Update app.py

app.py

@@ -1,59 +1,70 @@
 import numpy as np
-from skimage.metrics import structural_similarity as ssim
 import gradio as gr
+from PIL import Image
+from skimage.metrics import structural_similarity as ssim
 import cv2
-
-# Function to remove text from image (placeholder implementation)
-def remove_text(image):
-    # Placeholder implementation
-    # Implement your own text removal algorithm here
-    processed_image = image.copy()
-    # Your text removal logic goes here
-    return processed_image
+import pytesseract
+from difflib import SequenceMatcher
 
 # Function to calculate SSIM between two images
-def calculate_similarity(img1, img2):
-    if len(img1.shape) == 2:
-        img1 = cv2.cvtColor(img1, cv2.COLOR_GRAY2RGB)
-    if len(img2.shape) == 2:
-        img2 = cv2.cvtColor(img2, cv2.COLOR_GRAY2RGB)
-    max_height = max(img1.shape[0], img2.shape[0])
-    max_width = max(img1.shape[1], img2.shape[1])
-    img1_resized = cv2.resize(img1, (max_width, max_height))
-    img2_resized = cv2.resize(img2, (max_width, max_height))
-    
-    # Calculate similarity score based on SSIM
-    ssim_score = ssim(img1_resized, img2_resized, win_size=3, multichannel=True)
-    
-    return ssim_score
-
-# Rest of the code remains the same
-def image_similarity(img1, img2):
-    img1 = img1.astype(np.uint8)
-    img2 = img2.astype(np.uint8)
-    
-    # Remove text from images
-    img1_processed = remove_text(img1)
-    img2_processed = remove_text(img2)
-    
-    # Calculate similarity score based on SSIM
-    ssim_score = calculate_similarity(img1, img2)
-    
-    # Calculate similarity score after text removal
-    additional_score = calculate_similarity(img1_processed, img2_processed)
-    
-    result = f"SSIM Score: {ssim_score:.4f}\nAdditional Score (after text removal): {additional_score:.4f}"
+def calculate_ssim(img1, img2):
+    img1_gray = cv2.cvtColor(img1, cv2.COLOR_RGB2GRAY)
+    img2_gray = cv2.cvtColor(img2, cv2.COLOR_RGB2GRAY)
+    return ssim(img1_gray, img2_gray)
+
+# Function to compare trademarks based on text similarity
+def compare_text(trademark1, trademark2):
+    text1 = pytesseract.image_to_string(trademark1).lower()
+    text2 = pytesseract.image_to_string(trademark2).lower()
+    similarity_ratio = SequenceMatcher(None, text1, text2).ratio()
+    return similarity_ratio
+
+# Function to compare trademarks based on color similarity
+def compare_colors(trademark1, trademark2):
+    trademark1 = trademark1.convert("RGB")
+    trademark2 = trademark2.convert("RGB")
+    colors1 = trademark1.getcolors(trademark1.size[0] * trademark1.size[1])
+    colors2 = trademark2.getcolors(trademark2.size[0] * trademark2.size[1])
+    color_vector1 = np.zeros(3)
+    color_vector2 = np.zeros(3)
+    for count, color in colors1:
+        color_vector1 += np.array(color) * count
+    for count, color in colors2:
+        color_vector2 += np.array(color) * count
+    color_vector1 /= trademark1.size[0] * trademark1.size[1]
+    color_vector2 /= trademark2.size[0] * trademark2.size[1]
+    color_similarity = 1 - np.linalg.norm(color_vector1 - color_vector2)
+    return color_similarity
+
+# Function to compare trademarks based on multiple aspects
+def compare_trademarks(trademark1, trademark2):
+    ssim_score = calculate_ssim(np.array(trademark1), np.array(trademark2))
+    text_similarity = compare_text(trademark1, trademark2)
+    color_similarity = compare_colors(trademark1, trademark2)
+
+    # Adjust weights based on the importance of each aspect
+    ssim_weight = 0.6
+    text_weight = 0.2
+    color_weight = 0.2
+
+    overall_similarity = (ssim_weight * ssim_score) + (text_weight * text_similarity) + (color_weight * color_similarity)
+    return overall_similarity
+
+# Function to perform trademark conflict prevention
+def prevent_trademark_conflict(trademark1, trademark2):
+    similarity_score = compare_trademarks(trademark1, trademark2)
+    result = f"Trademark Similarity Score: {similarity_score:.4f}"
     return result
 
 iface = gr.Interface(
-    fn=image_similarity,
+    fn=prevent_trademark_conflict,
     inputs=[
-        gr.inputs.Image(type="numpy", label="Image 1"),
-        gr.inputs.Image(type="numpy", label="Image 2")
+        gr.inputs.Image(type="pil", label="Trademark Image 1"),
+        gr.inputs.Image(type="pil", label="Trademark Image 2")
     ],
     outputs="text",
-    title="Image Similarity Calculator",
-    description="Upload two images to compute their similarity."
+    title="Trademark Conflict Prevention",
+    description="Upload two trademark images to prevent conflict."
 )
 
-iface.launch()
+iface.launch(share=True)