def main(argv=None):
"""
Establishes a TensorFlow session, restores model, and runs detector
on image(s), writing bounding box information to a text file.
"""
image_filenames = data_tools.get_filenames(
FLAGS.image_path, str.split(FLAGS.filename_pattern, ','),
FLAGS.filename_extension)
if not image_filenames:
print("No matching images. Exiting...")
return
with tf.get_default_graph().as_default():
input_images = tf.placeholder(tf.float32,
shape=[None, None, None, 3],
name='input_images')
f_score, f_geometry = model.outputs(input_images, is_training=False)
with tf.Session() as sess:
restore_model(sess)
for image_file in image_filenames:
predict(sess, image_file, FLAGS.pyramid_levels, input_images,
f_score, f_geometry,
(FLAGS.tile_size, FLAGS.tile_size))
def main(argv=None):
"""Loads up ground truth and prediction files, calculates and
prints statistics
"""
# Load file lists
prediction_files = data_tools.get_filenames(
FLAGS.pred_path,
str.split(FLAGS.filename_pattern,','),
'txt')
ground_truth_files = data_tools.get_paired_filenames(
prediction_files, FLAGS.gt_path, 'json' )
assert len(ground_truth_files) == len(prediction_files)
# Load files contents and package for stats evaluation
predictions = {}
ground_truths = {}
for pred_file,truth_file in zip(prediction_files,ground_truth_files):
base = os.path.splitext(os.path.basename(pred_file))[0]
[_,gt_polys,gt_labels] = data_tools.parse_boxes_from_json( truth_file )
[_,polys,labels,scores] = data_tools.parse_boxes_from_text( pred_file )
if FLAGS.score_thresh: # Filter predictions if necessary
polys,labels,scores = threshold_predictions(
polys, labels, scores)
predictions[base] = { 'polygons' : polys,
'labels' : labels,
'scores' : scores }
ground_truths[base] = {'polygons' : gt_polys,
'labels' : gt_labels }
# Calculate statistics on predictions for ground truths
sample_stats,total_stats = stats.evaluate_predictions(
ground_truths,
predictions,
match_labels=FLAGS.match_labels,
iou_match_thresh=FLAGS.iou_thresh)
# Display save the results
print sample_stats
print total_stats
if FLAGS.save_result:
import json
with open(os.path.join(FLAGS.pred_path,FLAGS.save_result+'.json'),'w') \
as fd:
json.dump({'individual': sample_stats, 'overall': total_stats}, fd,
indent=4)
def _get_filenames(image_path, gt_path, file_patterns, image_ext, gt_ext):
"""
Construct pairs of complete file names from the base path and patterns for
images and their corresponding ground truths
Parameters:
image_path : Directory containing the images
gt_path : Directory containing the ground truth files
file_patterns : List of wildcard basename patterns for reading input files
image_ext : Extension of image files (appended to file_patterns)
gt_ext : Extension of ground truth files
Returns:
img_files : A list of image file names (with path and extension)
gt_files : A list of ground truth file names (with path and extension)
"""
img_files = data_tools.get_filenames(image_path, file_patterns, image_ext)
gt_files = data_tools.get_paired_filenames(img_files, gt_path, gt_ext)
return img_files, gt_files