ORB-HD / deface

I was looking into this project and was wondering if it would be possible to select specific faces out of the file to leave unblurred.

I second this

The face detection model used internally by deface doesn't support face recognition (which would be required for matching a specific face), only general face detection. This feature would be nice to have but I'm afraid implementing it properly would require quite some work and would make the code much more complicated.
If I do a rewrite some day, I'll keep this use case in mind.

Hi, I have quite the same need, but I have another idea for implementation ?
Could it be possible to define a "detection box" that, with a given pixel rectangle, would ignore the outter of this rectangle.
My use case : two person facing the camera, I want to blur the right one > I define a global rectangle on the right part and the face of the left one is not blurred.
Thanks

Hi @mthebaud, this feature might be a bit too specific for the main project but feel free to implement this in a fork by filtering the detections dets in this loop by their coordinate range:

deface/deface/deface.py

Lines 83 to 89 in 1e6a87f

    
           for i, det in enumerate(dets): 
        
               boxes, score = det[:4], det[4] 
        
               x1, y1, x2, y2 = boxes.astype(int) 
        
               x1, y1, x2, y2 = scale_bb(x1, y1, x2, y2, mask_scale) 
        
               # Clip bb coordinates to valid frame region 
        
               y1, y2 = max(0, y1), min(frame.shape[0] - 1, y2) 
        
               x1, x2 = max(0, x1), min(frame.shape[1] - 1, x2)

As you see, detection boxes are already being clipped to the valid frame coordinate range, so you could just change the first arguments of the max() and min() calls respectively.

That's great, thank for the information !

Honestly that is hugely specific. Using face recognition seems to be the way to go to achieve this. Just run face recognition and then contour recognition, place the person in a buffer then paste that on top of the blurred frame.

Yes, I understand that is specific, so I suggest that that this issue can be closed as we have answers to our questions.

I think it would be less specific if the faces were ordered from left to right and the indexes of that list could be an option.
Obviously someone might need to cut up a video into segments when that order doesn't change - but for interviews etc. having an option to select one or more faces to skip is a useful option.

	for i, det in enumerate(dets):
	boxes, score = det[:4], det[4]
	x1, y1, x2, y2 = boxes.astype(int)
	x1, y1, x2, y2 = scale_bb(x1, y1, x2, y2, mask_scale)
	# Clip bb coordinates to valid frame region
	y1, y2 = max(0, y1), min(frame.shape[0] - 1, y2)
	x1, x2 = max(0, x1), min(frame.shape[1] - 1, x2)

select faces to not be blurred