\cvarg{adaptive\_method}{Adaptive thresholding algorithm to use: \texttt{CV\_ADAPTIVE\_THRESH\_MEAN\_C} or \texttt{CV\_ADAPTIVE\_THRESH\_GAUSSIAN\_C} (see the discussion)}
\cvarg{thresholdType}{Thresholding type; must be one of
\begin{description}
- \cvarg{CV\_THRESH\_BINARY}{xxx}
- \cvarg{CV\_THRESH\_BINARY\_INV}{xxx}
+\cvarg{CV\_THRESH\_BINARY}{xxx}
+\cvarg{CV\_THRESH\_BINARY\_INV}{xxx}
\end{description}}
\cvarg{blockSize}{The size of a pixel neighborhood that is used to calculate a threshold value for the pixel: 3, 5, 7, and so on}
\cvarg{param1}{The method-dependent parameter. For the methods \texttt{CV\_ADAPTIVE\_THRESH\_MEAN\_C} and \texttt{CV\_ADAPTIVE\_THRESH\_GAUSSIAN\_C} it is a constant subtracted from the mean or weighted mean (see the discussion), though it may be negative}
The conventional range for R,G,B channel values is:
\begin{itemize}
- \item 0 to 255 for 8-bit images
- \item 0 to 65535 for 16-bit images and
- \item 0 to 1 for floating-point images.
+\item 0 to 255 for 8-bit images
+\item 0 to 65535 for 16-bit images and
+\item 0 to 1 for floating-point images.
\end{itemize}
Of course, in the case of linear transformations the range can be
The function can do the following transformations:
\begin{itemize}
- \item Transformations within RGB space like adding/removing the alpha channel, reversing the channel order, conversion to/from 16-bit RGB color (R5:G6:B5 or R5:G5:B5), as well as conversion to/from grayscale using:
- \[
- \text{RGB[A] to Gray:} Y \leftarrow 0.299 \cdot R + 0.587 \cdot G + 0.114 \cdot B
- \]
- and
- \[
- \text{Gray to RGB[A]:} R \leftarrow Y, G \leftarrow Y, B \leftarrow Y, A \leftarrow 0
- \]
+\item Transformations within RGB space like adding/removing the alpha channel, reversing the channel order, conversion to/from 16-bit RGB color (R5:G6:B5 or R5:G5:B5), as well as conversion to/from grayscale using:
+\[
+\text{RGB[A] to Gray:} Y \leftarrow 0.299 \cdot R + 0.587 \cdot G + 0.114 \cdot B
+\]
+and
+\[
+\text{Gray to RGB[A]:} R \leftarrow Y, G \leftarrow Y, B \leftarrow Y, A \leftarrow 0
+\]
The conversion from a RGB image to gray is done with:
\begin{lstlisting}
cvCvtColor(src ,bwsrc, CV_RGB2GRAY)
\end{lstlisting}
- \item RGB $\leftrightarrow$ CIE XYZ.Rec 709 with D65 white point (\texttt{CV\_BGR2XYZ, CV\_RGB2XYZ, CV\_XYZ2BGR, CV\_XYZ2RGB}):
- \[
- \begin{bmatrix}
- X \\
- Y \\
- Z
- \end{bmatrix}
- \leftarrow
- \begin{bmatrix}
+\item RGB $\leftrightarrow$ CIE XYZ.Rec 709 with D65 white point (\texttt{CV\_BGR2XYZ, CV\_RGB2XYZ, CV\_XYZ2BGR, CV\_XYZ2RGB}):
+\[
+\begin{bmatrix}
+X \\
+Y \\
+Z
+\end{bmatrix}
+\leftarrow
+\begin{bmatrix}
0.412453 & 0.357580 & 0.180423\\
0.212671 & 0.715160 & 0.072169\\
0.019334 & 0.119193 & 0.950227
- \end{bmatrix}
- \cdot
- \begin{bmatrix}
- R \\
- G \\
- B
- \end{bmatrix}
- \]
- \[
- \begin{bmatrix}
- R \\
- G \\
- B
- \end{bmatrix}
- \leftarrow
- \begin{bmatrix}
+\end{bmatrix}
+\cdot
+\begin{bmatrix}
+R \\
+G \\
+B
+\end{bmatrix}
+\]
+\[
+\begin{bmatrix}
+R \\
+G \\
+B
+\end{bmatrix}
+\leftarrow
+\begin{bmatrix}
3.240479 & -1.53715 & -0.498535\\
-0.969256 & 1.875991 & 0.041556\\
0.055648 & -0.204043 & 1.057311
- \end{bmatrix}
- \cdot
- \begin{bmatrix}
- X \\
- Y \\
- Z
- \end{bmatrix}
- \]
+\end{bmatrix}
+\cdot
+\begin{bmatrix}
+X \\
+Y \\
+Z
+\end{bmatrix}
+\]
$X$, $Y$ and $Z$ cover the whole value range (in the case of floating-point images $Z$ may exceed 1).
