def get_img(img_path):
img = read_file(img_path)
img = decode_jpeg(img, channels=3)
img = resize(img, [image_size, image_size])
img = 1 - img/255. # We would rather have the whole white void area be full of zeros than ones
img = tf.convert_to_tensor([img])
return img
def checkDecodingTF_Image_Decode(filename):
can_load = True
try:
im = io.read_file(filename)
im = image.decode_image(im, channels=3,
expand_animations=False).numpy()
except Exception as e:
can_load = False
return can_load
def process_path(file_path):
print(file_path)
file = read_file(file_path)
file = image.decode_jpeg(file, channels=3)
file = cast(file, float32)
file = preprocess_input(file)
file = image.resize(file, [ROW, COL])
return file
def load_image(image_path):
"""
Convert all image into array of shape (1, width, height, 3)
"""
image = io.read_file(image_path)
image = img.decode_image(image, channels=3)
image = img.convert_image_dtype(image, float32)
image = img.resize(image, (width, height))
image = image[newaxis, :]
return image
def image_generator(test_size=0.2, testing=False):
path = '/content/drive/My Drive/Flickr_Faces'
no_of_images = len(os.listdir(path))
if testing:
list_of_images = os.listdir(path)[int(no_of_images * test_size):]
else:
list_of_images = os.listdir(path)[:int(no_of_images * test_size)]
for filename in list_of_images:
img = os.path.join(path, filename)
img = read_file(img)
img = decode_png(img, channels=3)
yield img / 255
def load_img(img_path):
img = io.read_file(img_path)
img = tf.image.decode_image(img, channels=3)
img = tf.image.convert_image_dtype(img, tf.float32)
shape = tf.cast(tf.shape(img)[:-1], tf.float32)
long_dim = max(shape)
scale = FLAGS.max_dim / long_dim
new_shape = tf.cast(shape * scale, tf.int32)
img = tf.image.resize(img, new_shape, antialias=True)
img = img[tf.newaxis, :]
return img
def image_import(all_paths, img_size, ch=3):
"""Function to convert two images into tensors for putting it into the network
Arguments: all_path - list of paths to images,
img_size - image size
ch - kind of image color channels
Return: list of tensors"""
images = [
image.resize(image.decode_image(io.read_file(path)),
[img_size, img_size, ch]) for path in all_paths
]
images /= 255.0
print('Images are ready')
return images
def song_vectors(song_dir):
dir_name = '../tensors/'
with open('song_vectors.txt', 'w') as f:
f_index = 0
for file_name in os.listdir(song_dir):
raw_audio = io.read_file(song_dir + file_name)
song_vector, sample_rate = audio.decode_wav(raw_audio,
desired_samples=100000)
song_pickle = open(dir_name + 'song_tensor' + str(f_index), 'wb')
rate_pickle = open(dir_name + 'rate_tensor' + str(f_index), 'wb')
pickle.dump(song_vector, song_pickle)
pickle.dump(sample_rate, rate_pickle)
song_pickle.close()
rate_pickle.close()
f.write(str(sample_rate) + ':')
for tensor in song_vector:
f.write(str(tensor))
f.write('\n')
f_index += 1
def load_and_preprocess_image(self, path):
image = tf_io.read_file(path)
return self.preprocess_image(image)
def load_image(file_path):
img = read_file(file_path)
# decode and resize image
img = decode_png(img, channels=3)
img = resize(img, [32, 32])
return img
def handle_image_path(img_path):
img = read_file(img_path)
img = decode_jpeg(img, channels=3)
img = resize_images(img, [image_size, image_size])
img = img / 255.0
return img
def load_image(image_path, img_width=299, img_height=299):
img = io.read_file(image_path)
img = image.decode_jpeg(img, channels=3)
img = image.resize(img, (img_width, img_height))
return img
def from_file_system(cls, path: str):
image = read_file(path)
return cls.from_raw(image)
