def triplet_train_gen1(BASE_DIR,
IMG_ROW,
IMG_COL,
pathdata,
labeldata,
batch_size=50):
while (True):
imglist = []
for i in range(batch_size):
rndid = random.randint(0, len(pathdata) - 1)
imgpath = BASE_DIR + pathdata[pathdata.index[rndid]]
anchor = imgarr(imgpath)
anchor = cv2.resize(anchor, (IMG_ROW, IMG_COL))
positive = siamese_fetch_img(BASE_DIR,
labeldata[labeldata.index[rndid]],
labeldata,
pathdata,
kind=1)
negative = siamese_fetch_img(BASE_DIR,
labeldata[labeldata.index[rndid]],
labeldata,
pathdata,
kind=0)
positive = cv2.resize(positive, (IMG_ROW, IMG_COL))
negative = cv2.resize(negative, (IMG_ROW, IMG_COL))
imglist.append([anchor, positive, negative])
imglist = np.asarray(imglist)
yield ([imglist[:, 0], imglist[:, 1], imglist[:, 2]])
def triplet_train_gen(BASE_DIR,
IMG_ROW,
IMG_COL,
pathdata,
labeldata,
batch_size=50):
while (True):
anchorlist = []
positivelist = []
negativelist = []
for i in range(batch_size):
rndid = random.randint(0, len(pathdata) - 1)
imgpath = BASE_DIR + pathdata[pathdata.index[rndid]]
anchor = imgarr(imgpath)
anchor = cv2.resize(anchor, (IMG_ROW, IMG_COL))
anchorlist.append(anchor)
positive = siamese_fetch_img(BASE_DIR,
labeldata[labeldata.index[rndid]],
labeldata,
pathdata,
kind=1)
negative = siamese_fetch_img(BASE_DIR,
labeldata[labeldata.index[rndid]],
labeldata,
pathdata,
kind=0)
positive = cv2.resize(positive, (IMG_ROW, IMG_COL))
negative = cv2.resize(negative, (IMG_ROW, IMG_COL))
positivelist.append(positive)
negativelist.append(negative)
yield ([
np.asarray(anchorlist),
np.asarray(positivelist),
np.asarray(negativelist)
], None)
def triplet_valid_gen(BASE_DIR, IMG_ROW, IMG_COL, pathdata, labeldata):
anchorlist = []
positivelist = []
negativelist = []
for i in range(len(pathdata)):
imgpath = BASE_DIR + pathdata[pathdata.index[i]]
print(imgpath)
print(os.path.exists(imgpath))
anchor = imgarr(imgpath)
anchor = cv2.resize(anchor, (IMG_ROW, IMG_COL))
positive = siamese_fetch_img(BASE_DIR,
labeldata[labeldata.index[i]],
labeldata,
pathdata,
kind=1)
negative = siamese_fetch_img(BASE_DIR,
labeldata[labeldata.index[i]],
labeldata,
pathdata,
kind=0)
positive = cv2.resize(positive, (IMG_ROW, IMG_COL))
negative = cv2.resize(negative, (IMG_ROW, IMG_COL))
anchorlist.append(anchor)
positivelist.append(positive)
negativelist.append(negative)
return ([
np.asarray(anchorlist),
np.asarray(positivelist),
np.asarray(negativelist)
], None)
def siamese_train_gen(BASE_DIR,
IMG_ROW,
IMG_COL,
pathdata,
labeldata,
batch_size=50):
while (True):
imglist1 = []
imglist2 = []
labellist = []
for i in range(batch_size):
rndid = random.randint(0, len(pathdata) - 1)
imgpath = BASE_DIR + pathdata[pathdata.index[rndid]]
img1 = imgarr(imgpath)
if (i % 2 == 0):
kind = 0
labellist.append(0)
else:
kind = 1
labellist.append(1)
img2 = siamese_fetch_img(BASE_DIR,
labeldata[labeldata.index[rndid]],
labeldata,
pathdata,
kind=kind)
img1 = cv2.resize(img1, (IMG_ROW, IMG_COL))
img2 = cv2.resize(img2, (IMG_ROW, IMG_COL))
imglist1.append(img1)
imglist2.append(img2)
yield ([np.asarray(imglist1),
np.asarray(imglist2)], np.asarray(labellist))
def triplet_valid_gen1(BASE_DIR, IMG_ROW, IMG_COL, pathdata, labeldata):
imglist = []
for i in range(len(pathdata)):
imgpath = BASE_DIR + pathdata[pathdata.index[i]]
anchor = imgarr(imgpath)
anchor = cv2.resize(anchor, (IMG_ROW, IMG_COL))
positive = siamese_fetch_img(BASE_DIR,
labeldata[labeldata.index[i]],
labeldata,
pathdata,
kind=1)
negative = siamese_fetch_img(BASE_DIR,
labeldata[labeldata.index[i]],
labeldata,
pathdata,
kind=0)
positive = cv2.resize(positive, (IMG_ROW, IMG_COL))
negative = cv2.resize(negative, (IMG_ROW, IMG_COL))
imglist.append([anchor, positive, negative])
imglist = np.asarray(imglist)
return ([imglist[:, 0], imglist[:, 1], imglist[:, 2]])
def siamese_valid_gen(BASE_DIR, IMG_ROW, IMG_COL, pathdata, labeldata):
imglist1 = []
imglist2 = []
labellist = []
for i in range(len(pathdata)):
imgpath = BASE_DIR + pathdata[pathdata.index[i]]
img1 = imgarr(imgpath)
if (i % 2 == 0):
kind = 0
labellist.append(0)
else:
kind = 1
labellist.append(1)
img2 = siamese_fetch_img(BASE_DIR,
labeldata[labeldata.index[i]],
labeldata,
pathdata,
kind=kind)
img1 = cv2.resize(img1, (IMG_ROW, IMG_COL))
img2 = cv2.resize(img2, (IMG_ROW, IMG_COL))
imglist1.append(img1)
imglist2.append(img2)
return ([np.asarray(imglist1),
np.asarray(imglist2)], np.asarray(labellist))