def main():
# maybe make it read from multiple places?
# samples = ["CRAFT", "EAST", "USC", "GCP_lang_hints", "AWS", "GCP"]
samples = ["GCP", "GCP_crops", "CRAFT_attn"]
# samples = ["AWS"]
filenames = ["{}_indo.txt".format(name) for name in samples]
guessed_words = []
annotations = {}
data = {i: {} for i in range(len(filenames))}
name_dict = {i: name for i, name in enumerate(samples)}
for i, filename in enumerate(filenames):
with open(filename, "r") as f:
lines = f.readlines()
for line in lines:
file_path, *words = line.strip().split(",")
words = [x for x in words if len(x) != 0]
# Get the correct words
n = get_filename(file_path)
if n not in annotations:
img_annotations = get_annotations(n)
annotations[n] = img_annotations
# i represents which of the sources it came from - CRAFT, EAST,
# USC
guessed_words.append((i, n, words))
possible_true_words_dict = None
for i, n, words in guessed_words:
# i represents the source
# get the real annotations
annotation = annotations[n]
# Skip the annotation
if skip_annotation(annotation):
continue
matcher = Matcher(annotation,
words,
possible_true_words_dict=possible_true_words_dict)
possible_true_words_dict = matcher.possible_true_words_dict
word_n = len(annotation)
if word_n == 0:
# TODO remove this, if we end up testing with no text files
continue
if annotations:
perfect_matches = matcher.get_perfect_matches()
ign_symbols = matcher.get_perfect_matches_ignoring_symbols()
perf = len(perfect_matches) / word_n * 100
ign = len(ign_symbols) / word_n * 100
# vocab_matched = matcher.get_vocab_matches()
# vcb = len(vocab_matched) / word_n * 100
# mismatched = matcher.get_imperfect_matches(1)
# msm = len(mismatched)/word_n * 100
# percent_matched = perf + ign + vcb# maybe lets ignore the mismatched
percent_matched = perf + ign
# ones?
# percent_matched = perf
data[i][n] = percent_matched
unmatched = matcher.get_number_unmatched()
if unmatched > 4: # ignore all but craft
# if i == 3: # ignore all but craft
print(name_dict[i], n)
print("ANNOTATED", *matcher.get_unmatched_annotated())
print("DETECTED", *matcher.get_unmatched_detected())
print(">", matcher.char_level_accuracy)
print("=================================")
# print(n, perf, ign, unmatched)
for k in data:
avg = sum(data[k].values()) / (len(list(data[k].values())))
print(name_dict[k], '\t', round(avg, 2))
