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eval.py
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eval.py
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#!/usr/bin/python2.7
# adapted from: https://github.com/colincsl/TemporalConvolutionalNetworks/blob/master/code/metrics.py
import numpy as np
import argparse
def read_file(path):
with open(path, 'r') as f:
content = f.read()
f.close()
return content
def get_labels_start_end_time(frame_wise_labels, bg_class=["background"]):
labels = []
starts = []
ends = []
last_label = frame_wise_labels[0]
if frame_wise_labels[0] not in bg_class:
labels.append(frame_wise_labels[0])
starts.append(0)
for i in range(len(frame_wise_labels)):
if frame_wise_labels[i] != last_label:
if frame_wise_labels[i] not in bg_class:
labels.append(frame_wise_labels[i])
starts.append(i)
if last_label not in bg_class:
ends.append(i)
last_label = frame_wise_labels[i]
if last_label not in bg_class:
ends.append(i)
return labels, starts, ends
def levenstein(p, y, norm=False):
m_row = len(p)
n_col = len(y)
D = np.zeros([m_row+1, n_col+1], np.float)
for i in range(m_row+1):
D[i, 0] = i
for i in range(n_col+1):
D[0, i] = i
for j in range(1, n_col+1):
for i in range(1, m_row+1):
if y[j-1] == p[i-1]:
D[i, j] = D[i-1, j-1]
else:
D[i, j] = min(D[i-1, j] + 1,
D[i, j-1] + 1,
D[i-1, j-1] + 1)
if norm:
score = (1 - D[-1, -1]/max(m_row, n_col)) * 100
else:
score = D[-1, -1]
return score
def edit_score(recognized, ground_truth, norm=True, bg_class=["background"]):
P, _, _ = get_labels_start_end_time(recognized, bg_class)
Y, _, _ = get_labels_start_end_time(ground_truth, bg_class)
return levenstein(P, Y, norm)
def f_score(recognized, ground_truth, overlap, bg_class=["background"]):
p_label, p_start, p_end = get_labels_start_end_time(recognized, bg_class)
y_label, y_start, y_end = get_labels_start_end_time(ground_truth, bg_class)
tp = 0
fp = 0
hits = np.zeros(len(y_label))
for j in range(len(p_label)):
intersection = np.minimum(p_end[j], y_end) - np.maximum(p_start[j], y_start)
union = np.maximum(p_end[j], y_end) - np.minimum(p_start[j], y_start)
IoU = (1.0*intersection / union)*([p_label[j] == y_label[x] for x in range(len(y_label))])
# Get the best scoring segment
idx = np.array(IoU).argmax()
if IoU[idx] >= overlap and not hits[idx]:
tp += 1
hits[idx] = 1
else:
fp += 1
fn = len(y_label) - sum(hits)
return float(tp), float(fp), float(fn)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', default="gtea")
parser.add_argument('--split', default='1')
args = parser.parse_args()
ground_truth_path = "./data/"+args.dataset+"/groundTruth/"
recog_path = "./results/"+args.dataset+"/split_"+args.split+"/"
file_list = "./data/"+args.dataset+"/splits/test.split"+args.split+".bundle"
list_of_videos = read_file(file_list).split('\n')[:-1]
overlap = [.1, .25, .5]
tp, fp, fn = np.zeros(3), np.zeros(3), np.zeros(3)
correct = 0
total = 0
edit = 0
for vid in list_of_videos:
gt_file = ground_truth_path + vid
gt_content = read_file(gt_file).split('\n')[0:-1]
recog_file = recog_path + vid.split('.')[0]
recog_content = read_file(recog_file).split('\n')[1].split()
for i in range(len(gt_content)):
total += 1
if gt_content[i] == recog_content[i]:
correct += 1
edit += edit_score(recog_content, gt_content)
for s in range(len(overlap)):
tp1, fp1, fn1 = f_score(recog_content, gt_content, overlap[s])
tp[s] += tp1
fp[s] += fp1
fn[s] += fn1
print("Acc: %.4f" % (100*float(correct)/total))
print('Edit: %.4f' % ((1.0*edit)/len(list_of_videos)))
acc = (100*float(correct)/total)
edit = ((1.0*edit)/len(list_of_videos))
for s in range(len(overlap)):
precision = tp[s] / float(tp[s]+fp[s])
recall = tp[s] / float(tp[s]+fn[s])
f1 = 2.0 * (precision*recall) / (precision+recall)
f1 = np.nan_to_num(f1)*100
print('F1@%0.2f: %.4f' % (overlap[s], f1))
if __name__ == '__main__':
main()