4.5 Masks and Selections

Chapter 3 described in detail the use of the GIMP's selection tools; however, that discussion is incomplete. A full understanding of how to effectively work with selections requires a discussion on how to integrate masks. This section shows how masks are complementary to the selection tools and illustrates why the combination of selections and masks is so powerful.

          
4.5.1 Using Masks to Refine Selections

Masks are terrific tools for refining selections. A careful examination of a mask can often reveal several problems. Figure 4.36

  

Figure 4.36: Image Illustrating a Selection

illustrates a selection made with the Bezier Path tool. As will be seen in a moment, this selection exhibits the three basic types of selection problems. To better examine these problems, the selection is converted to a channel mask, and the selection itself is canceled.

The resulting channel mask is shown in Figure 4.37(a),

  

Figure 4.37: A Mask Converted from the Selection

and Figure 4.37(b) shows the associated Channels dialog. Because it is difficult to make out the light blue water background through a 50% transparent, black channel mask, the color of the mask has been changed to yellow, as shown in Figure 4.37(c).

To see the problems associated with the selection, the Zoom  tool is used to magnify the image window. This produces the result shown in Figure 4.38(a).

  

Figure 4.38: Illustrating the Three Basic Selection Problems

This figure shows that in several regions the light blue color of the background is showing through from around the edges of the yellow mask. This means that these pixels have been erroneously included as part of the selection.

Figure 4.38(b) shows the same image as in Figure 4.38(a), but with the colors of the mask inverted. The color inversion is done by making the channel mask active and then using the Invert    function found in the Image:Image/Colors menu. Inverting the colors inverts the regions of the mask that correspond to selected and unselected pixels in the image. Now it can be seen that in some places, the dark pixels from the subject are showing through around the mask edges. This means that they are mistakenly not included in the set of selected pixels.

Finally, in both Figures 4.38(a) and (b) a rough-edge, aliasing effect can be seen.

Each of these three problems can be solved by refining the mask. This can be accomplished using several different methods, but for this type of fine work near a mask edge, the best choice is the Airbrush  tool from the Toolbox. The Airbrush can apply a very light coat of paint, so it is a great touch-up tool. Working near the edge requires some blending of the background with the subject to avoid aliasing. When used with a light pressure the Airbrush is perfect for this.

Figure 4.39(a)

  

Figure 4.39: Introducing the Airbrush Tool

shows the Tool Options dialog  for the Airbrush. It is the Pressure option that interests us here. The Pressure slider is in units of percent, and the default value of 10% is shown in Figure 4.39(a). The effect of using 10% pressure in conjunction with the soft brush chosen in Figure 4.39(b) produces the top line painted in Figure 4.39(c). Each of the other lines is painted with the Pressure value labeled to the right of the line. This figure shows that, for low pressures, the Airbrush tool produces a very light layer of paint, great for touching up imperfect and aliased edges like the ones seen in Figure 4.38.

Using the Airbrush tool on the problem pixels shown in Figure 4.38 produces the results shown in Figure 4.40.

  

Figure 4.40: Solving the Three Basic Selection Problems with the Airbrush Tool

The technique used in applying the Airbrush tool is as follows:

1.

Make the channel mask active.

2.

Use the Zoom tool to magnify the image to a sufficient resolution so that the paint can be applied to the problem edge areas with precision.

3.

Set the Active Foreground Color to black by typing d in the image window. 

4.

Lightly apply black paint to the problem areas with the Airbrush tool. The black paint is useful for removing pixels which should not be part of the selection.

5.

Invert the mask colors using Invert from the Image:Image/Colors menu, and work the new problem areas. Because of the inversion of color, now the black paint is useful for including pixels that should be part of the selection.

6.

Evaluate the precision of the applied paint, and correct for mistakes by making liberal use of the Undo (C-z) and Redo (C-r) functions.

    
4.5.2 The Quick Mask

The previous section showed you how a channel mask could be used to refine a selection. This is so useful that the GIMP has a special pair of function buttons on the image window allowing a selection to be quickly converted to a channel mask and vice versa. These are called the Quick Mask buttons.

Figure 4.41(a)

  

Figure 4.41: Using the Quick Mask

illustrates an image with a selection. It also shows a button circled at the lower-left side of the image window containing a red square icon. This is the Quick Mask button. Clicking on it converts the selection to a mask, as shown in Figure 4.41(b). The button shown circled in Figure 4.41(b) contains an icon showing a square drawn in a dashed line and resembling the Marching Ants. Clicking on this button reverts the quick mask to a selection. Thus, the Quick Mask buttons can be used to quickly convert a selection to a mask that can then be edited, as described in Section 4.5.1, before being converted back to a selection.

