vouchervision/general_utils.py

import os, yaml, datetime, argparse, re, cv2, random, shutil, tiktoken, json, csv
from collections import Counter
import pandas as pd
from pathlib import Path
from dataclasses import dataclass
from tqdm import tqdm
import numpy as np
import concurrent.futures
from time import perf_counter
import torch

'''
TIFF --> DNG
Install
https://helpx.adobe.com/camera-raw/using/adobe-dng-converter.html
Read
https://helpx.adobe.com/content/dam/help/en/photoshop/pdf/dng_commandline.pdf

'''


# https://stackoverflow.com/questions/287871/how-do-i-print-colored-text-to-the-terminal

def make_zipfile(source_dir, output_filename):
    shutil.make_archive(output_filename, 'zip', source_dir)
    return output_filename + '.zip'

def validate_dir(dir):
    if not os.path.exists(dir):
        os.makedirs(dir, exist_ok=True)

def get_cfg_from_full_path(path_cfg):
    with open(path_cfg, "r") as ymlfile:
        cfg = yaml.full_load(ymlfile)
    return cfg

def num_tokens_from_string(string: str, encoding_name: str) -> int:
    encoding = tiktoken.get_encoding(encoding_name)
    if isinstance(string, dict):
        string = json.dumps(string, sort_keys=False)
    num_tokens = len(encoding.encode(string))
    return num_tokens

def add_to_expense_report(dir_home, data):
    path_expense_report = os.path.join(dir_home, 'expense_report','expense_report.csv')

    # Check if the file exists
    file_exists = os.path.isfile(path_expense_report)

    # Open the file in append mode if it exists, or write mode if it doesn't
    mode = 'a' if file_exists else 'w'
    
    with open(path_expense_report, mode=mode, newline='') as file:
        writer = csv.writer(file)
        
        # If the file does not exist, write the header first
        if not file_exists:
            writer.writerow(['run','date','api_version','total_cost', 'n_images', 'tokens_in', 'tokens_out', 'rate_in', 'rate_out', 'cost_in', 'cost_out',])
        
        # Write the data row
        writer.writerow(data)

def save_token_info_as_csv(Dirs, LLM_version0, path_api_cost, total_tokens_in, total_tokens_out, n_images):
    version_mapping = {
            'GPT 4': 'GPT_4',
            'GPT 3.5': 'GPT_3_5',
            'Azure GPT 3.5': 'GPT_3_5',
            'Azure GPT 4': 'GPT_4',
            'PaLM 2': 'PALM2'
        }
    LLM_version = version_mapping[LLM_version0]
    # Define the CSV file path
    csv_file_path = os.path.join(Dirs.path_cost, Dirs.run_name + '.csv')

    cost_in, cost_out, total_cost, rate_in, rate_out = calculate_cost(LLM_version, path_api_cost, total_tokens_in, total_tokens_out)
    
    # The data to be written to the CSV file
    data = [Dirs.run_name, get_datetime(),LLM_version, total_cost, n_images, total_tokens_in, total_tokens_out, rate_in, rate_out, cost_in, cost_out,]
    
    # Open the file in write mode
    with open(csv_file_path, mode='w', newline='') as file:
        writer = csv.writer(file)
        
        # Write the header
        writer.writerow(['run','date','api_version','total_cost', 'n_images', 'tokens_in', 'tokens_out', 'rate_in', 'rate_out', 'cost_in', 'cost_out',])
        
        # Write the data
        writer.writerow(data)
    # Create a summary string
    cost_summary = (f"Cost Summary for {Dirs.run_name}:\n"
                    f"     API Cost In: ${rate_in} per 1000 Tokens\n" 
                    f"     API Cost Out: ${rate_out} per 1000 Tokens\n" 
                    f"     Tokens In: {total_tokens_in} - Cost: ${cost_in:.4f}\n"
                    f"     Tokens In: {total_tokens_in} - Cost: ${cost_in:.4f}\n"
                    f"     Tokens Out: {total_tokens_out} - Cost: ${cost_out:.4f}\n"
                    f"     Images Processed: {n_images}\n"
                    f"     Total Cost: ${total_cost:.4f}")
    return cost_summary, data, total_cost

def summarize_expense_report(path_expense_report):
    # Initialize counters and sums
    run_count = 0
    total_cost_sum = 0
    tokens_in_sum = 0
    tokens_out_sum = 0
    rate_in_sum = 0
    rate_out_sum = 0
    cost_in_sum = 0
    cost_out_sum = 0
    n_images_sum = 0
    api_version_counts = Counter()

    # Try to read the CSV file into a DataFrame
    try:
        df = pd.read_csv(path_expense_report)

        # Process each row in the DataFrame
        for index, row in df.iterrows():
            run_count += 1
            total_cost_sum += row['total_cost']
            tokens_in_sum += row['tokens_in']
            tokens_out_sum += row['tokens_out']
            rate_in_sum += row['rate_in']
            rate_out_sum += row['rate_out']
            cost_in_sum += row['cost_in']
            cost_out_sum += row['cost_out']
            n_images_sum += row['n_images']
            api_version_counts[row['api_version']] += 1

    except FileNotFoundError:
        print(f"The file {path_expense_report} does not exist.")
        return None

    # Calculate API version percentages
    api_version_percentages = {version: (count / run_count) * 100 for version, count in api_version_counts.items()}

    # Calculate cost per image for each API version
    cost_per_image_dict = {}
    for version, count in api_version_counts.items():
        total_cost = df[df['api_version'] == version]['total_cost'].sum()
        n_images = df[df['api_version'] == version]['n_images'].sum()
        cost_per_image = total_cost / n_images if n_images > 0 else 0
        cost_per_image_dict[version] = cost_per_image

    # Return the DataFrame and all summaries
    return {
        'run_count': run_count,
        'total_cost_sum': total_cost_sum,
        'tokens_in_sum': tokens_in_sum,
        'tokens_out_sum': tokens_out_sum,
        'rate_in_sum': rate_in_sum,
        'rate_out_sum': rate_out_sum,
        'cost_in_sum': cost_in_sum,
        'cost_out_sum': cost_out_sum,
        'n_images_sum':n_images_sum,
        'api_version_percentages': api_version_percentages,
        'cost_per_image': cost_per_image_dict
    }, df

def calculate_cost(LLM_version, path_api_cost, total_tokens_in, total_tokens_out):
    # Load the rates from the YAML file
    with open(path_api_cost, 'r') as file:
        cost_data = yaml.safe_load(file)
    
