Обновлённая версия задания. Улучшен базовый кодек и вывод на экран, уточнено задание.

CNNImageCodec.py

 #This code is the simplest example of image compression based on neural networks
 #Comparison with JPEG is provided as well
 #It is a demonstation for Information Theory course
-#Written by Evgeny Belyaev, February 2024.
+#Written by Evgeny Belyaev, July 2024.
 import os
 import math
 import numpy
@@ -12,7 +12,12 @@ import tensorflow
 from tensorflow import keras
 from tensorflow.keras import layers
 from tensorflow.keras.models import Model
-from keras import backend as K
+#from keras import backend as K
+import tensorflow.keras.backend as K
+
+from skimage.metrics import structural_similarity as ssim
+
+#from tensorflow.keras.callbacks import ModelCheckpoint
 
 #import C-implementation of Witten&Neal&Cleary-1987 arithmetic coding as a external module
 from EntropyCodec import *
@@ -25,23 +30,25 @@ trainfolder = './train/'
 w=128
 h=128
 #If 0, then the training will be started, otherwise the model will be readed from a file
-LoadModel = 1
-#Training parameters
-batch_size = 10
+LoadModel = 0
+
 #Number of bits for representation of the layers sample in the training process
-bt = 3
-epochs = 3000
-#epochs = 100
+bt = 2
+#Training parameters 
+epochs = 2000
+#epochs = 500
+
 #Model parameters
-n1=128
-n2=32
-n3=16
+batch_sizeM1 = 24
+n1M1=128
+n2M1=32
+n3M1=16
 
 #Number of images to be compressed and shown from the test folder
 NumImagesToShow = 5
 
 #Number of bits for representation of the layers sample
-b = 3
+b = 2
 
 #Compute PSNR in RGB domain
 def PSNR_RGB(image1,image2):
@@ -95,22 +102,24 @@ def LoadImagesFromFolder (foldername):
     return x
 
 #Model training function
-def ImageCodecModel(trainfolder):
+def ImageCodecModel(trainfolder,trainwithnoise):
     input = layers.Input(shape=(w, h, 3))
     # Encoder
-    e1 = layers.Conv2D(n1, (7, 7), activation="relu", padding="same")(input)
-    e1 = layers.MaxPooling2D((2, 2), padding="same")(e1)
-    e2 = layers.Conv2D(n2, (5, 5), activation="relu", padding="same")(e1)
-    e2 = layers.MaxPooling2D((2, 2), padding="same")(e2)
-    e3 = layers.Conv2D(n3, (3, 3), activation="relu", padding="same")(e2)
-    e3 = layers.MaxPooling2D((2, 2), padding="same")(e3)
-    #add noise during training (needed for layer quantinzation)
-    e3 = e3 + tensorflow.random.uniform(tensorflow.shape(e3), 0, tensorflow.math.reduce_max(e3)/pow(2, bt+1))
+    e1 = layers.Conv2D(n1M1, (7, 7), activation="relu", padding="same")(input)
+    e1 = layers.AveragePooling2D((2, 2), padding="same")(e1)
+    e2 = layers.Conv2D(n2M1, (5, 5), activation="relu", padding="same")(e1)
+    e2 = layers.AveragePooling2D((2, 2), padding="same")(e2)
+    e3 = layers.Conv2D(n3M1, (3, 3), activation="relu", padding="same")(e2)
+    e3 = layers.AveragePooling2D((2, 2), padding="same")(e3)
+    layers.BatchNormalization()
+    if trainwithnoise==1:
+        maxt = tensorflow.keras.ops.max(e3)
+        e3 = e3 + maxt*keras.random.uniform(shape=(16,16,16), minval=-1.0/pow(2, bt+1), maxval=1.0/pow(2, bt+1), dtype=None, seed=None)
     
