamic adjustment module, and an imaging processing module.

In this preferred embodiment, the image preprocessing module is used to select a suitable image preprocessing method according to the temperature distribution of the scene to be observed, and to map the grayscale value to enhance the quality of the image. Specifically, the image preprocessing module can also be subdivided into a temperature distribution acquisition module, a temperature distribution judgment module, a linear grayscale mapping module and a histogram equalization module, wherein the temperature distribution acquisition module is used to observe the temperature distribution of the scene to be observed; the temperature distribution judgment module is used to judge whether the temperature distribution is uniform; the linear grayscale mapping module is used to use the linear grayscale mapping method to perform image preprocessing when the temperature distribution is relatively uniform; the histogram equalization module is used to use the histogram equalization method to perform image preprocessing when the temperature distribution is uneven. The specific processing process of each of the above modules corresponds to the detailed process of step 100 in Example 1, and will not be repeated here.

In this preferred embodiment, the dynamic adjustment module is used to enable the dynamic adjustment mode, map the maximum temperature in the actual scene to the maximum temperature level in the dynamic adjustment mode, and then determine the target temperature range that needs to be observed by repeatedly changing the temperature level range. Specifically, the dynamic adjustment module can be further divided into a maximum temperature level acquisition module, a minimum grayscale value assignment module, a maximum grayscale value assignment module and a target temperature range determination module, wherein the maximum temperature level acquisition module is used to enable the dynamic adjustment function, obtain the maximum temperature and the minimum temperature in the actual scene, and remap the grayscale according to the selected algorithm to obtain the maximum temperature level value _TH and the minimum temperature level value _TL ; the minimum grayscale value assignment module is used to select a temperature value _Tl higher than the current minimum temperature level value. If the temperature value _t1 of _a certain pixel _point satisfies _TL≤t1≤Tl , all the grayscale values of the pixel point are assigned to the minimum grayscale value, and the grayscale values of other pixels remain unchanged; the maximum grayscale value assignment module is used to select a temperature value _Th lower than _the current maximum temperature level value. If the temperature value _t2 of a certain pixel point _{satisfies Th≤t2≤TH} , then the grayscale values of the pixel points are all assigned to the highest grayscale value, and the grayscale values of other pixel points remain unchanged; the target temperature range determination module is used to observe the temperature image in the repeated work of the lowest grayscale value assignment module and the highest grayscale value assignment module until _t1 and _t2 are determined as the target temperature ranges that need to be focused on. The specific processing process of each of the above modules corresponds to the detailed process of step 200 and step 300 in embodiment 1, and will not be repeated here.

In this preferred embodiment, the imaging processing module is used to process the imaging result by using the acquired target temperature range, so as to obtain the infrared temperature picture of the observed target corresponding to the target temperature range.

In this embodiment, the above modules are processed collaboratively to achieve the following technical effects: flexibly retain the area of interest according to the needs of the user, weaken the non-area of interest, avoid the influence of the temperature change of the non-area of interest on the temperature of the area of interest, and ensure the image detail and authenticity of the area of interest. The process and steps of the collaborative processing between the modules are detailed in Example 1, which will not be repeated here.

Embodiment 3:

On the basis of the temperature width dynamic adjustment method and system provided in the above-mentioned embodiments 1 and 2, the present invention further provides a temperature width dynamic adjustment device that can be used to implement the above-mentioned method and system, as shown in FIG5 , which is a schematic diagram of the device architecture of an embodiment of the present invention. The temperature width dynamic adjustment device of this embodiment includes one or more processors 21 and a memory 22. Among them, FIG5 takes one processor 21 as an example.

The processor 21 and the memory 22 may be connected via a bus or other means, and FIG5 takes the connection via a bus as an example.

The memory 22 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as the temperature width dynamic adjustment method and system in Embodiments 1 to 2. The processor 21 executes various functional applications and data processing of the temperature width dynamic adjustment device by running the non-volatile software programs, instructions and modules stored in the memory 22, that is, the temperature width dynamic adjustment method and system in Embodiments 1 to 2 are implemented.

