Datasets:

LeoZhangzaolin
/

Graptoloidea-Specimens-Imaging

	#### 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)