Ask Your Question
0

Why i got stuck in faces = face_detector.detectMultiScale(gray, 1.3, 5)

asked 2017-11-06 17:45:29 -0600

updated 2019-12-09 07:49:20 -0600

ojesus gravatar image

I used this code for dataset recognition :

####################################################
# Modified by Nazmi Asri                           #
# Original code: http://thecodacus.com/            #
# All right reserved to the respective owner       #
####################################################

# Import OpenCV2 for image processing
import cv2

# Start capturing video 
vid_cam = cv2.VideoCapture(0)

# Detect object in video stream using Haarcascade Frontal Face
face_detector = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

# For each person, one face id
face_id = 1

# Initialize sample face image
count = 0

# Start looping
while(True):

    # Capture video frame
    _, image_frame = vid_cam.read()

    # Convert frame to grayscale
    gray = cv2.cvtColor(image_frame, cv2.COLOR_BGR2GRAY)

    # Detect frames of different sizes, list of faces rectangles
    faces = face_detector.detectMultiScale(gray, 1.3, 5)

    # Loops for each faces
    for (x,y,w,h) in faces:

        # Crop the image frame into rectangle
        cv2.rectangle(image_frame, (x,y), (x+w,y+h), (255,0,0), 2)

        # Increment sample face image
        count += 1

        # Save the captured image into the datasets folder
        cv2.imwrite("dataset/User." + str(face_id) + '.' + str(count) + ".jpg", gray[y:y+h,x:x+w])

        # Display the video frame, with bounded rectangle on the person's face
        cv2.imshow('frame', image_frame)

    # To stop taking video, press 'q' for at least 100ms
    if cv2.waitKey(100) & 0xFF == ord('q'):
        break

    # If image taken reach 100, stop taking video
    elif count>100:
        break

# Stop video
vid_cam.release()

# Close all started windows
cv2.destroyAllWindows()

sometime i got stuck in :

 cv2.waitKey(100) & 0xFF == ord('q'):
    break

Most of time i got stuck in:

 faces = face_detector.detectMultiScale(gray, 1.3, 5)
edit retag flag offensive close merge delete

1 answer

Sort by ยป oldest newest most voted
0

answered 2017-11-07 11:23:58 -0600

supra56 gravatar image

Try this:

import cv2
size = 4
webcam = cv2.VideoCapture(0) #Use camera 0

# We load the xml file
classifier = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

while True:
    (rval, im) = webcam.read()
    im=cv2.flip(im,1,1) #Flip to act as a mirror

    # Resize the image to speed up detection
    mini = cv2.resize(im, (im.shape[1] // size, im.shape[0] // size))

    # detect MultiScale / faces 
    faces = classifier.detectMultiScale(mini)

    # Draw rectangles around each face
    for f in faces:
        (x, y, w, h) = [v * size for v in f] #Scale the shapesize backup
        cv2.rectangle(im, (x, y), (x + w, y + h),(0,255,0),thickness=4)
        #Save just the rectangle faces in SubRecFaces
        sub_face = im[y:y+h, x:x+w]
        FaceFileName = "unknow_" + str(y) + ".jpg"
        #cv2.imwrite(FaceFileName, sub_face)

    # Show the image
    cv2.imshow('BCU Research by Waheed Rafiq (c)',   im)
    key = cv2.waitKey(10)
    # if Esc key is press then break out of the loop 
    if key == 27: #The Esc key
        break
# Stop video
webcam.release()

# Close all started windows
cv2.destroyAllWindows()
edit flag offensive delete link more

Comments

The waitKey will start saving in 10 sec. You can modified by yourself to suit your need

supra56 gravatar imagesupra56 ( 2017-11-07 11:26:10 -0600 )edit

Question Tools

1 follower

Stats

Asked: 2017-11-06 17:45:29 -0600

Seen: 4,342 times

Last updated: Nov 07 '17