def capture(self):
toolsConfig = ToolsConfig(self.captureName)
camera = toolsConfig.getCamera()
print('Capturing positive training images.')
print('Press enter to capture an image.')
print('Press Ctrl-C to quit.')
while True:
try:
input()
print('Capturing image...')
image = camera.read()
# Convert image to grayscale.
image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# Get coordinates of single face in captured image.
result = self.face.detect_single(image)
if result is None:
print('Could not detect single face!' +
' Check the image in capture.pgm' +
' to see what was captured and try' +
' again with only one face visible.')
continue
x, y, w, h = result
# Crop image as close as possible to desired face aspect ratio.
# Might be smaller if face is near edge of image.
crop = self.face.crop(image, x, y, w, h,
int(ToolsConfig.getFaceFactor() * w))
# Save image to file.
filename, count = toolsConfig.getNewCaptureFile()
cv2.imwrite(filename, crop)
print('Found face and wrote training image', filename)
except KeyboardInterrupt:
camera.stop()
break
def convert(self, rawDir):
toolsConfig = ToolsConfig(self.captureName)
filename, count = toolsConfig.getNewCaptureFile()
for filename in ToolsConfig.walkFiles(rawDir, '*'):
if not re.match('.+\.(jpg|jpeg)$', filename, re.IGNORECASE):
print("file {0} does not have the correct file extention.".
format(filename))
continue
print("processing {0}".format(filename))
image = cv2.imread(filename)
height, width, channels = image.shape
image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# TODO: check for multiple faces and warn
# Get coordinates of single face in captured image.
result = self.face.detect_single(image)
if result is None:
if (height + width > 800):
# it's a big image resize it and try again
mult = 0.5
print('Resizing from ({0},{1}) -> ({2},{3})'.format(
height, width, int(mult * height), int(mult * width)))
image2 = cv2.resize(image, None, fx=mult, fy=mult)
result = self.face.detect_single(image2)
if result is None:
mult = 0.25
print('Resizing from ({0},{1}) -> ({2},{3})'.format(
height, width, int(mult * height),
int(mult * width)))
image2 = cv2.resize(image, None, fx=mult, fy=mult)
result = self.face.detect_single(image2)
if result is not None:
print('It worked, found a face in resized image!')
image = image2
if result is None:
print('No face found')
continue
x, y, w, h = result
# Crop image as close as possible to desired face aspect ratio.
# Might be smaller if face is near edge of image.
crop = self.face.crop(image, x, y, w, h,
int(ToolsConfig.getFaceFactor() * w))
# Save image to file.
toFilename = os.path.join(ToolsConfig.TRAINING_DIR,
self.captureName, '%03d.pgm' % count)
cv2.imwrite(toFilename, crop)
print('Found face and wrote training image', toFilename)
count += 1
def capture(self, captureName):
camera = FacialRecognitionController.getCamera()
toolsConfig = ToolsConfig(captureName)
image = camera.read()
camera._camera.release()
# Convert image to grayscale.
image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# Get coordinates of single face in captured image.
result = self.face.detect_single(image)
if result is None:
return None
x, y, w, h = result
# Crop image as close as possible to desired face aspect ratio.
# Might be smaller if face is near edge of image.
crop = self.face.crop(image, x, y, w, h,
int(ToolsConfig.getFaceFactor() * w))
# Save image to file.
filename, count = toolsConfig.getNewCaptureFile()
cv2.imwrite(filename, crop)
return filename
def __init__(self, capName=None):
self.face = ToolsConfig.getFaceDetection()
self.captureName = capName
def __init__(self, facialRecognitionDir):
self.face = ToolsConfig.getFaceDetection()
ToolsConfig.TRAINING_DIR = facialRecognitionDir