![]() ![]() As such, our computed equivalent gray is Y=118 (118, 118, 118). ![]() ![]() loading image in numpy.ndarray format, save image converting image color format (RGB, YUV, Gray scale. When the weighted average does not equate to a whole number, we round it to the nearest whole. OpenCV is image processing library which supports. The weighted average for our RGB color (70, 130, 180) is as follows: As pure blue is darker than pure red and pure green, it is allocated the least weight.Ĭomputation Example: Let's say we have a color that can be represented by the RGB value 70, 130, 180. It is important to note that the colors are not equally weighted. Where R, G and B are integers representing red (R), green (G) and blue (B) with values in the range 0–255. The grayscale weighted average, Y, is represented by the following equation: To transform a color into its corresponding grayscale value, you need to calculate the average of the R, G, and B values using a weighted approach. RGB encodings can be converted to grayscale values by converting the RGB encoding into a set of three equal numbers that represent the range on the black-white spectrum on which the color appears. The higher the number is, the lighter the gray becomes. For example, white is represented by 255, 255, 255, black is represented by 0, 0, 0, and medium gray is 127, 127, 127. More advanced channel reordering can also be done with cv. They are given in the range of shades that lie between black and white. The conversion from a RGB image to gray is done with: cvtColor(src, bwsrc, cv::COLORRGB2GRAY). Grayscale images do not include any color. A range between 0 and 255 is used in the encodings. When RGB encodings are presented, the first value always represents the amount of red, the second the amount of green, and the third the amount of blue. This pure form of display is represented by the following encoding: pure red (255, 0, 0), pure green (0, 255, 0), and pure blue (0, 0, 255). The pure form of red, green, and blue are displayed as follows: R G B Vice versa if my ultimate source is grayscale then I'd open the files and the video capture in gray scale cv2.imread(path, cv2.The RGB color model is used to describe the way in which colors in different hues and tones are displayed by differing the amount of red, green, and blue light that is displayed through the pixels. ![]() Use img cv2.imread (filename, 0) to make sure img is a 1-channel image. You got the error because you didn't assign it to return a gray image. if you have capturing the image from a video camera in BGR, then I'd use BGR as the source, and do the BGR to grayscale conversion cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 1 img cv2.imread (filename, flags) returns a 3-channel color image when flag>0 returns a gray image when flag0 returns a image as the loaded file originally is. So I'd keep the image sources homogenous, e.g. In brief, let's not mix the versions and types in the processing pipeline. Cv2 rgb to gray code#Naturally what you want to avoid is fine tuning your code on a particular version of the image just to find out it was suboptimal for images coming from a different source. My guess is comes from the differences in the numeric calculations in the two methods (loading grayscale vs conversion to grayscale) img cv2.imread(path) imggray cv2.cvtColor(img, cv2.COLORBGR2GRAY) Level up your programming skills with exercises across 52 languages, and insightful discussion with our dedicated team of welcoming mentors. I've summed up the diff too to see import numpy as npĪmong all the IMREAD_ modes for cv2.imread(), only IMREAD_COLOR and IMREAD_ANYCOLOR can be converted using COLOR_BGR2GRAY, and both of them gave me the same diff against the image opened in IMREAD_GRAYSCALE So to convert the color image to grayscale we will be using cv2.imread(image-name.png,0) or you can also write cv2.IMREADGRAYSCALE in the place of 0 as it. Like you've documented, the diff between the two images is not perfectly 0, I can see diff pixels in towards the left and the bottom Img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)Īnd another by loading it in gray scale mode img_gray_mode = cv2.imread(path, cv2.IMREAD_GRAYSCALE) Some examples of RGB encodings and their equivalent grayscale values are. As such, our computed equivalent gray is Y118 (118, 118, 118). Once using the conversion img = cv2.imread(path) The weighted average for our RGB color (70, 130, 180) is as follows: Y 0.299 (70) + 0.587 (130) + 0.114 (180), When the weighted average does not equate to a whole number, we round it to the nearest whole. To illustrate, I've opened up this same color JPEG image: Note: This is not a duplicate, because the OP is aware that the image from cv2.imread is in BGR format (unlike the suggested duplicate question that assumed it was RGB hence the provided answers only address that issue) ![]()
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