CN105472239A - Photo processing method and photo processing device - Google Patents
Photo processing method and photo processing device Download PDFInfo
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- CN105472239A CN105472239A CN201510792628.4A CN201510792628A CN105472239A CN 105472239 A CN105472239 A CN 105472239A CN 201510792628 A CN201510792628 A CN 201510792628A CN 105472239 A CN105472239 A CN 105472239A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/80—Camera processing pipelines; Components thereof
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/60—Control of cameras or camera modules
- H04N23/61—Control of cameras or camera modules based on recognised objects
- H04N23/611—Control of cameras or camera modules based on recognised objects where the recognised objects include parts of the human body
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/60—Control of cameras or camera modules
- H04N23/68—Control of cameras or camera modules for stable pick-up of the scene, e.g. compensating for camera body vibrations
- H04N23/682—Vibration or motion blur correction
- H04N23/683—Vibration or motion blur correction performed by a processor, e.g. controlling the readout of an image memory
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Abstract
The invention relates to a photo processing method and a photo processing device. The method comprises the steps of grouping to-be-processed photos according to preset rules, detecting the quality of each group of to-be-processed photos to get the quality level of the group of to-be-processed photos, and storing the to-be-processed photos according to the quality level. By implementing the technical scheme, the storage space of a terminal or server is saved, and the storage pressure is reduced. Meanwhile, photos of high quality are screened out accurately for users, and the photos of high quality can be stored accurately and completely.
Description
Technical field
The disclosure relates to technical field of image processing, particularly relates to photo processing method and device.
Background technology
At present, along with the popularization degree of mobile phone is more and more higher, time user takes pictures, often worry that the photo effect of taking is bad, so often can take multiple pictures to Same Scene.Like this, bring great pressure to the backing up photos storage of high in the clouds or mobile phone, waste a lot of memory space.
Summary of the invention
Disclosure embodiment provides photo processing method and device.Described technical scheme is as follows:
According to the first aspect of disclosure embodiment, a kind of photo processing method is provided, comprises:
Pending photo is divided into groups by preset rules;
Carrying out quality testing to often organizing pending photo, obtaining the described credit rating often organizing pending photo;
According to described credit rating, described pending photo is stored.
Optionally, described pending photo to be divided into groups by preset rules, comprising:
Obtain the attribute information of pending photo, described attribute information comprises at least one information in the shooting time of photo and spot for photography;
According to described attribute information, described pending photo is divided into groups in advance;
Similarity Measure is carried out between the pending photo in described pre-grouping;
According to the similarity calculated, pending photo similar in described pre-grouping is divided into same group, obtains the grouping to described pending photo.
Optionally, describedly carrying out quality testing to often organizing pending photo, obtaining the described credit rating often organizing pending photo, comprising:
Detect the mass parameter of each pending photo in every group, described mass parameter comprises following at least one parameter: definition, color saturation and face;
The credit rating of each pending photo in every group is determined according to described mass parameter.
Optionally, when described mass parameter comprises face parameter, describedly carrying out quality testing to often organizing pending photo, obtaining the described credit rating often organizing pending photo, comprising:
Detect in described pending photo and whether comprise face;
When described pending photo comprises face, determine that the credit rating of described pending photo is higher than the credit rating of pending photo not comprising face.
Optionally, when described photo comprises face, describedly carrying out quality testing to often organizing pending photo, obtaining the described credit rating often organizing pending photo, also comprising:
Obtain the image information of facial image in described pending photo, described image information comprises following at least one information: the definition of the position of described facial image in described pending photo, described facial image proportion and described facial image in described pending photo;
The credit rating of described pending photo is determined according to the image information of described facial image; Wherein, the position of described facial image in described pending photo is the credit rating of predeterminated position is not the photo of predeterminated position higher than the position of described facial image in described pending photo; Described facial image proportion in described pending photo is higher, and the credit rating of described pending photo is higher; The definition of described facial image is higher, and the credit rating of described pending photo is higher.
Optionally, describedly according to described credit rating, described pending photo to be stored, comprises following at least one operation:
The default compression ratio corresponding according to described credit rating stores after described pending picture compression;
Delete according to described credit rating or store described pending photo;
By described pending photo storage in memory location corresponding to described credit rating.
Optionally, the attribute information of the pending photo of described acquisition, comprising:
Obtain the exchangeable image file of described pending photo;
At least one information in the shooting time of described pending photo and spot for photography is extracted from described exchangeable image file.
According to the second aspect of disclosure embodiment, a kind of picture processing device is provided, comprises:
Grouping module, for dividing into groups by preset rules to pending photo;
Detection module, for carrying out quality testing to often organizing pending photo, obtains the described credit rating often organizing pending photo;
Memory module, for storing described pending photo according to described credit rating.
Optionally, described grouping module comprises:
Obtain submodule, for obtaining the attribute information of pending photo, described attribute information comprises at least one information in the shooting time of photo and spot for photography;
First grouping submodule, for dividing into groups in advance to described pending photo according to described attribute information;
Calculating sub module, for carrying out Similarity Measure between the pending photo in described pre-grouping;
Second grouping submodule, for pending photo similar in described pre-grouping being divided into same group according to the similarity calculated, obtains the grouping to described pending photo.
Optionally, described detection module comprises:
First detection sub-module, for detecting the mass parameter of each pending photo in every group, described mass parameter comprises following at least one parameter: definition, color saturation and face;
Determine submodule, for determining the credit rating of each pending photo in every group according to described mass parameter.
Optionally, described detection module also comprises:
Second detection sub-module, for when described mass parameter comprises face parameter, detects in described pending photo whether comprise face;
Describedly determine submodule, for when described pending photo comprises face, determine that the photo grade of described pending photo is higher than the photo grade of pending photo not comprising face.
Optionally, described detection module also comprises:
Obtain submodule, for obtaining the image information of facial image in described pending photo, described image information comprises following at least one information: the definition of the position of described facial image in described pending photo, described facial image proportion and described facial image in described pending photo;
Describedly determine submodule, for determining the credit rating of described pending photo according to the image information of described facial image; Wherein, the position of described facial image in described pending photo is the credit rating of predeterminated position is not the photo of predeterminated position higher than the position of described facial image in described pending photo; Described facial image proportion in described pending photo is higher, and the credit rating of described pending photo is higher; The definition of described facial image is higher, and the credit rating of described pending photo is higher.
