Untitled.ipynb @4cd1a00 — view markup · raw · history · blame
In [2]:
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
tf.reset_default_graph()
import numpy as np
from PIL import Image
import src.model
import src.util
import os
import sys
In [3]:
model_PATH='./src/output/models/model227000.ckpt'
out_PATH='./results/test_output.png'
IMAGE_SZ = 128
In [4]:
import time
def run_time(func):
def inner(model_PATH, img_p):
back = func(model_PATH, img_p)
print("Runned time: {} s".format(round((time.time() - t)/10, 3)))
return back
t = time.time()
return inner
In [5]:
def load_demo_image(in_PATH):
img = np.array(Image.open(in_PATH).resize((128, 128), Image.ANTIALIAS).convert('RGB'))[np.newaxis] / 255.0
img_p = src.util.preprocess_images_outpainting(img)
return img_p
In [6]:
def image_to_path(img):
resize_img = img
path = out_PATH
resize_img.save(path)
return path
In [7]:
@run_time
def inference(model_PATH, img_p):
G_Z = tf.placeholder(tf.float32, shape=[None, IMAGE_SZ, IMAGE_SZ, 4], name='G_Z')
G_sample = src.model.generator(G_Z)
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, model_PATH)
output, = sess.run([G_sample], feed_dict={G_Z: img_p})
img_norm = (output[0] * 255.0).astype(np.uint8)
img = Image.fromarray(img_norm, 'RGB')
#util.save_image(output[0], out_PATH)
return img
In [8]:
def handle(conf):
"""
该方法是部署之后，其他人调用你的服务时候的处理方法。
请按规范填写参数结构，这样我们就能替你自动生成配置文件，方便其他人的调用。
范例：
params['key'] = value # value_type: str # description: some description
value_type 可以选择：img, video, audio, str, int, float, [int], [str], [float]
参数请放到params字典中，我们会自动解析该变量。
"""
base64_str = conf['Photo']
image = load_demo_image(base64_str)
res = inference(model_PATH, image)
image_str = image_to_path(res)
return {'Output': image_str}
In [9]:
handle({'Photo': '/home/jovyan/work/images/test.png'})
Out[9]: