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test_images_segmentation.py
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test_images_segmentation.py
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#!/usr/bin/env python
# Copyright (c) 2020 NVIDIA Corporation. All rights reserved.
# This work is licensed under the NVIDIA Source Code License - Non-commercial. Full
# text can be found in LICENSE.md
"""Test UCN on ros images"""
import torch
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.utils.data
import tf
import rosnode
import message_filters
import cv2
import torch.nn as nn
import threading
import argparse
import pprint
import time, os, sys
import os.path as osp
import numpy as np
import _init_paths
import networks
import rospy
import copy
from sensor_msgs.msg import Image, CameraInfo
from cv_bridge import CvBridge, CvBridgeError
from fcn.config import cfg, cfg_from_file, get_output_dir
from fcn.test_dataset import test_sample
from utils.mask import visualize_segmentation
lock = threading.Lock()
def compute_xyz(depth_img, fx, fy, px, py, height, width):
indices = np.indices((height, width), dtype=np.float32).transpose(1,2,0)
z_e = depth_img
x_e = (indices[..., 1] - px) * z_e / fx
y_e = (indices[..., 0] - py) * z_e / fy
xyz_img = np.stack([x_e, y_e, z_e], axis=-1) # Shape: [H x W x 3]
return xyz_img
class ImageListener:
def __init__(self, network, network_crop):
self.network = network
self.network_crop = network_crop
self.cv_bridge = CvBridge()
self.im = None
self.depth = None
self.rgb_frame_id = None
self.rgb_frame_stamp = None
# initialize a node
rospy.init_node("seg_rgb")
self.label_pub = rospy.Publisher('seg_label', Image, queue_size=10)
self.label_refined_pub = rospy.Publisher('seg_label_refined', Image, queue_size=10)
self.image_pub = rospy.Publisher('seg_image', Image, queue_size=10)
self.image_refined_pub = rospy.Publisher('seg_image_refined', Image, queue_size=10)
self.feature_pub = rospy.Publisher('seg_feature', Image, queue_size=10)
if cfg.TEST.ROS_CAMERA == 'D415':
# use RealSense D435
self.base_frame = 'measured/base_link'
rgb_sub = message_filters.Subscriber('/camera/color/image_raw', Image, queue_size=10)
depth_sub = message_filters.Subscriber('/camera/aligned_depth_to_color/image_raw', Image, queue_size=10)
msg = rospy.wait_for_message('/camera/color/camera_info', CameraInfo)
self.camera_frame = 'measured/camera_color_optical_frame'
self.target_frame = self.base_frame
elif cfg.TEST.ROS_CAMERA == 'Azure':
self.base_frame = 'measured/base_link'
rgb_sub = message_filters.Subscriber('/k4a/rgb/image_raw', Image, queue_size=10)
depth_sub = message_filters.Subscriber('/k4a/depth_to_rgb/image_raw', Image, queue_size=10)
msg = rospy.wait_for_message('/k4a/rgb/camera_info', CameraInfo)
self.camera_frame = 'rgb_camera_link'
self.target_frame = self.base_frame
else:
# use kinect
self.base_frame = '%s_rgb_optical_frame' % (cfg.TEST.ROS_CAMERA)
rgb_sub = message_filters.Subscriber('/%s/rgb/image_color' % (cfg.TEST.ROS_CAMERA), Image, queue_size=10)
depth_sub = message_filters.Subscriber('/%s/depth_registered/image' % (cfg.TEST.ROS_CAMERA), Image, queue_size=10)
msg = rospy.wait_for_message('/%s/rgb/camera_info' % (cfg.TEST.ROS_CAMERA), CameraInfo)
self.camera_frame = '%s_rgb_optical_frame' % (cfg.TEST.ROS_CAMERA)
self.target_frame = self.base_frame
# update camera intrinsics
intrinsics = np.array(msg.K).reshape(3, 3)
self.fx = intrinsics[0, 0]
self.fy = intrinsics[1, 1]
self.px = intrinsics[0, 2]
self.py = intrinsics[1, 2]
print(intrinsics)
queue_size = 1
slop_seconds = 0.1
ts = message_filters.ApproximateTimeSynchronizer([rgb_sub, depth_sub], queue_size, slop_seconds)
ts.registerCallback(self.callback_rgbd)
def callback_rgbd(self, rgb, depth):
if depth.encoding == '32FC1':
depth_cv = self.cv_bridge.imgmsg_to_cv2(depth)
elif depth.encoding == '16UC1':
depth_cv = self.cv_bridge.imgmsg_to_cv2(depth).copy().astype(np.float32)
depth_cv /= 1000.0
else:
rospy.logerr_throttle(
1, 'Unsupported depth type. Expected 16UC1 or 32FC1, got {}'.format(
depth.encoding))
return
im = self.cv_bridge.imgmsg_to_cv2(rgb, 'bgr8')
# rescale image if necessary
if cfg.TEST.SCALES_BASE[0] != 1:
im_scale = cfg.TEST.SCALES_BASE[0]
im = pad_im(cv2.resize(im, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR), 16)
depth_cv = pad_im(cv2.resize(depth_cv, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_NEAREST), 16)
with lock:
self.im = im.copy()
self.depth = depth_cv.copy()
self.rgb_frame_id = rgb.header.frame_id
self.rgb_frame_stamp = rgb.header.stamp
def run_network(self):
with lock:
if listener.im is None:
return
im_color = self.im.copy()
depth_img = self.depth.copy()
rgb_frame_id = self.rgb_frame_id
