CN105472361A - Method and system for image fluorescence processing of projector - Google Patents
Method and system for image fluorescence processing of projector Download PDFInfo
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- CN105472361A CN105472361A CN201510810937.XA CN201510810937A CN105472361A CN 105472361 A CN105472361 A CN 105472361A CN 201510810937 A CN201510810937 A CN 201510810937A CN 105472361 A CN105472361 A CN 105472361A
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- 238000000034 method Methods 0.000 title claims abstract description 109
- 238000005286 illumination Methods 0.000 claims abstract description 61
- 238000004043 dyeing Methods 0.000 claims abstract description 51
- 230000007797 corrosion Effects 0.000 claims abstract description 35
- 238000005260 corrosion Methods 0.000 claims abstract description 35
- 239000011159 matrix material Substances 0.000 claims description 30
- 230000004927 fusion Effects 0.000 claims description 13
- 238000001514 detection method Methods 0.000 claims description 12
- 239000000284 extract Substances 0.000 claims description 11
- 230000003287 optical effect Effects 0.000 claims description 11
- 238000002073 fluorescence micrograph Methods 0.000 abstract 6
- 238000007499 fusion processing Methods 0.000 abstract 1
- 230000000694 effects Effects 0.000 description 4
- 230000008030 elimination Effects 0.000 description 3
- 238000003379 elimination reaction Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 239000003086 colorant Substances 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 238000009499 grossing Methods 0.000 description 2
- 241000276489 Merlangius merlangus Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
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- 230000006870 function Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/20—Image enhancement or restoration using local operators
- G06T5/30—Erosion or dilatation, e.g. thinning
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N9/00—Details of colour television systems
- H04N9/12—Picture reproducers
- H04N9/31—Projection devices for colour picture display, e.g. using electronic spatial light modulators [ESLM]
- H04N9/3179—Video signal processing therefor
- H04N9/3182—Colour adjustment, e.g. white balance, shading or gamut
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/12—Edge-based segmentation
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N9/00—Details of colour television systems
- H04N9/12—Picture reproducers
- H04N9/31—Projection devices for colour picture display, e.g. using electronic spatial light modulators [ESLM]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10064—Fluorescence image
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Abstract
The invention discloses a method and system for image fluorescence processing of a projector. The method comprises: a projection image is obtained; gray-scale processing is carried out on the projection image to obtain a gray-scale image; a contour image is extracted from the gray-scale image and corrosion and expansion processing is carried out on the contour image; the illumination intensity of ambient light at a projection area of a projection module is detected and dyeing and fuzzy processing is carried out on the image after the corrosion and expansion processing according to the illumination intensity of the ambient light; fusion processing is carried out on the contour image and the image after the dyeing and fuzzy processing to obtain a fluorescence image; and the projection module projects the florescence image on the projection area. Therefore, fluorescence image projection by the projection module is realized and the fluorescence image and the ambient light can be distinguished, so that the user can watch the fluorescence image. Besides, the fluorescence degree of the fluorescence image can be adjusted according to the illumination intensity of the ambient light at the projection area of the projection module, so that the fluorescence image can meet the projection environment requirement.
Description
Technical field
The present invention relates to projection art, particularly relate to the method and system of a kind of projector image fluorescence process.
Background technology
Projecting apparatus (also known as projector), be a kind of can by image or VIDEO PROJECTION to the equipment on curtain, it projects to the image on curtain or video presents several times when keeping definition or decades of times amplifies, be convenient for people to viewing, also the visual field that people are open is given, therefore, projecting apparatus is deeply by the welcome of user.
Because projecting apparatus is that image or video are directly projected curtain, the drop shadow effect of projected image shown on curtain can by the impact of the surround lighting in the region at curtain place, if when surround lighting is crossed strong, surround lighting can cover the projected light of projecting apparatus outgoing, make curtain be the effect of looking whiting, user cannot watch projected picture, if surround lighting is excessively weak, then the projected picture of curtain display is excessively dark, can easily make people cannot know the information that acquisition projected image reflects.
Summary of the invention
The technical problem that the present invention mainly solves is to provide the method and system of a kind of projector image fluorescence process, projection module projection fluoroscopic image can be realized, wherein, fluoroscopic image can make a distinction with surround lighting, user is facilitated to watch fluoroscopic image, and according to the intensity of illumination of the surround lighting of the surround lighting of the view field of projection module, the Fluoresceinated degree of fluoroscopic image can be regulated, make fluoroscopic image more meet the needs of projection environment.
