import cv2
import numpy as np
from random import shuffle
from tqdm import tqdm
import os
TRAIN_DIR=r'C:\Users\Valued Customer\Desktop\Object detection\train'
TEST_DIR=r'C:\Users\Valued Customer\Desktop\Object detection\test'
IMG_SIZE=300
LR=1e-3
MODEL_NAME = 'dogsvscats-{}-{}.model'.format(LR,'2conv-basic')
def Label_img(img):
label = img.split('.')[-3]
if label == 'cat':
return [1,0]
elif label == 'dog':
return [0,1]
def create_train_data():
training_data = []
for img in tqdm(os.listdir(TRAIN_DIR)):
label = Label_img(img)
path = os.path.join(TRAIN_DIR,img)
img = cv2.resize(cv2.imread(path),(IMG_SIZE,IMG_SIZE), interpolation = cv2.INTER_AREA)
training_data.append([np.array(img),np.array(label)])
shuffle(training_data)
np.save('train_data.npy',training_data)
return training_data
def process_test_data():
testing_data = []
for img in tqdm(os.listdir(TEST_DIR)):
path = os.path.join(TEST_DIR,img)
img_num = img.split('.')[0]
img = cv2.resize(cv2.imread(path),(IMG_SIZE,IMG_SIZE), interpolation = cv2.INTER_AREA)
testing_data.append([np.array(img),img_num])
np.save('testing_data.npy',testing_data)
return testing_data
train_data = create_train_data()
#if U already have train data then:
#train_data = np.load('train_data.npy',allow_pickle=True)
print('data has been loaded')
import tflearn
from tflearn.layers.conv import conv_2d,max_pool_2d
from tflearn.layers.core import input_data,dropout,fully_connected
from tflearn.layers.estimator import regression
from tflearn.layers.normalization import batch_normalization as bn
import tensorflow as tf
tf.reset_default_graph()
convnet = input_data(shape=[None,IMG_SIZE,IMG_SIZE,3],name='input')
convnet = conv_2d(convnet,32,filter_size=[2,2],activation='relu')
convnet = conv_2d(convnet,64,filter_size=[2,2],activation='relu')
convnet = bn(convnet,trainable=True)
convnet = max_pool_2d(convnet,kernel_size=[3,3])
convnet = conv_2d(convnet,32,filter_size=[2,2],activation='relu')
convnet = conv_2d(convnet,64,filter_size=[2,2],activation='relu')
convnet = bn(convnet,trainable=True)
convnet = max_pool_2d(convnet,kernel_size=[3,3])
convnet = conv_2d(convnet,32,filter_size=[2,2],activation='relu')
convnet = conv_2d(convnet,64,filter_size=[2,2],activation='relu')
convnet = bn(convnet,trainable=True)
convnet = max_pool_2d(convnet,kernel_size=[3,3])
convnet = fully_connected(convnet,1024,activation='relu')
convnet = dropout(convnet,0.8)
convnet = tflearn.layers.normalization.batch_normalization(convnet,trainable=True)
convnet = fully_connected(convnet,2,activation='softmax')
convnet = regression(convnet,
optimizer='adam',
learning_rate= LR,
loss='categorical_crossentropy',
name='targets')
model = tflearn.DNN(convnet)
train = train_data[:-500]
test = train_data[-500:]
X = np.array([i[0] for i in train]).reshape(-1,IMG_SIZE,IMG_SIZE,3)
Y = np.array([i[1] for i in train])
test_x = np.array([i[0] for i in test]).reshape(-1,IMG_SIZE,IMG_SIZE,3)
test_y = np.array([i[1] for i in test])
model.fit({'input':X},{'targets':Y},
n_epoch=5,validation_set=({'input':test_x},{'targets':test_y}),
snapshot_step=500,show_metric=True,run_id=MODEL_NAME)
#
model.save(MODEL_NAME)
When ever I try to run this code it stops a 21% when it is creating the training data
def create_train_data():
training_data = []
for img in tqdm(os.listdir(TRAIN_DIR)):
label = Label_img(img)
path = os.path.join(TRAIN_DIR,img)
img = cv2.resize(cv2.imread(path),(IMG_SIZE,IMG_SIZE), interpolation = cv2.INTER_AREA)
And it keeps giving em a open cv error
error: OpenCV(4.1.1) C:\projects\opencv-python\opencv\modules\imgproc\src\resize.cpp:3720: error: (-215:Assertion failed) !ssize.empty() in function 'cv::resize
I am on windows ten using cuda for the first (not sure if i set it up right) Also does anyone know how i can check if i am using cuda Thanks