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server.py
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server.py
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from flask import Flask, render_template, request, jsonify
import os
import tensorflow as tf
os.environ["TFHUB_MODEL_LOAD_FORMAT"] = "COMPRESSED"
import numpy as np
import PIL.Image
import cv2
import glob
import tensorflow_hub as hub
import json
import base64
from io import BytesIO
style_predict_path = tf.keras.utils.get_file(
"style_predict.tflite",
"https://tfhub.dev/google/lite-model/magenta/arbitrary-image-stylization-v1-256/int8/prediction/1?lite-format=tflite",
)
style_transform_path = tf.keras.utils.get_file(
"style_transform.tflite",
"https://tfhub.dev/google/lite-model/magenta/arbitrary-image-stylization-v1-256/int8/transfer/1?lite-format=tflite",
)
def tensor_to_image(tensor):
tensor = tensor * 255
tensor = np.array(tensor, dtype=np.uint8)
if np.ndim(tensor) > 3:
assert tensor.shape[0] == 1
tensor = tensor[0]
return PIL.Image.fromarray(tensor)
def load_img(path_to_img):
max_dim = 512
img = tf.io.read_file(path_to_img)
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 = max_dim / long_dim
new_shape = tf.cast(shape * scale, tf.int32)
img = tf.image.resize(img, new_shape)
img = img[tf.newaxis, :]
return img
def convert_to_img(base_string, fileName):
imgdata = base64.b64decode(base_string)
filename = fileName
with open(filename, "wb") as f:
f.write(imgdata)
return filename
def preprocess_image(image, target_dim):
# Resize the image so that the shorter dimension becomes 256px.
shape = tf.cast(tf.shape(image)[1:-1], tf.float32)
short_dim = min(shape)
scale = target_dim / short_dim
new_shape = tf.cast(shape * scale, tf.int32)
image = tf.image.resize(image, new_shape)
# Central crop the image.
image = tf.image.resize_with_crop_or_pad(image, target_dim, target_dim)
return image
def run_style_predict(preprocessed_style_image):
# Load the model.
interpreter = tf.lite.Interpreter(model_path=style_predict_path)
# Set model input.
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
interpreter.set_tensor(input_details[0]["index"], preprocessed_style_image)
# Calculate style bottleneck.
interpreter.invoke()
style_bottleneck = interpreter.tensor(
interpreter.get_output_details()[0]["index"]
)()
return style_bottleneck
# Run style transform on preprocessed style image
def run_style_transform(style_bottleneck, preprocessed_content_image):
# Load the model.
interpreter = tf.lite.Interpreter(model_path=style_transform_path)
# Set model input.
input_details = interpreter.get_input_details()
interpreter.allocate_tensors()
# Set model inputs.
interpreter.set_tensor(input_details[0]["index"], preprocessed_content_image)
interpreter.set_tensor(input_details[1]["index"], style_bottleneck)
interpreter.invoke()
# Transform content image.
stylized_image = interpreter.tensor(interpreter.get_output_details()[0]["index"])()
return stylized_image
def processing(output):
_, style = output[0]["style"].split(",")
_, content = output[1]["target"].split(",")
style = convert_to_img(style, "style.jpg")
content = convert_to_img(content, "content.jpg")
style_image = load_img(style)
content_image = load_img(content)
style_image = preprocess_image(style_image, 256)
content_image = preprocess_image(content_image, 384)
style_bottleneck = run_style_predict(style_image)
stylized_image = run_style_transform(style_bottleneck, content_image)
if len(stylized_image.shape) > 3:
stylized_image = tf.squeeze(stylized_image, axis=0)
result = tensor_to_image(stylized_image)
buff = BytesIO()
result.save(buff, format="JPEG")
new_image_string = base64.b64encode(buff.getvalue()).decode("utf-8")
return new_image_string
app = Flask(
__name__,
static_url_path="",
static_folder="web/static",
template_folder="web/templates",
)
@app.route("/", methods=["GET", "POST"])
def root():
"""Return the home page."""
global output
if request.method == "POST":
output = request.get_json()
final_ouput = processing(output)
return jsonify(final_ouput)
else:
return render_template("index.html")
if __name__ == "__main__":
app.run(debug=True)