detect.py

#!/usr/bin/env python3
#-------------------------------------------------------------------------------
# Author: Lukasz Janyst <lukasz@jany.st>
# Date:   05.02.2018
#-------------------------------------------------------------------------------
# This file is part of SSD-TensorFlow.
#
# SSD-TensorFlow is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# SSD-TensorFlow is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with SSD-Tensorflow.  If not, see <http://www.gnu.org/licenses/>.
#-------------------------------------------------------------------------------

import tensorflow as tf
import argparse
import pickle
import numpy as np
import sys
import cv2
import os

from ssdutils import get_anchors_for_preset, decode_boxes, suppress_overlaps
from utils import draw_box
from tqdm import tqdm

#-------------------------------------------------------------------------------
# Start the show
#-------------------------------------------------------------------------------
def main():
    #---------------------------------------------------------------------------
    # Parse the commandline
    #---------------------------------------------------------------------------
    parser = argparse.ArgumentParser(description='SSD inference')
    parser.add_argument("files", nargs="*")
    parser.add_argument('--model', default='model300.pb',
                        help='model file')
    parser.add_argument('--training-data', default='training-data-300.pkl',
                        help='training data')
    parser.add_argument('--output-dir', default='test-out',
                        help='output directory')
    parser.add_argument('--batch-size', type=int, default=32,
                        help='batch size')
    args = parser.parse_args()

    #---------------------------------------------------------------------------
    # Print parameters
    #---------------------------------------------------------------------------
    print('[i] Model:         ', args.model)
    print('[i] Training data: ', args.training_data)
    print('[i] Output dir:    ', args.output_dir)
    print('[i] Batch size:    ', args.batch_size)

    #---------------------------------------------------------------------------
    # Load the graph and the training data
    #---------------------------------------------------------------------------
    graph_def = tf.GraphDef()
    with open(args.model, 'rb') as f:
        serialized = f.read()
        graph_def.ParseFromString(serialized)

    with open(args.training_data, 'rb') as f:
        data = pickle.load(f)
        preset = data['preset']
        colors = data['colors']
        lid2name = data['lid2name']
        anchors = get_anchors_for_preset(preset)

    #---------------------------------------------------------------------------
    # Create the output directory
    #---------------------------------------------------------------------------
    if not os.path.exists(args.output_dir):
        os.makedirs(args.output_dir)

    #---------------------------------------------------------------------------
    # Run the detections in batches
    #---------------------------------------------------------------------------
    with tf.Session() as sess:
        tf.import_graph_def(graph_def, name='detector')
        img_input = sess.graph.get_tensor_by_name('detector/image_input:0')
        result = sess.graph.get_tensor_by_name('detector/result/result:0')

        files = sys.argv[1:]

        for i in tqdm(range(0, len(files), args.batch_size)):
            batch_names = files[i:i+args.batch_size]
            batch_imgs = []
            batch = []
            for f in batch_names:
                img = cv2.imread(f)
                batch_imgs.append(img)
                img = cv2.resize(img, (300, 300))
                batch.append(img)

            batch = np.array(batch)
            feed = {img_input: batch}
            enc_boxes = sess.run(result, feed_dict=feed)

            for i in range(len(batch_names)):
                boxes = decode_boxes(enc_boxes[i], anchors, 0.5, lid2name, None)
                boxes = suppress_overlaps(boxes)[:200]
                name = os.path.basename(batch_names[i])

                with open(os.path.join(args.output_dir, name+'.txt'), 'w') as f:
                    for box in boxes:
                        draw_box(batch_imgs[i], box[1], colors[box[1].label])

                        box_data = '{} {} {} {} {} {}\n'.format(box[1].label,
                            box[1].labelid, box[1].center.x, box[1].center.y,
                            box[1].size.w, box[1].size.h)
                        f.write(box_data)

                cv2.imwrite(os.path.join(args.output_dir, name),
                            batch_imgs[i])

if __name__ == '__main__':
    main()