JP6443230B2 - Processing program, processing method, and processing apparatus - Google Patents

Description

  The present invention relates to a processing program, a processing method, and a processing apparatus.

  Conventionally, the growth status of crops cultivated in fields such as fields has often been judged based on human experience by a person visiting the field and observing the growth status of the crops. However, in recent years, ICT (Information and Communications Technology) has been actively introduced into the agricultural field. For example, the growth of crops using the image of crops taken with a camera or the result of measuring the wavelength of light reflected by the crops. Techniques for examining the situation have been proposed.

  When examining the growth status of a crop from an image obtained by imaging the crop, the growth status of the crop is determined with reference to the leaf color of the crop in the image. As a method for measuring the color of a subject such as a crop in an image, for example, a technique for comparing a reference color displayed on a color chart (hereinafter referred to as a color chart) including a plurality of color samples with the color of the subject Has been proposed.

  In addition, since the farm field is often provided outdoors, in some cases, a shadow may cover a part of the crop, which may cause a light / dark difference in an image of the same crop. That is, even if the leaf color is the same, if a bright portion and a dark portion are generated, the growth state of the crop may not be correctly measured from the leaf color of the crop. Therefore, there has been proposed a technique for determining the position of a shadow from an image using the difference in brightness of the captured image.

JP-A-5-223642 JP 2013-195243 A JP 2006-195622 A JP 2010-237976 A JP 2002-168771 A JP 2011-27600 A Japanese Patent Laid-Open No. 2004-3878 JP 2005-117524 A

  In other words, until now, in order to determine the growth status of crops from images of crops in a field outside the field, the crops and color charts are placed next to the crops so that the crops and color charts are in the same imaging area of the camera. Place a color chart. Then, the leaf color of the crop is measured by comparing the leaf color of the crop with the color of the color sample displayed on the color chart.

  At this time, even if the colors of the subjects are the same, the colors of the crops and the color chart may be the same with respect to the camera because the colors may look different if the sunlight reflection conditions are different. Thus, it is necessary to adjust the arrangement angle of the leaves of the crops and the color chart. Furthermore, even if the direction of the leaves of the crop and the color chart are adjusted to the same direction with respect to the camera, if the amount of reflected light on the leaves of the crop and the color chart exceeds a predetermined amount, the leaves of the crop and A situation called “tekari” where the color chart looks whitish may occur. Accordingly, it is necessary to further adjust the arrangement angles of the crop leaves and the color chart with respect to the camera so that no illusion occurs in the crop leaves and the color chart when viewed from the camera position.

  Moreover, since the crops grow daily, the direction of the leaves of the crops changes. Therefore, it is necessary to adjust the direction of the leaves of the crop every time the crop is imaged with a camera.

  In other words, even when crops are captured using a camera with a fixed imaging position and imaging range, there is a situation where the orientation of the crop leaves and color chart with respect to the camera is the same without adjusting the orientation of the crop leaves. hard. Therefore, in this case, it is difficult to accurately measure the leaf color of the crop as compared with the case where the orientation of the leaf of the crop is adjusted every time the image is captured by the camera.

  As one aspect, an object of the present invention is to obtain an image that can accurately measure the color of a subject even when the orientation of the subject and the direction of a color chart are different.

  In one aspect, the processing program causes the computer to determine whether the subject to be colorimetric and the color chart with a known colorimetric value are not irradiated with direct light from the light source. Then, the processing program causes the computer to capture an image of the subject and the color chart in the above situation on the subject in the image based on the color of the area corresponding to the color chart in the image and the colorimetric value of the color chart. The image is acquired as an image for color correction for obtaining a colorimetric value of the subject from the color of the corresponding region.

  As one aspect, the present invention has an effect that an image that can accurately measure the color of a subject can be obtained even when the orientation of the subject and the orientation of the color chart are different.

It is a schematic diagram used for description of the kind of light irradiated to an agricultural field. It is a schematic diagram which shows an example of the condition at the time of imaging crops. It is a figure which shows an example of the imaged image. It is a figure which shows an example of a structure of the processing apparatus which concerns on 1st Embodiment. It is a figure which shows an example of a structure in the case of implement | achieving the processing apparatus which concerns on 1st Embodiment with a computer. It is a flowchart which shows an example of the flow of the measurement process which concerns on 1st Embodiment. It is a flowchart which shows an example of the flow of a brightness calculation process. It is a flowchart which shows an example of the flow of a shadow area | region determination process. It is a figure which shows an example of a structure of the processing apparatus which concerns on 2nd Embodiment. It is a figure which shows an example of a structure in the case of implement | achieving the processing apparatus which concerns on 2nd Embodiment with a computer. It is a flowchart which shows an example of the flow of the measurement process which concerns on 2nd Embodiment. It is a figure which shows an example of shadowed information.

  Hereinafter, an example of an embodiment of the disclosed technology will be described in detail with reference to the drawings. In addition, the same code | symbol may be provided to the component or process which bears the same function through all the drawings, and the overlapping description may be abbreviate | omitted suitably.

  FIG. 1 is a diagram for explaining the types of light emitted to the outdoor field 58. As shown in FIG. 1, the outdoor field is irradiated with light from the sun 50, that is, sunlight, but the sunlight irradiating the field 58 is divided into two types of light: direct light 54 and sky light 56. Broadly divided.

  The direct light 54 is light that is directly emitted from the sun 50 to the field 58. Therefore, the direct light 54 irradiates the field 58 from one direction along a straight line connecting the sun 50 and the field 58.

  On the other hand, the sky light 56 refers to the scattered light that has reached the field 58 among the scattered light that is scattered in all directions by the cloud 52 and dust (not shown). That is, since the sky light 56 is scattered light, unlike the direct light 54, the field 58 is irradiated from all directions.

  In this way, since the direct light 54 irradiates the field 58 from one direction, depending on the direction of the leaves of the crop, the amount of light reflected by the leaves of the crop easily exceeds a predetermined amount, and shining may occur. is there. On the other hand, since the sky light 56 irradiates the field 58 from all directions, the reflected light from the leaves of the crop when the sky light 56 is irradiated is also dispersed, and the light is less likely to occur than the direct light 54.

  Further, the color temperature of the direct light 54 is lower than the color temperature of the sky light 56, and when the object is directly irradiated with the light 54, the object tends to have a tint from red to yellow. On the other hand, when sky light 56 having a color temperature higher than that of the light 54 is directly irradiated on the object, the object tends to have a blue color.

  Agricultural crops change in the range of leaf color from green to yellow according to their growth conditions. That is, the state of growth of the crop can be determined not from the brightness (lightness) of the leaf color of the crop but from the hue. Therefore, if the crop is irradiated with one of the direct light 54 and the sky light 56, the color temperature of each light is generally known, so the influence of the color temperature of one light on the hue is affected. By correcting for removal from the crop image, the leaf color of the actual crop can be estimated.