The memory 22 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some embodiments, the memory 22 may optionally include a memory remotely arranged relative to the processor 21, and these remote memories may be connected to the processor 21 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

The program instructions/modules are stored in the memory 22, and when executed by one or more processors 21, the temperature width dynamic adjustment method and system in the above-mentioned embodiments 1 to 2 are executed, for example, the various steps and module functions shown in Figures 1 and 4 described above are executed.

A person skilled in the art may understand that all or part of the steps in the various methods of the embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the storage medium may include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims (8)

1. A method for dynamically adjusting a temperature width, comprising: Select appropriate image preprocessing method according to the temperature distribution of the scene to be observed, and map the gray value to enhance the image quality; Enable the dynamic adjustment mode, and map the maximum temperature in the actual scene to the maximum temperature level in the dynamic adjustment mode; specifically, enable the dynamic adjustment function, obtain the highest temperature and the lowest temperature in the actual scene, and remap the grayscale according to the selected algorithm to obtain the highest temperature level value _TH and the lowest temperature level value _TL ; By repeatedly changing the temperature level range, determine the target temperature range that needs to be focused on. Specifically, select a temperature value T _L that is higher than the current minimum temperature level value. If a certain pixel point temperature value t ₁ satisfies T _L ≤ t ₁ ≤ T _l , then all grayscale values of the pixel point are assigned to the minimum grayscale value, and the grayscale values of other pixels remain unchanged. Select a temperature value _{Th that} is lower than the current maximum temperature level value. If a certain pixel point temperature value t ₂ satisfies _Th ≤ _{t 2} ≤ _TH , then all grayscale values of the pixel point are assigned to the maximum grayscale value, and the grayscale values of other pixels remain unchanged. Repeat the steps of selecting T _l and _Th , and observe the temperature image until t ₁ and t ₂ are determined to be the target temperature range that needs to be focused on. Get the final adjusted observation picture. 2. The method for dynamic temperature width adjustment according to claim 1 is characterized in that the step of selecting a suitable image preprocessing method according to the temperature distribution of the scene to be observed and mapping the grayscale values to enhance the image quality specifically comprises: Turn on the product in the scene to be observed and observe the temperature distribution of the scene to be observed; If the temperature distribution in the scene to be observed is relatively uniform, the linear grayscale mapping method is used for image preprocessing; If the temperature distribution in the scene to be observed is uneven, the histogram equalization method is used for image preprocessing. 3. The temperature width dynamic adjustment method according to claim 2 is characterized in that the linear grayscale mapping method specifically includes: modifying the grayscale value of each pixel of the original image according to the rule of linear mapping, thereby changing the dynamic range of the image grayscale. 4. The method for dynamic temperature and width adjustment according to claim 2, wherein the histogram equalization method specifically comprises: performing histogram equalization on the image so that the grayscale distribution probability of the image is the same. 5. The method for dynamic temperature and width adjustment according to claim 4 is characterized in that, before performing histogram equalization, the grayscale range of the image is normalized to [0, 1]. 6. The method for dynamic temperature-width adjustment according to claim 4, wherein the image grayscale is quantized to 8 bits and the image grayscale range is [0, 255]. 7. The method for dynamically adjusting the temperature width according to claim 1, wherein obtaining the observation picture after the final adjustment specifically comprises: The imaging result is processed by the acquired target temperature range, so as to obtain the infrared temperature picture of the observed target corresponding to the target temperature range. 8. A temperature width dynamic adjustment device, characterized in that: It includes at least one processor and a memory, the at least one processor and the memory are connected via a data bus, the memory stores instructions that can be executed by the at least one processor, and the instructions, after being executed by the processor, are used to complete the temperature width dynamic adjustment method described in any one of claims 1-7.