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data_loader_gan.py
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data_loader_gan.py
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import numpy as np
import cv2
from glob import glob
from tqdm import tqdm
import random
from skimage import io
from skimage.segmentation import mark_boundaries
from skimage.transform import rotate
import variables as var
TEST = False
def to_categorical(y, num_classes=None, dtype='float32'):
y = np.array(y, dtype='int')
input_shape = y.shape
if input_shape and input_shape[-1] == 1 and len(input_shape) > 1:
input_shape = tuple(input_shape[:-1])
y = y.ravel()
if not num_classes:
num_classes = np.max(y) + 1
n = y.shape[0]
categorical = np.zeros((n, num_classes), dtype=dtype)
categorical[np.arange(n), y] = 1
output_shape = input_shape + (num_classes,)
categorical = np.reshape(categorical, output_shape)
return categorical
class DataLoader():
def __init__(self, ws=512, nb=10000, bs=8):
self.nb = nb
self.bs = bs
self.ws = ws
#self.rgb_files = self.rgb_files[:10]
#self.dsm_files = self.dsm_files[:10]
#self.gti_files = self.gti_files[:10]
self.load_data()
self.num_tiles = len(self.rgb_imgs)
self.sliding_index = 0
def generator(self):
for _ in range(self.nb):
batch_rgb = []
batch_gti = []
batch_seg = []
for _ in range(self.bs):
rgb, gti, seg = self.extract_image()
batch_rgb.append(rgb)
# the ground truth is categorized
gti = to_categorical(gti != 0, 2)
batch_gti.append(gti)
# the segmentation is categorized
seg = to_categorical(seg != 0, 2)
batch_seg.append(seg)
batch_rgb = np.asarray(batch_rgb)
batch_gti = np.asarray(batch_gti)
batch_seg = np.asarray(batch_seg)
batch_rgb = batch_rgb / 255.0
#batch_gti = batch_gti[:,:,:,np.newaxis] / 255.0
yield (batch_rgb, batch_gti, batch_seg)
def test_shape(self, a):
ri = a.shape[0] % self.ws
rj = a.shape[1] % self.ws
return a[:-ri,:-rj]
def random_hsv(self, img, value_h=30, value_s=30, value_v=30):
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h, s, v = cv2.split(hsv)
h = np.int16(h)
s = np.int16(s)
v = np.int16(v)
h += value_h
h[h < 0] = 0
h[h > 255] = 255
s += value_s
s[s < 0] = 0
s[s > 255] = 255
v += value_v
v[v < 0] = 0
v[v > 255] = 255
h = np.uint8(h)
s = np.uint8(s)
v = np.uint8(v)
final_hsv = cv2.merge((h, s, v))
img = cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR)
return img
def extract_image(self, mode="sequential"):
if mode is "random":
rand_t = random.randint(0, self.num_tiles-1)
else:
if self.sliding_index < self.num_tiles:
rand_t = self.sliding_index
self.sliding_index = self.sliding_index + 1
else:
rand_t = 0
self.sliding_index = 0
rgb = self.rgb_imgs[rand_t].copy()
gti = self.gti_imgs[rand_t].copy()
seg = self.seg_imgs[rand_t].copy()
h = rgb.shape[1]
w = rgb.shape[0]
void = True
while void:
rot = random.randint(0,90)
ri = random.randint(0, int(h-self.ws*2))
rj = random.randint(0, int(w-self.ws*2))
win_rgb = rgb[ri:ri+int(self.ws*2), rj:rj+int(self.ws*2)]
win_gti = gti[ri:ri+int(self.ws*2), rj:rj+int(self.ws*2)]
win_seg = seg[ri:ri+int(self.ws*2), rj:rj+int(self.ws*2)]
win_rgb = np.uint8(rotate(win_rgb, rot, resize=False, preserve_range=True))
win_gti = np.uint8(rotate(win_gti, rot, resize=False, preserve_range=True))
win_seg = np.uint8(rotate(win_seg, rot, resize=False, preserve_range=True))
win_rgb = win_rgb[self.ws//2:-self.ws//2, self.ws//2:-self.ws//2]
win_gti = win_gti[self.ws//2:-self.ws//2, self.ws//2:-self.ws//2]
win_seg = win_seg[self.ws//2:-self.ws//2, self.ws//2:-self.ws//2]
if np.count_nonzero(win_seg):
void = False
# Perform some data augmentation
rot = random.randint(0,3)
win_rgb = np.rot90(win_rgb, k=rot)
win_gti = np.rot90(win_gti, k=rot)
win_seg = np.rot90(win_seg, k=rot)
if random.randint(0,1) is 1:
win_rgb = np.fliplr(win_rgb)
win_gti = np.fliplr(win_gti)
win_seg = np.fliplr(win_seg)
r_h = random.randint(-20,20)
r_s = random.randint(-20,20)
r_v = random.randint(-20,20)
win_rgb = self.random_hsv(win_rgb, r_h, r_s, r_v)
win_rgb = win_rgb.astype(np.float32)
win_gti = win_gti.astype(np.float32)
win_seg = win_seg.astype(np.float32)
return (win_rgb, win_gti, win_seg)
def load_data(self):
self.rgb_imgs = []
self.gti_imgs = []
self.seg_imgs = []
rgb_files = glob(var.DATASET_RGB)
gti_files = glob(var.DATASET_GTI)
seg_files = glob(var.DATASET_SEG)
rgb_files.sort()
gti_files.sort()
seg_files.sort()
combined = list(zip(rgb_files, gti_files, seg_files))
random.shuffle(combined)
rgb_files[:], gti_files[:], seg_files[:] = zip(*combined)
if TEST:
rgb_files = rgb_files[:4]
gti_files = gti_files[:4]
seg_files = seg_files[:4]
for rgb_name, gti_name, seg_name in tqdm(zip(rgb_files, gti_files, seg_files), total=len(rgb_files), desc="Loading dataset into RAM"):
tmp = io.imread(rgb_name)
tmp = tmp.astype(np.uint8)
self.rgb_imgs.append(tmp)
tmp = io.imread(gti_name)
tmp = tmp.astype(np.uint8)
self.gti_imgs.append(tmp)
tmp = io.imread(seg_name)
tmp = tmp.astype(np.uint8)
self.seg_imgs.append(tmp)