def __init__(self): # load config file VN_config.get_file("Smart_Camera") # get camera specs self.camera_index = VN_config.get_integer("camera", "camera_index", 0) self.camera_width = VN_config.get_integer("camera", "camera_width", 640) self.camera_height = VN_config.get_integer("camera", "camera_height", 480) self.camera_hfov = VN_config.get_float("camera", "horizontal-fov", 72.42) self.camera_vfov = VN_config.get_float("camera", "vertical-fov", 43.3) # simulator self.simulator = VN_config.get_boolean("simulator", "use_simulator", True) self.target_file = VN_config.get_string( "simulator", "target_location", os.environ["HOME"] + "/visnav/target.jpg" ) self.target_size = VN_config.get_float("algorithm", "outer_ring", 1.0) # Run the program no matter what mode or location; Useful for debug purposes self.always_run = VN_config.get_boolean("general", "always_run", True) # how many frames have been captured self.frames_captured = 0 # debugging: self.kill_camera = False
def __init__(self): #load config file VN_config.get_file('Smart_Camera') #get camera specs self.camera_index = VN_config.get_integer('camera', 'camera_index', 0) self.camera_width = VN_config.get_integer('camera', 'camera_width', 640) self.camera_height = VN_config.get_integer('camera', 'camera_height', 480) self.camera_hfov = VN_config.get_float('camera', 'horizontal-fov', 72.42) self.camera_vfov = VN_config.get_float('camera', 'vertical-fov', 43.3) #simulator self.simulator = VN_config.get_boolean('simulator', 'use_simulator', True) self.target_file = VN_config.get_string( 'simulator', 'target_location', os.environ['HOME'] + '/visnav/target.jpg') self.target_size = VN_config.get_float('algorithm', 'outer_ring', 1.0) #Run the program no matter what mode or location; Useful for debug purposes self.always_run = VN_config.get_boolean('general', 'always_run', True) #how many frames have been captured self.frames_captured = 0 #debugging: self.kill_camera = False
def __init__(self): # get which camera we will use self.camera_index = VN_config.get_integer('camera','index',0) # get image resolution self.img_width = VN_config.get_integer('camera','width',640) self.img_height = VN_config.get_integer('camera','height',480) # get image center self.img_center_x = self.img_width / 2 self.img_center_y = self.img_height / 2 # define field of view self.cam_hfov = VN_config.get_float('camera','horizontal-fov',70.42) self.cam_vfov = VN_config.get_float('camera','vertical-fov',43.3) #get camera distortion matrix and intrinsics. Defaults: logitech c920 mtx = np.array([[ 614.01269552,0,315.00073982], [0,614.43556296,237.14926858], [0,0,1.0]]) dist = np.array([0.12269303, -0.26618881,0.00129035, 0.00081791,0.17005303]) self.matrix = VN_config.get_array('camera','matrix', mtx) self.distortion = VN_config.get_array('camera', 'distortion', dist) self.newcameramtx, self.roi=cv2.getOptimalNewCameraMatrix(self.matrix,self.distortion,(self.img_width,self.img_height),1,(self.img_width,self.img_height)) #create a camera object self.camera = None #number of cores available for use desiredCores = VN_config.get_integer('processing', 'desired_cores', 4) self.cores_available = min(desiredCores, multiprocessing.cpu_count()) #does the user want to capture images in the background self.background_capture = VN_config.get_boolean('processing','background_capture', True) # background image processing variables self.proc = None # background process object self.parent_conn = None # parent end of communicatoin pipe self.img_counter = 0 # num images requested so far self.is_backgroundCap = False #state variable for background capture
def __init__(self): #load config file VN_config.get_file('Smart_Camera') #get camera specs self.camera_index = VN_config.get_integer('camera','camera_index',0) self.camera_width = VN_config.get_integer('camera', 'camera_width', 640) self.camera_height = VN_config.get_integer('camera', 'camera_height', 480) self.camera_hfov = VN_config.get_float('camera', 'horizontal-fov', 72.42) self.camera_vfov = VN_config.get_float('camera', 'vertical-fov', 43.3) #use simulator self.simulator = VN_config.get_boolean('simulator','use_simulator',True) #Run the program no matter what mode or location; Useful for debug purposes self.always_run = VN_config.get_boolean('general', 'always_run', True) #how many frames have been captured self.frame_count = 0 #debugging: self.kill_camera = False