Optionally, described memory module comprises at least one submodule following:
First sub module stored, stores after described pending picture compression for the default compression ratio corresponding according to described credit rating;
Second sub module stored, for deleting according to described credit rating or storing described pending photo;
3rd sub module stored, for by described pending photo storage in memory location corresponding to described credit rating.
Optionally, described acquisition submodule, for obtaining the exchangeable image file of described pending photo; At least one information in the shooting time of described pending photo and spot for photography is extracted from the exchangeable image file that described acquisition submodule obtains.
According to the third aspect of disclosure embodiment, a kind of picture processing device is provided, comprises:
Processor;
For the memory of storage of processor executable instruction;
Wherein, described processor is configured to:
Pending photo is divided into groups by preset rules;
Carrying out quality testing to often organizing pending photo, obtaining the described credit rating often organizing pending photo;
According to described credit rating, described pending photo is stored.
The technical scheme that embodiment of the present disclosure provides can comprise following beneficial effect:
In the present embodiment, by dividing into groups to pending photo, namely pick out the photo taken under Same Scene, to the photo taken under Same Scene, adopt different storage modes according to photographic quality grade, like this, save the memory space of terminal or server, reduce it and store pressure.Meanwhile, for user filters out the good photo of shooting quality exactly, making to take good photo can accurately intactly preserve.
In another embodiment, by dividing into groups to pending photo according to photo property information and photo similarity, the photo taken under Same Scene can be picked out exactly, to the photo taken under Same Scene, different storage modes is adopted according to photographic quality grade, like this, save the memory space of terminal or server, reduce it and store pressure.Meanwhile, for user filters out the good photo of shooting quality exactly, making to take good photo can accurately intactly preserve.
In another embodiment, by according at least one information in shooting time and spot for photography, can divide into groups by comparison film exactly, so that the follow-up division to similar group of photos is more accurate, and effectively reduce the quantity needing the photo calculating similarity, save the disposal ability of terminal or server.
In another embodiment, by dividing similar group of photos, can accurately find out the photo taken under Same Scene, accurately these photos being screened so that follow-up.
In another embodiment, according to multiple mass parameter determination photographic quality grade, can process according to photographic quality grade comparison film exactly so that follow-up.
In another embodiment, adopt different storage modes according to photographic quality grade comparison film, effectively can save the memory space of terminal or server, reduce it and store pressure.Meanwhile, for user filters out the good photo of shooting quality exactly, making to take good photo can accurately intactly preserve.
Should be understood that, it is only exemplary and explanatory that above general description and details hereinafter describe, and can not limit the disclosure.
Accompanying drawing explanation
Accompanying drawing to be herein merged in specification and to form the part of this specification, shows and meets embodiment of the present disclosure, and is used from specification one and explains principle of the present disclosure.
Fig. 1 is the flow chart of a kind of photo processing method according to an exemplary embodiment.
Fig. 2 is the flow chart of a kind of photo processing method according to another exemplary embodiment.
Fig. 3 is the flow chart of a kind of photo processing method according to another exemplary embodiment.
Fig. 4 is the flow chart of a kind of photo processing method according to another exemplary embodiment.
Fig. 5 is the flow chart of a kind of photo processing method according to another exemplary embodiment.
Fig. 6 is the block diagram of a kind of picture processing device according to an exemplary embodiment.
Fig. 7 is the block diagram of the grouping module according to an exemplary embodiment.
Fig. 8 a is the block diagram of the detection module according to an exemplary embodiment.
Fig. 8 b is the block diagram of the detection module according to another exemplary embodiment.
Fig. 8 c is the block diagram of the detection module according to another exemplary embodiment.
Fig. 9 is the block diagram of the memory module according to an exemplary embodiment.
Figure 10 is the block diagram of a kind of device for photo disposal according to an exemplary embodiment.
Figure 11 is the block diagram of a kind of device for photo disposal according to an exemplary embodiment.
Embodiment
Here will be described exemplary embodiment in detail, its sample table shows in the accompanying drawings.When description below relates to accompanying drawing, unless otherwise indicated, the same numbers in different accompanying drawing represents same or analogous key element.Execution mode described in following exemplary embodiment does not represent all execution modes consistent with the disclosure.On the contrary, they only with as in appended claims describe in detail, the example of apparatus and method that aspects more of the present disclosure are consistent.
The technical scheme that disclosure embodiment provides, relates to server or terminal.This terminal can be mobile phone, computer, digital camera, digital broadcast terminal, messaging devices, game console, flat-panel devices, Medical Devices, body-building equipment, arbitrary equipment with image processing function such as personal digital assistant.
Fig. 1 is the flow chart of a kind of photo processing method according to an exemplary embodiment, and as shown in Figure 1, photo processing method is used for, in terminal or server, comprising the following steps:
In step s 11, pending photo is divided into groups by preset rules;
In step s 12, carrying out quality testing to often organizing pending photo, often being organized the credit rating of pending photo;
In step s 13, according to credit rating, pending photo is stored.
In the present embodiment, by dividing into groups to pending photo, namely pick out the photo taken under Same Scene, to the photo taken under Same Scene, adopt different storage modes according to photographic quality grade, like this, save the memory space of terminal or server, reduce it and store pressure.Meanwhile, for user filters out the good photo of shooting quality exactly, making to take good photo can accurately intactly preserve.
Fig. 2 is the flow chart of a kind of photo processing method according to another exemplary embodiment, as shown in Figure 2, in another embodiment, divides into groups, comprising pending photo by preset rules:
In the step s 21, obtain the attribute information of pending photo, attribute information comprises at least one information in the shooting time of photo and spot for photography;
In step S22, according to attribute information, pending photo is divided into groups in advance;
In step S23, carry out Similarity Measure between the pending photo in pre-grouping;
In step s 24 which, according to the similarity calculated by advance grouping in similar pending photo be divided into same group, obtain the grouping to pending photo.
In the present embodiment, by dividing into groups to pending photo according to photo property information and photo similarity, the photo taken under Same Scene can be picked out exactly, to the photo taken under Same Scene, different storage modes is adopted according to photographic quality grade, like this, save the memory space of terminal or server, reduce it and store pressure.Meanwhile, for user filters out the good photo of shooting quality exactly, making to take good photo can accurately intactly preserve.
