CN111739041A - Image frame clipping method, device and equipment - Google Patents
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Abstract
The application discloses a method, a device and equipment for clipping image borders, and relates to the technical field of image processing. The method comprises the following steps: firstly, acquiring a target image of a frame to be cut; adjusting the target image into a black-and-white image by adjusting the color dimension information of the target image; then, acquiring a boundary value of an image frame according to a black-white boundary of the frame part and the effective picture part in the black-white image; and finally, cutting the image frame of the target image according to the boundary value. This application mainly to the RGB color processing of image pixel, can effectively get rid of the image frame in the picture, can improve the efficiency and the accuracy that the image frame was tailor, when treating the image quantity of tailorring the frame when more, more can realize batch processing, efficiency promotion is especially obvious.
Description
Technical Field
The present application relates to the field of image processing technologies, and in particular, to a method, an apparatus, and a device for clipping an image frame.
Background
With the development of network technology, internet resources are more and more abundant. Due to the diversity of the channels for obtaining pictures through the internet, the picture resources of the first hand cannot be directly used, and various picture frames are attached to a plurality of pictures, so that the picture display and the secondary use are influenced.
At present, an image frame area in a picture can be calibrated in a mode of manually cutting an image frame, and then the image frame is cut according to a calibration result. However, when the number of pictures in the image frame to be cut is large, the manual cutting of the image frame may result in low efficiency of cutting the image frame, and the manual cutting may easily result in a certain cutting error, which also affects the accuracy of cutting the image frame.
Disclosure of Invention
In view of this, the present application provides a method, an apparatus, and a device for clipping an image frame, and mainly aims to solve the technical problem that the efficiency and accuracy of clipping an image frame are affected by manually clipping an image frame at present.
According to an aspect of the present application, there is provided a method for cropping a frame of an image, the method including:
acquiring a target image of a frame to be cut;
adjusting the target image into a black-and-white image by adjusting the color dimension information of the target image;
acquiring a boundary value of an image frame according to a black-white boundary of the frame part and the effective picture part in the black-white image;
and cutting the image frame of the target image according to the boundary value.
According to another aspect of the present application, there is provided an image frame cropping device, including:
the acquisition module is used for acquiring a target image of a frame to be cut;
the adjusting module is used for adjusting the target image into a black-and-white image by adjusting the color dimension information of the target image;
the acquisition module is further used for acquiring a boundary value of an image frame according to a black-white boundary of the frame part and the effective picture part in the black-white image;
and the cutting module is used for cutting the image frame of the target image according to the boundary value.
According to yet another aspect of the present application, there is provided a storage medium having stored thereon a computer program which, when executed by a processor, implements the above-described cropping method for image borders.
According to another aspect of the present application, there is provided an image frame cropping device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, where the processor implements the image frame cropping method when executing the program.
By means of the technical scheme, compared with the conventional mode of manually cutting the image frame, the image frame cutting method, the image frame cutting device and the image frame cutting equipment can automatically cut the image frame, specifically can continuously reduce the color dimension until the color dimension is changed into a black-and-white image capable of identifying the frame part and the effective picture part by adjusting the color dimension information of the target image of the frame to be cut, so that the boundary value of the image frame is definitely detected according to the black-and-white boundary line of the frame part and the effective picture part in the black-and-white image, and the purpose of cutting the frame of the target image is further achieved according to the boundary value. The whole cutting process can be calibrated without manual participation, the efficiency and the accuracy of cutting the image frame can be improved, when the number of the images of the frame to be cut is large, batch processing can be realized, and the efficiency is improved obviously.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic flowchart illustrating a method for cropping an image border according to an embodiment of the present application;
fig. 2 is a schematic flowchart illustrating another method for cropping an image border according to an embodiment of the present application;
FIG. 3 is a graph illustrating color value changes of an image before and after being processed by a bilateral filter according to an embodiment of the present disclosure;
fig. 4 is a schematic diagram illustrating an example of determining a border of a frame by multipoint sampling according to an embodiment of the present application;
FIG. 5 is a schematic overall flow chart illustrating an example of image border cropping provided in an embodiment of the present application;
FIG. 6 is a diagram illustrating an effect of an exemplary picture after each step of processing according to an embodiment of the present application;
fig. 7 shows a schematic structural diagram of a cropping device of an image border according to an embodiment of the present application.
Detailed Description
The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The technical problem that the efficiency and the accuracy of image frame cutting are influenced by the manual image frame cutting mode at present is solved. The embodiment provides a method for cropping an image border, as shown in fig. 1, the method includes:
101. and acquiring a target image of the frame to be cut.
The target image may be an image corresponding to a frame video frame to be cut, or an image corresponding to a frame video frame to be cut.
The device or equipment for performing the cropping processing of the image frame can be configured on the client side or the server side, and can be used for automatically cropping the image frame so as to improve the efficiency and accuracy of the cropping of the image frame.
