def getPrediction(q):
# Open saved SVM model
loaded_model = pickle.load(open("bin/src/model.sav", "rb"))
while True:
if not q.empty():
# Pasamos la foto de la mano por el SVM
item = q.get()
# print("Se ha sacado 1 item, quedan {}".format(q.qsize()))
item = np.float32(item) / 255.0
item = cv.cvtColor(item, cv.COLOR_BGR2GRAY)
data = hog(item)
data = np.array([data])
print(loaded_model.predict(data))
img = cv.imread(dpath)
if (len(img) != None):
print(dpath + ' has been succesfully loaded!')
img = img[8:199 - 8, 8:199 - 8]
dim = (160, 160)
img = cv.resize(img, dim, interpolation=cv.INTER_AREA)
img = np.float32(img) / 255.0
img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
# cv.imshow('Imagen', img)
# cv.waitKey(0)
# The function hog will only accept 1 dimension images (Gray)
X[(num - 1) * samp + i - 1] = hog(img)
Y.append(l)
else:
print("Error! no image found")
i = samp
# Now that we already have the variables well fit the VSM
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)
svclassifier = SVC(kernel='linear')
svclassifier.fit(X_train, Y_train)
Y_pred = svclassifier.predict(X_test)
print("\n\nCLASSIFIER 1: Linear\n\n")
print(confusion_matrix(Y_test, Y_pred))
'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z')
dim = (160, 160)
data = np.zeros(shape=(len(opts),2916))
num = 0
for o in opts:
path = '../../../../asl_alphabet_test/asl_alphabet_test/{}_test.jpg'.format(o)
img = cv.imread(path)
img = img[8:199-8,8:199-8]
img = cv.resize(img, dim, interpolation = cv.INTER_AREA)
img = np.float32(img)/255.0
img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
data[num] = hog(img)
num=num+1
# # Now that we have our model we'll import some images to test the model outside the dataset
# img = cv.imread('../../../../fotoc.png')
# img = img[8:199-8,8:199-8]
# dim = (160, 160)
# img = cv.resize(img, dim, interpolation = cv.INTER_AREA)
# img = np.float32(img)/255.0
# img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
#
# # img2 = cv.imread('../../../../fotoj.png')
# # img2 = img2[8:199-8,8:199-8]
# # img2 = cv.resize(img2, dim, interpolation = cv.INTER_AREA)
# # img2 = np.float32(img2)/255.0
#Quitamos el fondo
BackGround = backgroundRemoval(noFace,bg)
#Binarizamos la imagen
imbin = binarization(BackGround, H_LowThreshold, H_HighThreshold, S_LowThreshold, S_HighThreshold, V_LowThreshold, V_HighThreshold)
#Situamos el Bounding box
bnd, origbnd = Bounding(imbin,frame)
# cv.imshow('Foreground',BackGround)
# cv.imshow('User hand', bnd)
cv.imshow('Bounding box', origbnd)
# Pasamos la foto de la mano por el SVM
bnd = np.float32(bnd)/255.0
bnd = cv.cvtColor(bnd, cv.COLOR_BGR2GRAY)
data = hog(bnd)
data = np.array([data])
print(loaded_model.predict(data))
#teclas
k = cv.waitKey(1) & 0xFF
if k == ord('q'):#quit
break
elif k == ord('b'):#remove background
print('Obtenemos nuevo background')
bg = frame
elif k == ord('s'):#capture skin
print('Capturamos la piel del usuario')
th = captureSkin(BackGround)
# Asign calculated thresholds to used ones
H_LowThreshold = th[0]
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import classification_report, confusion_matrix
import pickle
import math
from _hog import hog
# Open saved SVM model
loaded_model = pickle.load(open("bin/src/model.sav", "rb"))
# Now that we have our model we'll import some images to test the model outside the dataset
img = cv.imread('../../../../fotoc.png')
img = img[8:199 - 8, 8:199 - 8]
dim = (160, 160)
img = cv.resize(img, dim, interpolation=cv.INTER_AREA)
img = np.float32(img) / 255.0
img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
img2 = cv.imread('../../../../fotoj.png')
img2 = img2[8:199 - 8, 8:199 - 8]
img2 = cv.resize(img2, dim, interpolation=cv.INTER_AREA)
img2 = np.float32(img2) / 255.0
img2 = cv.cvtColor(img2, cv.COLOR_BGR2GRAY)
data = hog(img)
data2 = hog(img2)
data = np.array([data])
data2 = np.array([data2])
print(loaded_model.predict(data))
print(loaded_model.predict(data2))