     # Decoder
-    x = layers.Conv2DTranspose(n3, (3, 3), strides=2, activation="relu", padding="same")(e3)
-    x = layers.Conv2DTranspose(n2, (5, 5), strides=2, activation="relu", padding="same")(x)
-    x = layers.Conv2DTranspose(n1, (7, 7), strides=2, activation="relu", padding="same")(x)
+    x = layers.Conv2DTranspose(n3M1, (3, 3), strides=2, activation="relu", padding="same")(e3)
+    x = layers.Conv2DTranspose(n2M1, (5, 5), strides=2, activation="relu", padding="same")(x)
+    x = layers.Conv2DTranspose(n1M1, (7, 7), strides=2, activation="relu", padding="same")(x)
     x = layers.Conv2D(3, (3, 3), activation="sigmoid", padding="same")(x)
 
     # Autoencoder
@@ -121,18 +130,29 @@ def ImageCodecModel(trainfolder):
     autoencoder.summary()
 
     if LoadModel == 0:
+        print("Num GPUs Available: ", len(tensorflow.config.list_physical_devices('GPU')))
         xtrain = LoadImagesFromFolder(trainfolder)
         xtrain = xtrain / 255
-        autoencoder.fit(xtrain, xtrain, epochs=epochs, batch_size=batch_size,shuffle=True)
-        autoencoder.save('autoencodertemp.mdl')
-        encoder.save('encoder.mdl')
-        decoder.save('decoder.mdl')
+
+        with tensorflow.device('gpu'):    
+            autoencoder.fit(xtrain, xtrain, epochs=epochs, batch_size=batch_sizeM1,shuffle=True)
+        
+        if trainwithnoise==1:
+            encoder.save('encoder2.keras')
+            decoder.save('decoder2.keras')
         else:
-        autoencoder = keras.models.load_model('autoencodertemp.mdl')
-        encoder = keras.models.load_model('encoder.mdl')
-        decoder = keras.models.load_model('decoder.mdl')
+            encoder.save('encoder.keras')
+            decoder.save('decoder.keras')
+    else:
+        if trainwithnoise==1:
+            encoder = keras.models.load_model('encoder2.keras',safe_mode=False)
+            decoder = keras.models.load_model('decoder2.keras',safe_mode=False)
+        else:
+            encoder = keras.models.load_model('encoder.keras',safe_mode=False)
+            decoder = keras.models.load_model('decoder.keras',safe_mode=False)
     return encoder,decoder
 
+
 #Compresses input layer by multi-alphabet arithmetic coding using memoryless source model
 def EntropyEncoder (filename,enclayers,size_z,size_h,size_w):
     temp = numpy.zeros((size_z, size_h, size_w), numpy.uint8, 'C')
@@ -182,21 +202,24 @@ def JPEGRDSingleImage(X,TargetBPP,i):
             realbpp=bpp
             realpsnr=psnr
             realQ = Q
+    image.save('test.jpeg', "JPEG", quality=realQ)
+    image_dec = Image.open('test.jpeg')
+    I1 = numpy.array(image.getdata()).reshape(image.size[0], image.size[1], 3)
+    I2 = numpy.array(image_dec.getdata()).reshape(image_dec.size[0], image_dec.size[1], 3)
+
+    #print('\n\n Size = ',numpy.shape(I1))
+    psnr = ssim(I1[ :, :, 0], I2[ :, :, 0],data_range=255.0)
+    psnr = psnr + ssim(I1[ :, :, 1], I2[ :, :, 1],data_range=255.0)
+    psnr = psnr + ssim(I1[ :, :, 2], I2[ :, :, 2],data_range=255.0)
+    realpsnr=psnr/3.0
     JPEGfilename = 'image%i.jpeg' % i
     image.save(JPEGfilename, "JPEG", quality=realQ)
     return realQ, realbpp, realpsnr
 
-# Main function
-if __name__ == '__main__':
-    #Load test images
-    xtest = LoadImagesFromFolder(testfolder)
-    xtest = xtest / 255
-
-    #Train the model
-    encoder, decoder = ImageCodecModel(trainfolder)
 
+def NeuralCompressor(enc,dec):
     #Run the model for first NumImagesToShow images from the test set
-    encoded_layers = encoder.predict(xtest, batch_size=NumImagesToShow)
+    encoded_layers = enc.predict(xtest, batch_size=NumImagesToShow)
     max_encoded_layers = numpy.zeros(NumImagesToShow, numpy.float16, 'C')
 
     #normalization the layer to interval [0,1)
@@ -227,38 +250,75 @@ if __name__ == '__main__':
     for i in range(NumImagesToShow):
         encoded_layers_quantized[i] = K.cast(declayers[i]*max_encoded_layers[i], "float32")
         encoded_layers[i] = K.cast(encoded_layers[i] * max_encoded_layers[i], "float32")
-    decoded_imgs = decoder.predict(encoded_layers, batch_size=NumImagesToShow)
-    decoded_imgsQ = decoder.predict(encoded_layers_quantized, batch_size=NumImagesToShow)
+    decoded_imgs = dec.predict(encoded_layers, batch_size=NumImagesToShow)
+    decoded_imgsQ = dec.predict(encoded_layers_quantized, batch_size=NumImagesToShow)
+    return bpp, decoded_imgs, decoded_imgsQ
+   
+
+# Main function
+if __name__ == '__main__':
+    #Load test images
+    xtest = LoadImagesFromFolder(testfolder)
+    xtest = xtest / 255
+
+    #Train/load the model
+    encoder, decoder = ImageCodecModel(trainfolder,0)
+    encoder2, decoder2 = ImageCodecModel(trainfolder,1)
+    
+    bpp, decoded_imgs, decoded_imgsQ = NeuralCompressor(encoder,decoder)  
+    bpp2, decoded_imgs2, decoded_imgsQ2 = NeuralCompressor(encoder2,decoder2)    
 
     #Shows NumImagesToShow images from the test set
     #For each image the following results are presented
-    #Original image
-    #Image, represented by the model (without quantization)
-    #Image, represented by the model with quantization and compression of the layers samples
+    #Original image (RAW)
+    #Image, represented by the model (without noise additing during training)
+    #Image, represented by the model (with noise additing during training)
     #Corresponding JPEG image at the same compression level
+    #Q is the quality metric measured as SSIM
+    #bpp is bit per pixel after compression (bpp for RAW data is 24 bpp)
     for i in range(NumImagesToShow):
-        title = ''
+            title=''
             plt.subplot(4, NumImagesToShow, i + 1).set_title(title, fontsize=10)
+            if i==0:
+                plt.subplot(4, NumImagesToShow, i + 1).text(-50, 64, 'RAW')
             plt.imshow(xtest[i, :, :, :], interpolation='nearest')
             plt.axis(False)
     for i in range(NumImagesToShow):
-        psnr = PSNR(xtest[i, :, :, :], decoded_imgs[i, :, :, :])
-        title = '%2.2f' % psnr
+        #psnr = PSNR(xtest[i, :, :, :], decoded_imgsQ[i, :, :, :])
+        psnr = ssim(xtest[i, :, :, 0], decoded_imgsQ[i, :, :, 0],data_range=1.0)
+        psnr = psnr + ssim(xtest[i, :, :, 1], decoded_imgsQ[i, :, :, 1],data_range=1.0)
+        psnr = psnr + ssim(xtest[i, :, :, 2], decoded_imgsQ[i, :, :, 2],data_range=1.0)
+        psnr=psnr/3.0
+
+        #title = '%2.2f %2.2f' % (psnr, bpp[i])
+        title = 'Q=%2.2f bpp=%2.2f' % (psnr, bpp[i])
         plt.subplot(4, NumImagesToShow, NumImagesToShow + i + 1).set_title(title, fontsize=10)
-        plt.imshow(decoded_imgs[i, :, :, :], interpolation='nearest')
+        if i==0:
+            plt.subplot(4, NumImagesToShow, NumImagesToShow + i + 1).text(-50, 64, 'AE1')        
+        plt.imshow(decoded_imgsQ[i, :, :, :], interpolation='nearest')
         plt.axis(False)
     for i in range(NumImagesToShow):
-        psnr = PSNR(xtest[i, :, :, :], decoded_imgsQ[i, :, :, :])
-        title = '%2.2f %2.2f' % (psnr, bpp[i])
+        #psnr = PSNR(xtest[i, :, :, :], decoded_imgsQ2[i, :, :, :])
+        psnr = ssim(xtest[i, :, :, 0], decoded_imgsQ2[i, :, :, 0],data_range=1.0)
+        psnr = psnr + ssim(xtest[i, :, :, 1], decoded_imgsQ2[i, :, :, 1],data_range=1.0)
+        psnr = psnr + ssim(xtest[i, :, :, 2], decoded_imgsQ2[i, :, :, 2],data_range=1.0)
+        psnr=psnr/3.0
+        #title = '%2.2f %2.2f' % (psnr, bpp2[i])
+        title = 'Q=%2.2f bpp=%2.2f' % (psnr, bpp2[i])
         plt.subplot(4, NumImagesToShow, 2*NumImagesToShow + i + 1).set_title(title, fontsize=10)
-        plt.imshow(decoded_imgsQ[i, :, :, :], interpolation='nearest')
+        if i==0:
+            plt.subplot(4, NumImagesToShow, 2*NumImagesToShow + i + 1).text(-50, 64, 'AE2')        
+        plt.imshow(decoded_imgsQ2[i, :, :, :], interpolation='nearest')
         plt.axis(False)
     for i in range(NumImagesToShow):
         JPEGQP,JPEGrealbpp, JPEGrealpsnr = JPEGRDSingleImage(xtest[i, :, :, :], bpp[i],i)
         JPEGfilename = 'image%i.jpeg' % i
         JPEGimage = Image.open(JPEGfilename)
-        title = '%2.2f %2.2f' % (JPEGrealpsnr,JPEGrealbpp)
+        #title = '%2.2f %2.2f' % (JPEGrealpsnr,JPEGrealbpp)
+        title = 'Q=%2.2f bpp=%2.2f' % (JPEGrealpsnr,JPEGrealbpp)
         plt.subplot(4, NumImagesToShow, 3*NumImagesToShow + i + 1).set_title(title, fontsize=10)
+        if i==0:
+            plt.subplot(4, NumImagesToShow, 3*NumImagesToShow + i + 1).text(-50, 64, 'JPEG')        
         plt.imshow(JPEGimage, interpolation='nearest')
         plt.axis(False)
     plt.show()
\ No newline at end of file

EntropySetupUbuntu.py

+from setuptools import setup, Extension
+import os
+import sys
+import pybind11
+
+#functions_module = Extension(
+#    name='EntropyCodec',
+#    sources=['wrapper.cpp'],
+#   include_dirs=[os.path.join(os.getenv('PYTHON_DIR'), 'include'),
+#                os.path.join(pybind11.__path__[0], 'include')]
+#)
+
+functions_module = Extension(
+    name='EntropyCodec',
+    sources=['wrapper.cpp'],
+   include_dirs=[os.path.join('/home/eabelyaev/miniconda3/bin/', 'include'),
+                os.path.join(pybind11.__path__[0], 'include')]
+)
+
+#print(pybind11.__path__[0])
+#print(os.getenv('PYTHONPATH'))
+#print(sys.path)
+
+setup(ext_modules=[functions_module], options={"build_ext": {"build_lib": ".."}})

README.md

-# Учебный проект, посвященный сжатию изображений при помощи нейронных сетей
+# Учебный проект, посвященный сжатию изображений при автокодировщика
 
 ## Описание работы кодека
-Данный простой кодек сжимает изображения размером 128x128 при помощи свёрточных нейронных сетей, квантования и адаптивного арифметического кодирования.
+Данный простой кодек сжимает изображения размером 128x128 при помощи автокодировщика, квантования и адаптивного арифметического кодирования.
 В папке ./train/ находятся изображения, которые были использованы для обучения сети, в папке ./test/ находятся изображения для демонстрации результатов.
 
 Структура кодера:
-1. Слой 1. Принимает x размером 128x128x3. Выполняет свёртку 7x7, ReLu, MaxPooling2D 2x2. На выходе вектор y1 размером 64x64x128.
-2. Слой 2. Принимает y1. Выполняет свёртку 5x5, ReLu, MaxPooling2D 2x2. На выходе вектор y2 размером 32x32x32.
-3. Слой 3. Принимает y2. Выполняет свёртку 3x3, ReLu, MaxPooling2D 2x2. На выходе вектор y3 размером 16x16x16.
-4. Вычисление максимума $y_{max} = \max_i{y^i_3}$.
-5. Квантование x_i = \left\lfloor clip(y^i_3/y_{max},[0,1))\cdot 2^b\right\rfloor, в результате которого x_i \in \{0,1,...,2^{b}-1\}.
-6. Сжатие {x_i} при помощи адаптивного арифметического кодера (ААК) из [1].
+1. Слой 1. Принимает x размером 128x128x3. Выполняет свёртку 7x7, ReLu, AveragePooling2D 2x2. На выходе вектор y1 размером 64x64x128.
+2. Слой 2. Принимает y1. Выполняет свёртку 5x5, ReLu, AveragePooling2D 2x2. На выходе вектор y2 размером 32x32x32.
+3. Слой 3. Принимает y2. Выполняет свёртку 3x3, ReLu, AveragePooling2D 2x2. На выходе вектор y3 размером 16x16x16.
+4. Выполняется батч нормализация.
+5. Вычисление максимума $y_{max} = \max_i{y^i_3}$.
+6. Квантование x_i = \left\lfloor clip(y^i_3/y_{max},[0,1))\cdot 2^b\right\rfloor, в результате которого x_i \in \{0,1,...,2^{b}-1\}.
+7. Сжатие {x_i} при помощи адаптивного арифметического кодера (ААК) из [1].
 
 Структура декодера:
 1. Декодирование {x_i} при помощи АAК из [1].
@@ -19,21 +20,22 @@
 4. Слой 2. Принимает \hat y_2. Свёртка 5\times 5, ReLu. На выходе  \hat y_1 размером 64x64x32.
 4. Слой 3. Принимает \hat y_1. Свёртка 7\times 7, ReLu. На выходе  изображение \hat x размером 128x128x3.
 
-На следующем примере показан случай, когда при обучении к вектору y_3 не прибавляется шум, соразмерный квантованию с шагом 2^3. Как можно заметить, это приводит
-к появлению артефактов.
-![Обучения без добавления шума. Epoch=3000](./doc/AI_Epoch3000_NoNoise.png)
-
-На следующем примере показан случай, когда при обучении к вектору y_3 добавляется шум, соразмерный квантованию с шагом 2^3. Как можно заметить, в большей степени 
-артефакты устранились, что привело к тому, что данная простейшая модель выигрывает у JPEG по качеству на высоких степенях сжатия.
+На следующем примере показано сравнение эффективности работы данной модели, при b=2. Здесь RAW помечена строка исходных изображений,
+AE1 -- автокодировщик без добавления шума при обучении, AE2 -- автокодировщик c добавлением шума при обучении,
+JPEG -- алгоритм JPEG в котором степень сжатия подобрана так, чтобы быть близкой к тому, что достигают автокодировщики.
+Здесь Q -- это метрика качества изображения SSIM, а bpp -- это число бит на пиксель после сжатия (в исходном формате bpp=24).
 ![Обучения c добавлением шума. Epoch=3000](./doc/AI_Epoch3000_Noisebt3.png)
 
+Как можно заметить, автокодировщик AE2 выигрывает у JPEG по качеству на высоких степенях сжатия.  Также, пример показывает
+важность добавления шума при обучении, вследствие чего автокодировщик адаптируется к ошибке квантования.
+
 ААК из [1] реализован на языке Си и подключается к коду как библиотека. Для сборки модуля необходимо выполнить команды, которые находятся в файле
-EntropyCompile.bat
+EntropyCompile.bat или EntropyCompile.sh
 
 ## Описание задания к лабораторной работе
 Улучшить учебный кодек так, чтобы он:
 - либо на том же сжатии показывал лучшее субъективное визуальное качество; 
-- либо на том же сжатии обеспечивал выигрыш по PSNR от 0.1 дБ и выше;
+- либо на том же сжатии обеспечивал выигрыш по SSIM от 0.05 и выше;
 - либо при том же уровне качества обеспечивал от 3\% выигрыша по bpp.
 
 Можно улучшать следующие модули:
@@ -44,6 +46,8 @@ EntropyCompile.bat
 Требования к реализации:
 - Результаты должны быть продемонстрированы на изображениях из папки ./test/
 - При обучении должны использоваться только изображения из папки ./train/
+- При сравнении заменить строчку изображений автокодировщика AE1 на результаты разработанного кодека как показано на рисунке выше.
+- Построить график функции средний SSIM/bpp для тестовых изображений для b=2,3,4,5 для AE2, JPEG и предложенного кодека.
 
 На почту eabelyaev@itmo.ru прислать отчет в виде презентации в pdf формате, который включает в себя:
 - ФИО студента, номер группы.

decoder.mdl/fingerprint.pb

-ގœ浂
* ȾG(e2

\ No newline at end of file

decoder.mdl/keras_metadata.pb

-
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encoder.mdl/fingerprint.pb

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encoder.mdl/keras_metadata.pb

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-
-root.layer_with_weights-2"_tf_keras_layer*
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-	root.layer-8"_tf_keras_layer*{"name": "tf.compat.v1.shape", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "preserve_input_structure_in_config": true, "autocast": false, "class_name": "TFOpLambda", "config": {"name": "tf.compat.v1.shape", "trainable": true, "dtype": "float32", "function": "compat.v1.shape"}, "inbound_nodes": [["max_pooling2d_2", 0, 0, {"name": null, "out_type": "int32"}]], "shared_object_id": 14, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 16, 16]}}2

-
-root.layer-9"_tf_keras_layer*{"name": "tf.math.truediv", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "preserve_input_structure_in_config": true, "autocast": false, "class_name": "TFOpLambda", "config": {"name": "tf.math.truediv", "trainable": true, "dtype": "float32", "function": "math.truediv"}, "inbound_nodes": [["tf.math.reduce_max", 0, 0, {"y": 16, "name": null}]], "shared_object_id": 15, "build_input_shape": {"class_name": "TensorShape", "items": []}}2

-
root.layer-10"_tf_keras_layer*{"name": "tf.random.uniform", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "preserve_input_structure_in_config": true, "autocast": false, "class_name": "TFOpLambda", "config": {"name": "tf.random.uniform", "trainable": true, "dtype": "float32", "function": "random.uniform"}, "inbound_nodes": [["tf.compat.v1.shape", 0, 0, {"minval": 0, "maxval": ["tf.math.truediv", 0, 0]}]], "shared_object_id": 16, "build_input_shape": {"class_name": "TensorShape", "items": [4]}}2

-
root.layer-11"_tf_keras_layer*{"name": "tf.__operators__.add", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "preserve_input_structure_in_config": true, "autocast": false, "class_name": "TFOpLambda", "config": {"name": "tf.__operators__.add", "trainable": true, "dtype": "float32", "function": "__operators__.add"}, "inbound_nodes": [["max_pooling2d_2", 0, 0, {"y": ["tf.random.uniform", 0, 0], "name": null}]], "shared_object_id": 17, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 16, 16]}}2

\ No newline at end of file

install.bat

+pip install -r requirements.txt
+pause
\ No newline at end of file

requirements.txt

-keras==2.15.0
-matplotlib==3.8.4
-numpy==1.23.5
-Pillow==9.5.0
-Pillow==10.3.0
-pybind11==2.11.1
-setuptools==58.1.0
-tensorflow==2.15.0
-tensorflow_intel==2.15.0
+keras==3.4.1
+matplotlib==3.9.1
+numpy==1.24.2
+Pillow==10.4.0
+pybind11==2.13.1
+setuptools==69.5.1
+scikit-image
+tensorflow==2.16.2