- \item RGB $\leftrightarrow$ YCrCb JPEG (a.k.a. YCC) (\texttt{CV\_BGR2YCrCb, CV\_RGB2YCrCb, CV\_YCrCb2BGR, CV\_YCrCb2RGB})
- \[ Y \leftarrow 0.299 \cdot R + 0.587 \cdot G + 0.114 \cdot B \]
- \[ Cr \leftarrow (R-Y) \cdot 0.713 + delta \]
- \[ Cb \leftarrow (B-Y) \cdot 0.564 + delta \]
- \[ R \leftarrow Y + 1.403 \cdot (Cr - delta) \]
- \[ G \leftarrow Y - 0.344 \cdot (Cr - delta) - 0.714 \cdot (Cb - delta) \]
- \[ B \leftarrow Y + 1.773 \cdot (Cb - delta) \]
+\item RGB $\leftrightarrow$ YCrCb JPEG (a.k.a. YCC) (\texttt{CV\_BGR2YCrCb, CV\_RGB2YCrCb, CV\_YCrCb2BGR, CV\_YCrCb2RGB})
+\[ Y \leftarrow 0.299 \cdot R + 0.587 \cdot G + 0.114 \cdot B \]
+\[ Cr \leftarrow (R-Y) \cdot 0.713 + delta \]
+\[ Cb \leftarrow (B-Y) \cdot 0.564 + delta \]
+\[ R \leftarrow Y + 1.403 \cdot (Cr - delta) \]
+\[ G \leftarrow Y - 0.344 \cdot (Cr - delta) - 0.714 \cdot (Cb - delta) \]
+\[ B \leftarrow Y + 1.773 \cdot (Cb - delta) \]
where
- \[
- delta = \left\{
- \begin{array}{l l}
- 128 & \mbox{for 8-bit images}\\
- 32768 & \mbox{for 16-bit images}\\
- 0.5 & \mbox{for floating-point images}
- \end{array} \right.
- \]
+\[
+delta = \left\{
+\begin{array}{l l}
+128 & \mbox{for 8-bit images}\\
+32768 & \mbox{for 16-bit images}\\
+0.5 & \mbox{for floating-point images}
+\end{array} \right.
+\]
Y, Cr and Cb cover the whole value range.
- \item RGB $\leftrightarrow$ HSV (\texttt{CV\_BGR2HSV, CV\_RGB2HSV, CV\_HSV2BGR, CV\_HSV2RGB})
- in the case of 8-bit and 16-bit images
- R, G and B are converted to floating-point format and scaled to fit the 0 to 1 range
- \[ V \leftarrow max(R,G,B) \]
+\item RGB $\leftrightarrow$ HSV (\texttt{CV\_BGR2HSV, CV\_RGB2HSV, CV\_HSV2BGR, CV\_HSV2RGB})
+in the case of 8-bit and 16-bit images
+R, G and B are converted to floating-point format and scaled to fit the 0 to 1 range
+\[ V \leftarrow max(R,G,B) \]
\[ S \leftarrow \fork{\frac{V-min(R,G,B)}{V}}{if $V \neq 0$}{0}{otherwise} \]
\[ H \leftarrow \forkthree
H, S, V are left as is
\end{description}
- \item RGB $\leftrightarrow$ HLS (\texttt{CV\_BGR2HLS, CV\_RGB2HLS, CV\_HLS2BGR, CV\_HLS2RGB}).
- in the case of 8-bit and 16-bit images
- R, G and B are converted to floating-point format and scaled to fit the 0 to 1 range.
