I'm a little new to Python especially Imaging library that I'm currently working with. I'm working with a facial recognition code and running it in my Raspberry Pi 2 B+ running Jessie. I'm using Opencv 2.4.9 and Python 2.7. The code that I'm currently working with worked till a few minutes ago but now I keep getting an error. I didn't alter the code or update anything.
我对Python有点陌生，特别是我正在使用的映像库。我正在使用面部识别代码，并在我的树莓Pi 2b +运行Jessie。我用的是Opencv 2。4.9和Python 2。7。我目前正在处理的代码几分钟前还在工作，但现在我一直在出错。我没有修改代码或更新任何内容。
What I have tried: I uninstalled pillow and installed different versions, but it is still not working.
我试过的:我卸载了枕头，安装了不同的版本，但仍然不能使用。
I just don't understand what has changed. I tried by changing the variable names still no effect.
我就是不明白发生了什么变化。我尝试改变变量名，但仍然没有效果。
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()
And here's the Traceback:
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'
代码工作时的输出
I read it somewhere and if I try and edit im = Image.open(open(line,'rb')) I'm getting this error instead of the previous one
我在某个地方读到它，如果我尝试编辑im = Image.open(行，'rb')，我会得到这个错误，而不是之前的错误
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: ''
1 个解决方案
#1
1
Your subject line is confusing. You fixed that problem and then exposed a different bug.
你的主题很混乱。你解决了那个问题，然后暴露了另一个错误。
IOError: [Errno 2] No such file or directory: ''
The message shows that you are trying to open a file with no name and that means that the input file you are reading has at least one blank line. If its okay for that file to have blank lines, just skip those like:
消息显示您正在尝试打开一个没有名称的文件，这意味着您正在读取的输入文件至少有一个空行。如果该文件有空行是可以的，那么就跳过以下内容:
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
Otherwise, you have a legitimate error and you should catch and handle the exception.
否则，您有一个合法的错误，您应该捕获并处理异常。
#1
1
Your subject line is confusing. You fixed that problem and then exposed a different bug.
你的主题很混乱。你解决了那个问题，然后暴露了另一个错误。
IOError: [Errno 2] No such file or directory: ''
The message shows that you are trying to open a file with no name and that means that the input file you are reading has at least one blank line. If its okay for that file to have blank lines, just skip those like:
消息显示您正在尝试打开一个没有名称的文件，这意味着您正在读取的输入文件至少有一个空行。如果该文件有空行是可以的，那么就跳过以下内容:
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
Otherwise, you have a legitimate error and you should catch and handle the exception.
否则，您有一个合法的错误，您应该捕获并处理异常。