from imutils import paths
import matplotlib.pyplot as plt
import numpy as np
import argparse
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required=True, help="path to input dataset")
args = vars(ap.parse_args())
# grab the list of images that we'll be describing
print("[INFO] loading images...")
imagePaths = list(paths.list_images(args["dataset"]))
# initialize the image preprocessors
sp = SimplePreprocessor(32, 32)
iap = ImageToArrayPreprocessor()
# load the dataset from disk then scale the raw pixel intensities
# to the range [0, 1]
sdl = SimpleDatasetLoader(preprocessors=[sp, iap])
(data, labels) = sdl.load(imagePaths, verbose=500)
data = data.astype("float") / 255.0
# partition the data into training and testing splits using 75% of
# the data for training and the remaining 25% for testing
(trainX, testX, trainY, testY) = train_test_split(data,
labels,
test_size=0.25,
random_state=42)
bSize = 8 # 128
# construct the training image generator for data augmentation
aug = ImageDataGenerator(rotation_range=20,
zoom_range=0.15,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.15,
horizontal_flip=True,
fill_mode="nearest")
# load the RGB means for the training set
means = json.loads(open(config.DATASET_MEAN).read())
# initialize the image preprocessors
sp = SimplePreprocessor(227, 227)
pp = PatchPreprocessor(227, 227)
mp = MeanPreprocessor(means["R"], means["G"], means["B"])
iap = ImageToArrayPreprocessor()
# initialize the training and validation dataset generators
trainGen = HDF5DatasetGenerator(config.TRAIN_HDF5,
bSize,
aug=aug,
preprocessors=[pp, mp, iap],
classes=2)
valGen = HDF5DatasetGenerator(config.VAL_HDF5,
bSize,
preprocessors=[sp, mp, iap],
classes=2)
sys.append("../")
from pyimagesearch.utils.ranked import rank5_accuracy
from dog_vs_cat.configs import dog_vs_cat_configs as configs
from pyimagesearch.preprocessing.croppreprocessor import CropPreprocessor
from pyimagesearch.preprocessing.meanpreprocessor import MeanPreprocessor
from pyimagesearch.preprocessing.simplepreprocessor import SimplePreprocessor
from pyimagesearch.preprocessing.imagetoarraypreprocessor import ImageToArrayPreprocessor
# load RGB mean values
means = json.loads(open(configs.DATASET_MEAN).read())
# load preprocessors
ip = ImageToArrayPreprocessor()
cp = CropPreprocessor(width=227, height=227)
sp = SimplePreprocessor(width=227, height=227)
mp = MeanPreprocessor(rMean=means["R"], gMean=means["G"], bMean=means["B"])
# custom preprocessor class which utilizes custom preprocessors
class CustomPreprocessing:
def __init__(self, preprocessors=[]):
self.preprocessors = preprocessors
def preprocess(self, image):
""" applies preprocessors on image """
for preprocessor in self.preprocessors:
image = preprocessor.preprocess(image)
return image
args = vars(ap.parse_args())
# construct the training image generator for data augmentation
aug = ImageDataGenerator(rotation_range=18,
zoom_range=0.15,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.15,
horizontal_flip=True,
fill_mode="nearest")
# load the RGB means for the training set
means = json.loads(open(config.DATASET_MEAN).read())
# initialize the image preprocessors
sp = SimplePreprocessor(64, 64)
mp = MeanPreprocessor(means["R"], means["G"], means["B"])
iap = ImageToArrayPreprocessor()
# initialize the training and validation dataset generators
trainGen = HDF5DatasetGenerator(config.TRAIN_HDF5,
64,
aug=aug,
preprocessors=[sp, mp, iap],
classes=config.NUM_CLASSES)
valGen = HDF5DatasetGenerator(config.VAL_HDF5,
64,
preprocessors=[sp, mp, iap],
classes=config.NUM_CLASSES)
# if there is no specific model checkpoint supplied, then initialize
print("Driver: {}/{}".format(in_ds.GetDriver().ShortName,
in_ds.GetDriver().LongName))
print("Size is {} x {} x {}".format(in_ds.RasterXSize, in_ds.RasterYSize,
in_ds.RasterCount))
print("Projection is {}".format(in_ds.GetProjection()))
geotransform = in_ds.GetGeoTransform()
if geotransform:
print("Origin = ({}, {})".format(geotransform[0], geotransform[3]))
print("Pixel Size = ({}, {})".format(geotransform[1], geotransform[5]))
#plt.figure(1)
#plt.imshow(in_ds.GetRasterBand(1).ReadAsArray(), cmap="nipy_spectral")
return (in_ds, geotransform)
sp = SimplePreprocessor(config.IMAGE_SIZE_COLS, config.IMAGE_SIZE_ROWS)
# extract image paths and coordinates from stereo_pose_est.data
renav, o_lat, o_lon, ftype = rutil.read_renav(config.NAV_PATH)
# get bathymetry (gdal raster) and geotransform
bathy_ds, geotransform = extract_geotransform(config.BATHY_PATH)
# tranformation from lat lon to utm for bathymetry
utm_proj = pyproj.Proj(config.BATHY_UTM_PROJ_PARAMS)
# split into training, validation and testing
# load image, extract bathy patch
# save to HDF5
# perform sampling from the training set to build the
# testing split from the training data
