def _process_batch(tensor_dict,
sess,
batch_index,
counters,
losses_dict=None):
"""Evaluates tensors in tensor_dict, losses_dict and visualizes examples.
This function calls sess.run on tensor_dict, evaluating the original_image
tensor only on the first K examples and visualizing detections overlaid
on this original_image.
Args:
tensor_dict: a dictionary of tensors
sess: tensorflow session
batch_index: the index of the batch amongst all batches in the run.
counters: a dictionary holding 'success' and 'skipped' fields which can
be updated to keep track of number of successful and failed runs,
respectively. If these fields are not updated, then the success/skipped
counter values shown at the end of evaluation will be incorrect.
losses_dict: Optional dictonary of scalar loss tensors.
Returns:
result_dict: a dictionary of numpy arrays
result_losses_dict: a dictionary of scalar losses. This is empty if input
losses_dict is None.
"""
try:
if not losses_dict:
losses_dict = {}
result_dict, result_losses_dict = sess.run(
[tensor_dict, losses_dict])
counters['success'] += 1
except tf.errors.InvalidArgumentError:
logging.info('Skipping image')
counters['skipped'] += 1
return {}, {}
global_step = tf.compat.v1.train.global_step(
sess, tf.compat.v1.train.get_global_step())
if batch_index < eval_config.num_visualizations:
tag = 'image-{}'.format(batch_index)
eval_util.visualize_detection_results(
result_dict,
tag,
global_step,
categories=categories,
summary_dir=eval_dir,
export_dir=eval_config.visualization_export_dir,
show_groundtruth=eval_config.visualize_groundtruth_boxes,
groundtruth_box_visualization_color=eval_config.
groundtruth_box_visualization_color,
min_score_thresh=eval_config.min_score_threshold,
max_num_predictions=eval_config.max_num_boxes_to_visualize,
skip_scores=eval_config.skip_scores,
skip_labels=eval_config.skip_labels,
keep_image_id_for_visualization_export=eval_config.
keep_image_id_for_visualization_export)
return result_dict, result_losses_dict
def _process_batch(tensor_dict, sess, batch_index, counters,
losses_dict=None):
"""Evaluates tensors in tensor_dict, losses_dict and visualizes examples.
This function calls sess.run on tensor_dict, evaluating the original_image
tensor only on the first K examples and visualizing detections overlaid
on this original_image.
Args:
tensor_dict: a dictionary of tensors
sess: tensorflow session
batch_index: the index of the batch amongst all batches in the run.
counters: a dictionary holding 'success' and 'skipped' fields which can
be updated to keep track of number of successful and failed runs,
respectively. If these fields are not updated, then the success/skipped
counter values shown at the end of evaluation will be incorrect.
losses_dict: Optional dictonary of scalar loss tensors.
Returns:
result_dict: a dictionary of numpy arrays
result_losses_dict: a dictionary of scalar losses. This is empty if input
losses_dict is None.
"""
try:
if not losses_dict:
losses_dict = {}
result_dict, result_losses_dict = sess.run([tensor_dict, losses_dict])
counters['success'] += 1
except tf.errors.InvalidArgumentError:
logging.info('Skipping image')
counters['skipped'] += 1
return {}, {}
global_step = tf.train.global_step(sess, tf.train.get_global_step())
if batch_index < eval_config.num_visualizations:
tag = 'image-{}'.format(batch_index)
eval_util.visualize_detection_results(
result_dict,
tag,
global_step,
categories=categories,
summary_dir=eval_dir,
export_dir=eval_config.visualization_export_dir,
show_groundtruth=eval_config.visualize_groundtruth_boxes,
groundtruth_box_visualization_color=eval_config.
groundtruth_box_visualization_color,
min_score_thresh=eval_config.min_score_threshold,
max_num_predictions=eval_config.max_num_boxes_to_visualize,
skip_scores=eval_config.skip_scores,
skip_labels=eval_config.skip_labels,
keep_image_id_for_visualization_export=eval_config.
keep_image_id_for_visualization_export)
return result_dict, result_losses_dict
def _process_batch(tensor_dict, sess, batch_index, counters, update_op):
"""Evaluates tensors in tensor_dict, visualizing the first K examples.
This function calls sess.run on tensor_dict, evaluating the original_image
tensor only on the first K examples and visualizing detections overlaid
on this original_image.
