from PIL import Image import imagehash import os import numpy as np import csv def writeCSV(dirname, i1, i2): with open("duplicates.csv", "a") as file: writer = csv.writer(file, delimiter=',') writer.writerow([dirname+i1, dirname+i2]) class DuplicateRemover: extensions = ['png', 'jpg', 'jpeg', 'gif', 'webp'] def __init__(self,dirname,hash_size = 8): self.dirname = dirname self.hash_size = hash_size def find_duplicates(self, verbose=True, exportCSV=True): """ Find and Delete Duplicates """ fnames = os.listdir(self.dirname) hashes = {} duplicates = [] if verbose: print("Finding Duplicates Now!\n") for image in fnames: if list(image.lower().split('.'))[-1] in self.extensions: try: with Image.open(os.path.join(self.dirname,image)) as img: temp_hash = imagehash.average_hash(img, self.hash_size) if temp_hash in hashes: if verbose: print("Duplicate {} \nfound for Image {}!\n".format(image,hashes[temp_hash])) if exportCSV: writeCSV(self.dirname, image, hashes[temp_hash]) duplicates.append(image) else: hashes[temp_hash] = image except Exception as error: print("Error: The following error occured:\n",image,"\n",error,"\n") if len(duplicates) != 0: if verbose: a = input("Do you want to delete these {} Images? Press Y or N: ".format(len(duplicates))) else: a = 'y' space_saved = 0 if(a.strip().lower() == "y"): for duplicate in duplicates: space_saved += os.path.getsize(os.path.join(self.dirname,duplicate)) os.remove(os.path.join(self.dirname,duplicate)) if verbose: print("{} Deleted Succesfully!".format(duplicate)) if verbose: print("\n\nYou saved {} mb of Space!".format(round(space_saved/1000000),2)) else: if verbose: print("Thank you for Using Duplicate Remover") else: if verbose: print("No Duplicates Found :(") def find_similar(self,location,similarity=80,verbose=True): fnames = os.listdir(self.dirname) threshold = 1 - similarity/100 diff_limit = int(threshold*(self.hash_size**2)) with Image.open(location) as img: hash1 = imagehash.average_hash(img, self.hash_size).hash if verbose: print("Finding Similar Images to {} Now!\n".format(location)) for image in fnames: if list(image.lower().split('.'))[-1] in self.extensions: with Image.open(os.path.join(self.dirname,image)) as img: hash2 = imagehash.average_hash(img, self.hash_size).hash if np.count_nonzero(hash1 != hash2) <= diff_limit: if verbose: print("{} image found {}% similar to {}".format(image,similarity,location))