118 lines
4.0 KiB
Python
Executable File
118 lines
4.0 KiB
Python
Executable File
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
import os
|
|
import sys
|
|
__dir__ = os.path.dirname(os.path.abspath(__file__))
|
|
sys.path.append(__dir__)
|
|
sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
|
|
|
|
from ppocr.utils.logging import get_logger
|
|
logger = get_logger()
|
|
|
|
import cv2
|
|
import numpy as np
|
|
import time
|
|
from PIL import Image
|
|
from ppocr.utils.utility import get_image_file_list
|
|
from tools.infer.utility import draw_ocr, draw_boxes
|
|
|
|
import requests
|
|
import json
|
|
import base64
|
|
|
|
|
|
def cv2_to_base64(image):
|
|
return base64.b64encode(image).decode('utf8')
|
|
|
|
|
|
def draw_server_result(image_file, res):
|
|
img = cv2.imread(image_file)
|
|
image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
|
|
if len(res) == 0:
|
|
return np.array(image)
|
|
keys = res[0].keys()
|
|
if 'text_region' not in keys: # for ocr_rec, draw function is invalid
|
|
logger.info("draw function is invalid for ocr_rec!")
|
|
return None
|
|
elif 'text' not in keys: # for ocr_det
|
|
logger.info("draw text boxes only!")
|
|
boxes = []
|
|
for dno in range(len(res)):
|
|
boxes.append(res[dno]['text_region'])
|
|
boxes = np.array(boxes)
|
|
draw_img = draw_boxes(image, boxes)
|
|
return draw_img
|
|
else: # for ocr_system
|
|
logger.info("draw boxes and texts!")
|
|
boxes = []
|
|
texts = []
|
|
scores = []
|
|
for dno in range(len(res)):
|
|
boxes.append(res[dno]['text_region'])
|
|
texts.append(res[dno]['text'])
|
|
scores.append(res[dno]['confidence'])
|
|
boxes = np.array(boxes)
|
|
scores = np.array(scores)
|
|
draw_img = draw_ocr(
|
|
image, boxes, texts, scores, draw_txt=True, drop_score=0.5)
|
|
return draw_img
|
|
|
|
|
|
def main(url, image_path):
|
|
image_file_list = get_image_file_list(image_path)
|
|
is_visualize = False
|
|
headers = {"Content-type": "application/json"}
|
|
cnt = 0
|
|
total_time = 0
|
|
for image_file in image_file_list:
|
|
img = open(image_file, 'rb').read()
|
|
if img is None:
|
|
logger.info("error in loading image:{}".format(image_file))
|
|
continue
|
|
|
|
# 发送HTTP请求
|
|
starttime = time.time()
|
|
data = {'images': [cv2_to_base64(img)]}
|
|
r = requests.post(url=url, headers=headers, data=json.dumps(data))
|
|
elapse = time.time() - starttime
|
|
total_time += elapse
|
|
logger.info("Predict time of %s: %.3fs" % (image_file, elapse))
|
|
res = r.json()["results"][0]
|
|
logger.info(res)
|
|
|
|
if is_visualize:
|
|
draw_img = draw_server_result(image_file, res)
|
|
if draw_img is not None:
|
|
draw_img_save = "./server_results/"
|
|
if not os.path.exists(draw_img_save):
|
|
os.makedirs(draw_img_save)
|
|
cv2.imwrite(
|
|
os.path.join(draw_img_save, os.path.basename(image_file)),
|
|
draw_img[:, :, ::-1])
|
|
logger.info("The visualized image saved in {}".format(
|
|
os.path.join(draw_img_save, os.path.basename(image_file))))
|
|
cnt += 1
|
|
if cnt % 100 == 0:
|
|
logger.info("{} processed".format(cnt))
|
|
logger.info("avg time cost: {}".format(float(total_time) / cnt))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
if len(sys.argv) != 3:
|
|
logger.info("Usage: %s server_url image_path" % sys.argv[0])
|
|
else:
|
|
server_url = sys.argv[1]
|
|
image_path = sys.argv[2]
|
|
main(server_url, image_path)
|