Python畅游计算机视觉海洋：从图像分类到目标检测的精彩之旅

# 导入必要的库import numpy as npimport matplotlib.pyplot as pltfrom sklearn.model_selection import train_test_splitfrom sklearn.linear_model import LoGISticRegression# 加载和预处理图像数据data = np.load("data.npy")labels = np.load("labels.npy")data = data.reshape((data.shape[0], -1))data = data.astype("float32") / 255.0# 将数据分成训练集和测试集X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2)# 训练逻辑回归分类器classifier = LogisticRegression(max_iter=1000)classifier.fit(X_train, y_train)# 评估分类器score = classifier.score(X_test, y_test)print("准确率：", score)# 预测新图像image = np.load("new_image.npy")image = image.reshape((1, -1))image = image.astype("float32") / 255.0prediction = classifier.predict(image)print("预测标签：", prediction)

import numpy as npimport cv2# 加载并预处理图像image = cv2.imread("image.png")image = cv2.resize(image, (640, 480))# 创建目标检测器detector = cv2.dnn.readNetFromCaffe("deploy.prototxt.txt", "res10_300x300_ssd_iter_140000.caffemodel")# 检测图像中的对象blob = cv2.dnn.blobFromImage(image, 0.007843, (300, 300), 127.5)detector.setInput(blob)detections = detector.forward()# 绘制检测结果for i in range(0, detections.shape[2]):confidence = detections[0, 0, i, 2]if confidence > 0.5:x1 = int(detections[0, 0, i, 3] * image.shape[1])y1 = int(detections[0, 0, i, 4] * image.shape[0])x2 = int(detections[0, 0, i, 5] * image.shape[1])y2 = int(detections[0, 0, i, 6] * image.shape[0])cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)# 显示结果图像cv2.imshow("检测结果", image)cv2.waiTKEy(0)cv2.destroyAllwindows()