opencv检测不同颜色的斑点

Question

我是 opencv 的新手，对于一个学校项目，我需要用相机检测红色和绿色圆圈，所以我使用了 blobdetection，但它检测到了我的两种颜色，我认为我的掩模不好，每种颜色与特定操作相关联。

目前，我的代码在同一页面上检测到红色和绿色圆圈，但我希望它只检测白色页面上的红色圆圈。

感谢您的帮助

# Standard imports
import cv2
import numpy as np;

    # Read image
im = cv2.VideoCapture(0)

# Setup SimpleBlobDetector parameters.
params = cv2.SimpleBlobDetector_Params()

# Change thresholds
params.minThreshold = 100;
params.maxThreshold = 200;

# Filter by Area.
params.filterByArea = True
params.minArea = 200
params.maxArea = 20000

# Filter by Circularity
params.filterByCircularity = True
params.minCircularity = 0.1

# Filter by Convexity
params.filterByConvexity = True
params.minConvexity = 0.1

# Filter by Inertia
params.filterByInertia = True
params.minInertiaRatio = 0.1


blueLower = (0,85,170)  #100,130,50
blueUpper = (140,110,255) #200,200,130


while(1):

    ret, frame=im.read()

    mask = cv2.inRange(frame, blueLower, blueUpper)
    mask = cv2.erode(mask, None, iterations=0)
    mask = cv2.dilate(mask, None, iterations=0)
    frame = cv2.bitwise_and(frame,frame,mask = mask)

# Set up the detector with default parameters.
    detector = cv2.SimpleBlobDetector_create(params)

# Detect blobs.
    keypoints = detector.detect(mask)

# Draw detected blobs as red circles.
# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob
    im_with_keypoints = cv2.drawKeypoints(mask, keypoints, np.array([]), (0,0,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)


# Display the resulting frame

    frame = cv2.bitwise_and(frame,im_with_keypoints,mask = mask)

    cv2.imshow('frame',frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# When everything done, release the capture
im.release()
cv2.destroyAllWindows()

编辑1：代码更新

现在我遇到了一个问题，即未检测到我的完整圆圈。

无斑点检测

第二版

# Standard imports
import cv2
import numpy as np;

# Read image
im = cv2.VideoCapture(0)

while(1):
        ret, frame=im.read()


        lower = (130,150,80)  #130,150,80
        upper = (250,250,120) #250,250,120
        mask = cv2.inRange(frame, lower, upper)
        lower, contours, upper = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
        blob = max(contours, key=lambda el: cv2.contourArea(el))
        M = cv2.moments(blob)
        center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))
        canvas = im.copy()
        cv2.circle(canvas, center, 2, (0,0,255), -1)

        cv2.imshow('frame',frame)

        if cv2.waitKey(1) & 0xFF == ord('q'):
                break
im.release()
cv2.destroyAllWindows()

Answer 1

您需要计算出绿色的 BGR 数字是多少（为了论证起见[0, 255, 0]），然后创建一个蒙版，忽略绿色周围容差之外的任何颜色：

mask = cv2.inRange(image, lower, upper)

逐步查看本教程。

尝试使用下层和上层以获得正确的行为。然后你可以找到蒙版中的轮廓：

_, contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, 
                                                    cv2.CHAIN_APPROX_NONE)

然后遍历contours列表找到最大的一个（过滤掉任何可能的噪音）：

blob = max(contours, key=lambda el: cv2.contourArea(el))

这就是你最后的“斑点”。您可以通过执行以下操作找到中心：

M = cv2.moments(blob)
center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))

您可以将该中心绘制到图像的副本上，以进行检查：

canvas = im.copy()
cv2.circle(canvas, center, 2, (0,0,255), -1)

显然，这假设图像中只有一个绿色球，没有其他绿色球。但这是一个开始。

编辑 - 对第二篇文章的回复

我认为以下应该有效。我还没有测试过它，但您至少应该能够对显示的画布和蒙版进行更多调试：

# Standard imports
import cv2
import numpy as np;

# Read image
cam = cv2.VideoCapture(0)

while(1):
        ret, frame = cam.read()

        if not ret:
            break

        canvas = frame.copy()


        lower = (130,150,80)  #130,150,80
        upper = (250,250,120) #250,250,120
        mask = cv2.inRange(frame, lower, upper)
        try:
            # NB: using _ as the variable name for two of the outputs, as they're not used
            _, contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
            blob = max(contours, key=lambda el: cv2.contourArea(el))
            M = cv2.moments(blob)
            center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))

            cv2.circle(canvas, center, 2, (0,0,255), -1)

        except (ValueError, ZeroDivisionError):
            pass

        cv2.imshow('frame',frame)
        cv2.imshow('canvas',canvas)
        cv2.imshow('mask',mask)

        if cv2.waitKey(1) & 0xFF == ord('q'):
                break
im.release()
cv2.destroyAllWindows()