In another embodiment, the photographic intelligence of user grouping is extracted by the exchangeable image file (ExchangeableImageFile, be called for short exif) of photo.Obtain photographic intelligence in step S11, comprising:
Obtain the exif of pending photo, containing in exif is specially the photo of digital camera and the metadata that customizes, at least contains the information of following a few class record digital photograph:
Shooting time, shooting equipment (fuselage, camera lens, photoflash lamp etc.), acquisition parameters (shutter speed, aperture F value, ISO speed, focal length, metering mode etc.), Image Processing parameter (sharpening, contrast, saturation, white balance etc.), iamge description and copyright information, spot for photography (GPS locator data etc.), thumbnail etc.
At least one information in the shooting time of pending photo and spot for photography is extracted from exif.
In another embodiment, in step S12, only can divide into groups according to the shooting time of pending photo or spot for photography comparison film, also can divide into groups to pending photo according to shooting time and spot for photography.To according to shooting time and spot for photography, pending photo grouping is illustrated below.
Such as, user can select to be photo on the same day by shooting time, divides into groups according to spot for photography.
Obtain the photo that shooting time is on October 1st, 2015, these photos have three spots for photography respectively: the Forbidden City, the Tian'anmen Square and Qianmen.
Three groups can be divided into according to shooting time and these photos of spot for photography:
On October 1st, first group: 2015, the Forbidden City;
On October 1st, second group: 2015, the Tian'anmen Square;
3rd group: 2015 on October 1, Qianmen.
In the present embodiment, by according at least one information in shooting time and spot for photography, can divide into groups by comparison film exactly, so that the follow-up division to similar group of photos is more accurate, and effectively reduce the quantity needing the photo calculating similarity, save the disposal ability of terminal or server.
In another embodiment, the photo in every group is divided into similar group of photos according to similarity by step S13.Similarity each other in this group photo of technique computes of similar diagram search comparison can be utilized, select similarity to be more than or equal to the photo of 95% as similar group of photos.
Such as, in above-mentioned second group of photo, pass through Similarity Measure, similarity between photo E001-E010 is higher than 95%, then can determine that these ten photos are the photo taken under Same Scene, be namely all to the photo of same building thing shooting or to photo that same people takes in same place etc.
In the present embodiment, by dividing similar group of photos, can accurately find out the photo taken under Same Scene, accurately these photos being screened so that follow-up.
In another embodiment, in step S14, quality judging is carried out to the photo in each similar group of photos, to adopt different storage modes to store to the photo of different quality.
Fig. 3 is the flow chart of a kind of photo processing method according to another exemplary embodiment, as shown in Figure 3, carrying out quality testing, often being organized the credit rating of pending photo, comprising often organizing pending photo:
In step S31, detect the mass parameter of each pending photo in every group, mass parameter comprises following at least one parameter: definition, color saturation and face;
In step s 32, the credit rating of each pending photo in every group is determined according to mass parameter.
Fig. 4 is the flow chart of a kind of photo processing method according to another exemplary embodiment, and as shown in Figure 4, when order parameter comprises face parameter, step S32 comprises:
In step S41, detect in pending photo whether comprise face;
In step S42, when photo comprises face, determine that the credit rating of pending photo is higher than the credit rating of pending photo not comprising face.
Fig. 5 is the flow chart of a kind of photo processing method according to another exemplary embodiment, and as shown in Figure 5, when photo comprises face, step S32 comprises:
In step s 51, obtain the image information of facial image in pending photo, image information comprises following at least one information: the definition of the position of facial image in photo, facial image proportion and facial image in photo;
In step S52, the credit rating of pending photo is determined according to the image information of facial image, wherein, the position of facial image in pending photo is the credit rating of predeterminated position is not the photo of predeterminated position higher than the position of facial image in pending photo; Facial image proportion in pending photo is higher, and the credit rating of pending photo is higher; The definition of facial image is higher, and the credit rating of pending photo is higher.
The obtain manner of the mass parameter of following comparison film and the impact of credit rating is specifically described.
One, definition
The definition of photo can be adopted the Image Definition such as greyscale transformation function, gradient function, gradation of image entropy function, frequency domain class function, statistics function and be calculated.Such as adopt gradient function to calculate the shade of gray value of each photo, the photo that Grad is larger, definition is higher.
The photo that definition is higher, quality is higher.
Two, color saturation
The color saturation of photo refers to the bright-coloured degree of photographic quality.The photo that more color saturation is higher, and the more bright-coloured photographic quality higher grade of color.
Color saturation depends on the ratio containing colour content and colour killing composition (grey) in this look.Larger containing colour content, saturation is larger; Colour killing composition is larger, and saturation is less.
The color saturation of photo can be determined according to the value of S (saturation) parameter in HSV or the HSL color attribute parameter of photo.Wherein, HSL and form and aspect, saturation, brightness (Hue, Saturation, Lightness).HSV and form and aspect, saturation, lightness (Hue, Saturation, Value).
Or, also the color saturation of photo can be determined according to the histogram of the RGB color space of photo, and the ratio obtained from the histogram of photo shared by each color, the color number that proportion is greater than preset ratio value is more, illustrate that the color saturation of this photo is larger, photographic quality is more bright-coloured.
Three, face
By facial image in face recognition technology identification photo.If photo comprises face, can think that this photographic quality grade is higher than the photo not comprising face.
For the multiple pictures comprising face, can also the facial image recognized further be analyzed, obtain the image information of facial image, to judge the quality of these photos.
Can determine photographic quality grade according to the position of face in photo, the photographic quality higher grade being positioned at photo centre position as face.
Also can account for ratio-dependent photographic quality grade according to face in photo, such as, facial image proportion in photo is higher more, credit rating is higher, or proportion is in preset range in photo for facial image, and as 30% ~ 90%, credit rating is relatively high.
Can also detect the definition of facial image, the credit rating that the definition of facial image is higher is higher.
Mass parameter is not limited to above-mentioned three kinds of parameters, can also according to other factor determination photographic quality grades.As, photosensitivity, exposure, contrast, color range, color curve etc.The piece identity of face in photo can also be analyzed further, determine photographic quality grade according to piece identity.If photo comprises household, friend, colleague etc., photographic quality grade is higher than the photo comprising stranger's face.
