def main(rootDir):
detector = FaceDetector()
for iDir, subjectDirs, files in os.walk(rootDir):
for subjectDir in subjectDirs:
print('Walking subject folder at %s' % subjectDir)
subjectPath = os.path.join(rootDir, subjectDir)
# Each session
for jDir, sessionDirs, subjectFiles in os.walk(subjectPath):
for sessionDir in sessionDirs:
print('Walking sessions folder at %s' % sessionDir)
sessionPath = os.path.join(subjectPath, sessionDir)
img = cv2.imread(sessionPath + '/im0.bmp', 0)
face_coords = detector.detect(img)
for i in range(4):
imgFilePath = sessionPath + '/im%s.bmp' % i
imgCroppedFilePath = sessionPath + '/im%s_cropped.bmp' % i
if os.path.isfile(imgFilePath):
print('Cropping the image %s' % imgFilePath)
img = cv2.imread(imgFilePath)
if face_coords is None:
print(
'Did not find a face, just using normal image'
)
cv2.imwrite(imgCroppedFilePath, img)
else:
face = detector.crop_face(img, face_coords)
cv2.imwrite(imgCroppedFilePath, face)
def main(args):
# Create algorithm objects
lbp = LBP()
detector = FaceDetector()
svm = SVM()
knn = KNearest()
# Get subjects to train the svm on
imgs = [
'/home/arthur/Downloads/lfw_funneled/Gian_Marco/Gian_Marco_0001.jpg',
'/home/arthur/Downloads/lfw_funneled/Micky_Ward/Micky_Ward_0001.jpg',
'/home/arthur/Downloads/lfw_funneled/Ziwang_Xu/Ziwang_Xu_0001.jpg',
'/home/arthur/Downloads/lfw_funneled/Zhu_Rongji/Zhu_Rongji_0001.jpg'
]
# Load the subjects and extract their features
hists, labels = load_subjects(imgs, detector, lbp)
# Transform to np arrays
samples = np.array(hists, dtype=np.float32)
labels = np.array(labels, dtype=np.int)
# Train classifiers
svm.train(samples, labels)
knn.train(samples, labels)
# Check which mode the app is running in (image vs. video)
if args.image is not None:
# Read the image from the file path provided
img = cv2.imread(args.image, 0)
# Check the image exists
if img is not None:
# Run face recognition algorithm
classify_snapshot(img, detector, lbp, knn)
else:
print('The image could not be found...')
return
# Establish connection to camera
cap = cv2.VideoCapture(0)
# Continuously grab the next frame from the camera
while cap.isOpened():
# Capture frame-by-frame
ret, frame = cap.read()
# Start timer for performance logging
start = time.time()
# Convert frame to gray scale for face detector
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Detect a face in the frame and crop the image
face_coords = detector.detect(gray)
face = detector.crop_face(gray, face_coords)
# Check we have detected a face
if face is not None:
# Apply LBP operator to get feature descriptor
hist, bins = lbp.run(face, False)
# Convert the LBP descriptor to numpy array for opencv classifiers
test_sample = np.array([hist], dtype=np.float32)
# Get the class of id of the closest neighbour and its distance
dist, class_id = knn.predict(test_sample)
# Draw the face if found
util.draw_face(dist, class_id, frame, face_coords)
# util.segment_face(frame)
# Processing finished
end = time.time()
# Write the fps to the video
util.write_fps(start, end, frame)
# Display the resulting frame
cv2.imshow('frame', frame)
# Check if we should stop the application
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()