102. And adjusting the target image into a black-and-white image by adjusting the color dimension information of the target image.
Since the image of the frame to be cut may actually have a plurality of colors, which is not beneficial to distinguishing the frame region from the non-frame region in the image, the color dimension of the image can be continuously reduced by adjusting the color dimension information of the image in this embodiment until a black-and-white image capable of identifying the frame portion and the effective picture portion (the non-frame portion) is obtained. It should be noted that the color dimension reduction process may include at least one dimension reduction processing step, each dimension reduction processing step is for better achieving effect of the next dimension reduction processing step, and through the series of dimension reduction processing steps, until the black and white image capable of distinguishing the frame is obtained through the final color dimension reduction processing, the boundary value of the frame and the internal effective image may be better identified.
103. And acquiring the boundary value of the image frame according to the black-white boundary of the frame part and the effective picture part in the black-white image.
The black-white boundary between the frame portion and the effective picture portion in the black-white image can be used as a reference for distinguishing the frame region from the non-frame region in the image to a certain extent.
104. And cutting the image frame of the target image according to the boundary value of the image frame.
For example, according to the boundary value of the image frame, the frame region range and the non-frame region range in the image can be determined, that is, which of the image is the frame part and which of the image is the effective picture part, and then according to the frame region range and the non-frame region range, the corresponding clipping position of each side of the image is determined to perform frame clipping on the target image, so as to obtain the image with the frame removed, and then the image can be displayed or used for the second time.
Compared with the conventional manual image frame cutting method, the image frame cutting method provided by the embodiment can automatically cut the image frame, and particularly can continuously reduce the color dimension until the image frame becomes a black-and-white image capable of identifying a frame part and an effective picture part by adjusting the color dimension information of the target image of the frame to be cut, so that the boundary value of the image frame is definitely detected according to the black-and-white boundary line of the frame part and the effective picture part in the black-and-white image, and the purpose of cutting the frame of the target image according to the boundary value is further achieved. The whole cutting process can be calibrated without manual participation, the efficiency and the accuracy of cutting the image frame can be improved, when the number of the images of the frame to be cut is large, batch processing can be realized, and the efficiency is improved obviously.
Further, as a refinement and an extension of the specific implementation of the foregoing embodiment, in order to fully describe the implementation of this embodiment, this embodiment further provides another method for cropping an image frame, as shown in fig. 2, where the method includes:
201. and acquiring a target image of the frame to be cut.
In this embodiment, after obtaining the target image of the frame to be cropped, color dimension reduction processing needs to be performed on the target image, where the purpose of color dimension reduction is to obtain a black-and-white image capable of distinguishing the frame from an internal effective picture, and the specific color dimension reduction process may execute the processes shown in steps 202 to 204. Wherein, each step is for better realization effect of the next step.
202. And removing pixel points which are larger than a preset frequency threshold value in the target image of the frame to be cut.
The preset frequency threshold value can be preset according to actual conditions, and is used for determining high-frequency pixel points in the target image.
In this embodiment, the color value (RGB value) of each line of pixels of the image is regarded as a signal wave, and the portion where the color change is severe is a high-frequency portion, and the portion where the color change is gentle is a low-frequency portion. In order to reduce the color dimensionality of the target image to a black-and-white image, high-frequency pixel points in the target image, namely pixel points larger than a preset frequency threshold value in the target image, can be removed firstly to smooth the whole image. In order to implement the process accurately and quickly, the present embodiment may use a low-pass filter (LowPass filter) to remove high-frequency pixels in the target image. The low pass filter removes or attenuates the high frequency signal and retains the low frequency signal. Further, in order to achieve a better effect of removing high-frequency pixel points in the target image, optionally, step 202 may specifically include: and removing the pixel points which are larger than the preset frequency threshold value in the target image by using a bilateral filter.
The bilateral filter is one of the low-pass filters, and the bilateral filter may be defined as follows, for example:
in the above-mentioned formula one, the first,
in order to output the picture, the picture is output,
to input a picture, the value of the function represents the color information on a given pixel. The reason why the input picture and the output image are written as vectors is that the operation image is not limited to a single-channel gray-scale image, but may be a multi-channel color image. In formula one
Represents an arbitrary point in the image, and
is represented in
Adjacent points. And k is the normalization function:
the definition formula I and the formula II also comprise two core strategy formulas
And
these two formulas respectively represent
And
differences in spatial geometry and differences in luminosity/colour differences.
After the bilateral filter in the embodiment is used for processing, the color change of the whole image tends to be obviously relaxed, the aim of smoothing the whole image can be well achieved, and the subsequent processing from further reducing the color dimension to black and white is facilitated. For example, as shown in fig. 3, which is a graph showing the change of the color value of the image before and after the bilateral filter processing, line 1 is the original color value of the image, and line 2 is the color value after the bilateral filter processing, it is obvious that the color change of the whole image tends to be obviously relaxed after the bilateral filter processing.
