diff --git a/longinus/longinus/coeff_encoders/muzero.py b/longinus/longinus/coeff_encoders/muzero.py deleted file mode 100644 index e69de29..0000000 diff --git a/longinus/longinus/coeff_encoders/neural_network.py b/longinus/longinus/coeff_encoders/neural_network.py index 1722e10..71b827c 100644 --- a/longinus/longinus/coeff_encoders/neural_network.py +++ b/longinus/longinus/coeff_encoders/neural_network.py @@ -1,22 +1,46 @@ from keras.models import Sequential, load_model from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense +from tensorflow.keras import layers import pandas as pd +from tensorflow import keras +import tensorflow as tf +from tensorflow.keras import backend as K +from tensorflow.keras.utils import register_keras_serializable +NUM_THREADS = 14 + +@register_keras_serializable() +def custom_loss_function(y_true, y_pred): + size_minimizer_part = (- K.sum(K.abs(y_pred))) / (64 * 255) # minimizing the size equals to higher coefficients = more 0 in DCT table = smaller size + mse_original_jpeg_part = K.mean(K.square(y_pred - y_true)) + size_minimizer_part = size_minimizer_part * mse_original_jpeg_part + size_coefficient = 0.3 + quality_coefficient = 0.7 + return abs(size_minimizer_part * size_coefficient + mse_original_jpeg_part * quality_coefficient) class NeuralNetworkEncoder: - def __init__(self, internal_activation_function: str, external_activation_function: str, optimizer: str, loss_function: str, image_dimension_x: int, image_dimension_y: int): - self.model = Sequential() - self.model.add(Conv2D(32, (3, 3), activation=internal_activation_function, input_shape=(image_dimension_x, image_dimension_y, 1))) - self.model.add(MaxPooling2D((2, 2))) - self.model.add(Conv2D(64, (3, 3), activation=internal_activation_function)) - self.model.add(MaxPooling2D((2, 2))) - self.model.add(Flatten()) - self.model.add(Dense(64, activation=internal_activation_function)) - self.model.add(Dense((8, 8), activation=external_activation_function)) - self.model.compile(optimizer=optimizer, loss=loss_function) - - def __init__(self, pretrained_weights_path: str): - self.model = load_model(pretrained_weights_path) + def __init__(self, pretrained_weights_path: str = None, internal_activation_function: str = None, external_activation_function: str = None, optimizer: str = None, loss_function: str = None, image_dimension_x: int = None, image_dimension_y: int = None): + tf.config.threading.set_intra_op_parallelism_threads(NUM_THREADS) + + tf.config.threading.set_inter_op_parallelism_threads(NUM_THREADS) + if pretrained_weights_path: + self.model = load_model(pretrained_weights_path, custom_objects={'custom_loss_function': custom_loss_function}) + else: + self.model = keras.Sequential([ + layers.Reshape((512, 512, 1), input_shape=(262144,)), + #layers.InputLayer(input_shape=(512 * 512, 1, 1)), + layers.Conv2D(32, (3, 3), activation=internal_activation_function, padding='same'), + layers.MaxPooling2D((2, 2)), + layers.Conv2D(64, (3, 3), activation=internal_activation_function, padding='same'), + layers.MaxPooling2D((2, 2)), + layers.Conv2D(128, (3, 3), activation=internal_activation_function, padding='same'), + layers.MaxPooling2D((2, 2)), + layers.Conv2D(256, (3, 3), activation=internal_activation_function, padding='same'), + layers.Flatten(), + layers.Dense(64, activation=external_activation_function) + ]) + self.model.compile(optimizer=optimizer, loss=custom_loss_function) + def train(self, train_imageset: pd.DataFrame, train_quantization_dataset: pd.DataFrame, validation_dataset: pd.DataFrame, validation_quantization_dataset: pd.DataFrame, epochs: int, batch_size: int): self.model.fit(train_imageset, train_quantization_dataset, validation_data=(validation_dataset, validation_quantization_dataset), epochs=epochs, batch_size=batch_size) diff --git a/longinus/longinus/main.py b/longinus/longinus/main.py index ca2779a..f3724b4 100644 --- a/longinus/longinus/main.py +++ b/longinus/longinus/main.py @@ -1,4 +1,23 @@ -import click +import click +from numpy import require +from longinus.utils.dataset import Dataset +from longinus.coeff_encoders.neural_network import NeuralNetworkEncoder +from longinus.utils.image_encoder import ImageEncoder +from PIL import Image +from longinus.utils.tools import psnr +from rich.console import Console +from rich.spinner import Spinner +from rich.progress import Progress +from sklearn.model_selection import train_test_split +import traceback +import pandas as pd +import os +import time +import numpy as np +import turbojpeg + +from PIL.JpegImagePlugin import JpegImageFile +console = Console() @click.group() def cli(): @@ -6,20 +25,172 @@ def cli(): pass @click.command() -def reencode(): - """Reencodes the image using custom coefficient calculating algorithm""" - click.echo("Test") - pass +@click.option('-i', '--input-dataset-path', type=click.Path(exists=True), required=True) +@click.option('-o', '--output-images-path', type=click.Path(exists=True), required=True) +@click.option('-m', '--input-model-path', type=click.Path(exists=True), required=True) +@click.option('-e', '--export-data-path', type=str, required=True) +def bulk_bmp_reencode(input_dataset_path: str, input_model_path: str, output_images_path: str, export_data_path: str): + psnr_table = [] + with Progress() as progress: + task = progress.add_task("[bold yellow]Processing images from provided dataset...", total=len(os.listdir(input_dataset_path))) + for filename in os.listdir(input_dataset_path): + console.print(f"\n[yellow]Processing image: {filename}") + file = os.path.join(input_dataset_path, filename) + if os.path.isfile(file): + pass + else: + continue + nn_start_time = time.perf_counter_ns() + input_image = ImageEncoder(file) + input_image.read() + input_data_array = [] + input_data_array.append(input_image.luma_array) + data = pd.DataFrame(input_data_array) + encoder = NeuralNetworkEncoder( + pretrained_weights_path=input_model_path + ) + result_quant_table = encoder.predict_quantization_table(data) + result_quant_table = result_quant_table.flatten() + # print(result_quant_table) + output_image_path = os.path.join(output_images_path, filename.replace('.bmp', '.jpeg')) + jpeg_lib_output_image_path = os.path.join(output_images_path, f"jpeglib_{filename.replace('.bmp', '.jpeg')}") + turbojpeg_output_image_path = os.path.join(output_images_path, f"turbojpeg_{filename.replace('.bmp', '.jpeg')}") + output_image = ImageEncoder(output_image_path) + output_image.load_image_from_mem(input_image.luma_array_2d) + output_image.override_coefficients(result_quant_table) + output_image.write() + nn_end_time = time.perf_counter_ns() + test_output_image = ImageEncoder(output_image_path) + test_output_image.read() + jpeglib_start_time = time.perf_counter_ns() + jpeglib_image = Image.open(file) + jpeglib_image = jpeglib_image.convert('L') + jpeglib_image.save(jpeg_lib_output_image_path, "JPEG") + jpeglib_end_time = time.perf_counter_ns() + jpeglib_output_image = ImageEncoder(jpeg_lib_output_image_path) + jpeglib_output_image.read() + turbojpeg_start_time = time.perf_counter_ns() + turbojpeg_image = Image.open(file).convert('L') + turbojpeg_image_output = turbojpeg.compress(np.array(turbojpeg_image), 75, turbojpeg.SAMP.GRAY) + with open(turbojpeg_output_image_path, "wb") as turbojpeg_output_file: + turbojpeg_output_file.write(turbojpeg_image_output) + turbojpeg_stop_time = time.perf_counter_ns() + turbojpeg_image_new = ImageEncoder(turbojpeg_output_image_path) + turbojpeg_image_new.read() + #if isinstance(jpeglib_output_image.image_ptr, JpegImageFile): + # if hasattr(jpeglib_output_image.image_ptr, 'quantization'): + # print(jpeglib_output_image.image_ptr.quantization[0]) + calculated_psnr_original_neural_network = psnr(input_image.luma_array_2d, test_output_image.luma_array_2d) + calculated_psnr_original_jpeglib = psnr(input_image.luma_array_2d, jpeglib_output_image.luma_array_2d) + calculated_psnr_original_turbojpeg = psnr(input_image.luma_array_2d, turbojpeg_image_new.luma_array_2d) + result = { + 'input_image': file, + 'output_image': output_image_path, + 'psnr_orig_to_nn': calculated_psnr_original_neural_network, + 'input_size': os.path.getsize(file), + 'output_size': os.path.getsize(output_image_path), + 