import numpy as np import matplotlib.pyplot as plt from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LinearRegression
# 生成一些示例数据 np.random.seed(0) X = np.sort(5 * np.random.rand(80, 1), axis=0) y = np.sin(X).ravel() + np.random.normal(0, 0.1, X.shape[0])
import tensorflow as tf from tensorflow.keras.applications import VGG16 from tensorflow.keras.layers import Dense, Flatten from tensorflow.keras.models import Model from tensorflow.keras.preprocessing.image import ImageDataGenerator
# 加载YOLO模型 net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg") layer_names = net.getLayerNames() output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
# 显示结果 for i inrange(len(boxes)): if i in indexes: x, y, w, h = boxes[i] label = str(classes[class_ids[i]]) cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2) cv2.putText(img, label, (x, y + 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)
# 加载YOLO模型 net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg") layer_names = net.getLayerNames() output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
# 画出检测框 for i inrange(len(boxes)): if i in indexes: x, y, w, h = boxes[i] label = str(classes[class_ids[i]]) cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) cv2.putText(frame, label, (x, y + 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
cv2.imshow('Video', frame) if cv2.waitKey(1) & 0xFF == ord('q'): break