Initial commit

compare.py

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+import os
+import shutil
+
+import cv2
+import numpy as np
+from skimage.metrics import structural_similarity as ssim
+
+def load_images(folder):
+    """Load all PNG images from a folder, categorized by prefix."""
+    true_images = {}
+    false_images = {}
+    for filename in os.listdir(folder):
+        if filename.endswith('.png'):
+            filepath = os.path.join(folder, filename)
+            image = cv2.imread(filepath, cv2.IMREAD_UNCHANGED)  # Load image with alpha channel
+            if filename.startswith("True_"):
+                true_images[filename] = image
+            elif filename.startswith("False_"):
+                false_images[filename] = image
+    return true_images, false_images
+
+def resize_image(image, size=(125, 50)):
+    """Resize an image to a standard size."""
+    return cv2.resize(image, size, interpolation=cv2.INTER_AREA)
+
+def calculate_ssim(image1, image2):
+    """Calculate SSIM between two images."""
+    if image1.shape[2] == 4:  # Handle alpha channel
+        image1 = cv2.cvtColor(image1, cv2.COLOR_BGRA2BGR)
+    if image2.shape[2] == 4:  # Handle alpha channel
+        image2 = cv2.cvtColor(image2, cv2.COLOR_BGRA2BGR)
+    gray1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)
+    gray2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
+    score, _ = ssim(gray1, gray2, full=True)
+    return score
+
+def calculate_color_similarity(image1, image2):
+    """Compare color histograms of two images."""
+    if image1.shape[2] == 4:  # Handle alpha channel
+        image1 = cv2.cvtColor(image1, cv2.COLOR_BGRA2BGR)
+    if image2.shape[2] == 4:  # Handle alpha channel
+        image2 = cv2.cvtColor(image2, cv2.COLOR_BGRA2BGR)
+    hist1 = cv2.calcHist([image1], [0, 1, 2], None, [8, 8, 8], [0, 256, 0, 256, 0, 256])
+    hist2 = cv2.calcHist([image2], [0, 1, 2], None, [8, 8, 8], [0, 256, 0, 256, 0, 256])
+    hist1 = cv2.normalize(hist1, hist1).flatten()
+    hist2 = cv2.normalize(hist2, hist2).flatten()
+    return cv2.compareHist(hist1, hist2, cv2.HISTCMP_CORREL)
+
+def calculate_combined_similarity(image1, image2):
+    """Combine SSIM and color histogram similarity."""
+    # ssim_score = calculate_ssim(image1, image2)
+    color_score = calculate_color_similarity(image1, image2)
+    # Weight SSIM and color similarity equally (adjust weights as needed)
+    return 1 * color_score
+
+def find_most_similar_images(true_images, false_images):
+    """Find the most similar image for each 'True_' image among 'False_' images."""
+    results = {}
+    for true_name, true_image in true_images.items():
+        max_similarity = -1
+        most_similar_image = None
+        for false_name, false_image in false_images.items():
+            # resized_true = resize_image(true_image)
+            # resized_false = resize_image(false_image)
+
+            if true_name.split("_")[2] == false_name.split("_")[2]:
+                continue
+
+            similarity = calculate_combined_similarity(true_image, false_image)
+            if similarity > max_similarity:
+                max_similarity = similarity
+                most_similar_image = false_name
+        results[true_name] = (most_similar_image, max_similarity)
+        os.makedirs("output14\\"+true_name+"_"+most_similar_image,exist_ok=True)
+        shutil.copyfile('output13\\'+true_name, 'output14\\'+true_name+"_"+most_similar_image+"\\"+true_name)
+        shutil.copyfile('output13\\' + most_similar_image, 'output14\\' +true_name+"_"+most_similar_image+"\\" + most_similar_image)
+
+        # shutil.copyfile('output12\\'+ "segment_"+true_name.split("_")[2]+".png", 'output14\\'+true_name+"_"+most_similar_image+"\\segment_"+true_name.split("_")[2]+".png")
+        # shutil.copyfile('output12\\' +"segment_"+most_similar_image.split("_")[2]+".png", 'output14\\'+true_name+"_"+most_similar_image+"\\segment_"+most_similar_image.split("_")[2]+".png")
+
+        shutil.copyfile('output10\\'+ "segment_"+true_name.split("_")[2]+".png", 'output14\\'+true_name+"_"+most_similar_image+"\\segment_"+true_name.split("_")[2]+".png")
+        shutil.copyfile('output10\\' +"segment_"+most_similar_image.split("_")[2]+".png", 'output14\\'+true_name+"_"+most_similar_image+"\\segment_"+most_similar_image.split("_")[2]+".png")
+
+
+    return results
+
+def main():
+    folder = 'output13'  # Replace with your folder path
+    true_images, false_images = load_images(folder)
+    results = find_most_similar_images(true_images, false_images)
+    for true_image, (most_similar, similarity) in results.items():
+        print(f"True Image: {true_image}, Most Similar False Image: {most_similar}, Similarity: {similarity:.4f}")
+
+
+if __name__ == '__main__':
+    main()