Now we know how to convert BGR images to HSV, we can use this to extract a colored object. Here is the quick look at what HSV is. The concept remains the same, but now we add a range of pixel values we need. For BGR to HSV, we use the flag cv2.COLOR_BGR2HSV. Hue defines the color properties of the model, Saturation is the amount of gray in a color, and Value is the brightness of the intensity of the color. The saturation and value channels are also more intuitive to work with. For HSV, hue range is [0,179], saturation range is [0,255], and value range is [0,255]. Both HSV and HSL keep Here's an HSV color thresholder script to determine the lower and upper bounds using sliders. The HSV ranges are: 0 > H > 360 ⇒ OpenCV range = H/2 (0 > H > 180) 0 > S > 1 ⇒ OpenCV range = 255*S (0 > S > 255) HSV colorspace . Hue can be thought of an angle where 0 degree corresponds to the red color, 120 degrees corresponds to the green color, and 240 degrees corresponds to the blue color. In the below clip, a Tennis ball, which needs to be detected and tracked, is used to determine its HSV range. Hue represents the color and in this model, Hue is an angle from 0 to 360 degrees. What is HSV color space? Hi, could you please help me with a question defining the range of a certain color? Detect white color OpenCV Python. I want to detect the color, which I have done with the following code: import cv2 as cv import numpy as np cap = cv.VideoCapture(0) while(1): _, frame = cap.read() # Convert BGR to HSV hsv = cv.cvtColor(frame, cv.COLOR_BGR2HSV) # define range of blue color in HSV lower_blue = np.array([100,55,55]) upper_blue = np.array([105,255,255]) # Threshold the HSV image to get only blue … HSV (hue, saturation, value) colorspace is a model to represent the colorspace similar to the RGB color model. Hue: This channel encodes color color information. The HSV color space represents colors using three values. Colors are as follows: Red (0-60) Yellow (60-120) Green (120-180) Cyan (180-240) Blue (240-300) Magenta (300-360) The lower range is the minimum shade of red that will be detected, and the upper range is the maximum shade of red that will be detected. Many times, a typical red will still have some green and blue, so we would have to allow for some green and some blue, but then we'd want pretty much full red. Learning from the question and answers in the site /31305/why-is-this-simple-mask-not-working/ The lines limiting the red are 2 arrays: lower = n.array([0,100,100]) upper = n.array([20,255,255]) how are these arrays are formed? So if you are comparing OpenCV values with them, you need to normalize these ranges. In HSV, it is more easier to represent a color than RGB color-space. Now that the picture is in HSV, we need something called a “lower range” and an “upper range” for the hue that we are searching for. Different software use different scales. Tracking white color using python opencv, Then you would only have to look at lightness for detecting white and recognizing other colors would stay easy. In OpenCV HSV format, hue is represented by an integer from 0 to 179, while saturation and value are 0 to 255. The reason why HSV works best here is because we want a range of colors, and we generally want the same-ish color in this case. While in BGR, an image is treated as an additive result of three base colors (blue, green and red), HSV stands for Hue, Saturation and Value (Brightness). We can say that HSV is a rearrangement of RGB in a cylindrical shape. Object Tracking . The hue allows us to isolate the color range with a single value. Since the hue channel models the color type, it is very useful in image processing tasks that need to segment objects based on its color. The exact HSV or RGB range can be determined programmatically using OpenCV for an object to be identified or tracked. Now that we know how to convert a BGR image to HSV, we can use this to extract a colored object. hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) Great! In specifying the range , we have specified the range of blue color. Whereas you can enter the range of any colour you wish. For this reason, it's very helpful to first convert our image into HSV (hue-saturation-value) format.