基于形状特征的图像检索代码
时间: 2024-05-16 14:14:56 浏览: 46
以下是基于形状特征的图像检索代码示例,使用OpenCV和scikit-learn库:
```python
import cv2
import numpy as np
from sklearn.cluster import KMeans
from sklearn.neighbors import KNeighborsClassifier
# 加载图像
img = cv2.imread('image.jpg')
# 提取轮廓
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# 提取特征向量
features = []
for contour in contours:
area = cv2.contourArea(contour)
if area > 100: # 忽略面积小于100的区域
moments = cv2.moments(contour)
cx = int(moments['m10'] / moments['m00'])
cy = int(moments['m01'] / moments['m00'])
x, y, w, h = cv2.boundingRect(contour)
aspect_ratio = float(w) / h
rectangularity = w * h / area
circularity = 4 * np.pi * area / (cv2.arcLength(contour, True) ** 2)
features.append([aspect_ratio, rectangularity, circularity])
# 进行聚类
kmeans = KMeans(n_clusters=10).fit(features)
cluster_labels = kmeans.labels_
# 训练分类器
X_train = np.array(features)
y_train = np.array(cluster_labels)
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
# 测试图像
test_img = cv2.imread('test_image.jpg')
gray = cv2.cvtColor(test_img, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
test_contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
test_features = []
for contour in test_contours:
area = cv2.contourArea(contour)
if area > 100:
moments = cv2.moments(contour)
cx = int(moments['m10'] / moments['m00'])
cy = int(moments['m01'] / moments['m00'])
x, y, w, h = cv2.boundingRect(contour)
aspect_ratio = float(w) / h
rectangularity = w * h / area
circularity = 4 * np.pi * area / (cv2.arcLength(contour, True) ** 2)
test_features.append([aspect_ratio, rectangularity, circularity])
test_labels = knn.predict(test_features)
# 打印结果
print(test_labels)
```
以上代码将图像分为10个形状簇,并训练一个KNN分类器来识别测试图像的形状。你可以根据需要调整聚类数和分类器参数。