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lasttry.py
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lasttry.py
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import numpy as np
import cv2
import pickle #for label naming
#Rb -> Read Byte
labels = {}
with open("labels.pickle",'rb') as f:
og_labels = pickle.load(f)
labels = {v:k for k,v in og_labels.items()} #talk to me about this ;) (key, value pair conversion)
face_cascade = cv2.CascadeClassifier('cascade/data/haarcascade_frontalface_alt2.xml') #only front of a face
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read("trainer.yml")
cap = cv2.VideoCapture(0)
while(True):
ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.5, minNeighbors=5)
#scaleFactor aghe peeche krke dekho
#Bounding Box of Face
for (x, y, w, h) in faces:
print(x, y, w, h)
roi_gray = gray[y:y+h, x:x+w] #roi -> Region of Interest
roi_color = frame[y:y+h, x:x+w]
#Recognition part
id_, conf = recognizer.predict(roi_gray) #id's and confidence level
if(conf>=45 and conf<=85):
print(id_)
print(labels[id_])
font = cv2.FONT_HERSHEY_SIMPLEX
name = labels[id_]
color = (255,255,255)
stroke = 2
cv2.putText(frame, name, (x,y), font, 1, color, stroke, cv2.LINE_AA)
#Recognition part end
img_item = "my-image.png"
cv2.imwrite(img_item, roi_gray)
#drawing a rectangle to recognize
color = (255, 0, 0) #BGR
stroke = 2
end_cord_x = x+w
end_cord_y = y+h
cv2.rectangle(frame, (x, y), (end_cord_x, end_cord_y), color, stroke)
#Now Recognition Part to predict
cv2.imshow('frame', frame)
if(cv2.waitKey(20) & 0xFF == ord('q')):
break
cap.release()
cv2.destroyAllWindows()