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277 lignes
10 KiB
277 lignes
10 KiB
# coding=utf-8
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import os
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import shutil
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import sys
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import time
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import cv2
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import numpy as np
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import tensorflow as tf
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from main import preprocess
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import json
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import locale
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locale.setlocale(locale.LC_ALL, 'C')
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from scipy.misc import imread
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#current_directory = os.path.dirname(os.path.abspath(__file__))
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#root_path = os.path.abspath(os.path.dirname(current_directory) + os.path.sep + ".")
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#sys.path.append(sys.path.append(os.getcwd()))
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sys.path.append(os.getcwd())
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from nets import model_train as ctpnmodel
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from utils.rpn_msr.proposal_layer import proposal_layer
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from utils.text_connector.detectors import TextDetector
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from scipy.misc import imread
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import os
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from PIL import Image
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from model.img2seq import Img2SeqModel
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from model.utils.general import Config, run
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from model.utils.text import Vocab
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from model.utils.image import greyscale,predictsize
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tf.app.flags.DEFINE_string('test_data_path', '/app/image/1.png', '')
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tf.app.flags.DEFINE_string('output_path', '/app/im2latex_master/results/predict/', '')
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tf.app.flags.DEFINE_string('gpu', '0', '')
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tf.app.flags.DEFINE_string('checkpoint_path', '/app/im2latex_master/checkpoints_mlt/', '')
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FLAGS = tf.app.flags.FLAGS
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tf.app.flags.DEFINE_integer('language', '2', '')
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def get_images():
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files = []
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exts = ['jpg', 'png', 'jpeg', 'JPG']
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for parent, dirnames, filenames in os.walk(FLAGS.test_data_path):
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for filename in filenames:
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for ext in exts:
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if filename.endswith(ext):
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files.append(os.path.join(parent, filename))
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break
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print('Find {} images'.format(len(files)))
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return files
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def resize_image(img):
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img_size = img.shape
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im_size_min = np.min(img_size[0:2])
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im_size_max = np.max(img_size[0:2])
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im_scale = float(600) / float(im_size_min)
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if np.round(im_scale * im_size_max) > 1200:
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im_scale = float(1200) / float(im_size_max)
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new_h = int(img_size[0] * im_scale)
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new_w = int(img_size[1] * im_scale)
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new_h = new_h if new_h // 16 == 0 else (new_h // 16 + 1) * 16
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new_w = new_w if new_w // 16 == 0 else (new_w // 16 + 1) * 16
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re_im = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_LINEAR)
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#cv2.imshow("ss",img)
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#cv2.waitKey(0)
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return re_im, (new_h / img_size[0], new_w / img_size[1])
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def get_box():
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if os.path.exists(FLAGS.output_path):
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shutil.rmtree(FLAGS.output_path)
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os.makedirs(FLAGS.output_path)
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os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu
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input_image = tf.placeholder(tf.float32, shape=[None, None, None, 3], name='input_image')
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input_im_info = tf.placeholder(tf.float32, shape=[None, 3], name='input_im_info')
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global_step = tf.get_variable('global_step', [], initializer=tf.constant_initializer(0), trainable=False)
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bbox_pred, cls_pred, cls_prob = ctpnmodel.model(input_image,2)
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variable_averages = tf.train.ExponentialMovingAverage(0.997, global_step)
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saver = tf.train.Saver(variable_averages.variables_to_restore())
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ckpt_state = tf.train.get_checkpoint_state(FLAGS.checkpoint_path)
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model_path = os.path.join(FLAGS.checkpoint_path, os.path.basename(ckpt_state.model_checkpoint_path))
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# print('Restore from {}'.format(model_path))
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sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