  However, when the crop is irradiated with light in which direct light 54 and sky light 56 having different hues that are easily emphasized are mixed, the hue of the leaf color is complicatedly affected depending on the mixture ratio of the respective lights, and the crop is cropped. It is difficult to accurately estimate the leaf color of the actual crop from the image obtained by capturing the image.

  Therefore, as a result of studying the above contents, the inventor has found that the crop can be imaged with a camera in a state where the direct light 54 is blocked and the sky light 56 is irradiated on the crop. By taking an image in a state where the direct light 54 is blocked, it is possible to suppress the occurrence of the illusion on the leaves of the crop. In addition, since the direct light 54 is irradiated only from the direction along the straight line connecting the sun 50 and the field 58, the direct light 54 is more easily shielded by a light blocking object than the sky light 56 irradiated from all directions. . In other words, it can be said that the direct light 54 that is more likely to be shielded by the light-shielding object than the sky light 56 is not a stable light source as a light source for illuminating the crops at the time of imaging.

  As a matter of course, the area where the light 54 is directly shielded by the light shield is covered with a shadow. Since the area covered with the shadow can be regarded as an area irradiated with the skylight 56, correction is performed so as to remove the influence of the color temperature of the skylight 56 on the hue from the image of the crop covered with the shadow. Thus, the leaf color of actual crops can be estimated.

  However, since the crop grows in the sunlight, it is not possible to install a roof, which is an example of a shade, over the entire field 58 so that the crop is always covered with a shadow. Furthermore, if the area of the farm 58 is, for example, several ha or more, it is not practical to install a roof over the entire farm 58. Therefore, a method is conceivable in which a roof is installed on a part of the field 58 and a part of the field 58 is covered with a shadow.

  FIG. 2 is a diagram schematically illustrating an example of a situation when a farm crop is imaged in the field 58. As shown in FIG. 2, a column 68 </ b> A to which a roof 60 </ b> A and a color chart 66 are attached and a column 68 </ b> B to which a roof 60 </ b> B is attached are installed on a part of the site of the farm field 58. Around the field 58, a support column 68C to which a camera 76 is attached is installed, and a processing device 72 connected to the camera 76 by a cable 74 is installed. In FIG. 2, the processing apparatus 72 is installed outdoors, but it goes without saying that the processing apparatus 72 may be installed indoors.

  A shadow region 64A that covers the entire color chart 66 with shadow is formed by the roof 60A when the direct light 54 is incident from a predetermined direction, and the crop cultivated on the field 58 at the same time by the roof 60B. A shadow region 64B that covers a part of the image with a shadow is formed. Hereinafter, when it is not necessary to distinguish between the shadow area 64A and the shadow area 64B, the area including the shadow area 64A and the shadow area 64B is referred to as a shadow area 64.

  The camera 76 is mounted and mounted so that the color chart 66 in the shadow area 64A and the crop in the shadow area 64B are both within the imaging range at the time when the direct light 54 is incident from a predetermined direction. The direction is adjusted.

  In addition, the installation place of the support | pillar 68C is not restricted to the circumference | surroundings of the agricultural field 58, For example, you may be in the site of the agricultural field 58. In addition, here, the number of cameras 76 for imaging crops is described as one, but two or more cameras 76 may be installed and connected to the processing device 72. Further, the camera 76 and the processing device 72 may be connected not by the cable 74 but by a wireless communication line, for example, and the camera 76 and the processing device 72 may be connected via the Internet. Good. Further, the processing device 72 and the camera 76 may be housed in one housing and attached to the support column 68C. Further, the camera 76 may be moved in the field 58 by a driving force such as a motor, and the imaging direction may be adjustable.

  The color chart 66 is a chart in which color samples of a plurality of colors whose colorimetric values are known in advance are arranged, and the color of the object to be color-measured and the color sample color of the color chart 66 , Can be used to estimate the colorimetric value of the object. The type of color chart 66 to be used is not particularly limited. For example, a “Macbeth chart” in which color samples of 24 colors that reproduce colors existing in the natural world such as the farm 58 are arranged. The color chart 66 is not limited to a planar shape, and may be a three-dimensional shape.

  The colorimetric value of each color sample is represented by an RGB value representing the density of each color component of red, green, and blue, for example, by 8 bits from 0 to 255. The number of bits of the RGB value is an example, and the RGB density may be expressed by a number of bits other than 8 bits. Further, the colorimetric value of the color sample can also be expressed as the density of each color component of cyan, magenta, yellow, and black.

  FIG. 3 is a diagram illustrating an example of an image captured by the camera 76. The image 4 is a set of pixels having pixel values represented by RGB values, for example. The image 4 includes a color chart 66 covered with the shadow of the shadow region 64A and a crop covered with the shadow of the shadow region 64B. 70, and some of the crops 70 are covered with the shadow of the shadow region 64B. For convenience of explanation, an area surrounded by the outline of the crop 70 in the image 4 is called a subject area 6, and an area surrounded by the outline of the color chart 66 is called a color chart area 8. Here, the subject refers to an object that is a color measurement target.

  The position, shape, and size of the shadow region 64A formed by the roof 60A and the shadow region 64B formed by the roof 60B vary depending on the date and time. Accordingly, the entire color chart 66 and the crop 70 that is the measurement target of the growth status are not necessarily covered by the shadow.

  Therefore, in the first embodiment described below, an example will be described in which it is determined whether the entire color chart 66 and the crop 70 are both covered with shadows, and the image 4 is captured by the camera 76.

(First embodiment)
FIG. 4 is a functional block diagram illustrating an example of a functional configuration of the processing device 72. The processing device 72 includes an imaging unit 10, a shadow determination unit 12, an extraction unit 14, a measurement unit 16, an output unit 18, and reference color information 28. Further, the extraction unit 14 includes a subject area extraction unit 20, a color chart area extraction section 22, a subject area color information extraction section 24, and a color chart area color information extraction section 26. The reference color information 28 includes information on RGB values (reference colors) obtained by performing color measurement on each color sample of the color chart 66.

  The imaging unit 10 instructs the camera 76 to perform imaging, and outputs the image 4 captured by the camera 76 to the shadow determination unit 12.

  The shadow determination unit 12 determines the position of the shadow region 64 included in the image 4 received from the imaging unit 10. In addition, the shadow determination unit 12 determines whether or not both the crop 70 and the color chart 66 are included in the shadow region 64.

  When both the crop 70 and the color chart 66 are included in the shadow area 64 determined by the shadow determination section 12, the extraction unit 14 obtains the color information of the crop 70 and the color information of each color sample of the color chart 66. Extract from image 4. Then, the extraction unit 14 outputs the color information of the crop 70 extracted from the image 4 and the color information of each color sample of the color chart 66 to the measurement unit 16. Here, the color information includes, for example, RGB values of the object.

