34 lines
1.5 KiB
Text
34 lines
1.5 KiB
Text
Here, I introduce another library named "Dlib", which is a computer vision library always cooped with opencv.
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So to run the demo, we need to install Dlib on our system.
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1. I found tutorials to install dlib, and it worked for my device (Win10).
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https://www.learnopencv.com/install-opencv-3-and-dlib-on-windows-python-only/ (I have tried and it did work well)
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https://www.pyimagesearch.com/2017/05/01/install-dlib-raspberry-pi/ (I haven't get a chance to test on my Pi)
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Note that to install on windows, make sure you have CMAKE and Visual Studio 2017 installed.
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2. How to use:
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a. Add custom face dataset
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1. Open "Facial_Recognition_Registration.py".
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2. If using the laptop camera, make sure "cap = cv2.VideoCapture(0)" (at line 17);
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If using the external WebCam, make sure "cap = cv2.VideoCapture(1)" (at line 17).
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3. Run "Facial_Recognition_Registration.py"
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4. Enter the label as your name.
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Your face dataset:
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1. Folder "/Facial_images" -> "/face_rec" -> "/train", then you can see the folder of your name is in it.
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b. Run Facial_Recognition_Enrollment.py
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c. Test on videostream
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1. In "Facial_Recognition_Inference.py".
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2. Make sure line 27 to match your imaging device, same as above a.2
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3. Run
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3. Requirements for face registration:
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a. User can sometimes turn your head a little bit to let us get more face data, but you must make sure that there're 5 colles on each photo.
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b. Don't register you face when too much light is coming.
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welcome any try-out and comments!
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