OpenCv inRange function behaves differently when called from Objective C++ - ios

Question

I have used the excellent answer to the question here:
How to detect bullet holes on the target using python
I have verified that it works in both Python 2 and 3.6, but I would like to use the concept in an iOS application written in Objective C(++). This is my attempt at translating it. Ultimately, I need it to work with an image taken by the camera, so I don't want to use imread, but I've checked that this makes no difference.
UIImage *nsi = [UIImage imageNamed:#"CANDX.jpg"];
cv::Mat original;
UIImageToMat(nsi, original);
cv::Mat thresholded;
cv::inRange(original, cv::Scalar(40,40,40), cv::Scalar(160,160,160), thresholded);
cv::Mat kernel = cv::Mat::ones(10, 10, CV_64FC1);
cv::Mat opening;
cv::morphologyEx(thresholded, opening, cv::MORPH_OPEN, kernel);
vector<vector<cv::Point>> contours;
cv::findContours(opening, contours, CV_RETR_LIST, CV_CHAIN_APPROX_NONE);
The call to inRange, with the same values as the Python version, gives a completely black image. Indeed, it is impossible to pick values for lower- and upper-bounds that do not result in this outcome. I've tried converting the image to HSV and using HSV values for lower- and upper-bound. This makes a slight difference in that I can get some vaguely recognisable outcomes, but nothing like the useful result I should be getting.
If I substitute the 'thresholded' image from the answer and comment out the inRange call, the morphology and findContours calls work okay.
Am I doing something wrong in setting up the inRange call?

Answer 1

As you mention in the comments, the data type of original is CV_8UC4 -- i.e. it's a 4 channel image. However, in your call to cv::inRange, you provide ranges for only 3 channels.
cv::Scalar represents a 4-element vector. When you call the constructor with only 3 values, a default value of 0 is used for the 4-th element.
Hence, your call to inRange is actually equivalent to this:
cv::inRange(original, cv::Scalar(40,40,40,0), cv::Scalar(160,160,160,0), thresholded);
You're looking only for pixels that have the alpha channel set to 0 (fully transparent). Since the image came from a camera, it's highly unlikely there will be any transparent pixels -- the alpha channel is probably just all 255s.
There are 2 options to solve this:
Drop the unneeded alpha channel. One way to do this is to use cv::cvtColor, e.g.
cv::cvtColor(original, original, cv::COLOR_BGRA2BGR);
Specify desired range for all the channels, e.g.
cv::inRange(original, cv::Scalar(40,40,40,0), cv::Scalar(160,160,160,255), thresholded);