def evaluate_performance(params):
name, original_path, label_path = params
y_true = []
y_pred = []
video_segmentizer = Segmentizer(352, 288)
data_loader = LASIESTADataLoader(original_path, label_path)
for i, (original_frame, label_frame) in enumerate(data_loader):
predicted_background = video_segmentizer.fit_and_predict(original_frame)
label_frame = label_frame.tolist()
label_frame = [[background_map_conversion(rgb) for rgb in row] for row in label_frame]
y_true += list(chain.from_iterable(label_frame))
y_pred += list(chain.from_iterable(predicted_background))
y_pred, y_true = remove_uncertain_pixels(y_pred, y_true)
score = f1_score(y_true, y_pred)
print('Finished evaluation of dataset ' + name)
return name, score
def __init__(self,
source='kinect',
visuals=False,
filesource='default',
caldestination='default'):
# bit depth of the used depth image
self.imageDepth = 8
#current frame thats used for calibration
self.currentFrame = 0
#segmentizer, used for segmentating images into segments
#able to create a matrix with the size of the future obstaclematrix
self.segmentizer = Segmentizer()
# create visualizer to show region and segment size
# The visualizer is the same as the visualizer used to show obstacle avoidance
# using the visualizer the region of interest for obstacle detection and the segment size can be changed
if visuals == True:
import visualisation.avoidancevisualizer
self.visualizer = visualisation.avoidancevisualizer.AvoidanceVisualizer(
)
# filesource is the folder for testfiles. Used for testing
self.filesource = filesource
# caldestination is the folder where calibration files will be saved
# if this module is run on 'kinect-nav', the files will later automatically be send to the 'brain'
if caldestination == 'default':
self.caldestination = os.environ[
'BORG'] + "/brain/src/vision/obstacledetectorutil/"
else:
self.caldestination = caldestination
# specify the source (file or kinect) where the images will be taken from
# 'file' is used for testing
# When 'kinect', the images will be taken directly from the Kinect
self.source = source
if self.source == "file":
if self.filesource == 'default':
self.path = os.environ[
'BORG'] + '/brain/data/od_images/ball_none/'
else:
self.path = self.filesource
self.filelist = glob.glob(os.path.join(self.path, '*.png'))
#Used to retreive depth and RGB data:
self.rgb_retreiver = util.rosimage.RosImage("/camera/rgb/image_color",
"passthrough")
self.depth_retreiver = util.rosimage.RosImage(
"/camera/depth_registered/image_raw", "passthrough")
from segmentizer import Segmentizer
from segmentizer.data_loader import LASIESTADataLoader
import time
data_loader = LASIESTADataLoader('/Users/dsoellinger/Downloads/I_SI_01')
video_segmentizer = Segmentizer(352, 288)
start = time.time()
for i, frame in enumerate(data_loader):
if i == 10:
break
print("Frame: " + str(i + 1))
video_segmentizer.fit_and_predict(frame)
end = time.time()
print("Elapsed time: " + str(end - start))
