Added folder_stats.py

README.md

@@ -72,4 +72,17 @@ Script Logic: For every site in oneDrive CoastSnap directory, iterate through th
 * Starts tagging from the most recent image and stops for the site when an image has already been tagged. This way, the user can manually remove bad registered/tagged images, and they will not be automatically replaced.
 * Retrieves tide data for the site from the .mat file specified in Database/CoastSnapDB.xlsx
 
+### Statistics
 
+Run `generate_statistics_csv.bat`
+
+Generates `statistics.csv` which contains information about the Images directory. Columns include:
+
+site  | # processed | # photoshop | # registered | stability  | Most recent image deleted
+
+* stability = # registered / # processed. This formula is based on the assumption that someone will manually remove poorly registered images in `Images/Registered`. Thus stability represents the percentage of images that had good registration.
+
+## Future Improvements
+
+### Image metadata
+Currently (22/6/22) it would appear that images downloaded from Spotteron do not retain the images' metadata. This is based on looking in windows file explorer image->properties, as well as using the exif python package. Note: The metadata presented in file explorer is IPTC data. There is a python package to interact with this data, but I had issues with it.

coastsnap/folder_stats.py

@@ -0,0 +1,104 @@
+import os
+import pandas as pd
+from pathlib import Path
+from time import strptime
+
+code_dir = str(Path(os.getcwd()).parent)
+sites_csv_path = os.path.join(code_dir, "coastsnap_sites.csv")
+coastsnap_sites_csv = pd.read_csv(sites_csv_path)
+images_parent_dir = coastsnap_sites_csv.parent_directory[0] 
+images_dir = os.path.join(images_parent_dir, "Images")
+
+stats_csv = pd.DataFrame(columns = ['site','# processed', '# photoshop', '# registered', 'stability', 'most recently deleted'] )
+
+
+for site in os.listdir(images_dir): # Loop through SITES
+    i=0
+    to_append = [site, 0, 0, 0, 0, 0]
+    processed = False
+    photoshop = False
+    registered = False
+    latest_image_found = False
+    site_path = os.path.join(images_dir, site)
+    processed_path = os.path.join(site_path,'Processed')
+    photoshop_path = os.path.join(site_path,'Photoshop')
+    registered_path = os.path.join(site_path, 'Registered')
+    try: # Check if site contains 'Processed' directory
+        processed_years_list = os.listdir(processed_path)
+        processed_years_list = [x for x in processed_years_list if len(x) == 4] # remove files that aren't years
+        processed_years_list.reverse()
+        processed = True
+    except: 
+        continue
+    
+    try: # Check if site contains 'Processed' directory
+        photoshop_years_list = os.listdir(photoshop_path)
+        photoshop_years_list = [x for x in photoshop_years_list if len(x) == 4] # remove files that aren't years
+        photoshop_years_list.reverse()
+        photoshop = True
+    except: 
+        continue
+
+    try: # Check if site contains 'Processed' directory
+        registered_years_list = os.listdir(registered_path)
+        registered_years_list = [x for x in registered_years_list if len(x) == 4] # remove files that aren't years
+        registered_years_list.reverse()
+        registered = True
+    except: 
+        continue
+
+    if processed:
+        i=0
+        for year in processed_years_list: # Loop through YEARS
+            processed_year_path = os.path.join(processed_path, year)
+            processed_image_list = os.listdir(processed_year_path)
+            processed_image_list.reverse()
+            for image_filename in processed_image_list: # Loop through IMAGES
+                i += 1
+        to_append[1] = i
+    
+    if photoshop:
+        i=0
+        for year in photoshop_years_list: # Loop through YEARS
+            year_path = os.path.join(photoshop_path, year)
+            image_list = os.listdir(year_path)
+            image_list.reverse()
+            for image_filename in image_list: # Loop through IMAGES
+            
+                year_path = year_path.replace('Photoshop', 'Registered')
+                registered_image_path = year_path + '/' + image_filename[:-4] + '_registered.jpg'
+                if image_filename.endswith('.jpg') and not os.path.isfile(registered_image_path) and not latest_image_found:
+                    latest_image_found = True
+                    filename_list = image_filename.split(".")
+                    date = filename_list[3].split("_")
+                    image_date = date[0] + '-' + '{:02d}'.format(strptime(filename_list[2],'%b').tm_mon) +'-'+ filename_list[5]
+                    
+                    to_append[5] = image_date
+                    print(site)
+                    print(image_filename)
+                i += 1
+        to_append[2] = i
+    
+    if registered:
+        i=0
+        for year in registered_years_list: # Loop through YEARS
+            registered_year_path = os.path.join(registered_path, year)
+            registered_image_list = os.listdir(registered_year_path)
+            registered_image_list.reverse()
+            for image_filename in registered_image_list: # Loop through IMAGES
+                i += 1
+        to_append[3] = i
+    
+    stats_csv_length = len(stats_csv)
+    stats_csv.loc[stats_csv_length] = to_append
+    
+
+# Add site stability data (# registered / # processed)
+for i, row in stats_csv.iterrows():    
+    stability = "{0:.0%}".format(stats_csv.at[i, '# registered'] / stats_csv.at[i, '# processed'])
+    stats_csv.at[i,'stability'] = stability
+    
+stats_csv.set_index('site', inplace = True)
+
+output_file_path = os.path.join(code_dir, 'statistics.csv')
+stats_csv.to_csv(output_file_path)

