这是一外国人写的使用示例：
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OpenCV's face tracker uses an algorithm called Camshift. Camshift consists of four steps:
Here's how each step works: 1. Create a histogram Camshift represents the face it's tracking as a histogram (also called a barchart) of color values. Figure 1 shows two example histograms produced by the Camshift demo program that ships with OpenCV. The height of each colored bar indicates how many pixels in an image region have that "hue." Hue is one of three values describing a pixel's color in the HSV (Hue, Saturation, Value) color model. (For more on color, and color models, and see "The World of Color," SERVOMagazine, November 2005.)
In the image region represented by the top histogram, a bluish hue is most common, and a slightly more lavender hue is the next most common. The bottom histogram shows a region in which the most common hue is the rightmost bin. This hue is almost, but not quite, red. 2. Calculate face probability - simpler than it sounds! The histogram is created only once, at the start of tracking. Afterwards, it's used to assign a "face-probability" value to each image pixel in the video frames that follow. "Face probability" sounds terribly complicated, and heavily mathematical, but it's neither! Here's how it works. Figure 2 shows the bars from a histogram stacked one atop the other. After stacking them, it's clear that the rightmost bar accounts for about 45% of the pixels in the region. That means the probability that a pixel selected randomly from this region would fall into the rightmost bin is 45%. That's the "face probability" for a pixel with this hue. The same reasoning indicates that the face probability for the next histogram bin to the right is about 20%, since it accounts for about 20% of the stack's total height. That's all there is to it. |
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As new video frames arrive, the hue value for each pixel is determined. From that, the face histogram is used to assign a face probability to the pixel. This process is called "histogram backprojection" in OpenCV. There's a built-in method that implements it, called cvCalcBackProject(). Figure 3 shows the face-probability image in one video frame as Camshift tracks my face. Black pixels have the lowest probability value, and white, the highest. Gray pixels lie somewhere in the middle. 3. Shift to a new location With each new video frame, Camshift "shifts" its estimate of the face location, keeping it centered over the area with the highest concentration of bright pixels in the face-probability image. It finds this new location by starting at the previous location and computing the center of gravity of the face-probability values within a rectangle. It then shifts the rectangle so it's right over the center of gravity. It does this a few times to center the rectangle well. The OpenCV function cvCamShift()implements the steps for shifting to the new location. This process of shifting the rectangle to correspond with the center of gravity is based on an algorithm called "Mean Shift," by Dorin Comaniciu. In fact, Camshift stands for "Continuously Adaptive Mean Shift." 4. Calculate size and angle The OpenCV method is called "Continuously Adaptive," and not just "Mean Shift," because it also adjusts the size and angle of the face rectangle each time it shifts it. It does this by selecting the scale and orientation that are the best fit to the face-probability pixels inside the new rectangle location.
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