203. And carrying out gray level processing on the target image from which the pixel points with the preset frequency threshold are removed.
In this embodiment, after the target image is subjected to a process of smoothing color change, a Gray level (Gray) process may be performed to remove the color of the target image. The gray scale is no color, and the RGB color components are all equal, that is, R ═ G ═ B of one pixel point. There are many alternatives for the values, such as component, maximum, average, weighted average, etc. In order to achieve a better graying processing effect, optionally, a weighted average method may be used in this embodiment, and correspondingly, step 203 may specifically include: respectively calculating the gray value corresponding to each pixel point in the target image according to the weighted average rule of the RGB values of the pixel points; and then carrying out image gray processing by using the gray value of each pixel point obtained by calculation.
The weighted average rule can be preset according to actual requirements. Namely, the weight values corresponding to red (R), green (G) and blue (B) can be preset correspondingly. For example, the following formula three can be used to calculate the corresponding gray-scale value of each pixel point in the target image.
Gray (i, j) ═ 0.299 × R (i, j) +0.578 × G (i, j) +0.114 × B (i, j) (formula three)
In the formula III, Gray (i, j) represents the Gray value of the pixel point of the horizontal coordinate i and the vertical coordinate j; r (i, j) represents the R value of the pixel point of the horizontal coordinate i and the vertical coordinate j; g (i, j) represents the G value of the pixel point of the horizontal coordinate i and the vertical coordinate j; and B (i, j) represents the B value of the pixel point of the horizontal coordinate i and the vertical coordinate j.
204. And performing black-white processing on the target image subjected to the gray processing according to the gray value of each pixel point to obtain a black-white image comprising a frame part and an effective picture part.
And (4) rapidly and accurately performing blackening and whitening processing on the target image by using Binarization (BW) according to the gray value of each pixel point in the target image after gray processing. The binarization here means that the image has only two colors, white (RGB 255) and black (RGB 0).
Illustratively, step 204 may specifically include: acquiring the gray value of each pixel point in the target image after gray processing; if the gray value of the target pixel point is larger than or equal to the preset threshold value, setting the RGB value of the target pixel point as the RGB value of the first color; and if the gray value of the target pixel point is smaller than the preset threshold, setting the RGB value of the target pixel point as the RGB value of a second color, wherein the first color and the second color are opposite black and white.
For example, taking the pixel a after the gray processing in the target image as an example, if the gray value of the pixel a is greater than a certain threshold, the RGB value of the pixel a may be set to be a white RGB value (i.e., RGB is 255); if the gray value of the pixel point a is smaller than a certain threshold, the RGB value of the pixel point a may be set to be the black RGB value (i.e., RGB is 0). Through the optional mode, the target image can be quickly and accurately subjected to black-white processing, and the image frame cutting efficiency can be further improved.
205. Noise in black and white images is removed using a maximum filter.
After the black-and-white image is obtained through the blackening and whitening binarization processing, some noise points may appear in the black-and-white image, such as black points appearing in edge white. Therefore, in order to improve the accuracy of determining the border value of the image border and ensure that the subsequent image border is accurately cut, the embodiment may remove the noise in the black-and-white image by using a maximum Filter (Max Filter) to accurately remove the noise in the image, so that the border is more orderly and is not affected by the noise.
To illustrate a specific implementation process, step 205 may specifically include, as an example and optionally: firstly, setting at least one convolution area in a black-and-white image, wherein the size of the convolution area can be preset according to the actual situation; then comparing the brightness value of the central point with the brightness value of the peripheral points in each convolution area; if the brightness value of the central point is larger than the maximum value of the brightness values of the peripheral points, modifying the brightness value of the central point into the maximum value of the brightness values of the peripheral points; if the brightness value of the central point is smaller than the minimum value of the brightness values of the peripheral points, the brightness value of the central point is modified into the minimum value of the brightness values of the peripheral points. This removes dark spots in the image, making the bright spots larger.
For example, at least one convolution region of 3 × 3 is set in the black-and-white image, the brightness of the center point in the convolution region is compared with the bright spots of the peripheral pixels, if the brightness of the center point is greater than the maximum value of the nearby brightness values, the brightness of the center point is modified to the maximum value of the nearby brightness values, if the brightness of the center point is less than the minimum value of the nearby brightness values, the brightness of the center point is modified to the minimum value of the nearby brightness values, as shown in table 1, the brightness of each pixel point in the 3 × 3 region is the corresponding brightness value, the initial brightness of the center point is 150, and the maximum value of the brightness of the peripheral points is determined to be 144.
