201 lines
7.3 KiB
Python
201 lines
7.3 KiB
Python
# -*- coding:utf-8 -*-
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import argparse
|
|
import ast
|
|
import copy
|
|
import math
|
|
import os
|
|
import time
|
|
|
|
from paddle.fluid.core import AnalysisConfig, create_paddle_predictor, PaddleTensor
|
|
from paddlehub.common.logger import logger
|
|
from paddlehub.module.module import moduleinfo, runnable, serving
|
|
from PIL import Image
|
|
import cv2
|
|
import numpy as np
|
|
import paddle.fluid as fluid
|
|
import paddlehub as hub
|
|
|
|
from tools.infer.utility import draw_ocr, base64_to_cv2
|
|
from tools.infer.predict_system import TextSystem
|
|
|
|
|
|
class Config(object):
|
|
pass
|
|
|
|
@moduleinfo(
|
|
name="ocr_system",
|
|
version="1.0.0",
|
|
summary="ocr system service",
|
|
author="paddle-dev",
|
|
author_email="paddle-dev@baidu.com",
|
|
type="cv/text_recognition")
|
|
class OCRSystem(hub.Module):
|
|
def _initialize(self,
|
|
det_model_dir="",
|
|
det_algorithm="DB",
|
|
rec_model_dir="",
|
|
rec_algorithm="CRNN",
|
|
rec_char_dict_path="./ppocr/utils/ppocr_keys_v1.txt",
|
|
rec_batch_num=30,
|
|
use_gpu=False
|
|
):
|
|
"""
|
|
initialize with the necessary elements
|
|
"""
|
|
self.config = Config()
|
|
self.config.use_gpu = use_gpu
|
|
if use_gpu:
|
|
try:
|
|
_places = os.environ["CUDA_VISIBLE_DEVICES"]
|
|
int(_places[0])
|
|
print("use gpu: ", use_gpu)
|
|
print("CUDA_VISIBLE_DEVICES: ", _places)
|
|
except:
|
|
raise RuntimeError(
|
|
"Environment Variable CUDA_VISIBLE_DEVICES is not set correctly. If you wanna use gpu, please set CUDA_VISIBLE_DEVICES via export CUDA_VISIBLE_DEVICES=cuda_device_id."
|
|
)
|
|
self.config.ir_optim = True
|
|
self.config.gpu_mem = 8000
|
|
|
|
#params for text detector
|
|
self.config.det_algorithm = det_algorithm
|
|
self.config.det_model_dir = det_model_dir
|
|
# self.config.det_model_dir = "./inference/det/"
|
|
|
|
#DB parmas
|
|
self.config.det_db_thresh =0.3
|
|
self.config.det_db_box_thresh =0.5
|
|
self.config.det_db_unclip_ratio =2.0
|
|
|
|
#EAST parmas
|
|
self.config.det_east_score_thresh = 0.8
|
|
self.config.det_east_cover_thresh = 0.1
|
|
self.config.det_east_nms_thresh = 0.2
|
|
|
|
#params for text recognizer
|
|
self.config.rec_algorithm = rec_algorithm
|
|
self.config.rec_model_dir = rec_model_dir
|
|
# self.config.rec_model_dir = "./inference/rec/"
|
|
|
|
self.config.rec_image_shape = "3, 32, 320"
|
|
self.config.rec_char_type = 'ch'
|
|
self.config.rec_batch_num = rec_batch_num
|
|
self.config.rec_char_dict_path = rec_char_dict_path
|
|
self.config.use_space_char = True
|
|
|
|
def read_images(self, paths=[]):
|
|
images = []
|
|
for img_path in paths:
|
|
assert os.path.isfile(
|
|
img_path), "The {} isn't a valid file.".format(img_path)
|
|
img = cv2.imread(img_path)
|
|
if img is None:
|
|
logger.info("error in loading image:{}".format(img_path))
|
|
continue
|
|
images.append(img)
|
|
return images
|
|
|
|
def recognize_text(self,
|
|
images=[],
|
|
paths=[],
|
|
det_max_side_len=960,
|
|
draw_img_save='ocr_result',
|
|
visualization=False,
|
|
text_thresh=0.5):
|
|
"""
|
|
Get the chinese texts in the predicted images.