  Specifically, the subject region extraction unit 20 included in the extraction unit 14 extracts the crop 70 included in the shadow region 64, that is, the subject region 6 from the image 4. Then, the subject region color information extraction unit 24 extracts the color information of the subject region 6 from the subject region 6 extracted by the subject region extraction unit 20, and outputs the extracted color information of the subject region 6 to the measurement unit 16. Further, the color chart area extraction section 22 included in the extraction section 14 extracts the color chart 66 included in the shadow area 64, that is, the color chart area 8 from the image 4, and each color sample area included in the color chart area 8. (Color sample area) is extracted. Then, the color chart area color information extraction unit 26 extracts the color information of each color sample area included in the color chart area 8 from the color chart area 8 extracted by the color chart area extraction unit 22, and extracts the color sample area of each color sample area. The color information is output to the measurement unit 16.

  For each color sample area, the measurement unit 16 receives the color information of the color sample area included in the color chart area 8 received from the extraction unit 14 and the color sample reference corresponding to the color sample area included in the reference color information 28. The correction amount is calculated from the difference between the color information and the color information. This correction amount is calculated so that the color information of the color sample included in the color chart area 8 can be corrected to the reference color of the corresponding color sample. Then, the measuring unit 16 corrects the color information of the subject region 6 received from the extracting unit 14 with the calculated correction amount, thereby measuring the actual leaf color of the crop 70 that is the subject of the camera 76 and obtaining the measurement result. Output to the output unit 18.

  Note that the color information related to the reference color of each color sample is included in the reference color information 28, and the measurement unit 16 can acquire the color information of the reference color from the reference color information 28.

  The output unit 18 outputs information on the actual leaf color of the crop 70 estimated from the image 4 to the producer of the crop 70 by displaying the measurement result received from the measurement unit 16 on a display, for example.

  Next, FIG. 5 shows an example of a configuration when the processing device 72 is realized by a computer.

  The computer 100 includes a CPU 102, a memory 104, and a nonvolatile storage unit 106. The CPU 102, the memory 104, and the storage unit 106 are connected to each other via a bus 108. The computer 100 includes an input / output (I / O) 110 for connecting the input device 112, the output device 114, the camera 76, and the computer 100 to transmit / receive data to / from each other. Are connected to the bus 108.

  The input device 112 includes devices for allowing an operator of the computer 100 to give instructions to the computer 100, such as a keyboard and a mouse. The input device 112 may include a reading device for reading data recorded on the recording medium 116 such as a CD-ROM or a flash memory.

  The output device 114 includes a device for outputting a processing result in the computer 100, such as a display. Further, the output device 114 may include a writing device for writing the processing result in the computer 100 to the recording medium 116.

  Note that the various devices connected to the I / O 110 are merely examples. For example, the input device 112 is not necessarily connected to the I / O 110, and other devices not shown in FIG. May be connected. The storage unit 106 can be realized by an HDD (Hard Disk Drive), a flash memory, or the like.

  The storage unit 106 stores a processing program 120 for causing the computer 100 to function as the processing device 72 shown in FIG. The processing program 120 stored in the storage unit 106 includes an imaging process 122, a shadow determination process 124, a subject area extraction process 126, a color chart area extraction process 128, a subject area color information extraction process 130, and a color chart area color information extraction process. 132. Further, the processing program 120 includes a measurement process 134 and an output process 136.

  The CPU 102 reads out the processing program 120 from the storage unit 106, expands it in the memory 104, and executes each process included in the processing program 120.

  The CPU 102 reads out the processing program 120 from the storage unit 106, loads it into the memory 104, and executes the processing program 120, whereby the computer 100 operates as the processing device 72 shown in FIG.

  Further, when the CPU 102 executes the imaging process 122, the computer 100 operates as the imaging unit 10 illustrated in FIG. Further, when the CPU 102 executes the shadow determination process 124, the computer 100 operates as the shadow determination unit 12 illustrated in FIG. Further, when the CPU 102 executes the subject region extraction process 126, the computer 100 operates as the subject region extraction unit 20 shown in FIG. Further, when the CPU 102 executes the color chart area extraction process 128, the computer 100 operates as the color chart area extraction unit 22 shown in FIG. Further, when the CPU 102 executes the subject region color information extraction process 130, the computer 100 operates as the subject region color information extraction unit 24 shown in FIG. Further, when the CPU 102 executes the color chart area color information extraction process 132, the computer 100 operates as the color chart area color information extraction unit 26 shown in FIG. Further, when the CPU 102 executes the measurement process 134, the computer 100 operates as the measurement unit 16 illustrated in FIG. Further, when the CPU 102 executes the output process 136, the computer 100 operates as the output unit 18 illustrated in FIG.

  The CPU 100 executes the subject region extraction process 126, the color chart region extraction process 128, the subject region color information extraction process 130, and the color chart region color information extraction process 132, so that the computer 100 performs the extraction unit 14 shown in FIG. Works as.

  Further, the CPU 102 develops the reference color information stored in the reference color information storage area 138 in the memory 104, whereby the reference color information 28 is stored in the memory 104.

  The computer 100 can be realized by, for example, a semiconductor integrated circuit, more specifically, an ASIC (Application Specific Integrated Circuit) or the like.

  Next, the operation of the processing device 72 will be described. For example, the processing device 72 receives the instruction for starting the measurement of the leaf color of the crop 70 from the input device 112 to execute the measurement processing.

  FIG. 6 is a flowchart showing an example of the flow of measurement processing executed by the processing device 72.

  First, in step S <b> 10, the imaging unit 10 outputs an imaging instruction to the camera 76, acquires the image 4 captured by the camera 76, and stores it in a predetermined area of the memory 104, for example.

  In step S20, the shadow determination unit 12 divides the image 4 stored in the memory 104 into a plurality of blocks, and executes a brightness calculation process for calculating the brightness for each of the divided blocks. Details of the brightness calculation process will be described later.

  In step S30, the shadow determination unit 12 compares the brightness of each block with the average value of the brightness of each block for each block based on the brightness of each block calculated in the process of step S20. Then, the shadow determination unit 12 determines the area represented by the set of blocks whose brightness is lower than the average value of the brightness of each block as the shadow area 64, for example, the block determined to be included in the shadow area 64 , An identifier indicating that the block is included in the shadow area 64 is associated. Details of the process (shadow region determination process) in step S30 will be described later.

  If the weather is not sunny, there is a low possibility that the crops 70 and the color chart 66 are directly irradiated with the light 54. Therefore, for example, the average brightness of the image 4 obtained by averaging the brightness of each block of the image 4 is compared with a threshold value indicating the brightness of the image 4 in a fine weather, and the average brightness of the image 4 is higher than the threshold value, and the weather is When it can be determined that the sky is clear, the process of step S30 may be performed.

