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import gradio as gr
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import numpy as np
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import tensorflow as tf
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import cv2
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def parse_image(image):
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image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
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image = cv2.resize(image, (100, 100))
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image = image.astype(np.float32)
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image = image / 255.0
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image = np.expand_dims(image, axis=0)
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image = np.expand_dims(image, axis=-1)
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return image
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def cnn(input_shape, output_shape):
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num_classes = output_shape[0]
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dropout_seed = 708090
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kernel_seed = 42
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model = tf.keras.models.Sequential([
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tf.keras.layers.Conv2D(16, 3, activation='relu', input_shape=input_shape, kernel_initializer=tf.keras.initializers.GlorotUniform(seed=kernel_seed)),
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tf.keras.layers.MaxPooling2D(),
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tf.keras.layers.Dropout(0.1, seed=dropout_seed),
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tf.keras.layers.Conv2D(32, 5, activation='relu', kernel_initializer=tf.keras.initializers.GlorotUniform(seed=kernel_seed)),
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tf.keras.layers.MaxPooling2D(),
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tf.keras.layers.Dropout(0.1, seed=dropout_seed),
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tf.keras.layers.Conv2D(64, 10, activation='relu', kernel_initializer=tf.keras.initializers.GlorotUniform(seed=kernel_seed)),
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tf.keras.layers.MaxPooling2D(),
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tf.keras.layers.Dropout(0.1, seed=dropout_seed),
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tf.keras.layers.Flatten(),
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tf.keras.layers.Dense(128, activation='relu', kernel_regularizer='l2', kernel_initializer=tf.keras.initializers.GlorotUniform(seed=kernel_seed)),
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tf.keras.layers.Dropout(0.2, seed=dropout_seed),
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tf.keras.layers.Dense(16, activation='relu', kernel_regularizer='l2', kernel_initializer=tf.keras.initializers.GlorotUniform(seed=kernel_seed)),
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tf.keras.layers.Dropout(0.2, seed=dropout_seed),
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tf.keras.layers.Dense(num_classes, activation='sigmoid', kernel_initializer=tf.keras.initializers.GlorotUniform(seed=kernel_seed))
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])
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return model
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model = cnn((100, 100, 1), (1,))
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model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=False), optimizer='Adam', metrics='accuracy')
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model.load_weights('weights.h5')
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def segment(image):
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image = parse_image(image)
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output = model.predict(image)
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labels = {
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"farsi" : 1-float(output),
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"ruqaa" : float(output)
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}
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return labels
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iface = gr.Interface(fn=segment,
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inputs="image",
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outputs="label",
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examples=[["images/Farsi_1.jpg"],
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["images/Farsi_2.jpg"],
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["images/Ruqaa_1.jpg"],
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["images/Ruqaa_2.jpg"],
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["images/Ruqaa_3.jpg"],
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]).launch()
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