TABLE 1
| 123 | 123 | 128 |
| 122 | 150 | 90 |
| 144 | 112 | 135 |
After the black-white image is processed, the boundary is a more obvious black-white boundary, but the boundary is not necessarily absolutely vertical or horizontal due to the shooting angle of the original picture or other reasons; in addition, there may be some noise, so in order to minimize the damage to the effective artwork during the cropping, the embodiment may adopt multi-point sampling to determine the border of the border, i.e. execute the process shown in step 206.
206. And determining the boundary value of the image frame by adopting boundary multipoint sampling based on the black-white boundary of the black-white image after the noise points are removed.
By adopting multi-value filtering sampling, the border of the frame can be accurately determined by considering the actual condition of a black-white boundary, and the position of the frame of the image can be accurately determined, so that the accurate frame clipping of the original image is realized.
To illustrate a specific implementation process, step 206 may specifically include: respectively taking edge points on the black-white boundary lines of the black-white image after the noise points are removed by referring to a plurality of positions on each side of the black-white image; and then determining the boundary value of the image frame according to the pixel coordinates of the edge points. For example, in a normal rectangular picture, the image frame is also generally rectangular, and the black-and-white boundary obtained by the above processing may not meet the shape requirement of the rectangle, so that the maximum range of the rectangular non-frame region can be indirectly framed by the pixel coordinates of the edge points, and the boundary value of the image frame can be determined and obtained based on the edge point coordinate value used in the maximum range. By the alternative mode, the boundary value of the regular border can be accurately determined, and the accurate image border clipping is further realized.
For example, determining the boundary value of the image frame according to the pixel coordinates of the edge points may specifically include: comparing pixel coordinates of a plurality of edge points correspondingly selected by a target edge (taking one edge in the black-white image as an example) in the black-white image; and then, acquiring the position of the edge point with the maximum coordinate value corresponding to the target axis as the position of the cutting position of the frame corresponding to the target edge, wherein the straight line of the target edge is vertical to the target axis.
For example, as shown in fig. 4, points are respectively taken at 1/2, 1/4 and 3/4 of each edge of the black-and-white image to find edge points, and the maximum value of the border pixel is taken as the clipping position of the edge. As illustrated in fig. 4, the boundary corresponding to the right frame is determined to be not perpendicular to the X-axis according to the black and white boundary, and the boundary itself is not straight. By adopting boundary multipoint sampling, the X-axis (the horizontal axis of the default image is the X-axis, the vertical axis is the Y-axis, and the X-axis is perpendicular to the straight line of the right frame) coordinates of the edge point pixels are respectively 98 (corresponding to the position 1/4), 101 (corresponding to the position 1/2) and 102 (corresponding to the position 3/4) at three heights (1/2, 1/4 and 3/4) of the right frame, and then the position of the edge point corresponding to the position 102 can be taken as the position of the clipping part of the right frame. Similarly, the cropping positions of the upper, lower and left frames can be found, so that the boundary values x1 (corresponding to the cropping position of the left frame), x2 (corresponding to the cropping position of the right frame), y1 (corresponding to the cropping position of the upper frame) and y2 (corresponding to the cropping position of the lower frame) of the effective image (i.e., the image of the non-frame region) can be determined. Crop ((x1, x2, y1, y2)) of the PIL library of Python may then be called to obtain the desired de-framed image.
207. And cutting the image frame of the target image according to the boundary value of the image frame.
Based on the optional manner in step 206, correspondingly, step 207 may specifically include: and cutting the image frame of the target image according to the cutting position of the frame corresponding to each side of the black-white image. By the method, the image frame of the picture can be accurately cut. For example, as shown in fig. 4, the position of the edge point corresponding to X2 ═ 102 (corresponding to 3/4) may be taken as the cut position of the right frame, and when the right frame is specifically cut, the cut is performed along the Y-axis direction with X2 as the coordinate of the X-axis, and the right frame of the image may be removed; when the upper border is specifically cut, the position of the edge point corresponding to Y1 is taken as the cutting position of the upper border, and when the upper border is specifically cut, the upper border of the image can be removed by cutting along the X-axis direction according to Y1 as the coordinate of the Y-axis. Similarly, the left frame of the image can be removed by x1, and the lower frame of the image can be removed by y2, and finally the frame removal is completed.
Further, after step 207, the method of this embodiment may further include: outputting a target image cut by the frame; and/or outputting prompt information (such as prompt cutting success information of characters, audio, video, light, vibration and the like) of finishing cutting of the target image frame; and/or saving the target image after the frame cutting.
