CN113808135A - Image brightness abnormality detection method, electronic device, and storage medium - Google Patents
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- G06T2207/30196—Human being; Person
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Abstract
The embodiment of the invention relates to the field of image processing, and discloses an image brightness abnormity detection method, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring an infrared image containing a human face shot by a structured light camera; constructing a pixel histogram aiming at pixel points of which the gray value is not less than a specified gray value in the infrared image; counting a first probability smaller than a first gray value and a second probability larger than a second gray value in the pixel histogram; wherein a specified gray value < first gray value < second gray value; determining whether the first probability is greater than a first probability threshold and the second probability is greater than a second probability threshold; and when the first probability is larger than the first probability threshold or the second probability is larger than a second probability threshold, determining that the overall brightness of the infrared map is abnormal. The scheme can quickly detect the brightness abnormal condition of the infrared image containing the face so as to guide whether the infrared image is filtered or not.
Description
Technical Field
The present invention relates to the field of image processing, and in particular, to a method for detecting luminance abnormality of an image, an electronic device, and a storage medium.
Background
In 3D face recognition, the quality of face pixels of an infrared image and a depth image plays an absolute role in the accuracy of a recognition rate. In general, the 3D face recognition application acquires data by using a structured light camera, and in order to ensure the accuracy of recognition, the camera is required to filter data with abnormal face brightness in an infrared image in advance. Due to some scene limitations (such as time consumption and memory occupation), the camera end cannot directly perform face detection in the infrared image, so that the brightness of the infrared face cannot be directly judged. How to accurately identify the brightness of the human face in the infrared image at the structured light camera end becomes a troublesome problem.
Disclosure of Invention
An object of embodiments of the present invention is to provide an image brightness abnormality detection method, an electronic device, and a storage medium, which can quickly detect a brightness abnormality of an infrared image including a human face to guide an operation of filtering or not on the infrared image.
In order to solve the above technical problem, an embodiment of the present invention provides an image brightness abnormality detection method, including:
acquiring an infrared image containing a human face shot by a structured light camera;
constructing a pixel histogram aiming at pixel points of which the gray value is not less than a specified gray value in the infrared image;
counting a first probability smaller than a first gray value and a second probability larger than a second gray value in the pixel histogram; wherein a specified gray value < first gray value < second gray value;
determining whether the first probability is greater than a first probability threshold and the second probability is greater than a second probability threshold;
and when the first probability is larger than the first probability threshold or the second probability is larger than a second probability threshold, determining that the overall brightness of the infrared map is abnormal.
An embodiment of the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the image brightness anomaly detection method as described above.
Embodiments of the present invention also provide a computer-readable storage medium storing a computer program which, when executed by a processor, implements the image brightness abnormality detection method as described above.
Compared with the prior art, the method and the device have the advantages that the infrared image containing the human face shot by the structured light camera is obtained; constructing a pixel histogram aiming at pixel points of which the gray value is not less than the specified gray value in the infrared image; counting a first probability smaller than a first gray value and a second probability larger than a second gray value in the pixel histogram; wherein a specified gray value < first gray value < second gray value; judging whether the first probability is greater than a first probability threshold value or not and whether the second probability is greater than a second probability threshold value or not; and when the first probability is larger than the first probability threshold or the second probability is larger than the second probability threshold, determining that the overall brightness of the infrared image is abnormal. According to the scheme, the pixel histogram is constructed to count the pixel gray value probability of abnormal brightness in the infrared image, so that the brightness abnormal condition of the infrared image containing the human face is quickly detected, and whether the infrared image is filtered or not is guided.
Drawings
FIG. 1 is a first flowchart illustrating an exemplary method for detecting luminance abnormality of an image according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a method for detecting luminance abnormality of an image according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating a method for detecting luminance abnormality of an image according to an embodiment of the present invention;
FIG. 4 is a detailed flowchart of a fourth method for detecting luminance abnormality of an image according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, it will be appreciated by those of ordinary skill in the art that numerous technical details are set forth in order to provide a better understanding of the present application in various embodiments of the present invention. However, the technical solution claimed in the present application can be implemented without these technical details and various changes and modifications based on the following embodiments.
An embodiment of the present invention relates to an image brightness abnormality detection method, and as shown in fig. 1, the image brightness abnormality detection method provided in this embodiment includes the following steps.
