Update content/hardware/05.nicla/boards/nicla-vision/tutorials/blob-detection/content.md

Co-authored-by: BenjaminDannegard <[email protected]>

Author: sebromero

content/hardware/05.nicla/boards/nicla-vision/tutorials/blob-detection/content.md

@@ -97,7 +97,7 @@ Depending on how you hold the camera you may want to play with the `set_vflip` a
 
 ### 3. Defining the Color Thresholds
 
-In order to feed the blob detection algorithm with an image you have to take a snapshot from the camera or load the image from memory (e.g. SD card or internal Flash). In this case you will take a snapshot using the `snapshot()` function. The resulting image needs then to be fed to the algorithm using the `find_blobs` function. You will notice that a list of tuples gets passed to the algorithm. In this list you can specify the LAB color values that are mostly contained in the object that you would like to track. If you were for example to detect purely red objects on a black background the resulting range of colors would be very narrow. The corresponding LAB value for pure red is roughly (53,80,67). A slightly brighter red could be (55,73,50). Therefore the LAB range would be L: 53-55 A: 73-80 B: 50-67. OpenMV provides a convenient tool to figure out the desired color ranges: Threshold Editor. You can find it in the OpenMV IDE in the menu under Tools > Machine Vision > Threshold Editor. Place the desired object in front of the camera and open the tool. When it asks you about the "Source image location?" select "Frame Buffer". In the window that opens you will see a snapshot from the camera and a few sliders to adjust the LAB color ranges. As you move the sliders you will see in the black and white image on the right hand side which of the pixels would match the set color range. White pixels denote the matching pixels. As you can see in the following example, the pixels of a nice red apple on brown background are very nicely clustered. It results in mostly one big blob.
+In order to feed the blob detection algorithm with an image you have to take a snapshot from the camera or load the image from memory (e.g. SD card or internal Flash). In this case you will take a snapshot using the `snapshot()` function. The resulting image needs then to be fed to the algorithm using the `find_blobs` function. You will notice that a list of tuples gets passed to the algorithm. In this list you can specify the LAB color values that are mostly contained in the object that you would like to track. If you were for example to detect purely red objects on a black background the resulting range of colors would be very narrow. The corresponding LAB value for pure red is roughly (53,80,67). A slightly brighter red could be (55,73,50). Therefore the LAB range would be L: 53-55 A: 73-80 B: 50-67. OpenMV provides a convenient tool to figure out the desired color ranges: Threshold Editor. You can find it in the OpenMV IDE in the menu under **Tools > Machine Vision > Threshold Editor**. Place the desired object in front of the camera and open the tool. When it asks you about the "Source image location?" select "Frame Buffer". In the window that opens you will see a snapshot from the camera and a few sliders to adjust the LAB color ranges. As you move the sliders you will see in the black and white image on the right hand side which of the pixels would match the set color range. White pixels denote the matching pixels. As you can see in the following example, the pixels of a nice red apple on brown background are very nicely clustered. It results in mostly one big blob.
 
 ![LAB thresholds for an apple in the Threshold Editor](assets/lab_thresholds_apple.png)