def main():
#size = (28, 28)
size = (24, 36)
window_size = (40, 60)
img_size = (200, 100)
#imgs,labels=get_mnist_dataset(size)
path = '../../data2/image%d.gif.jpg'
classifier = CNN_number_classifier.CNN_number_classifier(size)
source_imgs, source_positions, source_labels = read_data(
'pos_and_lab.txt', '../../data2/image%d.gif.jpg')
train_s_imgs, train_s_positions, train_s_labels, test_s_imgs, test_s_positions, test_s_labels = divide_source_data(
source_imgs, source_positions, source_labels, 0.9, mode='r')
train_imgs, train_labels = create_data(train_s_imgs,
train_s_positions,
train_s_labels,
neg_num=1)
train_imgs = reshape_images(train_imgs, size)
test_imgs, test_labels = create_data(test_s_imgs,
test_s_positions,
test_s_labels,
neg_num=1)
test_imgs = reshape_images(test_imgs, size)
#train_imgs,train_labels,test_imgs,test_labels=divide_data(imgs,labels,0.9)
'''
while True:
c=cv2.waitKey(30)
if c==ord('q'):
break
cv2.imshow('img',imgs[1])
'''
#res0=classifier.predict(train_imgs[1])
classifier.restore('sess11')
iters = 7102
prob_space = np.linspace(0.95, 0.5, iters)
#classifier.fit(train_imgs,train_labels,None,None,iters,prob_space)
#classifier.save('sess11')
#res1=classifier.predict(imgs[1])
predict = create_predict_func(classifier, size)
matching_imgs, matching_labels = create_matching_images(
train_s_imgs, train_s_labels, img_size, window_size, predict,
sliding_window)
np.save('matching_imgs', matching_imgs)
np.save('matching_labels', matching_labels)
#matching_imgs=np.load('matching_imgs.npy')
#matching_labels=np.load('matching_labels.npy')
digit_localizer = Digit_localizer(
(img_size[0] - window_size[0], img_size[1] - window_size[1]))
digit_localizer.fit(matching_imgs, matching_labels, None, None, 5000)
thresh = 0.9
number = 4
i = 0
img = cv2.imread(path % i)
bgr = img.copy()
m = img.shape[0]
n = img.shape[1]
cv2.namedWindow('bgr')
#cv2.createTrackbar('s0','bgr',0,n-1,dummy)
#cv2.createTrackbar('e0', 'bgr', 0, n - 1, dummy)
c = ord('n')
while True:
if c == ord('q'):
break
if c == ord('n'):
i += 1
img = test_s_imgs[i]
gray = img
elem = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))
gray = cv2.dilate(img, elem)
#gray=cv2.erode(gray,elem)
#gray = cv2.GaussianBlur(gray,(3,3),-1)
bgr = cv2.cvtColor(gray, cv2.COLOR_GRAY2BGR)
res = sliding_window(predict,
gray.astype(np.float32) / 255, 4, window_size)
for item in res:
for num in range(10):
if item[0][0, num] >= thresh:
cv2.rectangle(bgr, (item[1], item[3]),
(item[2], item[4]),
(26 * num, 0, 255 - 26 * num))
continue
#matching_img=get_matching_img(res,(img.shape[1],img.shape[0]),window_size)
#digit_maps=digit_localizer.digit_maps.eval(feed_dict={digit_localizer.x:matching_img.reshape(-1,40,160,10)})
cv2.imshow('bgr', bgr)
c = cv2.waitKey(30)
def automatic_test():
size = (32, 44)
#size = (24,36)
window_size = (40, 60)
img_size = (200, 100)
#imgs,labels=get_mnist_dataset(size)
#path='../../data2/image%d.gif.jpg'
source_imgs, source_positions, source_labels = read_data(
'pos_and_lab.txt', '../../data2/image%d.gif.jpg')
train_s_imgs, train_s_positions, train_s_labels, test_s_imgs, test_s_positions, test_s_labels = divide_source_data(
source_imgs, source_positions, source_labels, 0.9, mode='r')
#train_imgs,train_labels=create_data(train_s_imgs,train_s_positions,train_s_labels,neg_num=1)
#train_imgs=reshape_images(train_imgs,size)
#test_imgs,test_labels=create_data(test_s_imgs,test_s_positions,test_s_labels,neg_num=1)
#test_imgs=reshape_images(test_imgs,size)
#saver=tf.train.Saver()
classifier = CNN_number_classifier.CNN_number_classifier(size)
classifier.restore('sess18_32x44_s')
print np.sum(classifier.W_fc1.eval(classifier.sess))
digit_localizer = Digit_localizer(
(img_size[0] - window_size[0], img_size[1] - window_size[1]))
digit_localizer.restore('loc_sess27_size32x44_as8')
print np.sum(classifier.W_fc1.eval(classifier.sess))
predict = create_predict_func(classifier, size)
correct_num = 0
for i in range(len(test_s_imgs)):
img = test_s_imgs[i]
label = test_s_labels[i]
elem = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))
gray = cv2.dilate(img, elem)
res = sliding_window(predict,
gray.astype(np.float32) / 255, 2, window_size)
matching_img = get_matching_img(res, img_size, window_size)
p_label = digit_localizer.predict(matching_img)