- \[ V_{max} \leftarrow {max}(R,G,B) \]
- \[ V_{min} \leftarrow {min}(R,G,B) \]
- \[ L \leftarrow \frac{V_{max} - V_{min}}{2} \]
- \[ S \leftarrow \fork
- {\frac{V_{max} - V_{min}}{V_{max} + V_{min}}}{if $L < 0.5$}
- {\frac{V_{max} - V_{min}}{2 - (V_{max} + V_{min})}}{if $L \ge 0.5$} \]
- \[ H \leftarrow \forkthree
- {{60(G - B)}/{S}}{if $V_{max}=R$}
- {{120+60(B - R)}/{S}}{if $V_{max}=G$}
- {{240+60(R - G)}/{S}}{if $V_{max}=B$} \]
- if $H<0$ then $H \leftarrow H+360$
-On output $0 \leq V \leq 1$, $0 \leq S \leq 1$, $0 \leq H \leq 360$.
+\item RGB $\leftrightarrow$ HLS (\texttt{CV\_BGR2HLS, CV\_RGB2HLS, CV\_HLS2BGR, CV\_HLS2RGB}).
+in the case of 8-bit and 16-bit images
+R, G and B are converted to floating-point format and scaled to fit the 0 to 1 range.
+\[ V_{max} \leftarrow {max}(R,G,B) \]
+\[ V_{min} \leftarrow {min}(R,G,B) \]
+\[ L \leftarrow \frac{V_{max} + V_{min}}{2} \]
+\[ S \leftarrow \fork
+{\frac{V_{max} - V_{min}}{V_{max} + V_{min}}}{if $L < 0.5$}
+{\frac{V_{max} - V_{min}}{2 - (V_{max} + V_{min})}}{if $L \ge 0.5$} \]
+\[ H \leftarrow \forkthree
+{{60(G - B)}/{S}}{if $V_{max}=R$}
+{{120+60(B - R)}/{S}}{if $V_{max}=G$}
+{{240+60(R - G)}/{S}}{if $V_{max}=B$} \]
+if $H<0$ then $H \leftarrow H+360$
+On output $0 \leq L \leq 1$, $0 \leq S \leq 1$, $0 \leq H \leq 360$.
The values are then converted to the destination data type:
\begin{description}
H, S, V are left as is
\end{description}
- \item RGB $\leftrightarrow$ CIE L*a*b* (\texttt{CV\_BGR2Lab, CV\_RGB2Lab, CV\_Lab2BGR, CV\_Lab2RGB})
- in the case of 8-bit and 16-bit images
- R, G and B are converted to floating-point format and scaled to fit the 0 to 1 range
+\item RGB $\leftrightarrow$ CIE L*a*b* (\texttt{CV\_BGR2Lab, CV\_RGB2Lab, CV\_Lab2BGR, CV\_Lab2RGB})
+in the case of 8-bit and 16-bit images
+R, G and B are converted to floating-point format and scaled to fit the 0 to 1 range
\[ \vecthree{X}{Y}{Z} \leftarrow \vecthreethree
{0.412453}{0.357580}{0.180423}
{0.212671}{0.715160}{0.072169}
L, a, b are left as is
\end{description}
- \item RGB $\leftrightarrow$ CIE L*u*v* (\texttt{CV\_BGR2Luv, CV\_RGB2Luv, CV\_Luv2BGR, CV\_Luv2RGB})
- in the case of 8-bit and 16-bit images
- R, G and B are converted to floating-point format and scaled to fit 0 to 1 range
- \[ \vecthree{X}{Y}{Z} \leftarrow \vecthreethree
+\item RGB $\leftrightarrow$ CIE L*u*v* (\texttt{CV\_BGR2Luv, CV\_RGB2Luv, CV\_Luv2BGR, CV\_Luv2RGB})
+in the case of 8-bit and 16-bit images
+R, G and B are converted to floating-point format and scaled to fit 0 to 1 range
+\[ \vecthree{X}{Y}{Z} \leftarrow \vecthreethree
{0.412453}{0.357580}{0.180423}
{0.212671}{0.715160}{0.072169}
{0.019334}{0.119193}{0.950227}
The above formulas for converting RGB to/from various color spaces have been taken from multiple sources on Web, primarily from
the Ford98
- at the Charles Poynton site.
+at the Charles Poynton site.
