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val_fcn8_vgg.py
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val_fcn8_vgg.py
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import numpy as np
import argparse
import os
import cv2
import tensorflow as tf
from models.fcn8_vgg import FCN8VGG
from collections import namedtuple
from utils import *
#--------------------------------------------------------------------------------
# Definitions
#--------------------------------------------------------------------------------
# a label and all meta information
Label = namedtuple( 'Label' , [
'name' , # The identifier of this label, e.g. 'car', 'person', ... .
# We use them to uniquely name a class
'id' , # An integer ID that is associated with this label.
# The IDs are used to represent the label in ground truth images
# An ID of -1 means that this label does not have an ID and thus
# is ignored when creating ground truth images (e.g. license plate).
# Do not modify these IDs, since exactly these IDs are expected by the
# evaluation server.
'trainId' , # Feel free to modify these IDs as suitable for your method. Then create
# ground truth images with train IDs, using the tools provided in the
# 'preparation' folder. However, make sure to validate or submit results
# to our evaluation server using the regular IDs above!
# For trainIds, multiple labels might have the same ID. Then, these labels
# are mapped to the same class in the ground truth images. For the inverse
# mapping, we use the label that is defined first in the list below.
# For example, mapping all void-type classes to the same ID in training,
# might make sense for some approaches.
# Max value is 255!
'category' , # The name of the category that this label belongs to
'categoryId' , # The ID of this category. Used to create ground truth images
# on category level.
'hasInstances', # Whether this label distinguishes between single instances or not
'ignoreInEval', # Whether pixels having this class as ground truth label are ignored
# during evaluations or not
'color' , # The color of this label
] )
#--------------------------------------------------------------------------------
# A list of all labels
#--------------------------------------------------------------------------------
# Please adapt the train IDs as appropriate for you approach.
# Note that you might want to ignore labels with ID 255 during training.
# Further note that the current train IDs are only a suggestion. You can use whatever you like.
# Make sure to provide your results using the original IDs and not the training IDs.
# Note that many IDs are ignored in evaluation and thus you never need to predict these!
labels = [
# name id trainId category catId hasInstances ignoreInEval color
Label( 'unlabeled' , 0 , 255 , 'void' , 0 , False , True , ( 0, 0, 0) ),
Label( 'ego vehicle' , 1 , 255 , 'void' , 0 , False , True , ( 0, 0, 0) ),
Label( 'rectification border' , 2 , 255 , 'void' , 0 , False , True , ( 0, 0, 0) ),
Label( 'out of roi' , 3 , 255 , 'void' , 0 , False , True , ( 0, 0, 0) ),
Label( 'static' , 4 , 255 , 'void' , 0 , False , True , ( 0, 0, 0) ),
Label( 'dynamic' , 5 , 255 , 'void' , 0 , False , True , (111, 74, 0) ),
Label( 'ground' , 6 , 255 , 'void' , 0 , False , True , ( 81, 0, 81) ),
Label( 'road' , 7 , 0 , 'flat' , 1 , False , False , (128, 64,128) ),
Label( 'sidewalk' , 8 , 1 , 'flat' , 1 , False , False , (244, 35,232) ),
Label( 'parking' , 9 , 255 , 'flat' , 1 , False , True , (250,170,160) ),
Label( 'rail track' , 10 , 255 , 'flat' , 1 , False , True , (230,150,140) ),
Label( 'building' , 11 , 2 , 'construction' , 2 , False , False , ( 70, 70, 70) ),
Label( 'wall' , 12 , 3 , 'construction' , 2 , False , False , (102,102,156) ),
Label( 'fence' , 13 , 4 , 'construction' , 2 , False , False , (2550,153,153) ),
Label( 'guard rail' , 14 , 255 , 'construction' , 2 , False , True , (180,165,180) ),
Label( 'bridge' , 15 , 255 , 'construction' , 2 , False , True , (150,100,100) ),
Label( 'tunnel' , 16 , 255 , 'construction' , 2 , False , True , (150,120, 90) ),
Label( 'pole' , 17 , 5 , 'object' , 3 , False , False , (153,153,153) ),
Label( 'polegroup' , 18 , 255 , 'object' , 3 , False , True , (153,153,153) ),
Label( 'traffic light' , 19 , 6 , 'object' , 3 , False , False , (250,170, 30) ),
Label( 'traffic sign' , 20 , 7 , 'object' , 3 , False , False , (220,220, 0) ),
Label( 'vegetation' , 21 , 8 , 'nature' , 4 , False , False , (107,142, 35) ),
