Upload ascr.py

ascr.py

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+
+# %%
+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')
+
+
+#%%
+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 segment(image):
+    image = parse_image(image)
+    # print(image.shape)
+    output = model.predict(image)
+    # print(output)
+    labels = {
+        "farsi" : 1-float(output),
+        "ruqaa" : float(output)
+    }
+    return labels
+
+iface = gr.Interface(fn=segment, inputs="image", outputs="label").launch()