Step 101: and acquiring an infrared image containing a human face shot by the structured light camera.
The structured light camera can be a monocular structured light camera.
Specifically, a face is shot through a structured light camera, and an infrared image containing the face is obtained.
Step 102: and constructing a pixel histogram aiming at the pixel points of which the gray value is not less than the specified gray value in the infrared image.
Specifically, traversing the infrared image pixel by pixel, counting the pixels with the gray value not less than the designated gray value (S1) in the infrared image and the total number sum of effective pixels, and constructing a histogram hist1[ c ] with the pixelsi]. Wherein the horizontal axis of the histogram is discrete gray scale valuei，iHas a value range of [ S1,255](ii) a The vertical axis of the histogram is the gray valueiProbability hist1[ alpha ] in the statistical gray scale valuei]I.e. grey value ofiIn sumThe ratio of (a) to (b). For example hist1[ S1 ]]Is the probability of the histogram having a gray value of S1. The infrared image belongs to a photo of an active light source, in an indoor environment, a foreground object often has brightness with stronger contrast, a background usually contains a large amount of low-brightness noise, the background noise can be effectively removed through a specified gray value (S1), and the accuracy of judging the abnormal brightness of the image is improved.
Step 103: counting a first probability smaller than a first gray value and a second probability larger than a second gray value in the pixel histogram; wherein a gray value < first gray value < second gray value is specified.
Specifically, the first probability P1 that the gradation value is smaller than the first gradation value K1 can be calculated using the histogram, as shown in (1).
Similarly, a second probability P2 that the gray-scale value is greater than the second gray-scale value K2 can be calculated using the histogram, as shown in (2).
Wherein S1< K1 < K2. K1 is the boundary gray scale value (e.g. 30) indicating the image brightness is too dark, and K2 is the boundary gray scale value (e.g. 230) indicating the image brightness is too bright.
Step 104: and judging whether the first probability is larger than a first probability threshold value or not and whether the second probability is larger than a second probability threshold value or not.
The first probability threshold (K3) is a boundary probability (e.g. 0.6) representing a gray scale with too dark brightness of the whole image, and the second probability threshold (K4) is a boundary probability (e.g. 0.7) representing a gray scale with too bright brightness of the whole image.
Specifically, comparing the first probability P1 counted in the previous step with the first probability threshold K3 and comparing the second probability P2 with the second probability threshold K4, it can be determined whether the overall brightness of the image is too dark or too bright.
When the first probability is greater than the first probability threshold or the second probability is greater than the second probability threshold, the following steps are performed.
Step 105: and determining the overall brightness abnormality of the infrared image.
Specifically, when the first probability P1 is greater than the first probability threshold K3, the image as a whole is considered too dark; when the second probability P2 is greater than the second probability threshold K4, the image is considered to be too bright overall. Whether the brightness of the image is totally too dark or totally too bright belongs to the abnormal brightness of the whole infrared image. The reason for the abnormal overall brightness is often caused by lens occlusion, and a large-area abnormal brightness value appears in the image.
In view of this, the following steps may be performed after step 105.
And when the overall brightness of the infrared image is determined to be abnormal, the infrared image is kept.
Specifically, due to the system robustness and safety of the structured light camera, when the overall brightness of the infrared image is abnormal, the infrared image does not need to be filtered, so that the normal data flow of the camera cannot be blocked.
Compared with the related art, the infrared image containing the human face shot by the structured light camera is obtained; constructing a pixel histogram aiming at pixel points of which the gray value is not less than the specified gray value in the infrared image; counting a first probability smaller than a first gray value and a second probability larger than a second gray value in the pixel histogram; wherein a specified gray value < first gray value < second gray value; judging whether the first probability is greater than a first probability threshold value or not and whether the second probability is greater than a second probability threshold value or not; and when the first probability is larger than the first probability threshold or the second probability is larger than the second probability threshold, determining that the overall brightness of the infrared image is abnormal. According to the scheme, the pixel histogram is constructed to count the pixel gray value probability of abnormal brightness in the infrared image, so that the brightness abnormal condition of the infrared image containing the human face is quickly detected, and whether the infrared image is filtered or not is guided.
Another embodiment of the present invention relates to an image luminance abnormality detection method, as shown in fig. 2, which is an improvement of the method steps shown in fig. 1 in that, when there is no global luminance abnormality in an infrared image, an operation of determining whether there is a local luminance abnormality is added. As shown in fig. 2, the step 104 includes the following steps.
