class App(object):
def __init__(self, model, camera_id, cascade_filename):
self.model = model
self.detector = CascadedDetector(cascade_fn=cascade_filename,
minNeighbors=5,
scaleFactor=1.1)
# self.camera_id = camera_id
def run(self):
while True:
array, _ = freenect.sync_get_video()
frame = cv2.cvtColor(array, cv2.COLOR_RGB2BGR)
# 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)
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)
# Get a prediction from the model:
prediction = self.model.predict(face)[0]
# Draw the face area in image:
requested_name = 'Hao'
if self.model.subject_names[prediction] == requested_name:
cv2.rectangle(imgout, (x0, y0), (x1, y1), (0, 255, 0), 2)
# Draw the predicted name (folder name...):
draw_str(imgout, (x0 - 20, y0 - 20),
self.model.subject_names[prediction])
cv2.imshow('videofacerec', imgout)
# Show image & exit on escape:
ch = cv2.waitKey(10)
if ch == 27:
break
def __init__(self, model, camera_id, cascade_filename):
self.model = model
self.detector = CascadedDetector(cascade_fn=cascade_filename,
minNeighbors=5,
scaleFactor=1.1)