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test.py
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test.py
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import numpy as np
import tensorflow as tf
import scipy.misc as sm
from eval.evaluate_depth import load_depths, eval_depth
from eval.evaluate_flow import get_scaled_intrinsic_matrix, eval_flow_avg
from eval.evaluate_mask import eval_mask
from eval.evaluate_disp import eval_disp_avg
from eval.pose_evaluation_utils import pred_pose
from eval.eval_pose import eval_snippet, kittiEvalOdom
import re, os
import sys
from tensorflow.python.platform import flags
opt = flags.FLAGS
def test(sess, eval_model, itr, gt_flows_2012, noc_masks_2012, gt_flows_2015,
noc_masks_2015, gt_masks):
with tf.name_scope("evaluation"):
sys.stderr.write("Evaluation at iter [" + str(itr) + "]: \n")
if opt.eval_pose != "":
seqs = opt.eval_pose.split(",")
odom_eval = kittiEvalOdom("./pose_gt_data/")
odom_eval.eval_seqs = seqs
pred_pose(eval_model, opt, sess, seqs)
for seq_no in seqs:
sys.stderr.write("pose seq %s: \n" % seq_no)
eval_snippet(
os.path.join(opt.trace, "pred_poses", seq_no),
os.path.join("./pose_gt_data/", seq_no))
odom_eval.eval(opt.trace + "/pred_poses/")
sys.stderr.write("pose_prediction_finished \n")
for eval_data in ["kitti_2012", "kitti_2015"]:
test_result_disp, test_result_flow_rigid, test_result_flow_optical, \
test_result_mask, test_result_disp2, test_image1 = [], [], [], [], [], []
if eval_data == "kitti_2012":
total_img_num = 194
gt_dir = opt.gt_2012_dir
else:
total_img_num = 200
gt_dir = opt.gt_2015_dir
for i in range(total_img_num):
img1 = sm.imread(
os.path.join(gt_dir, "image_2",
str(i).zfill(6) + "_10.png"))
img1_orig = img1
orig_H, orig_W = img1.shape[0:2]
img1 = sm.imresize(img1, (opt.img_height, opt.img_width))
img2 = sm.imread(
os.path.join(gt_dir, "image_2",
str(i).zfill(6) + "_11.png"))
img2 = sm.imresize(img2, (opt.img_height, opt.img_width))
imgr = sm.imread(
os.path.join(gt_dir, "image_3",
str(i).zfill(6) + "_10.png"))
imgr = sm.imresize(imgr, (opt.img_height, opt.img_width))
img2r = sm.imread(
os.path.join(gt_dir, "image_3",
str(i).zfill(6) + "_11.png"))
img2r = sm.imresize(img2r, (opt.img_height, opt.img_width))
img1 = np.expand_dims(img1, axis=0)
img2 = np.expand_dims(img2, axis=0)
imgr = np.expand_dims(imgr, axis=0)
img2r = np.expand_dims(img2r, axis=0)
calib_file = os.path.join(gt_dir, "calib_cam_to_cam",
str(i).zfill(6) + ".txt")
input_intrinsic = get_scaled_intrinsic_matrix(
calib_file,
zoom_x=1.0 * opt.img_width / orig_W,
zoom_y=1.0 * opt.img_height / orig_H)
pred_flow_rigid, pred_flow_optical, \
pred_disp, pred_disp2, pred_mask= sess.run([eval_model.pred_flow_rigid,
eval_model.pred_flow_optical,
eval_model.pred_disp,
eval_model.pred_disp2,
eval_model.pred_mask],
feed_dict = {eval_model.input_1: img1,
eval_model.input_2: img2,
eval_model.input_r: imgr,
eval_model.input_2r:img2r,
eval_model.input_intrinsic: input_intrinsic})
test_result_flow_rigid.append(np.squeeze(pred_flow_rigid))
test_result_flow_optical.append(np.squeeze(pred_flow_optical))
test_result_disp.append(np.squeeze(pred_disp))
test_result_disp2.append(np.squeeze(pred_disp2))
test_result_mask.append(np.squeeze(pred_mask))
test_image1.append(img1_orig)
## depth evaluation
if opt.eval_depth and eval_data == "kitti_2015":
print("Evaluate depth at iter [" + str(itr) + "] " + eval_data)
gt_depths, pred_depths, gt_disparities, pred_disp_resized = load_depths(
test_result_disp, gt_dir, eval_occ=True)
abs_rel, sq_rel, rms, log_rms, a1, a2, a3, d1_all = eval_depth(
gt_depths, pred_depths, gt_disparities, pred_disp_resized)
sys.stderr.write(
"{:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10} \n".
format('abs_rel', 'sq_rel', 'rms', 'log_rms', 'd1_all',
'a1', 'a2', 'a3'))
sys.stderr.write(
"{:10.4f}, {:10.4f}, {:10.3f}, {:10.3f}, {:10.3f}, {:10.3f}, {:10.3f}, {:10.3f} \n".
format(abs_rel, sq_rel, rms, log_rms, d1_all, a1, a2, a3))
disp_err = eval_disp_avg(
test_result_disp,
gt_dir,
disp_num=0,
moving_masks=gt_masks)
sys.stderr.write("disp err 2015 is \n")
sys.stderr.write(disp_err + "\n")
if opt.mode == "depthflow":
disp_err = eval_disp_avg(
test_result_disp2,
gt_dir,
disp_num=1,
moving_masks=gt_masks)
sys.stderr.write("disp2 err 2015 is \n")
sys.stderr.write(disp_err + "\n")
if opt.eval_depth and eval_data == "kitti_2012":
disp_err = eval_disp_avg(test_result_disp, gt_dir)
sys.stderr.write("disp err 2012 is \n")
sys.stderr.write(disp_err + "\n")
# flow evaluation
if opt.eval_flow and eval_data == "kitti_2012":
if opt.mode in ["depth", "depthflow"]:
epe = eval_flow_avg(gt_flows_2012, noc_masks_2012,
test_result_flow_rigid, opt)
sys.stderr.write("epe 2012 rigid is \n")
sys.stderr.write(epe + "\n")
epe = eval_flow_avg(gt_flows_2012, noc_masks_2012,
test_result_flow_optical, opt)
sys.stderr.write("epe 2012 optical is \n")
sys.stderr.write(epe + "\n")
if opt.eval_flow and eval_data == "kitti_2015":
if opt.mode in ["depth", "depthflow"]:
epe = eval_flow_avg(
gt_flows_2015,
noc_masks_2015,
test_result_flow_rigid,
opt,
moving_masks=gt_masks)
sys.stderr.write("epe 2015 rigid is \n")
sys.stderr.write(epe + "\n")
epe = eval_flow_avg(
gt_flows_2015,
noc_masks_2015,
test_result_flow_optical,
opt,
moving_masks=gt_masks)
sys.stderr.write("epe 2015 optical is \n")
sys.stderr.write(epe + "\n")
# mask evaluation
if opt.eval_mask and eval_data == "kitti_2015":
mask_err = eval_mask(test_result_mask, gt_masks, opt)
sys.stderr.write("mask_err is %s \n" % str(mask_err))