人脸识别代码（python）

'''' Real Time Face Recogition ==> Each face stored on dataset/ dir, should have a unique numeric integer ID as 1, 2, 3, etc ==> LBPH computed model (trained faces) should be on trainer/ dir Based on original code by Anirban Kar: https://github.com/thecodacus/Face-Recognition Developed by Marcelo Rovai - MJRoBot.org @ 21Feb18 ''' import cv2 import numpy as np import os # Create Local Binary Pattern Histograms for face recognization recognizer = cv2.face.LBPHFaceRecognizer_create() # Read the trained mode recognizer.read('trainer/trainer.yml') # Load prebuilt model for Frontal Face cascadePath = "haarcascade_frontalface_alt2.xml" # Create classifier from prebuilt model faceCascade = cv2.CascadeClassifier(cascadePath); # add the font text font = cv2.FONT_HERSHEY_SIMPLEX #iniciate id counter id = 0 # names related to ids: example ==> Marcelo: id=1, etc names = ['None', 'dh is cool', 'laoer', 'liyang', 'heqiang', 'xiu'] # Initialize and start realtime video capture cam = cv2.VideoCapture(0) #cam.set(3, 640) # set video widht #cam.set(4, 480) # set video height # Define min window size to be recognized as a face #minW = 0.1*cam.get(3) #minH = 0.1*cam.get(4) while True: # Read the video frame ret, img =cam.read() #img = cv2.flip(img, -1) # Flip vertically # Convert the captured frame into grayscale gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) # Get all face from the video frame faces = faceCascade.detectMultiScale( gray, scaleFactor = 1.2, minNeighbors = 5, #minSize = (int(minW), int(minH)), ) # For each face in faces for(x,y,w,h) in faces: # Create rectangle around the face cv2.rectangle(img, (x-20,y-20), (x+w+20,y+h+20), (0,255,0), 4) # Recognize the face belongs to which ID id, confidence = recognizer.predict(gray[y:y+h,x:x+w]) # Check if confidence is less them 100 ==> "0" is perfect match if (confidence < 100): id = names[id] confidence = " {0}%".format(round(100 - confidence)) else: id = "unknown" confidence = " {0}%".format(round(100 - confidence)) # put text describe who is in the picture cv2.putText(img, str(id), (x,y-40), font, 2, (255,255,255), 3) cv2.putText(img, str(confidence), (x+5,y+h-5), font, 1, (255,255,0), 1) # Dispaly the video frame with the bounded rectangle cv2.imshow('camera',img) # Press 'ESC' for exiting video k = cv2.waitKey(10) & 0xff if k == 27: break # Do a bit of cleanup print("\n [INFO] Exiting Program and cleanup stuff") cam.release() cv2.destroyAllWindows()