I have done tons of research into this , and I think my logic are worn out , almost there but can't seem to understand why nothing is display in the cv2.imshow() windows just a grey box , however good news is I am able to detect a face and crop that face then save it in the folder.
can you please shed some light of where I have gone wrong
#Author: Waheed Rafiq
#Research Student Birmingham City University
#Date: 03/11/2016
#Description :detect and Save capture face in a folder.
#Import library required for Capture face.
import cv2
#import the cascade for face detection
FaceClassifier =cv2.CascadeClassifier
('haarcascade_frontalface_default.xml')
# access the webcam (every webcam has
capture = cv2.VideoCapture(0)
while(True):
# Capture frame-by-frame
ret, frame = capture.read()
if not capture:
print "Error opening webcam device"
sys.exit(1)
# to detect faces in video
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = FaceClassifier.detectMultiScale(gray, 1.3, 5)
# Resize Image
minisize = (frame.shape[1],frame.shape[0])
miniframe = cv2.resize(frame, minisize)
# Store detected frames in variable name faces
faces = FaceClassifier.detectMultiScale(miniframe)
# Draw rectangle
for f in faces:
x, y, w, h = [ v for v in f ]
cv2.rectangle(frame, (x,y), (x+w,y+h), (255,255,255))
#Save just the rectangle faces in SubRecFaces
sub_face = frame[y:y+h, x:x+w]
FaceFileName = "unknowfaces/face_" + str(y) + ".jpg"
cv2.imwrite(FaceFileName, sub_face)
#Display the image
cv2.imshow('Result',frame)
break
# When everything done, release the capture
img.release()
cv2.waitKey(20)
cv2.destroyAllWindows()
really would appericate your support
I had to revamp my code , and re-think the logics again: for those of you who wish to know how to detect a face from webcam or Raspberry PI using Opencv and then crop that detected face this is how you do it in python 2.7 using OpenCV 2.4.12
# croppfacedetection.py
#Author: Waheed Rafiq
#Research Student Birmingham City University
#Date: 03/11/2016
#Description : Save capture face in a folder.
#Import library required for Capture face.
# Should you wish to use this code for
#education purpose in your assignment or dissertation
# please use the correct citation and give credit where required.
import cv2
size = 4
webcam = cv2.VideoCapture(0) #Use camera 0
# We load the xml file
classifier = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
# Above line normalTest
#classifier = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml')
#Above line test with different calulation
#classifier = cv2.CascadeClassifier('haarcascade_frontalface_alt_tree.xml')
#classifier = cv2.CascadeClassifier('lbpcascade_frontalface.xml')
while True:
(rval, im) = webcam.read()
im=cv2.flip(im,1,0) #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 = "unknowfaces/face_" + 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
remember you will need to create a folder and within that area you will need a folder named unknownfaces run the script from root of the folder and it should save any faces it detects into unknowfaces folder.
further information about this code will be available soon my site
waheedrafiq.net
It looks like cv2.waitKey(20) is not reached by your code. You should move it before the break statement.
In OpenCV cv2.waitKey completes the image display task. It is not just for adding a pause.
It's the 'working' version of the code with Python 3.6 OpenCV 4+. You don't have to refer anyone, use it freely.
import cv2
import os
classifier = cv2.CascadeClassifier(cv2.data.haarcascades+"haarcascade_frontalface_default.xml")
dirFace = 'cropped_face'
# Create if there is no cropped face directory
if not os.path.exists(dirFace):
os.mkdir(dirFace)
print("Directory " , dirFace , " Created ")
else:
print("Directory " , dirFace , " has found.")
webcam = cv2.VideoCapture(0) # Camera 0 according to USB port
# video = cv2.VideoCapture(r"use full windows path") # video path
while (True):
(f, im) = webcam.read() # f returns only True, False according to video access
# (f, im) = video.read() # video
if f != True:
break
# im=cv2.flip(im,1,0) #if you would like to give mirror effect
# detectfaces
faces = classifier.detectMultiScale(
im, # stream
scaleFactor=1.10, # change these parameters to improve your video processing performance
minNeighbors=20,
minSize=(30, 30) # min image detection size
)
# Draw rectangles around each face
for (x, y, w, h) in faces:
cv2.rectangle(im, (x, y), (x + w, y + h),(0,0,255),thickness=2)
# saving faces according to detected coordinates
sub_face = im[y:y+h, x:x+w]
FaceFileName = "cropped_face/face_" + str(y+x) + ".jpg" # folder path and random name image
cv2.imwrite(FaceFileName, sub_face)
# Video Window
cv2.imshow('Video Stream',im)
key = cv2.waitKey(1) & 0xFF
# q for exit
if key == ord('q'):
break
webcam.release()
