imagehash

from PIL import Image import imagehash import os # Create a dummy image for demonstration if not available dummy_image_path = 'dummy_image.png' if not os.path.exists(dummy_image_path): try: from PIL import ImageDraw img = Image.new('RGB', (200, 200), color = 'red') d = ImageDraw.Draw(img) d.text((10,10), "Hello", fill=(0,0,0)) img.save(dummy_image_path) print(f"Created dummy image: {dummy_image_path}") except ImportError: print("Pillow is needed to create a dummy image. Please install it.") exit() try: # Load an image image = Image.open(dummy_image_path) # Generate a perceptual hash (e.g., average hash) hash_value = imagehash.average_hash(image) print(f"Hash for '{dummy_image_path}': {hash_value}") # You can also generate other types of hashes: # phash_value = imagehash.phash(image) # dhash_value = imagehash.dhash(image) # whash_value = imagehash.whash(image) # colorhash_value = imagehash.colorhash(image) # To compare with another image: # For demonstration, let's pretend to load a slightly different image # In a real scenario, this would be another actual image file slightly_different_image = Image.new('RGB', (200, 200), color = 'red') d = ImageDraw.Draw(slightly_different_image) d.text((15,15), "Hello", fill=(0,0,0)) # slight shift other_hash_value = imagehash.average_hash(slightly_different_image) print(f"Hash for a slightly different image: {other_hash_value}") # Calculate the Hamming distance (difference) between hashes difference = hash_value - other_hash_value print(f"Difference between hashes: {difference}") # A smaller difference indicates greater similarity if difference < 5: print("The images are considered similar (difference < 5).") else: print("The images are considered different (difference >= 5).") except FileNotFoundError: print(f"Error: Image file not found at {dummy_image_path}") except Exception as e: print(f"An error occurred: {e}")