For solving the problems of the technologies described above, the technical scheme that the present invention adopts is: the method providing the process of a kind of projector image fluorescence, comprises acquisition projected image; Gray proces is carried out to described projected image, obtains gray level image; From described gray level image, extract contour images, and corrosion expansion process is carried out to described contour images; Detect the intensity of illumination that described projection module carries out the surround lighting of the view field projected, and according to the intensity of illumination of described surround lighting, dyeing Fuzzy Processing is carried out to the image after corrosion expansion process; Described contour images and the image after carrying out dyeing Fuzzy Processing are carried out fusion treatment and obtains fluoroscopic image; Described projection module to be projected described fluoroscopic image to described view field.
Wherein, carry out gray proces to described projected image, the step obtaining gray level image comprises: the rgb value obtaining the pixel of described projected image; According to the rgb value of the pixel of described projected image, calculate gray value; The rgb value of the pixel of described projected image is replaced into described gray value, obtains described gray level image.
Wherein, according to the rgb value of described pixel, the computing formula calculating the gray value of described pixel is:
Gray＝α*R+β*G+γ*B
Described Gray is gray value, described R is the color value of red component in the rgb value of described pixel, described G is the color value of the rgb value Green component of described pixel, described B is the color value of blue component in the rgb value of described pixel, described α be greater than 0.2 and be less than 0.4 numerical value, described β be greater than 0.5 and be less than 0.7 numerical value, sized by described γ 0.1 and be less than 0.3 numerical value.
Wherein, the described intensity of illumination according to described surround lighting comprises the step that the image after corrosion expansion process carries out dyeing Fuzzy Processing: first carry out dyeing process to the image after described corrosion expansion process; Select the matrix template of Gaussian Blur process according to the intensity of illumination of described surround lighting, between the matrix template of described Gaussian Blur process and the intensity of illumination of surround lighting, there is predetermined positive correlation; The matrix template of the image after described dyeing and selected Gaussian Blur process is carried out convolution algorithm, obtains the image after described dyeing Fuzzy Processing.
Wherein, describedly described contour images is carried out corroding the step of expansion process comprise: the contour edge extracting each object in described contour images; Eliminate the burr of described contour edge, and expand described contour edge, and fill up the blank parts of each interior of articles in described contour images.
For solving the problems of the technologies described above, another technical solution used in the present invention is: the system providing a kind of fluoroscopic image that projects, comprises projection module, optical detection device and processor, and described processor is connected with projection module and optical detection device respectively; Described processor is used for: obtain projected image; Gray proces is carried out to described projected image, obtains gray level image; From described gray level image, extract contour images, and corrosion expansion process is carried out to described contour images; Detect by described optical detection device the intensity of illumination that described projection module carries out the surround lighting of the view field projected, and according to the intensity of illumination of described surround lighting, dyeing Fuzzy Processing is carried out to the image after corrosion expansion process; Described contour images and the image after carrying out dyeing Fuzzy Processing are carried out fusion treatment and obtains fluoroscopic image; Send described fluoroscopic image to described projection module, and described projection module to be projected described fluoroscopic image to described view field.
Wherein, described processor carries out gray proces to described projected image, and the step obtaining gray level image comprises: the rgb value obtaining the pixel of described projected image; According to the rgb value of the pixel of described projected image, calculate gray value; The rgb value of the pixel of described projected image is replaced into described gray value, obtains described gray level image.
Wherein, according to the rgb value of described pixel, the computing formula calculating the gray value of described pixel is:
Gray＝α*R+β*G+γ*B
Described Gray is gray value, described R is the color value of red component in the rgb value of described pixel, described G is the color value of the rgb value Green component of described pixel, described B is the color value of blue component in the rgb value of described pixel, described α be greater than 0.2 and be less than 0.4 numerical value, described β be greater than 0.5 and be less than 0.7 numerical value, sized by described γ 0.1 and be less than 0.3 numerical value.
