#### Firstly, I read specimen data from a CSV file, merges and reformats certain columns, and then converts this data into a pandas DataFrame.
#### Then, I filter and process associated images by resizing them and saving them in a specified output directory. 
#### Next, I update the DataFrame with the paths to the processed images and save this enhanced dataset as a new CSV file.
#### Finally, I upload photos to github and replace the local paths with public URL.
#### Note: all these were done on local. And I upload the processed csv to github and get the URL 

import csv
import os
import cv2
import pandas as pd

# --- Initial Setup ---
initial_csv_file_path = '/Users/leozhangzaolin/Desktop/Graptolite specimens.csv'
image_dir_paths = ['/Users/leozhangzaolin/Desktop/project 1/graptolite specimens with scale 1', 
                   '/Users/leozhangzaolin/Desktop/project 1/graptolite specimens with scale 2']
output_image_dir = '/Users/leozhangzaolin/Desktop/project 1/output_images'
target_size = (256, 256)

# Ensure output directory exists
os.makedirs(output_image_dir, exist_ok=True)

# --- Read and Process CSV Data ---
with open(initial_csv_file_path, newline='', encoding='utf-8') as file:
    reader = csv.reader(file)
    data = list(reader)

header = data[0]

# Find indices for columns to merge
family_index = header.index('Family') if 'Family' in header else None
subfamily_index = header.index('Subfamily') if 'Subfamily' in header else None
locality_index = header.index('Locality') if 'Locality' in header else None
longitude_index = header.index('Longitude') if 'Longitude' in header else None
latitude_index = header.index('Latitude') if 'Latitude' in header else None
horizon_index = header.index('Horizon') if 'Horizon' in header else None

# Process rows: merge and delete columns
for row in data[1:]:
    # Merge columns
    if family_index is not None and subfamily_index is not None:
        family = row[family_index]
        subfamily = row[subfamily_index] if row[subfamily_index] else 'no subfamily'
        row[family_index] = f"{family} ({subfamily})"

    if locality_index is not None and all([longitude_index, latitude_index, horizon_index]):
        locality = row[locality_index]
        longitude = row[longitude_index]
        latitude = row[latitude_index]
        horizon = row[horizon_index]
        row[locality_index] = f"{locality} ({longitude}, {latitude}, {horizon})"

# Update header and remove unneeded columns
header[family_index] = 'Family (Subfamily)'
header[locality_index] = 'Locality (Longitude, Latitude, Horizon)'
indices_to_delete = [header.index(column) for column in columns_to_delete if column in header]
merged_indices = [subfamily_index, longitude_index, latitude_index, horizon_index]
indices_to_delete.extend(merged_indices)
indices_to_delete = list(set(indices_to_delete))
indices_to_delete.sort(reverse=True)
header = [col for i, col in enumerate(header) if i not in indices_to_delete]

for row in data[1:]:
    for index in indices_to_delete:
        del row[index]

# Convert processed data into a DataFrame
df = pd.DataFrame(data[1:], columns=header)

# Function to process and save the image, then return the file path
def process_and_save_image(image_name, max_size=target_size):
    image_base_name = os.path.splitext(image_name)[0]
    image_paths = [os.path.join(dir_path, image_base_name + suffix)
                   for dir_path in image_dir_paths
                   for suffix in ['_S.jpg', '_S.JPG']]

    image_path = next((path for path in image_paths if os.path.exists(path)), None)

    if image_path is None:
        return None

    # Read and resize the image
    img = cv2.imread(image_path, cv2.IMREAD_COLOR)
    img = cv2.resize(img, max_size, interpolation=cv2.INTER_AREA)

    # Save the image to the output directory
    output_path = os.path.join(output_image_dir, image_base_name + '.jpg')
    cv2.imwrite(output_path, img)

    return output_path

# Apply the function to process images and update the DataFrame
df['image file name'] = df['image file name'].apply(process_and_save_image)
df = df.dropna(subset=['image file name'])

# Rename the 'image file name' column to 'image'
df.rename(columns={'image file name': 'image'}, inplace=True)

# Save the DataFrame to a CSV file
final_csv_path = '/Users/leozhangzaolin/Desktop/Final_GS_with_Images5.csv'
df.to_csv(final_csv_path, index=False)

# take url path to each specimens
def update_csv_with_github_links(csv_file_path, github_repo_url, branch_name):
    updated_rows = []

    with open(csv_file_path, mode='r') as file:
        reader = csv.DictReader(file)
        for row in reader:
            image_name = row['image'].split('/')[-1] 
            row['image'] = f"{github_repo_url}/{branch_name}/{image_name}" 
            updated_rows.append(row)

    # Write updated data back to CSV
    with open(csv_file_path, mode='w', newline='') as file:
        writer = csv.DictWriter(file, fieldnames=reader.fieldnames)
        writer.writeheader()
        writer.writerows(updated_rows)

csv_file = '/Users/leozhangzaolin/Desktop/Final_GS_with_Images5.csv'
github_repo_url = 'https://raw.githubusercontent.com/LeoZhangzaolin/photos' 
branch_name = 'main' 
update_csv_with_github_links(csv_file, github_repo_url, branch_name)