[pull] master from ageitgey:master

.github/workflows/main.yml

@@ -5,7 +5,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.5, 3.6, 3.7, 3.8]
+        python-version: [3.8, 3.9, "3.10"]
     steps:
     - name: Checkout
       uses: actions/checkout@v2

Dockerfile

@@ -1,6 +1,6 @@
 # This is a sample Dockerfile you can modify to deploy your own app based on face_recognition
 
-FROM python:3.6-slim-stretch
+FROM python:3.10.3-slim-bullseye
 
 RUN apt-get -y update
 RUN apt-get install -y --fix-missing \
@@ -46,5 +46,7 @@ RUN cd /root/face_recognition && \
     pip3 install -r requirements.txt && \
     python3 setup.py install
 
+# Add pip3 install opencv-python==4.1.2.30 if you want to run the live webcam examples
+
 CMD cd /root/face_recognition/examples && \
     python3 recognize_faces_in_pictures.py

LICENSE

@@ -1,7 +1,7 @@
 
 MIT License
 
-Copyright (c) 2017, Adam Geitgey
+Copyright (c) 2021, Adam Geitgey
 
 Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
 

README.md

@@ -7,7 +7,7 @@ the world's simplest face recognition library.
 
 Built using [dlib](http://dlib.net/)'s state-of-the-art face recognition
 built with deep learning. The model has an accuracy of 99.38% on the
-[Labeled Faces in the Wild](http://vis-www.cs.umass.edu/lfw/) benchmark.
+Labeled Faces in the Wild benchmark (see the [LFW paper (PDF)](https://people.cs.umass.edu/~elm/papers/lfw.pdf)).
 
 This also provides a simple `face_recognition` command line tool that lets
 you do face recognition on a folder of images from the command line!

examples/facerec_from_webcam_faster.py

@@ -42,14 +42,14 @@
     # 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)
-
-    # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
-    rgb_small_frame = small_frame[:, :, ::-1]
-
     # Only process every other frame of video to save time
     if process_this_frame:
+        # 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)
+
+        # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
+        rgb_small_frame = small_frame[:, :, ::-1]
+
         # Find all the faces and face encodings in the current frame of video
         face_locations = face_recognition.face_locations(rgb_small_frame)
         face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)

examples/facerec_ipcamera_knn.py

@@ -209,6 +209,6 @@ def show_prediction_labels_on_image(frame, predictions):
             frame = show_prediction_labels_on_image(frame, predictions)
             cv2.imshow('camera', frame)
             if ord('q') == cv2.waitKey(10):
-                cap1.release()
+                cap.release()
                 cv2.destroyAllWindows()
                 exit(0)

requirements_dev.txt

@@ -1,4 +1,4 @@
-pip==19.2
+pip==21.1
 bumpversion==0.5.3
 wheel==0.29.0
 watchdog==0.8.3

setup.py

@@ -57,6 +57,7 @@
         'Programming Language :: Python :: 3.6',
         'Programming Language :: Python :: 3.7',
         'Programming Language :: Python :: 3.8',
+        'Programming Language :: Python :: 3.9',
     ],
     test_suite='tests',
     tests_require=test_requirements