Python load_dataset Examples

Example #1

File: testing_extension_trainset.py Project: holasyb/DepthPrediction-1

from image_handling_with_xy import real_world_values
import glob
import pickle
import os
import scipy.io


def normalise_x(x):
    return np.abs(x - 320.0) / 320.0


def normalise_y(y):
    return y / 480.0


[image_set, depths] = load_dataset('/home/nico/data/nyu_depth_v2_labeled.mat')
splits_path = '/home/nico/data/splits.mat'
splits = scipy.io.loadmat(splits_path)
train_inds = splits['trainNdxs']
train_slices = np.array_split(train_inds - 1, 10)

pretrained = "/home/nico/DepthPrediction/xy_extension/models/snapshot_cord_redo/_iter_33000.caffemodel"
net = "/home/nico/DepthPrediction/xy_extension/models/cord_extension_deploy.prototxt"
CNN = caffe.Net(net, pretrained, caffe.TEST)

no_superpixels = 400

for s in train_slices:
    images = tuple(s)
    for img_ind in images:
        print "processing image: " + str(img_ind[0])

Example #2

File: testing_old.py Project: likojack/DepthPrediction

from pylab import *
from image_handling_with_xy import load_dataset
from image_handling_with_xy import preprocess_image
from image_handling_with_xy import apply_depths
from image_handling_with_xy import real_world_values
import glob
import pickle
import os
import scipy.io

def normalise_x(x):
    return np.abs(x-320.0)/320.0
def normalise_y(y):
    return y/480.0

[image_set, depths] = load_dataset('/home/nico/data/nyu_depth_v2_labeled.mat')
splits_path = '/home/nico/data/splits.mat'
splits = scipy.io.loadmat(splits_path)
test_inds = splits['testNdxs']
test_slices = np.array_split(test_inds - 1, 10)

pretrained = "/home/nico/DepthPrediction/xy_extension/models/snapshot_cord_redo/_iter_33000.caffemodel"
net = "/home/nico/DepthPrediction/xy_extension/models/cord_extension_deploy.prototxt"
CNN = caffe.Net(net, pretrained, caffe.TEST)

no_superpixels = 400

for s in test_slices:
    images = tuple(s)
    for img_ind in images:
        print "processing image: "+str(img_ind[0])