Figure 4.42

  

Figure 4.42: Quick Mask Options

shows two features of the Quick Mask buttons. Double-clicking either of the buttons brings up the Edit Qmask Attributes  dialog. This dialog, shown in Figure 4.42(a), permits the default opacity and color of the mask to be modified. The second feature of the quick mask is shown in Figure 4.42(b). When the quick mask is created, it also appears in the Channels dialog with the label Qmask in the Channel Title area. This channel exists only as long as the quick mask and disappears as soon as the mask is reverted to a selection.

         
4.5.3 Finding the Natural Mask

Performing a selection requires separating the subject, the part of the image that interests us, from the background. Often the subject has colorspace features that differentiate it from the background, and the goal of this section is to explain how to exploit this fact. Since the techniques described in this section depend on using an image's natural color features to make the selection, I call this finding the natural mask. The methods are based on using two primary tools: Threshold, found in the Image:Image/Colors menu, and Decompose, found in Image:Image/Mode. The natural mask approach often allows the subject to be extracted in a single, bold operation.

   
4.5.3.1 Working with the Threshold Tool

The Threshold tool allows you to specify a range of values in an image. All the pixels that are in the range of the selected values are mapped to white, and the rest are mapped to black. Threshold is a powerful tool for automatically creating masks. This is illustrated in the following example.

Figure 4.43

  

Figure 4.43: Pasting the Image into Its Own Channel Mask

illustrates the first step of using Threshold to create a natural mask. In the example, we want to make a selection of the partially blooming flower in Figure 4.43(a). We begin by copying the image in Figure 4.43(a) into a channel mask. This is done by creating a new channel mask in the Channels dialog, and then copying and pasting the image layer to the mask using C-c and C-v (see Section 2.4). Figure 4.43(b) shows the resulting Channels dialog, and Figure 4.43(c) shows that yellow is chosen as the mask color. This color was chosen to contrast against the dark background of Figure 4.43(a). Since a channel mask is only 8 bits deep, pasting the color image into the channel mask immediately converts it to a grayscale. This can be seen in Figure 4.43(d), which was obtained by toggling on the channel mask's Eye icon and toggling off the image layer's Eye icon.

The Threshold dialog works by clicking and dragging out a part of the range of values in the histogram. The range of values in the histogram is in [0,255], and, as can be seen in Figure 4.44(b),

  

Figure 4.44: Applying Threshold to the Channel Mask

the range that has been selected is from 72 to 253. Sweeping out values in the Threshold dialog's histogram immediately maps to white the pixels in the active layer (here the channel mask) having these values. The pixels having values outside the swept range are mapped to black. Thus, the channel mask that was a continuously varying grayscale image is converted to a binary black-and-white one. Figure 4.44(a) shows the channel mask before the application of Threshold, and Figure 4.44(c) shows the channel mask after the application of Threshold.

Toggling the image layer's Eye icon back on allows the channel mask to be seen over the image, as illustrated in Figure 4.45.

  

Figure 4.45: The Resulting Mask Defects as Seen in the Image Window

The parts of the image layer corresponding to the white parts of the channel mask can be seen clearly in the image window. The parts of the image corresponding to black parts of the channel mask are masked by a partially transparent yellow film.

As shown in Figure 4.45, the result of using Threshold produces an almost perfect mask for the flower. However, several defect regions remain. There are certain parts of the image that should be masked but aren't, and there are parts that are masked but that shouldn't be. These regions are easily removed using the Lasso and the Paintbrush tool.

Figure 4.46(a)

  

Figure 4.46: Using the Lasso Tool to Remove Defect Regions

shows how the Lasso   has been used to select parts of the image that should be masked but aren't. Because there are several offending regions, their selections have been combined using the methods described in Section 3.2. The parts of the channel mask that are in the selected regions are repaired (that is, converted to black) in three steps. The channel mask is made active by clicking on its thumbnail in the Channels dialog, the Active Background Color is set to black, as shown in Figure 4.46(b), and the selections are cut by typing C-x in the image window. The result is shown in Figure 4.46(c).

Figure 4.47 shows how the stalk of the flower, which was not included in the mask, is restored using the Paintbrush  tool. Figure 4.47(a)

  

Figure 4.47: Using the Paintbrush Tool to Fill in Missing Regions

shows the stalk of the flower zoomed by 300%, and Figure 4.47(b) and (c) show that white is chosen as the Active Foreground Color and that a small hard brush has been chosen from the Brush Selection dialog. The Paintbrush cursor can be seen applying white paint to the mask over the region of the flower stalk in Figure 4.47(a). The semi-transparency of the mask facilitates the painting process. Figure 4.47(d) shows the result of having fully restored the flower stalk.

