import pandas as pd from collections import Counter import json import os from PIL import Image import numpy as np import torch import matplotlib.pyplot as plt from IPython import get_ipython import sys import gc import streamlit as st class VQADataProcessor: """ A class to process OKVQA dataset. Attributes: questions_file_path (str): The file path for the questions JSON file. annotations_file_path (str): The file path for the annotations JSON file. questions (list): List of questions extracted from the JSON file. annotations (list): List of annotations extracted from the JSON file. df_questions (DataFrame): DataFrame created from the questions list. df_answers (DataFrame): DataFrame created from the annotations list. merged_df (DataFrame): DataFrame resulting from merging questions and answers. """ def __init__(self, questions_file_path, annotations_file_path): """ Initializes the VQADataProcessor with file paths for questions and annotations. Parameters: questions_file_path (str): The file path for the questions JSON file. annotations_file_path (str): The file path for the annotations JSON file. """ self.questions_file_path = questions_file_path self.annotations_file_path = annotations_file_path self.questions, self.annotations = self.read_json_files() self.df_questions = pd.DataFrame(self.questions) self.df_answers = pd.DataFrame(self.annotations) self.merged_df = None def read_json_files(self): """ Reads the JSON files for questions and annotations. Returns: tuple: A tuple containing two lists: questions and annotations. """ with open(self.questions_file_path, 'r') as file: data = json.load(file) questions = data['questions'] with open(self.annotations_file_path, 'r') as file: data = json.load(file) annotations = data['annotations'] return questions, annotations @staticmethod def find_most_frequent(my_list): """ Finds the most frequent item in a list. Parameters: my_list (list): A list of items. Returns: The most frequent item in the list. Returns None if the list is empty. """ if not my_list: return None counter = Counter(my_list) most_common = counter.most_common(1) return most_common[0][0] def merge_dataframes(self): """ Merges the questions and answers DataFrames on 'question_id' and 'image_id'. """ self.merged_df = pd.merge(self.df_questions, self.df_answers, on=['question_id', 'image_id']) def join_words_with_hyphen(self, sentence): return '-'.join(sentence.split()) def process_answers(self): """ Processes the answers by extracting raw and processed answers and finding the most frequent ones. """ if self.merged_df is not None: self.merged_df['raw_answers'] = self.merged_df['answers'].apply(lambda x: [ans['raw_answer'] for ans in x]) self.merged_df['processed_answers'] = self.merged_df['answers'].apply( lambda x: [ans['answer'] for ans in x]) self.merged_df['most_frequent_raw_answer'] = self.merged_df['raw_answers'].apply(self.find_most_frequent) self.merged_df['most_frequent_processed_answer'] = self.merged_df['processed_answers'].apply( self.find_most_frequent) self.merged_df.drop(columns=['answers'], inplace=True) else: print("DataFrames have not been merged yet.") # Apply the function to the 'most_frequent_processed_answer' column self.merged_df['single_word_answers'] = self.merged_df['most_frequent_processed_answer'].apply( self.join_words_with_hyphen) def get_processed_data(self): """ Retrieves the processed DataFrame. Returns: DataFrame: The processed DataFrame. Returns None if the DataFrame is empty or not processed. """ if self.merged_df is not None: return self.merged_df else: print("DataFrame is empty or not processed yet.") return None def save_to_csv(self, df, saved_file_name): if saved_file_name is not None: if ".csv" not in saved_file_name: df.to_csv(os.path.join(saved_file_name, ".csv"), index=None) else: df.to_csv(saved_file_name, index=None) else: df.to_csv("data.csv", index=None) def display_dataframe(self): """ Displays the processed DataFrame. """ if self.merged_df is not None: print(self.merged_df) else: print("DataFrame is empty.") def process_okvqa_dataset(questions_file_path, annotations_file_path, save_to_csv=False, saved_file_name=None): """ Processes the OK-VQA dataset given the file paths for questions and annotations. Parameters: questions_file_path (str): The file path for the questions JSON file. annotations_file_path (str): The file path for the annotations JSON file. Returns: DataFrame: The processed DataFrame containing merged and processed VQA data. """ # Create an instance of the class processor = VQADataProcessor(questions_file_path, annotations_file_path) # Process the data processor.merge_dataframes() processor.process_answers() # Retrieve the processed DataFrame processed_data = processor.get_processed_data() if save_to_csv: processor.save_to_csv(processed_data, saved_file_name) return processed_data def show_image(image): """ Display an image in various environments (Jupyter, PyCharm, Hugging Face Spaces). Handles different types of image inputs (file path, PIL Image, numpy array, OpenCV, PyTorch tensor). Args: image (str or PIL.Image or numpy.ndarray or torch.Tensor): The image to display. """ in_jupyter = is_jupyter_notebook() in_colab = is_google_colab() # Convert image to PIL Image if it's a file path, numpy array, or PyTorch tensor if isinstance(image, str): if os.path.isfile(image): image = Image.open(image) else: raise ValueError("File path provided does not exist.") elif isinstance(image, np.ndarray): if image.ndim == 3 and image.shape[2] in [3, 4]: image = Image.fromarray(image[..., ::-1] if image.shape[2] == 3 else image) else: image = Image.fromarray(image) elif torch.is_tensor(image): image = Image.fromarray(image.permute(1, 2, 0).numpy().astype(np.uint8)) # Display the image if in_jupyter or in_colab: from IPython.display import display display(image) else: image.show() def show_image_with_matplotlib(image): if isinstance(image, str): image = Image.open(image) elif isinstance(image, np.ndarray): image = Image.fromarray(image) elif torch.is_tensor(image): image = Image.fromarray(image.permute(1, 2, 0).numpy().astype(np.uint8)) plt.imshow(image) plt.axis('off') # Turn off axis numbers plt.show() def is_jupyter_notebook(): """ Check if the code is running in a Jupyter notebook. Returns: bool: True if running in a Jupyter notebook, False otherwise. """ try: from IPython import get_ipython if 'IPKernelApp' not in get_ipython().config: return False if 'ipykernel' in str(type(get_ipython())): return True # Running in Jupyter Notebook except (NameError, AttributeError): return False # Not running in Jupyter Notebook return False # Default to False if none of the above conditions are met def is_pycharm(): return 'PYCHARM_HOSTED' in os.environ def is_google_colab(): return 'COLAB_GPU' in os.environ or 'google.colab' in sys.modules def get_image_path(name, path_type): """ Generates a path for models, images, or data based on the specified type. Args: name (str): The name of the model, image, or data folder/file. path_type (str): The type of path needed ('models', 'images', or 'data'). Returns: str: The full path to the specified resource. """ # Get the current working directory (assumed to be inside 'code' folder) current_dir = os.getcwd() # Get the directory one level up (the parent directory) parent_dir = os.path.dirname(current_dir) # Construct the path to the specified folder folder_path = os.path.join(parent_dir, path_type) # Construct the full path to the specific resource full_path = os.path.join(folder_path, name) return full_path def get_model_path(model_name): """ Get the path to the 'model1' folder. Returns: str: Absolute path to the 'model1' folder. """ # Directory of the current script current_script_dir = os.path.dirname(os.path.abspath(__file__)) # Directory of the 'my_project' folder (parent of the 'my_model' folder) project_dir = os.path.dirname(current_script_dir) # Path to the 'models/{model_name}' folder model_path = os.path.join(project_dir, "models", model_name) return model_path def free_gpu_resources(): """ Clears GPU memory. """ if torch.cuda.is_available(): torch.cuda.empty_cache() torch.cuda.empty_cache() gc.collect() gc.collect() if __name__ == "__main__": pass #val_data = process_okvqa_dataset('OpenEnded_mscoco_val2014_questions.json', 'mscoco_val2014_annotations.json', save_to_csv=True, saved_file_name="okvqa_val.csv") #train_data = process_okvqa_dataset('OpenEnded_mscoco_train2014_questions.json', 'mscoco_train2014_annotations.json', save_to_csv=True, saved_file_name="okvqa_train.csv")