err = False
print i
for j in range(6):
if label[j] != p_label[0][j]:
err = True
print 'l = ', label, ' p = ', p_label
break
if not err:
correct_num += 1
print float(correct_num) / len(test_s_labels)
def train_classifier():
restorer = Restorer()
restorer.restore('r_sess2')
#size = (28, 28)
size = (32, 44)
window_size = (40, 60)
img_size = (200, 100)
#imgs,labels=get_mnist_dataset(size)
path = '../../data2/image%d.gif.jpg'
classifier = CNN_number_classifier.CNN_number_classifier(size)
source_imgs, source_positions, source_labels = read_data(
'pos_and_lab.txt', '../../data2/image%d.gif.jpg')
#prepare_func=create_prepare_func(restorer)
#prepared_imgs=prepare_imgs(source_imgs,prepare_func)
prepared_imgs = np.load('restored_imgs.npy')
train_s_imgs, train_s_positions, train_s_labels, test_s_imgs, test_s_positions, test_s_labels = divide_source_data(
prepared_imgs, source_positions, source_labels, 0.9, mode='r')
train_imgs, train_labels = create_data(train_s_imgs,
train_s_positions,
train_s_labels,
neg_num=1)
train_imgs = reshape_images(train_imgs, size)
test_imgs, test_labels = create_data(test_s_imgs,
test_s_positions,
test_s_labels,
neg_num=1)
test_imgs = reshape_images(test_imgs, size)
classifier.restore('sess18_32x44_s')
iters = 7102
prob_space = np.linspace(0.6, 0.6, iters)
classifier.fit(train_imgs, train_labels, test_imgs, test_labels, iters,
prob_space)
classifier.save('sess18_32x44_s')
def test():
#size = (28, 28)
size = (32, 44)
window_size = (40, 60)
img_size = (200, 100)
#imgs,labels=get_mnist_dataset(size)
#path='../../data2/image%d.gif.jpg'
source_imgs, source_positions, source_labels = read_data(
'pos_and_lab.txt', '../../data2/image%d.gif.jpg')
train_s_imgs, train_s_positions, train_s_labels, test_s_imgs, test_s_positions, test_s_labels = divide_source_data(
source_imgs, source_positions, source_labels, 0.9, mode='r')
#train_imgs,train_labels=create_data(train_s_imgs,train_s_positions,train_s_labels,neg_num=1)
#train_imgs=reshape_images(train_imgs,size)
#test_imgs,test_labels=create_data(test_s_imgs,test_s_positions,test_s_labels,neg_num=1)
#test_imgs=reshape_images(test_imgs,size)
#saver=tf.train.Saver()
classifier = CNN_number_classifier.CNN_number_classifier(size)
classifier.restore('sess13')
print np.sum(classifier.W_fc1.eval(classifier.sess))
digit_localizer = Digit_localizer(
(img_size[0] - window_size[0], img_size[1] - window_size[1]))
digit_localizer.restore('loc_sess15_size32x44')
print np.sum(classifier.W_fc1.eval(classifier.sess))
predict = create_predict_func(classifier, size)
for i in range(len(test_s_imgs)):
img = test_s_imgs[i]
label = test_s_labels[i]
elem = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))
gray = cv2.dilate(img, elem)
res = sliding_window(predict,
gray.astype(np.float32) / 255, 4, window_size)
matching_img = get_matching_img(res, img_size, window_size)
p_label = digit_localizer.predict(matching_img)
print 'l = ', label, ' p = ', p_label
while True:
c = cv2.waitKey(30)
if c == ord('n'):
break
if c == ord('q'):
break
cv2.imshow('img', img)
if c == ord('q'):
break
def prepare_matching_imgs():
#size = (28, 28)
size = (32, 44)
window_size = (40, 60)
img_size = (200, 100)
step = 2
restorer = Restorer()
restorer.restore('r_sess2')
prepare_func = create_prepare_func(restorer)
classifier = CNN_number_classifier.CNN_number_classifier(size)
source_imgs, source_positions, source_labels = read_data(
'pos_and_lab.txt', '../../data2/image%d.gif.jpg')
#prepared_imgs=prepare_imgs(source_imgs,prepare_func)
prepared_imgs = np.load('restored_imgs.npy')
train_s_imgs, train_s_positions, train_s_labels, test_s_imgs, test_s_positions, test_s_labels = divide_source_data(
prepared_imgs, source_positions, source_labels, 0.9, mode='r')
classifier.restore('sess18_32x44_s')
predict = create_predict_func(classifier, size)
matching_imgs, matching_labels = create_matching_images(
train_s_imgs, train_s_labels, img_size, window_size, predict,
sliding_window, step)
np.save('matching_imgs5', matching_imgs)
np.save('matching_labels5', matching_labels)
matching_imgs, matching_labels = create_matching_images(
test_s_imgs, test_s_labels, img_size, window_size, predict,
sliding_window, step)
np.save('test_matching_imgs5', matching_imgs)
np.save('test_matching_labels5', matching_labels)