Args:
tensor_dict: a dictionary of tensors
sess: tensorflow session
batch_index: the index of the batch amongst all batches in the run.
counters: a dictionary holding 'success' and 'skipped' fields which can
be updated to keep track of number of successful and failed runs,
respectively. If these fields are not updated, then the success/skipped
counter values shown at the end of evaluation will be incorrect.
update_op: An update op that has to be run along with output tensors. For
example this could be an op to compute statistics for slim metrics.
Returns:
result_dict: a dictionary of numpy arrays
"""
def delete_key(dic, keys):
for key in keys:
if key in dic.keys():
del dic[key]
b_visualize = batch_index < eval_config.num_visualizations
try:
out = sess.run([tensor_dict, update_op])
result_dict = out[0]
image_id = result_dict['image_id']
tag = image_id
counters['success'] += 1
except tf.errors.InvalidArgumentError:
logging.info('Skipping image')
counters['skipped'] += 1
return {}
global_step = tf.train.global_step(sess, slim.get_global_step())
if b_visualize:
eval_util.visualize_detection_results(
result_dict,
tag,
global_step,
categories=categories,
summary_dir=eval_dir,
export_dir=eval_config.visualization_export_dir,
show_groundtruth=True)
return result_dict
def _process_batch(tensor_dict, sess, batch_index, counters, update_op):
"""Evaluates tensors in tensor_dict, visualizing the first K examples.
This function calls sess.run on tensor_dict, evaluating the original_image
tensor only on the first K examples and visualizing detections overlaid
on this original_image.
Args:
tensor_dict: a dictionary of tensors
sess: tensorflow session
batch_index: the index of the batch amongst all batches in the run.
counters: a dictionary holding 'success' and 'skipped' fields which can
be updated to keep track of number of successful and failed runs,
respectively. If these fields are not updated, then the success/skipped
counter values shown at the end of evaluation will be incorrect.
update_op: An update op that has to be run along with output tensors. For
example this could be an op to compute statistics for slim metrics.
Returns:
result_dict: a dictionary of numpy arrays
"""
duration = time.time() - start
print(batch_index, ": %.1f" % duration)
if batch_index >= eval_config.num_visualizations:
if 'original_image' in tensor_dict:
tensor_dict = {
k: v
for (k, v) in tensor_dict.items() if k != 'original_image'
}
try:
(result_dict, _) = sess.run([tensor_dict, update_op])
counters['success'] += 1
except tf.errors.InvalidArgumentError:
logging.info('Skipping image')
counters['skipped'] += 1
return {}
global_step = tf.train.global_step(sess, slim.get_global_step())
if batch_index < eval_config.num_visualizations:
tag = 'image-{}'.format(batch_index)
eval_util.visualize_detection_results(
result_dict,
tag,
global_step,
categories=categories,
summary_dir=eval_dir,
export_dir=eval_config.visualization_export_dir,
show_groundtruth=eval_config.visualization_export_dir)
return result_dict
def _process_batch(tensor_dict, sess, batch_index, counters, update_op):
"""Evaluates tensors in tensor_dict, visualizing the first K examples.
This function calls sess.run on tensor_dict, evaluating the original_image
tensor only on the first K examples and visualizing detections overlaid
on this original_image.
Args:
tensor_dict: a dictionary of tensors
sess: tensorflow session
batch_index: the index of the batch amongst all batches in the run.
counters: a dictionary holding 'success' and 'skipped' fields which can
be updated to keep track of number of successful and failed runs,
respectively. If these fields are not updated, then the success/skipped
counter values shown at the end of evaluation will be incorrect.
update_op: An update op that has to be run along with output tensors. For
example this could be an op to compute statistics for slim metrics.
Returns:
result_dict: a dictionary of numpy arrays
"""
if batch_index >= eval_config.num_visualizations:
if 'original_image' in tensor_dict:
tensor_dict = {k: v for (k, v) in tensor_dict.items()
if k != 'original_image'}
try:
(result_dict, _) = sess.run([tensor_dict, update_op])
counters['success'] += 1
except tf.errors.InvalidArgumentError:
logging.info('Skipping image')
counters['skipped'] += 1
return {}
global_step = tf.train.global_step(sess, slim.get_global_step())
if batch_index < eval_config.num_visualizations:
tag = 'image-{}'.format(batch_index)
eval_util.visualize_detection_results(
result_dict, tag, global_step, categories=categories,
summary_dir=eval_dir,
export_dir=eval_config.visualization_export_dir,
show_groundtruth=eval_config.visualization_export_dir)
return result_dict
def _process_batch(tensor_dict, sess, batch_index, counters):
"""Evaluates tensors in tensor_dict, visualizing the first K examples.
