Face detection

Author: saimj7

.gitattributes

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+import cv2
+
+cap = cv2.VideoCapture(0)
+
+# haar trained model on frontal faces only
+face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+
+
+while(True):
+
+    ret, frame = cap.read() #read frame by frame
+    #A frame is a array of 3 matrices where each matrix is for the respective color blue, green, red
+
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) #convert into gray
+
+    #gray is a single matrix now
+
+    #detectMultiScale function detect faces and return an array of position coordinates and sizes
+    # 1.3 is scaled factor, if its high we may miss some pixels and thus faces
+    # 5 is minNeighbors, defines how many neighbor rectangles should be identified to retain it, higher value = less false positives
+    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+
+
+
+    #define rectangle if faces detected, passed red in BGR format
+    for (x,y,w,h) in faces:
+
+      cv2.rectangle(frame, (x, y), (x+w, y+h), (0 , 0, 255), 2)
+
+    cv2.imshow('frame', frame)
+
+    if cv2.waitKey(1) & 0xFF == ord('q'):#q terminates script
+
+        break
+
+cap.release()
+
+cv2.destroyAllWindows()