虚拟现实和增强现实之场景理解算法：物体识别：计算机视觉与物体检测.docx

model = torch.hub.load('ultralytics/yolov5', 'yolov5s')

img = Image.open('path/to/your/image.jpg')

results = model(img)

results.show()

import cv2

import numpy as np

image = cv2.imread('example.jpg')

gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

cv2.imshow('Original Image', image)

cv2.imshow('Gray Image', gray_image)

sift = cv2.SIFT_create()

keypoints, descriptors = sift.detectAndCompute(image, None)

image_with_keypoints = cv2.drawKeypoints(image, keypoints, np.array([]), (0,0,255), cv2.DRAW_

MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)

cv2.imshow('SIFT Keypoints', image_with_keypoints)

model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3), activation='relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())

model.add(Dense(128, activation='relu'))

model.add(Dense(1, activation='sigmoid'))