  On the other hand, if it is determined that the average brightness of the image is equal to or less than the threshold value and the weather is not clear, the processes in steps S30 to S50 are omitted, and the process after step S60 is performed to extract the subject area 6 from the shadow area 64 described later. May be performed. Further, instead of determining the weather condition by comparing the average brightness of the image 4 and the threshold value, for example, information regarding the weather condition that allows determination as to whether or not the weather is clear is acquired from the outside of the processing device 72. You may do it.

  In step S40, the shadow determination unit 12 determines whether or not the subject is included in the shadow region 64 determined in the process of step S30, that is, whether or not the subject region 6 is included in the shadow region 64. The shadow determination unit 12 uses, for example, contour data of an object extracted from the shadow area 64 by a known edge extraction technique such as a sobel filter or a Hough transform, and the outline of the crop 70 stored in advance in a predetermined area of the memory 104. The contour data shown is compared. Then, the shadow determination unit 12 calculates the similarity between the contour data of the object extracted from the shadow region 64 and the contour data indicating the outline of the crop 70, and when the calculated similarity is higher than the threshold, 64, it is determined that the subject area 6 is included. Note that the determination method in step S40 is an example, and it may be determined whether or not the subject region 6 is included in the shadow region 64 by another known image recognition method.

  If the determination process in step S40 is negative, that is, if the shadow area 64 does not include the subject area 6, it means that the crop 70, which is the measurement target of the growth status, is not covered with a shadow. The measurement process shown in FIG. On the other hand, if the determination is affirmative, the process proceeds to step S50.

  In step S50, the shadow determination unit 12 determines whether or not the color chart 66 is included in the shadow area 64 determined in the process of step S30, that is, whether or not the color chart area 8 is included in the shadow area 64.

  The shadow determination unit 12 uses the contour data indicating the outline of the color chart 66 in place of the outline data indicating the outline of the crop 70, so that the color chart area is added to the shadow area 64 by the same method as the determination process in step S <b> 40. 8 can be determined. The outline data of the color chart 66 may be stored in advance in a predetermined area of the memory 104, for example.

  If the determination process in step S50 is negative, that is, if the color chart area 8 is not included in the shadow area 64, it means that the color chart 66 is not covered with a shadow, so the measurement process shown in FIG. To do. On the other hand, if the determination is affirmative, the process proceeds to step S60.

  In step S <b> 60, the subject region extraction unit 20 extracts the region surrounded by the contour data of the crop 70 extracted from the shadow region 64 in the determination process in step S <b> 40 as the subject region 6 from the image 4. Specifically, an identifier indicating the subject area 6 is associated with a block included in the area surrounded by the contour data of the crop 70 extracted from the shadow area 64.

  In step S <b> 70, the color chart area extraction unit 22 extracts the area surrounded by the outline data of the color chart 66 extracted from the shadow area 64 in the determination process of step S <b> 50 as the color chart area 8 from the image 4. Specifically, an identifier indicating the color chart area 8 is associated with a block included in an area surrounded by the outline data of the color chart 66 extracted from the shadow area 64.

  In step S80, the subject region color information extraction unit 24 calculates the RGB value of the subject region 6. For example, the subject area color information extraction unit 24 converts the RGB values, which are the pixel values of all the pixels included in the subject area 6 extracted from the image 4 in the process of step S60, into red (R), green (G), and An average RGB value averaged for each blue (B) is calculated. Then, the subject area color information extraction unit 24 stores, for example, this average RGB value as a RGB value of the subject area 6 in a predetermined area of the memory 104.

  In the above-described example, the average of the RGB values of all the pixels included in the subject area 6 is the RGB value of the subject area 6. However, the RGB value calculation method for the subject area 6 is not limited to this. For example, another calculation method may be used such that the RGB value of the pixel at the center of gravity of the subject region 6 is set as the RGB value of the subject region 6.

  In step S90, the color chart area extracting unit 22 further extracts color sample areas corresponding to individual color samples from the color chart area 8 extracted from the image 4 in the process of step S70. The color sample area extraction method can use the same technique as the determination process in step S40 by using outline data indicating the outline of the color sample area instead of the outline data indicating the outline of the crop 70.

  Since the color chart 66 includes a plurality of color samples, a plurality of color sample areas are extracted from the color chart area 8 by the process of step S90. At this time, since the arrangement location of each color sample in the color chart 66 is known, for example, arrangement information indicating the arrangement order of each color sample in the color chart 66 is stored in advance in the memory 104, for example. Each color sample area extracted from the color chart area 8 by associating the color sample color with the color sample area extracted from the color chart area 8 based on the arrangement information of the color samples stored in the memory 104. The color of becomes clear.

  In step S100, the color chart area color information extraction unit 26 calculates the RGB value of the color sample area for each color sample area extracted from the color chart area 8 in the process of step S90. For example, for each color sample area, the color chart area color information extraction unit 26 calculates an average RGB value obtained by averaging the RGB values of all the pixels included in the color sample area for each of R, G, and B. Then, the color chart area color information extraction unit 26 stores the calculated average RGB value in a predetermined area of the memory 104 as the RGB value of the corresponding color sample area, for example.

  Note that the RGB value calculation method for the color sample area is not limited to this. For example, another calculation method may be used such that the RGB value of the pixel at the center of gravity of the color sample area is set as the RGB value of the color sample area.

  In step S <b> 110, the measurement unit 16 acquires the reference color information of each color sample included in the color chart 66 from the reference color information 28. The reference color information is assumed to be indicated by RGB values, for example.

  The color of the color sample area on the image 4 is the reference color information acquired in step S110 depending on, for example, the characteristics of the image sensor of the camera 76 and the illumination conditions different from those at the time of color measurement including the influence of the shadow covering the color chart 66. Is different from the reference color represented by Since the color chart 66 and the crop 70 are captured in a situation where they are covered with shadows by the same camera 76, the crop 70 included in the image 4 is the same as the color change amount of the color sample of the color chart 66. It can be considered that the color of the leaf color has also changed. Therefore, if the color of the subject area 6 is corrected by the change in the color sample color of the color chart 66, the actual leaf color of the crop 70 can be estimated from the color of the subject area 6.

  Therefore, in step S120, the measurement unit 16 determines, for each color sample area, the RGB value of the color sample area calculated in the process of step S100 and the RGB of the reference color information of the color sample area acquired in the process of step S110. The difference between the value and the value is calculated. The difference is expressed as a vector in the RGB color space with the origin at (R, G, B) = (0, 0, 0), for example.

  Then, the measurement unit 16 obtains, for example, a vector corresponding to the average of the difference vectors calculated for each color sample area as a correction amount (correction vector). The calculation method of the correction amount (correction vector) is not limited to this, and a known method can be used. For example, another method may be used, such as a correction amount (correction vector) obtained by weighting and averaging the difference vector calculated for each color sample area.