This function calls sess.run on tensor_dict, evaluating the original_image
tensor only on the first K examples and visualizing detections overlaid
on this original_image.
Args:
tensor_dict: a dictionary of tensors
sess: tensorflow session
batch_index: the index of the batch amongst all batches in the run.
counters: a dictionary holding 'success' and 'skipped' fields which can
be updated to keep track of number of successful and failed runs,
respectively. If these fields are not updated, then the success/skipped
counter values shown at the end of evaluation will be incorrect.
Returns:
result_dict: a dictionary of numpy arrays
"""
try:
result_dict = sess.run(tensor_dict)
counters['success'] += 1
except tf.errors.InvalidArgumentError:
logging.info('Skipping image')
counters['skipped'] += 1
return {}
global_step = tf.train.global_step(sess, tf.train.get_global_step())
if batch_index < eval_config.num_visualizations:
tag = 'image-{}'.format(batch_index)
eval_util.visualize_detection_results(
result_dict,
tag,
global_step,
categories=categories,
summary_dir=eval_dir,
export_dir=eval_config.visualization_export_dir,
show_groundtruth=False,
max_num_predictions=None)
return result_dict
def eval_wrapper(original_image, filename, det_boxes, det_scores, det_classes, det_transcriptions, gt_boxes, gt_classes, gt_transcriptions, global_step):
original_image = original_image
tensor_dict = {}
tensor_dict['original_image'] = original_image
tensor_dict['filename'] = filename
tensor_dict['detection_boxes'] = det_boxes
tensor_dict['detection_scores'] = det_scores
tensor_dict['detection_classes'] = det_classes
tensor_dict['detection_transcriptions'] = det_transcriptions
tensor_dict['groundtruth_boxes'] = gt_boxes
tensor_dict['groundtruth_classes'] = gt_classes
tensor_dict['groundtruth_transcriptions'] = gt_transcriptions
tensor_dict['image_id'] = 'aaa'
print gt_transcriptions
gt_transcriptions_str = []
for a in gt_transcriptions:
gt_transcriptions_str += ["".join([chr(item) for item in a if item > 0])]
print gt_transcriptions_str
print det_transcriptions
det_transcriptions_str = []
for a in det_transcriptions:
det_transcriptions_str += ["".join([chr(item) for item in a if item > 0])]
print det_transcriptions_str
print ''
print 'eval wrapper'
print filename
print original_image.shape
print det_boxes.shape
print det_scores.shape
print det_classes.shape
print det_transcriptions.shape
print gt_boxes.shape
print gt_classes.shape
print gt_transcriptions.shape
print global_step
sys.stdout.flush()
categories = [{'id': 0, 'name': 'background'}, {'id': 1, 'name': 'text'}]
eval_util.visualize_detection_results(tensor_dict, 'tag' + str(global_step),
global_step,
categories = categories,
summary_dir = '/home/zbychuj/Desktop/models/object_detection/models/eval',
export_dir = '/home/zbychuj/Desktop/models/object_detection/models/eval',
show_groundtruth = True,
max_num_predictions = 100000,
min_score_thresh=.5,
gt_transcriptions = gt_transcriptions_str,
det_transcriptions = det_transcriptions_str)
#f = open('/home/zbychuj/Desktop/test_results/' + filename + '.txt', 'w')
#for i in range(0, 64):
# f.write(str(det_scores[i]) + ',' + str(det_boxes[i][0]) + ',' + str(det_boxes[i][1]) + ',' + str(det_boxes[i][2]) + ',' + str(det_boxes[i][3]) + ',')
# f.write(str(det_transcriptions[i]) + '\n')
#f.close()
tensor_dict = {}
tensor_dict['detection_boxes'] = [det_boxes]
tensor_dict['detection_scores'] = [det_scores]
tensor_dict['detection_classes'] = [det_classes]
tensor_dict['groundtruth_boxes'] = [gt_boxes]
tensor_dict['groundtruth_classes'] = [gt_classes]
tensor_dict['image_id'] = ['aaa']
#metrics = eval_util.evaluate_detection_results_pascal_voc(tensor_dict, categories, label_id_offset=1)
#mAP = metrics['Precision/[email protected]']
#print mAP
print 'dupadupa'
print 'dupadupa'
print 'dupadupa'
print 'dupadupa'
sys.stdout.flush()
return float(global_step)
def _process_batch(tensor_dict,
sess,
batch_index,
counters,
losses_dict=None):
"""Evaluates tensors in tensor_dict, losses_dict and visualizes examples.