Wherein, described processor comprises the step that the image after corrosion expansion process carries out dyeing Fuzzy Processing according to the intensity of illumination of described surround lighting: carry out dyeing process to the image after described corrosion expansion process; Select the matrix template of Gaussian Blur process according to the intensity of illumination of described surround lighting, between the matrix template of described Gaussian Blur process and the intensity of illumination of surround lighting, there is predetermined positive correlation; The matrix template of the image after described dyeing and selected Gaussian Blur process is carried out convolution algorithm, obtains the image after described dyeing Fuzzy Processing.
Wherein said processor comprises the step that described contour images carries out corroding expansion process: the contour edge extracting each object in described contour images; Eliminate the burr of described contour edge, and expand described contour edge, and fill up the blank parts of each interior of articles in described contour images.
The invention has the beneficial effects as follows: the situation being different from prior art, the present invention, after getting projected image, carries out projected image to projected image successively and carries out gray proces, obtain gray level image; From gray level image, extract contour images, and corrosion expansion process is carried out to contour images; Dyeing Fuzzy Processing is carried out to the image after corrosion expansion process, finally contour images and the image after carrying out dyeing Fuzzy Processing are carried out fusion treatment and obtain fluoroscopic image, projection module is made to send fluoroscopic image, realize projection fluoroscopic image, wherein, fluoroscopic image can make a distinction with surround lighting, facilitates user to watch fluoroscopic image; Further, when carrying out dyeing Fuzzy Processing to the image after corrosion expansion process, that the intensity of illumination of the surround lighting of view field is carried out, the intensity of illumination being specially surround lighting is stronger, and the degree of Fuzzy Processing is darker, and image is Fluoresceinated stronger, the intensity of illumination of surround lighting is more weak, the degree of Fuzzy Processing is more shallow, and image is Fluoresceinated more weak, makes fluoroscopic image more meet the needs of projection environment.
Accompanying drawing explanation
Fig. 1 is that the present invention projects the schematic diagram of system embodiment of fluoroscopic image;
Fig. 2 is the flow chart of the method execution mode of projector image fluorescence of the present invention process;
Fig. 3 is the flow chart generating gray level image in the method execution mode of projector image fluorescence of the present invention process;
Fig. 4 is that the method execution mode of projector image fluorescence of the present invention process carries out to image the flow chart of Fuzzy Processing of dyeing.
Embodiment
Below in conjunction with drawings and embodiments, the present invention is described in detail.
Refer to Fig. 1, the system 20 of projection fluoroscopic image comprises projection module 21, optical detection device 22 and processor 23, and processor 23 is connected with projection module 21 and optical detection device 22 respectively.Optical detection device 22 is for detecting the intensity of illumination of the surround lighting of the view field of projection module 21.The intensity of illumination of surround lighting to refer in unit are accept the energy of visible ray.Except the intensity of illumination of the surround lighting by optical detection device 22 direct-detection view field, also the lens assembly with acquisition function can be utilized to take a width projection ambient image, again gray proces is carried out to this image, the intensity of illumination of the surround lighting of picture is drawn by the gray value of each pixel calculating gray level image, be the intensity of illumination of the surround lighting in projection environment, certainly, those skilled in the art also can adopt other method to detect the intensity of illumination of the surround lighting of view field, repeat no longer one by one herein.
Processor 23 for: obtain projected image, gray proces is carried out to projected image, obtain gray level image, from gray level image, extract contour images, and corrosion expansion process is carried out to contour images, the intensity of illumination that projection module 21 carries out the surround lighting of the view field projected is detected by optical detection device 22, and environmentally the intensity of illumination of light carries out dyeing Fuzzy Processing to the image after corrosion expansion process, contour images and the image after carrying out dyeing Fuzzy Processing are carried out fusion treatment and obtains fluoroscopic image, fluoroscopic image is sent to projection module 21, and make projection module 21 to view field's projection fluoroscopic image.
What deserves to be explained is: after extracting the contour images obtained, the copy of contour images can be preserved, wherein, the copy of contour images is identical with former contour images, after contour images and carrying out the dyeed image after Fuzzy Processing carry out fusion treatment and be specially: the image after the copy of contour images and Fuzzy Processing of carrying out dyeing is carried out fusion treatment.
