def create_dataset(dataset_folder, thumbs_folder, ext, num_clusters, thumb_size):
for pathfile in glob.iglob(dataset_folder + '/**/*', recursive=True):
filename = basename(pathfile)
if filename.endswith(tuple(ext)):
image = image_utils.open_image(pathfile, "RGB")
color, image = dominant_color.find_dominant_color_and_generate_thumb(image, filename, thumbs_folder,
num_clusters, thumb_size, True)
dataset_thumbs.write_pic(color.red, color.green, color.blue, filename)
return True
def get_candidate_image(candidate_images, thumbs_folder):
"""
:param candidate_images: array of filename
:param thumbs_folder: folder containing thumbnails
"""
output = None
while not output and candidate_images:
filename_candidate = candidate_images.pop(random.randrange(len(candidate_images)))
output = image_utils.open_image(thumbs_folder + filename_candidate, "RGB")
return output
def to_gray_and_save_pic(uri, elaborated_folder):
image = image_utils.open_image(uri, "RGB")
if not image:
return False
else:
if image.format == "PNG":
image = image.convert('LA')
else:
image = image.convert('L')
image_utils.save_image(image, basename(uri), elaborated_folder)
return True
def create_dataset(dataset_folder, thumbs_folder, ext, num_clusters,
thumb_size):
for pathfile in glob.iglob(dataset_folder + '/**/*', recursive=True):
filename = basename(pathfile)
if filename.endswith(tuple(ext)):
image = image_utils.open_image(pathfile, "RGB")
color, image = dominant_color.find_dominant_color_and_generate_thumb(
image, filename, thumbs_folder, num_clusters, thumb_size, True)
dataset_thumbs.write_pic(color.red, color.green, color.blue,
filename)
return True
def __init__(self, path2data, path2Individual, path2postures, number_sequences, size, use_memory, number_data, transforms_target_label=None, transforms_individual=None):
self.sequence_number = number_sequences
self.size = size
self.transforms_target_label = transforms_target_label
self.transforms_individual = transforms_individual
self.use_memory = use_memory
self.target_path, self.label_path = self.get_target_label_path(path2data, path2Individual, path2postures, number_data)
self.path2Image_map = {}
if use_memory:
path2Individual = os.path.join(path2data, path2Individual)
path2postures = os.path.join(path2data, path2postures)
iu.folder_to_map(path2Individual, self.path2Image_map, lambda image_path: iu.open_image(image_path, self.size), depth=2)
iu.folder_to_map(path2postures, self.path2Image_map, lambda image_path: iu.open_image(image_path, self.size), depth=2)
def to_sepia_and_save_pic(uri, elaborated_folder):
"""
Convert before in grayscale and then in sepia
"""
img = image_utils.open_image(uri, "RGB")
if img:
orig_mode = img.mode
if orig_mode != "L":
img = img.convert("L")
sepia = make_linear_ramp()
img.putpalette(sepia)
img = img.convert(orig_mode)
image_utils.save_image(img, basename(uri), elaborated_folder)
return True
else:
return False
def resize_and_save_pic(uri, width, height, elaborated_folder):
image = image_utils.open_image(uri, "RGB")
image = image_utils.resize_image(image, (width,height))
image_utils.save_image(image, basename(uri), elaborated_folder)
return True