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import os
import time
from PIL import Image
import tensorflow as tf
import tensorflow_hub as hub
from numpy import asarray
from tensorflow.python.ops.numpy_ops import np_config
np_config.enable_numpy_behavior()
os.environ["TFHUB_DOWNLOAD_PROGRESS"] = "True"
from huggingface_hub import from_pretrained_keras
def tensor_to_image(tensor):
tensor = tf.cast(tf.clip_by_value(tensor, 0, 255), tf.uint8)
tensor = Image.fromarray(tensor.numpy())
tensor.save('upscaled_image.jpg')
return 'upscaled_image.jpg'
def request_to_image(request):
image = request.files.get('image', False)
print(image)
if image:
return asarray(Image.open(image))
else:return asarray(Image.open(request.files['image']))
def _monet(image, upscale=False):
IMAGE_SIZE = (256, 256)
def decode_image(image):
#image = tf.image.decode_jpeg(image, channels=3)
image = (tf.cast(image, tf.float32) / 127.5) - 1
image = tf.reshape(image, [*IMAGE_SIZE, 3])
return image
#new_model = tf.keras.models.load_model('models/monet_generator', compile=False)
new_model = from_pretrained_keras("JoshuaPeddle/MonetGenerator")
#new_model.summary()
image = tf.image.resize(image, IMAGE_SIZE)
image = decode_image(image)
image = tf.expand_dims(image, 0)
start = time.time()
prediction = new_model(image, training=False)
prediction = tf.reshape(prediction, [256, 256, 3])
prediction = (prediction + 1) / 2
prediction = tf.image.convert_image_dtype(prediction, tf.uint8)
print("Time Taken: %f" % (time.time() - start))
if upscale:
return _upscale(prediction)
return tensor_to_image(prediction)
def _upscale(image):
SAVED_MODEL_PATH = "https://tfhub.dev/captain-pool/esrgan-tf2/1"
def preprocess_image(_image):
""" Loads image from path and preprocesses to make it model ready
Args:
image_path: Path to the image file
"""
#hr_image = tf.image.decode_image(tf.io.read_file(image_path))
hr_image = _image
# If PNG, remove the alpha channel. The model only supports
# images with 3 color channels.
if hr_image.shape[-1] == 4:
hr_image = hr_image[...,:-1]
hr_size = (tf.convert_to_tensor(hr_image.shape[:-1]) // 4) * 4
hr_image = tf.image.crop_to_bounding_box(hr_image, 0, 0, hr_size[0], hr_size[1])
hr_image = tf.cast(hr_image, tf.float32)
return tf.expand_dims(hr_image, 0)
hr_image = preprocess_image(image)
model = hub.load(SAVED_MODEL_PATH)
start = time.time()
fake_image = model(hr_image)
fake_image = tf.squeeze(fake_image)
print("Time Taken: %f" % (time.time() - start))
fake_image = tf.image.resize(fake_image, (512,512))
return tensor_to_image(fake_image) |