rgb_frame_stamp = self.rgb_frame_stamp
print('===========================================')
# bgr image
im = im_color.astype(np.float32)
im_tensor = torch.from_numpy(im) / 255.0
pixel_mean = torch.tensor(cfg.PIXEL_MEANS / 255.0).float()
im_tensor -= pixel_mean
image_blob = im_tensor.permute(2, 0, 1)
sample = {'image_color': image_blob.unsqueeze(0)}
if cfg.INPUT == 'DEPTH' or cfg.INPUT == 'RGBD':
height = im_color.shape[0]
width = im_color.shape[1]
xyz_img = compute_xyz(depth_img, self.fx, self.fy, self.px, self.py, height, width)
depth_blob = torch.from_numpy(xyz_img).permute(2, 0, 1)
sample['depth'] = depth_blob.unsqueeze(0)
out_label, out_label_refined = test_sample(sample, self.network, self.network_crop)
# publish segmentation mask
label = out_label[0].cpu().numpy()
label_msg = self.cv_bridge.cv2_to_imgmsg(label.astype(np.uint8))
label_msg.header.stamp = rgb_frame_stamp
label_msg.header.frame_id = rgb_frame_id
label_msg.encoding = 'mono8'
self.label_pub.publish(label_msg)
num_object = len(np.unique(label)) - 1
print('%d objects' % (num_object))
if out_label_refined is not None:
label_refined = out_label_refined[0].cpu().numpy()
label_msg_refined = self.cv_bridge.cv2_to_imgmsg(label_refined.astype(np.uint8))
label_msg_refined.header.stamp = rgb_frame_stamp
label_msg_refined.header.frame_id = rgb_frame_id
label_msg_refined.encoding = 'mono8'
self.label_refined_pub.publish(label_msg_refined)
# publish segmentation images
im_label = visualize_segmentation(im_color[:, :, (2, 1, 0)], label, return_rgb=True)
rgb_msg = self.cv_bridge.cv2_to_imgmsg(im_label, 'rgb8')
rgb_msg.header.stamp = rgb_frame_stamp
rgb_msg.header.frame_id = rgb_frame_id
self.image_pub.publish(rgb_msg)
if out_label_refined is not None:
im_label_refined = visualize_segmentation(im_color[:, :, (2, 1, 0)], label_refined, return_rgb=True)
rgb_msg_refined = self.cv_bridge.cv2_to_imgmsg(im_label_refined, 'rgb8')
rgb_msg_refined.header.stamp = rgb_frame_stamp
rgb_msg_refined.header.frame_id = rgb_frame_id
self.image_refined_pub.publish(rgb_msg_refined)
def parse_args():
"""
Parse input arguments
"""
parser = argparse.ArgumentParser(description='Test a PoseCNN network')
parser.add_argument('--gpu', dest='gpu_id', help='GPU id to use',
default=0, type=int)
parser.add_argument('--instance', dest='instance_id', help='PoseCNN instance id to use',
default=0, type=int)
parser.add_argument('--pretrained', dest='pretrained',
help='initialize with pretrained checkpoint',
default=None, type=str)
parser.add_argument('--pretrained_crop', dest='pretrained_crop',
help='initialize with pretrained checkpoint for crops',
default=None, type=str)
parser.add_argument('--cfg', dest='cfg_file',
help='optional config file', default=None, type=str)
parser.add_argument('--dataset', dest='dataset_name',
help='dataset to train on',
default='shapenet_scene_train', type=str)
parser.add_argument('--rand', dest='randomize',
help='randomize (do not use a fixed seed)',
action='store_true')
parser.add_argument('--network', dest='network_name',
help='name of the network',
default=None, type=str)
parser.add_argument('--background', dest='background_name',
help='name of the background file',
default=None, type=str)
if len(sys.argv) == 1:
parser.print_help()
sys.exit(1)
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
print('Called with args:')
print(args)
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
print('Using config:')
pprint.pprint(cfg)
if not args.randomize:
# fix the random seeds (numpy and caffe) for reproducibility
np.random.seed(cfg.RNG_SEED)
# device
cfg.gpu_id = 0
cfg.device = torch.device('cuda:{:d}'.format(cfg.gpu_id))
cfg.instance_id = args.instance_id
num_classes = 2
cfg.MODE = 'TEST'
cfg.TEST.VISUALIZE = False
print('GPU device {:d}'.format(args.gpu_id))
# prepare network
if args.pretrained:
network_data = torch.load(args.pretrained)
print("=> using pre-trained network '{}'".format(args.pretrained))
else:
network_data = None
print("no pretrained network specified")
sys.exit()
network = networks.__dict__[args.network_name](num_classes, cfg.TRAIN.NUM_UNITS, network_data).cuda(device=cfg.device)
network = torch.nn.DataParallel(network, device_ids=[0]).cuda(device=cfg.device)
cudnn.benchmark = True
network.eval()
if args.pretrained_crop:
network_data_crop = torch.load(args.pretrained_crop)
network_crop = networks.__dict__[args.network_name](num_classes, cfg.TRAIN.NUM_UNITS, network_data_crop).cuda(device=cfg.device)
network_crop = torch.nn.DataParallel(network_crop, device_ids=[cfg.gpu_id]).cuda(device=cfg.device)
network_crop.eval()
else:
network_crop = None
# image listener
listener = ImageListener(network, network_crop)
while not rospy.is_shutdown():
listener.run_network()