提交 4af18109 编写于 作者: L LDOUBLEV

opt visualized func and add docker usage in cpu

上级 fcfdd0a3
......@@ -15,6 +15,9 @@ cd /home/Projects
# 首次运行需创建一个docker容器,再次运行时不需要运行当前命令
# 创建一个名字为ppocr的docker容器,并将当前目录映射到容器的/paddle目录下
如果您希望在CPU环境下使用docker,使用docker而不是nvidia-docker创建docker
sudo docker run --name ppocr -v $PWD:/paddle --network=host -it hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev /bin/bash
如果您的机器安装的是CUDA9,请运行以下命令创建容器
sudo nvidia-docker run --name ppocr -v $PWD:/paddle --network=host -it hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev /bin/bash
......@@ -24,7 +27,7 @@ sudo nvidia-docker run --name ppocr -v $PWD:/paddle --network=host -it hub.baidu
您也可以访问[DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/)获取与您机器适配的镜像。
# ctrl+P+Q可退出docker,重新进入docker使用如下命令
sudo nvidia-docker container exec -it ppocr /bin/bash
sudo docker container exec -it ppocr /bin/bash
```
注意:如果docker pull过慢,可以按照如下步骤手动下载后加载docker,以cuda9 docker为例,使用cuda10 docker只需要将cuda9改为cuda10即可。
......
......@@ -23,6 +23,7 @@ import cv2
import numpy as np
import json
from PIL import Image, ImageDraw, ImageFont
import math
def parse_args():
......@@ -127,6 +128,18 @@ def resize_img(img, input_size=600):
def draw_ocr(image, boxes, txts, scores, draw_txt=True, drop_score=0.5):
"""
Visualize the results of OCR detection and recognition
args:
image(Image): image from Image.open
boxes(list): boxes with shape(N, 4, 2)
txts(list): the texts
scores(list): txxs corresponding scores
draw_txt(bool): whether draw text or not
drop_score(float): only scores greater than drop_threshold will be visualized
return(array):
the visualized img
"""
from PIL import Image, ImageDraw, ImageFont
img = image.copy()
......@@ -154,35 +167,123 @@ def draw_ocr(image, boxes, txts, scores, draw_txt=True, drop_score=0.5):
fill='red')
if draw_txt:
txt_color = (0, 0, 0)
img = np.array(resize_img(img))
_h = img.shape[0]
blank_img = np.ones(shape=[_h, 600], dtype=np.int8) * 255
img = np.array(resize_img(img, input_size=600))
txt_img = text_visual(
txts, scores, img_h=img.shape[0], img_w=600, threshold=drop_score)
img = np.concatenate([np.array(img), np.array(txt_img)], axis=1)
return img
def str_count(s):
"""
Count the number of Chinese characters,
a single English character and a single number
equal to half the length of Chinese characters.
args:
s(string): the input of string
return(int):
the number of Chinese characters
"""
import string
count_zh = count_pu = 0
s_len = len(s)
en_dg_count = 0
for c in s:
if c in string.ascii_letters or c.isdigit() or c.isspace():
en_dg_count += 1
elif c.isalpha():
count_zh += 1
else:
count_pu += 1
return s_len - math.ceil(en_dg_count / 2)
def text_visual(texts, scores, img_h=400, img_w=600, threshold=0.):
"""
create new blank img and draw txt on it
args:
texts(list): the text will be draw
scores(list|None): corresponding score of each txt
img_h(int): the height of blank img
img_w(int): the width of blank img
return(array):
"""
if scores is not None:
assert len(texts) == len(
scores), "The number of txts and corresponding scores must match"
def create_blank_img():
blank_img = np.ones(shape=[img_h, img_w], dtype=np.int8) * 255
blank_img[:, img_w - 1:] = 0
blank_img = Image.fromarray(blank_img).convert("RGB")
draw_txt = ImageDraw.Draw(blank_img)
return blank_img, draw_txt
blank_img, draw_txt = create_blank_img()
font_size = 20
gap = 20
title = "index text score"
txt_color = (0, 0, 0)
font = ImageFont.truetype(
"./doc/simfang.ttf", font_size, encoding="utf-8")
"../../doc/simfang.ttf", font_size, encoding="utf-8")
draw_txt.text((20, 0), title, txt_color, font=font)
count = 0
for idx, txt in enumerate(txts):
if scores[idx] < drop_score:
gap = font_size + 5
txt_img_list = []
count, index = 0, 0
for idx, txt in enumerate(texts):
index += 1
if scores[idx] < threshold:
index -= 1
continue
font = ImageFont.truetype(
"./doc/simfang.ttf", font_size, encoding="utf-8")
new_txt = str(idx) + ': ' + txt + ' ' + '%.3f' % (scores[idx])
draw_txt.text(
(20, gap * (count + 1)), new_txt, txt_color, font=font)
first_line = True
while str_count(txt) >= img_w // font_size - 4:
tmp = txt
txt = tmp[:img_w // font_size - 4]
if first_line:
new_txt = str(index) + ': ' + txt
first_line = False
else:
new_txt = ' ' + txt
draw_txt.text((0, gap * (count + 1)), new_txt, txt_color, font=font)
txt = tmp[img_w // font_size - 4:]
count += 1
img = np.concatenate([np.array(img), np.array(blank_img)], axis=1)
return img
if count >= img_h // gap - 1:
txt_img_list.append(np.array(blank_img))
blank_img, draw_txt = create_blank_img()
count = 0
if first_line:
new_txt = str(index) + ': ' + txt + ' ' + '%.3f' % (scores[idx])
else:
new_txt = " " + txt + " " + '%.3f' % (scores[idx])
draw_txt.text((0, gap * (count + 1)), new_txt, txt_color, font=font)
count += 1
# whether add new blank img or not
if count >= img_h // gap - 1 and idx + 1 < len(texts):
txt_img_list.append(np.array(blank_img))
blank_img, draw_txt = create_blank_img()
count = 0
txt_img_list.append(np.array(blank_img))
if len(txt_img_list) == 1:
blank_img = np.array(txt_img_list[0])
else:
blank_img = np.concatenate(txt_img_list, axis=1)
# cv2.imwrite("./draw_txt.jpg", np.array(blank_img))
return np.array(blank_img)
if __name__ == '__main__':
text = [
"旨在打造一套丰富领先、且实用的工具库助力使、用者训练出更好的模型,并应用落地",
"以下代码实现了文本检测、识别串联推理,在执行预测时,需要通过参数image_dir指定单张图像或者图像集合",
"上述DB模型的训练和评估,需设置后处理参数box_thresh=0.6,unclip_ratio=1.5,使用不同数据集"
]
img = text_visual(text, scores=[0.999, 0.999, 0.999], img_h=100)
cv2.imwrite("./draw_txt.jpg", np.array(img))
"""
test_img = "./doc/test_v2"
predict_txt = "./doc/predict.txt"
f = open(predict_txt, 'r')
......@@ -202,3 +303,4 @@ if __name__ == '__main__':
new_img = draw_ocr(image, boxes, txts, scores, draw_txt=True)
cv2.imwrite(img_name, new_img)
"""
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