寄生虫卵检测与识别Python代码

以下是一个基于Python的寄生虫卵检测与识别的简单代码示例，仅供参考：

import cv2
import numpy as np
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# 加载寄生虫卵图像数据集
def load_data():
    x = []
    y = []
    for i in range(1, 101):
        for j in range(1, 11):
            filename = f"parasite_{i}_{j}.png"
            img = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)
            x.append(img)
            y.append(i)
    return np.array(x), np.array(y)

# 图像预处理
def preprocess(img):
    # 去噪
    img = cv2.GaussianBlur(img, (5, 5), 0)
    # 边缘检测
    edges = cv2.Canny(img, 50, 150)
    # 膨胀操作
    kernel = np.ones((5, 5), np.uint8)
    edges = cv2.dilate(edges, kernel, iterations=1)
    return edges

# 提取图像特征
def extract_features(img):
    # 计算图像的Hu矩
    moments = cv2.moments(img)
    hu_moments = cv2.HuMoments(moments)
    return hu_moments.flatten()

# 训练分类模型
def train_model(x, y):
    # 划分训练集和测试集
    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
    # 图像预处理和特征提取
    x_train = [extract_features(preprocess(img)) for img in x_train]
    x_test = [extract_features(preprocess(img)) for img in x_test]
    # 训练SVM分类器
    svm = SVC(kernel='linear', C=1.0)
    svm.fit(x_train, y_train)
    # 测试模型性能
    y_pred = svm.predict(x_test)
    accuracy = accuracy_score(y_test, y_pred)
    print(f"Accuracy: {accuracy}")
    # 返回分类器
    return svm

# 加载测试图像并进行预测
def predict(svm, filename):
    img = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)
    feature = extract_features(preprocess(img))
    label = svm.predict([feature])
    return label[0]

# 加载数据集
x, y = load_data()
# 训练分类模型
svm = train_model(x, y)
# 进行单个图像预测
label = predict(svm, "test.png")
print(f"Predicted label: {label}")

这个示例代码只是一个简单的实现，实际上寄生虫卵检测与识别的任务比较复杂，需要更加完整和细致的实现。