    # Get the rates for the specified LLM version
    if LLM_version in cost_data:
        rates = cost_data[LLM_version]
        cost_in = rates['in'] * (total_tokens_in/1000)
        cost_out = rates['out'] * (total_tokens_out/1000)
        total_cost = cost_in + cost_out
    else:
        raise ValueError(f"LLM version {LLM_version} not found in the cost data")
    
    return cost_in, cost_out, total_cost, rates['in'], rates['out']

def create_google_ocr_yaml_config(output_file, dir_images_local, dir_output):
    # Define the configuration dictionary
    config = {
        'leafmachine': {
            'LLM_version': 'PaLM 2',
            'archival_component_detector': {
                'detector_iteration': 'PREP_final',
                'detector_type': 'Archival_Detector',
                'detector_version': 'PREP_final',
                'detector_weights': 'best.pt',
                'do_save_prediction_overlay_images': True,
                'ignore_objects_for_overlay': [],
                'minimum_confidence_threshold': 0.5
            },
            'cropped_components': {
                'binarize_labels': False,
                'binarize_labels_skeletonize': False,
                'do_save_cropped_annotations': True,
                'save_cropped_annotations': ['label', 'barcode'],
                'save_per_annotation_class': True,
                'save_per_image': False
            },
            'data': {
                'do_apply_conversion_factor': False,
                'include_darwin_core_data_from_combined_file': False,
                'save_individual_csv_files_landmarks': False,
                'save_individual_csv_files_measurements': False,
                'save_individual_csv_files_rulers': False,
                'save_individual_efd_files': False,
                'save_json_measurements': False,
                'save_json_rulers': False
            },
            'do': {
                'check_for_corrupt_images_make_vertical': True,
                'check_for_illegal_filenames': False
            },
            'logging': {
                'log_level': None
            },
            'modules': {
                'specimen_crop': True
            },
            'overlay': {
                'alpha_transparency_archival': 0.3,
                'alpha_transparency_plant': 0,
                'alpha_transparency_seg_partial_leaf': 0.3,
                'alpha_transparency_seg_whole_leaf': 0.4,
                'ignore_archival_detections_classes': [],
                'ignore_landmark_classes': [],
                'ignore_plant_detections_classes': ['leaf_whole', 'specimen'],
                'line_width_archival': 12,
                'line_width_efd': 12,
                'line_width_plant': 12,
                'line_width_seg': 12,
                'overlay_background_color': 'black',
                'overlay_dpi': 300,
                'save_overlay_to_jpgs': True,
                'save_overlay_to_pdf': False,
                'show_archival_detections': True,
                'show_landmarks': True,
                'show_plant_detections': True,
                'show_segmentations': True
            },
            'print': {
                'optional_warnings': True,
                'verbose': True
            },
            'project': {
                'batch_size': 500,
                'build_new_embeddings_database': False,
                'catalog_numerical_only': False,
                'continue_run_from_partial_xlsx': '',
                'delete_all_temps': False,
                'delete_temps_keep_VVE': False,
                'dir_images_local': dir_images_local,
                'dir_output': dir_output,
                'embeddings_database_name': 'SLTP_UM_AllAsiaMinimalInRegion',
                'image_location': 'local',
                'num_workers': 1,
                'path_to_domain_knowledge_xlsx': '',
                'prefix_removal': '',
                'prompt_version': 'Version 2 PaLM 2',
                'run_name': 'google_vision_ocr_test',
                'suffix_removal': '',
                'use_domain_knowledge': False
            },
            'use_RGB_label_images': False
        }
    }
    # Generate the YAML string from the data structure
    validate_dir(os.path.dirname(output_file))
    yaml_str = yaml.dump(config)

    # Write the YAML string to a file
    with open(output_file, 'w') as file:
        file.write(yaml_str)

def test_GPU():
    info = []
    success = False

    if torch.cuda.is_available():
        num_gpus = torch.cuda.device_count()
        info.append(f"Number of GPUs: {num_gpus}")

        for i in range(num_gpus):
            gpu = torch.cuda.get_device_properties(i)
            info.append(f"GPU {i}: {gpu.name}")

        success = True
    else:
        info.append("No GPU found!")
        info.append("LeafMachine2 image cropping and embedding search will be slow or not possible.")

    return success, info


# def load_cfg(pathToCfg):
#     try:
#         with open(os.path.join(pathToCfg,"LeafMachine2.yaml"), "r") as ymlfile:
#             cfg = yaml.full_load(ymlfile)
#     except:
#         with open(os.path.join(os.path.dirname(os.path.dirname(pathToCfg)),"LeafMachine2.yaml"), "r") as ymlfile:
#             cfg = yaml.full_load(ymlfile)
#     return cfg

# def load_cfg_VV(pathToCfg):
#     try:
#         with open(os.path.join(pathToCfg,"VoucherVision.yaml"), "r") as ymlfile:
#             cfg = yaml.full_load(ymlfile)
#     except:
#         with open(os.path.join(os.path.dirname(os.path.dirname(pathToCfg)),"VoucherVision.yaml"), "r") as ymlfile:
#             cfg = yaml.full_load(ymlfile)
#     return cfg

def load_cfg(pathToCfg, system='LeafMachine2'):
    if system not in ['LeafMachine2', 'VoucherVision', 'SpecimenCrop']:
        raise ValueError("Invalid system. Expected 'LeafMachine2', 'VoucherVision' or 'SpecimenCrop'.")

    try:
        with open(os.path.join(pathToCfg, f"{system}.yaml"), "r") as ymlfile:
            cfg = yaml.full_load(ymlfile)
    except:
        with open(os.path.join(os.path.dirname(os.path.dirname(pathToCfg)), f"{system}.yaml"), "r") as ymlfile:
            cfg = yaml.full_load(ymlfile)
    return cfg


def import_csv(full_path):
    csv_data = pd.read_csv(full_path,sep=',',header=0, low_memory=False, dtype=str)
    return csv_data

def import_tsv(full_path):
    csv_data = pd.read_csv(full_path,sep='\t',header=0, low_memory=False, dtype=str)
    return csv_data

def parse_cfg():
    parser = argparse.ArgumentParser(
            description='Parse inputs to read  config file',
            formatter_class=argparse.ArgumentDefaultsHelpFormatter)

    optional_args = parser._action_groups.pop()
    required_args = parser.add_argument_group('MANDATORY arguments')
    required_args.add_argument('--path-to-cfg',
                                type=str,
                                required=True,
                                help='Path to config file - LeafMachine.yaml. Do not include the file name, just the parent dir.')

    parser._action_groups.append(optional_args)
    args = parser.parse_args()
    return args

def check_for_subdirs(cfg):
    original_in = cfg['leafmachine']['project']['dir_images_local']
    dirs_list = []
    run_name = []
    has_subdirs = False
    if os.path.isdir(original_in):
        # list contents of the directory
        contents = os.listdir(original_in)
        
        # check if any of the contents is a directory
        subdirs = [f for f in contents if os.path.isdir(os.path.join(original_in, f))]
        
        if len(subdirs) > 0:
            print("The directory contains subdirectories:")
            for subdir in subdirs:
                has_subdirs = True
                print(os.path.join(original_in, subdir))
                dirs_list.append(os.path.join(original_in, subdir))
                run_name.append(subdir)
        else:
            print("The directory does not contain any subdirectories.")
            dirs_list.append(original_in)
            run_name.append(cfg['leafmachine']['project']['run_name'])

    else:
        print("The specified path is not a directory.")

    return run_name, dirs_list, has_subdirs

def check_for_subdirs_VV(cfg):
    original_in = cfg['leafmachine']['project']['dir_images_local']
    dirs_list = []
    run_name = []
    has_subdirs = False
    if os.path.isdir(original_in):
        dirs_list.append(original_in)
        run_name.append(os.path.basename(os.path.normpath(original_in)))
        # list contents of the directory
        contents = os.listdir(original_in)
        
