def __init__(self, model, camera_id, cascade_filename, run_local, wait=50):
self.model = model
self.wait = wait
self.detector = CascadedDetector(cascade_fn=cascade_filename,
minNeighbors=5,
scaleFactor=1.1)
if run_local:
self.cam = create_capture(camera_id)
class Recognizer(object):
def __init__(self, model, camera_id, cascade_filename, run_local, wait=50):
self.model = model
self.wait = wait
self.detector = CascadedDetector(cascade_fn=cascade_filename,
minNeighbors=5,
scaleFactor=1.1)
if run_local:
self.cam = create_capture(camera_id)
def run(self):
while True:
ret, frame = self.cam.read()
# Resize the frame to half the original size for speeding up the detection process:
# img = cv2.resize(frame, (frame.shape[1] / 2, frame.shape[0] / 2), interpolation=cv2.INTER_CUBIC)
img = cv2.resize(frame, (320, 240), interpolation=cv2.INTER_CUBIC)
imgout = img.copy()
for i, r in enumerate(self.detector.detect(img)):
x0, y0, x1, y1 = r
# (1) Get face, (2) Convert to grayscale & (3) resize to image_size:
face = img[y0:y1, x0:x1]
face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)
face = cv2.resize(face,
self.model.image_size,
interpolation=cv2.INTER_CUBIC)
prediction = self.model.predict(face)
predicted_label = prediction[0]
classifier_output = prediction[1]
# Now let's get the distance from the assuming a 1-Nearest Neighbor.
# Since it's a 1-Nearest Neighbor only look take the zero-th element:
distance = classifier_output['distances'][0]
# Draw the face area in image:
cv2.rectangle(imgout, (x0, y0), (x1, y1), (0, 0, 255), 2)
# Draw the predicted name (folder name...):
draw_str(imgout, (x0 - 20, y0 - 40),
"Label " + self.model.subject_names[predicted_label])
draw_str(imgout, (x0 - 20, y0 - 20),
"Feature Distance " + "%1.1f" % distance)
cv2.imshow('OCVFACEREC < LOCAL', imgout)
key = cv2.waitKey(self.wait)
if 'q' == chr(key & 255):
print ">> 'q' Pressed. Exiting."
break
def __init__(self, model, cascade_filename, run_local, wait, rp):
self.rp = rp
self.wait = wait
self.doRun = True
self.model = model
self.restart = False
self.ros_restart_request = False
self.detector = CascadedDetector(cascade_fn=cascade_filename,
minNeighbors=5,
scaleFactor=1.1)
if run_local:
print ">> Error: Run local selected in ROS based Recognizer"
sys.exit(1)
else:
self.bridge = CvBridge()
def signal_handler(signal, frame):
print ">> ROS Exiting"
self.doRun = False
signal.signal(signal.SIGINT, signal_handler)
def __init__(self,
model,
camera_id,
cascade_filename,
run_local,
inscope="/rsbopencv/ipl",
outscope="/ocvfacerec/rsb/people",
wait=50,
notification="/ocvfacerec/rsb/restart/",
show_gui=False):
self.model = model
self.wait = wait
self.notification_scope = notification
self.show_gui = show_gui
self.detector = CascadedDetector(cascade_fn=cascade_filename,
minNeighbors=5,
scaleFactor=1.1)
if run_local:
print ">> Error Run local selected in RSB based Recognizer"
sys.exit(1)
self.doRun = True
self.restart = False
def signal_handler(signal, frame):
print "\n>> RSB Exiting"
self.doRun = False
signal.signal(signal.SIGINT, signal_handler)
# RSB
try:
rsb.converter.registerGlobalConverter(IplimageConverter())
except Exception, e:
# If they are already loaded, the lib throws an exception ...
# print ">> [Error] While loading RST converter: ", e
pass
class Recognizer(object):
def __init__(self, model, camera_id, cascade_filename, run_local, wait=50):
self.model = model
self.wait = wait
self.detector = CascadedDetector(cascade_fn=cascade_filename, minNeighbors=5, scaleFactor=1.1)
if run_local:
self.cam = create_capture(camera_id)
def run(self):
while True:
ret, frame = self.cam.read()
# Resize the frame to half the original size for speeding up the detection process:
# img = cv2.resize(frame, (frame.shape[1] / 2, frame.shape[0] / 2), interpolation=cv2.INTER_CUBIC)
img = cv2.resize(frame, (320, 240), interpolation=cv2.INTER_CUBIC)
imgout = img.copy()
for i, r in enumerate(self.detector.detect(img)):
x0, y0, x1, y1 = r
# (1) Get face, (2) Convert to grayscale & (3) resize to image_size:
face = img[y0:y1, x0:x1]
face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)
face = cv2.resize(face, self.model.image_size, interpolation=cv2.INTER_CUBIC)
prediction = self.model.predict(face)
predicted_label = prediction[0]
classifier_output = prediction[1]
# Now let's get the distance from the assuming a 1-Nearest Neighbor.
# Since it's a 1-Nearest Neighbor only look take the zero-th element:
distance = classifier_output['distances'][0]
# Draw the face area in image:
cv2.rectangle(imgout, (x0, y0), (x1, y1), (0, 0, 255), 2)
# Draw the predicted name (folder name...):
draw_str(imgout, (x0 - 20, y0 - 40), "Label " + self.model.subject_names[predicted_label])
draw_str(imgout, (x0 - 20, y0 - 20), "Feature Distance " + "%1.1f" % distance)
cv2.imshow('OCVFACEREC < LOCAL', imgout)
key = cv2.waitKey(self.wait)
if 'q' == chr(key & 255):
print ">> 'q' Pressed. Exiting."
break
def __init__(self, model, camera_id, cascade_filename, run_local, wait=50):
self.model = model
self.wait = wait
self.detector = CascadedDetector(cascade_fn=cascade_filename, minNeighbors=5, scaleFactor=1.1)
if run_local:
self.cam = create_capture(camera_id)
