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import gradio as gr
import cv2
import numpy as np
from skimage.metrics import structural_similarity as ssim
from transformers import BertForSequenceClassification, BertTokenizer
import torch
from PIL import Image
# Load pre-trained model and tokenizer
model = BertForSequenceClassification.from_pretrained("bert-base-uncased")
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
def calculate_ssim(img1, img2):
img1_resized = cv2.resize(img1, (img2.shape[1], img2.shape[0]))
img2_resized = cv2.resize(img2, (img1.shape[1], img1.shape[0]))
img1_gray = cv2.cvtColor(img1_resized, cv2.COLOR_BGR2GRAY)
img2_gray = cv2.cvtColor(img2_resized, cv2.COLOR_BGR2GRAY)
return ssim(img1_gray, img2_gray)
def calculate_text_similarity(text1, text2):
encoded_text1 = tokenizer(text1, truncation=True, padding=True, return_tensors="pt")
encoded_text2 = tokenizer(text2, truncation=True, padding=True, return_tensors="pt")
with torch.no_grad():
outputs_text1 = model(**encoded_text1)
outputs_text2 = model(**encoded_text2)
embeddings_text1 = outputs_text1.last_hidden_state.squeeze(0)
embeddings_text2 = outputs_text2.last_hidden_state.squeeze(0)
text_similarity = ssim(embeddings_text1.numpy(), embeddings_text2.numpy())
return text_similarity
def calculate_color_similarity(img1, img2):
img1_hsv = cv2.cvtColor(img1, cv2.COLOR_BGR2HSV)
img2_hsv = cv2.cvtColor(img2, cv2.COLOR_BGR2HSV)
hist1 = cv2.calcHist([img1_hsv], [0, 1], None, [180, 256], [0, 180, 0, 256])
hist2 = cv2.calcHist([img2_hsv], [0, 1], None, [180, 256], [0, 180, 0, 256])
color_similarity = cv2.compareHist(hist1, hist2, cv2.HISTCMP_CORREL)
return color_similarity
def compare_trademarks(trademark1, trademark2):
img1 = np.array(trademark1)
img2 = np.array(trademark2)
if img1.shape != img2.shape:
img1_resized = cv2.resize(img1, (img2.shape[1], img2.shape[0]))
img2_resized = cv2.resize(img2, (img1.shape[1], img1.shape[0]))
img1 = img1_resized
img2 = img2_resized
ssim_score = calculate_ssim(img1, img2)
text1 = "Trademark text 1"
text2 = "Trademark text 2"
text_similarity = calculate_text_similarity(text1, text2)
color_similarity = calculate_color_similarity(img1, img2)
return ssim_score, text_similarity, color_similarity
def prevent_trademark_conflict(trademark1, trademark2):
similarity_scores = compare_trademarks(trademark1, trademark2)
return similarity_scores
# Interface
trademark_comparison_interface = gr.Interface(
fn=prevent_trademark_conflict,
inputs=[
gr.inputs.Image(type="pil", label="Trademark Image 1"),
gr.inputs.Image(type
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