def merge_labelact_annolist(annolist):
name_to_idx = {};
res_annolist = AnnotationLib.AnnoList();
for a in annolist:
if a.imageName in name_to_idx:
aidx = name_to_idx[a.imageName];
res_annolist[aidx].rects.extend(a.rects);
else:
res_annolist.append(a);
name_to_idx[a.imageName] = len(res_annolist) - 1;
return res_annolist;
def eval(self, weights, test_boxes, min_conf, tau, show_suppressed, expname):
self.H["grid_width"] = self.H["image_width"] / self.H["region_size"]
self.H["grid_height"] = self.H["image_height"] / self.H["region_size"]
x_in = tf.placeholder(tf.float32, name='x_in', shape=[self.H['image_height'], self.H['image_width'], 3])
if self.H['use_rezoom']:
pred_boxes, pred_logits, pred_confidences, pred_confs_deltas, pred_boxes_deltas = self.build_forward(tf.expand_dims(x_in, 0), 'test', reuse=None)
grid_area = self.H['grid_height'] * self.H['grid_width']
pred_confidences = tf.reshape(tf.nn.softmax(tf.reshape(pred_confs_deltas, [grid_area * self.H['rnn_len'], 2])),
[grid_area, self.H['rnn_len'], 2])
if self.H['reregress']:
pred_boxes = pred_boxes + pred_boxes_deltas
else:
pred_boxes, pred_logits, pred_confidences = self.build_forward(tf.expand_dims(x_in, 0), 'test', reuse=None)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver.restore(sess, weights)
pred_annolist = al.AnnoList()
true_annolist = al.parse(test_boxes)
data_dir = os.path.dirname(test_boxes)
image_dir = self.get_image_dir(weights, expname, test_boxes)
subprocess.call('mkdir -p %s' % image_dir, shell=True)
for i in range(len(true_annolist)):
true_anno = true_annolist[i]
orig_img = imread('%s/%s' % (data_dir, true_anno.imageName))[:,:,:3]
img = imresize(orig_img, (self.H["image_height"], self.H["image_width"]), interp='cubic')
feed = {x_in: img}
(np_pred_boxes, np_pred_confidences) = sess.run([pred_boxes, pred_confidences], feed_dict=feed)
pred_anno = al.Annotation()
pred_anno.imageName = true_anno.imageName
new_img, rects = add_rectangles(self.H, [img], np_pred_confidences, np_pred_boxes,
use_stitching=True, rnn_len=self.H['rnn_len'], min_conf=min_conf, tau=tau, show_suppressed=show_suppressed)
pred_anno.rects = rects
pred_anno.imagePath = os.path.abspath(data_dir)
pred_anno = rescale_boxes((self.H["image_height"], self.H["image_width"]), pred_anno, orig_img.shape[0], orig_img.shape[1])
pred_annolist.append(pred_anno)
imname = '%s/%s' % (image_dir, os.path.basename(true_anno.imageName))
misc.imsave(imname, new_img)
if i % 25 == 0:
print(i)
return pred_annolist, true_annolist
def pal2al(_annolist):
#annotations = [];
annotations = AnnotationLib.AnnoList()
for adesc in _annolist.attribute_desc:
annotations.attribute_desc[adesc.name] = adesc
print "attribute: ", adesc.name, adesc.id
for valdesc in adesc.val_to_str:
annotations.add_attribute_val(adesc.name, valdesc.s, valdesc.id)
attribute_name_from_id = {
adesc.id: aname
for aname, adesc in annotations.attribute_desc.iteritems()
}
attribute_dtype_from_id = {
adesc.id: adesc.dtype
for aname, adesc in annotations.attribute_desc.iteritems()
}
for _a in _annolist.annotation:
anno = AnnotationLib.Annotation()
anno.imageName = _a.imageName
anno.rects = []
for _r in _a.rect:
rect = AnnotationLib.AnnoRect()
rect.x1 = _r.x1
rect.x2 = _r.x2
rect.y1 = _r.y1
rect.y2 = _r.y2
if _r.HasField("id"):
rect.id = _r.id
if _r.HasField("track_id"):
rect.track_id = _r.track_id
if _r.HasField("score"):
rect.score = _r.score
for _at in _r.attribute:
try:
cur_aname = attribute_name_from_id[_at.id]
cur_dtype = attribute_dtype_from_id[_at.id]
except KeyError as e:
print "attribute: ", _at.id
print e
assert (False)
if cur_dtype == AnnotationLib.AnnoList.TYPE_INT32:
rect.at[cur_aname] = _at.val
elif cur_dtype == AnnotationLib.AnnoList.TYPE_FLOAT:
rect.at[cur_aname] = _at.fval
elif cur_dtype == AnnotationLib.AnnoList.TYPE_STRING:
rect.at[cur_aname] = _at.strval
else:
assert (False)
anno.rects.append(rect)
annotations.append(anno)
return annotations
def main():
parser = argparse.ArgumentParser()
parser.add_argument("hits_dir", help="hit directory")
parser.add_argument("--print_empty", action="store_true", help="print info on empty hits");
