#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
Explanation: Copyright 2020 The TensorFlow Authors.
End of explanation
"""


!pip install opencv-python matplotlib tensorflow

"""
Explanation: Super resolution with TensorFlow Lite
Overview
The task of recovering a high resolution (HR) image from its low resolution counterpart is commonly referred to as Single Image Super Resolution (SISR). 
The model used here is ESRGAN
(ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks). And we are going to use TensorFlow Lite to run inference on the pretrained model.
The TFLite model is converted from this
implementation hosted on TF Hub. Note that the model we converted upsamples a 50x50 low resolution image to a 200x200 high resolution image (scale factor=4). If you want a different input size or scale factor, you need to re-convert or re-train the original model.
Setup
Since we are going to need to do some color space transformation, let's install OpenCV first.
End of explanation
"""


import tensorflow as tf
import tensorflow_hub as hub
import cv2
import matplotlib.pyplot as plt
print(tf.__version__)

"""
Explanation: Import dependencies.
End of explanation
"""


model = hub.load("https://tfhub.dev/captain-pool/esrgan-tf2/1")
concrete_func = model.signatures[tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
concrete_func.inputs[0].set_shape([1, 50, 50, 3])
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func], model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()

# Save the TF Lite model.
with tf.io.gfile.GFile('ESRGAN.tflite', 'wb') as f:
  f.write(tflite_model)

esrgan_model_path = './ESRGAN.tflite'

"""
Explanation: Download and convert the ESRGAN model
End of explanation
"""


test_img_path = tf.keras.utils.get_file('lr.jpg', 'https://raw.githubusercontent.com/tensorflow/examples/master/lite/examples/super_resolution/android/app/src/main/assets/lr-1.jpg')

"""
Explanation: Download a test image (insect head).
End of explanation
"""


lr = cv2.imread(test_img_path)
lr = cv2.cvtColor(lr, cv2.COLOR_BGR2RGB)
lr = tf.expand_dims(lr, axis=0)
lr = tf.cast(lr, tf.float32)

# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path=esrgan_model_path)
interpreter.allocate_tensors()

# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

# Run the model
interpreter.set_tensor(input_details[0]['index'], lr)
interpreter.invoke()

# Extract the output and postprocess it
output_data = interpreter.get_tensor(output_details[0]['index'])
sr = tf.squeeze(output_data, axis=0)
sr = tf.clip_by_value(sr, 0, 255)
sr = tf.round(sr)
sr = tf.cast(sr, tf.uint8)

"""
Explanation: Generate a super resolution image using TensorFlow Lite
End of explanation
"""


lr = tf.cast(tf.squeeze(lr, axis=0), tf.uint8)
plt.figure(figsize = (1, 1))
plt.title('LR')
plt.imshow(lr.numpy());

plt.figure(figsize=(10, 4))
plt.subplot(1, 2, 1)        
plt.title(f'ESRGAN (x4)')
plt.imshow(sr.numpy());

bicubic = tf.image.resize(lr, [200, 200], tf.image.ResizeMethod.BICUBIC)
bicubic = tf.cast(bicubic, tf.uint8)
plt.subplot(1, 2, 2)   
plt.title('Bicubic')
plt.imshow(bicubic.numpy());

"""
Explanation: Visualize the result
End of explanation
"""