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saver.restore(sess, model_path)
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dir_output = "/app/im2latex_master/results/full/"
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config_vocab = Config(dir_output + "vocab.json")
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config_model = Config(dir_output + "model.json")
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vocab = Vocab(config_vocab)
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model = Img2SeqModel(config_model, dir_output, vocab)
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model.build_pred()
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model.restore_session(dir_output + "model.weights4/test-model.ckpt")
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# print(FLAGS.test_data_path)
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img = cv2.imread(FLAGS.test_data_path)[:, :, ::-1]
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h, w, c = img.shape
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if h > 121:
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approx, image, (rh, rw) = preprocess.draw_rec(img)
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img = preprocess.Perspective(image, approx)
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img = cv2.resize(img, None, None, fx=1.0 / rw, fy=1.0 / rh, interpolation=cv2.INTER_LINEAR)
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#cv2.imshow("Dd",img)
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#cv2.waitKey(0)
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img, (rh, rw) = resize_image(img)
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h, w, c = img.shape
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im_info = np.array([h, w, c]).reshape([1, 3])
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bbox_pred_val, cls_prob_val = sess.run([bbox_pred, cls_prob],
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feed_dict={input_image: [img],
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input_im_info: im_info})
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textsegs, _ = proposal_layer(cls_prob_val, bbox_pred_val, im_info, img)
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scores = textsegs[:, 0:2] # 改
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textsegs = textsegs[:, 2:6] # 改
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textdetector = TextDetector(DETECT_MODE='H')
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boxes = textdetector.detect(textsegs, scores, img.shape[:2], img)
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boxes = np.array(boxes, dtype=np.int)
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image_box = sorted(boxes, key=(lambda x: (x[1] + x[3], x[0] + x[6])))
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for i, box in enumerate(image_box):
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cv2.polylines(img, [box[:8].astype(np.int32).reshape((-1, 1, 2))], True, color=(0, 255, 0),
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thickness=2)
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img = cv2.resize(img, None, None, fx=1.0 / rh, fy=1.0 / rw, interpolation=cv2.INTER_LINEAR)
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cv2.imshow("ss",img)
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cv2.waitKey(0)
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return 0
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def save_to_file():
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if os.path.exists(FLAGS.output_path):
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shutil.rmtree(FLAGS.output_path)
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os.makedirs(FLAGS.output_path)
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os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu
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input_image = tf.placeholder(tf.float32, shape=[None, None, None, 3], name='input_image')
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input_im_info = tf.placeholder(tf.float32, shape=[None, 3], name='input_im_info')
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global_step = tf.get_variable('global_step', [], initializer=tf.constant_initializer(0), trainable=False)
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bbox_pred, cls_pred, cls_prob = ctpnmodel.model(input_image,2.0)
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variable_averages = tf.train.ExponentialMovingAverage(0.997, global_step)
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saver = tf.train.Saver(variable_averages.variables_to_restore())
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ckpt_state = tf.train.get_checkpoint_state(FLAGS.checkpoint_path)
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model_path = os.path.join(FLAGS.checkpoint_path, os.path.basename(ckpt_state.model_checkpoint_path))
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sess=tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
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saver.restore(sess, model_path)
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dir_output = "/app/im2latex_master/results/full/"
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config_vocab = Config(dir_output + "vocab.json")
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config_model = Config(dir_output + "model.json")
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vocab = Vocab(config_vocab)
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#英文
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config_vocab_en = Config(dir_output + "vocabe.json")
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vocab_en = Vocab(config_vocab_en)
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model_en = Img2SeqModel(config_model, dir_output, vocab_en)
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model_en.build_pred()
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model_en.restore_session(dir_output + "model.weights_en/test-model.ckpt")
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#print(FLAGS.test_data_path)
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img = imread(FLAGS.test_data_path)
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h, w, c = img.shape
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res = ""
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if h>40:
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approx, image, (rh, rw) = preprocess.draw_rec(img)
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img = preprocess.Perspective(image, approx)
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img = cv2.resize(img, None, None, fx=1.0 / rw, fy=1.0 / rh, interpolation=cv2.INTER_LINEAR)
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img, (rh, rw) = resize_image(img)
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h, w, c = img.shape
