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)