  The measurement unit 16 moves the RGB value obtained by moving (correcting) the position of the RGB value of the subject area 6 calculated in step S80 in the RGB color space with the calculated correction amount (correction vector). The estimated leaf color of the crop 70 is estimated.

  In step S130, the output unit 18 displays the color represented by the RGB value indicating the leaf color of the crop 70 estimated in the process of step S120 on, for example, a display that is an example of the output device 114 illustrated in FIG. At this time, the output unit 18 may further display not only the leaf color of the crop 70 but also RGB values corresponding to the leaf color, for example.

  The output unit 18 may output the RGB value indicating the leaf color of the crop 70 to another electronic device such as a computer connected to the Internet via the Internet, for example. In this case, for example, it is possible to request an expert regarding the cultivation of the crop 70, which is different from the producer of the crop 70, to judge the growth status of the crop 70 or to receive advice.

  Next, the lightness calculation process in step S20 described above will be described. FIG. 7 is a flowchart illustrating an example of the flow of brightness calculation processing executed by the processing device 72.

  In step S200, the shadow determination unit 12 divides the image 4 into a plurality of regions, that is, blocks, and calculates the lightness for each block. First, the shadow determination unit 12 determines the size of a block that is a division unit of the image 4.

  As will be described later, the shadow area 64 in the image 4 is determined in units of blocks. Accordingly, as the size of the block is set larger, a situation in which an area that is not actually covered with a shadow is extracted as the shadow area 64 from the image 4 is likely to occur, so the determination accuracy of the shadow area 64 is low. Become. However, as the block size is set larger, the number of blocks into which the image 4 is divided decreases, so that the processing time required for the brightness calculation process in step S20 and the shadow area determination process in step S30 is shortened. Therefore, the block size is determined based on the determination accuracy of the shadow region 64 and the balance between the processing times.

  Here, as an example, the size of the block set by examining the determination accuracy of the shadow region 64 and the processing time in advance by an experiment with an actual machine of the processing device 72 or a computer simulation based on the design specifications of the processing device 72 is used. use. The preset block size may be stored in advance in a predetermined area of the memory 104, for example.

  In step S210, the shadow determination unit 12 divides the image 4 acquired in the process of step S10 in FIG. 6 into blocks having a size determined in the process of step S200. Then, the shadow determination unit 12 associates each of the divided blocks with a block number that does not overlap. For the block number, for example, an integer increasing from 0 to 1 is used.

  In step S <b> 220, the shadow determination unit 12 sets the selected block number N to 0, and stores the selected block number N (= 0) in a predetermined area of the memory 104.

  In step S230, the shadow determination unit 12 acquires a block (block of interest) whose block number corresponds to the selected block number N from the blocks associated with the block number in the process of step S210. Then, the shadow determination unit 12 calculates the lightness in the block of interest.

Specifically, the shadow determination unit 12 calculates an average RGB value of all pixels included in the block of interest. Then, the shadow determination unit 12 converts the calculated average RGB value from the RGB color space to a value on the CIE (Commission Internationale de l'Eclairage) -L ^* A ^* B ^* color space, and the lightness in the block of interest is converted. calculate. In the CIE-L ^* A ^* B ^* color space, a color is represented by a value L ^* representing brightness, and a value A ^* and a value B ^* representing chromaticity. Therefore, by calculating the value L ^* from the average RGB value, it is possible to calculate the brightness in the block of interest.

For this purpose, the average RGB value is once converted into a value (XYZ value) on the CIE-XYZ color space, and the XYZ value is further converted into a value on the CIE-L ^* A ^* B ^* color space. Therefore, a conversion formula for converting RGB values into XYZ values when the standard light source D65 defined by the CIE is used is shown in Formula (1).

  Here, M is a transformation matrix, and is expressed by equation (2).

The lightness L ^* is calculated by the expression (3) using Y among the XYZ values obtained by the expression (1).

Here, the value Y _n represents the value Y on a perfect diffuse reflection surface which is a reflection surface having a reflectance of 100%, and Y _n = 1.0000 is used.

  In the process of step S230, in order to extract the shadow area 64 from the image 4, the brightness of the block is calculated. However, in order to extract the shadow area 64 from the image 4, it is only necessary to know the brightness of the block. Therefore, another index indicating the brightness of the block, for example, luminance may be calculated.

Therefore, instead of the lightness L ^*, the block luminance L may be calculated from the average RGB value using equation (4).

Note that the block of calculating the lightness L ^* is calculated lightness L ^*, and, in association with an identifier indicating that lightness L ^* has already been calculated and stored in a predetermined area of the memory 104.

In step S240, the shadow determination unit 12 determines whether or not the lightness L ^* of the block has been calculated in the process of step S230 for all the blocks divided from the image 4 in the process of step S210. This determination is performed by referring to whether or not an identifier indicating that the lightness L ^* has been calculated is associated with the block. If the determination is negative, the process proceeds to step S250.

In step S250, the shadow determination unit 12 increments the selected block number N by one and stores the selected block number N whose value has been updated in a predetermined area of the memory 104. Then, by repeating the processing in steps S230 to S250, the lightness L ^* of the block can be calculated for each block divided from the image 4 in the processing in step S210.

On the other hand, the determination process is affirmative determination in step S240, i.e., the processing each of the blocks divided from the image 4 in the step S210, when the lightness L ^* of the block has already been calculated, the brightness calculation process shown in FIG. 7 finish.

  Next, the shadow area determination process in step S30 described above will be described. FIG. 8 is a flowchart showing an example of the flow of the shadow area determination process executed by the processing device 72.

First, in step S300, the shadow determination unit 12 acquires the brightness L ^* of each block divided from the image 4 from the memory 104, and calculates an average value (average brightness) of the acquired brightness L ^* of each block. .

  In step S <b> 310, the shadow determination unit 12 sets the selected block number N to 0, and stores the selected block number N (= 0) in a predetermined area of the memory 104.

In step S320, the shadow determination unit 12 acquires the lightness L ^* of the block whose block number corresponds to the selected block number N, that is, the block of interest, from each of the blocks divided from the image 4. Then, the shadow determination unit 12 determines whether or not the lightness L ^{* of the} block of interest is lower than the average lightness calculated in the process of step S300. If the determination is negative, the process proceeds to step S330.

The shadow determination unit 12 associates an identifier indicating that the lightness L ^* has been compared with the average lightness with an identifier indicating that it has been compared with the average lightness, and stores them in a predetermined area of the memory 104.

In step S330, the shadow determination unit 12 increments the selected block number N by one and stores the selected block number N whose value has been updated in a predetermined area of the memory 104. Then, the process proceeds to step S320, and the process of comparing the lightness L ^{* of the} block of interest with the average lightness is repeatedly executed for the block of interest corresponding to the updated selected block number N.