The judgement of comparison film credit rating can adopt above at least one parameter comparison film.If adopt multiple parameter comparison film credit rating to evaluate, weights that then can be different to each setting parameter according to actual needs, adopting weighting algorithm to calculate credit rating, by quantizing credit rating, being convenient to follow-uply to process accordingly according to credit rating comparison film.
Be described for ten determinations of photo to credit rating of above-mentioned similar group of photos E001-E010 below.
Such as, the mass parameter of above-mentioned similar group of photos E001-E010 is as shown in table 1 below,
Table 1
Each mass parameter weights corresponding with it are as shown in table 2 below,
Table 2
The mass value that can calculate the photo of E001-E010 according to table 1, table 2 is as shown in table 3 below,
Table 3
| Photo | Mass value |
| E001 | 40 |
| E002 | 33.2 |
| E003 | 38.7 |
| E004 | 68.7 |
| E005 | 81.2 |
| E006 | 72.7 |
| E007 | 87.45 |
| E008 | 66.55 |
| E009 | 68.45 |
| E010 | 36 |
Can find out according to table 3, the mass value of photo E007, E005, E006, E004, E009, E008 these several photos is all more than 60%, relatively high.
Such as, can set the credit rating of mass value more than 60% is A; Mass value is B lower than the credit rating of 60%.
To sum up, according to multiple mass parameter determination credit rating, can process according to credit rating comparison film exactly so that follow-up.
In another embodiment, according to the difference of credit rating, different storage modes can be adopted by comparison film, following at least one mode can be adopted:
(1) store after compressing according to the default compression ratio comparison film that credit rating is corresponding
Such as, the A of photo E007, E005, E006, E004, E009 and E008 be to(for) credit rating adopt the compression ratio of 80% to compress, and credit rating is that photo E001-E003 and E010 of B adopts the compression ratio of 25% to compress.
Or photo is divided into Three Estate by mass value: the photo E007 that mass value is the highest is credit rating A, E005, E006, E004, E009 and E008 credit rating is B, E001-E003 and E010 credit rating is C.
The compression ratio of 100% is adopted to compress to the photo that credit rating is A, carry out guarantee photographic quality, photo E005, E006, E004, E009 and E008 for credit rating B adopt the compression ratio of 50% to compress, and photo E001-E003 and E010 of credit rating C adopts the compression ratio of 25% to compress.
(2) delete or storage picture according to credit rating
Such as, only store photo E007, E005, E006, E004, E009 and E008 that credit rating is A, delete photo E001-E003 and E010 that credit rating is B.
(3) by photo storage in memory location corresponding to credit rating
Be that photo E007, E005, E006, E004, E009 and E008 of A is stored in terminal by credit rating, credit rating is that B photo E001-E003 and E010 is stored in server end.
Or three kinds of modes can be combined: photo is divided into Three Estate by mass value: the photo E007 that mass value is the highest is credit rating A, E005, E006, E004, E009 and E008 credit rating is B, E001-E003 and E010 credit rating is C.
In terminal, store photo E007, E005, E006, E004, E009 and E008 that credit rating is A, B, delete photo E001-E003 and E010 that credit rating is C.Meanwhile, adopt the compression ratio of 100% to compress, carry out guarantee photographic quality to the photo E007 that credit rating is A, the B of photo E005, E006, E004, E009 and E008 be to(for) credit rating adopt the compression ratio of 50% to compress.
Store after server photo E001-E003 and E010 that be C to credit rating adopts the compression ratio of 25% to compress.
In the present embodiment, adopt different storage modes according to credit rating comparison film, effectively can save the memory space of terminal or server, reduce it and store pressure.Meanwhile, for user filters out the good photo of shooting quality exactly, making to take good photo can accurately intactly preserve.
Following is disclosure device embodiment, may be used for performing disclosure embodiment of the method.
Fig. 6 is the block diagram of a kind of picture processing device according to an exemplary embodiment, and this device can realize becoming the some or all of of electronic equipment by software, hardware or both combinations.As shown in Figure 6, this picture processing device comprises:
Grouping module 61, for dividing into groups by preset rules to pending photo;
Detection module 62, for carrying out quality testing to often organizing pending photo, obtains the described credit rating often organizing pending photo;
Memory module 63, for storing described pending photo according to described credit rating.
In the present embodiment, grouping module 61 comparison film divides into groups, detection module 62 carries out quality testing to often organizing pending photo, obtains the described credit rating often organizing pending photo, and memory module 65 adopts different storage modes to store according to photographic quality grade reference substance.Like this, save the memory space of terminal or server, reduce it and store pressure.Meanwhile, for user filters out the good photo of shooting quality exactly, making to take good photo can accurately intactly preserve.
Fig. 7 is the block diagram of the grouping module according to an exemplary embodiment, and as shown in Figure 7, grouping module 61 comprises:
Obtain submodule 71, for obtaining the attribute information of pending photo, described attribute information comprises at least one information in the shooting time of photo and spot for photography;
First grouping submodule 72, for dividing into groups in advance to described pending photo according to described attribute information;
Calculating sub module 73, for carrying out Similarity Measure between the pending photo in described pre-grouping;
Second grouping submodule 74, for pending photo similar in described pre-grouping being divided into same group according to the similarity calculated, obtains the grouping to described pending photo.
In the present embodiment, by dividing into groups to pending photo according to photo property information and photo similarity, the photo taken under Same Scene can be picked out exactly, to the photo taken under Same Scene, different storage modes is adopted according to photographic quality grade, like this, save the memory space of terminal or server, reduce it and store pressure.Meanwhile, for user filters out the good photo of shooting quality exactly, making to take good photo can accurately intactly preserve.
In another embodiment, the photographic intelligence of user grouping is extracted by the exchangeable image file (ExchangeableImageFile, be called for short exif) of photo.Obtain submodule 71 and be configured to the exchangeable image file obtaining pending photo; At least one information in the shooting time of photo and spot for photography is extracted from exchangeable image file.