Gray level image refers to the image of color depth also having many levels between black and white, and R, G, B are respectively the color value of the red component of each pixel of projected image, green component, blue component three.The color of each pixel in projected image has R, G, B tri-components to determine, and each component has 255 intermediate values desirable, and such pixel can have the excursion of the color of more than 1,600 ten thousand (255*255*255).And gray level image is R, G, a kind of special coloured image that B tri-components are identical, the excursion of an one pixel is 255 kinds, gray proces narrows down to excursion between B&W by the color of image by changeable colored excursion, then processor 23 pairs of projected images carry out gray proces, the step obtaining gray level image comprises: the rgb value obtaining the pixel of projected image, according to the rgb value of the pixel of projected image, calculate gray value, the rgb value of the pixel of projected image is replaced into gray value, obtain gray level image, wherein, the computing formula of the gray value of calculating pixel point is:
Gray＝α*R+β*G+γ*B
Gray is gray value, R is the color value of red component in the rgb value of pixel, G is the color value of the rgb value Green component of pixel, B is the color value of blue component in the rgb value of pixel, α be greater than 0.2 and be less than 0.4 numerical value, β be greater than 0.5 and be less than 0.7 numerical value, sized by γ 0.1 and be less than 0.3 numerical value.。Certainly, above-mentioned just explanation a kind of wherein method preferably projected image being converted to gray level image, those skilled in the art also can adopt other method by projected image converting gradation image.
Contour images refers to the image of the profile that can embody each object in projected image, and the step that processor 23 pairs of contour images carry out corroding expansion process specifically comprises the contour edge extracting each object in contour images; The burr at contour elimination edge, and expand contour edge, and fill up the space of each interior of articles in contour images.Specifically, after the burr at contour elimination edge, expand the profile of each object, then fill the blank parts of each interior of articles in contour images.By above-mentioned process, map contour image becomes more level and smooth, thus reduces the requirement of the detail in contour images.
When Fuzzy Processing is carried out to image, that the intensity of illumination of environmentally light is carried out, concrete, the intensity of illumination of surround lighting is stronger, Fuzzy Processing is darker, the intensity of illumination of surround lighting is more weak, Fuzzy Processing is more shallow, then the intensity of illumination of processor 23 environmentally light comprises carry out the dyeing step of Fuzzy Processing of the image after corrosion expansion process: carry out dyeing to the image after corrosion expansion process and process, environmentally the intensity of illumination of light selects the matrix template of Gaussian Blur process, between the matrix template of Gaussian Blur process and the intensity of illumination of surround lighting, there is predetermined positive correlation, the matrix template of the image after dyeing and selected Gaussian Blur process is carried out convolution algorithm, obtain the image after dyeing Fuzzy Processing.There is between the matrix template of Gaussian Blur process and the intensity of illumination of surround lighting predetermined positive correlation refer to: the intensity of illumination of surround lighting is stronger, the matrix template of Gaussian Blur process selected larger, and the matrix template of Gaussian Blur process is larger, then the program of Fuzzy Processing carried out to image larger, the intensity of illumination of surround lighting is more weak, the matrix template of Gaussian Blur process selected less, and the matrix template of Gaussian Blur process is less, then carry out fuzzy program to image less; Certainly, the predetermined positive correlation between the size of the matrix template of Gaussian Blur process and the intensity of illumination of surround lighting can set according to actual conditions.
Further, processor 23 carries out fusion treatment at the image after Fuzzy Processing that contour images and carrying out dyeed and obtains being the prospect using contour images as image in fluoroscopic image, after dyeing Fuzzy Processing, image is as the background of image, wherein, image after dyeing Fuzzy Processing is a fuzzyyer image, can be formed with contour images and set off by contrast, thus make the Fluoresceinated display of image after merging more outstanding, the effect projected is better.
Certainly, after obtaining fluoroscopic image, before projection fluoroscopic image, process can also be optimized to fluoroscopic image, such as: to the smoothing process of fluoroscopic image, improve quality of fluoroscopic image etc.
In embodiments of the present invention, after getting projected image, successively projected image is carried out to projected image and carry out gray proces, obtain gray level image; From gray level image, extract contour images, and corrosion expansion process is carried out to contour images; Dyeing Fuzzy Processing is carried out to the image after corrosion expansion process, finally contour images and the image after carrying out dyeing Fuzzy Processing are carried out fusion treatment and obtain fluoroscopic image, projection module is made to send fluoroscopic image, realize projection fluoroscopic image, wherein, fluoroscopic image can make a distinction with surround lighting, facilitates user to watch fluoroscopic image; Further, when carrying out dyeing Fuzzy Processing to the image after corrosion expansion process, that the intensity of illumination of the surround lighting of view field is carried out, the intensity of illumination being specially surround lighting is stronger, and the degree of Fuzzy Processing is darker, and image is Fluoresceinated stronger, the intensity of illumination of surround lighting is more weak, the degree of Fuzzy Processing is more shallow, and image is Fluoresceinated more weak, makes fluoroscopic image more meet the needs of projection environment.