        # check if any of the contents is a directory
        subdirs = [f for f in contents if os.path.isdir(os.path.join(original_in, f))]
        
        if len(subdirs) > 0:
            print("The directory contains subdirectories:")
            for subdir in subdirs:
                has_subdirs = True
                print(os.path.join(original_in, subdir))
                dirs_list.append(os.path.join(original_in, subdir))
                run_name.append(subdir)
        else:
            print("The directory does not contain any subdirectories.")
            dirs_list.append(original_in)
            run_name.append(cfg['leafmachine']['project']['run_name'])

    else:
        print("The specified path is not a directory.")

    return run_name, dirs_list, has_subdirs

def get_datetime():
    day = "_".join([str(datetime.datetime.now().strftime("%Y")),str(datetime.datetime.now().strftime("%m")),str(datetime.datetime.now().strftime("%d"))])
    time = "-".join([str(datetime.datetime.now().strftime("%H")),str(datetime.datetime.now().strftime("%M")),str(datetime.datetime.now().strftime("%S"))])
    new_time = "__".join([day,time])
    return new_time

def save_config_file(cfg, logger, Dirs):
    logger.info("Save config file")
    name_yaml = ''.join([Dirs.run_name,'.yaml'])
    write_yaml(cfg, os.path.join(Dirs.path_config_file, name_yaml))

def write_yaml(cfg, path_cfg):
    with open(path_cfg, 'w') as file:
        yaml.dump(cfg, file)

def split_into_batches(Project, logger, cfg):
    logger.name = 'Creating Batches'
    n_batches, n_images = Project.process_in_batches(cfg)
    m = f'Created {n_batches} Batches to Process {n_images} Images'
    logger.info(m)
    return Project, n_batches, m 

def make_images_in_dir_vertical(dir_images_unprocessed, cfg):
    if cfg['leafmachine']['do']['check_for_corrupt_images_make_vertical']:
        n_rotate = 0
        n_corrupt = 0
        n_total = len(os.listdir(dir_images_unprocessed))
        for image_name_jpg in tqdm(os.listdir(dir_images_unprocessed), desc=f'{bcolors.BOLD}     Checking Image Dimensions{bcolors.ENDC}',colour="cyan",position=0,total = n_total):
            if image_name_jpg.endswith((".jpg",".JPG",".jpeg",".JPEG")):
                try:
                    image = cv2.imread(os.path.join(dir_images_unprocessed, image_name_jpg))
                    h, w, img_c = image.shape
                    image, img_h, img_w, did_rotate = make_image_vertical(image, h, w, do_rotate_180=False)
                    if did_rotate:
                        n_rotate += 1
                    cv2.imwrite(os.path.join(dir_images_unprocessed,image_name_jpg), image)
                except:
                    n_corrupt +=1
                    os.remove(os.path.join(dir_images_unprocessed, image_name_jpg))
            # TODO check that below works as intended 
            elif image_name_jpg.endswith((".tiff",".tif",".png",".PNG",".TIFF",".TIF",".jp2",".JP2",".bmp",".BMP",".dib",".DIB")):
                try:
                    image = cv2.imread(os.path.join(dir_images_unprocessed, image_name_jpg))
                    h, w, img_c = image.shape
                    image, img_h, img_w, did_rotate = make_image_vertical(image, h, w, do_rotate_180=False)
                    if did_rotate:
                        n_rotate += 1
                    image_name_jpg = '.'.join([image_name_jpg.split('.')[0], 'jpg'])
                    cv2.imwrite(os.path.join(dir_images_unprocessed,image_name_jpg), image)
                except:
                    n_corrupt +=1
                    os.remove(os.path.join(dir_images_unprocessed, image_name_jpg))
        m = ''.join(['Number of Images Rotated: ', str(n_rotate)])
        Print_Verbose(cfg, 2, m).bold()
        m2 = ''.join(['Number of Images Corrupted: ', str(n_corrupt)])
        if n_corrupt > 0:
            Print_Verbose(cfg, 2, m2).warning
        else:
            Print_Verbose(cfg, 2, m2).bold

def make_image_vertical(image, h, w, do_rotate_180):
    did_rotate = False
    if do_rotate_180:
        # try:
        image = cv2.rotate(image, cv2.ROTATE_180)
        img_h, img_w, img_c = image.shape
        did_rotate = True
        # print("      Rotated 180")
    else:
        if h < w:
            # try:
            image = cv2.rotate(image, cv2.ROTATE_90_CLOCKWISE)
            img_h, img_w, img_c = image.shape
            did_rotate = True
            # print("      Rotated 90 CW")
        elif h >= w:
            image = image
            img_h = h
            img_w = w
            # print("      Not Rotated")
    return image, img_h, img_w, did_rotate
    

def make_image_horizontal(image, h, w, do_rotate_180):
    if h > w:
        if do_rotate_180:
            image = cv2.rotate(image, cv2.ROTATE_180)
        return cv2.rotate(image, cv2.ROTATE_90_COUNTERCLOCKWISE), w, h, True
    return image, w, h, False

def make_images_in_dir_horizontal(dir_images_unprocessed, cfg):
    # if cfg['leafmachine']['do']['check_for_corrupt_images_make_horizontal']:
    n_rotate = 0
    n_corrupt = 0
    n_total = len(os.listdir(dir_images_unprocessed))
    for image_name_jpg in tqdm(os.listdir(dir_images_unprocessed), desc=f'{bcolors.BOLD}     Checking Image Dimensions{bcolors.ENDC}', colour="cyan", position=0, total=n_total):
        if image_name_jpg.endswith((".jpg",".JPG",".jpeg",".JPEG")):
            try:
                image = cv2.imread(os.path.join(dir_images_unprocessed, image_name_jpg))
                h, w, img_c = image.shape
                image, img_h, img_w, did_rotate = make_image_horizontal(image, h, w, do_rotate_180=False)
                if did_rotate:
                    n_rotate += 1
                cv2.imwrite(os.path.join(dir_images_unprocessed,image_name_jpg), image)
            except:
                n_corrupt +=1
                os.remove(os.path.join(dir_images_unprocessed, image_name_jpg))
        # TODO check that below works as intended 
        elif image_name_jpg.endswith((".tiff",".tif",".png",".PNG",".TIFF",".TIF",".jp2",".JP2",".bmp",".BMP",".dib",".DIB")):
            try:
                image = cv2.imread(os.path.join(dir_images_unprocessed, image_name_jpg))
                h, w, img_c = image.shape
                image, img_h, img_w, did_rotate = make_image_horizontal(image, h, w, do_rotate_180=False)
                if did_rotate:
                    n_rotate += 1
                image_name_jpg = '.'.join([image_name_jpg.split('.')[0], 'jpg'])
                cv2.imwrite(os.path.join(dir_images_unprocessed,image_name_jpg), image)
            except:
                n_corrupt +=1
                os.remove(os.path.join(dir_images_unprocessed, image_name_jpg))
    m = ''.join(['Number of Images Rotated: ', str(n_rotate)])
    print(m)
    # Print_Verbose(cfg, 2, m).bold()
    m2 = ''.join(['Number of Images Corrupted: ', str(n_corrupt)])
    print(m2)