Obtain in the exif that submodule 71 obtains that to contain be specially the photo of digital camera and the metadata that customizes, at least contain the information of following a few class record digital photograph:
Shooting time, shooting equipment (fuselage, camera lens, photoflash lamp etc.), acquisition parameters (shutter speed, aperture F value, ISO speed, focal length, metering mode etc.), Image Processing parameter (sharpening, contrast, saturation, white balance etc.), iamge description and copyright information, spot for photography (GPS locator data etc.), thumbnail etc.
In another embodiment, grouping module 61 can only be divided into groups according to the shooting time of photo or spot for photography comparison film, also can divide into groups according to shooting time and spot for photography comparison film.Be illustrated according to shooting time and the grouping of spot for photography comparison film below.
Such as, user can select to be photo on the same day by shooting time, divides into groups according to spot for photography.
Obtain the photo that shooting time is on October 1st, 2015, these photos have three spots for photography respectively: the Forbidden City, the Tian'anmen Square and Qianmen.
Three groups can be divided into according to shooting time and these photos of spot for photography:
On October 1st, first group: 2015, the Forbidden City;
On October 1st, second group: 2015, the Tian'anmen Square;
3rd group: 2015 on October 1, Qianmen.
In the present embodiment, by according at least one information in shooting time and spot for photography, can divide into groups by comparison film exactly, so that the follow-up division to similar group of photos is more accurate, and effectively reduce the quantity needing the photo calculating similarity, save the disposal ability of terminal or server.
In another embodiment, the photo in every group is divided into similar group of photos according to similarity by the second grouping submodule 74.Similarity each other in this group photo of technique computes of similar diagram search comparison can be utilized, select similarity to be more than or equal to the photo of 95% as similar group of photos.
Such as, in above-mentioned second group of photo, pass through Similarity Measure, similarity between photo E001-E010 is higher than 95%, then can determine that these ten photos are the photo taken under Same Scene, be namely all to the photo of same building thing shooting or to photo that same people takes in same place etc.
In the present embodiment, by dividing similar group of photos, can accurately find out the photo taken under Same Scene, accurately these photos being screened so that follow-up.
Fig. 8 a is the block diagram of the detection module according to an exemplary embodiment, and as shown in Figure 8 a, optionally, detection module 62 comprises:
First detection sub-module 81, be configured to detect the mass parameter of each pending photo in every group, mass parameter comprises following at least one parameter: definition, color saturation and face;
Determine submodule 82, be configured to the credit rating determining each pending photo in every group according to described mass parameter.
Fig. 8 b is the block diagram of the detection module according to another exemplary embodiment, and as shown in Figure 8 b, optionally, detection module 62 comprises:
Second detection sub-module 83, for when described mass parameter comprises face parameter, detects in described pending photo whether comprise face;
Describedly determine submodule 82, for when described pending photo comprises face, determine that the photo grade of described pending photo is higher than the photo grade of pending photo not comprising face.
Fig. 8 c is the block diagram of the detection module according to another exemplary embodiment, and as shown in Figure 8 c, optionally, detection module 62 also comprises:
Obtain submodule 84, for obtaining the image information of facial image in described pending photo, described image information comprises following at least one information: the definition of the position of described facial image in described pending photo, described facial image proportion and described facial image in described pending photo;
Describedly determine submodule 82, for determining the credit rating of described pending photo according to the image information of described facial image; Wherein, the position of described facial image in described pending photo is the credit rating of predeterminated position is not the photo of predeterminated position higher than the position of described facial image in described pending photo; Described facial image proportion in described pending photo is higher, and the credit rating of described pending photo is higher; The definition of described facial image is higher, and the credit rating of described pending photo is higher.
The obtain manner of the mass parameter of following comparison film and the impact of comparison film credit rating are specifically described.
One, definition
The definition of photo can be adopted the Image Definition such as greyscale transformation function, gradient function, gradation of image entropy function, frequency domain class function, statistics function and be calculated.Such as adopt gradient function to calculate the shade of gray value of each photo, the photo that Grad is larger, definition is higher.
The photo that definition is higher, quality is higher.
Two, color saturation
The color saturation of photo refers to the bright-coloured degree of photographic quality.The photo that more color saturation is higher, and the more bright-coloured photographic quality higher grade of color.
Color saturation depends on the ratio containing colour content and colour killing composition (grey) in this look.Larger containing colour content, saturation is larger; Colour killing composition is larger, and saturation is less.
The color saturation of photo can be determined according to the value of S (saturation) parameter in HSV or the HSL color attribute parameter of photo.Wherein, HSL and form and aspect, saturation, brightness (Hue, Saturation, Lightness).HSV and form and aspect, saturation, lightness (Hue, Saturation, Value).
Or, also the color saturation of photo can be determined according to the histogram of the RGB color space of photo, and the ratio obtained from the histogram of photo shared by each color, the color number that proportion is greater than preset ratio value is more, illustrate that the color saturation of this photo is larger, photographic quality is more bright-coloured.
Three, face
By facial image in face recognition technology identification photo.If photo comprises face, can think that this photographic quality grade is higher than the photo not comprising face.
For the multiple pictures comprising face, can also the facial image recognized further be analyzed, obtain the image information of facial image, to judge the quality of these photos.
Can determine photographic quality grade according to the position of face in photo, the photographic quality higher grade being positioned at photo centre position as face.
Also can account for ratio-dependent photographic quality grade according to face in photo, such as, facial image proportion in photo is higher more, photographic quality higher grade, or proportion is in preset range in photo for facial image, and as 30% ~ 90%, photographic quality grade is relatively high.
Can also detect the definition of facial image, the photographic quality better grade that the definition of facial image is higher.
Mass parameter is not limited to above-mentioned three kinds of parameters, can also according to other factor determination photographic quality grades.As, photosensitivity, exposure, contrast, color range, color curve etc.The piece identity of face in photo can also be analyzed further, determine photographic quality grade according to piece identity.If photo comprises household, friend, colleague etc., photographic quality grade is higher than the photo comprising stranger's face.
The judgement of comparison film credit rating can adopt above at least one parameter comparison film.If adopt multiple parameter comparison film credit rating to evaluate, weights that then can be different to each setting parameter according to actual needs, adopting weighting algorithm to calculate credit rating, by quantizing credit rating, being convenient to follow-uply to process accordingly according to credit rating comparison film.
