cv2.error: OpenCV(4.3.0) 输入图像中的通道数无效

Question

这是错误代码。

Traceback (most recent call last):
      File "C:\Users\user\Desktop\Python\opencv.py", line 46, in <module>
        color = color_mapping(j)
      File "C:\Users\user\Desktop\Python\opencv.py", line 26, in color_mapping
        return hsv_to_rgb([base % 1.2, 0.95, 0.80])
      File "C:\Users\user\Desktop\Python\opencv.py", line 13, in hsv_to_rgb
        out = cv.cvtColor(in2, cv.COLOR_HSV2RGB)
    cv2.error: OpenCV(4.3.0) c:\projects\opencv-python\opencv\modules\imgproc\src\color.simd_helpers.hpp:92: error: (-2:Unspecified error) in function '__cdecl cv::impl::`anonymous-namespace'::CvtHelper<struct cv::impl::`anonymous namespace'::Set<3,-1,-1>,struct cv::impl::A0x3b52564f::Set<3,4,-1>,struct cv::impl::A0x3b52564f::Set<0,5,-1>,2>::CvtHelper(const class cv::_InputArray &,const class cv::_OutputArray &,int)'
    > Invalid number of channels in input image:
    >     'VScn::contains(scn)'
    > where
    >     'scn' is 1

C++ 代码：graphsegmentation-demo.cpp

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#include "opencv2/ximgproc/segmentation.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/core.hpp"
#include "opencv2/imgproc.hpp"
#include <iostream>
#include <fstream>

using namespace cv;
using namespace cv::ximgproc::segmentation;

Scalar hsv_to_rgb(Scalar);
Scalar color_mapping(int);

static void help() {
    std::cout << std::endl <<
    "A program demonstrating the use and capabilities of a particular graph based image" << std::endl <<
    "segmentation algorithm described in P. Felzenszwalb, D. Huttenlocher," << std::endl <<
    "             \"Efficient Graph-Based Image Segmentation\"" << std::endl <<
    "International Journal of Computer Vision, Vol. 59, No. 2, September 2004" << std::endl << std::endl <<
    "Usage:" << std::endl <<
    "./graphsegmentation_demo input_image output_image [simga=0.5] [k=300] [min_size=100]" << std::endl;
}

Scalar hsv_to_rgb(Scalar c) {
    Mat in(1, 1, CV_32FC3);
    Mat out(1, 1, CV_32FC3);

    float * p = in.ptr<float>(0);

    p[0] = (float)c[0] * 360.0f;
    p[1] = (float)c[1];
    p[2] = (float)c[2];

    cvtColor(in, out, COLOR_HSV2RGB);

    Scalar t;

    Vec3f p2 = out.at<Vec3f>(0, 0);

    t[0] = (int)(p2[0] * 255);
    t[1] = (int)(p2[1] * 255);
    t[2] = (int)(p2[2] * 255);

    return t;

}

Scalar color_mapping(int segment_id) {

    double base = (double)(segment_id) * 0.618033988749895 + 0.24443434;

    return hsv_to_rgb(Scalar(fmod(base, 1.2), 0.95, 0.80));

}

int main(int argc, char** argv) {

    if (argc < 2 || argc > 6) {
        help();
        return -1;
    }

    Ptr<GraphSegmentation> gs = createGraphSegmentation();

    if (argc > 3)
        gs->setSigma(atof(argv[3]));

    if (argc > 4)
        gs->setK((float)atoi(argv[4]));

    if (argc > 5)
        gs->setMinSize(atoi(argv[5]));

    if (!gs) {
        std::cerr << "Failed to create GraphSegmentation Algorithm." << std::endl;
        return -2;
    }

    Mat input, input2, output, output_image;

    input2 = imread(argv[1]);

    if (!input2.data) {
        std::cerr << "Failed to load input image" << std::endl;
        return -3;
    }

    resize(input2, input, Size(input2.cols / 10, input2.rows / 10), 0, 0, INTER_LINEAR);

    gs->processImage(input, output);

    double min, max;
    Point minL, maxL;
    minMaxLoc(output, &min, &max, &minL, &maxL);

    std::cout << "Min: " << min << " Max: " << max << " MinL: " << minL << " MaxL: " << maxL << "\n";

    int nb_segs = (int)max + 1;

    std::cout << nb_segs << " segments" << std::endl;

    output_image = Mat::zeros(output.rows, output.cols, CV_8UC3);

    uint* p;
    uchar* p2;

    for (int i = 0; i < output.rows; i++) {

        p = output.ptr<uint>(i);
        p2 = output_image.ptr<uchar>(i);

        for (int j = 0; j < output.cols; j++) {
            Scalar color = color_mapping(p[j]);
            p2[j*3] = (uchar)color[0];
            p2[j*3 + 1] = (uchar)color[1];
            p2[j*3 + 2] = (uchar)color[2];