parser.add_argument("--save_worker_results", action="store_true", help="save results for each individual worker");
#parser.add_argument("output_ext", default=".al", help="output format .idl/.al/.pal")
#parser.add_argument("--url_prefix", help="path on S3 that should be removed when converting to annolist")
args = parser.parse_args()
#url_prefix = bash_var_to_py("./data_utils_init.sh", "S3_HOST_DIR")
url_prefix = bash_var_to_py(args.hits_dir + "/hit_params.sh", "S3_HOST_DIR")
args.output_ext = ".pal"
if url_prefix[-1] != os.sep:
url_prefix += os.sep;
hit_name = get_hit_name(args.hits_dir)
#pal_name = args.hits_dir + '/' + hit_name + '.pal'
pal_name = args.hits_dir + '/' + hit_name + args.output_ext
results_filename = args.hits_dir + '/' + hit_name + '.results'
results_by_worker_dir = '%s/results_by_worker_%s' % (args.hits_dir, hit_name)
subprocess.call(['mkdir', '-p', results_by_worker_dir])
try:
with open(args.hits_dir + '/bad_workers.txt', 'r') as f:
bad_workerids = set(x.strip() for x in f.readlines())
except IOError:
bad_workerids = set()
with open(results_filename, 'r') as results_file:
results_list = list(csv.reader(results_file, delimiter='\t'))
invert = lambda d: {v:k for k,v in d.iteritems()}
columns = invert(dict(enumerate(results_list[0])))
annotation_list = AnnotationLib.AnnoList();
hit_type = "";
for each in results_list[1:]:
if each[columns['hitstatus']] == 'Reviewable' and each[columns['workerid']] not in bad_workerids:
cur_hit_type = each[columns['Answer.results']].split(',')[0];
if not hit_type:
hit_type = cur_hit_type;
else:
assert(hit_type == cur_hit_type);
a = annotation_from_result(each, columns, url_prefix);
annotation_list.append(a);
# MA: some hit types require special post-processing
if hit_type == "label_act":
annotation_list = merge_labelact_annolist(annotation_list);
annotation_list.add_attribute("gender", int);
annotation_list.add_attribute("ptype", int);
annotation_list.add_attribute("act", int);
annotation_list.add_attribute_val("gender", "male", ATTR_VAL_GENDER_MALE);
annotation_list.add_attribute_val("gender", "female", ATTR_VAL_GENDER_FEMALE);
annotation_list.add_attribute_val("ptype", "sales", ATTR_VAL_PTYPE_SALES);
annotation_list.add_attribute_val("ptype", "customer", ATTR_VAL_PTYPE_CUST);
annotation_list.add_attribute_val("act", "interact_with_customer", ATTR_VAL_ACT_SALES_INT);
annotation_list.add_attribute_val("act", "clean", ATTR_VAL_ACT_SALES_CLEAN);
annotation_list.add_attribute_val("act", "other_sales", ATTR_VAL_ACT_SALES_OTHER);
annotation_list.add_attribute_val("act", "queue", ATTR_VAL_ACT_CUST_QUEUE);
annotation_list.add_attribute_val("act", "interact_with_sales", ATTR_VAL_ACT_CUST_INT);
annotation_list.add_attribute_val("act", "browse", ATTR_VAL_ACT_CUST_BROWSE);
annotation_list.add_attribute_val("act", "other_customer", ATTR_VAL_ACT_CUST_OTHER);
AnnotationLib.save(pal_name, annotation_list)
if args.save_worker_results:
for workerid in set(x[columns['workerid']] for x in results_list[1:]):
if workerid == '':
print '%s missing entries' % sum(1 for each in results_list[1:]
if each[columns['workerid']] == '')
continue
annotation_list = AnnotationLib.AnnoList([annotation_from_result(each, columns, url_prefix)
for each in results_list[1:]
if each[columns['hitstatus']] == 'Reviewable'
and each[columns['workerid']] == workerid]);
output_filename = '%s/%s.pal' % (results_by_worker_dir, workerid);
#print "saving ", output_filename;
print "worker: {}, number of annotations: {}".format(workerid, len(annotation_list));
AnnotationLib.save(output_filename, annotation_list)
if args.print_empty:
for res in results_list[1:]:
if res[columns['Answer.results']] == "":
print "empty hit: ", res
# show statistics on empty results (happens due to unknown bug in javascript tool)
empty_res_workers = [each[columns['workerid']] for each in results_list[1:] if each[columns['Answer.results']] == ""];
print "empty output by workerid: "
for workerid, worker_empty_count in collections.Counter(empty_res_workers).items():
print workerid, worker_empty_count
num_empty = sum((1 for each in results_list[1:] if each[columns['Answer.results']] == ""));
print "number of empty results: ", num_empty