Be described for ten determinations of photo to credit rating of above-mentioned similar group of photos E001-E010 below.As shown in Table 1, each mass parameter weights corresponding with it are as above shown in table 2 for the mass parameter of above-mentioned similar group of photos E001-E010, can calculate the photographic quality value of E001-E010 according to table 1, table 2.As above shown in table 3, can find out according to table 3, the mass value of photo E007, E005, E006, E004, E009, E008 these several photos is all more than 60%, relatively high.
Such as, can set the credit rating of mass value more than 60% is A; Mass value is B lower than the credit rating of 60%.
To sum up, according to multiple mass parameter determination credit rating, can process according to credit rating comparison film exactly so that follow-up.
Fig. 9 is the block diagram of the memory module according to an exemplary embodiment, and as shown in Figure 9, optionally, memory module 63 comprises at least one submodule following:
First sub module stored 91, the default compression ratio be configured to according to credit rating is corresponding stores after pending picture compression.
Such as, photo E007, E005, E006, E004, E009 and E008 that first sub module stored 91 is more than or equal to 60% for credit rating adopt the compression ratio of 80% to compress, and the compression ratio that photographic quality grade is less than photo E001-E003 and the E010 employing 25% of 60% compresses.
Or the first sub module stored 91 credit rating is that photo E007, E005, E006, E004, E009 and E008 of A adopts the compression ratio of 80% to compress, credit rating is that photo E001-E003 and E010 of B adopts the compression ratio of 25% to compress.
Or photo is divided into Three Estate by mass value: the photo E007 that mass value is the highest is credit rating A, E005, E006, E004, E009 and E008 credit rating is B, E001-E003 and E010 credit rating is C.
The compression ratio of 100% is adopted to compress to the photo that credit rating is A, carry out guarantee photographic quality, photo E005, E006, E004, E009 and E008 for credit rating B adopt the compression ratio of 50% to compress, and photo E001-E003 and E010 of credit rating C adopts the compression ratio of 25% to compress.
Second sub module stored 92, is configured to delete according to credit rating or store pending photo.
Such as, the second sub module stored 92 only stores photo E007, E005, E006, E004, E009 and E008 that credit rating is A, deletes photo E001-E003 and E010 that credit rating is B.
3rd sub module stored 93, is configured to pending photo storage in memory location corresponding to credit rating.
Credit rating is that photo E007, E005, E006, E004, E009 and E008 of A is stored in terminal by the 3rd sub module stored 93, and credit rating is that B photo E001-E003 and E010 is stored in server end.
In addition, three modules can be combined:
In terminal,
Photo is divided into Three Estate by mass value: the photo E007 that mass value is the highest is credit rating A, E005, E006, E004, E009 and E008 credit rating is B, E001-E003 and E010 credit rating is C.
In terminal, store photo E007, E005, E006, E004, E009 and E008 that credit rating is A, B, delete photo E001-E003 and E010 that credit rating is C.Meanwhile, adopt the compression ratio of 100% to compress, carry out guarantee photographic quality to the photo E007 that credit rating is A, the B of photo E005, E006, E004, E009 and E008 be to(for) credit rating adopt the compression ratio of 50% to compress.
Store after server photo E001-E003 and E010 that be C to credit rating adopts the compression ratio of 25% to compress.
In the present embodiment, adopt different storage modes according to credit rating comparison film, effectively can save the memory space of terminal or server, reduce it and store pressure.Meanwhile, for user filters out the good photo of shooting quality exactly, making to take good photo can accurately intactly preserve.
According to the third aspect of disclosure embodiment, a kind of picture processing device is provided, comprises:
Processor;
For the memory of storage of processor executable instruction;
Wherein, described processor is configured to:
Pending photo is divided into groups by preset rules;
Carrying out quality testing to often organizing pending photo, obtaining the described credit rating often organizing pending photo;
According to described credit rating, described pending photo is stored.
Figure 10 is the block diagram of a kind of device for photo disposal according to an exemplary embodiment, and this device is applicable to terminal equipment.Such as, device 1700 can be video camera, sound pick-up outfit, mobile phone, computer, digital broadcast terminal, messaging devices, game console, flat-panel devices, Medical Devices, body-building equipment, personal digital assistant etc.
Device 1700 can comprise following one or more assembly: processing components 1702, memory 1704, power supply module 1706, multimedia groupware 1708, audio-frequency assembly 1710, the interface 1712 of I/O (I/O), sensor cluster 1714, and communications component 1716.
The integrated operation of the usual control device 1700 of processing components 1702, such as with display, call, data communication, camera operation and record operate the operation be associated.Treatment element 1702 can comprise one or more processor 1720 to perform instruction, to complete all or part of step of above-mentioned method.In addition, processing components 1702 can comprise one or more module, and what be convenient between processing components 1702 and other assemblies is mutual.Such as, processing unit 1702 can comprise multi-media module, mutual with what facilitate between multimedia groupware 1708 and processing components 1702.
Memory 1704 is configured to store various types of data to be supported in the operation of equipment 1700.The example of these data comprises for any application program of operation on device 1700 or the instruction of method, contact data, telephone book data, message, picture, video etc.Memory 1704 can be realized by the volatibility of any type or non-volatile memory device or their combination, as static RAM (SRAM), Electrically Erasable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory EPROM (EPROM), programmable read only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or CD.
The various assemblies that power supply module 1706 is device 1700 provide electric power.Power supply module 1706 can comprise power-supply management system, one or more power supply, and other and the assembly generating, manage and distribute electric power for device 1700 and be associated.
Multimedia groupware 1708 is included in the screen providing an output interface between described device 1700 and user.In certain embodiments, screen can comprise liquid crystal display (LCD) and touch panel (TP).If screen comprises touch panel, screen may be implemented as touch-screen, to receive the input signal from user.Touch panel comprises one or more touch sensor with the gesture on sensing touch, slip and touch panel.Described touch sensor can the border of not only sensing touch or sliding action, but also detects the duration relevant to described touch or slide and pressure.In certain embodiments, multimedia groupware 1708 comprises a front-facing camera and/or post-positioned pick-up head.When equipment 1700 is in operator scheme, during as screening-mode or video mode, front-facing camera and/or post-positioned pick-up head can receive outside multi-medium data.Each front-facing camera and post-positioned pick-up head can be fixing optical lens systems or have focal length and optical zoom ability.
