Python 图片聚类算法 - 使用 KMeans 将图片分组

以下是一个简单的聚类算法示例，用于将一组图片聚成 k 个类。\n\npython\nimport numpy as np\nimport os\nfrom PIL import Image\nfrom sklearn.cluster import KMeans\n\ndef load_images(directory):\n images = []\n for filename in os.listdir(directory):\n if filename.endswith('.jpg') or filename.endswith('.png'):\n image = Image.open(os.path.join(directory, filename))\n image = np.array(image)\n images.append(image)\n return images\n\ndef preprocess_images(images):\n preprocessed_images = []\n for image in images:\n # 对图片进行预处理，例如调整大小或转换为灰度图像\n # preprocessed_image = ...\n preprocessed_images.append(preprocessed_image)\n return preprocessed_images\n\ndef cluster_images(images, k):\n # 将图片转换为特征向量\n features = []\n for image in images:\n feature = image.flatten() # 将图像展平为一维向量\n features.append(feature)\n features = np.array(features)\n \n # 使用K-means算法进行聚类\n kmeans = KMeans(n_clusters=k)\n kmeans.fit(features)\n labels = kmeans.labels_\n \n # 将图片根据聚类结果分组\n clusters = {}\n for i, label in enumerate(labels):\n if label not in clusters:\n clusters[label] = []\n clusters[label].append(images[i])\n \n return clusters\n\n# 示例用法\nimage_directory = 'path_to_directory_containing_images'\nk = 5 # 聚类的簇数\nimages = load_images(image_directory)\npreprocessed_images = preprocess_images(images)\nclusters = cluster_images(preprocessed_images, k)\n\n# 打印每个类别中的图片数量\nfor label, cluster in clusters.items():\n print(f"Cluster {label}: {len(cluster)} images")\n\n\n请注意，上述代码是一个简单示例，并且需要根据你的具体需求进行修改和完善。你可能需要根据实际情况进行图像预处理、特征提取和聚类算法的参数调整等操作。