Update DataClean and ImageTransfer.py

DataClean and ImageTransfer.py

@@ -1,17 +1,21 @@
-#### This code shows how I process the data and transfer the images to Numpy arrays on local. After processing, I upload the final csv to github and get the URL.
+#### 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 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.
+
 import csv
 import os
-import numpy as np
-from PIL import Image
+import cv2
 import pandas as pd
 
 # --- Initial Setup ---
-initial_csv_file_path = 'https://raw.githubusercontent.com/LeoZhangzaolin/photos/main/Graptolite%20specimens.csv'
-columns_to_delete = [
-    "species ID", "Phylum", "Class", "Order", "revised species name", 
-    "total number of specimens", "specimens Serial No", "显微镜照片数量", "SLR photo No",
-    "相机照片数量", "跑数据照片总数", "备注", "age  from", "age  to", "collection No", "Microscrope photo No"
-]
+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:
@@ -60,49 +64,35 @@ for row in data[1:]:
 # Convert processed data into a DataFrame
 df = pd.DataFrame(data[1:], columns=header)
 
-# --- Image Processing ---
-# Image directories
-image_dir_paths = ['/Users/leozhangzaolin/Desktop/project 1/graptolite specimens with scale 1', 
-                   '/Users/leozhangzaolin/Desktop/project 1/graptolite specimens with scale 2']
-
-# Normalize file extensions in the image directories
-def normalize_file_extensions(dir_path):
-    for filename in os.listdir(dir_path):
-        if filename.lower().endswith('.jpg') and not filename.endswith('.jpg'):
-            base, ext = os.path.splitext(filename)
-            new_filename = base + '.jpg'
-            os.rename(os.path.join(dir_path, filename), os.path.join(dir_path, new_filename))
-
-for path in image_dir_paths:
-    normalize_file_extensions(path)
-
-# Function to process and return the image array
-def process_image_array(image_name, max_size=(1024, 1024)):
+# 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']]  
+                   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
 
-    with Image.open(image_path) as img:
-        img.thumbnail(max_size, Image.Resampling.LANCZOS)
-        return np.array(img)
+    # Read and resize the image
+    img = cv2.imread(image_path, cv2.IMREAD_COLOR)
+    img = cv2.resize(img, max_size, interpolation=cv2.INTER_AREA)
 
-# Apply the function to embed image arrays in the 'image file name' column
-df['image file name'] = df['image file name'].apply(process_image_array)
-df = df.dropna(subset=['image file name'])
+    # Save the image to the output directory
+    output_path = os.path.join(output_image_dir, image_base_name + '.jpg')
+    cv2.imwrite(output_path, img)
 
-# Since arrays can't be directly saved in CSV, convert them to a string representation
-df['image file name'] = df['image file name'].apply(lambda x: np.array2string(x))
+    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 Final DataFrame to a CSV File ---
-final_csv_path = '/Users/leozhangzaolin/Desktop/Final_GS_with_Images.csv'
+# 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)
-