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
예제 #2
0
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))