打开 fp = io.BytesIO(fp.read()) AttributeError: 'str' 对象在 PIL/image.py 中没有属性 'read'

Question

我对 Python 有点陌生，尤其是我目前正在使用的 Imaging 库。 我正在使用面部识别代码并在运行 Jessie 的 Raspberry Pi 2 B+ 中运行它。 我正在使用 Opencv 2.4.9 和 Python 2.7。 我目前正在使用的代码一直工作到几分钟前，但现在我不断收到错误消息。 我没有更改代码或更新任何内容。

我尝试过的：我卸载了枕头并安装了不同的版本，但它仍然无法正常工作。

我只是不明白发生了什么变化。 我尝试通过更改变量名称仍然没有效果。

import cv
import cv2
import sys
import os
import datetime
import time
from PIL import Image
import numpy as np


def EuclideanDistance(p, q):
    p = np.asarray(p).flatten()
    q = np.asarray(q).flatten()
    return np.sqrt(np.sum(np.power((p-q),2)))


class EigenfacesModel():

    def __init__(self, X=None, y=None, num_components=0):
        self.num_components = 0
        self.projections = []
        self.W = []
        self.mu = []
        if (X is not None) and (y is not None):
            self.compute(X,y)

    def compute(self, X, y):
        [D, self.W, self.mu] = pca(asRowMatrix(X),y, self.num_components)
        # store labels
        self.y = y
        # store projections
        for xi in X:
            self.projections.append(project(self.W, xi.reshape(1,-1), self.mu))

    def predict(self, X):
        minDist = np.finfo('float').max
        minClass = -1
        Q = project(self.W, X.reshape(1,-1), self.mu)
        for i in xrange(len(self.projections)):
            dist = EuclideanDistance(self.projections[i], Q)
            #print i,dist
            if dist < minDist:
                minDist = dist
                minClass = self.y[i]
        print "\nMinimum distance ", minDist        
        return minClass,minDist


def asRowMatrix(X):
    if len(X) == 0:
        return np.array([])
    mat = np.empty((0, X[0].size), dtype=X[0].dtype)
    for row in X:
        mat = np.vstack((mat, np.asarray(row).reshape(1,-1)))
    return mat

def read_images(filename, sz=None):
    c = 0
    X,y = [], []
    with open(filename) as f:
        for line in f:
            line = line.rstrip()
            im = Image.open(line)
            im = im.convert("L")
            # resize to given size (if given)
            if (sz is None):
                im = im.resize((92,112), Image.ANTIALIAS)
            X.append(np.asarray(im, dtype=np.uint8))
            y.append(c)
            c = c+1

    print c
    return [X,y]

def pca(X, y, num_components=0):
    [n,d] = X.shape
    print n
    if (num_components <= 0) or (num_components>n):
        num_components = n
    mu = X.mean(axis=0)
    X = X - mu
    if n>d:
        C = np.dot(X.T,X)
        [eigenvalues,eigenvectors] = np.linalg.eigh(C)
    else:
        C = np.dot(X,X.T)
        [eigenvalues,eigenvectors] = np.linalg.eigh(C)
        eigenvectors = np.dot(X.T,eigenvectors)
        for i in xrange(n):
            eigenvectors[:,i] = eigenvectors[:,i]/np.linalg.norm(eigenvectors[:,i])
    # or simply perform an economy size decomposition
    # eigenvectors, eigenvalues, variance = np.linalg.svd(X.T, full_matrices=False)
    # sort eigenvectors descending by their eigenvalue
    idx = np.argsort(-eigenvalues)
    eigenvalues = eigenvalues[idx]
    eigenvectors = eigenvectors[:,idx]
    # select only num_components
    num_components = 25
    eigenvalues = eigenvalues[0:num_components].copy()
    eigenvectors = eigenvectors[:,0:num_components].copy()
    return [eigenvalues, eigenvectors, mu]

def project(W, X, mu=None):
    if mu is None:
        return np.dot(X,W)
    return np.dot(X - mu, W)




def reconstruct(W, Y, mu=None):
    if mu is None:
        return np.dot(W.T,Y)
    return np.dot(W.T,Y) + mu

#if __name__ == "__main__":

def FaceRecognitionWrapper(Database_Address,TestImages_Address):

    out_dir = "Output_Directory"

    [X,y] = read_images(Database_Address)
    y = np.asarray(y, dtype=np.int32)
    #print len(X)

    model = EigenfacesModel(X[0:], y[0:])
    # get a prediction for the first observation
    [X1,y1] = read_images(TestImages_Address) 
    y1 = np.asarray(y1, dtype=np.int32)
    OutputFile = open("Output.txt",'a')
    for i in xrange(len(X1)):
        predicted,difference = model.predict(X1[i])
        predicted1 = int(predicted/10) + 1
        if difference <= 1000:
            print i+1 , "th image was recognized as individual" , predicted+1
            OutputFile.write(str(predicted1))
            OutputFile.write("\n")
        else:
            os.chdir(out_dir)
            print i+1,"th image could not be recognized. Storing in error folder."

            errorImage = Image.fromarray(X1[i])
            current_time = datetime.datetime.now().time()
            error_img_name=current_time.isoformat()+'.png'
            errorImage.save(error_img_name)
            os.chdir('..')
    OutputFile.close()





#Create Model Here

cascPath = '/home/pi/opencv-2.4.9/data/haarcascades/haarcascade_frontalface_default.xml'
faceCascade = cv2.CascadeClassifier(cascPath)
Test_Files = []

video_capture = cv2.VideoCapture(0)

i = 0

while True:
    # Capture frame-by-frame
    ret, frame = video_capture.read()

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

    faces = faceCascade.detectMultiScale(
        gray,
        scaleFactor=1.1,
        minNeighbors=5,
        minSize=(30, 30),
        flags=cv2.cv.CV_HAAR_SCALE_IMAGE
    )

    # Draw a rectangle around the faces
    for (x, y, w, h) in faces:
        dummy_image = frame
        cv2.rectangle(dummy_image, (x, y), (x+w, y+h), (0, 255, 0), 2)
        dummy_image=dummy_image[y:y+h, x:x+w]
        dirname = 'detection_output'
        os.chdir(dirname)
        current_time = datetime.datetime.now().time()   
        final_img_name=current_time.isoformat()+'.png' 
        Test_Files.append(final_img_name)  
        dummy_image = cv2.cvtColor(dummy_image, cv2.COLOR_BGR2GRAY) 
        cv2.imwrite(final_img_name,dummy_image)
        os.chdir('..')


    # Display the resulting frame
    cv2.imshow('Video', frame)

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

    if i % 20 == 0:
        dirname = 'detection_output'
        os.chdir(dirname)
        TestFile = open('CameraFeedFaces.txt',"w")
        for Files in Test_Files:
            TestFile.write(os.getcwd()+"/"+Files+"\n")
        TestFile.close()
        os.chdir("..")
        #Call testing.py
        FaceRecognitionWrapper("/home/pi/train_faces/temp.txt",os.getcwd()+"/detection_output/CameraFeedFaces.txt")
        #Open Output File and Copy in a separate folder where distance greater than threshold
        #Then delete all the files in the folder
        break


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

这是回溯：

Traceback (most recent call last):
  File "hel.py", line 213, in <module>
    FaceRecognitionWrapper("/home/pi/train_faces/temp.txt",os.getcwd()+"/detection_output/CameraFeedFaces.txt")
  File "hel.py", line 127, in FaceRecognitionWrapper
    [X,y] = read_images(Database_Address)
  File "hel.py", line 68, in read_images
    im = Image.open(line)
  File "/usr/local/lib/python2.7/dist-packages/PIL/Image.py", line 2277, in open
    fp = io.BytesIO(fp.read())
AttributeError: 'str' object has no attribute 'read'

代码运行时的输出

我在某处读过它，如果我尝试编辑 im = Image.open(open(line,'rb')) 我得到这个错误而不是前一个

Traceback (most recent call last):
  File "hel.py", line 208, in <module>
    FaceRecognitionWrapper("/home/pi/train_faces/temp.txt",os.getcwd()+"/detection_output/CameraFeedFaces.txt")
  File "hel.py", line 122, in FaceRecognitionWrapper
    [X,y] = read_images(Database_Address)
  File "hel.py", line 63, in read_images
    im = Image.open(open(line,'rb'))
IOError: [Errno 2] No such file or directory: ''

Answer 1

您的主题行令人困惑。 你修复了这个问题，然后暴露了一个不同的错误。

IOError: [Errno 2] No such file or directory: ''

该消息显示您正在尝试打开一个没有名称的文件，这意味着您正在阅读的输入文件至少有一个空行。 如果该文件可以有空行，请跳过以下内容：

def read_images(filename, sz=None):
    c = 0
    X,y = [], []
    with open(filename) as f:
        for line in f:
            line = line.rstrip()
            if line:
                im = Image.open(open(line,'rb')) 
                im = im.convert("L"
                # resize to given size (if given)
                if (sz is None):
                    im = im.resize((92,112), Image.ANTIALIAS)
                X.append(np.asarray(im, dtype=np.uint8))
                y.append(c)
                c = c+1

否则，您会遇到合法的错误，您应该捕获并处理异常。