diff options
| -rw-r--r-- | face-detection/03_face_recogition.py | 120 |
1 files changed, 66 insertions, 54 deletions
diff --git a/face-detection/03_face_recogition.py b/face-detection/03_face_recogition.py index d5b18cc..185d3c2 100644 --- a/face-detection/03_face_recogition.py +++ b/face-detection/03_face_recogition.py @@ -1,68 +1,80 @@ import cv2 -import numpy as np import os +import numpy as np +from picamera2 import Picamera2 + +#Parameters +id = 0 +font = cv2.FONT_HERSHEY_COMPLEX +height=1 +boxColor=(0,0,255) #BGR- GREEN +nameColor=(255,255,255) #BGR- WHITE +confColor=(255,255,0) #BGR- TEAL + +face_detector=cv2.CascadeClassifier('haarcascade_frontalface_default.xml') recognizer = cv2.face.LBPHFaceRecognizer_create() recognizer.read('trainer/trainer.yml') -cascadePath = "haarcascade_frontalface_default.xml" -faceCascade = cv2.CascadeClassifier(cascadePath); -font = cv2.FONT_HERSHEY_COMPLEX -#font = cv2.FONT_HERSHEY_TRIPLEX -id = 0 # names related to id -names = ['None', 'Junaid', 'Bikram', 'Saumit'] -# 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) -#reads frame, converts to grayscale, for each detected face it +names = ['None', 'Saumit', 'Bikram', 'Junaid'] + +# Create an instance of the PiCamera2 object +cam = Picamera2() +## Initialize and start realtime video capture +# Set the resolution of the camera preview +cam.preview_configuration.main.size = (640, 360) +cam.preview_configuration.main.format = "RGB888" +cam.preview_configuration.controls.FrameRate=30 +cam.preview_configuration.align() +cam.configure("preview") +cam.start() + while True: - ret, img =cam.read() - gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) - - faces = faceCascade.detectMultiScale( - gray, - scaleFactor = 1.2, - minNeighbors = 5, - minSize = (int(minW), int(minH)), - ) + # Capture a frame from the camera + frame=cam.capture_array() + + #Convert fram from BGR to grayscale + frameGray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + #Create a DS faces- array with 4 elements- x,y coordinates top-left corner), width and height + faces = face_detector.detectMultiScale( + frameGray, # The grayscale frame to detect + scaleFactor=1.1,# how much the image size is reduced at each image scale-10% reduction + minNeighbors=5, # how many neighbors each candidate rectangle should have to retain it + minSize=(150, 150)# Minimum possible object size. Objects smaller than this size are ignored. + ) for(x,y,w,h) in faces: - cv2.rectangle(img, (x,y), (x+w,y+h), (0,255,0), 2) - id, confidence = recognizer.predict(gray[y:y+h,x:x+w]) + namepos=(x+5,y-5) #shift right and up/outside the bounding box from top + confpos=(x+5,y+h-5) #shift right and up/intside the bounding box from bottom + #create a bounding box across the detected face + cv2.rectangle(frame, (x,y), (x+w,y+h), boxColor, 3) #5 parameters - frame, topleftcoords,bottomrightcooords,boxcolor,thickness + + #recognizer.predict() method takes the ROI as input and + #returns the predicted label (id) and confidence score for the given face region. + id, confidence = recognizer.predict(frameGray[y:y+h,x:x+w]) - # If confidence is less them 100 ==> "0" : perfect match - if (confidence < 100): + # If confidence is less than 100, it is considered a perfect match + if confidence < 100: id = names[id] - confidence = " {0}%".format(round(100 - confidence)) + confidence = f"{100 - confidence:.0f}%" else: id = "unknown" - confidence = " {0}%".format(round(100 - confidence)) - - cv2.putText( - img, - str(id), - (x+5,y-5), - font, - 1, - (255,255,255), - 2 - ) - cv2.putText( - img, - str(confidence), - (x+5,y+h-5), - font, - 1, - (255,255,0), - 1 - ) - - cv2.imshow('camera',img) - k = cv2.waitKey(10) & 0xff # Press 'ESC' for exiting video - if k == 27: + confidence = f"{100 - confidence:.0f}%" + + #Display name and confidence of person who's face is recognized + cv2.putText(frame, str(id), namepos, font, height, nameColor, 2) + cv2.putText(frame, str(confidence), confpos, font, height, confColor, 1) + + # Display realtime capture output to the user + cv2.imshow('Raspi Face Recognizer',frame) + + # Wait for 30 milliseconds for a key event (extract sigfigs) and exit if 'ESC' or 'q' is pressed + key = cv2.waitKey(100) & 0xff + #Checking keycode + if key == 27: # ESCAPE key + break + elif key == 113: # q key break + +# Release the camera and close all windows print("\n [INFO] Exiting Program and cleaning up stuff") -cam.release() +cam.stop() cv2.destroyAllWindows() |