def __readimage(self, fn):
try:
im = imageio.volread(fn)
except Exception:
# require tifffile only if imageio fails
from skimage.external.tifffile import tifffile
im = tifffile.imread(fn)
return self.__fix_image_dimensions(im)
def getmaskarray(self):
if self.mfn is None:
return None
try:
im = imageio.imread(self.mfn, flatten=True)
except Exception:
# require tifffile only if imageio fails
from skimage.external.tifffile import tifffile
im = tifffile.imread(self.mfn)
return im
def false_color_images(folder, files1, files2, k):
beta2 = 0.05
beta4 = 1.00
beta6 = 0.544
beta1 = 0.65
beta3 = 0.85
beta5 = 0.35
temp_0 = tiff.imread(files1[k])
temp_1 = tiff.imread(files2[k])
temp_0 = temp_0.astype(float)
temp_1 = temp_1.astype(float)
temp_0 = temp_0 - 50
temp_1 = temp_1 - 50
ind = np.where(temp_0 < 0)
temp_0[ind] = 0
ind = np.where(temp_1 < 0)
temp_1[ind] = 0
#temp_0 = cv.resize(temp_0,(int(temp_0.shape[0]*1.414), temp_0.shape[1]), interpolation = cv.INTER_CUBIC)
#temp_1 = cv.resize(temp_1,(int(temp_1.shape[0]*1.414), temp_1.shape[1]), interpolation = cv.INTER_CUBIC)
im = np.zeros((temp_0.shape[0], temp_0.shape[1], 3))
im[:, :, 0] = np.multiply(np.exp(-temp_0 * beta1 / 1200),
np.exp(-temp_1 * beta2 / 1200))
im[:, :, 1] = np.multiply(np.exp(-temp_0 * beta3 / 1200),
np.exp(-temp_1 * beta4 / 1200))
im[:, :, 2] = np.multiply(np.exp(-temp_0 * beta5 / 1200),
np.exp(-temp_1 * beta6 / 1200))
im = im * 255
im = im.astype('uint8')
oldname = files1[k]
#newname = oldname.replace('CH000001', '')
newname = oldname.replace(folder, folder + '_rgb')
tifffile.imsave(newname, im)
return
def _get_batches_of_transformed_samples(self, index_array):
batch_x = []
batch_y = []
for batch_index, image_index in enumerate(index_array):
city, id = self.image_ids[image_index]
for data_dir in self.data_dirs:
city_dir_name = data_dir.split("/")[-1]
if city in data_dir:
img_name = self.image_name_template.format(id=id)
if self.clahe:
data_dir = os.path.join(self.wdata_dir, city_dir_name)
path = os.path.join(data_dir, img_name)
else:
path = os.path.join(data_dir, img_name)
break
arr = tifffile.imread(path)
image = np.stack([
arr[..., 4], arr[..., 2], arr[..., 1], arr[..., 0],
arr[..., 3], arr[..., 5], arr[..., 6], arr[..., 7]
],
axis=-1)
if self.stretch_and_mean:
image = stretch_8bit(image) * 255
if self.ohe_city:
ohe_city = np.zeros((image.shape[0], image.shape[1], 4),
dtype="float32")
ohe_city[..., cities.index(city)] = 2047
image = np.concatenate([image, ohe_city], axis=-1)
image = np.array(image, dtype="float32")
lines = self.masks_dict[id]
mask = np.zeros((image.shape[0], image.shape[1], 1))
# lines in wkt format, pygeoif
if os.path.exists("masks/" + id + ".png"):
mask = img_to_array(
load_img("masks/" + id + ".png", grayscale=True)) / 255.
else:
mask = np.zeros((image.shape[0], image.shape[1], 1))
# lines in wkt format, pygeoif
for line in lines:
if "LINESTRING EMPTY" == line:
continue
points = pygeoif.from_wkt(line).coords
for i in range(1, len(points)):
pt1 = (int(points[i - 1][0]), int(points[i - 1][1]))
pt2 = (int(points[i][0]), int(points[i][1]))
cv2.line(mask,
pt1,
pt2, (1, ),
thickness=self.thickness)
cv2.imwrite("masks/" + id + ".png", mask * 255)
ori_height = image.shape[0]
ori_width = image.shape[1]
mask = self.transform_mask(mask, image)
if self.random_transformer is not None:
image, mask = self.random_transformer.random_transform(
image, mask)
if self.stretch_and_mean:
mean_band = mean_bands[city]
for band in range(len(mean_band)):
image[..., band] -= mean_band[band]
if self.crop_shape is not None:
crops = 0
tries = 0
while crops < self.crops_per_image:
tries += 1
if self.random_transformer is None:
y_start = (ori_height - self.crop_shape[0]) // 2
x_start = (ori_width - self.crop_shape[1]) // 2
else:
y_start = random.randint(
0, ori_height - self.crop_shape[0] - 1)
x_start = random.randint(
0, ori_width - self.crop_shape[1] - 1)
y_end = y_start + self.crop_shape[0]
x_end = x_start + self.crop_shape[1]
crop_image = image[y_start:y_end, x_start:x_end, :]
crop_mask = mask[y_start:y_end, x_start:x_end, :]
if self.random_transformer is None:
batch_x.append(crop_image)
batch_y.append(crop_mask)
crops += 1
