cleanup

face_detectio.py

@@ -1,52 +0,0 @@
-import face_recognition
-import cv2
-
-# This is a demo of blurring faces in 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("http://192.168.178.53:8000/stream.mjpg")
-
-# Initialize some variables
-face_locations = []
-
-while True:
-    # Grab a single frame of video
-    ret, frame = video_capture.read()
-
-    # Resize frame of video to 1/4 size for faster face detection processing
-    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
-
-    # Find all the faces and face encodings in the current frame of video
-    face_locations = face_recognition.face_locations(small_frame, model="cnn")
-
-    # Display the results
-    for top, right, bottom, left in face_locations:
-        # 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
-
-        # Extract the region of the image that contains the face
-        face_image = frame[top:bottom, left:right]
-
-        # Blur the face image
-        face_image = cv2.GaussianBlur(face_image, (9, 9), 30)
-
-        # Put the blurred face region back into the frame image
-        frame[top:bottom, left:right] = face_image
-
-    # 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()