@dataclass
class Print_Verbose_Error():
    cfg: str = ''
    indent_level: int = 0
    message: str = ''
    error: str = ''

    def __init__(self, cfg,indent_level,message,error) -> None:
        self.cfg = cfg
        self.indent_level = indent_level
        self.message = message
        self.error = error

    def print_error_to_console(self):
        white_space = " " * 5 * self.indent_level
        if self.cfg['leafmachine']['print']['optional_warnings']:
            print(f"{bcolors.FAIL}{white_space}{self.message} ERROR: {self.error}{bcolors.ENDC}")

    def print_warning_to_console(self):
        white_space = " " * 5 * self.indent_level
        if self.cfg['leafmachine']['print']['optional_warnings']:
            print(f"{bcolors.WARNING}{white_space}{self.message} ERROR: {self.error}{bcolors.ENDC}")

@dataclass
class Print_Verbose():
    cfg: str = ''
    indent_level: int = 0
    message: str = ''

    def __init__(self, cfg, indent_level, message) -> None:
        self.cfg = cfg
        self.indent_level = indent_level
        self.message = message

    def bold(self):
        white_space = " " * 5 * self.indent_level
        if self.cfg['leafmachine']['print']['verbose']:
            print(f"{bcolors.BOLD}{white_space}{self.message}{bcolors.ENDC}")

    def green(self):
        white_space = " " * 5 * self.indent_level
        if self.cfg['leafmachine']['print']['verbose']:
            print(f"{bcolors.OKGREEN}{white_space}{self.message}{bcolors.ENDC}")

    def cyan(self):
        white_space = " " * 5 * self.indent_level
        if self.cfg['leafmachine']['print']['verbose']:
            print(f"{bcolors.OKCYAN}{white_space}{self.message}{bcolors.ENDC}")

    def blue(self):
        white_space = " " * 5 * self.indent_level
        if self.cfg['leafmachine']['print']['verbose']:
            print(f"{bcolors.OKBLUE}{white_space}{self.message}{bcolors.ENDC}")

    def warning(self):
        white_space = " " * 5 * self.indent_level
        if self.cfg['leafmachine']['print']['verbose']:
            print(f"{bcolors.WARNING}{white_space}{self.message}{bcolors.ENDC}")

    def plain(self):
        white_space = " " * 5 * self.indent_level
        if self.cfg['leafmachine']['print']['verbose']:
            print(f"{white_space}{self.message}")

def print_main_start(message):
    indent_level = 1
    white_space = " " * 5 * indent_level
    end = " " * int(80 - len(message) - len(white_space))
    # end_white_space = " " * end
    blank = " " * 80
    print(f"{bcolors.CBLUEBG2}{blank}{bcolors.ENDC}")
    print(f"{bcolors.CBLUEBG2}{white_space}{message}{end}{bcolors.ENDC}")
    print(f"{bcolors.CBLUEBG2}{blank}{bcolors.ENDC}")

def print_main_success(message):
    indent_level = 1
    white_space = " " * 5 * indent_level
    end = " " * int(80 - len(message) - len(white_space))
    blank = " " * 80
    # end_white_space = " " * end
    print(f"{bcolors.CGREENBG2}{blank}{bcolors.ENDC}")
    print(f"{bcolors.CGREENBG2}{white_space}{message}{end}{bcolors.ENDC}")
    print(f"{bcolors.CGREENBG2}{blank}{bcolors.ENDC}")

def print_main_warn(message):
    indent_level = 1
    white_space = " " * 5 * indent_level
    end = " " * int(80 - len(message) - len(white_space))
    # end_white_space = " " * end
    blank = " " * 80
    print(f"{bcolors.CYELLOWBG2}{blank}{bcolors.ENDC}")
    print(f"{bcolors.CYELLOWBG2}{white_space}{message}{end}{bcolors.ENDC}")
    print(f"{bcolors.CYELLOWBG2}{blank}{bcolors.ENDC}")

def print_main_fail(message):
    indent_level = 1
    white_space = " " * 5 * indent_level
    end = " " * int(80 - len(message) - len(white_space))
    # end_white_space = " " * end
    blank = " " * 80
    print(f"{bcolors.CREDBG2}{blank}{bcolors.ENDC}")
    print(f"{bcolors.CREDBG2}{white_space}{message}{end}{bcolors.ENDC}")
    print(f"{bcolors.CREDBG2}{blank}{bcolors.ENDC}")

def print_main_info(message):
    indent_level = 2
    white_space = " " * 5 * indent_level
    end = " " * int(80 - len(message) - len(white_space))
    # end_white_space = " " * end
    print(f"{bcolors.CGREYBG}{white_space}{message}{end}{bcolors.ENDC}")
    
# def report_config(dir_home, cfg_file_path):
#     print_main_start("Loading Configuration File")
#     if cfg_file_path == None:
#         print_main_info(''.join([os.path.join(dir_home, 'LeafMachine2.yaml')]))
#     elif cfg_file_path == 'test_installation':
#         print_main_info(''.join([os.path.join(dir_home, 'demo','LeafMachine2_demo.yaml')]))
#     else:
#         print_main_info(cfg_file_path)

# def report_config_VV(dir_home, cfg_file_path):
#     print_main_start("Loading Configuration File")
#     if cfg_file_path == None:
#         print_main_info(''.join([os.path.join(dir_home, 'VoucherVision.yaml')]))
#     elif cfg_file_path == 'test_installation':
#         print_main_info(''.join([os.path.join(dir_home, 'demo','VoucherVision_demo.yaml')]))
#     else:
#         print_main_info(cfg_file_path)

def report_config(dir_home, cfg_file_path, system='VoucherVision'):
    print_main_start("Loading Configuration File")
    
    if system not in ['LeafMachine2', 'VoucherVision', 'SpecimenCrop']:
        raise ValueError("Invalid system. Expected 'LeafMachine2' or 'VoucherVision' or 'SpecimenCrop'.")
    
    if cfg_file_path == None:
        print_main_info(''.join([os.path.join(dir_home, f'{system}.yaml')]))
    elif cfg_file_path == 'test_installation':
        print_main_info(''.join([os.path.join(dir_home, 'demo', f'{system}_demo.yaml')]))
    else:
        print_main_info(cfg_file_path)


def make_file_names_valid(dir, cfg):
    if cfg['leafmachine']['do']['check_for_illegal_filenames']:
        n_total = len(os.listdir(dir))
        for file in tqdm(os.listdir(dir), desc=f'{bcolors.HEADER}     Removing illegal characters from file names{bcolors.ENDC}',colour="cyan",position=0,total = n_total):
            name = Path(file).stem
            ext = Path(file).suffix
            name_cleaned = re.sub(r"[^a-zA-Z0-9_-]","-",name)
            name_new = ''.join([name_cleaned,ext])
            i = 0
            try:
                os.rename(os.path.join(dir,file), os.path.join(dir,name_new))
            except:
                while os.path.exists(os.path.join(dir,name_new)):
                    i += 1
                    name_new = '_'.join([name_cleaned, str(i), ext])
                os.rename(os.path.join(dir,file), os.path.join(dir,name_new))

# def load_config_file(dir_home, cfg_file_path):
#     if cfg_file_path == None: # Default path
#         return load_cfg(dir_home)
#     else:
#         if cfg_file_path == 'test_installation':
#             path_cfg = os.path.join(dir_home,'demo','LeafMachine2_demo.yaml')                     
#             return get_cfg_from_full_path(path_cfg)
#         else: # Custom path
#             return get_cfg_from_full_path(cfg_file_path)
        