Audio-frequency assembly 1710 is configured to export and/or input audio signal.Such as, audio-frequency assembly 1710 comprises a microphone (MIC), and when device 1700 is in operator scheme, during as call model, logging mode and speech recognition mode, microphone is configured to receive external audio signal.The audio signal received can be stored in memory 1704 further or be sent via communications component 1716.In certain embodiments, audio-frequency assembly 1710 also comprises a loud speaker, for output audio signal.
I/O interface 1712 is for providing interface between processing components 1702 and peripheral interface module, and above-mentioned peripheral interface module can be keyboard, some striking wheel, button etc.These buttons can include but not limited to: home button, volume button, start button and locking press button.
Sensor cluster 1714 comprises one or more transducer, for providing the state estimation of various aspects for device 1700.Such as, sensor cluster 1714 can detect the opening/closing state of equipment 1700, the relative positioning of assembly, such as described assembly is display and the keypad of device 1700, the position of all right checkout gear 1700 of sensor cluster 1714 or device 1700 assemblies changes, the presence or absence that user contacts with device 1700, the variations in temperature of device 1700 orientation or acceleration/deceleration and device 1700.Sensor cluster 1714 can comprise proximity transducer, be configured to without any physical contact time detect near the existence of object.Sensor cluster 1714 can also comprise optical sensor, as CMOS or ccd image sensor, for using in imaging applications.In certain embodiments, this sensor cluster 1714 can also comprise acceleration transducer, gyro sensor, Magnetic Sensor, pressure sensor or temperature sensor.
Communications component 1716 is configured to the communication being convenient to wired or wireless mode between device 1700 and other equipment.Device 1700 can access the wireless network based on communication standard, as WiFi, 2G or 3G, or their combination.In one exemplary embodiment, communications component 1716 receives from the broadcast singal of external broadcasting management system or broadcast related information via broadcast channel.In one exemplary embodiment, described communications component 1716 also comprises near-field communication (NFC) module, to promote junction service.Such as, can based on radio-frequency (RF) identification (RFID) technology in NFC module, Infrared Data Association (IrDA) technology, ultra broadband (UWB) technology, bluetooth (BT) technology and other technologies realize.
In the exemplary embodiment, device 1700 can be realized, for performing said method by one or more application specific integrated circuit (ASIC), digital signal processor (DSP), digital signal processing appts (DSPD), programmable logic device (PLD), field programmable gate array (FPGA), controller, microcontroller, microprocessor or other electronic components.
In the exemplary embodiment, additionally provide a kind of non-transitory computer-readable recording medium comprising instruction, such as, comprise the memory 1704 of instruction, above-mentioned instruction can perform said method by the processor 1720 of device 1700.Such as, described non-transitory computer-readable recording medium can be ROM, random access memory (RAM), CD-ROM, tape, floppy disk and optical data storage devices etc.
Figure 11 is the block diagram of a kind of device for photo disposal according to an exemplary embodiment.Such as, device 1900 may be provided in a server.Device 1900 comprises processing components 1922, and it comprises one or more processor further, and the memory resource representated by memory 1932, can such as, by the instruction of the execution of processing unit 1922, application program for storing.The application program stored in memory 1932 can comprise each module corresponding to one group of instruction one or more.In addition, processing components 1922 is configured to perform instruction, to perform the above method.
Device 1900 can also comprise the power management that a power supply module 1926 is configured to final controlling element 1900, and a wired or wireless network interface 1950 is configured to device 1900 to be connected to network, and input and output (I/O) interface 1958.Device 1900 can operate the operating system based on being stored in memory 1932, such as WindowsServerTM, MacOSXTM, UnixTM, LinuxTM, FreeBSDTM or similar.
A kind of non-transitory computer-readable recording medium, when the instruction in described storage medium is performed by the processor of device 1700 or device 1900, make device 1700 or device 1900 can perform the method for above-mentioned photo disposal, described method comprises:
Pending photo is divided into groups by preset rules;
Carrying out quality testing to often organizing pending photo, obtaining the described credit rating often organizing pending photo;
According to described credit rating, described pending photo is stored.
Optionally, described pending photo to be divided into groups by preset rules, comprising:
Obtain the attribute information of pending photo, described attribute information comprises at least one information in the shooting time of photo and spot for photography;
According to described attribute information, described pending photo is divided into groups in advance;
Similarity Measure is carried out between the pending photo in described pre-grouping;
According to the similarity calculated, pending photo similar in described pre-grouping is divided into same group, obtains the grouping to described pending photo.
Optionally, describedly carrying out quality testing to often organizing pending photo, obtaining the described credit rating often organizing pending photo, comprising:
Detect the mass parameter of each pending photo in every group, described mass parameter comprises following at least one parameter: definition, color saturation and face;
The credit rating of each pending photo in every group is determined according to described mass parameter.
Optionally, when described mass parameter comprises face parameter, describedly carrying out quality testing to often organizing pending photo, obtaining the described credit rating often organizing pending photo, comprising:
Detect in described pending photo and whether comprise face;
When described pending photo comprises face, determine that the credit rating of described pending photo is higher than the credit rating of pending photo not comprising face.
Optionally, when described photo comprises face, describedly carrying out quality testing to often organizing pending photo, obtaining the described credit rating often organizing pending photo, also comprising:
Obtain the image information of facial image in described pending photo, described image information comprises following at least one information: the definition of the position of described facial image in described pending photo, described facial image proportion and described facial image in described pending photo;
The credit rating of described pending photo is determined according to the image information of described facial image; Wherein, the position of described facial image in described pending photo is the credit rating of predeterminated position is not the photo of predeterminated position higher than the position of described facial image in described pending photo; Described facial image proportion in described pending photo is higher, and the credit rating of described pending photo is higher; The definition of described facial image is higher, and the credit rating of described pending photo is higher.
Optionally, describedly according to described credit rating, described pending photo to be stored, comprises following at least one operation:
The default compression ratio corresponding according to described credit rating stores after described pending picture compression;
Delete according to described credit rating or store described pending photo;
By described pending photo storage in memory location corresponding to described credit rating.
Optionally, the attribute information of the pending photo of described acquisition, comprising:
Obtain the exchangeable image file of described pending photo;
At least one information in the shooting time of described pending photo and spot for photography is extracted from described exchangeable image file.