  On the other hand, if the determination process in step S320 is affirmative, the process proceeds to step S340.

In step S340, the shadow determination unit 12 recognizes that the block of interest is an area covered with shadow, that is, a block included in the shadow area 64, because the lightness L ^{* of the} block of interest is lower than the average lightness. Therefore, the shadow determination unit 12 associates an identifier indicating the shadow region 64 with the block of interest, and stores the association in a predetermined region of the memory 104.

In step S350, the shadow determination unit 12 determines, for each block divided from the image 4, whether or not the lightness L ^* of the block is compared with the average lightness in the process of step S320. This determination is performed by referring to whether or not an identifier indicating that the block has been compared with the average brightness is associated with the block. If the determination is negative, the process proceeds to step S330.

  As already described, in the process of step S330, the shadow determination unit 12 increments the selected block number N by one and proceeds to step S320. Therefore, it is possible to determine all the blocks included in the shadow region 64 by repeating the processes of steps S320 to S350.

  As described above, according to the first embodiment, the processing device 72 determines whether the crop 70 and the color chart 66 are both covered by the shadow from the contrast of the image 4. Then, the processing device 72 extracts the subject area 6 and the color chart area 8 from the image 4 determined that both the crop 70 and the color chart 66 are covered with shadows, and the color sample area included in the color chart area 8. The color of the subject region 6 is corrected based on the difference between the color of the subject color and the reference color of the color sample region.

  Therefore, even if the orientation of the crop 70 and the color chart 66 with respect to the sun 50 and the camera 76 is different, the crop 70 is obtained by using the image 4 including the crop 70 and the color chart 66 covered with shadows. In addition, the occurrence of light on the color chart 66 is suppressed. That is, the leaf color of the crop 70 can be measured with higher accuracy than when the image 4 in which at least one of the crop 70 and the color chart 66 is not covered with a shadow is used.

  Further, when the actual leaf color of the crop 70 is estimated, the image 4 when the crop 70 and the color chart 66 are both covered with shadows is used. Therefore, the direct light 54 and the sky light 56 are mixed in the image 4. The crop 70 and the color chart 66 illuminated by the sky light 56 are reflected instead of the reflected light. Therefore, the leaf color of the crop 70 can be accurately estimated as compared to the case where the crop 70 illuminated with light in which the direct light 54 and the sky light 56 are mixed and the image 4 showing the color chart 66 is used.

(Second Embodiment)
In the processing device 72 according to the first embodiment, when the shadow region 64 is extracted from the image 4 and it is determined that both the subject region 6 and the color chart region 8 are included in the shadow region 64, the leaf color of the crop 70 is changed. measure. However, when the image 4 in which the crop 70 and the color chart 66 are clearly covered with shadows is used for measuring the leaf color of the crop 70, the shadow area 64 is extracted from the image 4, and the subject area 6 and the color chart area are extracted. The process of determining whether both are included in the shadow area 64 becomes unnecessary.

  Therefore, in the second embodiment, a time zone in which both the crop 70 and the color chart 66 are covered with shadows is calculated in advance from the position information of the place where the crop 70 and the color chart 66 exist. Then, by capturing the image 4 with the camera 76 in accordance with the time zone, the image 4 in which the crop 70 and the color chart 66 are both covered with shadows is acquired.

  FIG. 9 is a functional block diagram illustrating an example of a functional configuration of the processing device 72A according to the second embodiment. The difference between the processing device 72A and the processing device 72 according to the first embodiment is that the shadow determination unit 12 is replaced with the shadow determination unit 12A, and the shadowed information 30 is newly added. In addition, the subject area extraction unit 20 is replaced with a subject area extraction unit 20A, and the color chart area extraction unit 22 is replaced with a color chart area extraction unit 22A, and along with this replacement, the extraction unit 14 is replaced with an extraction unit 14A.

  The shadow determination unit 12A acquires information about the time zone in which both the crop 70 and the color chart 66 are covered with shadow from the shaded information 30, and outputs an instruction to start imaging to the imaging unit 10 in the time zone. The image 4 captured by the camera 76 is received from the imaging unit 10. Then, the shadow determination unit 12 determines the position of the shadow region 64 included in the image 4 received from the imaging unit 10, and determines whether the crop 70 and the color chart 66 are both included in the shadow region 64. .

  Unlike the subject region extraction unit 20, the subject region extraction unit 20 </ b> A extracts the subject region 6 from the image 4 without determining whether or not the subject region 6 is included in the shadow region 64.

  Further, unlike the color chart area extraction section 22, the color chart area extraction unit 22A extracts the color chart area 8 from the image 4 without determining whether or not the shadow area 64 includes the color chart area 8. The color sample area is extracted from the color chart area 8.

  Next, FIG. 10 shows an example of a configuration when the processing device 72A is realized by a computer. The configuration of the computer 100A illustrated in FIG. 10 is different from the configuration of the computer 100 according to the first embodiment illustrated in FIG. 5 in that the shadow determination process 124 is replaced with the shadow determination process 124A, and the shadowed information storage area 140 is newly added. It is a point added to. The subject area extraction process 126 is replaced with a subject area extraction process 126A, and the color chart area extraction process 128 is replaced with a color chart area extraction process 128A.

  The CPU 102 reads the processing program 120A from the storage unit 106, expands it in the memory 104, and executes each process included in the processing program 120A.

  The CPU 102 reads out the processing program 120A from the storage unit 106, expands it in the memory 104, and executes the processing program 120A, whereby the computer 100A operates as the processing device 72A shown in FIG.

  Further, when the CPU 102 executes the shadow determination process 124A, the computer 100A operates as the shadow determination unit 12A illustrated in FIG. Further, when the CPU 102 executes the subject region extraction process 126A, the computer 100A operates as the subject region extraction unit 20A shown in FIG. Further, when the CPU 102 executes the color chart area extraction process 128A, the computer 100A operates as the color chart area extraction unit 22A shown in FIG.

  Furthermore, the shadow information 30 is stored in the memory 104 by the CPU 102 expanding the shadow information stored in the shadow information storage area 140 in the memory 104.

  Note that the computer 100A can be realized by, for example, a semiconductor integrated circuit, more specifically, an ASIC or the like.

  Next, the operation of the processing device 72A will be described. For example, the processing device 72A executes measurement processing when the processing device 72A is turned on and started.

  FIG. 11 is a flowchart illustrating an example of the flow of measurement processing executed by the processing device 72A. The flowchart of the measurement process shown in FIG. 11 is different from the flowchart of the measurement process according to the first embodiment shown in FIG. 6 in that steps S2 to S8 are added and the processes of steps S40 and S50 are deleted instead. It is. Therefore, differences from the flowchart of the measurement process according to the first embodiment will be described below.

