本文将为您提供关于Pythoncv2ORB检测和计算返回“输入图像中的通道数无效”的详细介绍，我们还将为您解释python输出图像通道数的相关知识，同时，我们还将为您提供关于-->8_,ctrs,_=

本文将为您提供关于Python cv2 ORB检测和计算返回“输入图像中的通道数无效”的详细介绍，我们还将为您解释python输出图像通道数的相关知识，同时，我们还将为您提供关于--> 8_,ctrs,_=cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE) ValueError: 没有足够的值来解包预期 3，得到 2、AttributeError: module cv2.cv2 has no attribute create ThinPlateSplineShapeTransformer 报错、AttributeError：模块'cv2.cv2'没有属性'createLBPHFaceRecognizer'、AttributeError：模块'cv2.cv2'没有属性'xfeatures2d'[Opencv 3.4.3]的实用信息。

• Python cv2 ORB检测和计算返回“输入图像中的通道数无效”（python输出图像通道数）
• --> 8_,ctrs,_=cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE) ValueError: 没有足够的值来解包预期 3，得到 2
• AttributeError: module cv2.cv2 has no attribute create ThinPlateSplineShapeTransformer 报错
• AttributeError：模块'cv2.cv2'没有属性'createLBPHFaceRecognizer'
• AttributeError：模块'cv2.cv2'没有属性'xfeatures2d'[Opencv 3.4.3]

Python cv2 ORB检测和计算返回“输入图像中的通道数无效”（python输出图像通道数）

如何解决Python cv2 ORB检测和计算返回“输入图像中的通道数无效”

我正在尝试从两个不同的图像中提取和匹配特征，但由于某种原因，“detectAndCompute”方法不适用于我的 orb 对象： orb = cv2.ORB_create() kp,corners = orb.detectAndCompute(image,None 我正在传递单个灰度图像（函数 np.float32(cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)) 的返回）。由于某种原因，程序返回以下错误： 回溯（最近一次调用最后一次）： 文件“C:\\Users\\levxr\\Desktop\\Visual-positioning-bot-main\\alloverlay.py”，第 37 行，在 cv2.imshow("相机"+str(i),corn1.updateanddisplay()) 文件“C:\\Users\\levxr\\Desktop\\Visual-positioning-bot-main\\features.py”，第 33 行，在 updateanddisplay dst = self.update(image=self.image) 文件“C:\\Users\\levxr\\Desktop\\Visual-positioning-bot-main\\features.py”，第 23 行，更新中 kp,None) cv2.error: OpenCV(4.4.0) c:\\users\\appveyor\\appdata\\local\\temp\\1\\pip-req-build-95hbg2jt\\opencv\\modules\\imgproc\\src\\color.simd_helpers.hpp:92: 错误: (-2:Unspecified error) in function ''__cdecl cv::impl::anonymous-namespace''::CvtHelper<struct cv::impl::anonymous namespace''::Set,struct cv::impl::A0x2980c61a::Set,2>::CvtHelper(const class cv::_InputArray &,const class cv::_OutputArray &,int)''

输入图像中的通道数无效： ''VScn::包含（scn）'' 在哪里 ''scn'' 是 1

程序分为3个文件，alloverlay.py（主文件）：

import sys
import cv2
import numpy as np
import features as corn
import camera as cali
cv2.ocl.setUSEOpenCL(False)
#videoname = input("enter input")
videoname = "camera10001-0200.mkv"
try:
    videoname = int(videoname)
    cap = cv2.VideoCapture(videoname)
except:
    cap = cv2.VideoCapture(videoname)
videoname2 = "camera 20000-0200.mkv"
try:
    videoname = int(videoname)
    cap2 = cv2.VideoCapture(videoname)
except:
    cap2 = cv2.VideoCapture(videoname)
if cap.isOpened()and cap2.isOpened():
    ret1,image1 = cap.read()
    ret2,image2 = cap2.read()
    ret = [ret1,ret2]
    image = [np.float32(cv2.cvtColor(image1,cv2.COLOR_BGR2GRAY)),np.float32(cv2.cvtColor(image2,cv2.COLOR_BGR2GRAY))]
    cali1 = cali.Calibrator()
    corn1 = corn.Corner_detector(image)
while cap.isOpened() and cap2.isOpened():
    ret[0],image[0] = cap.read()
    ret[1],image[1] = cap2.read()
    if ret:
        backupimg = image
        for i,img in enumerate(image):
            if cali1.calibrated:
                backupimg[i] = corn1.image = cali1.undistort(np.float32(cv2.cvtColor(image[i],cali1.mtx,cali1.dist)
            else:
                backupimg[i] = corn1.image = np.float32(cv2.cvtColor(img,cv2.COLOR_BGR2GRAY))
            cv2.imshow("camera "+str(i),corn1.updateanddisplay())
        image = backupimg
        print(ret,image)
        #cv2.imshow("test",image)
        key = cv2.waitKey(1)
        if key == ord("c"):
            cali1.calibrate(cali1.image)
        if cv2.waitKey(25) & 0xFF == ord("q"):
            break
    else:
        print("capture not reading")
        break
cap.release()