def __init__(self): # get which camera we will use self.camera_index = VN_config.get_integer('camera', 'index', 0) # get image resolution self.img_width = VN_config.get_integer('camera', 'width', 640) self.img_height = VN_config.get_integer('camera', 'height', 480) # get image center self.img_center_x = self.img_width / 2 self.img_center_y = self.img_height / 2 # define field of view self.cam_hfov = VN_config.get_float('camera', 'horizontal-fov', 70.42) self.cam_vfov = VN_config.get_float('camera', 'vertical-fov', 43.3) #get camera distortion matrix and intrinsics. Defaults: logitech c920 mtx = np.array([[614.01269552, 0, 315.00073982], [0, 614.43556296, 237.14926858], [0, 0, 1.0]]) dist = np.array( [0.12269303, -0.26618881, 0.00129035, 0.00081791, 0.17005303]) self.matrix = VN_config.get_array('camera', 'matrix', mtx) self.distortion = VN_config.get_array('camera', 'distortion', dist) self.newcameramtx, self.roi = cv2.getOptimalNewCameraMatrix( self.matrix, self.distortion, (self.img_width, self.img_height), 1, (self.img_width, self.img_height)) #create a camera object self.camera = None #number of cores available for use desiredCores = VN_config.get_integer('processing', 'desired_cores', 4) self.cores_available = min(desiredCores, multiprocessing.cpu_count()) #does the user want to capture images in the background self.background_capture = VN_config.get_boolean( 'processing', 'background_capture', True) # background image processing variables self.proc = None # background process object self.parent_conn = None # parent end of communicatoin pipe self.img_counter = 0 # num images requested so far self.is_backgroundCap = False #state variable for background capture
def __init__(self): #The number of core to be used while processing image data #This number may be less than the actaul number of cores on the CPU depending on the users specifications #cores = min(desiredCores, multiprocessing.cpu_count()) //dont allow more cores than the CPU has available and don;t run more than the user wants desired_cores = VN_config.get_integer('processing', 'desired_cores', 4) available_cores = min(desired_cores, multiprocessing.cpu_count()) #check if a core is already in use for background image capture cores_image_capture = int( VN_config.get_boolean('processing', 'background_capture', True)) self.cores_processing = max(available_cores - cores_image_capture, 1) #The time(in millis) is takes to capture an Image #Frame rate = 1000/captureTime #****On some cameras frame rate is dependent on camera exposure level #****i.e. dark = slower fps and light = fast frame rate #****Capture time is dependent on available CPU #This time will be dynamically calculated using an rolling average self.captureTime = 0 self.captureTimeSet = np.zeros(4) #The time(in millis) it takes to process an image #****Process time is dependent on available CPU and the image #This time will be dynamically calculated using an rolling average self.processTime = 0 self.processTimeSet = np.zeros(4) #How often a image is dispatched to be processed(in millis) #Determined by splitting up processTime among available number of cores #*****This will not be smaller than captureTime because we won't allow it to process frames more often than capturing frames; it will cause sync issues with Pipe() #*****If the CPU is slow and has limited cores, we may process everyother frame or every nth frame!!! #runTime = max(processTime/processing_cores,captureTime) self.runTime = 0 #set up a pipe to pass info between background processes and dispatcher self.parent_conn, self.child_conn = multiprocessing.Pipe() #last time we started to process an image self.lastDispatch = 0 #last time an image process completed self.lastRetreival = 0
def __init__(self): #The number of core to be used while processing image data #This number may be less than the actaul number of cores on the CPU depending on the users specifications #cores = min(desiredCores, multiprocessing.cpu_count()) //dont allow more cores than the CPU has available and don;t run more than the user wants desired_cores = VN_config.get_integer('processing', 'desired_cores', 4) available_cores = min(desired_cores, multiprocessing.cpu_count()) #check if a core is already in use for background image capture cores_image_capture = int(VN_config.get_boolean('processing','background_capture', True)) self.cores_processing = max(available_cores - cores_image_capture,1) #The time(in millis) is takes to capture an Image #Frame rate = 1000/captureTime #****On some cameras frame rate is dependent on camera exposure level #****i.e. dark = slower fps and light = fast frame rate #****Capture time is dependent on available CPU #This time will be dynamically calculated using an rolling average self.captureTime = 0 self.captureTimeSet = np.zeros(4) #The time(in millis) it takes to process an image #****Process time is dependent on available CPU and the image #This time will be dynamically calculated using an rolling average self.processTime = 0 self.processTimeSet = np.zeros(4) #How often a image is dispatched to be processed(in millis) #Determined by splitting up processTime among available number of cores #*****This will not be smaller than captureTime because we won't allow it to process frames more often than capturing frames; it will cause sync issues with Pipe() #*****If the CPU is slow and has limited cores, we may process everyother frame or every nth frame!!! #runTime = max(processTime/processing_cores,captureTime) self.runTime = 0 #set up a pipe to pass info between background processes and dispatcher self.parent_conn, self.child_conn = multiprocessing.Pipe() #last time we started to process an image self.lastDispatch = 0 #last time an image process completed self.lastRetreival = 0