Add a second webcam example that runs a lot faster

README.md

@@ -200,7 +200,8 @@ All the examples are available [here](https://github.com/ageitgey/face_recogniti
 * [Identify specific facial features in a photograph](https://github.com/ageitgey/face_recognition/blob/master/examples/find_facial_features_in_picture.py)
 * [Apply (horribly ugly) digital make-up](https://github.com/ageitgey/face_recognition/blob/master/examples/digital_makeup.py)
 * [Find and recognize unknown faces in a photograph based on photographs of known people](https://github.com/ageitgey/face_recognition/blob/master/examples/recognize_faces_in_pictures.py)
-* [Recognize faces in live video using your webcam (Requires OpenCV to be installed)](https://github.com/ageitgey/face_recognition/blob/master/examples/facerec_from_webcam.py)
+* [Recognize faces in live video using your webcam - Simple / Slower Version (Requires OpenCV to be installed)](https://github.com/ageitgey/face_recognition/blob/master/examples/facerec_from_webcam.py)
+* [Recognize faces in live video using your webcam - Faster Version (Requires OpenCV to be installed)](https://github.com/ageitgey/face_recognition/blob/master/examples/facerec_from_webcam_faster.py)
 
 ## How Face Recognition Works
 
@@ -228,7 +229,7 @@ to any service that supports Docker images.
 Solution: `dlib` is compiled with SSE4 or AVX support, but your CPU is too old and doesn't support that.
 You'll need to recompile `dlib` after [making the code change outlined here](https://github.com/ageitgey/face_recognition/issues/11#issuecomment-287398611).
 
-##### Issue: `RuntimeError: Unsupported image type, must be 8bit gray or RGB image.` when running the webcam example.
+##### Issue: `RuntimeError: Unsupported image type, must be 8bit gray or RGB image.` when running the webcam examples.
 
 Solution: Your webcam probably isn't set up correctly with OpenCV. [Look here for more](https://github.com/ageitgey/face_recognition/issues/21#issuecomment-287779524).
 

examples/facerec_from_webcam.py

 import face_recognition
 import cv2
 
-# This is a super simple demo of running face recognition on live video from your webcam.
+# This is a super simple (but slow) example of running face recognition on live video from your webcam.
+# There's a second example that's a little more complicated but runs faster.
 
 # PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.
 # OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this

examples/facerec_from_webcam_faster.py

+import face_recognition
+import cv2
+
+# This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the
+# other example, but it includes some basic performance tweaks to make things run a lot faster:
+#   1. Process each video frame at 1/4 resolution (though still display it at full resolution)
+#   2. Only detect faces in every other frame of video.
+
+# PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.
+# OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this
+# specific demo. If you have trouble installing it, try any of the other demos that don't require it instead.
+
+# Get a reference to webcam #0 (the default one)
+video_capture = cv2.VideoCapture(0)
+
+# Load a sample picture and learn how to recognize it.
+obama_image = face_recognition.load_image_file("obama.jpg")
+obama_face_encoding = face_recognition.face_encodings(obama_image)[0]
+
+# Initialize some variables
+face_locations = []
+face_encodings = []
+face_names = []
+process_this_frame = True
+
+while True:
+    # Grab a single frame of video
+    ret, frame = video_capture.read()
+
+    # Resize frame of video to 1/4 size for faster face recognition processing
+    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
+
+    # Only process every other frame of video to save time
+    if process_this_frame:
+        # Find all the faces and face encodings in the current frame of video
+        face_locations = face_recognition.face_locations(small_frame)
+        face_encodings = face_recognition.face_encodings(small_frame, face_locations)
+
+        face_names = []
+        for face_encoding in face_encodings:
+            # See if the face is a match for the known face(s)
+            match = face_recognition.compare_faces([obama_face_encoding], face_encoding)
+            name = "Unknown"
+
+            if match[0]:
+                name = "Barack"
+
+            face_names.append(name)
+
+    process_this_frame = not process_this_frame
+
+
+    # Display the results
+    for (top, right, bottom, left), name in zip(face_locations, face_names):
+        # Scale back up face locations since the frame we detected in was scaled to 1/4 size
+        top *= 4
+        right *= 4
+        bottom *= 4
+        left *= 4
+
+        # Draw a box around the face
+        cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
+
+        # Draw a label with a name below the face
+        cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
+        font = cv2.FONT_HERSHEY_DUPLEX
+        cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
+
+    # Display the resulting image
+    cv2.imshow('Video', frame)
+
+    # Hit 'q' on the keyboard to quit!
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+# Release handle to the webcam
+video_capture.release()
+cv2.destroyAllWindows()