Based on the content of the above embodiments, the overall flow of the method of this embodiment may be as shown in fig. 5, after obtaining the Original picture (Original) with a frame, first processing the Original picture through a Low Pass filter (Low Pass); performing Gray level (Gray) processing; then black and white Binarization (BW) processing is carried out, and the color dimension of the image can be reduced to black and white through the series of modes; at the moment, noise points which have influence on determining the boundary value of the image frame exist in the black-and-white image, and the noise points can be removed by using a maximum value Filter (Max Filter); and finally, determining the border of the frame by adopting border multipoint sampling to cut so as to obtain a picture (No frame) with the frame removed. As shown in fig. 6, the effect graph of the picture after each step of processing is shown.
The clipping method for the image frame provided by the embodiment relates to various algorithms of image processing, and mainly combines a series of algorithms of image processing based on image color application for RGB color processing of image pixels, so that the color dimension is continuously reduced until black and white, and the boundary value of the frame can be definitely detected, thereby achieving the purpose of removing the frame and removing the regular frame of most images. Compared with the conventional mode of manually cutting the image frame, the method can automatically cut the image frame, particularly, the color dimension can be continuously reduced until the image frame becomes a black-and-white image capable of identifying the frame part and the effective picture part by adjusting the color dimension information of the target image of the frame to be cut, so that the boundary value of the image frame can be definitely detected according to the black-and-white boundary line of the frame part and the effective picture part in the black-and-white image, and the purpose of cutting the frame of the target image according to the boundary value can be further realized. The whole cutting process can be calibrated without manual participation, the efficiency and the accuracy of cutting the image frame can be improved, when the number of the images of the frame to be cut is large, batch processing can be realized, and the efficiency is improved obviously.
Further, as a specific implementation of the method shown in fig. 1 and fig. 2, this embodiment provides a cropping device for an image frame, as shown in fig. 7, the device includes: an acquisition module 31, an adjustment module 32, and a cutting module 33.
The obtaining module 31 may be configured to obtain a target image of a frame to be cut;
an adjusting module 32, configured to adjust the target image into a black-and-white image by adjusting color dimension information of the target image;
the obtaining module 31 may be further configured to obtain a boundary value of an image frame according to a black-white boundary between a frame portion and an effective picture portion in the black-white image;
and the cropping module 33 is configured to crop the image border of the target image according to the boundary value.
In a specific application scenario, the obtaining module 31 may be specifically configured to remove noise in the black-and-white image by using a maximum filter; and determining the boundary value of the image frame by adopting boundary multipoint sampling based on the black-white boundary of the black-white image after the noise points are removed.
In a specific application scenario, the obtaining module 31 may be further configured to refer to a plurality of positions on each side of the black-and-white image, and respectively obtain edge points on the black-and-white boundary line of the black-and-white image from which the noise points are removed; and determining the boundary value of the image frame according to the pixel coordinates of the edge points.
In a specific application scenario, the obtaining module 31 may be further configured to compare pixel coordinates of a plurality of edge points correspondingly selected by a target edge in a black-and-white image; and acquiring the position of the edge point with the maximum coordinate value on the target axis as the position of the cutting position of the corresponding frame of the target edge, wherein the extending direction of the target edge is vertical to the target axis.
In a specific application scenario, the cropping module 33 may be specifically configured to crop the image border of the target image with reference to a cropping position of a corresponding border on each side of the black-and-white image.
In a specific application scenario, the obtaining module 31 may be further configured to set at least one convolution region in a black-and-white image; comparing the brightness value of the central point with the brightness value of the peripheral point in the convolution region; if the brightness value of the central point is larger than the maximum value of the peripheral point brightness values, modifying the brightness value of the central point to be the maximum value of the peripheral point brightness values; and if the brightness value of the central point is smaller than the minimum value of the peripheral point brightness values, modifying the brightness value of the central point into the minimum value of the peripheral point brightness values.
In a specific application scenario, the adjusting module 32 may be specifically configured to remove a pixel point in the target image that is greater than a preset frequency threshold; carrying out gray level processing on the target image from which the pixel points with the preset frequency threshold are removed; and performing black-white processing on the target image subjected to the gray processing according to the gray value of each pixel point to obtain a black-white image comprising a frame part and an effective picture part.
In a specific application scenario, the adjusting module 32 may be further configured to remove, by using a bilateral filter, a pixel point in the target image that is greater than a preset frequency threshold.
In a specific application scenario, the adjusting module 32 may be further configured to calculate, according to a weighted average rule of RGB values of pixel points, a gray value corresponding to each pixel point in the target image; and carrying out image gray processing by using the gray value.
In a specific application scenario, the adjusting module 32 may be further configured to compare the gray value of each pixel in the target image after the gray processing; if the gray value of the target pixel point is larger than or equal to a preset threshold value, setting the RGB value of the target pixel point as the RGB value of the first color; and if the gray value of the target pixel point is smaller than a preset threshold value, setting the RGB value of the target pixel point as the RGB value of a second color, wherein the first color and the second color are opposite black and white.
