基于颜色块检索代码
时间: 2023-11-09 10:07:40 浏览: 33
以下是一个基于颜色块检索的简单代码示例:
```python
import os
import numpy as np
from PIL import Image
from sklearn.cluster import KMeans
# 定义颜色块大小和聚类数
BLOCK_SIZE = 32
CLUSTER_NUM = 16
# 获取所有图片文件的路径
def get_image_paths(image_dir):
image_paths = []
for filename in os.listdir(image_dir):
if filename.endswith('.jpg') or filename.endswith('.png'):
image_path = os.path.join(image_dir, filename)
image_paths.append(image_path)
return image_paths
# 将图片转换成颜色块,并使用K-Means算法进行聚类
def extract_features(image_path):
image = Image.open(image_path)
width, height = image.size
# 将图片分成若干个颜色块
blocks = []
for x in range(0, width, BLOCK_SIZE):
for y in range(0, height, BLOCK_SIZE):
block = image.crop((x, y, x+BLOCK_SIZE, y+BLOCK_SIZE))
blocks.append(block)
# 对每个颜色块进行聚类
features = []
for block in blocks:
# 将颜色块转换成向量
vector = np.array(block).reshape(-1, 3)
# 使用K-Means算法进行聚类
kmeans = KMeans(n_clusters=CLUSTER_NUM, random_state=0).fit(vector)
feature = kmeans.cluster_centers_.flatten()
features.append(feature)
return features
# 建立倒排索引表,记录每个关键词出现在哪些图片中
def build_index(image_dir):
index = {}
image_paths = get_image_paths(image_dir)
for image_path in image_paths:
features = extract_features(image_path)
for feature in features:
# 将向量转换成字符串作为关键词
key = ' '.join(str(x) for x in feature)
if key in index:
index[key].append(image_path)
else:
index[key] = [image_path]
return index
# 根据输入的颜色块查询倒排索引表,返回与该颜色块颜色最接近的图片
def search(index, color_block):
# 将颜色块转换成向量
vector = np.array(color_block).reshape(-1, 3)
# 对颜色块进行聚类
kmeans = KMeans(n_clusters=CLUSTER_NUM, random_state=0).fit(vector)
feature = kmeans.cluster_centers_.flatten()
# 将向量转换成字符串作为查询关键词
key = ' '.join(str(x) for x in feature)
# 查询倒排索引表
if key in index:
return index[key]
else:
return []
# 测试代码
if __name__ == '__main__':
image_dir = './images'
index = build_index(image_dir)
# 输入一个颜色块,查询倒排索引表
color_block = Image.new('RGB', (BLOCK_SIZE, BLOCK_SIZE), (255, 0, 0))
result = search(index, color_block)
print(result)
```
在以上代码中,我们先定义了颜色块的大小和聚类数。然后,通过`extract_features`函数将每张图片分成若干个颜色块,并使用K-Means算法进行聚类,得到每个颜色块的特征向量。接着,通过`build_index`函数建立倒排索引表,记录每个关键词出现在哪些图片中。最后,通过`search`函数根据输入的颜色块查询倒排索引表,返回与该颜色块颜色最接近的图片。在测试代码中,我们输入一个红色的颜色块,查询倒排索引表,返回相应的图片路径。