DataCleaning and ImageTransfering.py

Project _1.py

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+#### delete unuseful columns 
+import csv
+
+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)
+
+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"
+]
+
+header = data[0]
+indices_to_delete = [header.index(column) for column in columns_to_delete if column in header]
+
+indices_to_delete.sort(reverse=True)
+
+for row in data:
+    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)
+
+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_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):
+    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)
+
+# 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)):
+    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
+
+    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
+
+# 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
+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 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)