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import gradio as gr
import tensorflow as tf
import numpy as np
from tensorflow.keras.layers import (
Conv2D,
MaxPool2D,
Dropout,
Conv2DTranspose,
concatenate,
)
import matplotlib.pyplot as plt
class EncoderBlock(tf.keras.layers.Layer):
def __init__(self, filters, rate=None, pooling=True, **kwargs):
super(EncoderBlock, self).__init__(**kwargs)
self.filters = filters
self.rate = rate
self.pooling = pooling
self.conv1 = Conv2D(
self.filters,
kernel_size=3,
strides=1,
padding="same",
activation="relu",
kernel_initializer="he_normal",
)
self.conv2 = Conv2D(
self.filters,
kernel_size=3,
strides=1,
padding="same",
activation="relu",
kernel_initializer="he_normal",
)
if self.pooling:
self.pool = MaxPool2D(pool_size=(2, 2))
if self.rate is not None:
self.drop = Dropout(rate)
def call(self, inputs):
x = self.conv1(inputs)
if self.rate is not None:
x = self.drop(x)
x = self.conv2(x)
if self.pooling:
y = self.pool(x)
return y, x
else:
return x
def get_config(self):
base_config = super().get_config()
return {
**base_config,
"filters": self.filters,
"rate": self.rate,
"pooling": self.pooling,
}
class DecoderBlock(tf.keras.layers.Layer):
def __init__(self, filters, rate=None, axis=-1, **kwargs):
super(DecoderBlock, self).__init__(**kwargs)
self.filters = filters
self.rate = rate
self.axis = axis
self.convT = Conv2DTranspose(
self.filters, kernel_size=3, strides=2, padding="same"
)
self.conv1 = Conv2D(
self.filters,
kernel_size=3,
activation="relu",
kernel_initializer="he_normal",
padding="same",
)
if rate is not None:
self.drop = Dropout(self.rate)
self.conv2 = Conv2D(
self.filters,
kernel_size=3,
activation="relu",
kernel_initializer="he_normal",
padding="same",
)
def call(self, inputs):
X, short_X = inputs
ct = self.convT(X)
c_ = concatenate([ct, short_X], axis=self.axis)
x = self.conv1(c_)
if self.rate is not None:
x = self.drop(x)
y = self.conv2(x)
return y
def get_config(self):
base_config = super().get_config()
return {
**base_config,
"filters": self.filters,
"rate": self.rate,
"axis": self.axis,
}
# Load the model with custom layers
unet = tf.keras.models.load_model(
"final.h5",
custom_objects={
"EncoderBlock": EncoderBlock,
"DecoderBlock": DecoderBlock,
},
)
def show_image(image, cmap=None, title=None):
plt.imshow(image, cmap=cmap)
if title is not None:
plt.title(title)
plt.axis("off")
def predict(image):
real_img = tf.image.resize(image, [128, 128])
real_img = real_img / 255.0
real_img = np.expand_dims(real_img, axis=0)
pred_mask = unet.predict(real_img).reshape(128, 128)
real_img = real_img[0]
fig, ax = plt.subplots(1, 2, figsize=(10, 5))
ax[0].imshow(real_img)
ax[0].set_title("Original Image")
ax[0].axis("off")
ax[1].imshow(pred_mask, cmap="gray")
ax[1].set_title("Predicted Mask")
ax[1].axis("off")
plt.tight_layout()
plt.show()
return pred_mask
# Create Gradio interface
iface = gr.Interface(
fn=predict,
inputs=gr.Image(type="numpy"),
outputs=gr.Image(type="numpy"),
examples=["./images/water_body_11.jpg", "./images/water_body_1011.jpg"],
title="Water Body Segmentation",
)
iface.launch()
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