coastsnap/generate_statistics_csv.bat

@@ -0,0 +1,3 @@
+call activate coastsnap 
+python "%~dp0folder_stats.py"
+call conda deactivate

coastsnap/spotteron_batch_download.py

@@ -6,7 +6,7 @@ import datetime
 import re
 from pathlib import Path
 from urllib.parse import urljoin
-from os import path, makedirs
+from os import path, makedirs, getcwd
 
 import attr
 import pytz
@@ -17,10 +17,9 @@ from loguru import logger
 from timezonefinder import TimezoneFinder
 from werkzeug.utils import secure_filename
 
-#app = typer.Typer()
-coastsnap_sites = pd.read_csv("C:/Users/z5079346/OneDrive - UNSW/Projects/Coastsnap_test/CoastSnap_Sites.csv")
-
-
+code_images_dir = str(Path(getcwd()).parent)
+coastsnap_sites_path = path.join(code_images_dir, "coastsnap_sites.csv")
+coastsnap_sites = pd.read_csv(coastsnap_sites_path)
 
 @attr.s()
 class SpotteronImage:

statistics.csv

@@ -0,0 +1,35 @@
+site,# processed,# photoshop,# registered,stability,most recently deleted
+alex,75,74,73,97%,25-08-2021
+birubi,65,64,0,0%,12-06-2022
+blacksmiths,1581,1577,1381,87%,30-04-2022
+broulee,190,191,93,49%,14-06-2022
+buddina,117,114,85,73%,15-06-2022
+burleigh,245,242,0,0%,17-06-2022
+byron,1255,1329,676,54%,13-06-2022
+cathieillaroo,97,103,66,68%,16-06-2022
+cathielagoon,74,75,73,99%,10-04-2022
+coolum,80,36,22,28%,13-06-2022
+cooya,55,46,0,0%,15-11-2021
+cowbay,34,34,0,0%,16-06-2022
+era,56,56,45,80%,14-04-2022
+fourmile,244,247,115,47%,01-06-2022
+frankston,191,189,157,82%,16-06-2022
+garie,48,48,0,0%,13-06-2022
+hungry,128,128,92,72%,20-05-2022
+macsnth,73,72,0,0%,13-06-2022
+macssth,60,59,0,0%,13-06-2022
+manly,1167,1262,1122,96%,14-06-2022
+moffat,114,131,215,189%,09-03-2022
+newell,23,34,0,0%,27-04-2022
+nthnarra,2315,2444,1097,47%,16-06-2022
+queenscliff,85,79,0,0%,14-06-2022
+rainbow,50,50,0,0%,24-04-2022
+seaford,69,57,0,0%,11-06-2022
+shortpoint,222,222,0,0%,06-06-2022
+stockton1,262,261,33,13%,24-05-2022
+stockton2,214,213,62,29%,11-05-2022
+stockton3,257,256,69,27%,15-05-2022
+tomakin,211,211,142,67%,13-06-2022
+tugun,288,289,132,46%,01-06-2022
+wamberal,372,366,0,0%,15-06-2022
+wonga,46,46,0,0%,30-05-2022