This function calls sess.run on tensor_dict, evaluating the original_image
tensor only on the first K examples and visualizing detections overlaid
on this original_image.
Args:
tensor_dict: a dictionary of tensors
sess: tensorflow session
batch_index: the index of the batch amongst all batches in the run.
counters: a dictionary holding 'success' and 'skipped' fields which can
be updated to keep track of number of successful and failed runs,
respectively. If these fields are not updated, then the success/skipped
counter values shown at the end of evaluation will be incorrect.
losses_dict: Optional dictonary of scalar loss tensors.
Returns:
result_dict: a dictionary of numpy arrays
result_losses_dict: a dictionary of scalar losses. This is empty if input
losses_dict is None.
"""
try:
if not losses_dict:
losses_dict = {}
'''
testing
'''
# # tensor_dict = [n.name for n in tf.get_default_graph().as_graph_def().node]
# test_layer = tf.get_default_graph().get_tensor_by_name('SecondStageFeatureExtractor/InceptionV2/Mixed_5b/Branch_1/Conv2d_0a_1x1/Relu:0')
# test_layer_dict = {"test_layer":test_layer}
# feature_dict, result_dict, result_losses_dict = sess.run([test_layer, tensor_dict, losses_dict])
# # feature_dict = sess.run([test_layer, tensor_dict])
# import numpy as np
# import scipy.misc
# feature_tensor = feature_dict[0]
# origin_image = result_dict['original_image']
# feature_map = np.sum(feature_tensor,axis=(2))
# # feature_map /= feature_map.max()
# # feature_map = 1 - feature_map
# # rgb_feature_map = np.zeros((256, 256, 3))
# # rgb_feature_map[..., 0] = feature_map * 30
# # rgb_feature_map[..., 1] = feature_map * 144
# # rgb_feature_map[..., 2] = feature_map * 255
# # rgb_feature_map[..., 0] = np.where(feature_map==0, 30, 255)
# # rgb_feature_map[..., 1] = np.where(feature_map==0, 144, 255)
# # rgb_feature_map[..., 2] = np.where(feature_map==0, 255, 255)
# origin_image = origin_image[0]
# # origin_image[..., 0] = np.where(origin_image[..., 0]==0, 30, 255)
# # origin_image[..., 1] = np.where(origin_image[..., 1]==0, 144, 255)
# # origin_image[..., 2] = np.where(origin_image[..., 2]==0, 255, 255)
# scipy.misc.imsave('feature_map_{}.jpg'.format(counters['success']), feature_map)
# # scipy.misc.imsave('origin_image_{}.jpg'.format(counters['success']), origin_image)
# # from IPython import embed;embed()
''''''
result_dict, result_losses_dict = sess.run(
[tensor_dict, losses_dict])
counters['success'] += 1
except tf.errors.InvalidArgumentError:
logging.info('Skipping image')
counters['skipped'] += 1
return {}, {}
global_step = tf.train.global_step(sess, tf.train.get_global_step())
if batch_index < eval_config.num_visualizations:
tag = 'image-{}'.format(batch_index)
eval_util.visualize_detection_results(
eval_config.num_visualizations,
result_dict,
tag,
global_step,
categories=categories,
summary_dir=eval_dir,
export_dir=eval_config.visualization_export_dir,
show_groundtruth=eval_config.visualize_groundtruth_boxes,
groundtruth_box_visualization_color=eval_config.
groundtruth_box_visualization_color,
min_score_thresh=eval_config.min_score_threshold,
max_num_predictions=eval_config.max_num_boxes_to_visualize,
skip_scores=eval_config.skip_scores,
skip_labels=eval_config.skip_labels,
num_examples=eval_config.num_examples,
keep_image_id_for_visualization_export=eval_config.
keep_image_id_for_visualization_export)
return result_dict, result_losses_dict
