forgetest5
/
PaddleOCR
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Merge pull request #1299 from WenmuZhou/fix_predict_system 

add predict_cls to predict_system
This commit is contained in:
zhoujun 2020-12-06 20:28:37 -06:00 committed by GitHub
parent 2985e7b897 34cd39194d
commit 99ee41d8db
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 60 additions and 41 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

16
tools/infer/predict_cls.py
View File

@ -23,7 +23,7 @@ import copy
import numpy as np
import math
import time
import traceback
import paddle.fluid as fluid
import tools.infer.utility as utility
@ -106,10 +106,10 @@ class TextClassifier(object):
norm_img_batch = fluid.core.PaddleTensor(norm_img_batch)
self.predictor.run([norm_img_batch])
prob_out = self.output_tensors[0].copy_to_cpu()
cls_res = self.postprocess_op(prob_out)
cls_result = self.postprocess_op(prob_out)
elapse += time.time() - starttime
for rno in range(len(cls_res)):
label, score = cls_res[rno]
for rno in range(len(cls_result)):
label, score = cls_result[rno]
cls_res[indices[beg_img_no + rno]] = [label, score]
if '180' in label and score > self.cls_thresh:
img_list[indices[beg_img_no + rno]] = cv2.rotate(
@ -133,8 +133,8 @@ def main(args):
img_list.append(img)
try:
img_list, cls_res, predict_time = text_classifier(img_list)
except Exception as e:
print(e)
except:
logger.info(traceback.format_exc())
logger.info(
"ERROR!!!! \n"
"Please read the FAQ：https://github.com/PaddlePaddle/PaddleOCR#faq \n"
@ -143,9 +143,9 @@ def main(args):
"Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ")
exit()
for ino in range(len(img_list)):
print("Predicts of {}:{}".format(valid_image_file_list[ino], cls_res[
logger.info("Predicts of {}:{}".format(valid_image_file_list[ino], cls_res[
ino]))
print("Total predict time for {} images, cost: {:.3f}".format(
logger.info("Total predict time for {} images, cost: {:.3f}".format(
len(img_list), predict_time))
if __name__ == "__main__":

5
tools/infer/predict_det.py
View File

@ -178,11 +178,12 @@ if __name__ == "__main__":
if count > 0:
total_time += elapse
count += 1
print("Predict time of {}: {}".format(image_file, elapse))
logger.info("Predict time of {}: {}".format(image_file, elapse))
src_im = utility.draw_text_det_res(dt_boxes, image_file)
img_name_pure = os.path.split(image_file)[-1]
img_path = os.path.join(draw_img_save,
"det_res_{}".format(img_name_pure))
cv2.imwrite(img_path, src_im)
logger.info("The visualized image saved in {}".format(img_path))
if count > 1:
print("Avg Time:", total_time / (count - 1))
logger.info("Avg Time:", total_time / (count - 1))

10
tools/infer/predict_rec.py
View File

@ -22,7 +22,7 @@ import cv2
import numpy as np
import math
import time
import traceback
import paddle.fluid as fluid
import tools.infer.utility as utility
@ -135,8 +135,8 @@ def main(args):
img_list.append(img)
try:
rec_res, predict_time = text_recognizer(img_list)
except Exception as e:
print(e)
except:
logger.info(traceback.format_exc())
logger.info(
"ERROR!!!! \n"
"Please read the FAQ：https://github.com/PaddlePaddle/PaddleOCR#faq \n"
@ -145,9 +145,9 @@ def main(args):
"Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ")
exit()
for ino in range(len(img_list)):
print("Predicts of {}:{}".format(valid_image_file_list[ino], rec_res[
logger.info("Predicts of {}:{}".format(valid_image_file_list[ino], rec_res[
ino]))
print("Total predict time for {} images, cost: {:.3f}".format(
logger.info("Total predict time for {} images, cost: {:.3f}".format(
len(img_list), predict_time))