The present invention provides again the method execution mode of projector image fluorescence process.Refer to Fig. 2, method comprises:
Step S201: obtain projected image;
Projected image is provided by external equipment, also can obtain from the memory extraction of internal system.
Step S202: gray proces is carried out to projected image, obtains gray level image;
Gray level image refers to the image only having black and white two kinds of colors, and it can make beholder can distinguish shape and the profile of each object in image more intuitively.The rgb value of each pixel of projected image comprises R value, G value and B value, the color of each pixel in projected image has R, G, B tri-color components to determine, and each color component has 255 intermediate values desirable, such pixel can have the excursion of the color of more than 1,600 ten thousand (255*255*255).And gray level image is a kind of special coloured image that R, G, B tri-components are identical, the excursion of an one pixel is 255 kinds, and gray proces narrows down to excursion between B&W by the color of image by changeable colored excursion.And projected image is converted to recovers to be exactly the rgb value of for a change each pixel in projected image, make projected image only have black and white two kinds of colors, then as shown in Figure 3, step S202 comprises:
Step S2021: the rgb value obtaining the pixel of projected image;
Step S2022: according to the rgb value of the pixel of projected image, calculate gray value, wherein, the computing formula of the gray value of calculating pixel point is:
Gray＝α*R+β*G+γ*B
Gray is gray value, R is the color value of red component in the rgb value of pixel, G is the color value of the rgb value Green component of pixel, B is the color value of blue component in the rgb value of pixel, α be greater than 0.2 and be less than 0.4 numerical value, β be greater than 0.5 and be less than 0.7 numerical value, sized by γ 0.1 and be less than 0.3 numerical value.
Step S2023: the rgb value of the pixel of projected image is replaced into gray value, obtains gray level image.
Step S203: from gray level image, extracts contour images, and carries out corrosion expansion process to contour images;
What deserves to be explained is: after extraction obtains contour images, preserve the copy of contour images, to facilitate follow-up process.
And to contour images carry out corrosion expansion process be for making the profile of each object in contour images more clear, concrete step S203 comprises: the contour edge extracting each object in contour images, the burr at contour elimination edge, and expand contour edge, and fill up the blank portion of each interior of articles in contour images.After carrying out corrosion expansion process to contour images, image is more level and smooth, thus can reduce some details in image.
Step S204: detect the intensity of illumination that projection module carries out the surround lighting of the view field projected, and environmentally the intensity of illumination of light carries out dyeing Fuzzy Processing to the image after corrosion expansion process;
Projected picture region when view field refers to that projection module projects.When carrying out Fuzzy Processing to image, the intensity of illumination of surround lighting is stronger, and Fuzzy Processing is darker, and the intensity of illumination of surround lighting is more weak, and Fuzzy Processing is more shallow, and with the needs making fluoroscopic image more meet projection environment, then as shown in Figure 4, step S204 comprises again:
Step S2041: environmentally the intensity of illumination of light selects the matrix template of Gaussian Blur process, has predetermined positive correlation between the matrix template of Gaussian Blur process and the intensity of illumination of surround lighting;
There is between the matrix template of Gaussian Blur process and the intensity of illumination of surround lighting predetermined positive correlation refer to: the intensity of illumination of surround lighting is stronger, the matrix template of Gaussian Blur process selected larger, and the matrix template of Gaussian Blur process is larger, then the program of Fuzzy Processing carried out to image larger, the intensity of illumination of surround lighting is more weak, the matrix template of Gaussian Blur process selected less, and the matrix template of Gaussian Blur process is less, then carry out fuzzy program to image less; Certainly, the predetermined positive correlation between the size of the matrix template of Gaussian Blur process and the intensity of illumination of surround lighting can set according to actual conditions.
Step S2042: the matrix template of the image after dyeing and selected Gaussian Blur process is carried out convolution algorithm, obtains the image after dyeing Fuzzy Processing.