In a specific application scenario, the apparatus further comprises: an output module;
the output module is used for cutting an image frame of the target image according to the boundary value and outputting the target image after frame cutting; and/or outputting prompt information of finishing the cutting of the target image frame; and/or storing the target image cut by the frame.
It should be noted that other corresponding descriptions of the functional units related to the cropping device of the image frame provided in this embodiment may refer to the corresponding descriptions in fig. 1 and fig. 2, and are not described herein again.
Based on the method shown in fig. 1 and fig. 2, correspondingly, the present embodiment further provides a storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the cropping method of the image border shown in fig. 1 and fig. 2.
Based on such understanding, the technical solution of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.), and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method of the embodiments of the present application.
Based on the method shown in fig. 1 and fig. 2 and the virtual device embodiment shown in fig. 7, in order to achieve the above object, an embodiment of the present application further provides a device for clipping an image border, which may specifically be a personal computer, a smart television, a notebook computer, a smart phone, or other network devices, and the device includes a storage medium and a processor; a storage medium for storing a computer program; a processor for executing a computer program to implement the cropping method of the image borders as shown in fig. 1 and 2.
Optionally, the entity device may further include a user interface, a network interface, a camera, a Radio Frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, and the like. The user interface may include a Display screen (Display), an input unit such as a keypad (Keyboard), etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), etc.
It will be understood by those skilled in the art that the above-described physical device structure provided in the present embodiment is not limited to the physical device, and may include more or less components, or combine some components, or arrange different components.
The storage medium may further include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the above-described physical devices, and supports the operation of the information processing program as well as other software and/or programs. The network communication module is used for realizing communication among components in the storage medium and communication with other hardware and software in the information processing entity device.
Through the above description of the embodiments, those skilled in the art will clearly understand that the present application can be implemented by software plus a necessary general hardware platform, and can also be implemented by hardware. By applying the scheme of the embodiment, compared with the current mode of manually cutting the image frame, the image frame can be automatically cut, specifically, the color dimension can be continuously reduced until the image frame is changed into a black-and-white image by adjusting the color dimension information of the target image of the frame to be cut, so that the boundary value of the image frame can be definitely detected according to the black-and-white boundary line of the frame part and the effective picture part in the black-and-white image, and the purpose of cutting the frame of the target image according to the boundary value can be further realized. The whole cutting process can be calibrated without manual participation, the efficiency and the accuracy of cutting the image frame can be improved, when the number of the images of the frame to be cut is large, batch processing can be realized, and the efficiency is improved obviously.
Those skilled in the art will appreciate that the figures are merely schematic representations of one preferred implementation scenario and that the blocks or flow diagrams in the figures are not necessarily required to practice the present application. Those skilled in the art will appreciate that the modules in the devices in the implementation scenario may be distributed in the devices in the implementation scenario according to the description of the implementation scenario, or may be located in one or more devices different from the present implementation scenario with corresponding changes. The modules of the implementation scenario may be combined into one module, or may be further split into a plurality of sub-modules.
The above application serial numbers are for description purposes only and do not represent the superiority or inferiority of the implementation scenarios. The above disclosure is only a few specific implementation scenarios of the present application, but the present application is not limited thereto, and any variations that can be made by those skilled in the art are intended to fall within the scope of the present application.
These and other aspects are also encompassed by the present embodiments as specified in the following numbered clauses:
1. a cropping method of an image frame comprises the following steps:
acquiring a target image of a frame to be cut;
adjusting the target image into a black-and-white image by adjusting the color dimension information of the target image;
acquiring a boundary value of an image frame according to a black-white boundary of the frame part and the effective picture part in the black-white image;
and cutting the image frame of the target image according to the boundary value.
2. The method according to clause 1, wherein the obtaining of the boundary value of the image border according to the black-white boundary between the border portion and the effective picture portion in the black-white image specifically includes:
removing noise points in the black-and-white image by using a maximum filter;
and determining the boundary value of the image frame by adopting boundary multipoint sampling based on the black-white boundary of the black-white image after the noise points are removed.
3. According to the method in clause 2, determining the boundary value of the image frame by using boundary multipoint sampling based on the black-white boundary of the black-white image without the noise point specifically comprises:
respectively taking edge points on the black-white boundary lines of the black-white image after the noise points are removed by referring to a plurality of positions on each side of the black-white image;
and determining the boundary value of the image frame according to the pixel coordinates of the edge points.
4. The method according to clause 3, determining a boundary value of an image border according to the pixel coordinates of the edge point, specifically including:
comparing pixel coordinates of a plurality of edge points correspondingly selected by the target edge in the black-and-white image;
and acquiring the position of the edge point with the maximum coordinate value corresponding to the target axis as the position of the cutting position of the frame corresponding to the target edge, wherein the straight line of the target edge is vertical to the target axis.
5. According to the method in clause 4, clipping the image border of the target image according to the boundary value, specifically including:
and cutting the image frame of the target image according to the cutting position of the frame corresponding to each side of the black-white image.
