def main(models, n):
assert six.PY3
if not common_tools.exists_and_newer(settings.TEST_CLS_CROPPED, settings.TEST_CLASSIFICATION):
print('creating', settings.TEST_CLS_CROPPED)
predictions2html.create_pkl()
with open(settings.TEST_CLS_CROPPED, 'rb') as f:
gts = cPickle.load(f)
with open(settings.STAT_FREQUENCY) as f:
stat_freq = json.load(f)
preds = []
for model in models:
all = []
with open(model['predictions_file_path']) as f:
for line in f:
obj = json.loads(line.strip())
all += list(zip(obj['predictions'], obj['probabilities']))
assert len(gts) == len(all)
preds.append(all)
np.random.seed(n + 2018)
sampled = np.array(range(len(gts)))
np.random.shuffle(sampled)
sampled = sampled[:n]
dir_name = 'cls_examples'
root = os.path.join(settings.PRODUCTS_ROOT, dir_name)
if not os.path.isdir(root):
os.makedirs(root)
def text2minipage(text):
i = [o['text'] for o in stat_freq].index(text)
return r'\begin{minipage}{3.5mm} \includegraphics[width=\linewidth]{figure/texts/' + '0_{}.png'.format(i) + r'} \end{minipage}'
for no, i in enumerate(sampled):
file_name = '{}.png'.format(i)
image, gt = gts[i]
image = misc.toimage(image)
image.save(os.path.join(root, file_name), format='png')
s = '{} & '.format(no + 1) + r'\begin{minipage}{6.0mm} \includegraphics[width=\linewidth]{figure/cls_examples/' + '{}.png'.format(i) + r'} \end{minipage} &' + '\n'
s += '{} &\n'.format(text2minipage(gt['text']))
for j, preds_model in enumerate(preds):
texts, probs = preds_model[i]
prob_text = '{:5.1f}'.format(round(probs[0] * 1000) / 10.)
prob_text = prob_text.replace(' ', r'\,\,\,')
col = '{} {}'.format(text2minipage(texts[0]), prob_text)
if texts[0] == gt['text']:
col = r'\multicolumn{1}{>{\columncolor{cls_correct}}r}{' + col + '}'
if j == len(preds) - 1:
col += r' \\'
else:
col += ' &'
s += col + '\n'
print(s.replace('\n', ' ').strip())
def main():
write_darknet_data()
write_darknet_cfg()
write_darknet_names()
assert os.path.isfile(settings.DARKNET_PRETRAIN) and 79327120 == os.path.getsize(settings.DARKNET_PRETRAIN), \
'please download {} to {}'.format('https://pjreddie.com/media/files/darknet19_448.conv.23', settings.DARKNET_PRETRAIN)
if not common_tools.exists_and_newer(settings.DARKNET_TRAIN_LIST,
settings.CATES):
crop_train_images()
def main():
write_darknet_test_cfg()
if not common_tools.exists_and_newer(settings.DARKNET_VALID_LIST,
settings.CATES):
crop_test_images(settings.DARKNET_VALID_LIST)
def main():
if not common_tools.exists_and_newer(settings.TEST_LIST, settings.CATES):
dn_prepare = imp.load_source('dn_prepare',
'../detection/prepare_test_data.py')
dn_prepare.crop_test_images(settings.TEST_LIST)
def main(models, n):
assert six.PY3
if not common_tools.exists_and_newer(settings.TEST_CLS_CROPPED,
settings.TEST_CLASSIFICATION):
print('creating', settings.TEST_CLS_CROPPED)
create_pkl()
with open(settings.TEST_CLS_CROPPED, 'rb') as f:
gts = cPickle.load(f)
preds = []
for model in models:
all = []
with open(model['predictions_file_path']) as f:
for line in f:
obj = json.loads(line.strip())
all += list(zip(obj['predictions'], obj['probabilities']))
assert len(gts) == len(all)
preds.append(all)
np.random.seed(0)
sampled = np.array(range(len(gts)))
np.random.shuffle(sampled)
sampled = sampled[105:105 + n]
with open('predictions_compare.template.html') as f:
template = Template(f.read())
rows = []
for i in sampled:
row = dict()
image, gt = gts[i]
image = misc.toimage(image)
bytesio = six.BytesIO()
image.save(bytesio, format='png')
png_base64 = base64.b64encode(bytesio.getvalue())
row['png_base64'] = png_base64.decode('ascii')
row['ground_truth'] = gt['text']
row_models = []
for preds_model in preds:
texts, probs = preds_model[i]
prob_text = '{:5.1f}%'.format(round(probs[0] * 1000) / 10.)
prob_text = prob_text.replace(' ', ' ')
row_models.append({
'text': texts[0],
'prob': prob_text,
})
row['models'] = row_models
rows.append(row)
with open(settings.PREDICTIONS_HTML, 'w') as f:
f.write(
template.render({
'models':
list(map(operator.itemgetter('display_name'), models)),
'rows':
rows,
}))
with open(settings.STAT_FREQUENCY) as f:
freq = json.load(f)
text2idx = {o['text']: i for i, o in enumerate(freq)}
dir_name = 'cls_examples'
root = os.path.join(settings.PRODUCTS_ROOT, dir_name)
if not os.path.isdir(root):
os.makedirs(root)
def text2minipage(text):
return r'\begin{minipage}{3mm} \includegraphics[width=\linewidth]{figure/texts/' \
+ '0_{}.png'.format(text2idx[text]) + r'} \end{minipage}'
for no, i in enumerate(sampled):
file_name = '{}.png'.format(i)
image, gt = gts[i]
image = misc.toimage(image)
image.save(os.path.join(root, file_name), format='png')
s = r'\begin{minipage}{6.0mm} \includegraphics[width=\linewidth]{figure/cls_examples/' + '{}.png'.format(
i) + r'} \end{minipage} &' + '\n'
s += '{} &\n'.format(text2minipage(gt['text']))
for j, preds_model in enumerate(preds):
texts, probs = preds_model[i]
prob_text = '{:5.1f}'.format(round(probs[0] * 1000) / 10.)
prob_text = prob_text.replace(' ', r'\,\,\,')
col = '{} {}'.format(text2minipage(texts[0]), prob_text)
if texts[0] == gt['text']:
col = r'\multicolumn{1}{>{\columncolor{cls_correct}}r}{' + col + '}'
if j == len(preds) - 1:
col += r' \\'
else:
col += ' &'
s += col + '\n'