Upload resnet50_classification.py

resnet50_classification.py

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+import numpy as np
+from tensorflow.keras.applications import ResNet50
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.resnet50 import preprocess_input
+from sklearn.metrics.pairwise import cosine_similarity
+import os
+
+# Load the pre-trained ResNet50 model
+model = ResNet50(weights='imagenet', include_top=False, pooling='avg')
+
+
+# Function to extract feature vector from an image
+def extract_features(img_path, model):
+    img = image.load_img(img_path, target_size=(224, 224))
+    img_data = image.img_to_array(img)
+    img_data = np.expand_dims(img_data, axis=0)
+    img_data = preprocess_input(img_data)
+    features = model.predict(img_data)
+    return features.flatten()
+
+
+# Directory containing images
+image_dir = './images/forward_facing'
+
+# Extract features for all images
+image_features = {}
+for img_file in os.listdir(image_dir):
+    img_path = os.path.join(image_dir, img_file)
+    features = extract_features(img_path, model)
+    image_features[img_file] = features
+
+# Convert feature dictionary to list for processing
+feature_list = list(image_features.values())
+file_list = list(image_features.keys())
+
+# Calculate similarities
+num_images = len(file_list)
+similarity_matrix = np.zeros((num_images, num_images))
+
+for i in range(num_images):
+    for j in range(i, num_images):
+        if i != j:
+            similarity = cosine_similarity(
+                [feature_list[i]],
+                [feature_list[j]]
+            )[0][0]
+            similarity_matrix[i][j] = similarity
+            similarity_matrix[j][i] = similarity
+
+# Identify and remove duplicates
+threshold = 0.9  # Similarity threshold for duplicates
+duplicates = set()
+for i in range(num_images):
+    for j in range(i + 1, num_images):
+        if similarity_matrix[i][j] > threshold:
+            duplicates.add(file_list[j])
+
+# Remove duplicates
+# for duplicate in duplicates:
+#     os.remove(os.path.join(image_dir, duplicate))
+print("Duplicate Images No => ", len(duplicates))
+
+# print(f"Removed {len(duplicates)} duplicate images.")