When the first probability is not greater than the first probability threshold and the second probability is not greater than the second probability threshold, the following steps are performed.
Step 106: judging whether the first probability is greater than a third probability threshold or whether the second probability is greater than a fourth probability threshold; wherein the third probability threshold < the first probability threshold, and the fourth probability threshold < the second probability threshold.
Specifically, when the first probability P1 is not greater than the first probability threshold K3, the image as a whole is considered to be not excessively dark; when the second probability P2 is not greater than the second probability threshold K4, the image as a whole is considered to be not too bright. And when the whole image is not too dark or too bright, the representation infrared image is not abnormal in whole brightness. At this time, the first probability P1 and the second probability P2 may be further analyzed to determine whether there is a local luminance abnormality in the infrared map. The first probability P1 is further compared to a third probability threshold (K5) and the second probability P2 is further compared to a fourth probability threshold (K6). Wherein the third probability threshold K5 is a boundary probability (e.g. 0.56) of a gray scale with too dark local brightness of the image, and the fourth probability threshold K6 is a boundary probability (e.g. 0.38) of a gray scale with too bright local brightness of the image.
When the first probability is greater than the third probability threshold, or the second probability is greater than the fourth probability threshold, the following steps are performed.
Step 107: and determining local brightness abnormality of the infrared image.
Specifically, when the first probability P1 is greater than the third probability threshold K5, the image is considered to be locally too dark; when the second probability P2 is greater than the fourth probability threshold K6, the image is considered locally too bright. Whether the brightness of the image is locally too dark or locally too bright belongs to the local brightness abnormity of the infrared image.
Compared with the related art, the embodiment continuously analyzes the first probability and the first probability when the infrared image is determined not to belong to the overall brightness abnormality, and determines whether the infrared image has local brightness abnormality, so that the brightness abnormality of the infrared image including the human face is more accurately detected, and whether the infrared image is filtered is guided.
Another embodiment of the present invention relates to an image brightness abnormality detection method, as shown in fig. 3, which is an improvement of the method steps shown in fig. 2, and the improvement is that when there is a local brightness abnormality in an infrared image, the operation of determining whether there is a human face brightness abnormality is added. As shown in fig. 3, the method (after step 102) further includes the following steps.
Step 108: and counting the gray level mean value and the gray level standard deviation of the pixel histogram.
Specifically, the mean value of the gray scale can be calculated by using the histogrammSum gray standard deviationstdThe calculation formulas are shown in (3) and (4).
Accordingly, after step 107, namely when the local brightness of the infrared image is determined to be abnormal, the following steps can be further included.
Step 109: and judging whether the gray average value is larger than a preset average value or not and whether the gray standard deviation is smaller than a preset standard deviation or not.
In general, the face brightness abnormality is often in the case of local brightness abnormality. In order to further extract the situation of face brightness abnormality, according to the brightness expression of face overexposure, the contrast of the image is often higher than other situations, and the contrast is calculated by standard deviationstdIs embodied. In addition, the image mean value in the case of face overexposuremWill be smaller than other exposure scenarios. So by determining the gray in the pixel histogramThe relation between the degree mean value and the gray standard deviation and the preset mean value and the preset standard deviation can judge whether the face brightness is abnormal or not in the infrared image. The preset mean value (K7) and the preset standard deviation (K8) are boundary values for judging the human face brightness abnormity. Wherein K7 can take the value of 170, and K8 can take the value of 82.
And when the gray average value is larger than the preset average value and the gray standard deviation is smaller than the preset standard deviation, executing the step 110, otherwise, executing the step 111.
Step 110: and determining that the brightness of the human face in the infrared image is normal.
Step 111: and determining the brightness abnormality of the human face in the infrared image.
Specifically, for a structured light camera, when a face of an infrared image is too dark or too bright, a corresponding depth image often has a situation that the face is too dark or too bright, and in payment-level 3D face recognition application, the situation undoubtedly causes a serious influence on the success probability of face recognition, so that current frame data needs to be filtered when the face brightness of the infrared image is abnormal, and next normal data is introduced to subsequent application.
When in usem>K7 andstd<k8, the situation is not considered as the face brightness abnormality, and after step 110, that is, when the face brightness in the infrared image is determined to be normal, the infrared image can be retained; when in usemK7 or lessstdAnd when the brightness of the human face is determined to be abnormal in the infrared image, the infrared image can be filtered after the step 111.
