def __init__(self, tca, img_source, config=None, frame_type=0, data_proc=None):
self.img_source = img_source
self.last_frame = None
self.__avg_frame = None
self.frame_type = FRAME_TYPES[frame_type]
self.cal_data = None
# Tactical Computer Application
self.tca = tca
# Source Calibration Module
self.scm = SourceCalibrationModule(self, config)
# ObjectDetectionModule
self.odm = ObjectDetectionModule(self, config)
self.config = config
if data_proc is None:
self.data_proc = DataProcessor()
else:
self.data_proc = data_proc
class ImageProcessor(object):
def __init__(self, tca, img_source, config=None, frame_type=0, data_proc=None):
self.img_source = img_source
self.last_frame = None
self.__avg_frame = None
self.frame_type = FRAME_TYPES[frame_type]
self.cal_data = None
# Tactical Computer Application
self.tca = tca
# Source Calibration Module
self.scm = SourceCalibrationModule(self, config)
# ObjectDetectionModule
self.odm = ObjectDetectionModule(self, config)
self.config = config
if data_proc is None:
self.data_proc = DataProcessor()
else:
self.data_proc = data_proc
def process(self):
self.last_frame = self.img_source.read()
if self.avg_frame is None:
self.avg_frame = self.last_frame
# Find objects from the image source
self.last_frame, self.avg_frame, img_data = self.odm.findObjects(
self.last_frame, self.avg_frame, self.frame_type)
# Display calibration points
if self.cal_data and self.frame_type == 'main':
for num, cal_point in enumerate(self.cal_data.image_points, 1):
point = (cal_point[0], cal_point[1])
color_intensity = ((num - 1) % 3) / 3.0 * 200 + 55
color = (0, 0, color_intensity) if num > 3 else (0, color_intensity, 0)
cv.circle(self.last_frame, point, 5, color, thickness=-1)
cv.circle(self.last_frame, point, 5, [0, 0, 0], thickness=2)
# Display calibration status
cal_status_color = [0, 255, 0] if self.cal_data else [0, 0, 255]
cv.circle(self.last_frame, (self.img_source.width - 25, 25), 20, [0, 0, 0], thickness=5)
cv.circle(self.last_frame, (self.img_source.width - 25, 25), 20, cal_status_color, thickness=-1)
cv.circle(self.last_frame, (self.img_source.width - 25, 25), 20, [255, 255, 255], thickness=2)
self.data_proc.process(img_data, self)
return self.last_frame
@property
def avg_frame(self):
#define average frame property to be used in process
return self.__avg_frame
@avg_frame.setter
def avg_frame(self, frame):
#set average frame using numpy float
if self.__avg_frame is None:
self.__avg_frame = np.float32(frame)
else:
self.__avg_frame = frame
def saveFrame(self, filename=""):
self.img_source.save(filename, self.last_frame)
def setFrameType(self, frame_type):
if isinstance(frame_type, basestring):
if frame_type in FRAME_TYPES:
self.frame_type = frame_type
else:
raise Exception("Invalid frame type '%s'" % frame_type)
else:
self.frame_type = FRAME_TYPES[frame_type]
def __string__(self):
return 'Image Processor{%r}' % self.image_source
def __repr__(self):
return self.__string__()
