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Unverified Commit adbe225a authored by dyning's avatar dyning Committed by GitHub
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Merge pull request #17 from LDOUBLEV/fixocr 

fix rec bug and delete infer/predict_eval.py
parents 80fdcebc c65f3308
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Showing with 9 additions and 167 deletions
tools/infer/predict_eval.py deleted 100755 → 0
View file @ 80fdcebc
# 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 utility
from ppocr.utils.utility import initial_logger
logger = initial_logger()
import cv2
import predict_system
import copy
import numpy as np
import math
import time
import json
import os
from PIL import Image, ImageDraw, ImageFont
from tools.infer.utility import draw_ocr
from ppocr.utils.utility import get_image_file_list
if __name__ == "__main__":
args = utility.parse_args()
text_sys = predict_system.TextSystem(args)
if not os.path.exists(args.image_dir):
raise Exception("{} not exists !!".format(args.image_dir))
image_file_list = get_image_file_list(args.image_dir)
total_time_all = 0
count = 0
save_path = "./inference_output/predict.txt"
if not os.path.exists(os.path.dirname(save_path)):
os.makedirs(os.path.dirname(save_path))
fout = open(save_path, "wb")
for image_name in image_file_list:
image_file = image_name
img = cv2.imread(image_file)
if img is None:
logger.info("error in loading image:{}".format(image_file))
continue
count += 1
total_time = 0
starttime = time.time()
dt_boxes, rec_res = text_sys(img)
elapse = time.time() - starttime
total_time_all += elapse
print("Predict time of %s(%d): %.3fs" % (image_file, count, elapse))
dt_num = len(dt_boxes)
bbox_list = []
for dno in range(dt_num):
box = dt_boxes[dno]
text, score = rec_res[dno]
points = []
for tno in range(len(box)):
points.append([box[tno][0] * 1.0, box[tno][1] * 1.0])
bbox_list.append({
"transcription": text,
"points": points,
"scores": score * 1.0
})
# draw predict box and text in image
# and save drawed image in save_path
image = Image.open(image_file)
boxes, txts, scores = [], [], []
for dic in bbox_list:
boxes.append(dic['points'])
txts.append(dic['transcription'])
scores.append(round(dic['scores'], 3))
new_img = draw_ocr(image, boxes, txts, scores, draw_txt=True)
draw_img_save = os.path.join(
os.path.dirname(save_path), "inference_draw",
os.path.basename(image_file))
if not os.path.exists(os.path.dirname(draw_img_save)):
os.makedirs(os.path.dirname(draw_img_save))
cv2.imwrite(draw_img_save, new_img[:, :, ::-1])
print("drawed img saved in {}".format(draw_img_save))
# save predicted results in txt file
otstr = image_name + "\t" + json.dumps(bbox_list) + "\n"
fout.write(otstr.encode('utf-8'))
avg_time = total_time_all / count
logger.info("avg_time: {0}".format(avg_time))
logger.info("avg_fps: {0}".format(1.0 / avg_time))
fout.close()
tools/infer/predict_eval_new.py deleted 100755 → 0
View file @ 80fdcebc
# 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 utility
from ppocr.utils.utility import initial_logger
logger = initial_logger()
import cv2
import predict_system
import copy
import numpy as np
import math
import time
import json
import os
if __name__ == "__main__":
args = utility.parse_args()
text_sys = predict_system.TextSystem(args)
image_file_list = []
img_set_path = "/paddle/code/dyn/test_imgs/rctw_samples/"
image_file_list = os.listdir(img_set_path)
total_time_all = 0
count = 0
save_path = "./output/predict.txt"
fout = open(save_path, "wb")
for image_name in image_file_list:
image_file = img_set_path + image_name
img = cv2.imread(image_file)
if img is None:
logger.info("error in loading image:{}".format(image_file))
continue
count += 1
starttime = time.time()
dt_boxes, rec_res = text_sys(img)
if dt_boxes is None:
count -= 1
continue
elapse = time.time() - starttime
total_time_all += elapse
print("Predict time of %s(%d): %.3fs" % (image_file, count, elapse))
dt_num = len(dt_boxes)
bbox_list = []
for dno in range(dt_num):
box = dt_boxes[dno]
text, score = rec_res[dno]
points = []
for tno in range(len(box)):
points.append([box[tno][0] * 1.0, box[tno][1] * 1.0])
bbox_list.append({
"transcription": text,
"points": points,
"scores": score * 1.0
})
otstr = image_name + "\t" + json.dumps(bbox_list) + "\n"
fout.write(otstr.encode('utf-8'))
avg_time = total_time_all / count
logger.info("avg_time: {0}".format(avg_time))
logger.info("avg_fps: {0}".format(1.0 / avg_time))
fout.close()
tools/infer/predict_rec.py
View file @ adbe225a
...@@ -36,8 +36,9 @@ class TextRecognizer(object): ...@@ -36,8 +36,9 @@ class TextRecognizer(object):
char_ops_params['loss_type'] = 'ctc' char_ops_params['loss_type'] = 'ctc'
self.char_ops = CharacterOps(char_ops_params) self.char_ops = CharacterOps(char_ops_params)
def resize_norm_img(self, img): def resize_norm_img(self, img, max_wh_ratio):
imgC, imgH, imgW = self.rec_image_shape imgC, imgH, imgW = self.rec_image_shape
imgW = int(32 * max_wh_ratio)
h = img.shape[0] h = img.shape[0]
w = img.shape[1] w = img.shape[1]
ratio = w / float(h) ratio = w / float(h)
...@@ -56,14 +57,19 @@ class TextRecognizer(object): ...@@ -56,14 +57,19 @@ class TextRecognizer(object):
def __call__(self, img_list): def __call__(self, img_list):
img_num = len(img_list) img_num = len(img_list)
batch_num = 15 batch_num = 30
rec_res = [] rec_res = []
predict_time = 0 predict_time = 0
for beg_img_no in range(0, img_num, batch_num): for beg_img_no in range(0, img_num, batch_num):
end_img_no = min(img_num, beg_img_no + batch_num) end_img_no = min(img_num, beg_img_no + batch_num)
norm_img_batch = [] norm_img_batch = []
max_wh_ratio = 0
for ino in range(beg_img_no, end_img_no): for ino in range(beg_img_no, end_img_no):
norm_img = self.resize_norm_img(img_list[ino]) h, w = img_list[ino].shape[0:2]
wh_ratio = w * 1.0 / h
max_wh_ratio = max(max_wh_ratio, wh_ratio)
for ino in range(beg_img_no, end_img_no):
norm_img = self.resize_norm_img(img_list[ino], max_wh_ratio)
norm_img = norm_img[np.newaxis, :] norm_img = norm_img[np.newaxis, :]
norm_img_batch.append(norm_img) norm_img_batch.append(norm_img)
norm_img_batch = np.concatenate(norm_img_batch) norm_img_batch = np.concatenate(norm_img_batch)
......
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