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# %%
from cProfile import label
import gradio as gr
import numpy as np
# import random as rn
# import os
import tensorflow as tf
import cv2
tf.config.experimental.set_visible_devices([], 'GPU')
#%% constantes
COLOR = np.array([163, 23, 252])/255.0
ALPHA = 0.8
#%%
def parse_image(image):
image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
image = cv2.resize(image, (100, 100))
image = image.astype(np.float32)
image = image / 255.0
image = np.expand_dims(image, axis=0)
image = np.expand_dims(image, axis=-1)
return image
#%%
def cnn(input_shape, output_shape):
num_classes = output_shape[0]
dropout_seed = 708090
kernel_seed = 42
model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(16, 3, activation='relu', input_shape=input_shape, kernel_initializer=tf.keras.initializers.GlorotUniform(seed=kernel_seed)),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Dropout(0.1, seed=dropout_seed),
tf.keras.layers.Conv2D(32, 5, activation='relu', kernel_initializer=tf.keras.initializers.GlorotUniform(seed=kernel_seed)),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Dropout(0.1, seed=dropout_seed),
tf.keras.layers.Conv2D(64, 10, activation='relu', kernel_initializer=tf.keras.initializers.GlorotUniform(seed=kernel_seed)),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Dropout(0.1, seed=dropout_seed),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation='relu', kernel_regularizer='l2', kernel_initializer=tf.keras.initializers.GlorotUniform(seed=kernel_seed)),
tf.keras.layers.Dropout(0.2, seed=dropout_seed),
tf.keras.layers.Dense(16, activation='relu', kernel_regularizer='l2', kernel_initializer=tf.keras.initializers.GlorotUniform(seed=kernel_seed)),
tf.keras.layers.Dropout(0.2, seed=dropout_seed),
tf.keras.layers.Dense(num_classes, activation='sigmoid', kernel_initializer=tf.keras.initializers.GlorotUniform(seed=kernel_seed))
])
return model
#%%
model = cnn((100, 100, 1), (1,))
model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=False), optimizer='Adam', metrics='accuracy')
model.load_weights('weights.h5')
#%%
def saliency_map(img):
"""
return the normalized gradients overs the image, and also the prediction of the model
"""
inp = tf.convert_to_tensor(
img[None, :, :, None],
dtype = tf.float32
)
inp_var = tf.Variable(inp)
with tf.GradientTape() as tape:
pred = model(inp_var, training=False)
loss = pred[0][0]
grads = tape.gradient(loss, inp_var)
grads = tf.math.abs(grads) / (tf.math.reduce_max(tf.math.abs(grads))+1e-14)
return grads, round(float(model(inp_var, training = False)))
#%%
def segment(image):
# c = image
print(image.shape)
image = parse_image(image)
print(image.shape)
output = model.predict(image)
# print(output)
labels = {
"Farsi" : 1-float(output),
"Ruqaa" : float(output)
}
grads, _ = saliency_map(image[0, :, :, 0])
s_map = grads.numpy()[0, :, :, 0]
reconstructed_image = cv2.cvtColor(image.squeeze(0), cv2.COLOR_GRAY2RGB)
for i in range(reconstructed_image.shape[0]):
for j in range(reconstructed_image.shape[1]):
reconstructed_image[i, j, :] = reconstructed_image[i, j, :] * (1-ALPHA) + s_map[i, j]* COLOR * ALPHA
# reconstructed_image = reconstructed_image.astype(np.uint8)
V = reconstructed_image
# print("i shape:", i.shape)
# print("type(i):", type(i))
return labels, reconstructed_image
iface = gr.Interface(fn=segment,
description="""
This is an Arab Calligraphy Style Recognition.
This model predicts the style (binary classification) of the image.
The model also outputs the Saliency map.
""",
inputs="image",
outputs=[
gr.outputs.Label(num_top_classes=2, label="Style"),
gr.outputs.Image(label = "Saliency map")
],
examples=[["images/Farsi_1.jpg"],
["images/Farsi_2.jpg"],
["images/real_Farsi.jpg"],
["images/Ruqaa_1.jpg"],
["images/Ruqaa_2.jpg"],
["images/Ruqaa_3.jpg"],
["images/real_Ruqaa.jpg"],
]).launch()
# %%
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