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clustering.py
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clustering.py
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from __future__ import division
import numpy as np
import cPickle as pkl
from stacked_autoencoder import SdA
import theano
import cv2
import io
import math
from picamera.array import PiRGBArray
from picamera import PiCamera
import time
N_DIM = 100
PARTITION = 10
IS_KMEANS = 1
train_mean = np.load('new_data/store_mean.npy')
def label_faces_from_video(centers):
# initialize the camera and grab a reference to the raw camera capture
camera = PiCamera()
camera.resolution = (640, 480)
camera.framerate = 32
camera.sharpness = 50
rawCapture = PiRGBArray(camera, size=(640, 480))
face_cascade = cv2.CascadeClassifier('/home/pi/mainak/opencv-3.0.0/data/haarcascades/haarcascade_frontalface_default.xml')
# allow the camera to warmup
time.sleep(0.1)
# loading the trained model
model_file = file('models/pretrained_model.save', 'rb')
sda = pkl.load(model_file)
model_file.close()
get_single_encoded_data = sda.single_encoder_function()
#time = 1
# capture frames from the camera
for frame in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):
image, face_images = capture_and_detect(frame, face_cascade)
for face in face_images:
encoded_x = get_single_encoded_data(train_x=face)
if (IS_KMEANS == 1):
label_x, dist = get_kmeans_labels(centers, encoded_x)
# else:
# label_x = cluster.get_tseries_labels(encoded_x,time)
print("This is person: ", label_x, dist)
# time += 1
# show the frame
cv2.imshow("Frame", image)
key = cv2.waitKey(1) & 0xFF
# clear the stream in preparation for the next frame
rawCapture.truncate(0)
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
def get_kmeans_labels(centers, x):
dist = []
for center in centers:
dist.append(np.linalg.norm(center-x))
return np.argmin(np.asarray(dist)), min(np.asarray(dist))
def capture_and_detect(frame, face_cascade):
image = frame.array
im_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(im_gray, 1.3, 5)
face_images = []
for (x,y,w,h) in faces:
cv2.rectangle(image,(x,y),(x+w,y+h),(255,0,0),2)
face_gray = np.array(im_gray[y:y+h, x:x+w], 'uint8')
face_sized = cv2.resize(face_gray, (30, 30))
flat_face = face_sized.reshape(1, face_sized.shape[0]*face_sized.shape[1])
flat_face = flat_face/255
face_x = flat_face - train_mean
face_images.append(face_x)
return image, face_images
def cluster_train_data():
train_set = np.load('new_data/train_faces.npy')
test_set = np.load('new_data/test_faces.npy')
tr_x = [i[0] for i in train_set]
tr_y = [i[1] for i in train_set]
te_x = [i[0] for i in test_set]
te_y = [i[1] for i in test_set]
train_set_x = theano.shared(value=np.asarray(tr_x), borrow=True)
test_set_x = theano.shared(value=np.asarray(te_x), borrow=True)
train_set_l = np.asarray(tr_y)
test_set_l = np.asarray(te_y)
# compute number of minibatches for training, validation and testing
n_train_data = train_set_x.get_value(borrow=True).shape[0]
print "n_train_data: ", n_train_data
n_test_data = test_set_x.get_value(borrow=True).shape[0]
print "n_test_data: ", n_test_data
train_x = np.zeros((n_train_data, N_DIM), dtype=np.float32)
test_x = np.zeros((n_test_data, N_DIM), dtype=np.float32)
# loading the trained model
model_file = file('models/pretrained_model.save', 'rb')
sda = pkl.load(model_file)
model_file.close()
get_encoded_data = sda.encoder_function(train_set_x=train_set_x)
get_single_encoded_data = sda.single_encoder_function()
for i in range(n_train_data):
encoded_x = get_encoded_data(index=i)
if (IS_KMEANS == 1):
train_x[i] = encoded_x
# else:
# cluster.getDimensionInfo(endoded_x)
if (IS_KMEANS == 1):
#flags = cv2.KMEANS_RANDOM_CENTERS
flags = cv2.KMEANS_PP_CENTERS
# Apply KMeans
compactness, labels, centers = cv2.kmeans(data=train_x, K=3, bestLabels=None, criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_MAX_ITER, 100000, 0.001), attempts=10, flags=flags)
#print "Error: ", compactness, len(labels)
get_accuracy(train_x, train_set_l, labels)
test_labels = []
for i in range(n_test_data):
encoded_x = get_single_encoded_data(train_x=test_set_x.get_value(borrow=True)[i:i+1])
label, _ = get_kmeans_labels(centers, encoded_x)
test_labels.append(label)
test_x[i] = encoded_x
# else:
# cluster.getDimensionInfo(endoded_x)
get_accuracy(test_x, test_set_l, test_labels)
# else:
# time = 1
# for data in train_x:
# centers[time] = cluster.get_tseries_labels(data,time)
# time += 1
return centers
def get_accuracy(data_x, data_y, labels):
A = []
B = []
C = []
A_l1 =0
A_l2 =0
A_l3 =0
B_l1 =0
B_l2 =0
B_l3 =0
C_l1 =0
C_l2 =0
C_l3 =0
# Now split the data depending on their labels
for i in xrange(len(labels)):
if (labels[i] == 0):
if data_y[i] == "label1":
A_l1 += 1
elif data_y[i] == "label2":
A_l2 += 1
elif data_y[i] == "label3":
A_l3 += 1
A.append(data_x[i])
elif (labels[i] == 1):
if data_y[i] == "label1":
B_l1 += 1
elif data_y[i] == "label2":
B_l2 += 1
elif data_y[i] == "label3":
B_l3 += 1
B.append(data_x[i])
elif (labels[i] == 2):
if data_y[i] == "label1":
C_l1 += 1
elif data_y[i] == "label2":
C_l2 += 1
elif data_y[i] == "label3":
C_l3 += 1
C.append(data_x[i])
print "Length: ", len(A), len(B), len(C)
len_A = len(A)
len_B = len(B)
len_C = len(C)
max_A = max(A_l1,A_l2,A_l3)
max_B = max(B_l1,B_l2,B_l3)
max_C = max(C_l1,C_l2,C_l3)
#accuracy = (max_A/len_A) + (max_B/len_B) + (max_C/len_C)
accuracy = (max_A + max_B + max_C)/len(labels)
print "Acc: ", accuracy
print "Cluster A Count: ", A_l1, A_l2, A_l3
print "Cluster B Count: ", B_l1, B_l2, B_l3
print "Cluster C Count: ", C_l1, C_l2, C_l3
if __name__ == '__main__':
centers = cluster_train_data()
label_faces_from_video(centers)