/
evaluate.py
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evaluate.py
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import tensorflow as tf
import os
import json
import subprocess
from scipy.misc import imread, imresize
from scipy import misc
from train import build_forward
from utils.annolist import AnnotationLib as al
from utils.train_utils import add_rectangles, rescale_boxes
import cv2
import argparse
def get_image_dir(args):
weights_iteration = int(args.weights.split('-')[-1])
print('weights_iteration: %d' % (weights_iteration,))
expname = '_' + args.expname if args.expname else ''
image_dir = '%s/images_%s_%d%s' % (os.path.dirname(args.weights), os.path.basename(args.test_boxes)[:-5], weights_iteration, expname)
return image_dir
def get_results(args, H):
tf.reset_default_graph()
x_in = tf.placeholder(tf.float32, name='x_in', shape=[H['image_height'], H['image_width'], 3])
if H['use_rezoom']:
pred_boxes, pred_logits, pred_confidences, pred_confs_deltas, pred_boxes_deltas = build_forward(H, tf.expand_dims(x_in, 0), 'test', reuse=None)
grid_area = H['grid_height'] * H['grid_width']
pred_confidences = tf.reshape(tf.nn.softmax(tf.reshape(pred_confs_deltas, [grid_area * H['rnn_len'], 2])), [grid_area, H['rnn_len'], 2])
if H['reregress']:
pred_boxes = pred_boxes + pred_boxes_deltas
else:
pred_boxes, pred_logits, pred_confidences = build_forward(H, tf.expand_dims(x_in, 0), 'test', reuse=None)
saver = tf.train.Saver()
with tf.Session() as sess:
# sess.run(tf.initialize_all_variables())
sess.run(tf.global_variables_initializer())
# saver.restore(sess, './output/lstm_rezoom_lung_2017_01_17_18.24/save.ckpt-1000000')
print('args.weights: %s' % (args.weights,))
saver.restore(sess, args.weights)
print('run')
pred_annolist = al.AnnoList()
true_annolist = al.parse(args.test_boxes)
data_dir = os.path.dirname(args.test_boxes)
image_dir = get_image_dir(args)
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, (H["image_height"], 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(H, [img], np_pred_confidences, np_pred_boxes,
use_stitching=True, rnn_len=H['rnn_len'], min_conf=0.2, tau=args.tau)
pred_anno.rects = rects
pred_anno.imagePath = os.path.abspath(data_dir)
pred_anno = rescale_boxes((H["image_height"], 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 main():
parser = argparse.ArgumentParser()
parser.add_argument('--weights', required=True)
# parser.add_argument('--weights', type=str)
parser.add_argument('--expname', default='')
parser.add_argument('--test_boxes', required=True)
# parser.add_argument('--test_boxes', type=str)
parser.add_argument('--gpu', default=0)
parser.add_argument('--logdir', default='output')
parser.add_argument('--iou_threshold', default=0.5, type=float)
parser.add_argument('--tau', default=0.25, type=float)
parser.add_argument('--min_conf', default=0.2, type=float)
parser.add_argument('--show_suppressed', default=True, type=bool)
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
hypes_file = '%s/hypes.json' % os.path.dirname(args.weights)
# args.weights= './output/lstm_rezoom_lung_2016_12_13_17.39/save.ckpt-1000000'
# hypes_file = './hypes/lstm_rezoom_lung.json'
with open(hypes_file, 'r') as f:
H = json.load(f)
expname = args.expname + '_' if args.expname else ''
# args.test_boxes = './data/lung_data/lung_test.json'
# print('test_load')
pred_boxes = '%s.%s%s' % (args.weights, expname, os.path.basename(args.test_boxes))
true_boxes = '%s.gt_%s%s' % (args.weights, expname, os.path.basename(args.test_boxes))
pred_annolist, true_annolist = get_results(args, H)
pred_annolist.save(pred_boxes)
true_annolist.save(true_boxes)
print('pred_boxes')
try:
rpc_cmd = './utils/annolist/doRPC.py --minOverlap %f %s %s' % (args.iou_threshold, true_boxes, pred_boxes)
print('$ %s' % rpc_cmd)
rpc_output = subprocess.check_output(rpc_cmd, shell=True)
print(rpc_output)
txt_file = [line for line in rpc_output.split('\n') if line.strip()][-1]
output_png = '%s/results.png' % get_image_dir(args)
plot_cmd = './utils/annolist/plotSimple.py %s --output %s' % (txt_file, output_png)
print('$ %s' % plot_cmd)
plot_output = subprocess.check_output(plot_cmd, shell=True)
print('output results at: %s' % plot_output)
except Exception as e:
print(e)
if __name__ == '__main__':
main()