Those skilled in the art, at consideration specification and after putting into practice disclosed herein disclosing, will easily expect other embodiment of the present disclosure.The application is intended to contain any modification of the present disclosure, purposes or adaptations, and these modification, purposes or adaptations are followed general principle of the present disclosure and comprised the undocumented common practise in the art of the disclosure or conventional techniques means.Specification and embodiment are only regarded as exemplary, and true scope of the present disclosure and spirit are pointed out by claim below.
Should be understood that, the disclosure is not limited to precision architecture described above and illustrated in the accompanying drawings, and can carry out various amendment and change not departing from its scope.The scope of the present disclosure is only limited by appended claim.
Claims (15)
1. a photo processing method, is characterized in that, comprising:
Pending photo is divided into groups by preset rules;
Carrying out quality testing to often organizing pending photo, obtaining the described credit rating often organizing pending photo;
According to described credit rating, described pending photo is stored.
2. method according to claim 1, is characterized in that, describedly divides into groups by preset rules to pending photo, comprising:
Obtain the attribute information of pending photo, described attribute information comprises at least one information in the shooting time of photo and spot for photography;
According to described attribute information, described pending photo is divided into groups in advance;
Similarity Measure is carried out between the pending photo in described pre-grouping;
According to the similarity calculated, pending photo similar in described pre-grouping is divided into same group, obtains the grouping to described pending photo.
3. method according to claim 1, is characterized in that, describedly carries out quality testing to often organizing pending photo, obtains the described credit rating often organizing pending photo, comprising:
Detect the mass parameter of each pending photo in every group, described mass parameter comprises following at least one parameter: definition, color saturation and face;
The credit rating of each pending photo in every group is determined according to described mass parameter.
4. method according to claim 3, is characterized in that, when described mass parameter comprises face parameter, describedly carrying out quality testing to often organizing pending photo, obtaining the described credit rating often organizing pending photo, comprising:
Detect in described pending photo and whether comprise face;
When described pending photo comprises face, determine that the credit rating of described pending photo is higher than the credit rating of pending photo not comprising face.
5. method according to claim 4, is characterized in that, when described photo comprises face, describedly carrying out quality testing to often organizing pending photo, obtaining the described credit rating often organizing pending photo, also comprising:
Obtain the image information of facial image in described pending photo, described image information comprises following at least one information: the definition of the position of described facial image in described pending photo, described facial image proportion and described facial image in described pending photo;
The credit rating of described pending photo is determined according to the image information of described facial image; Wherein, the position of described facial image in described pending photo is the credit rating of predeterminated position is not the photo of predeterminated position higher than the position of described facial image in described pending photo; Described facial image proportion in described pending photo is higher, and the credit rating of described pending photo is higher; The definition of described facial image is higher, and the credit rating of described pending photo is higher.
6. the method according to any one of claim 1-5, is characterized in that, describedly stores described pending photo according to described credit rating, comprises following at least one operation:
The default compression ratio corresponding according to described credit rating stores after described pending picture compression;
Delete according to described credit rating or store described pending photo;
By described pending photo storage in memory location corresponding to described credit rating.
7. method according to claim 2, is characterized in that, the attribute information of the pending photo of described acquisition, comprising:
Obtain the exchangeable image file of described pending photo;
At least one information in the shooting time of described pending photo and spot for photography is extracted from described exchangeable image file.
8. a picture processing device, is characterized in that, comprising:
Grouping module, for dividing into groups by preset rules to pending photo;
Detection module, for carrying out quality testing to often organizing pending photo, obtains the described credit rating often organizing pending photo;
Memory module, for storing described pending photo according to described credit rating.
9. device according to claim 8, is characterized in that, described grouping module comprises:
Obtain submodule, for obtaining the attribute information of pending photo, described attribute information comprises at least one information in the shooting time of photo and spot for photography;
First grouping submodule, for dividing into groups in advance to described pending photo according to described attribute information;
Calculating sub module, for carrying out Similarity Measure between the pending photo in described pre-grouping;
Second grouping submodule, for pending photo similar in described pre-grouping being divided into same group according to the similarity calculated, obtains the grouping to described pending photo.
10. device according to claim 8, is characterized in that, described detection module comprises:
First detection sub-module, for detecting the mass parameter of each pending photo in every group, described mass parameter comprises following at least one parameter: definition, color saturation and face;
Determine submodule, for determining the credit rating of each pending photo in every group according to described mass parameter.
11. devices according to claim 10, is characterized in that, described detection module also comprises:
Second detection sub-module, for when described mass parameter comprises face parameter, detects in described pending photo whether comprise face;
Describedly determine submodule, for when described pending photo comprises face, determine that the photo grade of described pending photo is higher than the photo grade of pending photo not comprising face.
12. devices according to claim 11, is characterized in that, described detection module also comprises:
Obtain submodule, for obtaining the image information of facial image in described pending photo, described image information comprises following at least one information: the definition of the position of described facial image in described pending photo, described facial image proportion and described facial image in described pending photo;
Describedly determine submodule, for determining the credit rating of described pending photo according to the image information of described facial image; Wherein, the position of described facial image in described pending photo is the credit rating of predeterminated position is not the photo of predeterminated position higher than the position of described facial image in described pending photo; Described facial image proportion in described pending photo is higher, and the credit rating of described pending photo is higher; The definition of described facial image is higher, and the credit rating of described pending photo is higher.
13. devices according to Claim 8 according to any one of-12, it is characterized in that, described memory module comprises at least one submodule following:
First sub module stored, stores after described pending picture compression for the default compression ratio corresponding according to described credit rating;
Second sub module stored, for deleting according to described credit rating or storing described pending photo;
3rd sub module stored, for by described pending photo storage in memory location corresponding to described credit rating.
14. devices according to claim 9, is characterized in that, described acquisition submodule, for obtaining the exchangeable image file of described pending photo; At least one information in the shooting time of described pending photo and spot for photography is extracted from the exchangeable image file that described acquisition submodule obtains.
15. 1 kinds of picture processing devices, is characterized in that, comprising:
Processor;
For the memory of storage of processor executable instruction;
Wherein, described processor is configured to:
Pending photo is divided into groups by preset rules;
Carrying out quality testing to often organizing pending photo, obtaining the described credit rating often organizing pending photo;
According to described credit rating, described pending photo is stored.
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