# ema = tf.train.ExponentialMovingAverage(0.999)o
# vars = ema.variables_to_restore()
# load the normalization params if the data is normalized
if normalized_dataset:
text_file = open(normalized_file_name, "r")
normalization_params = text_file.read().split()
text_file.close()
saver = tf.train.Saver()
file_names = os.listdir(dataset_path + validation_path)
file_names = filterImages(file_names)
file_names = addDatasetPath(dataset_path + validation_path, file_names)
# shuffle(file_names)
labels = extractLabels(file_names)
with tf.Session() as sess:
saver.restore(sess, checkpoint)
for i in range(len(file_names)):
x = logits.eval(feed_dict={file_input: file_names[i]})
print(x)
x = x[0]
unnormalizeFromParams(x, normalization_params)
image = cv2.imread(file_names[i])
image = drawOneLane(image, x[0], x[1], x[2], "blue")
image = drawOneLane(image, x[3], x[4], x[5], "yellow")
label = unnormalizeFromParams(labels[i], normalization_params)
image = drawOneLane(image, label[0], label[1], label[2], "green")
# newArr.append(name) # return newArr ###Begin main function here### # create an input tensor prepare_file_system(summaries_dir) #load the data set # A vector of filenames. image_files = os.listdir(dataset_path) image_files = filterImages(image_files) random.shuffle(image_files) #make the dataset the full path image_files = addDatasetPath(dataset_path, image_files) #files variable will be left with only the train ground truth train_groundTruth = extractLabels(image_files) #load validation fileset validation_files = os.listdir(validation_path) validation_files = filterImages(validation_files) validation_files = addDatasetPath(validation_path, validation_files) random.shuffle(validation_files) validation_groundTruth = extractLabels(validation_files) assert len(train_groundTruth) == len(image_files) assert len(validation_groundTruth) == len(validation_files) filenames_placeholder = tf.placeholder(tf.string) # filenames = tf.constant(["/var/data/image1.jpg", "/var/data/image2.jpg"]) # `labels[i]` is the label for the image in `filenames[i]. labels_inputPlaceholder = tf.placeholder(tf.float32)
import shutil
from dataHelperFunctions import filterImages, addDatasetPath, extractLabels
def normalize(data, mean, stdev):
return str((data - mean) / stdev)
dataset_path = "D:/cuLane/culane_preprocessing/converted_dataset_percentage_augmented/" #don't forget the slash at the end!
new_dataset_path = "D:/LaneDetectionV2/d_aug_two_lanes_percentage_dataset/"
print("reading data...")
files = os.listdir(dataset_path)
image_files = filterImages(files)
image_files = addDatasetPath(dataset_path, image_files) #merging the full path with the image file names in order to copy them properly later
labels = extractLabels(image_files)
print("read all data...")
#calculate the mean of each
meansOfAllData = []
stdevOfAllData = []
for i in range(len(labels[0])): #assuming that all the length of data will be constant
print("calculating mean and stddev at index: ", i)
allDataAcrossSingleIndex = []
for j in range(len(labels)):
allDataAcrossSingleIndex.append(float(labels[j][i]))
mean = statistics.mean(allDataAcrossSingleIndex)
std = statistics.stdev(allDataAcrossSingleIndex)
meansOfAllData.append(mean)
stdevOfAllData.append(std)