        }
    }

    imwrite(argv[2], output_image);

    std::cout << "Image written to " << argv[2] << std::endl;

    return 0;
}

我对 Python 的转换：

import cv2 as cv
import sys
import numpy as np

def hsv_to_rgb(c):
    in2 = np.array((1,1,3), np.float32)
    out = np.array((1,1,3), np.float32)

    in2[0] = c[0] * 360.0
    in2[1] = c[1]
    in2[2] = c[2]

    out = cv.cvtColor(in2, cv.COLOR_HSV2RGB) 
 
    t =  [0,0,0]

    t[0] = (int)(out[0] * 255)
    t[1] = (int)(out[1] * 255)
    t[2] = (int)(out[2] * 255)

    return t;


def color_mapping(segment_id):
    base = (segment_id) * 0.618033988749895 + 0.24443434
    return hsv_to_rgb([base % 1.2, 0.95, 0.80])


img = cv.imread("IMG1.jpg", cv.IMREAD_COLOR)

absolute = 10
newdim = (int(img.shape[0]/absolute),int(img.shape[1]/absolute))

img = cv.resize(img, newdim, interpolation = cv.INTER_AREA)

gs = cv.ximgproc.segmentation.createGraphSegmentation()
gs.setSigma(10)
gs.setK(300)
gs.setMinSize(1000)
rimg = gs.processImage(img)

output_image = np.zeros(img.shape, dtype=np.uint8)

for idx1, i in enumerate(rimg):
    for idx2, j in enumerate(i):
        color = color_mapping(j)
        output_image[idx1][idx2*3] = color[0]
        output_image[idx1][idx2*3 + 1] = color[1]
        output_image[idx1][idx2*3 + 2] = color[2]

cv.imshow("Output", output_image);        
cv.waitKey(0)
cv.destroyAllWindows()

我的猜测：

output_image = np.zeros(img.shape, dtype=np.uint8)

对比

output_image = Mat::zeros(output.rows, output.cols, CV_8UC3);

没有正确转换？

for (int i = 0; i < output.rows; i++) {

    p = output.ptr<uint>(i);
    p2 = output_image.ptr<uchar>(i);

    for (int j = 0; j < output.cols; j++) {
        Scalar color = color_mapping(p[j]);
        p2[j*3] = (uchar)color[0];
        p2[j*3 + 1] = (uchar)color[1];
        p2[j*3 + 2] = (uchar)color[2];

    }
}

对比

for idx1, i in enumerate(rimg):
    for idx2, j in enumerate(i):
        color = color_mapping(j)
        output_image[idx1][idx2*3] = color[0]
        output_image[idx1][idx2*3 + 1] = color[1]
        output_image[idx1][idx2*3 + 2] = color[2]

数组未正确使用。 至少，我不记得可以在 Python 中完成。 但是，我找不到解决方案。 第二个索引无法扩展，此外，我认为 CV_8UC3 应该是一个三通道数组（400、300、3），所以我不明白为什么它会扩展第二个维度。

Answer 1

我自己设法解决了这个问题。 但是，我将直接向 OpenCV 开发人员解决这个问题，以便他们给我答案。

import cv2 as cv
import sys
import numpy as np
import colorsys

def hsv_to_rgb(c):
    in2 = np.array((1,1,3), np.float32)
    out = np.array((1,1,3), np.float32)

    in2[0] = c[0] * 360.0
    in2[1] = c[1]
    in2[2] = c[2]    
    
    out = colorsys.hsv_to_rgb(in2[0],in2[1],in2[2])
 
    t =  [0,0,0]

    t[0] = (int)(out[0] * 255)
    t[1] = (int)(out[1] * 255)
    t[2] = (int)(out[2] * 255)

    return t;


def color_mapping(segment_id):
    base = (segment_id) * 0.618033988749895 + 0.24443434
    return hsv_to_rgb([base % 1.2, 0.95, 0.80])


img = cv.imread("IMG1.jpg", cv.IMREAD_COLOR)

absolute = 5
newdim = (int(img.shape[0]/absolute),int(img.shape[1]/absolute))

img = cv.resize(img, newdim, interpolation = cv.INTER_AREA)

gs = cv.ximgproc.segmentation.createGraphSegmentation()
gs.setSigma(0.001)
gs.setK(10)
gs.setMinSize(50)
rimg = gs.processImage(img)

min = 0.0
max = 0.0
minL = (0,0)
maxL = (0,0)
min, max, minL, maxL = cv.minMaxLoc(rimg)

print("Min: {} Max: {} MinL: {} MaxL: {}".format(min,max,minL,maxL))

nb_segs = max + 1

print("Segments: {}".format(nb_segs))

output_image = np.zeros(img.shape, dtype=np.uint8)

rows, cols = rimg.shape

for idx1 in range(rows):
    for idx2 in range(cols):
        color = color_mapping(rimg[idx1][idx2])
        output_image[idx1][idx2] = color

cv.imwrite("IMG1-XXX.jpg", output_image)

print("Image written to {}".format("IMG1-XXX.jpg"))
cv.imshow("Output", output_image);        
cv.waitKey(0)
cv.destroyAllWindows()

更改我的代码：

使用 colorsys 将 hsv 更改为 rgb。 该问题可能与附加到 cvtColor function 的尺寸、形状或类型问题有关。

更新循环。 使用形状中的行和列范围，而不是枚举行和列以提高效率。 此外，片段到像素的转换是在删除索引分配并将元组 [R,G,B] 直接插入到 400x300 索引中完成的。