I'm trying to do face recognition for my project similar to this. But I need to detect it in a video. So I'm taking a video ([Friends](https://www.youtube.com/watch?v=t0BFOi5o0YY) Video) and take some images from this video for training purpose. I'm using the following code to get the frames:
import cv2
vidcap = cv2.VideoCapture('pathToFolder/Friends - Bad monkey, Hot girls and Phoebe saves the monkey.mp4')
success,image = vidcap.read()
count = 0
success = True
while success:
success,image = vidcap.read()
print('Read a new frame: ', success)
cv2.imwrite("pathToFolder/Friends/frame%d.jpg" % count, image) # save frame as JPEG file
count += 1
And then running the following code:
import cv2, sys, numpy, os
size = 3
#fn_haar = 'haarcascade_frontalface_default.xml'
fn_dir = 'pathToFolder/Friends_Train'
# Part 1: Create fisherRecognizer
print('Training...')
# Create a list of images and a list of corresponding names
(images, lables, names, id) = ([], [], {}, 0)
# Get the folders containing the training data
for (subdirs, dirs, files) in os.walk(fn_dir):
# Loop through each folder named after the subject in the photos
for subdir in dirs:
names[id] = subdir
subjectpath = os.path.join(fn_dir, subdir)
# Loop through each photo in the folder
for filename in os.listdir(subjectpath):
# Skip non-image formates
f_name, f_extension = os.path.splitext(filename)
if(f_extension.lower() not in
['.png','.jpg','.jpeg','.gif','.pgm']):
print("Skipping "+filename+", wrong file type")
continue
path = subjectpath + '/' + filename
lable = id
# Add to training data
images.append(cv2.imread(path, 0))
lables.append(int(lable))
id += 1
(im_width, im_height) = (112, 92)
# Create a Numpy array from the two lists above
(images, lables) = [numpy.array(lis) for lis in [images, lables]]
# OpenCV trains a model from the images
# NOTE FOR OpenCV2: remove '.face'
model = cv2.face.createFisherFaceRecognizer()
model.train(images, lables)
# Part 2: Use fisherRecognizer on camera stream
haar_cascade = cv2.CascadeClassifier('C:/opencv-3.2.0/data/haarcascades/haarcascade_frontalface_default.xml')
webcam = cv2.VideoCapture('pathToFolder/Friends - Bad monkey, Hot girls and Phoebe saves the monkey.mp4')
while True:
# Loop until the camera is working
rval = False
while(not rval):
# Put the image from the webcam into 'frame'
(rval, frame) = webcam.read()
if(not rval):
print("Failed to open webcam. Trying again...")
# Flip the image (optional)
#frame=cv2.flip(frame,1,0)
# Convert to grayscalel
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Resize to speed up detection (optinal, change size above)
mini = cv2.resize(gray, (int(gray.shape[1] / size), int(gray.shape[0] / size)))
# Detect faces and loop through each one
faces = haar_cascade.detectMultiScale(mini)
for i in range(len(faces)):
face_i = faces[i]
# Coordinates of face after scaling back by `size`
(x, y, w, h) = [v * size for v in face_i]
face = gray[y:y + h, x:x + w]
face_resize = cv2.resize(face, (im_width, im_height))
# Try to recognize the face
prediction = model.predict(face_resize)
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 3)
# [1]
# Write the name of recognized face
cv2.putText(frame,
'%s - %.0f' % (names[prediction[0]],prediction[1]),
(x-10, y-10), cv2.FONT_HERSHEY_PLAIN,1,(0, 255, 0))
# Show the image and check for ESC being pressed
cv2.imshow('OpenCV', frame)
key = cv2.waitKey(10)
if key == 27:
break
# When everything is done, release the capture
video_capture.release()
cv2.destroyAllWindows()
But I'm getting the following error:
But I'm getting the following error:
> D:\Build\OpenCV\opencv_contrib-3.2.0\modules\face\src\fisher_faces.cpp:132:> error: (-5) Wrong input image size.> Reason: Training and Test images must> be of equal size! Expected an image> with 40000 elements, but got 10304. in> function> cv::face::Fisherfaces::predict
And I'm not understanding why this error is coming. Also, the solutions I'm getting online are not helping me. Can anyone please help?
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