# def load_config_file_VV(dir_home, cfg_file_path):
#     if cfg_file_path == None: # Default path
#         return load_cfg_VV(dir_home)
#     else:
#         if cfg_file_path == 'test_installation':
#             path_cfg = os.path.join(dir_home,'demo','VoucherVision_demo.yaml')                     
#             return get_cfg_from_full_path(path_cfg)
#         else: # Custom path
#             return get_cfg_from_full_path(cfg_file_path)

def load_config_file(dir_home, cfg_file_path, system='LeafMachine2'):
    if system not in ['LeafMachine2', 'VoucherVision', 'SpecimenCrop']:
        raise ValueError("Invalid system. Expected 'LeafMachine2' or 'VoucherVision' or 'SpecimenCrop'.")

    if cfg_file_path is None:  # Default path
        if system == 'LeafMachine2':
            return load_cfg(dir_home, system='LeafMachine2')  # For LeafMachine2

        elif system == 'VoucherVision': # VoucherVision
            return load_cfg(dir_home, system='VoucherVision')  # For VoucherVision

        elif system == 'SpecimenCrop': # SpecimenCrop
            return load_cfg(dir_home, system='SpecimenCrop')  # For SpecimenCrop

    else:
        if cfg_file_path == 'test_installation':
            path_cfg = os.path.join(dir_home, 'demo', f'{system}_demo.yaml')                     
            return get_cfg_from_full_path(path_cfg)
        else:  # Custom path
            return get_cfg_from_full_path(cfg_file_path)

        
def load_config_file_testing(dir_home, cfg_file_path):
    if cfg_file_path == None: # Default path
        return load_cfg(dir_home)
    else:
        if cfg_file_path == 'test_installation':
            path_cfg = os.path.join(dir_home,'demo','demo.yaml')                     
            return get_cfg_from_full_path(path_cfg)
        else: # Custom path
            return get_cfg_from_full_path(cfg_file_path)

def subset_dir_images(cfg, Project, Dirs):
    if cfg['leafmachine']['project']['process_subset_of_images']:
        dir_images_subset = cfg['leafmachine']['project']['dir_images_subset']
        num_images_per_species = cfg['leafmachine']['project']['n_images_per_species']
        if cfg['leafmachine']['project']['species_list'] is not None:
            species_list = import_csv(cfg['leafmachine']['project']['species_list'])
            species_list = species_list.iloc[:, 0].tolist()
        else:
            species_list = None

        validate_dir(dir_images_subset)

        species_counts = {}
        filenames = os.listdir(Project.dir_images)
        random.shuffle(filenames)
        for filename in filenames:
            species_name = filename.split('.')[0]
            species_name = species_name.split('_')[2:]
            species_name = '_'.join([species_name[0], species_name[1], species_name[2]])

            if (species_list is None) or ((species_name in species_list) and (species_list is not None)):
            
                if species_name not in species_counts:
                    species_counts[species_name] = 0
                
                if species_counts[species_name] < num_images_per_species:
                    species_counts[species_name] += 1
                    src_path = os.path.join(Project.dir_images, filename)
                    dest_path = os.path.join(dir_images_subset, filename)
                    shutil.copy(src_path, dest_path)
        
        Project.dir_images = dir_images_subset
        
        subset_csv_name = os.path.join(Dirs.dir_images_subset, '.'.join([Dirs.run_name, 'csv']))
        df = pd.DataFrame({'species_name': list(species_counts.keys()), 'count': list(species_counts.values())})
        df.to_csv(subset_csv_name, index=False)
        return Project
    else:
        return Project

'''# Define function to be executed by each worker
def worker_crop(rank, cfg, dir_home, Project, Dirs):
    # Set worker seed based on rank
    np.random.seed(rank)
    # Call function for this worker
    crop_detections_from_images(cfg, dir_home, Project, Dirs)

def crop_detections_from_images(cfg, dir_home, Project, Dirs):
    num_workers = 6
    
    # Initialize and start worker processes
    processes = []
    for rank in range(num_workers):
        p = mp.Process(target=worker_crop, args=(rank, cfg, dir_home, Project, Dirs))
        p.start()
        processes.append(p)

    # Wait for all worker processes to finish
    for p in processes:
        p.join()'''

def crop_detections_from_images_worker_VV(filename, analysis, Project, Dirs, save_per_image, save_per_class, save_list, binarize_labels):
    try:
        full_image = cv2.imread(os.path.join(Project.dir_images, '.'.join([filename, 'jpg'])))
    except:
        full_image = cv2.imread(os.path.join(Project.dir_images, '.'.join([filename, 'jpeg'])))

    try:
        archival = analysis['Detections_Archival_Components']
        has_archival = True
    except: 
        has_archival = False

    try:
        plant = analysis['Detections_Plant_Components']
        has_plant = True
    except: 
        has_plant = False

    if has_archival and (save_per_image or save_per_class):
        crop_component_from_yolo_coords_VV('ARCHIVAL', Dirs, analysis, archival, full_image, filename, save_per_image, save_per_class, save_list)
 
def crop_detections_from_images_worker(filename, analysis, Project, Dirs, save_per_image, save_per_class, save_list, binarize_labels):
    try:
        full_image = cv2.imread(os.path.join(Project.dir_images, '.'.join([filename, 'jpg'])))
    except:
        full_image = cv2.imread(os.path.join(Project.dir_images, '.'.join([filename, 'jpeg'])))

    try:
        archival = analysis['Detections_Archival_Components']
        has_archival = True
    except: 
        has_archival = False

    try:
        plant = analysis['Detections_Plant_Components']
        has_plant = True
    except: 
        has_plant = False

    if has_archival and (save_per_image or save_per_class):
        crop_component_from_yolo_coords('ARCHIVAL', Dirs, analysis, archival, full_image, filename, save_per_image, save_per_class, save_list)
    if has_plant and (save_per_image or save_per_class):
        crop_component_from_yolo_coords('PLANT', Dirs, analysis, plant, full_image, filename, save_per_image, save_per_class, save_list)


def crop_detections_from_images(cfg, logger, dir_home, Project, Dirs, batch_size=50):
    t2_start = perf_counter()
    logger.name = 'Crop Components'
    
    if cfg['leafmachine']['cropped_components']['do_save_cropped_annotations']:
        detections = cfg['leafmachine']['cropped_components']['save_cropped_annotations']
        logger.info(f"Cropping {detections} components from images")

        save_per_image = cfg['leafmachine']['cropped_components']['save_per_image']
        save_per_class = cfg['leafmachine']['cropped_components']['save_per_annotation_class']
        save_list = cfg['leafmachine']['cropped_components']['save_cropped_annotations']
        try:
            binarize_labels = cfg['leafmachine']['cropped_components']['binarize_labels']
        except:
            binarize_labels = False
        if cfg['leafmachine']['project']['batch_size'] is None:
            batch_size = 50
        else:
            batch_size = int(cfg['leafmachine']['project']['batch_size'])
        if cfg['leafmachine']['project']['num_workers'] is None:
            num_workers = 4 
        else:
            num_workers = int(cfg['leafmachine']['project']['num_workers'])

