Question

我正在开发一个基本的图像识别程序，该程序将获取像素数据，然后使用SVD分解将其分解。我已经获取了所有数据，剩下要做的唯一一件事就是通过一些矩阵运算将所有数据推到最前面。但是，考虑到我已经处理了矩阵，并且遇到np.matmul（）时，它仍然失败，因此我总是收到一个似乎很愚蠢的错误。如果函数getEigenvalues会中断。这是我得到的错误。预先感谢。

Traceback (most recent call last):
  File "C:/Users/Bryson M/Documents/CS 1400/MORE PYTHON/Image Recognition/camera-test/venv/main.py", line 117, in <module>
    main()
  File "C:/Users/Bryson M/Documents/CS 1400/MORE PYTHON/Image Recognition/camera-test/venv/main.py", line 96, in main
    eigenComparisionMatrix = makeEigenVectors(cleanedData) #makes the eigenvectors that will be used to compare against other pictures
  File "C:/Users/Bryson M/Documents/CS 1400/MORE PYTHON/Image Recognition/camera-test/venv/main.py", line 57, in makeEigenVectors
    matrixM = np.matmul(np.transpose(matrix), matrix) #gives really big rank one matrix
TypeError: Object arrays are not currently supported


import cv2
import numpy as np
import time
import matplotlib as plt

def smallestDimensions(imageList):
    smallestColumns = 100000  # gives a very large starting columns and rows
    smallestRows = 100000
    for image in imageList:
        img = cv2.imread(image)  # reads in images one by one.
        rows, columns, channels = img.shape
        if rows < smallestRows:  # if image has a smaller width, then makes that the smallest width.
            smallestRows = rows
        if columns < smallestColumns:
            smallestColumns = columns
    print("rows: ", smallestRows, "columns: ", smallestColumns)
    return smallestRows, smallestColumns

def makePictureMatrixBGR(imageList, maxRows, maxColumns): #makes the numpy array with pixel data
    pictureDataMatrix = []
    for image in imageList: #takes image one at a time
        bluePixelData = []
        greenPixelData = []
        redPixelData = []

        img = cv2.imread(image)  # reads in images one by one.
        rows, columns, channels = img.shape
        startPosX = (columns - maxColumns) // 2  # centers the area of interest in the middle despite differences in pixel count
        startPosY = (rows - maxRows) // 2

        for x in range(startPosX):
            for y in range(startPosY):
                blue, green, red = (img[x + startPosX, y + startPosY])  # makes blue, green, red arrays for each picture
                bluePixelData.append((blue))  # adds pixels values to three seperate lists
                greenPixelData.append((green))
                redPixelData.append((red))

        print("green pixel data: ", greenPixelData)
        pictureDataMatrix.append(bluePixelData) #makes each pixel value list into a line on the main data matrix
        pictureDataMatrix.append(greenPixelData)
        pictureDataMatrix.append(redPixelData)
    return pictureDataMatrix

def muCenteredData(matrix): #centers each set of data relative to its mean.
    for row in range(len(matrix)):
        sum = 0
        for cell in range(len(matrix[row])):
            sum += matrix[row][cell]
        for cell in range(len(matrix[row])):
            matrix[row][cell] -= sum/(len(matrix[row])) #subtracting mu from each value. mu is the mean of each row.
    return matrix

def covarianceCentered(baseDataMatrix):
    pass

def makeEigenVectors(matrix):
    matrixM = np.matmul(np.transpose(matrix), matrix) #gives really big rank one matrix
    uVectors , eigenValues, vVectors = np.linalg.svd(matrixM) #SVD decomposition
    return vVectors[:,[0,1]] #for now, i take only two columns so that i can nicely print out two numbers, x and y, on a graph

def getMatrixValues(testedMatrix, eigenvectorMatrix):
    x,y = np.matmul(eigenvectorMatrix , testedMatrix)
    return x , y


def main():
    imageList = ["C:\\Users\\Bryson M\\Documents\\CS 1400\\MORE PYTHON\\Image Recognition\\camera-test\\venv\\Images\\SpongbobJump.png",
                 "C:\\Users\\Bryson M\\Documents\\CS 1400\\MORE PYTHON\\Image Recognition\\camera-test\\venv\\Images\\SpongebobHug.png",
                 "C:\\Users\\Bryson M\\Documents\\CS 1400\\MORE PYTHON\\Image Recognition\\camera-test\\venv\\Images\\SpongebobJogging.png",
                 "C:\\Users\\Bryson M\\Documents\\CS 1400\\MORE PYTHON\\Image Recognition\\camera-test\\venv\\Images\\SpongbobShowing.png"]
    observedImage = ["C:\\Users\\Bryson M\\Documents\\CS 1400\\MORE PYTHON\\Image Recognition\\camera-test\\venv\\Images\\mario.png"]

    smallestRows, smallestColumns = smallestDimensions(imageList + observedImage) #finds the smallest dimentions out of all the data for clean linalg operations

    pictureDataMatrix = makePictureMatrixBGR(imageList, smallestRows, smallestColumns)
    observedDataMatrix = makePictureMatrixBGR(observedImage, smallestRows, smallestColumns)

    cleanedData = muCenteredData(pictureDataMatrix)#cleans the data for a more consistant value
    cleanedObservedData = muCenteredData(observedDataMatrix)

    eigenComparisionMatrix = makeEigenVectors(cleanedData) #makes the eigenvectors that will be used to compare against other pictures
#find x y points
    finalPoints = []
    for image in range(imageList):
        x,y = getMatrixValues(cleanedData, eigenComparisionMatrix)
        finalPoints.append([x,y])

#plot points
    plt.suptitle("Base pictures plotted")
    plt.scatter(*zip(*finalPoints),'r*') #base points are red stars. https://stackoverflow.com/questions/21519203/plotting-a-list-of-x-y-coordinates-in-python-matplotlib
    plt.show()
    plt.waitforbuttonpress()
    x,y = getMatrixValues(cleanedObservedData, eigenComparisionMatrix)
    plt.scatter(x,y , 'gs') #observed image is representied by a green square
    plt.suptitle("Oberved Point ")
    plt.show()


startTime = time.time()

if __name__ == "__main__":
    main()

print(time.time() - startTime)

列表无法与numpy相乘

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