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metric.py
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metric.py
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import numpy as np
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])
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"]):
print(111111)
print(recognized.shape)
p_label, p_start, p_end = get_labels_start_end_time(recognized, bg_class)
print(len(p_label))
y_label, y_start, y_end = get_labels_start_end_time(ground_truth, bg_class)
print(len(y_label))
tp = 0
fp = 0
hits = np.zeros(len(y_label))
# print(p_label)
print(p_start)
print(p_end)
# print(y_label)
print(y_start)
print(y_end)
for j in range(len(p_label)):
intersection = np.minimum(p_end[j], y_end) - np.maximum(p_start[j], y_start)
print(np.minimum(p_end[j], y_end))
print(np.maximum(p_start[j], y_start))
print(intersection)
union = np.maximum(p_end[j], y_end) - np.minimum(p_start[j], y_start)
print(np.maximum(p_end[j], y_end))
print(np.minimum(p_start[j], y_start))
print(union)
IoU = (1.0 * intersection / union) * ([p_label[j] == y_label[x] for x in range(len(y_label))])
print((1.0 * intersection / union))
print(([p_label[j] == y_label[x] for x in range(len(y_label))]))
# Get the best scoring segment
idx = np.array(IoU).argmax()
print(IoU)
print(idx)
print(np.array(IoU).shape)
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)
if __name__ == '__main__':
import torch
import os
from tqdm import tqdm
path ='/mnt/data3/chai/openpack_dataset/journal_v0.3.1/log/openpack-2d-kpt/CTRGCN4Seg/CTRGCN_base/pred'
pred = []
label = []
tp, fp, fn = np.zeros(3), np.zeros(3), np.zeros(3)
overlap = [.1, .25, .5]
correct = 0
total = 0
edit = 0
flap = 0
for item in tqdm(os.listdir(path)):
if flap ==1:
break
flap = 1
gt_content = np.load(os.path.join(path, item, 't.npy')).reshape(-1)
recog_content = np.load(os.path.join(path, item, 'y.npy'))
recog_content = np.argmax(recog_content,axis=1).reshape(-1)
print(recog_content.shape,gt_content.shape)
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
acc = 100 * float(correct) / total
edit = (1.0 * edit) / len(os.listdir(path))
print('Accyracy: %.4f' % (acc))
print('edit: %.4f' % (edit))
f1s = np.array([0, 0, 0], dtype=float)
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))
f1s[s] = f1