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im_info = np.array([h, w, c]).reshape([1, 3])
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bbox_pred_val, cls_prob_val = sess.run([bbox_pred, cls_prob],
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feed_dict={input_image: [img],
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input_im_info: im_info})
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textsegs, _ = proposal_layer(cls_prob_val, bbox_pred_val, im_info,img)
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scores = textsegs[:, 0:2] # 改
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textsegs = textsegs[:, 2:6] # 改
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textdetector = TextDetector(DETECT_MODE='H')
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boxes = textdetector.detect(textsegs, scores, img.shape[:2],img)
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boxes = np.array(boxes, dtype=np.int)
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img2=img.copy()
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for i, box in enumerate(boxes):
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if box[8]==1:
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cv2.polylines(img2, [box[:8].astype(np.int32).reshape((-1, 1, 2))], True, color=(0, 255, 0),
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thickness=2)
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else:
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cv2.polylines(img2, [box[:8].astype(np.int32).reshape((-1, 1, 2))], True, color=(255, 0, 0),
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thickness=2)
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img2 = cv2.resize(img2, None, None, fx=1.0 / rh, fy=1.0 / rw, interpolation=cv2.INTER_LINEAR)
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#cv2.imshow("ss", img2)
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#cv2.waitKey(0)
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for i,b in enumerate(boxes):
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lan=b[8]
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box = boxes[i]
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img0 = img[min(box[1], box[3]) - 1:max(box[5], box[7]) + 1, min(box[0], box[2]) - 1:max(box[4], box[6]) + 1,
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::-1]
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#cv2.imshow("ss",img0)
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#cv2.waitKey(0)
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"""
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if lan == 2:
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img0 = predictsize(img0)
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#cv2.imshow("ss",img0)
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#cv2.waitKey(0)
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img0 = greyscale(img0)
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hyp = model.predict(img0)
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res = res + hyp[0] + "\n"
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model.logger.info(hyp[0])
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else:
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"""
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img0 = predictsize(img0)
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#cv2.imshow("ss",img0)
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#cv2.waitKey(0)
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img0 = greyscale(img0)
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hyp = model_en.predict(img0)
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res = res + hyp[0] + "\n"
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model_en.logger.info(hyp[0])
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#hyp=pytesseract.image_to_string(img0)
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#res = res + hyp + "\n"
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#model.logger.info(hyp)
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res = json.dumps({"res": res})
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model_en.logger.info(res)
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else:
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#print(0)
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img = predictsize(img)
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img0 = greyscale(img)
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#cv2.imshow("ss", img0)
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#cv2.waitKey(0)
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hyps = model_en.predict(img0)
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res = res + hyps[0] + "\n"
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model_en.logger.info(hyps[0])
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res = json.dumps({"res": res})
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model_en.logger.info(res)
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return 0
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'''
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cv2.imwrite(os.path.join(FLAGS.output_path, str(i) +'.png'),img[min(box[1],box[3]):max(box[5],box[7]),min(box[0],box[2]) :max(box[4],box[6]), ::-1])
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cv2.polylines(img, [box[:8].astype(np.int32).reshape((-1, 1, 2))], True, color=(0, 255, 0),
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thickness=2)
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img = cv2.resize(img, None, None, fx=1.0 / rh, fy=1.0 / rw, interpolation=cv2.INTER_LINEAR)
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cv2.imwrite(os.path.join(FLAGS.output_path, os.path.basename(im_fn)), img[:, :, ::-1])
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with open(os.path.join(FLAGS.output_path, os.path.splitext(os.path.basename(im_fn))[0]) + ".txt",
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"w") as f:
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for i, box in enumerate(boxes):
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line = ",".join(str(box[k]) for k in range(8))
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line += "," + str(scores[i]) + "\r\n"
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f.writelines(line)
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'''
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def main(argv=None):
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res=save_to_file()
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#res=get_box()
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return res
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if __name__ == '__main__':
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tf.app.run()
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