Label( 'terrain' , 22 , 9 , 'nature' , 4 , False , False , (152,251,152) ),
Label( 'sky' , 23 , 10 , 'sky' , 5 , False , False , ( 70,130,180) ),
Label( 'person' , 24 , 11 , 'human' , 6 , True , False , (220, 20, 60) ),
Label( 'rider' , 25 , 12 , 'human' , 6 , True , False , (255, 0, 0) ),
Label( 'car' , 26 , 13 , 'vehicle' , 7 , True , False , ( 0, 0,142) ),
Label( 'truck' , 27 , 14 , 'vehicle' , 7 , True , False , ( 0, 0, 70) ),
Label( 'bus' , 28 , 15 , 'vehicle' , 7 , True , False , ( 0, 60,100) ),
Label( 'caravan' , 29 , 255 , 'vehicle' , 7 , True , True , ( 0, 0, 90) ),
Label( 'trailer' , 30 , 255 , 'vehicle' , 7 , True , True , ( 0, 0,110) ),
Label( 'train' , 31 , 16 , 'vehicle' , 7 , True , False , ( 0, 80,100) ),
Label( 'motorcycle' , 32 , 17 , 'vehicle' , 7 , True , False , ( 0, 0,230) ),
Label( 'bicycle' , 33 , 18 , 'vehicle' , 7 , True , False , (1255, 11, 32) ),
]
trainId2name = { label.trainId : label.name for label in labels }
ignore_label = 255
parser = argparse.ArgumentParser(description='Evaluation on the cityscapes validation set')
parser.add_argument('--checkpoint_dir', type=str, help='folder containing checkpoints', required=True)
parser.add_argument('--gt_file', type=str, help='path to filelist.txt', required=True)
parser.add_argument('--num_classes', type=int, default= 19, help='num classes')
parser.add_argument('--output_path', type=str, default='validation.txt')
args = parser.parse_args()
### INPUTS ###
image_placeholder = tf.placeholder(tf.float32)
sem_gt_placeholder = tf.placeholder(tf.int32)
input_images = tf.cast(tf.expand_dims(image_placeholder, axis=0),tf.float32)
sem_gt = tf.expand_dims(sem_gt_placeholder, axis=0)
with tf.name_scope("content_vgg"):
vgg_fcn = FCN8VGG()
vgg_fcn.build(input_images,train=False, debug=False, num_classes=args.num_classes)
sem_pred = vgg_fcn.pred_up
### MIOU ###
weightsValue = tf.to_float(tf.not_equal(sem_gt,ignore_label))
sem_gt = tf.where(tf.equal(sem_gt, ignore_label), tf.zeros_like(sem_gt), sem_gt)
sem_pred = tf.where(tf.equal(sem_pred, ignore_label), tf.zeros_like(sem_pred), sem_pred)
miou, update_op = tf.metrics.mean_iou(labels=tf.reshape(sem_gt,[-1]),predictions=tf.reshape(sem_pred,[-1]), num_classes=args.num_classes, weights=tf.reshape(weightsValue,[-1]))
summary_miou = tf.summary.scalar("miou",miou)
print('Finished building Network.')
if not os.path.exists(os.path.join(args.checkpoint_dir,"val")):
os.mkdir(os.path.join(args.checkpoint_dir,"val"))
writer = tf.summary.FileWriter(os.path.join(args.checkpoint_dir,"val"))
init = [tf.global_variables_initializer(),tf.local_variables_initializer()]
list_checkpoints = {}
while True:
print("Waiting for new checkpoint", end='\r')
best=0
for f in sorted(os.listdir(args.checkpoint_dir)):
if "fcn8s-" in f:
output = open(args.output_path,"a")
basename=f.split(".")[0]
if basename not in list_checkpoints.keys():
list_checkpoints[basename]=os.path.join(args.checkpoint_dir, basename)
with tf.Session() as sess:
sess.run(init)
step = load(sess,list_checkpoints[basename])
print("Loading last checkpoint")
if step >= 0:
print("Restored step: ", step)
print(" [*] Load SUCCESS")
else:
step=0
print(" [!] Load failed...")
coord = tf.train.Coordinator()
tf.train.start_queue_runners()
print('Thread running')
print('Running the Network')
lenght=len(open(args.gt_file).readlines())
with open(args.gt_file) as filelist:
for idx,line in enumerate(filelist):
print("Image evaluated: ",idx + 1,"/",lenght,end='\r')
image = cv2.imread(line.split(";")[0])
semgt = cv2.imread(line.split(";")[-1].strip(),cv2.IMREAD_GRAYSCALE)
_=sess.run(update_op,feed_dict={image_placeholder : image , sem_gt_placeholder : semgt})
miou_value =sess.run(miou,feed_dict={image_placeholder : image , sem_gt_placeholder : semgt})
sum_str = sess.run(summary_miou)
writer.add_summary(sum_str,step)
if miou_value > best:
output.write("!!!!!!!!NEW BEST!!!!!!!!\n")
best = miou_value
output.write("########" + str(step) + "########\n")
mean=0
confusion_matrix=tf.get_default_graph().get_tensor_by_name("mean_iou/total_confusion_matrix:0").eval()
for cl in range(confusion_matrix.shape[0]):
tp_fn = np.sum(confusion_matrix[cl,:])
tp_fp = np.sum(confusion_matrix[:,cl])
tp = confusion_matrix[cl,cl]
IoU_cl = tp / (tp_fn + tp_fp - tp)
output.write(trainId2name[cl] + ": {:.4f}\n".format(IoU_cl))
output.write("#######################\n")
output.write("mIoU: " + str(miou_value) +"\n")
coord.request_stop()
coord.join(stop_grace_period_secs=30)
output.close()