python3项目源代码下载_人脸检测和识别源代码下载-opencv3+python3.6完整实战项目源代码识别视频《欢乐颂》中人物，-opencv3《欢乐颂》...

人脸检测和识别源代码下载-opencv3+python3.6完整实战项目源代码识别视频《欢乐颂》中人物，-opencv3《欢乐颂》

效果图

源代码import os

import sys

import cv2

import numpy as np

def 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 (X) #2017-6-11 add

print (y)

return [X,y]

def face_rec():

names = ['fanshengmei']

if len(sys.argv) < 2:

print ("USAGE: facerec_demo.py /to/images> [/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:

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']

# 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 /to/images> [/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()

完整项目文档下载

http://download.csdn.net/download/wyx100/9867602

程序运行方法

http://blog.csdn.net/wyx100/article/details/73017614

开发环境快速搭建（30分钟）

http://blog.csdn.net/wyx100/article/details/73008528

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python3项目源代码下载_人脸检测和识别源代码下载-opencv3+python3.6完整实战项目源代码识别视频《欢乐颂》中人物，-opencv3《欢乐颂》...相关推荐

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python3项目源代码下载_人脸检测和识别 源代码 下载-opencv3+python3.6完整实战项目源代码 识别视频《欢乐颂》中人物，-opencv3《欢乐颂》...

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