|
|
Args:
|
|
images (list(numpy.ndarray)): images data, shape of each is [H, W, C]. If images not paths
|
|
paths (list[str]): The paths of images. If paths not images
|
|
use_gpu (bool): Whether to use gpu.
|
|
batch_size(int): the program deals once with one
|
|
output_dir (str): The directory to store output images.
|
|
visualization (bool): Whether to save image or not.
|
|
box_thresh(float): the threshold of the detected text box's confidence
|
|
text_thresh(float): the threshold of the recognize chinese texts' confidence
|
|
Returns:
|
|
res (list): The result of chinese texts and save path of images.
|
|
"""
|
|
|
|
if images != [] and isinstance(images, list) and paths == []:
|
|
predicted_data = images
|
|
elif images == [] and isinstance(paths, list) and paths != []:
|
|
predicted_data = self.read_images(paths)
|
|
else:
|
|
raise TypeError("The input data is inconsistent with expectations.")
|
|
|
|
assert predicted_data != [], "There is not any image to be predicted. Please check the input data."
|
|
|
|
self.config.det_max_side_len = det_max_side_len
|
|
text_sys = TextSystem(self.config)
|
|
cnt = 0
|
|
all_results = []
|
|
for img in predicted_data:
|
|
result = {'save_path': ''}
|
|
if img is None:
|
|
logger.info("error in loading image")
|
|
result['data'] = []
|
|
all_results.append(result)
|
|
continue
|
|
starttime = time.time()
|
|
dt_boxes, rec_res = text_sys(img)
|
|
elapse = time.time() - starttime
|
|
cnt += 1
|
|
print("Predict time of image %d: %.3fs" % (cnt, elapse))
|
|
dt_num = len(dt_boxes)
|
|
rec_res_final = []
|
|
for dno in range(dt_num):
|
|
text, score = rec_res[dno]
|
|
# if the recognized text confidence score is lower than text_thresh, then drop it
|
|
if score >= text_thresh:
|
|
# text_str = "%s, %.3f" % (text, score)
|
|
# print(text_str)
|
|
rec_res_final.append(
|
|
{
|
|
'text': text,
|
|
'confidence': float(score),
|
|
'text_box_position': dt_boxes[dno].astype(np.int).tolist()
|
|
}
|
|
)
|
|
result['data'] = rec_res_final
|
|
|
|
if visualization:
|
|
image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
|
|
boxes = dt_boxes
|
|
txts = [rec_res[i][0] for i in range(len(rec_res))]
|
|
scores = [rec_res[i][1] for i in range(len(rec_res))]
|
|
|
|
draw_img = draw_ocr(image, boxes, txts, scores, draw_txt=True, drop_score=0.5)
|
|
if not os.path.exists(draw_img_save):
|
|
os.makedirs(draw_img_save)
|
|
saved_name = 'ndarray_{}.jpg'.format(time.time())
|
|
save_file_path = os.path.join(draw_img_save, saved_name)
|
|
cv2.imwrite(save_file_path, draw_img[:, :, ::-1])
|
|
print("The visualized image saved in {}".format(save_file_path))
|
|
result['save_path'] = save_file_path
|
|
|
|
all_results.append(result)
|
|
return all_results
|
|
|
|
@serving
|
|
def serving_method(self, images, **kwargs):
|
|
"""
|
|
Run as a service.
|
|
"""
|
|
images_decode = [base64_to_cv2(image) for image in images]
|
|
results = self.recognize_text(images_decode, **kwargs)
|
|
return results
|
|
|
|
|
|
if __name__ == '__main__':
|
|
ocr = OCRSystem()
|
|
image_path = [
|
|
'./doc/imgs/11.jpg',
|
|
'./doc/imgs/12.jpg',
|
|
]
|
|
res = ocr.recognize_text(paths=image_path, visualization=True)
|
|
print(res) |