USAGE: facerec_demo.py </path/to/images> [</path/to/store/images/at>] 问题解决

报错

C:\ProgramData\Anaconda3\python.exe D:/PycharmProjects/pycv-master/chapter5/video_rec.pyUSAGE: facerec_demo.py </path/to/images> [</path/to/store/images/at>]Process finished with exit code 0

原因

未配置输入参数

解决办法

执行py文件时，输入文件

>python D:/PycharmProjects/pycv-master/chapter5/video_rec.py D:\PycharmProjects\pycv-master\data\at D:\PycharmProjects\pycv-master\chapter5\faces.csv

黄色背景表示空格

源代码

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")                    else:                        print (filepath)                    # resize to given size (if given)                    if (sz is not None):                        im = cv2.resize(im, (200, 200))                    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            print (c)            c = c+1                print (y)    return [X,y]def face_rec():    names = ['Joe', 'Jane', 'Jack']    if len(sys.argv) < 2:        print ("USAGE: facerec_demo.py </path/to/images> [</path/to/store/images/at>]")        sys.exit()    [X,y] = read_images(sys.argv[1])    y = np.asarray(y, dtype=np.int32)        if len(sys.argv) == 3:        out_dir = sys.argv[2]        model = cv2.face.createEigenFaceRecognizer()    model.train(np.asarray(X), np.asarray(y))    camera = cv2.VideoCapture(0)    face_cascade = cv2.CascadeClassifier('./cascades/haarcascade_frontalface_default.xml')    while (True):      read, img = camera.read()      faces = face_cascade.detectMultiScale(img, 1.3, 5)      for (x, y, w, h) in faces:        img = cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)        roi = gray[x:x+w, y:y+h]        try:            roi = cv2.resize(roi, (200, 200), interpolation=cv2.INTER_LINEAR)            print (roi.shape)            params = model.predict(roi)            print ("Label: %s, Confidence: %.2f" % (params[0], params[1]))            cv2.putText(img, names[params[0]], (x, y - 20), cv2.FONT_HERSHEY_SIMPLEX, 1, 255, 2)            if (params[0] == 0):                cv2.imwrite('face_rec.jpg', img)        except:            continue      cv2.imshow("camera", img)      if cv2.waitKey(1000 // 12) & 0xff == ord("q"):        break    cv2.destroyAllWindows()if __name__ == "__main__":    face_rec()def original():    # This is where we write the images, if an output_dir is given    # in command line:    out_dir = None    names = ['Joe', 'Jane', 'Jack']    # 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.createLBPHFaceRecognizer()    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:    camera = cv2.VideoCapture(0)    face_cascade = cv2.CascadeClassifier('./cascades/haarcascade_frontalface_default.xml')    while (True):      read, img = camera.read()      faces = face_cascade.detectMultiScale(img, 1.3, 5)      for (x, y, w, h) in faces:        img = cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)        roi = gray[x:x+w, y:y+h]        roi = cv2.resize(roi, (200, 200), interpolation=cv2.INTER_LINEAR)        print (roi.shape)        params = model.predict(roi)        print ("Label: %s, Confidence: %.2f" % (params[0], params[1]))        cv2.putText(img, names[params[0]], (x,y - 20), cv2.FONT_HERSHEY_SIMPLEX, 1, 255, 3)      cv2.imshow("camera", img)      if cv2.waitKey(1000 / 12) & 0xff == ord("q"):        break    [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)):    for i in range(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)    cv2.destroyAllWindows()