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from sentence_transformers import SentenceTransformer, util
class ImageSimilarity(object):
def __init__(self, minimum_commutative_image_diff):
self.minimum_commutative_image_diff = minimum_commutative_image_diff
def check(self, pil_images):
results = []
# Load the OpenAI CLIP Model
print('Loading CLIP Model...')
model = SentenceTransformer('clip-ViT-B-32')
print("Images:", len(pil_images))
encoded_image = model.encode([image["pil"] for image in pil_images], batch_size=128, convert_to_tensor=True, show_progress_bar=True)
# Now we run the clustering algorithm. This function compares images aganist
# all other images and returns a list with the pairs that have the highest
# cosine similarity score
processed_images = util.paraphrase_mining_embeddings(encoded_image)
# NUM_SIMILAR_IMAGES = 10
# =================
# DUPLICATES
# =================
# print('Finding duplicate images...')
# Filter list for duplicates. Results are triplets (score, image_id1, image_id2) and is scorted in decreasing order
# A duplicate image will have a score of 1.00
# It may be 0.9999 due to lossy image compression (.jpg)
# duplicates = [image for image in processed_images if image[0] >= 0.999]
# Output the top X duplicate images
# for score, image_id1, image_id2 in duplicates[0:NUM_SIMILAR_IMAGES]:
# print("\nScore: {:.3f}%".format(score * 100))
# print(pil_images[image_id1])
# print(pil_images[image_id2])
# =================
# NEAR DUPLICATES
# =================
print('Finding near duplicate images...')
# Use a threshold parameter to identify two images as similar. By setting the threshold lower,
# you will get larger clusters which have less similar images in it. Threshold 0 - 1.00
# A threshold of 1.00 means the two images are exactly the same. Since we are finding near
# duplicate images, we can set it at 0.99 or any number 0 < X < 1.00.
# threshold = 0.99
# near_duplicates = [image for image in processed_images if image[0] < threshold]
for score, image_id1, image_id2 in processed_images:
results.append({
'score': score,
'image1': pil_images[image_id1]["key"],
'image2': pil_images[image_id2]["key"]
})
# print("\nScore: {:.3f}%".format(score * 100))
# print(pil_images[image_id1]["key"])
# print(pil_images[image_id2]["key"])
return results |