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

asked 2017-11-06 17:45:29 -0500

updated 2019-12-09 07:49:20 -0500

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)
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1 answer

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answered 2017-11-07 11:23:58 -0500

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()
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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 -0500 )edit
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Asked: 2017-11-06 17:45:29 -0500

Seen: 1,221 times

Last updated: Nov 07 '17