AttributeError: 'module' object has no attribute 'face' 问题解决 opencv+python

运行环境

win7+opencv3.1+python2.7.13

运行问题

Traceback (most recent call last):  File "C:/Project/pycv-master/chapter5/face_recognition.py", line 122, in <module>    model = cv2.face.createEigenFaceRecognizer()AttributeError: 'module' object has no attribute 'face'Process finished with exit code 1

问题原因

未安装

opencv_contrib，所以

model = cv2.face.createEigenFaceRecognizer() 行找不到face

下载地址

https://github.com/opencv/opencv_contrib

解决办法

安装并配置opencv_contrib

文件源代码

#!/usr/bin/env python# Software License Agreement (BSD License)## Copyright (c) 2012, Philipp Wagner <bytefish[at]gmx[dot]de>.# All rights reserved.## Redistribution and use in source and binary forms, with or without# modification, are permitted provided that the following conditions# are met:##  * Redistributions of source code must retain the above copyright#    notice, this list of conditions and the following disclaimer.#  * Redistributions in binary form must reproduce the above#    copyright notice, this list of conditions and the following#    disclaimer in the documentation and/or other materials provided#    with the distribution.#  * Neither the name of the author nor the names of its#    contributors may be used to endorse or promote products derived#    from this software without specific prior written permission.## THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE# POSSIBILITY OF SUCH DAMAGE.# ------------------------------------------------------------------------------------------------# Note:# When using the FaceRecognizer interface in combination with Python, please stick to Python 2.# Some underlying scripts like create_csv will not work in other versions, like Python 3.# ------------------------------------------------------------------------------------------------import osimport sysimport cv2import numpy as npdef normalize(X, low, high, dtype=None):    """Normalizes a given array in X to a value between low and high."""    X = np.asarray(X)    minX, maxX = np.min(X), np.max(X)    # normalize to [0...1].    X = X - float(minX)    X = X / float((maxX - minX))    # scale to [low...high].    X = X * (high-low)    X = X + low    if dtype is None:        return np.asarray(X)    return np.asarray(X, dtype=dtype)def read_images(path, sz=None):    """Reads the images in a given folder, resizes images on the fly if size is given.    Args:        path: Path to a folder with subfolders representing the subjects (persons).        sz: A tuple with the size Resizes    Returns:        A list [X,y]            X: The images, which is a Python list of numpy arrays.            y: The corresponding labels (the unique number of the subject, person) in a Python list.    """    c = 0    X,y = [], []    for dirname, dirnames, filenames in os.walk(path):        for subdirname in dirnames:            subject_path = os.path.join(dirname, subdirname)            for filename in os.listdir(subject_path):                try:                    if (filename == ".directory"):                        continue                    filepath = os.path.join(subject_path, filename)                    im = cv2.imread(os.path.join(subject_path, filename), cv2.IMREAD_GRAYSCALE)                    if (im is None):                        print "image " + filepath + " is none"                     # resize to given size (if given)                    if (sz is not None):                        im = cv2.resize(im, sz)                    X.append(np.asarray(im, dtype=np.uint8))                    y.append(c)                except IOError, (errno, strerror):                    print "I/O error({0}): {1}".format(errno, strerror)                except:                    print "Unexpected error:", sys.exc_info()[0]                    raise            c = c+1    return [X,y]if __name__ == "__main__":    # This is where we write the images, if an output_dir is given    # in command line:    out_dir = None    # You'll need at least a path to your image data, please see    # the tutorial coming with this source code on how to prepare    # your image data:    if len(sys.argv) < 2:        print "USAGE: facerec_demo.py </path/to/images> [</path/to/store/images/at>]"        sys.exit()    # Now read in the image data. This must be a valid path!    [X,y] = read_images(sys.argv[1])    # Convert labels to 32bit integers. This is a workaround for 64bit machines,    # because the labels will truncated else. This will be fixed in code as    # soon as possible, so Python users don't need to know about this.    # Thanks to Leo Dirac for reporting:    y = np.asarray(y, dtype=np.int32)    # If a out_dir is given, set it:    if len(sys.argv) == 3:        out_dir = sys.argv[2]    # Create the Eigenfaces model. We are going to use the default    # parameters for this simple example, please read the documentation    # for thresholding:    model = cv2.face.createEigenFaceRecognizer()    # Read    # Learn the model. Remember our function returns Python lists,    # so we use np.asarray to turn them into NumPy lists to make    # the OpenCV wrapper happy:    model.train(np.asarray(X), np.asarray(y))    # We now get a prediction from the model! In reality you    # should always use unseen images for testing your model.    # But so many people were confused, when I sliced an image    # off in the C++ version, so I am just using an image we    # have trained with.    #    # model.predict is going to return the predicted label and    # the associated confidence:    [p_label, p_confidence] = model.predict(np.asarray(X[0]))    # Print it:    print "Predicted label = %d (confidence=%.2f)" % (p_label, p_confidence)    # Cool! Finally we'll plot the Eigenfaces, because that's    # what most people read in the papers are keen to see.    #    # Just like in C++ you have access to all model internal    # data, because the cv::FaceRecognizer is a cv::Algorithm.    #    # You can see the available parameters with getParams():    print model.getParams()    # Now let's get some data:    mean = model.getMat("mean")    eigenvectors = model.getMat("eigenvectors")    # We'll save the mean, by first normalizing it:    mean_norm = normalize(mean, 0, 255, dtype=np.uint8)    mean_resized = mean_norm.reshape(X[0].shape)    if out_dir is None:        cv2.imshow("mean", mean_resized)    else:        cv2.imwrite("%s/mean.png" % (out_dir), mean_resized)    # Turn the first (at most) 16 eigenvectors into grayscale    # images. You could also use cv::normalize here, but sticking    # to NumPy is much easier for now.    # Note: eigenvectors are stored by column:    for i in xrange(min(len(X), 16)):        eigenvector_i = eigenvectors[:,i].reshape(X[0].shape)        eigenvector_i_norm = normalize(eigenvector_i, 0, 255, dtype=np.uint8)        # Show or save the images:        if out_dir is None:            cv2.imshow("%s/eigenface_%d" % (out_dir,i), eigenvector_i_norm)        else:            cv2.imwrite("%s/eigenface_%d.png" % (out_dir,i), eigenvector_i_norm)    # Show the images:    if out_dir is None:        cv2.waitKey(0)