'jpeglib_size': os.path.getsize(jpeg_lib_output_image_path), + 'turbojpeg_size': os.path.getsize(turbojpeg_output_image_path), + 'psnr_orig_to_jpeglib': calculated_psnr_original_jpeglib, + 'psnr_orig_to_turbojpeg': calculated_psnr_original_turbojpeg, + 'compression_duration_nn': nn_end_time - nn_start_time, + 'compression_duration_jpeglib': jpeglib_end_time - jpeglib_start_time, + 'compression_duration_turbojpeg': turbojpeg_stop_time - turbojpeg_start_time + } + psnr_table.append(result) + progress.update(task, advance=1) + console.print("[bold green]Done!") + psnr_dataframe = pd.DataFrame(psnr_table) + psnr_dataframe.to_csv(export_data_path) + +@click.command() +@click.option('-i', '--input-image-path', type=click.Path(exists=True), required=True) +@click.option('-m', '--input-model-path', type=click.Path(exists=True), required=True) +def reencode(input_image_path: str, input_model_path: str): + """Reencodes the BMP image using custom coefficient calculating algorithm""" + click.echo("Reencode") + input_image = ImageEncoder(input_image_path) + input_image.read() + input_data_array = [] + input_data_array.append(input_image.luma_array) + data = pd.DataFrame(input_data_array) + encoder = NeuralNetworkEncoder( + pretrained_weights_path=input_model_path + ) + result_quant_table = encoder.predict_quantization_table(data) + result_quant_table = result_quant_table.flatten() + print(result_quant_table) + output_image = ImageEncoder(f"{input_image_path}_converted.jpeg") + output_image.load_image_from_mem(input_image.luma_array_2d) + output_image.override_coefficients(result_quant_table) + output_image.write() @click.command() -@click.argument('dataset_path', type=click.Path(exists=True), required=True) -def train(dataset_path: str): +@click.option('-p', 'dataset_path', type=click.Path(exists=True), required=True) +@click.option('-o', 'output_model_path', type=str) +@click.option('-i', 'input_model_path', type=click.Path(exists=True)) +def train(dataset_path: str, output_model_path: str, input_model_path: str): """Train the models using the dataset""" click.echo("Train") - pass + if not input_model_path: + encoder = NeuralNetworkEncoder( + internal_activation_function="relu", + external_activation_function="linear", + optimizer="adam", + loss_function="mean_squared_error", + image_dimension_x=512, + image_dimension_y=512 + ) + dataset = Dataset(dataset_path=dataset_path) + dataset.load_prepared_dataset() + dataset_size = dataset.dataset_size + current_index = 0 + for batch in range(100, dataset_size, 100): + if (current_index > 0): + print("Loading training model...") + encoder = NeuralNetworkEncoder(pretrained_weights_path=output_model_path) + print(f"Extracting luma values for first index={current_index}") + extracted_luma_values = dataset.extract_luma_table(batch_size=100, first_item=current_index) + print(f"Extracting dct values for first index={current_index}") + extracted_dct_values = dataset.extract_dct_table(batch_size=100, first_item=current_index) + + print("Splitting the dataset") + train_luma_df, temp_luma_df = train_test_split(extracted_luma_values, test_size=0.4, random_state=42) + validation_luma_df, test_luma_df = train_test_split(temp_luma_df, test_size=0.5, random_state=42) + train_dct_df, temp_dct_df = train_test_split(extracted_dct_values, test_size=0.4, random_state=42) + validation_dct_df, test_dct_df = train_test_split(temp_dct_df, test_size=0.5, random_state=42) + print("Training the model...") + encoder.train( + train_imageset=train_luma_df, + train_quantization_dataset=train_dct_df, + validation_dataset=validation_luma_df, + validation_quantization_dataset=validation_dct_df, + epochs=32, + batch_size=5 + ) + print("Exporting the model") + encoder.export_weights(output_model_path) + current_index = batch + +@click.command() +@click.option('-p', 'dataset_path', type=click.Path(exists=True), required=True) +def prepare_dataset(dataset_path: str): + click.echo("Prepare dataset") + try: + dataset = Dataset(dataset_path=dataset_path) + with console.status("[bold yellow]Preparing the dataset for training...") as status: + spinner = Spinner("dots", text="Processing") + dataset.prepare_dataset() + console.print("[bold green]Done! Processed files are now in directory: tmp_database") + except Exception as e: + console.print("[bold red]Error when preparing dataset") + print(e) + print(traceback.print_exc()) cli.add_command(reencode) cli.add_command(train) +cli.add_command(prepare_dataset) +cli.add_command(bulk_bmp_reencode) if __name__ == "__main__": cli() diff --git a/longinus/longinus/utils/dataset.py b/longinus/longinus/utils/dataset.py index 91ac3db..3eddd80 100644 --- a/longinus/longinus/utils/dataset.py +++ b/longinus/longinus/utils/dataset.py @@ -16,48 +16,77 @@ class Dataset(): def prepare_dataset(self): counter = 0 for filename in os.listdir(self.dataset_path): + file = os.path.join(self.dataset_path, filename) if os.path.isfile(file): counter += 1 else: continue - file = os.path.join(self.dataset_path, filename) image = Image.open(file) for i in range(0, 20): - sub_x = random.randint(0, image.width - 512) - sub_y = random.randint(0, image.height - 512) - subimage = image.crop(sub_x, sub_x + 512, sub_y, sub_y + 512) + sub_x = random.randint(0, image.width - 513) + sub_y = random.randint(0, image.height - 513) + cropbox = (sub_x, sub_y, sub_x + 512, sub_y + 512) + subimage = image.crop(cropbox) new_filename = os.path.join('tmp_database', filename.split('.')[0] + f'_subimage_{i}.jpg') subimage = subimage.convert('L') + if not os.path.exists('tmp_database'): + os.makedirs('tmp_database') subimage.save(new_filename) self.dataset_filenames.append(new_filename) + print(new_filename) self.dataset_size = counter def load_prepared_dataset(self): + counter = 0 for filename in os.listdir(self.dataset_path): + file = os.path.join(self.dataset_path, filename) if os.path.isfile(file): counter += 1 else: continue - file = os.path.join(self.dataset_path, filename) self.dataset_filenames.append(file) + self.dataset_size = counter def __len__(self): return self.dataset_size - def extract_luma_table(self): - dataframe = pd.DataFrame() + def extract_luma_table(self, batch_size: int, first_item: int): + data = [] + counter = 0 for image_file in self.dataset_filenames: + if counter < first_item: + counter+=1 + continue + if (counter - first_item) > batch_size: + break + print(f"Processing image no={counter}") image = Image.open(image_file) + image = image.convert('L') image_luma = image.getdata() image_luma = np.array(image_luma) - dataframe = dataframe.add(image_luma) + data.append(image_luma.flatten()) + counter+=1 + print("Converting to dataframe") + dataframe = pd.DataFrame(data) + print("Done!") return dataframe - def extract_dct_table(self): - dataframe = pd.DataFrame() + def extract_dct_table(self, batch_size: int, first_item: int): + data = [] + counter = 0 for image_file in self.dataset_filenames: + if counter < first_item: + counter+=1 + continue + if (counter - first_item) > batch_size: + break + print(f"Processing image no={counter}") image = Image.open(image_file) image_dct = np.array(image.quantization[0]) - dataframe = dataframe.add(image_dct) + data.append(image_dct.flatten()) + counter+=1 + print("Converting to dataframe") + dataframe = pd.DataFrame(data) + print("Done!") return dataframe \ No newline at end of file diff --git a/longinus/longinus/utils/image.py b/longinus/longinus/utils/image.py deleted file mode 100644 index 8a44625..0000000 --- a/longinus/longinus/utils/image.py +++ /dev/null @@ -1,26 +0,0 @@ -from scipy import misc -import numpy as np -import scipy -import cv2 as cv - -class Image: - def __init__(self, path): - self.path = path - self.bgr_array = None - self.dct_array = None - - def read(self): - self.bgr_array = cv.imread(self.path, cv.IMREAD_GRAYSCALE) - self.dct_array = cv.dct(np.float32(self.bgr_array)) - - def write(self): - pass - - def extract_coefficients(self): - return self.dct_array - - def load_image_from_mem(self, image_array): - self.bgr_array = image_array - - def override_coefficients(self, new_dct_array): - self.dct_array = new_dct_array diff --git a/longinus/poetry.lock 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