CYAN = '\033[96m'
BLUE = '\033[94m'
GREEN = '\033[92m'
YELLOW = '\033[93m'
RED = '\033[91m'
END = '\033[0m'
while True:
try:
cin = input('image path: ')
imagePath = Path(cin)
img = Image.open(imagePath)
img.load()
img = resize_image(img, 100, 125)
data = np.asarray(img, dtype="float32")
data /= 255
data = np.reshape(data, (1, 125, 100, 4))
result = model.predict(data)
print(color.BLUE + 'Result:' + color.END)
if result[0][0] > .5:
if result[0][0] > .9:
print(color.GREEN + 'Male (' + str(result[0][0]) + ')' +
color.END)
print(color.RED + 'Female (' + str(result[0][1]) + ')' +
color.END)
else:
print(color.CYAN + 'Male (' + str(result[0][0]) + ')' +
color.END)
print(color.BLUE + 'Female (' + str(result[0][1]) + ')' +
loss='categorical_crossentropy',
learning_rate=0.001)
#training
model = tflearn.DNN(network, tensorboard_verbose=3)
model.fit(X,
Y,
n_epoch=13,
shuffle=True,
validation_set=0.2,
show_metric=True,
batch_size=96,
run_id='brandberg')
#classifier
while True:
try:
cin = input('image path: ')
imagePath = Path(cin)
img = Image.open(imagePath)
img.load()
img = resize_image(img, 160, 180)
data = np.asarray(img, dtype="float32")
data /= 255
data = np.reshape(data, (1, 180, 160, 4))
result = model.predict(data)
print('Male (' + str(result[0][0]) + ')')
print('Female (' + str(result[0][1]) + ')')
except FileNotFoundError:
print('File not found.')
csv_dir = "/home/goerlab/Bilinear-CNN-TensorFlow/core/model/20180110/record/"
csvfile = open(csv_dir + "test_detail.csv", "wb")
writer = csv.writer(csvfile)
writer.writerow(["labels", "prediction", "confidence_score", "file_name"])
image_dir = "/media/goerlab/My Passport/Welder_detection/dataset/20180109/Data/val/"
cnt = 0
for dirc in os.listdir(image_dir):
subdir = image_dir + dirc + '/'
if not os.path.isfile(subdir):
for subdirc in os.listdir(subdir):
file_name = subdir + subdirc
print(file_name)
file_image = data_utils.load_image(file_name)
file_image = data_utils.resize_image(file_image, 448, 448)
#file_image=cv2.imread(file_name)
#file_image=cv2.resize(file_image,(448,448))
#file_image=file_image*(1./255)-0.5
trans_image = np.asarray(file_image).reshape((1, 448, 448, 3))
#trans_image=trans_image(1./255)-0.5
real_predict, real_fc3l, real_confidence_score = sess.run(
[prediction, vgg.fc3l, confidence_score],
feed_dict={imgs: trans_image})
#writer.writerow([str(dirc),real_predict[0],real_proba[0],file_name])
# print(trans_image)
real_label = int(dirc)
print("label:%d, prediction:%d, confidence score:%f" %
(real_label, real_predict, real_confidence_score))
cnt=0
print("here")
for root,dirs,files in os.walk(image_dir):
print(root)
for i in files:
print("file:%s" % (i))
filename=os.path.splitext(i)
if filename[1]=='.bmp':
image_file=root+"/"+i
real_label=0
#file_name=image_dir+dirc
print(image_file)
file_image=data_utils.load_image(image_file)
croped=crop_image(file_image)
file_image=data_utils.resize_image(croped,448,448)
#file_image=cv2.imread(file_name)
#file_image=cv2.resize(file_image,(448,448))
#file_image=file_image*(1./255)-0.5
trans_image=np.asarray(file_image).reshape((1,448,448,3))
#trans_image=trans_image(1./255)-0.5
real_predict,real_fc3l,real_confidence_score,top1_out,top2_out=sess.run([prediction,vgg.fc3l,confidence_score,top1,top2],feed_dict={imgs:trans_image})
#writer.writfor i in files:
print("file:%s" % (i))
filename=os.path.splitext(i)
if filename[1]=='.bmp':
image_file=root+"/"+i