- \item Bayer $\rightarrow$ RGB (\texttt{CV\_BayerBG2BGR, CV\_BayerGB2BGR, CV\_BayerRG2BGR, CV\_BayerGR2BGR, CV\_BayerBG2RGB, CV\_BayerGB2RGB, CV\_BayerRG2RGB, CV\_BayerGR2RGB}) The Bayer pattern is widely used in CCD and CMOS cameras. It allows one to get color pictures from a single plane where R,G and B pixels (sensors of a particular component) are interleaved like this:
+\item Bayer $\rightarrow$ RGB (\texttt{CV\_BayerBG2BGR, CV\_BayerGB2BGR, CV\_BayerRG2BGR, CV\_BayerGR2BGR, CV\_BayerBG2RGB, CV\_BayerGB2RGB, CV\_BayerRG2RGB, CV\_BayerGR2RGB}) The Bayer pattern is widely used in CCD and CMOS cameras. It allows one to get color pictures from a single plane where R,G and B pixels (sensors of a particular component) are interleaved like this:
\cvarg{lo\_diff}{Maximal lower brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component. In the case of 8-bit color images it is a packed value}
\cvarg{up\_diff}{Maximal upper brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component. In the case of 8-bit color images it is a packed value}
\ifC % {
-\cvarg{comp}{Pointer to the structure that the function fills with the information about the repainted domain}
+\cvarg{comp}{Pointer to the structure that the function fills with the information about the repainted domain.
+Note that the function does not fill \texttt{comp->contour} field. The boundary of the filled component can be retrieved from the output mask image using \cvCPyCross{FindContours}}
\else % }{
-\cvarg{comp}{Returned connected component for the repainted domain}
+\cvarg{comp}{Returned connected component for the repainted domain. Note that the function does not fill \texttt{comp->contour} field. The boundary of the filled component can be retrieved from the output mask image using \cvCPyCross{FindContours}}
\fi % }
\cvarg{flags}{The operation flags. Lower bits contain connectivity value, 4 (by default) or 8, used within the function. Connectivity determines which neighbors of a pixel are considered. Upper bits can be 0 or a combination of the following flags:
\begin{description}
\]
\[
-\texttt{tiltedSum}(X,Y) = \sum_{y<Y,abs(x-X)<y} \texttt{image}(x,y)
+\texttt{tiltedSum}(X,Y) = \sum_{y<Y,abs(x-X+1)\leq Y-y-1} \texttt{image}(x,y)
\]
Using these integral images, one may calculate sum, mean and standard deviation over a specific up-right or rotated rectangular region of the image in a constant time, for example:
R, G and B are converted to floating-point format and scaled to fit the 0 to 1 range.
\[ V_{max} \leftarrow {max}(R,G,B) \]
\[ V_{min} \leftarrow {min}(R,G,B) \]
- \[ L \leftarrow \frac{V_{max} - V_{min}}{2} \]
+ \[ L \leftarrow \frac{V_{max} + V_{min}}{2} \]
\[ S \leftarrow \fork
{\frac{V_{max} - V_{min}}{V_{max} + V_{min}}}{if $L < 0.5$}
{\frac{V_{max} - V_{min}}{2 - (V_{max} + V_{min})}}{if $L \ge 0.5$} \]
{{120+60(B - R)}/{S}}{if $V_{max}=G$}
{{240+60(R - G)}/{S}}{if $V_{max}=B$} \]
if $H<0$ then $H \leftarrow H+360$
-On output $0 \leq V \leq 1$, $0 \leq S \leq 1$, $0 \leq H \leq 360$.
+On output $0 \leq L \leq 1$, $0 \leq S \leq 1$, $0 \leq H \leq 360$.
The values are then converted to the destination data type:
\begin{description}
\]
\[
-\texttt{tilted}(X,Y) = \sum_{y<Y,abs(x-X)<y} \texttt{image}(x,y)
+\texttt{tilted}(X,Y) = \sum_{y<Y,abs(x-X+1)\leq Y-y-1} \texttt{image}(x,y)
\]
Using these integral images, one may calculate sum, mean and standard deviation over a specific up-right or rotated rectangular region of the image in a constant time, for example:
It makes possible to do a fast blurring or fast block correlation with variable window size, for example. In the case of multi-channel images, sums for each channel are accumulated independently.
+As a practical example, the next figure shows the calculation of the integral of a straight rectangle \texttt{Rect(3,3,3,2)} and of a tilted rectangle \texttt{Rect(5,1,2,3)}. The selected pixels in the original \texttt{image} are shown, as well as the relative pixels in the integral images \texttt{sum} and \texttt{tilted}.
+
+\begin{center}\includegraphics[width=0.8\textwidth]{pics/integral.png}\end{center}
\cvCppFunc{threshold}
Applies a fixed-level threshold to each array element
projects / opencv.git / blobdiff