regional_feature_based_object_recognition.py

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#!/usr/bin/env python

from __future__ import print_function

import os.path as osp

import itertools, pkg_resources, sys
from distutils.version import LooseVersion
if LooseVersion(pkg_resources.get_distribution("chainer").version) >= LooseVersion('7.0.0') and \
        sys.version_info.major == 2:
    print('''Please install chainer < 7.0.0:

    sudo pip install chainer==6.7.0

c.f https://github.com/jsk-ros-pkg/jsk_recognition/pull/2485
''', file=sys.stderr)
    sys.exit(1)
if [p for p in list(itertools.chain(*[pkg_resources.find_distributions(_) for _ in sys.path])) if "cupy-" in p.project_name ] == []:
    print('''Please install CuPy

    sudo pip install cupy-cuda[your cuda version]
i.e.
    sudo pip install cupy-cuda91

''', file=sys.stderr)
    # sys.exit(1)
import chainer
from chainer import cuda
import numpy as np
from sklearn.neighbors import KNeighborsClassifier

import cv_bridge
from jsk_recognition_msgs.msg import ClassificationResult
from jsk_recognition_utils.chainermodels import ResNet152
from jsk_recognition_utils.chainermodels import ResNet152Feature
from jsk_topic_tools import ConnectionBasedTransport
import message_filters
import rospy
from sensor_msgs.msg import Image


import rospkg
PKG_PATH = rospkg.RosPack().get_path('jsk_perception')


class RegionalFeatureBasedObjectRecognition(ConnectionBasedTransport):

    def __init__(self):
        super(RegionalFeatureBasedObjectRecognition, self).__init__()
        # parameters
        db_file = rospy.get_param('~db_file')
        self.gpu = rospy.get_param('~gpu', 0)
        # setup chainer
        chainer.global_config.train = False
        chainer.global_config.enable_backprop = False
        # model
        pretrained_model = osp.join(
            PKG_PATH, 'trained_data/resnet152_from_caffe.npz')
        rospy.loginfo('Loading pretrained model: %s' % pretrained_model)
        # TODO(wkentaro): Support Resnet50/101
        self.model = ResNet152Feature()
        chainer.serializers.load_npz(pretrained_model, self.model)
        if self.gpu >= 0:
            chainer.cuda.get_device_from_id(self.gpu).use()
            self.model.to_gpu()
        rospy.loginfo('Finished loading pretrained model')
        # mean
        mean_file = osp.join(
            PKG_PATH, 'trained_data/resnet_lsvrc2012_mean.npy')
        self.mean = np.load(mean_file)
        assert self.mean.shape == (224, 224, 3)  # BGR order
        # knn
        rospy.loginfo('Fitting KNN from db')
        db = np.load(db_file)
        X, y, self.target_names = db['X'], db['y'], db['target_names']
        self.knn = KNeighborsClassifier(n_neighbors=10)
        self.knn.fit(X, y)
        rospy.loginfo('Finished fitting KNN from db')
        # setup publishers
        self.pub = self.advertise(
            '~output', ClassificationResult, queue_size=1)

    def subscribe(self):
        self.subs = []
        self.subs.append(message_filters.Subscriber('~input', Image))
        self.subs.append(message_filters.Subscriber('~input/mask', Image))
        queue_size = 100
        slop = 0.1
        self.sync = message_filters.ApproximateTimeSynchronizer(
            self.subs, queue_size=queue_size, slop=slop)
        self.sync.registerCallback(self.callback)

    def unsubscribe(self):
        for sub in self.subs:
            sub.unregister()

    def callback(self, imgmsg, mask_msg):
        bridge = cv_bridge.CvBridge()
        img = bridge.imgmsg_to_cv2(imgmsg, desired_encoding='bgr8')
        mask = bridge.imgmsg_to_cv2(mask_msg, desired_encoding='mono8')
        if mask.ndim == 3:
            mask = np.squeeze(mask, axis=2)
        mask = mask >= 127  # uint8 -> bool

        img = img.astype(np.float64)
        img[mask] -= self.mean[mask]
        img[~mask] = 0

        img = img.transpose(2, 0, 1)
        img = img.astype(np.float32)
        x_data = np.asarray([img])
        if self.gpu >= 0:
            x_data = cuda.to_gpu(x_data)
        x = chainer.Variable(x_data)
        y = self.model(x)

        feat = cuda.to_cpu(y.data)
        feat = feat.squeeze(axis=(2, 3))
        X_query = feat

        y_pred_proba = self.knn.predict_proba(X_query)
        y_pred = np.argmax(y_pred_proba, axis=1)

        classes = self.knn.classes_
        target_names = self.target_names[classes]

        msg = ClassificationResult()
        msg.header = imgmsg.header
        msg.labels = y_pred.tolist()
        msg.label_names = target_names[y_pred].tolist()
        msg.label_proba = y_pred_proba[:, y_pred].flatten().tolist()
        msg.probabilities = y_pred_proba.flatten().tolist()
        msg.target_names = target_names.tolist()
        self.pub.publish(msg)


if __name__ == '__main__':
    rospy.init_node('regional_feature_based_object_recognition')
    app = RegionalFeatureBasedObjectRecognition()
    rospy.spin()