40
tools/infer/predict_system.py
View File

@ -23,17 +23,21 @@ import numpy as np
import time
from PIL import Image
import tools.infer.utility as utility
from tools.infer.utility import draw_ocr
import tools.infer.predict_rec as predict_rec
import tools.infer.predict_det as predict_det
import tools.infer.predict_cls as predict_cls
from ppocr.utils.utility import get_image_file_list, check_and_read_gif
from ppocr.utils.logging import get_logger
from tools.infer.utility import draw_ocr_box_txt
class TextSystem(object):
def __init__(self, args):
self.text_detector = predict_det.TextDetector(args)
self.text_recognizer = predict_rec.TextRecognizer(args)
self.use_angle_cls = args.use_angle_cls
if self.use_angle_cls:
self.text_classifier = predict_cls.TextClassifier(args)
def get_rotate_crop_image(self, img, points):
'''
@ -72,12 +76,12 @@ class TextSystem(object):
bbox_num = len(img_crop_list)
for bno in range(bbox_num):
cv2.imwrite("./output/img_crop_%d.jpg" % bno, img_crop_list[bno])
print(bno, rec_res[bno])
logger.info(bno, rec_res[bno])
def __call__(self, img):
ori_im = img.copy()
dt_boxes, elapse = self.text_detector(img)
print("dt_boxes num : {}, elapse : {}".format(len(dt_boxes), elapse))
logger.info("dt_boxes num : {}, elapse : {}".format(len(dt_boxes), elapse))
if dt_boxes is None:
return None, None
img_crop_list = []
@ -88,8 +92,14 @@ class TextSystem(object):
tmp_box = copy.deepcopy(dt_boxes[bno])
img_crop = self.get_rotate_crop_image(ori_im, tmp_box)
img_crop_list.append(img_crop)
if self.use_angle_cls:
img_crop_list, angle_list, elapse = self.text_classifier(
img_crop_list)
logger.info("cls num : {}, elapse : {}".format(
len(img_crop_list), elapse))
rec_res, elapse = self.text_recognizer(img_crop_list)
print("rec_res num : {}, elapse : {}".format(len(rec_res), elapse))
logger.info("rec_res num : {}, elapse : {}".format(len(rec_res), elapse))
# self.print_draw_crop_rec_res(img_crop_list, rec_res)
return dt_boxes, rec_res
@ -119,7 +129,8 @@ def main(args):
image_file_list = get_image_file_list(args.image_dir)
text_sys = TextSystem(args)
is_visualize = True
tackle_img_num = 0
font_path = args.vis_font_path
drop_score = args.drop_score
for image_file in image_file_list:
img, flag = check_and_read_gif(image_file)
if not flag:
@ -128,20 +139,16 @@ def main(args):
logger.info("error in loading image:{}".format(image_file))
continue
starttime = time.time()
tackle_img_num += 1
if not args.use_gpu and args.enable_mkldnn and tackle_img_num % 30 == 0:
text_sys = TextSystem(args)
dt_boxes, rec_res = text_sys(img)
elapse = time.time() - starttime
print("Predict time of %s: %.3fs" % (image_file, elapse))
logger.info("Predict time of %s: %.3fs" % (image_file, elapse))
drop_score = 0.5
dt_num = len(dt_boxes)
for dno in range(dt_num):
text, score = rec_res[dno]
if score >= drop_score:
text_str = "%s, %.3f" % (text, score)
print(text_str)
logger.info(text_str)
if is_visualize:
image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
@ -149,15 +156,20 @@ def main(args):
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, drop_score=drop_score)
draw_img = draw_ocr_box_txt(
image,
boxes,
txts,
scores,
drop_score=drop_score,
font_path=font_path)
draw_img_save = "./inference_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])
print("The visualized image saved in {}".format(
logger.info("The visualized image saved in {}".format(
os.path.join(draw_img_save, os.path.basename(image_file))))

18
tools/infer/utility.py
View File

@ -71,6 +71,7 @@ def parse_args():
parser.add_argument("--use_space_char", type=str2bool, default=True)
parser.add_argument(
"--vis_font_path", type=str, default="./doc/simfang.ttf")
parser.add_argument("--drop_score", type=float, default=0.5)
# params for text classifier
parser.add_argument("--use_angle_cls", type=str2bool, default=False)
@ -202,7 +203,12 @@ def draw_ocr(image,
return image
def draw_ocr_box_txt(image, boxes, txts):
def draw_ocr_box_txt(image,
boxes,
txts,
scores=None,
drop_score=0.5,
font_path="./doc/simfang.ttf"):
h, w = image.height, image.width
img_left = image.copy()
img_right = Image.new('RGB', (w, h), (255, 255, 255))
@ -212,7 +218,9 @@ def draw_ocr_box_txt(image, boxes, txts):
random.seed(0)
draw_left = ImageDraw.Draw(img_left)
draw_right = ImageDraw.Draw(img_right)
for (box, txt) in zip(boxes, txts):
for idx, (box, txt) in enumerate(zip(boxes, txts)):
if scores is not None and scores[idx] < drop_score:
continue
color = (random.randint(0, 255), random.randint(0, 255),
random.randint(0, 255))
draw_left.polygon(box, fill=color)
@ -228,8 +236,7 @@ def draw_ocr_box_txt(image, boxes, txts):
1]) ** 2)
if box_height > 2 * box_width:
font_size = max(int(box_width * 0.9), 10)
font = ImageFont.truetype(
"./doc/simfang.ttf", font_size, encoding="utf-8")
font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
cur_y = box[0][1]
for c in txt:
char_size = font.getsize(c)
@ -238,8 +245,7 @@ def draw_ocr_box_txt(image, boxes, txts):
cur_y += char_size[1]
else:
font_size = max(int(box_height * 0.8), 10)
font = ImageFont.truetype(
"./doc/simfang.ttf", font_size, encoding="utf-8")
font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
draw_right.text(
[box[0][0], box[0][1]], txt, fill=(0, 0, 0), font=font)
img_left = Image.blend(image, img_left, 0.5)