Update DataClean and ImageTransfer.py

DataClean and ImageTransfer.py

@@ -1,87 +1,69 @@
-#### delete unuseful columns 
+#### 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.
 import csv
+import os
+import numpy as np
+from PIL import Image
+import pandas as pd
 
-file_path = '/Users/leozhangzaolin/Downloads/Graptolite specimens.csv'
-
-with open(file_path, newline='', encoding='utf-8') as file:
-    reader = csv.reader(file)
-    data = list(reader)
-
+# --- Initial Setup ---
+initial_csv_file_path = 'https://github.com/LeoZhangzaolin/photos/blob/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"
 ]
 
+# --- 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]
-indices_to_delete = [header.index(column) for column in columns_to_delete if column in header]
 
+# 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:
+for row in data[1:]:
     for index in indices_to_delete:
         del row[index]
 
-new_file_path = '/Users/leozhangzaolin/desktop/New_GS.csv'  
-
-with open(new_file_path, mode='w', newline='', encoding='utf-8') as file:
-    writer = csv.writer(file)
-    writer.writerows(data)
-
-
-#### merge columns
-
-file_path1 = '/Users/leozhangzaolin/desktop/New_GS.csv'
-
-with open(file_path1, newline='', encoding='utf-8') as file1:
-    reader1 = csv.reader(file1)
-    data1 = list(reader1)
+# Convert processed data into a DataFrame
+df = pd.DataFrame(data[1:], columns=header)
 
-header1 = data1[0]
-family_index1 = header1.index('Family') if 'Family' in header1 else None
-subfamily_index1 = header1.index('Subfamily') if 'Subfamily' in header1 else None
-locality_index1 = header1.index('Locality') if 'Locality' in header1 else None
-longitude_index1 = header1.index('Longitude') if 'Longitude' in header1 else None
-latitude_index1 = header1.index('Latitude') if 'Latitude' in header1 else None
-horizon_index1 = header1.index('Horizon') if 'Horizon' in header1 else None
-
-for row1 in data1[1:]:  
-    family1 = row1[family_index1] if family_index1 is not None else ''
-    subfamily1 = row1[subfamily_index1] if subfamily_index1 is not None else 'no subfamily'
-    row1[family_index1] = f"{family1} ({subfamily1})" if subfamily_index1 is not None else family1
-
-    locality1 = row1[locality_index1] if locality_index1 is not None else ''
-    longitude1 = row1[longitude_index1] if longitude_index1 is not None else ''
-    latitude1 = row1[latitude_index1] if latitude_index1 is not None else ''
-    horizon1 = row1[horizon_index1] if horizon_index1 is not None else ''
-    row1[locality_index1] = f"{locality1} ({longitude1}, {latitude1}, {horizon1})"
-
-header1[family_index1] = 'Family (Subfamily)'
-header1[locality_index1] = 'Locality (Longitude, Latitude, Horizon)'
-
-indices_to_delete1 = sorted([subfamily_index1, longitude_index1, latitude_index1, horizon_index1], reverse=True)
-for index1 in indices_to_delete1:
-    if index1 is not None:
-        for row1 in data1:
-            del row1[index1]
-
-new_file_path1 = '/Users/leozhangzaolin/desktop/New_GS_1.csv'
-
-with open(new_file_path1, mode='w', newline='', encoding='utf-8') as file1:
-    writer1 = csv.writer(file1)
-    writer1.writerows(data1)
-
-#### read images
-import os
-import numpy as np
-from PIL import Image
-import pandas as pd
-
-# Paths
-csv_file_path = '/Users/leozhangzaolin/desktop/New_GS_1.csv'
+# --- 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']
-npy_save_dir = '/Users/leozhangzaolin/Desktop/arrays'
 
 # Normalize file extensions in the image directories
 def normalize_file_extensions(dir_path):
@@ -89,17 +71,13 @@ def normalize_file_extensions(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))
+            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)
 
-# Load the CSV file
-df = pd.read_csv(csv_file_path)
-
-# Function to process and resize the image
-def process_image(image_name, save_dir, max_size=(1024, 1024)):
+# Function to process and return the image array
+def process_image_array(image_name, max_size=(1024, 1024)):
     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
@@ -111,26 +89,20 @@ def process_image(image_name, save_dir, max_size=(1024, 1024)):
         return None
 
     with Image.open(image_path) as img:
-        # Resize the image using LANCZOS filter (replacement for ANTIALIAS)
         img.thumbnail(max_size, Image.Resampling.LANCZOS)
-        image_array = np.array(img)
-        npy_filename = image_base_name + '_S.npy'  
-        npy_path = os.path.join(save_dir, npy_filename)
-        np.save(npy_path, image_array)
-        return npy_path
+        return np.array(img)
 
-# Create a directory to save the numpy arrays
-os.makedirs(npy_save_dir, exist_ok=True)
-
-# Process each image and update the 'image file name' column
-df['image file name'] = df['image file name'].apply(lambda x: process_image(x, npy_save_dir))
-
-# Remove rows where the image was not found
+# 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'])
 
+# 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))
+
 # Rename the 'image file name' column to 'image'
 df.rename(columns={'image file name': 'image'}, inplace=True)
 
-# Save the updated DataFrame to a new CSV file
-updated_csv_path = '/Users/leozhangzaolin/Desktop/New_GS_2_updated_images.csv'
-df.to_csv(updated_csv_path, index=False)
+# --- Save the Final DataFrame to a CSV File ---
+final_csv_path = '/Users/leozhangzaolin/Desktop/Final_GS_with_Images.csv'
+df.to_csv(final_csv_path, index=False)
+