OpenCV中常用的代码

以下是一些OpenCV中常用的代码片段，涵盖了一些基本的图像处理和计算机视觉任务。

1. 加载和显示图像:

   import cv2
   
   # 读取图像
   img = cv2.imread('image.jpg')
   
   # 显示图像
   cv2.imshow('Image', img)
   cv2.waitKey(0)
   cv2.destroyAllWindows()
   1
   2
   3
   4
   5
   6
   7
   8
   9

2. 调整图像大小:

   resized_img = cv2.resize(img, (width, height))
   1

3. 灰度转换:

   gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
   1

4. 图像平滑:

   smoothed_img = cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)
   1

5. 边缘检测:

   edges = cv2.Canny(gray_img, threshold1, threshold2)
   1

6. 图像阈值处理:

   ret, binary_img = cv2.threshold(gray_img, threshold_value, max_value, cv2.THRESH_BINARY)
   1

7. 图像轮廓检测:

   contours, hierarchy = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
   1

8. 绘制轮廓:

   cv2.drawContours(img, contours, -1, (0, 255, 0), 2)
   1

9. 人脸检测:

   face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
   faces = face_cascade.detectMultiScale(gray_img, scaleFactor=1.3, minNeighbors=5)
   1
   2

10. 图像旋转:

    rows, cols = img.shape[:2]
    M = cv2.getRotationMatrix2D((cols / 2, rows / 2), angle, scale)
    rotated_img = cv2.warpAffine(img, M, (cols, rows))
    1
    2
    3

11. 图像腐蚀与膨胀:

    kernel = np.ones((kernel_size, kernel_size), np.uint8)
    eroded_img = cv2.erode(img, kernel, iterations=1)
    dilated_img = cv2.dilate(img, kernel, iterations=1)
    1
    2
    3

12. 图像直方图均衡化:

    equ_img = cv2.equalizeHist(gray_img)
    1

13. 图像混合:

    blended_img = cv2.addWeighted(img1, alpha, img2, beta, gamma)
    1

14. 图像拼接:

    stitched_img = cv2.hconcat([img1, img2])
    1

15. 图像截取:

    roi = img[y:y+h, x:x+w]
    1

16. 图像相加:

    added_img = cv2.add(img1, img2)
    1

17. 图像减法:

    subtracted_img = cv2.subtract(img1, img2)
    1

18. 图像位运算:

    bitwise_and = cv2.bitwise_and(img1, img2)
    bitwise_or = cv2.bitwise_or(img1, img2)
    bitwise_xor = cv2.bitwise_xor(img1, img2)
    bitwise_not = cv2.bitwise_not(img)
    1
    2
    3
    4

19. 图像平均模糊:

    averaged_img = cv2.blur(img, (kernel_size, kernel_size))
    1

20. 中值滤波:

    median_blurred_img = cv2.medianBlur(img, ksize)
    1

21. 自适应阈值:

    adaptive_thresh = cv2.adaptiveThreshold(
        gray_img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
    1
    2

22. 图像旋转缩放:

    M = cv2.getRotationMatrix2D((cols / 2, rows / 2), angle, scale)
    rotated_resized_img = cv2.warpAffine(img, M, (new_cols, new_rows))
    1
    2

23. SIFT 特征提取和匹配:

    sift = cv2.SIFT_create()
    keypoints1, descriptors1 = sift.detectAndCompute(img1, None)
    keypoints2, descriptors2 = sift.detectAndCompute(img2, None)
    1
    2
    3

24. SURF 特征提取和匹配:

    surf = cv2.SURF_create()
    keypoints1, descriptors1 = surf.detectAndCompute(img1, None)
    keypoints2, descriptors2 = surf.detectAndCompute(img2, None)
    1
    2
    3

25. ORB 特征提取和匹配:

    orb = cv2.ORB_create()
    keypoints1, descriptors1 = orb.detectAndCompute(img1, None)
    keypoints2, descriptors2 = orb.detectAndCompute(img2, None)
    1
    2
    3

26. 使用 FLANN 匹配器进行特征匹配:

    FLANN_INDEX_KDTREE = 1
    index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
    search_params = dict(checks=50)
    flann = cv2.FlannBasedMatcher(index_params, search_params)
    matches = flann.knnMatch(descriptors1, descriptors2, k=2)
    1
    2
    3
    4
    5

27. 简单图像深度学习任务:

    net = cv2.dnn.readNet('model.weights', 'model.cfg')
    blob = cv2.dnn.blobFromImage(img, scalefactor=1.0, size=(300, 300), mean=(104.0, 177.0, 123.0))
    net.setInput(blob)
    detections = net.forward()
    1
    2
    3
    4

28. HOG 特征提取与行人检测:

    hog = cv2.HOGDescriptor()
    hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
    boxes, weights = hog.detectMultiScale(img, winStride=(8, 8), padding=(0, 0), scale=1.05)
    1
    2
    3

这些是一些基本的OpenCV代码示例，覆盖了图像处理和计算机视觉中的常见任务。在实际应用中，可能需要根据具体场景和需求进一步调整参数和算法。