def process(self):
import numpy
from calculators.image import ImageData
audio = self.get(0)
if isinstance(audio, AudioData):
hop_length = 256 # number of samples per time-step in spectrogram
n_mels = 128 # number of bins in spectrogram. Height of image
time_steps = 384 # number of time-steps. Width of image
start_sample = 0 # starting at beginning
length_samples = time_steps*hop_length
sr = 16000
y = numpy.fromstring(audio.audio, numpy.int16) / 32768.0
if self.audio is None:
self.audio = y
else:
self.audio = numpy.append(self.audio, y)
if len(self.audio) > length_samples:
self.audio = self.audio[len(self.audio) - length_samples:]
y = self.audio
window = y[start_sample:start_sample+length_samples]
img = spectrogram_image(window, sr=sr, hop_length=hop_length, n_mels=n_mels)
self.set_output(0, ImageData(img, audio.timestamp))
return True
return False
def process(self):
image = self.get(0)
if isinstance(image, ImageData):
nf = image.image.copy()
nf = self._process_image(nf)
self.set_output(0, ImageData(nf, image.timestamp))
return True
return False
def process(self):
if self.input_data[0] is not None and self.input_data[1] is not None:
image = self.get(0)
(kp, box) = self.get(1)
if isinstance(image, ImageData):
nf = image.image.copy()
if kp is not None:
handtracker.hand_tracker.draw_hand(nf, kp)
handtracker.hand_tracker.draw_box(nf, box)
self.set_output(0, ImageData(nf, image.timestamp))
return True
return False
def process(self):
image = self.get(0)
if isinstance(image, ImageData):
nf = image.image.copy()
img = nf[:, :, ::-1]
kp, box = self.detector(img)
if kp is not None:
handtracker.hand_tracker.draw_hand(nf, kp)
handtracker.hand_tracker.draw_box(nf, box)
self.set_output(0, ImageData(nf, image.timestamp))
self.set_output(1, (kp, box))
return True
return False
def process(self):
image = self.get(0)
if isinstance(image, ImageData):
frame = image.image.copy()
# Scale image for faster processing
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
face_locations = face_recognition.face_locations(small_frame)
# Loop through each face found in the unknown image
for (top, right, bottom, left) in face_locations:
# Scale coordinates back to original image size
top *= 4
right *= 4
bottom *= 4
left *= 4
cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 3)
self.set_output(0, ImageData(frame, image.timestamp))
return True
return False