Datasets:
jblitzar
/
github-python

Dataset card Data Studio Files
xet
Community
text
stringlengths
0
93.6k
assert len(FLAGS.labels) == 2
labels_a, labels_b = FLAGS.labels[0], FLAGS.labels[1]
label_a_paths = os.path.join(FLAGS.dataset, "sequences", FLAGS.sequence, labels_a)
label_b_paths = os.path.join(FLAGS.dataset, "sequences", FLAGS.sequence, labels_b)
if os.path.isdir(label_a_paths):
print("Labels folder a exists! Using labels from %s" % label_a_paths)
else:
print("Labels folder a doesn't exist! Exiting...")
quit()
if os.path.isdir(label_b_paths):
print("Labels folder b exists! Using labels from %s" % label_b_paths)
else:
print("Labels folder b doesn't exist! Exiting...")
quit()
# populate the pointclouds
label_a_names = [os.path.join(dp, f) for dp, dn, fn in os.walk(os.path.expanduser(label_a_paths)) for f in fn]
label_a_names.sort()
label_b_names = [os.path.join(dp, f) for dp, dn, fn in os.walk(os.path.expanduser(label_b_paths)) for f in fn]
label_b_names.sort()
# check that there are same amount of labels and scans
if not FLAGS.ignore_safety:
assert len(label_a_names) == len(scan_names)
assert len(label_b_names) == len(scan_names)
# create scans
color_dict = CFG["color_map"]
if FLAGS.color_learning_map:
learning_map_inv = CFG["learning_map_inv"]
learning_map = CFG["learning_map"]
color_dict = {key: color_dict[learning_map_inv[learning_map[key]]] for key, value in color_dict.items()}
scan_b = SemLaserScan(color_dict, project=True)
scan_a = SemLaserScan(color_dict, project=True)
# create a visualizer
images = not FLAGS.ignore_images
vis = LaserScanComp(scans=(scan_a, scan_b),
scan_names=scan_names,
label_names=(label_a_names, label_b_names),
offset=FLAGS.offset, images=images, instances=FLAGS.do_instances, link=FLAGS.link)
# print instructions
print("To navigate:")
print("\tb: back (previous scan)")
print("\tn: next (next scan)")
print("\tq: quit (exit program)")
# run the visualizer
vis.run()
# <FILESEP>
import difflib
import logging
import sys
def reconstruct_full_result(result, processed_text):
words_full_postprocessed = []
words_full = result["result"]
words = [wi["word"] for wi in words_full]
words_postprocessed = processed_text.split()
#print(f"seq matching: {words} --- {words_postprocessed}")
s = difflib.SequenceMatcher(None, words, words_postprocessed)
for tag, i1, i2, j1, j2 in s.get_opcodes():
if tag in ["delete"]:
print("Warning: postprocessor should only replace or insert words (or word blocks), but [%s] detected" % tag, file=sys.stderr)
words_full_postprocessed = words_full
break
else:
if tag == "equal":
words_full_postprocessed.extend(words_full[i1:i2])
elif tag == "insert":
if len(words_full_postprocessed) > 0:
words_full_postprocessed[-1]["word"] += " " + " ".join(words_postprocessed[j1:j2])
elif tag == "replace":
new_word = {"word" : " ".join(words_postprocessed[j1:j2])}
new_word["start"] = words_full[i1]["start"]
for key in words_full[i2-1].keys():
if key not in ["word", "start", "phones"]:
new_word[key] = words_full[i2-1][key]
if "word_with_punctuation" in new_word:
new_word["word_with_punctuation"] = new_word["word"] + new_word["punctuation"]
new_word["unnormalized_words"] = words_full[i1:i2]
if "confidence" in new_word:
new_word["confidence"] = min([w["confidence"] for w in words_full[i1:i2]])
words_full_postprocessed.append(new_word)
result["result"] = words_full_postprocessed
result["text"] = " ".join(wi["word"] for wi in result["result"])
return result
def test_reconstruct():
result = {
"final" : False,