Spaces:
Build error
Build error
File size: 1,826 Bytes
e9df5bd c771339 e9df5bd 1194684 fd5e8cf e9df5bd c771339 e9df5bd c771339 e9df5bd c771339 f1e328a e9df5bd caa78b5 e9df5bd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 |
import gradio as gr
import keras
from keras.models import load_model
from tensorflow_addons.layers import InstanceNormalization
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
cust = {'InstanceNormalization': InstanceNormalization}
model=load_model('g-cycleGAN-photo2monet-500images-epoch10_30_30_30_30_30_1000images_30_30_30.h5',cust)
path = [['ex1.jpg'], ['ex2.jpg'], ['ex4.jpg'],['ex6.jpg'],['ex7.jpg'],['ex8.jpg'],['ex9.jpg'],['ex10.jpg'],['ex12.jpg'],['ex13.jpg']]
# preprocess
AUTOTUNE = tf.data.AUTOTUNE
BUFFER_SIZE = 400
BATCH_SIZE = 1
IMG_WIDTH = 256
IMG_HEIGHT = 256
def resize(image,height,width):
resized_image = tf.image.resize(image,[height,width],method = tf.image.ResizeMethod.NEAREST_NEIGHBOR)
return resized_image
def normalize(input_image):
input_image = (input_image/127.5) - 1
return input_image
def load(img_file):
img = tf.io.read_file(img_file)
img = tf.io.decode_jpeg(img)
real_image = tf.cast(img,tf.float32)
return real_image
def load_image_test(image_file):
re = load(image_file)
re = resize(re,IMG_HEIGHT,IMG_WIDTH)
re = normalize(re)
return re
def show_preds_image(image_path):
A = load_image_test(image_path)
A = np.expand_dims(A,axis=0)
B = model(A)
B = B[0]
B = B * 0.5 + 0.5
B = B.numpy()
return B
inputs_image = [
gr.components.Image(shape=(256,256),type="filepath", label="Input Image"),
]
outputs_image = [
gr.components.Image(shape=(256,256),type="numpy", label="Output Image").style(width=256, height=256),
]
interface_image = gr.Interface(
fn=show_preds_image,
inputs=inputs_image,
outputs=outputs_image,
title="photo2monet",
examples=path,
cache_examples=False,
)
gr.TabbedInterface(
[interface_image],
tab_names=['Image inference']
).queue().launch()
|