Compared with the related art, in the embodiment, when the infrared image is determined to be local brightness abnormality, the gray level mean value and the gray level standard deviation of the pixel histogram are continuously analyzed to determine whether the infrared image has face brightness abnormality, so that the brightness abnormality of the infrared image including the face is more accurately detected to guide whether the infrared image is filtered.
Another embodiment of the present invention relates to an image brightness abnormality detection method, as shown in fig. 4, which is an improvement of any one of the method steps shown in fig. 1 to 3, and the improvement is that a color image of a human face synchronously photographed by a structured light camera when an infrared image is photographed is used to assist in judging whether the human face brightness in the infrared image is abnormal. As shown in fig. 4, the above method further includes the following steps.
Step 112: and acquiring a color image of a human face synchronously shot by the structured light camera when the infrared image is shot.
Specifically, while step 101 is executed, the structured light camera also synchronously captures a color image of the same face by using a color lens.
Step 113: and judging whether the face position is detected in the color map. When the face position in the color map is detected, step 114 is executed; otherwise, step 102 is performed.
Specifically, a face recognition algorithm is used for recognizing the face in the color map, determining whether the color map contains a face area, and determining the position of the face area under the condition that the color map contains the face area. When the face position in the color image is detected, the face position in the infrared image can be positioned based on the detected face position, and whether the brightness of the face position in the infrared image is abnormal or not is further judged; when the face position in the color image is not detected, the determination of the abnormal brightness of the infrared image can be completed by the step 102 and the subsequent steps in the methods of the above embodiments.
Step 114: and mapping the face position in the color image to the infrared image according to camera parameters of a color lens and an infrared lens of the structured light camera to obtain the face position in the infrared image.
Specifically, because the acquisition interval between the infrared image and the color image is very short, the color image and the infrared image have a large-area overlapped area according to the arrangement of the lens of the camera. Usually, the face will exist in two images at the same time, and there is only a slight difference in angle and position. In order to obtain the face position on the infrared image, the color face frame is mapped to the infrared image through the color lens and the camera parameters of the infrared lens when the color image face detection is successful, so that the face position in the infrared image is obtained.
Step 115: and determining whether the face brightness in the infrared image is abnormal or not according to the gray average value of the face position in the infrared image.
Specifically, the gray level average value at the face position in the infrared image can represent the brightness of the face, so that whether the face brightness is abnormal or not can be determined.
In one example, when the average gray value is smaller than the first gray value or the average gray value is larger than the second gray value, it is determined that the brightness of the face in the infrared image is abnormal, otherwise, it is determined that the brightness of the face in the infrared image is normal.
Specifically, pixels in the face frame in the infrared image are traversed pixel by pixel, and the gray average value m1 of the face frame area is counted. The average grayscale value m1 is used as the brightness value of the face region, and this is determined. When m1 is smaller than the first gray value K1, the human face is judged to be too dark; and when m1 is larger than the second gray value K2, the human face brightness is judged to be too bright. The face brightness is considered to be either too dark or too bright, which belongs to the face brightness abnormal condition.
On the basis, when the brightness of the human face in the infrared image is determined to be normal, the infrared image is reserved; and when the brightness of the human face in the infrared image is determined to be abnormal, filtering the infrared image.
Compared with the related art, the embodiment acquires the color image of the face shot by the structured light camera, maps the face position successfully detected in the color image into the infrared image, determines the face position in the infrared image, and then determines whether the face brightness abnormality exists in the infrared image based on the analysis of the gray level average value of the face position in the infrared image, so that the brightness abnormality condition of the infrared image containing the face is more accurately detected to guide the operation of whether the infrared image is filtered.
Another embodiment of the invention relates to an electronic device, as shown in FIG. 5, comprising at least one processor 202; and a memory 201 communicatively coupled to the at least one processor 202; wherein the memory 201 stores instructions executable by the at least one processor 202, the instructions being executable by the at least one processor 202 to enable the at least one processor 202 to perform any of the method embodiments described above.
Where the memory 201 and the processor 202 are coupled in a bus, the bus may comprise any number of interconnected buses and bridges that couple one or more of the various circuits of the processor 202 and the memory 201 together. The bus may also connect various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface provides an interface between the bus and the transceiver. The transceiver may be one element or a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. The data processed by the processor 202 is transmitted over a wireless medium through an antenna, which further receives the data and transmits the data to the processor 202.
The processor 202 is responsible for managing the bus and general processing and may also provide various functions including timing, peripheral interfaces, voltage regulation, power management, and other control functions. And memory 201 may be used to store data used by processor 202 in performing operations.
Another embodiment of the present invention relates to a computer-readable storage medium storing a computer program. The computer program realizes any of the above-described method embodiments when executed by a processor.
That is, as can be understood by those skilled in the art, all or part of the steps in the method for implementing the embodiments described above may be implemented by a program instructing related hardware, where the program is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
It will be understood by those of ordinary skill in the art that the foregoing embodiments are specific examples for carrying out the invention, and that various changes in form and details may be made therein without departing from the spirit and scope of the invention in practice.
Claims (10)
1. An image brightness abnormality detection method is characterized by comprising the following steps:
acquiring an infrared image containing a human face shot by a structured light camera;
constructing a pixel histogram aiming at pixel points of which the gray value is not less than a specified gray value in the infrared image;
counting a first probability smaller than a first gray value and a second probability larger than a second gray value in the pixel histogram; wherein a specified gray value < first gray value < second gray value;
determining whether the first probability is greater than a first probability threshold and the second probability is greater than a second probability threshold;
and when the first probability is larger than the first probability threshold or the second probability is larger than a second probability threshold, determining that the overall brightness of the infrared map is abnormal.
2. The method of claim 1, wherein said determining whether the first probability is greater than a first probability threshold or the second probability is greater than a second probability threshold further comprises:
when the first probability is not greater than the first probability threshold and the second probability is not greater than the second probability threshold, determining whether the first probability is greater than a third probability threshold or whether the second probability is greater than a fourth probability threshold; wherein the third probability threshold < the first probability threshold, the fourth probability threshold < the second probability threshold;
when the first probability is larger than the third probability threshold or the second probability is larger than the fourth probability threshold, determining that the local brightness of the infrared image is abnormal.
3. The method of claim 2, further comprising:
counting the gray level mean value and the gray level standard deviation of the pixel histogram;
when the local brightness of the infrared image is determined to be abnormal, judging whether the gray average value is larger than a preset average value or not and whether the gray standard deviation is smaller than a preset standard deviation or not;
and when the gray average value is larger than the preset average value and the gray standard deviation is smaller than the preset standard deviation, determining that the brightness of the face in the infrared image is normal, otherwise, determining that the brightness of the face in the infrared image is abnormal.
4. The method of claim 1, further comprising:
and when the overall brightness of the infrared image is determined to be abnormal, the infrared image is reserved.
5. The method of claim 3, further comprising:
when the brightness of the face in the infrared image is determined to be normal, the infrared image is reserved;
and when the human face brightness in the infrared image is determined to be abnormal, filtering the infrared image.
6. The method according to any one of claims 1-5, further comprising:
acquiring a color image of the human face synchronously shot by the structured light camera when the infrared image is shot;
judging whether the face position is detected in the color image;
when the face position in the color image is detected, mapping the face position in the color image to the infrared image according to camera parameters of a color lens and an infrared lens of the structured light camera to obtain the face position in the infrared image; and determining whether the brightness of the face in the infrared image is abnormal according to the gray average value of the face position in the infrared image.
7. The method as claimed in claim 6, wherein after determining whether the face position is detected in the color map, the method further comprises:
and when the face position in the color image is not detected, executing the step of constructing a pixel histogram for the pixel points of which the gray value is not less than the designated gray value in the infrared image.
8. The method of claim 6, wherein the determining whether the face brightness in the infrared image is abnormal according to the gray level average value of the face position in the infrared image comprises:
and when the gray average value is smaller than the first gray value or the gray average value is larger than the second gray value, determining that the brightness of the face in the infrared image is abnormal, otherwise, determining that the brightness of the face in the infrared image is normal.
9. An electronic device, comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the image brightness anomaly detection method of any one of claims 1-8.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the image luminance abnormality detection method according to any one of claims 1 to 8.
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| CN115049658A (en) * | 2022-08-15 | 2022-09-13 | 合肥的卢深视科技有限公司 | RGB-D camera quality detection method, electronic device and storage medium |
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