Do conversion to grayscale after face detection.

[iclemens]

#2129

The proposed change would convert the training image to grayscale AFTER running the face detector. The default face detector already converts the images to grayscale and a custom detector should be able to decide if it want to use grayscale/color images.

I've kept the conversion itself as I'm not sure if the actual training requires grayscale images.

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[alalek]

Perhaps, it makes sense to convert color of cropped image only. Update the code below:

Mat crop = img(Rect((int)min_x, (int)min_y, (int)w, (int)h)).clone();

(BTW, avoid using of in-place src/dst parameters - they cause performance regressions)

[iclemens]

Oh, I completely agree... as you can see I just moved some existing code around (i.e. tried to change as little as possible).

To be honest, I don't really get why the face detector needs to be called from this code in the first place. It would make more sense to me if the face rectangles were passed from the calling code as is done when fitting:

facemark = cv2.face.createFacemarkLBF()
facemark.fit(image, faces)

That would probably also make it easier to do the training from Python (#2071), as the main issue here seems to be that passing a function from Python to C++ is difficult.

[alalek]

I believe it is OK to make API more friendly for Python or other bindings.