        if binarize_labels:
            save_per_class = True

        with concurrent.futures.ThreadPoolExecutor(max_workers=num_workers) as executor:
            futures = []
            for i in range(0, len(Project.project_data), batch_size):
                batch = list(Project.project_data.items())[i:i+batch_size]
                # print(f'Cropping Detections from Images {i} to {i+batch_size}')
                logger.info(f'Cropping {detections} from images {i} to {i+batch_size} [{len(Project.project_data)}]')
                for filename, analysis in batch:
                    if len(analysis) != 0:
                        futures.append(executor.submit(crop_detections_from_images_worker, filename, analysis, Project, Dirs, save_per_image, save_per_class, save_list, binarize_labels))

                for future in concurrent.futures.as_completed(futures):
                    pass
                futures.clear()

    t2_stop = perf_counter()
    logger.info(f"Save cropped components --- elapsed time: {round(t2_stop - t2_start)} seconds")

def crop_detections_from_images_VV(cfg, logger, dir_home, Project, Dirs, batch_size=50):
    t2_start = perf_counter()
    logger.name = 'Crop Components'
    
    if cfg['leafmachine']['cropped_components']['do_save_cropped_annotations']:
        detections = cfg['leafmachine']['cropped_components']['save_cropped_annotations']
        logger.info(f"Cropping {detections} components from images")

        save_per_image = cfg['leafmachine']['cropped_components']['save_per_image']
        save_per_class = cfg['leafmachine']['cropped_components']['save_per_annotation_class']
        save_list = cfg['leafmachine']['cropped_components']['save_cropped_annotations']
        binarize_labels = cfg['leafmachine']['cropped_components']['binarize_labels']
        if cfg['leafmachine']['project']['batch_size'] is None:
            batch_size = 50
        else:
            batch_size = int(cfg['leafmachine']['project']['batch_size'])
        if cfg['leafmachine']['project']['num_workers'] is None:
            num_workers = 4 
        else:
            num_workers = int(cfg['leafmachine']['project']['num_workers'])

        if binarize_labels:
            save_per_class = True

        with concurrent.futures.ThreadPoolExecutor(max_workers=num_workers) as executor:
            futures = []
            for i in range(0, len(Project.project_data), batch_size):
                batch = list(Project.project_data.items())[i:i+batch_size]
                # print(f'Cropping Detections from Images {i} to {i+batch_size}')
                logger.info(f'Cropping {detections} from images {i} to {i+batch_size} [{len(Project.project_data)}]')
                for filename, analysis in batch:
                    if len(analysis) != 0:
                        futures.append(executor.submit(crop_detections_from_images_worker_VV, filename, analysis, Project, Dirs, save_per_image, save_per_class, save_list, binarize_labels))

                for future in concurrent.futures.as_completed(futures):
                    pass
                futures.clear()

    t2_stop = perf_counter()
    logger.info(f"Save cropped components --- elapsed time: {round(t2_stop - t2_start)} seconds")
# def crop_detections_from_images_VV(cfg, logger, dir_home, Project, Dirs, batch_size=50):
#     t2_start = perf_counter()
#     logger.name = 'Crop Components'
    
#     if cfg['leafmachine']['cropped_components']['do_save_cropped_annotations']:
#         detections = cfg['leafmachine']['cropped_components']['save_cropped_annotations']
#         logger.info(f"Cropping {detections} components from images")

#         save_per_image = cfg['leafmachine']['cropped_components']['save_per_image']
#         save_per_class = cfg['leafmachine']['cropped_components']['save_per_annotation_class']
#         save_list = cfg['leafmachine']['cropped_components']['save_cropped_annotations']
#         binarize_labels = cfg['leafmachine']['cropped_components']['binarize_labels']
#         if cfg['leafmachine']['project']['batch_size'] is None:
#             batch_size = 50
#         else:
#             batch_size = int(cfg['leafmachine']['project']['batch_size'])

#         if binarize_labels:
#             save_per_class = True

#         for i in range(0, len(Project.project_data), batch_size):
#             batch = list(Project.project_data.items())[i:i+batch_size]
#             logger.info(f"Cropping {detections} from images {i} to {i+batch_size} [{len(Project.project_data)}]")
#             for filename, analysis in batch:
#                 if len(analysis) != 0:
#                     crop_detections_from_images_worker_VV(filename, analysis, Project, Dirs, save_per_image, save_per_class, save_list, binarize_labels)

#     t2_stop = perf_counter()
#     logger.info(f"Save cropped components --- elapsed time: {round(t2_stop - t2_start)} seconds")


# def crop_detections_from_images_SpecimenCrop(cfg, logger, dir_home, Project, Dirs, original_img_dir=None, batch_size=50):
#     t2_start = perf_counter()
#     logger.name = 'Crop Components --- Specimen Crop'
    
#     if cfg['leafmachine']['modules']['specimen_crop']:
#         # save_list = ['ruler', 'barcode', 'colorcard', 'label', 'map', 'envelope', 'photo', 'attached_item', 'weights',
#         #               'leaf_whole', 'leaf_partial', 'leaflet', 'seed_fruit_one', 'seed_fruit_many', 'flower_one', 'flower_many', 'bud', 'specimen', 'roots', 'wood']
#         save_list = cfg['leafmachine']['cropped_components']['include_these_objects_in_specimen_crop']

#         logger.info(f"Cropping to include {save_list} components from images")

#         if cfg['leafmachine']['project']['batch_size'] is None:
#             batch_size = 50
#         else:
#             batch_size = int(cfg['leafmachine']['project']['batch_size'])
#         if cfg['leafmachine']['project']['num_workers'] is None:
#             num_workers = 4 
#         else:
#             num_workers = int(cfg['leafmachine']['project']['num_workers'])

#         with concurrent.futures.ThreadPoolExecutor(max_workers=num_workers) as executor:
#             futures = []
#             for i in range(0, len(Project.project_data), batch_size):
#                 batch = list(Project.project_data.items())[i:i+batch_size]
#                 # print(f'Cropping Detections from Images {i} to {i+batch_size}')
#                 logger.info(f'Cropping {save_list} from images {i} to {i+batch_size} [{len(Project.project_data)}]')
#                 for filename, analysis in batch:
#                     if len(analysis) != 0:
#                         futures.append(executor.submit(crop_detections_from_images_worker_SpecimenCrop, filename, analysis, Project, Dirs, save_list, original_img_dir))

#                 for future in concurrent.futures.as_completed(futures):
#                     pass
#                 futures.clear()

#     t2_stop = perf_counter()
#     logger.info(f"Save cropped components --- elapsed time: {round(t2_stop - t2_start)} seconds")