Step S205: contour images and the image after carrying out dyeing Fuzzy Processing are carried out fusion treatment and obtains fluoroscopic image;
The image after Fuzzy Processing that contour images and carrying out dyeed carries out fusion treatment and obtains referring in fluoroscopic image prospect using contour images as image, after dyeing Fuzzy Processing, image is as the background of image, wherein, image after dyeing Fuzzy Processing is a fuzzyyer image, can be formed with contour images and set off by contrast, thus making the Fluoresceinated display of image after merging more outstanding, the effect projected is better.
Step S206: projection module to be projected fluoroscopic image to view field.
Certainly, after obtaining fluoroscopic image, before projection fluoroscopic image, process can also be optimized to fluoroscopic image, such as: to the smoothing process of fluoroscopic image, improve quality of fluoroscopic image etc.
In embodiments of the present invention, after getting projected image, successively projected image is carried out to projected image and carry out gray proces, obtain gray level image; From gray level image, extract contour images, and corrosion expansion process is carried out to contour images; Dyeing Fuzzy Processing is carried out to the image after corrosion expansion process, finally contour images and the image after carrying out dyeing Fuzzy Processing are carried out fusion treatment and obtain fluoroscopic image, projection module is made to send fluoroscopic image, realize projection fluoroscopic image, wherein, fluoroscopic image can make a distinction with surround lighting, facilitates user to watch fluoroscopic image; Further, when carrying out dyeing Fuzzy Processing to the image after corrosion expansion process, that the intensity of illumination of the surround lighting of view field is carried out, the intensity of illumination being specially surround lighting is stronger, and the degree of Fuzzy Processing is darker, and image is Fluoresceinated stronger, the intensity of illumination of surround lighting is more weak, the degree of Fuzzy Processing is more shallow, and image is Fluoresceinated more weak, makes fluoroscopic image more meet the needs of projection environment.
The foregoing is only embodiments of the present invention; not thereby the scope of the claims of the present invention is limited; every utilize specification of the present invention and accompanying drawing content to do equivalent structure or equivalent flow process conversion; or be directly or indirectly used in other relevant technical fields, be all in like manner included in scope of patent protection of the present invention.
Claims (10)
1. a method for projector image fluorescence process, is characterized in that, comprising:
Obtain projected image;
Gray proces is carried out to described projected image, obtains gray level image;
From described gray level image, extract contour images, and corrosion expansion process is carried out to described contour images;
Detect the intensity of illumination that projection module carries out the surround lighting of the view field projected, and according to the intensity of illumination of described surround lighting, dyeing Fuzzy Processing is carried out to the image after corrosion expansion process;
Described contour images and the image after carrying out dyeing Fuzzy Processing are carried out fusion treatment and obtains fluoroscopic image;
Described projection module to be projected described fluoroscopic image to described view field.
2. method according to claim 1, is characterized in that,
Carry out gray proces to described projected image, the step obtaining gray level image comprises:
Obtain the rgb value of the pixel of described projected image;
According to the rgb value of the pixel of described projected image, calculate gray value;
The rgb value of the pixel of described projected image is replaced into described gray value, obtains described gray level image.
3. method according to claim 1, is characterized in that,
According to the rgb value of described pixel, the computing formula calculating the gray value of described pixel is:
Gray＝α*R+β*G+γ*B
Described Gray is gray value, described R is the color value of red component in the rgb value of described pixel, described G is the color value of the rgb value Green component of described pixel, described B is that the color of blue component in the rgb value of described pixel can go up, described α be greater than 0.2 and be less than 0.4 numerical value, described β be greater than 0.5 and be less than 0.7 numerical value, sized by described γ 0.1 and be less than 0.3 numerical value.
4. method according to claim 1, is characterized in that,
The described intensity of illumination according to described surround lighting comprises the step that the image after corrosion expansion process carries out dyeing Fuzzy Processing:
Dyeing process is first carried out to the image after described corrosion expansion process;
Select the matrix template of Gaussian Blur process according to the intensity of illumination of described surround lighting, between the matrix template of described Gaussian Blur process and the intensity of illumination of surround lighting, there is predetermined positive correlation;
The matrix template of the image after described dyeing and selected Gaussian Blur process is carried out convolution algorithm, obtains the image after described dyeing Fuzzy Processing.
