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
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))
