![]() Instead of using pixel values from 0 to 255 ( Figure 3, left), this format codifies the information in 16 bits, that is, 0 to 65,535 values ( Figure 3, right).įigure 3: Gray8 thermal image ( left, lighter_gray8.jpg) vs. Great, now we understand where these amazing colorful images come from, but how can we calculate the temperature (you might be wondering)?įor this purpose, thermal cameras also provide gray16 images. For extra information, you can visit ColorMaps in OpenCV.įor example, the Inferno palette shows the temperature variation between purple (0) and yellow (255). ![]() ![]() Colormaps from OpenCV: Grayscale ( top-left), Inferno ( top-right), Jet ( bottom-left), and Viridis ( bottom-right). These maps are known as thermal color palettes. This format, shown in Figure 1 ( right) and known as grayscale or gray8 (8 bits = pixel values from 0 to 255), is the most extended in thermal vision.Īs Figure 2 shows, gray8 images are colored with different colormaps or color scales to enhance the visualization of the temperature distribution in thermal images. When the image is represented in a grayscale space ( Figure 1, center), each pixel is only characterized by a single channel or value, usually between 0 and 255 (i.e., black and white). Black and White or Grayscale visible-light image ( center) vs. Figure 1: Color RGB visible-light image ( left) vs. Therefore, we have three 8-bit unsigned channels that represent how Red, Green, and Blue our image is ( Figure 1, left). However, as you’ve probably learned from the OpenCV 101 - OpenCV Basics course at PyImageSearch University, the information is usually codified in RGB color space (i.e., the Red, Green, and Blue colors) are presented by an integer in the range from 0 to 255. We all are familiar with common color visible pictures or RGB images ( Figure 1, left).
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