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copied!<p>You can do this with <a href="http://opencv.itseez.com/modules/imgproc/doc/miscellaneous_transformations.html?cv2.threshold#threshold" rel="noreferrer">threshold</a>, <a href="http://opencv.itseez.com/modules/imgproc/doc/structural_analysis_and_shape_descriptors.html?highlight=findcontour#void%20findContours%28InputOutputArray%20image,%20OutputArrayOfArrays%20contours,%20OutputArray%20hierarchy,%20int%20mode,%20int%20method,%20Point%20offset%29" rel="noreferrer">findContours</a>, and <a href="http://opencv.itseez.com/modules/imgproc/doc/structural_analysis_and_shape_descriptors.html?highlight=findcontour#boundingrect" rel="noreferrer">boundingRect</a>.</p> <p>So, here is a quick script doing this with the python interface.</p> <pre><code>stitched = cv2.imread('stitched.jpg', 0) (_, mask) = cv2.threshold(stitched, 1.0, 255.0, cv2.THRESH_BINARY); # findContours destroys input temp = mask.copy() (contours, _) = cv2.findContours(temp, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # sort contours by largest first (if there are more than one) contours = sorted(contours, key=lambda contour:len(contour), reverse=True) roi = cv2.boundingRect(contours[0]) # use the roi to select into the original 'stitched' image stitched[roi[1]:roi[3], roi[0]:roi[2]] </code></pre> <p>Ends up looking like this: <img src="https://i.stack.imgur.com/aGccT.png" alt="enter image description here"></p> <p><strong>NOTE :</strong> Sorting may not be necessary with raw imagery, but using the compressed image caused some compression artifacts to show up when using a low threshold, so that is why I post-processed with sorting.</p> <p>Hope that helps!</p>

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