，camera.py（用于校准和不失真相机并三角测量点的相对位置的模块（该项目的不同部分，与此问题无关））：

import sys
import cv2
#import glob
import numpy as np
cv2.ocl.setUSEOpenCL(False)
class Missing_calibration_data_error(Exception):
    def __init__():
        pass
class Calibrator():
    def __init__(self,image=None,mtx=None,dist=None,camera_data={"pixelsize":None,"matrixsize":None,"baseline":None,"lens_distance":None},criteria=(cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER,30,0.001),calibrated = False):
        self.criteria = criteria
        self.objpoints = []
        self.imgpoints = []
        self.objp = np.zeros((6*7,3),np.float32)
        self.objp[:,:2] = np.mgrid[0:7,0:6].T.reshape(-1,2)
        self.image = image
        self.mtx = mtx
        self.dist = dist
        self.calibrated = calibrated
        self.pixelsize = camera_data["pixelsize"]
        self.matrixsize = camera_data["matrixsize"]
        self.baseline = camera_data["baseline"]
        self.lens_distance = camera_data["lens_distance"]
    def calibrate(self,image):
        gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
        ret,corners = cv2.findChessboardCorners(gray,(7,6),None)
        if ret == True:
            self.objpoints.append(self.objp)
            corners2 = cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),self.criteria)
            self.imgpoints.append(corners2)
            h,w = image.shape[:2]
            ret,mtx,dist,rvecs,tvecs = cv2.calibrateCamera(objpoints,imgpoints,gray.shape[::-1],None,None)
            self.mtx = mtx
            self.dist = dist
            self.calibrated = True
            return mtx,dist
    def undistort(self,image,dist):
        if dist == None or mtx == None or image == None:
            raise Missing_calibration_data_error
            h,w = image.shape[:2]
        newcameramtx,roi=cv2.getoptimalNewCameraMatrix(mtx,(w,h),1,h))
        dst = cv2.undistort(image,newcameramtx)
        x,y,w,h = roi
        dst = dst[y:y+h,x:x+w]
        return image
    def calculate_point_relative_position(self,point_location2d):
        angle = self.baseline/(point_location2d[left][x]-point_location2d[right][x])
        x = angle * (point_location2d[left][x]-self.matrixsize[0]/2)
        y = angle * (point_location2d[left][y]-self.matrixsize[1]/2)
        z = self.lens_distance * (1-angle/self.pixelsize)
        return [x,z]
´´´,and features.py(module to detect and match the features,aparently where the issue happens):
´´´
import sys
import cv2
import numpy as np
cv2.ocl.setUSEOpenCL(False)
class UnkNown_algorythm_error(Exception):
    def __init__(self):
        pass
class No_image_passed_error(Exception):
    def __int__ (self):
        pass
class Corner_detector():
    def __init__(self,detectortype="ORB",corners=[]):
        self.corners = corners
        self.image = image
        self.detectortype = detectortype
    def update(self,image=None):
        if self.detectortype == "Harris":
            self.corners = cv2.cornerHarris(image,3,1)
        elif self.detectortype == "Shi-Tomasi":
            self.corners = cv2.goodFeaturesToTrack(image,1)
        elif self.detectortype == "ORB":
            orb = cv2.ORB_create()
            kp,None)
        elif self.detectortype == "SURF":
            minHessian = 400
            detector = cv2.features2d_SURF(hessianThreshold=minHessian)
            keypoints1,descriptors1 = detector.detectAndCompute(img1,None)
            keypoints2,descriptors2 = detector.detectAndCompute(img2,None)
        else:
            raise UnkNown_algoryth_error
        return self.corners
    def updateanddisplay(self):
        dst = self.update(image=self.image)
        self.image[dst>0.01*dst.max()] = 0
        return self.image
class Feature_matcher():
    def __init__(self,matcher = cv2.DescriptorMatcher_create(cv2.DescriptorMatcher_FLANNBASED)):
        self.matcher = matcher
´´´
Does anyone kNow how to fix this? I''ve been looking for the answer for quite a while but i only find the answer for when you''re converting the image to grayscale and it doesnt work for me.

解决方法

# image type is uint8:
image = np.zeros((100,100),np.uint8)
orb = cv2.ORB_create()
kp,corners = orb.detectAndCompute(image,None)

# image type is float32:
image = np.float32(np.zeros((100,np.uint8))
orb = cv2.ORB_create()
kp,None)

kp,corners = orb.detectAndCompute(image.astype(np.uint8),None)

--> 8_,ctrs,_=cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE) ValueError: 没有足够的值来解包预期 3，得到 2

如何解决--> 8_,ctrs,_=cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE) ValueError: 没有足够的值来解包预期 3，得到 2

我收到这样的错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-38-c01841a65106> in <module>
      1 #assign "-"=10
----> 2 data=load_images_from_folder("D:/Handwritten-Equation-Solver-master (1)/Handwritten-Equation-Solver-master/extracted_images/-")
      3 len(data)
      4 for i in range(0,len(data)):
      5     data[i]=np.append(data[i],["10"])
<ipython-input-36-de2a2236b032> in load_images_from_folder(folder)
      6         if img is not None:
      7             _,thresh=cv2.threshold(img,127,255,cv2.THRESH_BINARY)
----> 8             _,ctrs,_=cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
      9             cnt=sorted(ctrs,key=lambda ctr:cv2.boundingRect(ctr)[0])
     10             w=int(28)
ValueError: not enough values to unpack (expected 3,got 2)

请帮我解决这个问题。

AttributeError: module cv2.cv2 has no attribute create ThinPlateSplineShapeTransformer 报错

使用opencv-python报错：

AttributeError: module ''cv2.cv2'' has no attribute ''createThinPlateSplineShapeTransformer''

解决方法：

pip install opencv-contrib-python

AttributeError：模块'cv2.cv2'没有属性'createLBPHFaceRecognizer'

AttributeError: module ''cv2.cv2'' has no attribute ''xfeatures2d''