img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) center = (int((match[0] + match[2])/2), int((match[1] + match[3])/2)) # 指定圆心坐标 radius = int((((match[2] - match[0]) + (match[3] - match[1]))/4)) # 指定半径大小 result = cv2.circle(img, center, radius, color, -1) # 绘制圆形这样有什么问题

这段代码的问题在于将原始图像 `img` 直接转换为了 BGR 彩色图像，而原始图像类型可能并不是灰度图像。如果原始图像本身已经是彩色图像，则直接使用 `cv2.cvtColor()` 函数进行转换会导致图像颜色失真。因此，在进行颜色空间转换之前，应该先判断原始图像的类型，如果是灰度图像，则进行转换，否则直接使用原始图像。另外，对于圆心坐标和半径大小的计算，可以使用更简洁的表达式，如下所示：

center = ((match[0] + match[2]) // 2, (match[1] + match[3]) // 2)
radius = ((match[2] - match[0]) + (match[3] - match[1])) // 4

这样可以避免使用 `int()` 函数进行类型转换。最终的代码应该如下所示：

if len(img.shape) == 2:
    img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)

center = ((match[0] + match[2]) // 2, (match[1] + match[3]) // 2)
radius = ((match[2] - match[0]) + (match[3] - match[1])) // 4
result = cv2.circle(img, center, radius, color, -1)

相关问题

Python cv 几个常用信息提取的功能，给出示例代码

Python cv（OpenCV）提供了许多信息提取的功能，以下是几个常用的示例代码：

1. 图像特征匹配

import cv2

img1 = cv2.imread('img1.jpg',0)
img2 = cv2.imread('img2.jpg',0)

orb = cv2.ORB_create()

kp1, des1 = orb.detectAndCompute(img1,None)
kp2, des2 = orb.detectAndCompute(img2,None)

bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)

matches = bf.match(des1,des2)

matches = sorted(matches, key = lambda x:x.distance)

img3 = cv2.drawMatches(img1,kp1,img2,kp2,matches[:10],None,flags=2)

cv2.imshow("Image", img3)
cv2.waitKey(0)
cv2.destroyAllWindows()

2. 人脸检测

import cv2

face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

img = cv2.imread('img.jpg')

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

faces = face_cascade.detectMultiScale(gray, 1.3, 5)

for (x,y,w,h) in faces:
    cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)

cv2.imshow("Image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()

3. 目标检测

import cv2

net = cv2.dnn.readNetFromDarknet("yolov4.cfg", "yolov4.weights")

classes = []
with open("coco.names", "r") as f:
    classes = [line.strip() for line in f.readlines()]

layer_names = net.getLayerNames()
output_layers = [layer_names[i[0]-1] for i in net.getUnconnectedOutLayers()]

img = cv2.imread("person.jpg")
height, width, channels = img.shape

blob = cv2.dnn.blobFromImage(img, 0.00392, (416,416), (0,0,0), True, crop=False)
net.setInput(blob)
outs = net.forward(output_layers)

class_ids = []
confidences = []
boxes = []

for out in outs:
    for detection in out:
        scores = detection[5:]
        class_id = np.argmax(scores)
        confidence = scores[class_id]
        if confidence > 0.5:
            center_x = int(detection[0] * width)
            center_y = int(detection[1] * height)
            w = int(detection[2] * width)
            h = int(detection[3] * height)

            x = int(center_x - w/2)
            y = int(center_y - h/2)

            boxes.append([x, y, w, h])
            confidences.append(float(confidence))
            class_ids.append(class_id)

indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

font = cv2.FONT_HERSHEY_PLAIN

for i in range(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), font, 3, (0,255,0), 3)

cv2.imshow("Image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()