def init_algorithm(
self,
save_training_file='datasets/faces_training.pkl',
load_preprocessed_data='datasets/faces_preprocessed.pkl',
load_mlp='params/mlp.xml',
face_casc='params/haarcascade_frontalface_default.xml',
left_eye_casc='params/haarcascade_lefteye_2splits.xml',
right_eye_casc='params/haarcascade_righteye_2splits.xml'):
self.data_file = save_training_file
self.faces = FaceDetector(face_casc, left_eye_casc, right_eye_casc)
self.head = None
# load preprocessed dataset to access labels and PCA params
if path.isfile(load_preprocessed_data):
(_, y_train), (_, y_test), V, m = homebrew.load_from_file(
load_preprocessed_data)
self.pca_V = V
self.pca_m = m
self.all_labels = np.unique(np.hstack((y_train, y_test)))
# load pre-trained multi-layer perceptron
if path.isfile(load_mlp):
layer_sizes = np.array([self.pca_V.shape[1],
len(self.all_labels)])
self.MLP = MultiLayerPerceptron(layer_sizes, self.all_labels)
self.MLP.load(load_mlp)
else:
print "Warning: Testing is disabled"
print "Could not find pre-trained MLP file ", load_mlp
self.testing.Disable()
else:
print "Warning: Testing is disabled"
print "Could not find data file ", load_preprocessed_data
self.testing.Disable()
def InitAlgorithm(self, faceCasc, leftEyeCasc, rightEyeCasc, loadPreprocessedData, loadMLP):
# load pre-trained cascades
self.faceCasc = cv3.CascadeClassifier(faceCasc)
if self.faceCasc.empty():
print 'Warning: Could not load face cascade:', faceCasc
raise SystemExit
self.leftEyeCasc = cv3.CascadeClassifier(leftEyeCasc)
if self.leftEyeCasc.empty():
print 'Warning: Could not load left eye cascade:', leftEyeCasc
raise SystemExit
self.rightEyeCasc = cv3.CascadeClassifier(rightEyeCasc)
if self.rightEyeCasc.empty():
print 'Warning: Could not load right eye cascade:', rightEyeCasc
raise SystemExit
# load preprocessed dataset to access labels and PCA params
if path.isfile(loadPreprocessedData):
(_,y_train),(_,y_test),self.pca_V,self.pca_m = homebrew.load_from_file(loadPreprocessedData)
self.allLabels = np.unique(np.hstack((y_train,y_test)))
# load pre-trained multi-layer perceptron
if path.isfile(loadMLP):
self.MLP = MultiLayerPerceptron(np.array([self.pca_V.shape[1], len(self.allLabels)]),
self.allLabels)
self.MLP.load(loadMLP)
else:
print "Warning: Testing is disabled"
print "Could not find pre-trained MLP file ", loadMLP
self.testing.Disable()
else:
print "Warning: Testing is disabled"
print "Could not find preprocessed data file ", loadPreprocessedData
self.testing.Disable()
def init_algorithm(
self,
save_training_file='datasets/faces_training.pkl',
load_preprocessed_data='datasets/faces_preprocessed.pkl',
load_mlp='params/mlp.xml',
face_casc='params/haarcascade_frontalface_default.xml',
left_eye_casc='params/haarcascade_lefteye_2splits.xml',
right_eye_casc='params/haarcascade_righteye_2splits.xml'):
"""Initializes face detector and facial expression classifier
This method initializes both the face detector and the facial
expression classifier.
:param save_training_file: filename for storing the assembled
training set
:param load_preprocessed_data: filename for loading a previously
preprocessed dataset (for
classification in Testing Mode)
:param load_mlp: filename for loading a pre-trained
MLP classifier (use the script
train_test_mlp.py)
:param face_casc: path to a face cascade
:param left_eye_casc: path to a left-eye cascade
:param right_eye_casc: path to a right-eye cascade
"""
self.data_file = save_training_file
self.faces = FaceDetector(face_casc, left_eye_casc, right_eye_casc)
self.head = None
# load preprocessed dataset to access labels and PCA params
if path.isfile(load_preprocessed_data):
(_, y_train), (
_,
y_test), V, m = homebrew.load_from_file(load_preprocessed_data)
self.pca_V = V
self.pca_m = m
self.all_labels = np.unique(np.hstack((y_train, y_test)))
# load pre-trained multi-layer perceptron
if path.isfile(load_mlp):
layer_sizes = np.array(
[self.pca_V.shape[1],
len(self.all_labels)])
self.MLP = MultiLayerPerceptron(layer_sizes, self.all_labels)
self.MLP.load(load_mlp)
else:
print "Warning: Testing is disabled"
print "Could not find pre-trained MLP file ", load_mlp
self.testing.Disable()
else:
print "Warning: Testing is disabled"
print "Could not find data file ", load_preprocessed_data
self.testing.Disable()
def init_algorithm(
self,
save_training_file='datasets/faces_training.pkl',
load_preprocessed_data='datasets/faces_preprocessed.pkl',
load_mlp='params/mlp.xml',
face_casc='params/haarcascade_frontalface_default.xml',
left_eye_casc='params/haarcascade_lefteye_2splits.xml',
right_eye_casc='params/haarcascade_righteye_2splits.xml'):
"""Initializes face detector and facial expression classifier
This method initializes both the face detector and the facial
expression classifier.
