写出上述实验的源代码
时间: 2024-09-24 19:04:17 浏览: 29
编写这样一个实验的源代码涉及到多个步骤,这里提供一个简单的示例,使用OpenCV和scikit-learn库来创建一个基本的颜色和纹理特征提取及分类的鸡肉生熟度识别系统。这只是一个简化版的代码框架,实际项目可能更复杂,需要更多的图像处理和深度学习技术。
```python
import cv2
import numpy as np
from sklearn.svm import SVC
from skimage.feature import hog
# 数据预处理函数
def preprocess_image(img_path):
img = cv2.imread(img_path)
# 图像归一化并转换到HSV色彩空间
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
return hsv
# 特征提取函数
def extract_features(image):
gray = cv2.cvtColor(image, cv2.COLOR_HSV2GRAY)
# 使用HOG特征
hog_descriptors = hog(gray, orientations=9, pixels_per_cell=(8,8), cells_per_block=(2,2))
# 提取颜色直方图
color_histogram = cv2.calcHist([image], [0,1,2], None, [8,8,8], [0,256,0,256,0,256])
color_histogram = cv2.normalize(color_histogram, color_histogram).flatten()
return np.hstack((hog_descriptors, color_histogram))
# 主程序
if __name__ == "__main__":
images = ['chicken_raw.jpg', 'chicken_half.jpg', 'chicken_full.jpg'] # 假设这些是你的鸡肉图片路径
labels = [0, 1, 2] # 对应生、熟、非常熟的标签
features = []
for img_path in images:
image = preprocess_image(img_path)
feature_vector = extract_features(image)
features.append(feature_vector)
X = np.array(features)
y = np.array(labels)
# 划分训练集和测试集
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 使用SVM进行训练
svm = SVC(kernel='linear')
svm.fit(X_train, y_train)
# 预测新图片
new_image_path = 'new_chicken.jpg'
new_image_features = extract_features(preprocess_image(new_image_path))
prediction = svm.predict(new_image_features)
print(f"预测的新图片生熟度为:{prediction[0]}")
# 模型评估
score = svm.score(X_test, y_test)
print(f"模型在测试集上的准确率为:{score}")
```