The Image Recognition process performs a background extraction to identify the object, and captures the u, v coodinates from its center (pixel coordinates from the image detect). The Real World XYZ process, then loads all the Initial Calibrations we did, and calculates the X Y Z points, with the "magic" happening in this specific function. Accept #4, F1 Score → 0.959. Reject #5 (F1 Score → 0.888) Applying this crop to the original image, you get this: That's 875x233, whereas the original was 1328x2048. That's a 92.5% decrease in the number of pixels, with no loss of text! This will help any OCR tool focus on what's important, rather than the noise. One practical application of cropping in OpenCV can be to divide an image into smaller patches. Use loops to crop out a fragment from the image. Start by getting the height and width of the required patch from the shape of the image. Python img = cv2.imread ("test_cropped.jpg") image_copy = img.copy () imgheight=img.shape [0] imgwidth=img.shape [1].

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