'''
# Single threaded
def crop_detections_from_images(cfg, dir_home, Project, Dirs):
    if cfg['leafmachine']['cropped_components']['do_save_cropped_annotations']:
        save_per_image = cfg['leafmachine']['cropped_components']['save_per_image']
        save_per_class = cfg['leafmachine']['cropped_components']['save_per_annotation_class']
        save_list = cfg['leafmachine']['cropped_components']['save_cropped_annotations']
        binarize_labels = cfg['leafmachine']['cropped_components']['binarize_labels']
        if binarize_labels:
            save_per_class = True

        for filename, analysis in  tqdm(Project.project_data.items(), desc=f'{bcolors.BOLD}     Cropping Detections from Images{bcolors.ENDC}',colour="cyan",position=0,total = len(Project.project_data.items())):
            if len(analysis) != 0:
                try:
                    full_image = cv2.imread(os.path.join(Project.dir_images, '.'.join([filename, 'jpg'])))
                except:
                    full_image = cv2.imread(os.path.join(Project.dir_images, '.'.join([filename, 'jpeg'])))

                try:
                    archival = analysis['Detections_Archival_Components']
                    has_archival = True
                except: 
                    has_archival = False

                try:
                    plant = analysis['Detections_Plant_Components']
                    has_plant = True
                except: 
                    has_plant = False

                if has_archival and (save_per_image or save_per_class):
                    crop_component_from_yolo_coords('ARCHIVAL', Dirs, analysis, archival, full_image, filename, save_per_image, save_per_class, save_list)
                if has_plant and (save_per_image or save_per_class):
                    crop_component_from_yolo_coords('PLANT', Dirs, analysis, plant, full_image, filename, save_per_image, save_per_class, save_list)
'''


def process_detections(success, save_list, detections, detection_type, height, width, min_x, min_y, max_x, max_y):
    for detection in detections:
        detection_class = detection[0]
        detection_class = set_index_for_annotation(detection_class, detection_type)

        if (detection_class in save_list) or ('save_all' in save_list):
            location = yolo_to_position_ruler(detection, height, width)
            ruler_polygon = [
                (location[1], location[2]), 
                (location[3], location[2]), 
                (location[3], location[4]), 
                (location[1], location[4])
            ]

            x_coords = [x for x, y in ruler_polygon]
            y_coords = [y for x, y in ruler_polygon]

            min_x = min(min_x, *x_coords)
            min_y = min(min_y, *y_coords)
            max_x = max(max_x, *x_coords)
            max_y = max(max_y, *y_coords)
            success = True

    return min_x, min_y, max_x, max_y, success


def crop_component_from_yolo_coords_VV(anno_type, Dirs, analysis, all_detections, full_image, filename, save_per_image, save_per_class, save_list):
    height = analysis['height']
    width = analysis['width']

    # Initialize a list to hold all the cropped images
    cropped_images = []

    if len(all_detections) < 1:
        print('     MAKE THIS HAVE AN EMPTY PLACEHOLDER') # TODO ###################################################################################
    else:
        for detection in all_detections:
            detection_class = detection[0]
            detection_class = set_index_for_annotation(detection_class, anno_type)

            if (detection_class in save_list) or ('save_all' in save_list):

                location = yolo_to_position_ruler(detection, height, width)
                ruler_polygon = [(location[1], location[2]), (location[3], location[2]), (location[3], location[4]), (location[1], location[4])]

                x_coords = [x for x, y in ruler_polygon]
                y_coords = [y for x, y in ruler_polygon]

                min_x, min_y = min(x_coords), min(y_coords)
                max_x, max_y = max(x_coords), max(y_coords)

                detection_cropped = full_image[min_y:max_y, min_x:max_x]
                cropped_images.append(detection_cropped)
                loc = '-'.join([str(min_x), str(min_y), str(max_x), str(max_y)])
                detection_cropped_name = '.'.join(['__'.join([filename, detection_class, loc]), 'jpg'])
                # detection_cropped_name = '.'.join([filename,'jpg'])

                # save_per_image
                if (detection_class in save_list) and save_per_image:
                    if detection_class == 'label':
                        detection_class2 = 'label_ind'
                    else:
                        detection_class2 = detection_class
                    dir_destination = os.path.join(Dirs.save_per_image, filename, detection_class2)
                    # print(os.path.join(dir_destination,detection_cropped_name))
                    validate_dir(dir_destination)
                    # cv2.imwrite(os.path.join(dir_destination,detection_cropped_name), detection_cropped)
                    
                # save_per_class
                if (detection_class in save_list) and save_per_class:
                    if detection_class == 'label':
                        detection_class2 = 'label_ind'
                    else:
                        detection_class2 = detection_class
                    dir_destination = os.path.join(Dirs.save_per_annotation_class, detection_class2)
                    # print(os.path.join(dir_destination,detection_cropped_name))
                    validate_dir(dir_destination)
                    # cv2.imwrite(os.path.join(dir_destination,detection_cropped_name), detection_cropped)
            else:
                # print(f'detection_class: {detection_class} not in save_list: {save_list}')
                pass

    # Initialize a list to hold all the acceptable cropped images
    acceptable_cropped_images = []

    for img in cropped_images:
        # Calculate the aspect ratio of the image
        aspect_ratio = min(img.shape[0], img.shape[1]) / max(img.shape[0], img.shape[1])
        # Only add the image to the acceptable list if the aspect ratio is more square than 1:8
        if aspect_ratio >= 1/8:
            acceptable_cropped_images.append(img)

    # Sort acceptable_cropped_images by area (largest first)
    acceptable_cropped_images.sort(key=lambda img: img.shape[0] * img.shape[1], reverse=True)


    # If there are no acceptable cropped images, set combined_image to None or to a placeholder image
    if not acceptable_cropped_images:
        combined_image = None  # Or a placeholder image here
    else:
    #     # Recalculate max_width and total_height for acceptable images
    #     max_width = max(img.shape[1] for img in acceptable_cropped_images)
    #     total_height = sum(img.shape[0] for img in acceptable_cropped_images)

    #     # Now, combine all the acceptable cropped images into a single image
    #     combined_image = np.zeros((total_height, max_width, 3), dtype=np.uint8)

    #     y_offset = 0
    #     for img in acceptable_cropped_images:
    #         combined_image[y_offset:y_offset+img.shape[0], :img.shape[1]] = img
    #         y_offset += img.shape[0]
        # Start with the first image
        # Recalculate max_width and total_height for acceptable images
        max_width = max(img.shape[1] for img in acceptable_cropped_images)
        total_height = sum(img.shape[0] for img in acceptable_cropped_images)
        combined_image = np.zeros((total_height, max_width, 3), dtype=np.uint8)

        y_offset = 0
        y_offset_next_row = 0
        x_offset = 0

        # Start with the first image
        combined_image[y_offset:y_offset+acceptable_cropped_images[0].shape[0], :acceptable_cropped_images[0].shape[1]] = acceptable_cropped_images[0]
        y_offset_next_row += acceptable_cropped_images[0].shape[0]

        # Add the second image below the first one
        y_offset = y_offset_next_row
        combined_image[y_offset:y_offset+acceptable_cropped_images[1].shape[0], :acceptable_cropped_images[1].shape[1]] = acceptable_cropped_images[1]
        y_offset_next_row += acceptable_cropped_images[1].shape[0]

        # Create a list to store the images that are too tall for the current row
        too_tall_images = []

        # Now try to fill in to the right with the remaining images
        current_width = acceptable_cropped_images[1].shape[1]

        for img in acceptable_cropped_images[2:]:
            if current_width + img.shape[1] > max_width:
                # If this image doesn't fit, start a new row
                y_offset = y_offset_next_row
                combined_image[y_offset:y_offset+img.shape[0], :img.shape[1]] = img
                current_width = img.shape[1]
                y_offset_next_row = y_offset + img.shape[0]
            else:
                # If this image fits, add it to the right
                max_height = y_offset_next_row - y_offset
                if img.shape[0] > max_height:
                    too_tall_images.append(img)
                else:
                    combined_image[y_offset:y_offset+img.shape[0], current_width:current_width+img.shape[1]] = img
                    current_width += img.shape[1]