6. The method according to clause 2, wherein the removing noise in the black-and-white image by using the maximum filter specifically includes:
setting at least one convolution area in the black and white image;
comparing the brightness value of the central point with the brightness value of the peripheral point in the convolution region;
if the brightness value of the central point is larger than the maximum value of the peripheral point brightness values, modifying the brightness value of the central point to be the maximum value of the peripheral point brightness values;
and if the brightness value of the central point is smaller than the minimum value of the peripheral point brightness values, modifying the brightness value of the central point into the minimum value of the peripheral point brightness values.
7. According to the method of clause 1, the adjusting the target image into a black-and-white image by adjusting the color dimension information of the target image specifically includes:
removing pixel points larger than a preset frequency threshold value in the target image;
carrying out gray level processing on the target image from which the pixel points with the preset frequency threshold are removed;
and performing black-white processing on the target image subjected to the gray processing according to the gray value of each pixel point to obtain a black-white image comprising a frame part and an effective picture part.
8. According to the method in clause 7, removing the pixel points in the target image that are greater than the preset frequency threshold specifically includes:
and removing pixel points larger than a preset frequency threshold value in the target image by using a bilateral filter.
9. According to the method in clause 7, the performing gray processing on the target image from which the pixel point with the preset frequency threshold is removed specifically includes:
respectively calculating the gray value corresponding to each pixel point in the target image according to the weighted average rule of the RGB values of the pixel points;
and carrying out image gray processing by using the gray value.
10. According to the method in clause 7, the performing black-and-white processing on the target image after the gray processing according to the gray value of each pixel point to obtain a black-and-white image including a frame part and an effective picture part specifically includes:
acquiring the gray value of each pixel point in the target image after gray processing;
if the gray value of the target pixel point is larger than or equal to a preset threshold value, setting the RGB value of the target pixel point as the RGB value of the first color;
and if the gray value of the target pixel point is smaller than a preset threshold value, setting the RGB value of the target pixel point as the RGB value of a second color, wherein the first color and the second color are opposite black and white.
11. The method of any of clauses 1-10, after cropping an image border of the target image in accordance with the boundary value, further comprising:
outputting the target image with the cut frame; and/or the presence of a gas in the gas,
outputting prompt information of finishing the cutting of the target image frame; and/or the presence of a gas in the gas,
and saving the target image cut by the frame.
12. A cropping device for an image border, comprising:
the acquisition module is used for acquiring a target image of a frame to be cut;
the adjusting module is used for adjusting the target image into a black-and-white image by adjusting the color dimension information of the target image;
the acquisition module is further used for acquiring a boundary value of an image frame according to a black-white boundary of the frame part and the effective picture part in the black-white image;
and the cutting module is used for cutting the image frame of the target image according to the boundary value.
13. The apparatus according to the clause 12, wherein,
the acquisition module is specifically used for removing noise points in the black-and-white image by using a maximum filter;
and determining the boundary value of the image frame by adopting boundary multipoint sampling based on the black-white boundary of the black-white image after the noise points are removed.
14. The apparatus according to the clause 13, wherein,
the acquisition module is specifically further configured to refer to a plurality of positions on each side of the black-and-white image, and respectively take edge points on the black-and-white boundary line of the black-and-white image from which the noise points are removed;
and determining the boundary value of the image frame according to the pixel coordinates of the edge points.
15. The apparatus according to the clause 14, wherein,
the acquisition module is specifically used for comparing pixel coordinates of a plurality of edge points correspondingly selected by the target edge in the black-and-white image;
and acquiring the position of the edge point with the maximum coordinate value on the target axis as the position of the cutting position of the frame corresponding to the target edge, wherein the straight line of the target edge is vertical to the target axis.
16. The apparatus according to the clause 15, wherein,
and the cutting module is specifically used for cutting the image frame of the target image according to the cutting position of the frame corresponding to each side of the black-white image.
17. The apparatus according to the clause 13, wherein,
the acquisition module is specifically used for setting at least one convolution area in the black and white image;
comparing the brightness value of the central point with the brightness value of the peripheral point in the convolution region;
if the brightness value of the central point is larger than the maximum value of the peripheral point brightness values, modifying the brightness value of the central point to be the maximum value of the peripheral point brightness values;
and if the brightness value of the central point is smaller than the minimum value of the peripheral point brightness values, modifying the brightness value of the central point into the minimum value of the peripheral point brightness values.
18. The apparatus according to the clause 12, wherein,
the adjusting module is specifically configured to remove pixel points in the target image that are greater than a preset frequency threshold;
carrying out gray level processing on the target image from which the pixel points with the preset frequency threshold are removed;
and performing black-white processing on the target image subjected to the gray processing according to the gray value of each pixel point to obtain a black-white image comprising a frame part and an effective picture part.