  First, in step S <b> 2, the shadow determination unit 12 </ b> A acquires the shadowed information 30 stored in a predetermined area of the memory 104.

  FIG. 12 is a diagram illustrating an example of the shadowed information 30. As shown in FIG. 12, the shadow information 30 is information in which a number, date, shadow time, and shadow time are associated with each other in the row direction. The number is a number for uniquely identifying the row data associated with the row direction of the shaded information 30, and the date indicates date information. The shadowed time indicates the start time of a period in which the crop 70 and the color chart 66 are covered by the roof 60A and the roof 60B shown in FIG. 2, and the shadow time indicates the crop 70 and the color chart 66. Both show the end times of periods covered with shadows.

  For example, the row data represented by the number 1 indicates that the crop 70 and the color chart 66 are both covered with shadows from 12:30 to 13:00 on January 1, 2000. Such shadowing time and shadowing time for each date can be calculated in advance from the size and installation height of the roofs 60A and 60B, the orbit of the sun, and the longitude and latitude where the crop 70 and the color chart 66 exist. The calculation result is stored in the memory 104 as the shaded information 30.

  The shadow determination unit 12A acquires the current date by using a calendar function built in the CPU 102, for example. Then, the shadow determination unit 12A acquires from the memory 104 the row data of the shadowed information 30 that matches the current date.

  In step S4, the shadow determination unit 12A obtains the current time (current time) using, for example, a calendar function built in the CPU 102. Then, the shadow determination unit 12A determines whether or not the acquired current time is after the shadow insertion time included in the row data acquired in the process of step S2. In the case of negative determination, that is, if the current time is before the shadowing time, it is considered that the crop 70 and the color chart 66 are not covered by the shadow, and the process proceeds to step S8.

  In step S8, the shadow determination unit 12A waits until a predetermined time (for example, 1 minute) elapses, and executes the process of step S4 again. On the other hand, if the determination process in step S4 is affirmative, the process proceeds to step S6.

  In step S6, the shadow determination unit 12A determines whether or not the current time acquired in the process of step S4 is earlier than the shadow time included in the row data acquired in the process of step S2. In the case of a negative determination, that is, if the current time is after the shadowing time, it is considered that the crop 70 and the color chart 66 are not covered with shadows, and the process proceeds to step S8 and waits for a predetermined time.

  On the other hand, when the determination process in step S6 is affirmative, the current time is included in the time zone in which the start time is the shadowing time and the last time is the shadowing time, and the process proceeds to step S10. In step S <b> 10, the shadow determination unit 12 </ b> A outputs an instruction to start imaging to the imaging unit 10, receives the image 4 captured by the camera 76 from the imaging unit 10, and stores the image 4 in a predetermined area of the memory 104, for example. .

  Since the captured image 4 is the image 4 captured in the time zone from the shadowing time to the shadowing time, it is clear that both the crop 70 and the color chart 66 are covered with shadows. Therefore, the process of step S40 for determining whether or not the subject area 6 is included in the shadow area 64 and the step of determining whether or not the color chart area 8 is included in the shadow area 64 in FIG. The process of S50 is deleted from the flowchart shown in FIG.

  Thereafter, the processing device 72A can measure the leaf color of the crop 70 by executing the processes of steps S20, S30, and S60 to S130 already described in the first embodiment. In addition, when the measurement process shown in FIG. 11 is complete | finished, the leaf color of the crops 70 after the next day can be measured by performing again from step S2.

  The current date and time may be acquired from a device such as a clock connected to the I / O 110 as an example of the input device 112.

  As described above, according to the second embodiment, the processing device 72A measures the leaf color of the crop 70 based on the image 4 captured in advance in a time zone in which both the crop 70 and the color chart 66 are covered with shadows. In this case, as shown in the first embodiment, a process for determining whether the crop area 70 and the color chart 66 are included in the shadow area 64 of the image 4 is unnecessary, and compared with the measurement process according to the first embodiment. Acceleration of measurement processing can be expected.

  Note that the processing device 72 according to the first embodiment and the processing device 72A according to the second embodiment output the imaging instruction to the camera 76 to capture the image 4, but the acquisition method of the image 4 is limited to this. Absent. For example, the data of the image 4 that has already been captured may be acquired via the I / O 110 and stored in the memory 104, and the processing after step S20 in FIGS. 6 and 11 may be executed. At this time, in the processing device 72A according to the second embodiment, the captured image 4 in which the crop 70 and the color chart 66 are both covered with shadows may be acquired via the I / O 110. In addition, the image is periodically captured by the camera 76, and the processing device 72 according to the first embodiment is applied to a mode in which the image 4 used for estimating the leaf color of the crop 70 is selected from the plurality of captured images 4. It is also possible.

  Moreover, although the example which measures the leaf color of the crop 70 was demonstrated in 1st Embodiment and 2nd Embodiment, it cannot be overemphasized that the measuring object of the color by the processing apparatus 72 and the processing apparatus 72A is not restricted to the crop 70. The disclosed technology can also be applied to color measurement of an outdoor object, such as a building wall. By comparing the color of the measured wall of the building with the color of the wall at the time of new construction, it can be used as reference data when judging the repair time of the wall.

  As described above, the disclosed technology has been described using each embodiment, but the disclosed technology is not limited to the scope described in each embodiment. Various modifications or improvements can be added to each embodiment without departing from the spirit of the disclosed technology, and forms to which the modifications or improvements are added are also included in the technical scope of the disclosed technology. For example, the processing order may be changed without departing from the scope of the disclosed technology.

  The following additional notes are further disclosed regarding the embodiment including the first embodiment and the second embodiment.

(Appendix 1)
On the computer,
It is determined whether the subject to be colorimetric and the color chart whose colorimetric value is known are in a situation where direct light from the light source is not irradiated,
Based on the color of the area corresponding to the color chart in the image and the colorimetric value of the color chart, an image of the subject and the color chart in the situation is captured. A processing program for executing processing including obtaining an image for performing color correction for obtaining a colorimetric value of the subject from color.

(Appendix 2)
In the computer,
From the acquired image, an area corresponding to the color chart and an area corresponding to the subject in the image are extracted, and a color of the area corresponding to the color chart and a color of the area corresponding to the subject are obtained. ,
Additional processing for further executing a process including: performing color correction to obtain a colorimetric value of the subject from a color of the region corresponding to the subject based on a color of the region corresponding to the color sample and a colorimetric value of the color sample The processing program according to 1.

(Appendix 3)
A low brightness area with a brightness less than a threshold value is extracted from the image obtained by capturing the subject and the color chart, and an area corresponding to the color chart and an area corresponding to the subject in the image are included in the low brightness area. The processing program according to Supplementary Note 1 or Supplementary Note 2, wherein it is determined whether or not the subject and the color chart are both in a state where direct light from the light source is not irradiated.