        # Process the images that were too tall for their rows
        for img in too_tall_images:
            y_offset = y_offset_next_row
            combined_image[y_offset:y_offset+img.shape[0], :img.shape[1]] = img
            y_offset_next_row += img.shape[0]

        # Trim the combined_image to remove extra black space
        combined_image = combined_image[:y_offset_next_row]


        # save the combined image
        # if (detection_class in save_list) and save_per_class:
        dir_destination = os.path.join(Dirs.save_per_annotation_class, 'label')
        validate_dir(dir_destination)
        # combined_image_name = '__'.join([filename, detection_class]) + '.jpg'
        combined_image_name = '.'.join([filename,'jpg'])
        cv2.imwrite(os.path.join(dir_destination, combined_image_name), combined_image)

        original_image_name = '.'.join([filename,'jpg'])
        cv2.imwrite(os.path.join(Dirs.save_original, original_image_name), full_image)
        


def crop_component_from_yolo_coords(anno_type, Dirs, analysis, all_detections, full_image, filename, save_per_image, save_per_class, save_list):
    height = analysis['height']
    width = analysis['width']
    if len(all_detections) < 1:
        print('     MAKE THIS HAVE AN EMPTY PLACEHOLDER') # TODO ###################################################################################
    else:
        for detection in all_detections:
            detection_class = detection[0]
            detection_class = set_index_for_annotation(detection_class, anno_type)

            if (detection_class in save_list) or ('save_all' in save_list):

                location = yolo_to_position_ruler(detection, height, width)
                ruler_polygon = [(location[1], location[2]), (location[3], location[2]), (location[3], location[4]), (location[1], location[4])]

                x_coords = [x for x, y in ruler_polygon]
                y_coords = [y for x, y in ruler_polygon]

                min_x, min_y = min(x_coords), min(y_coords)
                max_x, max_y = max(x_coords), max(y_coords)

                detection_cropped = full_image[min_y:max_y, min_x:max_x]
                loc = '-'.join([str(min_x), str(min_y), str(max_x), str(max_y)])
                detection_cropped_name = '.'.join(['__'.join([filename, detection_class, loc]), 'jpg'])

                # save_per_image
                if (detection_class in save_list) and save_per_image:
                    dir_destination = os.path.join(Dirs.save_per_image, filename, detection_class)
                    # print(os.path.join(dir_destination,detection_cropped_name))
                    validate_dir(dir_destination)
                    cv2.imwrite(os.path.join(dir_destination,detection_cropped_name), detection_cropped)
                    
                # save_per_class
                if (detection_class in save_list) and save_per_class:
                    dir_destination = os.path.join(Dirs.save_per_annotation_class, detection_class)
                    # print(os.path.join(dir_destination,detection_cropped_name))
                    validate_dir(dir_destination)
                    cv2.imwrite(os.path.join(dir_destination,detection_cropped_name), detection_cropped)
            else:
                # print(f'detection_class: {detection_class} not in save_list: {save_list}')
                pass

def yolo_to_position_ruler(annotation, height, width):
    return ['ruler', 
        int((annotation[1] * width) - ((annotation[3] * width) / 2)), 
        int((annotation[2] * height) - ((annotation[4] * height) / 2)), 
        int(annotation[3] * width) + int((annotation[1] * width) - ((annotation[3] * width) / 2)), 
        int(annotation[4] * height) + int((annotation[2] * height) - ((annotation[4] * height) / 2))]


class bcolors:
    HEADER = '\033[95m'
    OKBLUE = '\033[94m'
    OKCYAN = '\033[96m'
    OKGREEN = '\033[92m'
    WARNING = '\033[93m'
    FAIL = '\033[91m'
    ENDC = '\033[0m'
    BOLD = '\033[1m'
    UNDERLINE = '\033[4m'
    CEND      = '\33[0m'
    CBOLD     = '\33[1m'
    CITALIC   = '\33[3m'
    CURL      = '\33[4m'
    CBLINK    = '\33[5m'
    CBLINK2   = '\33[6m'
    CSELECTED = '\33[7m'

    CBLACK  = '\33[30m'
    CRED    = '\33[31m'
    CGREEN  = '\33[32m'
    CYELLOW = '\33[33m'
    CBLUE   = '\33[34m'
    CVIOLET = '\33[35m'
    CBEIGE  = '\33[36m'
    CWHITE  = '\33[37m'

    CBLACKBG  = '\33[40m'
    CREDBG    = '\33[41m'
    CGREENBG  = '\33[42m'
    CYELLOWBG = '\33[43m'
    CBLUEBG   = '\33[44m'
    CVIOLETBG = '\33[45m'
    CBEIGEBG  = '\33[46m'
    CWHITEBG  = '\33[47m'

    CGREY    = '\33[90m'
    CRED2    = '\33[91m'
    CGREEN2  = '\33[92m'
    CYELLOW2 = '\33[93m'
    CBLUE2   = '\33[94m'
    CVIOLET2 = '\33[95m'
    CBEIGE2  = '\33[96m'
    CWHITE2  = '\33[97m'

    CGREYBG    = '\33[100m'
    CREDBG2    = '\33[101m'
    CGREENBG2  = '\33[102m'
    CYELLOWBG2 = '\33[103m'
    CBLUEBG2   = '\33[104m'
    CVIOLETBG2 = '\33[105m'
    CBEIGEBG2  = '\33[106m'
    CWHITEBG2  = '\33[107m'
    CBLUEBG3   = '\33[112m'


def set_index_for_annotation(cls,annoType):
    if annoType == 'PLANT':
        if cls == 0:
            annoInd = 'Leaf_WHOLE'
        elif cls == 1:
            annoInd = 'Leaf_PARTIAL'
        elif cls == 2:
            annoInd = 'Leaflet'
        elif cls == 3:
            annoInd = 'Seed_Fruit_ONE'
        elif cls == 4:
            annoInd = 'Seed_Fruit_MANY'
        elif cls == 5:
            annoInd = 'Flower_ONE'
        elif cls == 6:
            annoInd = 'Flower_MANY'
        elif cls == 7:
            annoInd = 'Bud'
        elif cls == 8:
            annoInd = 'Specimen'
        elif cls == 9:
            annoInd = 'Roots'
        elif cls == 10:
            annoInd = 'Wood'
    elif annoType == 'ARCHIVAL':
        if cls == 0:
            annoInd = 'Ruler'
        elif cls == 1:
            annoInd = 'Barcode'
        elif cls == 2:
            annoInd = 'Colorcard'
        elif cls == 3:
            annoInd = 'Label'
        elif cls == 4:
            annoInd = 'Map'
        elif cls == 5:
            annoInd = 'Envelope'
        elif cls == 6:
            annoInd = 'Photo'
        elif cls == 7:
            annoInd = 'Attached_item'
        elif cls == 8:
            annoInd = 'Weights'
    return annoInd.lower()
# def set_yaml(path_to_yaml, value):
#     with open('file_to_edit.yaml') as f:
#         doc = yaml.load(f)

#     doc['state'] = state

#     with open('file_to_edit.yaml', 'w') as f:
#         yaml.dump(doc, f)