19. The apparatus according to the clause 18, wherein,
the adjusting module is specifically further configured to remove, by using a bilateral filter, pixel points in the target image that are greater than a preset frequency threshold.
20. The apparatus according to the clause 18, wherein,
the adjusting module is specifically further configured to calculate a gray value corresponding to each pixel point in the target image according to a weighted average rule of RGB values of the pixel points;
and carrying out image gray processing by using the gray value.
21. The apparatus according to the clause 18, wherein,
the adjusting module is specifically further configured to compare the gray value of each pixel point in the target image after the gray processing;
if the gray value of the target pixel point is larger than or equal to a preset threshold value, setting the RGB value of the target pixel point as the RGB value of the first color;
and if the gray value of the target pixel point is smaller than a preset threshold value, setting the RGB value of the target pixel point as the RGB value of a second color, wherein the first color and the second color are opposite black and white.
22. The apparatus of any of clauses 12-21, further comprising:
the output module is used for cutting the image frame of the target image according to the boundary value and outputting the target image after frame cutting; and/or the presence of a gas in the gas,
outputting prompt information of finishing the cutting of the target image frame; and/or the presence of a gas in the gas,
and saving the target image cut by the frame.
23. A storage medium having stored thereon a computer program which, when executed by a processor, implements the method of any of clauses 1 to 11.
24. An apparatus for cropping a frame of an image, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, the processor implementing the method of any of clauses 1 to 11 when executing the program.
Claims (10)
1. A cropping method of an image frame is characterized by comprising the following steps:
acquiring a target image of a frame to be cut;
adjusting the target image into a black-and-white image by adjusting the color dimension information of the target image;
acquiring a boundary value of an image frame according to a black-white boundary of the frame part and the effective picture part in the black-white image;
and cutting the image frame of the target image according to the boundary value.
2. The method according to claim 1, wherein the obtaining a boundary value of a frame of an image according to a black-and-white boundary between a frame portion and an effective picture portion in the black-and-white image comprises:
removing noise points in the black-and-white image by using a maximum filter;
and determining the boundary value of the image frame by adopting boundary multipoint sampling based on the black-white boundary of the black-white image after the noise points are removed.
3. The method according to claim 2, wherein the determining the boundary value of the image border by using boundary multi-point sampling based on the black-white boundary of the black-white image after the noise removal specifically comprises:
respectively taking edge points on the black-white boundary lines of the black-white image after the noise points are removed by referring to a plurality of positions on each side of the black-white image;
and determining the boundary value of the image frame according to the pixel coordinates of the edge points.
4. The method according to claim 3, wherein determining the boundary value of the image border according to the pixel coordinates of the edge point specifically comprises:
comparing pixel coordinates of a plurality of edge points correspondingly selected by the target edge in the black-and-white image;
and acquiring the position of the edge point with the maximum coordinate value corresponding to the target axis as the position of the cutting position of the frame corresponding to the target edge, wherein the straight line of the target edge is vertical to the target axis.
5. The method of claim 4, wherein clipping the image border of the target image according to the boundary value comprises:
and cutting the image frame of the target image according to the cutting position of the frame corresponding to each side of the black-white image.
6. The method according to claim 2, wherein the removing noise in the black-and-white image using a maximum filter comprises:
setting at least one convolution area in the black and white image;
comparing the brightness value of the central point with the brightness value of the peripheral point in the convolution region;
if the brightness value of the central point is larger than the maximum value of the peripheral point brightness values, modifying the brightness value of the central point to be the maximum value of the peripheral point brightness values;
and if the brightness value of the central point is smaller than the minimum value of the peripheral point brightness values, modifying the brightness value of the central point into the minimum value of the peripheral point brightness values.
7. The method according to claim 1, wherein the adjusting the target image into a black-and-white image by adjusting color dimension information of the target image specifically comprises:
removing pixel points larger than a preset frequency threshold value in the target image;
carrying out gray level processing on the target image from which the pixel points with the preset frequency threshold are removed;
and performing black-white processing on the target image subjected to the gray processing according to the gray value of each pixel point to obtain a black-white image comprising a frame part and an effective picture part.
8. A cropping device for an image border, comprising:
the acquisition module is used for acquiring a target image of a frame to be cut;
the adjusting module is used for adjusting the target image into a black-and-white image by adjusting the color dimension information of the target image;
the acquisition module is further used for acquiring a boundary value of an image frame according to a black-white boundary of the frame part and the effective picture part in the black-white image;
and the cutting module is used for cutting the image frame of the target image according to the boundary value.
9. A storage medium on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the method of any one of claims 1 to 7.
10. An image border cropping device comprising a storage medium, a processor and a computer program stored on the storage medium and executable on the processor, characterized in that the processor implements the method of any one of claims 1 to 7 when executing the program.
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