5. method according to claim 1, is characterized in that,
Describedly described contour images carried out corroding the step of expansion process comprise:
Extract the contour edge of each object in described contour images;
Eliminate the burr of described contour edge, and expand described contour edge, and fill up the blank parts of each interior of articles in described contour images.
6. project the system of fluoroscopic image, and comprise projection module, optical detection device and processor, described processor is connected with projection module and optical detection device respectively;
Described processor is used for:
Obtain projected image;
Gray proces is carried out to described projected image, obtains gray level image;
From described gray level image, extract contour images, and corrosion expansion process is carried out to described contour images;
Detect by described optical detection device the intensity of illumination that described projection module carries out the surround lighting of the view field projected, and according to the intensity of illumination of described surround lighting, dyeing Fuzzy Processing is carried out to the image after corrosion expansion process;
Described contour images and the image after carrying out dyeing Fuzzy Processing are carried out fusion treatment and obtains fluoroscopic image;
Send described fluoroscopic image to described projection module, and described projection module to be projected described fluoroscopic image to described view field.
7. system according to claim 6, is characterized in that,
Described processor carries out gray proces to described projected image, and the step obtaining gray level image comprises:
Obtain the rgb value of the pixel of described projected image;
According to the rgb value of the pixel of described projected image, calculate gray value;
The rgb value of the pixel of described projected image is replaced into described gray value, obtains described gray level image.
8. system according to claim 7, is characterized in that,
According to the rgb value of described pixel, the computing formula calculating the gray value of described pixel is:
Gray＝α*R+β*G+γ*B
Described Gray is gray value, described R is the color value of red component in the rgb value of described pixel, described G is the color value of the rgb value Green component of described pixel, described B is the color value of blue component in the rgb value of described pixel, described α be greater than 0.2 and be less than 0.4 numerical value, described β be greater than 0.5 and be less than 0.7 numerical value, sized by described γ 0.1 and be less than 0.3 numerical value.
9. system according to claim 6, is characterized in that,
Described processor comprises the step that the image after corrosion expansion process carries out dyeing Fuzzy Processing according to the intensity of illumination of described surround lighting:
Dyeing process is carried out to the image after described corrosion expansion process;
Select the matrix template of Gaussian Blur process according to the intensity of illumination of described surround lighting, between the matrix template of described Gaussian Blur process and the intensity of illumination of surround lighting, there is predetermined positive correlation;
The matrix template of the image after described dyeing and selected Gaussian Blur process is carried out convolution algorithm, obtains the image after described dyeing Fuzzy Processing.
10. system according to claim 6, is characterized in that,
Described processor comprises the step that described contour images carries out corroding expansion process:
Extract the contour edge of each object in described contour images;
Eliminate the burr of described contour edge, and expand described contour edge, and fill up the blank parts of each interior of articles in described contour images.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510810937.XA CN105472361B (en) | 2015-11-19 | 2015-11-19 | A kind of method and system of projector image fluorescence processing |
| PCT/CN2016/093404 WO2017084391A1 (en) | 2015-11-19 | 2016-08-05 | Method and system for performing fluorescence processing on an image of a projector |
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Cited By (3)
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| WO2017084391A1 (en) * | 2015-11-19 | 2017-05-26 | 广景视睿科技（深圳）有限公司 | Method and system for performing fluorescence processing on an image of a projector |
| CN108174170A (en) * | 2017-12-29 | 2018-06-15 | 安徽慧视金瞳科技有限公司 | View field's size self-sensing method, system and equipment based on camera |
| CN109410236A (en) * | 2018-06-12 | 2019-03-01 | 佛山市顺德区中山大学研究院 | The method and system that fluorescent staining image reflective spot is identified and redefined |
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| CN110689926A (en) * | 2018-06-19 | 2020-01-14 | 上海交通大学 | Accurate detection method for high-throughput digital PCR image droplets |
| CN110909747B (en) * | 2019-05-13 | 2023-04-07 | 河南理工大学 | Coal gangue identification method based on multi-color space principal component analysis description |
| CN111489322B (en) * | 2020-04-09 | 2023-05-26 | 广州光锥元信息科技有限公司 | Method and device for adding sky filter to static picture |
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| CN105472361B (en) | 2018-03-27 |
| WO2017084391A1 (en) | 2017-05-26 |
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