For the final step in this example, Figure 4.48(a)

  

Figure 4.48: Converting the Mask to a Selection

shows how the Channel to Selection function is applied by clicking on its icon in the Channel dialog's button bar. Turning off the visibility of the channel mask, the resulting selection is seen in Figure 4.48(b).

This example shows how using Threshold can produce a selection much more quickly than would have been possible with the Bezier Path tool. Making a Bezier path would have required placing and refining a large number of control points. In contrast, the procedure employed with the Threshold tool required some experimentation with values in the tool's dialog, followed by some rough selections with the Lasso and some painting with the Paintbrush.

A key element to making the Threshold tool work efficiently is finding a reasonable range of values in the tool dialog's histogram. The example used in this section shows that it is not necessary to find a perfect mask. Rather, the goal is to find a mask that separates the subject from the background enough so that tools such as the Lasso and the Paintbrush can be used to easily clean up the defects.

The range of values used to create the mask in this example is shown in Figure 4.44(b). It is important to understand that this result was obtained by using a trial-and-error, experimental approach. Several contiguous regions of the histogram were swept out by the mouse, and, each time, the parts of the image that mapped to white and black were observed. A tip for finding useful regions is to examine the ranges of values supporting the main bumps in the histogram. These are usually associated with major image features, and it is often the case that one of these bumps is the solution to our search. When a reasonable range has been discovered, the data entry boxes can be used to refine the end points of the range.

Although the Threshold tool is not a panacea and isn't guaranteed to work, it is often successful. It is worth trying to apply the Threshold tool before launching into a long selection process with the Bezier Path tool. Some good examples of using Threshold to make selection masks are illustrated in Sections 7.3 and 7.4.

      
4.5.3.2 The Threshold Tool Versus the Magic Wand

The Magic Wand, presented in Section 3.1.1, is very similar in principle to Threshold but not nearly as effective. As already described, the Magic Wand works by choosing a seed pixel in the image and interactively setting a threshold that controls how many pixels around the seed are included in the selection. Thus, if the value of the pixel at the seed is S, and the value of the threshold is T, then the range of pixel values that are included in the selection is [S-T,S+T].

Now suppose that the range of pixel values that separates the subject from the background is [R₁,R₂]. To make the Magic Wand work on this image, the threshold must have the value T=(R₂-R₁)/2 and the seed must have the value S=(R₁+R₂)/2. The problem, then, is finding a pixel in the subject having the correct seed value that, when experimenting with threshold values, will produce an acceptable result. This is impractical for several reasons, the main difficulty being that there is no way to use the visual feedback from several tries of the Magic Wand to discover a more refined solution.

On the other hand, Threshold requires only that the end points of the range be specified, so it's much better adapted to experimentation. It is easy to try several contiguous value-regions, and the visual feedback from this is very useful for improving the search. In addition, the histogram in the Threshold dialog provides important clues as to which regions may be most useful.

Finally, the algorithm used by the Magic Wand is slow, because for each change in the threshold value, it must recursively grow the selected region around the seed. In comparison, the algorithm for Threshold is very fast, because it must only compare each pixel in the image with a threshold.

    
4.5.3.3 Threshold and Decompose

In the previous sections, Threshold was applied directly to the image. However, this tool can often be more effective when applied to an image color component. The function Decompose, found in the Image:Image/Mode menu, can be used to separate an image into its RGB and HSV components. When the decomposition is RGB, Decompose creates three grayscale images containing the red, green, and blue channels of the image. For HSV, three grayscales are also created, but now they represent the hue, saturation, and value components of the image. (See Chapter 5 for an in-depth discussion of the relationship between an image and its RGB and HSV color components.)

Figure 4.49(a)

  

Figure 4.49: A Flower Image and the Decompose dialog

illustrates an image of a flower, and Figure 4.49(b) shows the Decompose dialog. Either an RGB or HSV decomposition of the image can be performed by clicking on the appropriate radio button. The CMY  decomposition is not useful, because it produces results that are identical to RGB when used with the Threshold tool. CMYK  may produce interesting results, but only RGB and HSV are discussed here.

Figure 4.50(a), (b), and (c)

  

Figure 4.50: The RGB and HSV Decompositions of the Flower

show the red, green, and blue components of the flower shown in Figure 4.49(a). Figure 4.50(d), (e), and (f) show the hue, saturation, and value components. Note that for each of the components, the relationship between the flower and its background is different. For example, the flower in both the red component and the saturation component seems to be better separated from the background than for the other components. Because the flower is a brightly saturated orange-red, this should not be a surprise. However, the point of using the Decompose tool is that it gives the Threshold tool an advantage that can be exploited when trying to extract a natural mask. Examples of using this technique can be found in Sections 7.3 and 7.4.

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