def _preprocess(self,
image_filename,
output_dir,
aggregate,
labelling=True):
"""Resize/crop then save the training & label images
Parameters
----------
datadir : str
image_filaname : str
aggregate : boolean
labelling : boolean
Returns
-------
dict
Key/values with the filenames and label ids
"""
# open original images
img_in = Image.open(image_filename)
# resize images (self.image_size*larger_size or larger_size*self.image_size)
img_in = utils.resize_image(img_in, self.image_size)
# crop images to get self.image_size*self.image_size dimensions
crop_pix = np.random.randint(0, 1 + max(img_in.size) - self.image_size)
final_img_in = utils.mono_crop_image(img_in, crop_pix)
# save final image
new_in_filename = os.path.join(output_dir, 'images',
image_filename.split('/')[-1])
final_img_in.save(new_in_filename)
# label_filename vs label image
if labelling:
label_filename = image_filename.replace("images/", "labels/")
label_filename = label_filename.replace(".jpg", ".png")
img_out = Image.open(label_filename)
img_out = utils.resize_image(img_out, self.image_size)
final_img_out = utils.mono_crop_image(img_out, crop_pix)
# group some labels
if aggregate:
final_img_out = self.group_image_label(final_img_out)
labels = utils.mapillary_label_building(final_img_out,
self.label_ids)
new_out_filename = os.path.join(output_dir, 'labels',
label_filename.split('/')[-1])
final_img_out.save(new_out_filename)
else:
new_out_filename = None
labels = {i: 0 for i in range(self.get_nb_labels())}
return {
"raw_filename": image_filename,
"image_filename": new_in_filename,
"label_filename": new_out_filename,
"labels": labels
}
def _preprocess(self, image_filename, output_dir, labelling=True):
"""Resize/crop then save the training & label images
Parameters
----------
datadir : str
image_filaname : str
labelling : boolean
Returns
-------
dict
Key/values with the filenames and label ids
"""
# open original images
img_in = Image.open(image_filename)
# resize images
# (self.image_size*larger_size or larger_size*self.image_size)
img_in = utils.resize_image(img_in, self.image_size)
# crop images to get self.image_size*self.image_size dimensions
crop_pix = np.random.randint(0, 1 + max(img_in.size) - self.image_size)
final_img_in = utils.mono_crop_image(img_in, crop_pix)
# save final image
new_in_filename = os.path.join(output_dir, "images",
os.path.basename(image_filename))
final_img_in.save(new_in_filename)
# label_filename vs label image
if labelling:
label_filename = image_filename.replace("images/", "labels/")
label_filename = label_filename.replace(".jpg", ".png")
img_out = Image.open(label_filename)
img_out = utils.resize_image(img_out, self.image_size)
img_out = utils.mono_crop_image(img_out, crop_pix)
# aggregate some labels
img_out = self.group_image_label(img_out)
labels = utils.build_labels(img_out,
self.label_ids,
dataset="mapillary")
new_out_filename = os.path.join(output_dir, "labels",
os.path.basename(label_filename))
label_out = np.array(img_out)
final_img_out = utils.build_image_from_config(
label_out, self.label_info)
final_img_out.save(new_out_filename)
else:
new_out_filename = None
labels = {i: 0 for i in range(self.get_nb_labels())}
return {
"raw_filename": image_filename,
"image_filename": new_in_filename,
"label_filename": new_out_filename,
"labels": labels,
}
def _preprocess(self, image_filename, output_dir, labelling):
"""Resize/crop then save the training & label images
Parameters
----------
image_filename : str
Full path towards the image on the disk
datadir : str
Output path where preprocessed image must be saved
Returns
-------
dict
Key/values with the filenames and label ids
"""
img_in = Image.open(image_filename)
raw_img_size = img_in.size[0]
result_dicts = []
# crop tile_size*tile_size tiles into 5000*5000 raw images
buffer_tiles = []
for x in range(0, raw_img_size, self.tile_size):
for y in range(0, raw_img_size, self.tile_size):
tile = img_in.crop(
(x, y, x + self.tile_size, y + self.tile_size))
tile = utils.resize_image(tile, self.image_size)
img_id = int(
(raw_img_size / self.tile_size * x / self.tile_size +
y / self.tile_size))
basename_decomp = os.path.splitext(
os.path.basename(image_filename))
new_in_filename = (basename_decomp[0] + '_' + str(img_id) +
basename_decomp[1])
new_in_path = os.path.join(output_dir, 'images',
new_in_filename)
tile.save(new_in_path.replace(".tif", ".png"))
result_dicts.append({
"raw_filename": image_filename,
"image_filename": new_in_path
})
if labelling:
label_filename = image_filename.replace("images/", "gt/")
img_out = Image.open(label_filename)
buffer_tiles = []
for x in range(0, raw_img_size, self.tile_size):
for y in range(0, raw_img_size, self.tile_size):
tile = img_out.crop(
(x, y, x + self.tile_size, y + self.tile_size))
tile = utils.resize_image(tile, self.image_size)
img_id = int(
(raw_img_size / self.tile_size * x / self.tile_size +
y / self.tile_size))
basename_decomp = os.path.splitext(
os.path.basename(image_filename))
new_out_filename = (basename_decomp[0] + '_' +
str(img_id) + basename_decomp[1])
new_out_path = os.path.join(output_dir, 'labels',
new_out_filename)
tile.save(new_out_path.replace(".tif", ".png"))
labels = utils.build_labels(tile,
self.label_ids,
dataset='aerial')
result_dicts[img_id]["label_filename"] = new_out_path
result_dicts[img_id]["labels"] = labels
return result_dicts