def train():
mask_model = MaskModel()
mask_model.build()
input_images = mask_model.input_images
output = mask_model.output
output_shape = output.shape.dims[3].value
output = mask_model.conv_layer(output,
1,
output_shape,
1,
1,
name='tmp_output')
output = tf.squeeze(output, axis=0)
output = tf.sigmoid(output)
# out_put = tf.sigmoid(output)
saver = tf.train.Saver()
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
saver.restore(sess=sess, save_path='./model/model')
image_path = 'C:/Users\lr\Desktop/21\H10420180821041413/RIGHT_6.jpg'
image = cv2.imread(image_path)
image = input_images_preprocess(image)
image = resize_image(image)[0]
image_shape = image.shape
image = np.reshape(image,
[1, image_shape[0], image_shape[1], image_shape[2]])
feed_dict = {input_images: image}
loss_result = sess.run(output, feed_dict=feed_dict)
loss_result = loss_result * 255
image_result = loss_result.astype(np.int)
save_img(image_result, 'result.jpg')
pass
def clip_label_image(annotation_info, image_map):
"""
clip text area
:param annotation_info:
:param image_map:
:return:
"""
for info in annotation_info:
name = info['image_name']
if 'region' not in info.keys():
continue
region = info['region']
if region is None or len(region) == 0:
continue
image_path = IMAGE_PATH + name
original_image = cv2.imread(image_path)
for r in region:
try:
label = r['label']
print(label)
p1 = r['p1']
p2 = r['p2']
text_area = original_image[int(p1[0]):int(p2[0]), int(p1[1]):int(p2[1]), :]
path = TEXT_AREA_DIR_PATH + label + '-' + str(uuid.uuid4()) + '.jpg'
save_img(text_area, path)
print(label)
except Exception as e:
print(e)
def main():
all_image_path = get_all_file_from_dir(HORIZONTAL_PATH)
index = 0
for path in all_image_path:
index += 1
if index % 1000 == 0:
print(index)
new_img = get_resize_image(path)
new_img = np.rot90(new_img)
save_img(new_img, path)
def main():
all_image_path = get_all_file_from_dir(HORIZONTAL_PATH)
length = len(all_image_path)
index = 0
for path in all_image_path:
index += 1
if index % 100 == 0:
print(str(index * 1.0 / length))
new_img = get_resize_image(path)
# print(path)
save_img(new_img, path)
def save_orig_char(im, char):
"""
保存生成的原始图片
:param im:
:param char:
:return:
"""
dir_path = GENERATE_ORIG_CHAR_DIE_PATH + char + '/'
create_dir(dir_path)
path = dir_path + str(uuid.uuid4()) + '.jpg'
save_img(im, path)
def merge_image_template(bg_path, template_path_list):
"""
合成文本模板和图片
:param bg_path:
:param template_path_list:
:return:
"""
bg = cv2.imread(bg_path)
bg_shape = bg.shape
text_area = [] # [[[h_start,h_end],[w_start,w_end]]。。。。。]
region = []
for template_path in template_path_list:
text_img = cv2.imread(template_path)
text_img = resize_text_img(text_img)
text_shape = text_img.shape
point = get_available_point(bg_shape, text_area, text_shape)
text_area.append([[point[0], point[0] + text_shape[0]],
[point[1], point[1] + text_shape[1]]])
text_color = np.array(get_random_text_color())
select_bg = bg[point[0]:point[0] + text_shape[0],
point[1]:point[1] + text_shape[1], :]
location = np.where(text_img > 120)
for i in range(len(location[0])):
select_bg[location[0][i], location[1][i], :] = text_color
start_point = [int(point[0]), int(point[1])]
end_point = [
int(text_shape[0] + point[0]),
int(point[1] + text_shape[1])
]
info = {"info": template_path, "p1": start_point, "p2": end_point}
region.append(info)
"""保存相关图片和信息"""
image_size = int(bg_shape[0]), int(bg_shape[1])
file_name = str(uuid.uuid4())
training_data_dir = GENERATE_DIE_PATH + '/training_data/'
image_info = {
'image_size': image_size,
'region': region,
'image_name': file_name + '.jpg'
}
save_image_path = training_data_dir + file_name + '.jpg'
save_json_path = training_data_dir + file_name + '.json'
save_img(bg, save_image_path)
with open(save_json_path, 'w') as f:
r = json.dumps(image_info)
f.write(r)
def main():
for i in range(1000000):
try:
print(i)
text_img, label = gen_template_text_image()
background = gen_background()
if i % 3 == 0:
background[text_img > 150] = np.random.choice(range(0, 30),
1)[0]
else:
background[text_img > 150] = np.random.choice(
range(200, 255), 1)[0]
path = GENERATE_DIE_PATH + '/data/' + label + '-' + str(
uuid.uuid4()) + '.jpg'
save_img(background, path)
except Exception as e:
print(e)
def save_text_image(arr_list):
"""
保存文字区域图片
:param arr_list:
:return:
"""
i = 0
for content in arr_list:
try:
image_path = content['image_path']
info = content['info']
text_lines = info['text_lines']
if len(text_lines) > 0:
for area in text_lines:
img = cv2.imread(image_path)
text_area = get_text_area(img, area)
save_img(text_area, TEXT_AREA + str(i) + '.jpg')
i += 1
except Exception as e:
print(e)