(Appendix 4)
A period in which direct light from the light source to the subject and the color chart is blocked by a light blocking object is stored in advance.
The processing program according to Supplementary Note 1 or Supplementary Note 2, wherein it is determined whether or not the subject and the color chart are not irradiated with direct light from the light source based on whether or not a date and time is included in the period.

(Appendix 5)
On the computer,
It is determined whether the subject to be colorimetric and the color chart whose colorimetric value is known are in a situation where direct light from the light source is not irradiated,
Based on the color of the area corresponding to the color chart in the image and the colorimetric value of the color chart, an image of the subject and the color chart in the situation is captured. A processing method for executing processing including obtaining an image for performing color correction for obtaining a colorimetric value of the subject from color.

(Appendix 6)
In the computer,
From the acquired image, an area corresponding to the color chart and an area corresponding to the subject in the image are extracted, and a color of the area corresponding to the color chart and a color of the area corresponding to the subject are obtained. ,
Additional processing for further executing a process including: performing color correction to obtain a colorimetric value of the subject from a color of the region corresponding to the subject based on a color of the region corresponding to the color sample and a colorimetric value of the color sample 5. The processing method according to 5.

(Appendix 7)
A low brightness area with a brightness less than a threshold value is extracted from the image obtained by capturing the subject and the color chart, and an area corresponding to the color chart and an area corresponding to the subject in the image are included in the low brightness area. The processing method according to appendix 5 or appendix 6, wherein it is determined whether or not both the subject and the color chart are not irradiated with direct light from the light source.

(Appendix 8)
A period in which direct light from the light source to the subject and the color chart is blocked by a light blocking object is stored in advance.
The processing method according to appendix 5 or appendix 6, wherein whether or not the subject and the color chart are not irradiated with direct light from the light source is determined based on whether the date and time is included in the period.

(Appendix 9)
A shadow determination unit that determines whether or not a colorimetric object and a color chart having a known colorimetric value are not irradiated with direct light from a light source;
Based on the color of the area corresponding to the color chart in the image and the colorimetric value of the color chart, an image of the subject and the color chart in the situation is captured. An imaging unit that acquires an image for performing color correction to obtain a colorimetric value of the subject from colors;
Processing equipment.

(Appendix 10)
From the acquired image, an area corresponding to the color chart and an area corresponding to the subject in the image are extracted, and a color of the area corresponding to the color chart and a color of the area corresponding to the subject are obtained. An extractor;
A measurement unit that performs color correction to obtain a colorimetric value of the subject from the color of the region corresponding to the subject based on the color of the region corresponding to the color sample and the colorimetric value of the color sample;
The processing apparatus according to appendix 9, further comprising:

(Appendix 11)
The shadow determination unit extracts a low brightness area whose brightness is less than a threshold from the image obtained by capturing the subject and the color chart, and an area corresponding to the color chart and an area corresponding to the subject in the image are the The processing apparatus according to appendix 9 or appendix 10, wherein it is determined whether or not both the subject and the color chart are not irradiated with direct light from the light source based on whether or not they are included in a low brightness area.

(Appendix 12)
A period in which direct light from the light source to the subject and the color chart is blocked by a light blocking object is stored in advance.
The shadow determining unit determines whether or not the subject and the color chart are not irradiated with direct light from the light source based on whether the date and time is included in the period. The processing apparatus according to 10.

(Appendix 13)
On the computer,
It is determined whether the subject to be colorimetric and the color chart whose colorimetric value is known are in a situation where direct light from the light source is not irradiated,
Based on the color of the area corresponding to the color chart in the image and the colorimetric value of the color chart, an image of the subject and the color chart in the situation is captured. A computer-readable recording medium storing a processing program for executing processing including obtaining an image for performing color correction for obtaining a colorimetric value of the subject from color.

4 ... Image, 6 ... Subject area, 8 ... Color chart area, 10 (10A) ... Imaging unit, 12 ... Shadow determination unit, 14 (14A) ... Extracting unit, 16 ... Measurement unit, 18 ... Output unit, 20 (20A) ... Subject region extraction unit, 22 (22A) ... Color chart region extraction unit, 24 ... Subject region color information extraction unit, 26 ... color chart area color information extraction unit, 28 ... reference color information, 30 ... shaded information, 50 ... sun, 52 ... clouds, 54 ... direct light, 56 ... Sky light, 58 ... farm field, 60A (60B) ... roof, 64 (64A, 64B) ... shadow area, 66 ... color chart, 70 ... crop, 72 (72A) ... Processing unit, 76 ... camera, 100 (100A) ... computer, 102 ... CPU, 104 ... memory, 106 ...憶部, 108 ... bus, 112 ... input device, 114 ... output device, 116 ... recording medium, 120 (120A) ... program

Claims (6)

On the computer,
It is determined whether the subject to be colorimetric and the color chart whose colorimetric value is known are in a situation where direct light from the light source is not irradiated,
Based on the color of the area corresponding to the color chart in the image and the colorimetric value of the color chart, an image of the subject and the color chart in the situation is captured. A processing program for executing processing including obtaining an image for performing color correction for obtaining a colorimetric value of the subject from color. In the computer,
From the acquired image, an area corresponding to the color chart and an area corresponding to the subject in the image are extracted, and a color of the area corresponding to the color chart and a color of the area corresponding to the subject are obtained. ,
And further performing a process including: performing color correction to obtain a colorimetric value of the subject from a color of the region corresponding to the subject based on a color of the region corresponding to the color sample and a colorimetric value of the color sample. Item 1. A processing program according to item 1. A low brightness area with a brightness less than a threshold value is extracted from the image obtained by capturing the subject and the color chart, and an area corresponding to the color chart and an area corresponding to the subject in the image are included in the low brightness area. The processing program according to claim 1, wherein it is determined whether or not the subject and the color chart are both in a state where direct light from the light source is not irradiated. A period in which direct light from the light source to the subject and the color chart is blocked by a light blocking object is stored in advance.
The process according to claim 1 or 2, wherein it is determined whether or not the subject and the color chart are not irradiated with direct light from the light source based on whether or not date and time is included in the period. program. On the computer,
It is determined whether the subject to be colorimetric and the color chart whose colorimetric value is known are in a situation where direct light from the light source is not irradiated,
Based on the color of the area corresponding to the color chart in the image and the colorimetric value of the color chart, an image of the subject and the color chart in the situation is captured. A processing method for executing processing including obtaining an image for performing color correction for obtaining a colorimetric value of the subject from color. A shadow determination unit that determines whether or not a colorimetric object and a color chart having a known colorimetric value are not irradiated with direct light from a light source;
Based on the color of the area corresponding to the color chart in the image and the colorimetric value of the color chart, an image of the subject and the color chart in the situation is captured. An imaging unit that acquires an image for performing color correction to obtain a colorimetric value of the subject from colors;
Processing equipment.