import os
import sys
import cv2
import 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 + lowif 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 ResizesReturns: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 = 0X,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"):continuefilepath = 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])raiseprint (c)c = c+1# print (X) #2017-6-11 addprint (y)return [X,y]def face_rec():names = ['fanshengmei']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("2.mp4")face_cascade = cv2.CascadeClassifier('./cascades/haarcascade_frontalface_alt2.xml')while (True):read, img = camera.read()# faces = face_cascade.detectMultiScale(img, 1.3, 5)faces = face_cascade.detectMultiScale(img, 1.4, 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)# print(gray)roi = gray[x:x+w, y:y+h]# print(roi)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:continuecv2.imshow("camera", img)if cv2.waitKey(1000 // 12) & 0xff == ord("q"):breakcv2.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 = Nonenames = ['Joe', 'Jane', 'Jack']# jm->Joe、 jb->Jane、sw->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()