elif np.count_nonzero(crop_image) > 100 or tries > 20:
batch_x.append(crop_image)
batch_y.append(crop_mask)
crops += 1
else:
batch_x.append(image)
batch_y.append(mask)
batch_x = np.array(batch_x, dtype="float32")
batch_y = np.array(batch_y, dtype="float32")
if self.preprocessing_function == 'caffe':
batch_x_rgb = batch_x[..., :3]
batch_x_bgr = batch_x_rgb[..., ::-1]
batch_x[..., :3] = batch_x_bgr
if not self.stretch_and_mean:
batch_x = batch_x / 8. - 127.5
else:
if self.stretch_and_mean:
batch_x = batch_x / 255
else:
batch_x = batch_x / 1024. - 1
return self.transform_batch_x(batch_x), self.transform_batch_y(batch_y)
def _get_batches_of_transformed_samples(self, index_array):
batch_x = []
batch_y = []
for batch_index, image_index in enumerate(index_array):
city, id = self.image_ids[image_index]
for data_dir in self.data_dirs:
city_dir_name = data_dir.split("/")[-1]
if city in data_dir:
img_name = self.image_name_template.format(id=id)
if self.clahe:
data_dir = os.path.join(self.wdata_dir, city_dir_name)
path = os.path.join(data_dir, img_name)
else:
path = os.path.join(data_dir, img_name)
break
arr = tifffile.imread(path)
image = np.stack([arr[..., 4], arr[..., 2], arr[..., 1], arr[..., 0], arr[..., 3], arr[..., 5], arr[..., 6], arr[..., 7]], axis=-1)
if self.stretch_and_mean:
image = stretch_8bit(image) * 255
if self.ohe_city:
ohe_city = np.zeros((image.shape[0], image.shape[1], 4), dtype="float32")
ohe_city[..., cities.index(city)] = 2047
image = np.concatenate([image, ohe_city], axis=-1)
image = np.array(image, dtype="float32")
lines = self.masks_dict[id]
mask = np.zeros((image.shape[0], image.shape[1], 1))
# lines in wkt format, pygeoif
if os.path.exists("masks/" + id + ".png"):
mask = img_to_array(load_img("masks/" + id + ".png", grayscale=True)) / 255.
else:
mask = np.zeros((image.shape[0], image.shape[1], 1))
# lines in wkt format, pygeoif
for line in lines:
if "LINESTRING EMPTY" == line:
continue
points = pygeoif.from_wkt(line).coords
for i in range(1, len(points)):
pt1 = (int(points[i - 1][0]), int(points[i - 1][1]))
pt2 = (int(points[i][0]), int(points[i][1]))
cv2.line(mask, pt1, pt2, (1,), thickness=self.thickness)
cv2.imwrite("masks/" + id + ".png", mask * 255)
ori_height = image.shape[0]
ori_width = image.shape[1]
mask = self.transform_mask(mask, image)
if self.random_transformer is not None:
image, mask = self.random_transformer.random_transform(image, mask)
if self.stretch_and_mean:
mean_band = mean_bands[city]
for band in range(len(mean_band)):
image[..., band] -= mean_band[band]
if self.crop_shape is not None:
crops = 0
tries = 0
while crops < self.crops_per_image:
tries += 1
if self.random_transformer is None:
y_start = (ori_height - self.crop_shape[0]) // 2
x_start = (ori_width - self.crop_shape[1]) // 2
else:
y_start = random.randint(0, ori_height - self.crop_shape[0] - 1)
x_start = random.randint(0, ori_width - self.crop_shape[1] - 1)
y_end = y_start + self.crop_shape[0]
x_end = x_start + self.crop_shape[1]
crop_image = image[y_start:y_end, x_start:x_end, :]
crop_mask = mask[y_start:y_end, x_start:x_end, :]
if self.random_transformer is None:
batch_x.append(crop_image)
batch_y.append(crop_mask)
crops += 1
elif np.count_nonzero(crop_image) > 100 or tries > 20:
batch_x.append(crop_image)
batch_y.append(crop_mask)
crops += 1
else:
batch_x.append(image)
batch_y.append(mask)
batch_x = np.array(batch_x, dtype="float32")
batch_y = np.array(batch_y, dtype="float32")
if self.preprocessing_function == 'caffe':
batch_x_rgb = batch_x[..., :3]
batch_x_bgr = batch_x_rgb[..., ::-1]
batch_x[..., :3] = batch_x_bgr
if not self.stretch_and_mean:
batch_x = batch_x / 8. - 127.5
else:
if self.stretch_and_mean:
batch_x = batch_x / 255
else:
batch_x = batch_x / 1024. - 1
return self.transform_batch_x(batch_x), self.transform_batch_y(batch_y)
from skimage import filters
gfiltA = filters.gaussian(A_grey, sigma=(10,10), multichannel=True)
plt.imshow(gfiltA, cmap=plt.cm.gray)
# In[184]:
from skimage import filters
sofiltA = filters.sobel(A_grey)
plt.imshow(sofiltA, cmap=plt.cm.gray)
# In[194]:
from skimage.external.tifffile import tifffile
# In[195]:
mdimage=tifffile.imread("multipage_tif_example.tif", key=0)
# In[ ]:
