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

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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 </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:
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 </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()

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