:param save_training_file: filename for storing the assembled
training set
:param load_preprocessed_data: filename for loading a previously
preprocessed dataset (for
classification in Testing Mode)
:param load_mlp: filename for loading a pre-trained
MLP classifier (use the script
train_test_mlp.py)
:param face_casc: path to a face cascade
:param left_eye_casc: path to a left-eye cascade
:param right_eye_casc: path to a right-eye cascade
"""
self.data_file = save_training_file
self.faces = FaceDetector(face_casc, left_eye_casc, right_eye_casc)
self.head = None
# load preprocessed dataset to access labels and PCA params
if path.isfile(load_preprocessed_data):
(_, y_train), (_, y_test), V, m = homebrew.load_from_file(
load_preprocessed_data)
self.pca_V = V
self.pca_m = m
self.all_labels = np.unique(np.hstack((y_train, y_test)))
# load pre-trained multi-layer perceptron
if path.isfile(load_mlp):
layer_sizes = np.array([self.pca_V.shape[1],
len(self.all_labels)])
self.MLP = MultiLayerPerceptron(layer_sizes, self.all_labels)
self.MLP.load(load_mlp)
else:
print "Warning: Testing is disabled"
print "Could not find pre-trained MLP file ", load_mlp
self.testing.Disable()
else:
print "Warning: Testing is disabled"
print "Could not find data file ", load_preprocessed_data
self.testing.Disable()
def InitAlgorithm(self, faceCasc, leftEyeCasc, rightEyeCasc,
loadPreprocessedData, loadMLP):
# load pre-trained cascades
self.faceCasc = cv3.CascadeClassifier(faceCasc)
if self.faceCasc.empty():
print 'Warning: Could not load face cascade:', faceCasc
raise SystemExit
self.leftEyeCasc = cv3.CascadeClassifier(leftEyeCasc)
if self.leftEyeCasc.empty():
print 'Warning: Could not load left eye cascade:', leftEyeCasc
raise SystemExit
self.rightEyeCasc = cv3.CascadeClassifier(rightEyeCasc)
if self.rightEyeCasc.empty():
print 'Warning: Could not load right eye cascade:', rightEyeCasc
raise SystemExit
# load preprocessed dataset to access labels and PCA params
if path.isfile(loadPreprocessedData):
(_, y_train), (
_, y_test), self.pca_V, self.pca_m = homebrew.load_from_file(
loadPreprocessedData)
self.allLabels = np.unique(np.hstack((y_train, y_test)))
# load pre-trained multi-layer perceptron
if path.isfile(loadMLP):
self.MLP = MultiLayerPerceptron(
np.array([self.pca_V.shape[1],
len(self.allLabels)]), self.allLabels)
self.MLP.load(loadMLP)
else:
print "Warning: Testing is disabled"
print "Could not find pre-trained MLP file ", loadMLP
self.testing.Disable()
else:
print "Warning: Testing is disabled"
print "Could not find preprocessed data file ", loadPreprocessedData
self.testing.Disable()
import socket
from socket import *
'''new=socket()
new.connect(('192.168.43.34',7000))'''
#new.listen(0)
#a,addr=new.accept()
load_preprocessed_data = 'datasets/faces_preprocessed.pkl'
load_mlp = 'params/mlp.xml'
light1 = 0
light2 = 0
if path.isfile(load_preprocessed_data):
(_,
y_train), (_,
y_test), V, m = homebrew.load_from_file(load_preprocessed_data)
pca_V = V
pca_m = m
all_labels = np.unique(np.hstack((y_train, y_test)))
# load pre-trained multi-layer perceptron
if path.isfile(load_mlp):
layer_sizes = np.array([pca_V.shape[1], len(all_labels)])
MLP = MultiLayerPerceptron(layer_sizes, all_labels)
MLP.load(load_mlp)
#############################################################################
import cv2
import numpy as np
import copy
import math