opt visualized func and add docker usage in cpu
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LDOUBLEV
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@ -15,6 +15,9 @@ cd /home/Projects
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# 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
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# 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
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如果您希望在CPU环境下使用docker，使用docker而不是nvidia-docker创建docker
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sudo docker run --name ppocr -v $PWD:/paddle --network=host -it hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev /bin/bash
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如果您的机器安装的是CUDA9，请运行以下命令创建容器
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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
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@ -24,7 +27,7 @@ sudo nvidia-docker run --name ppocr -v $PWD:/paddle --network=host -it hub.baidu
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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。
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# ctrl+P+Q可退出docker，重新进入docker使用如下命令
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sudo nvidia-docker container exec -it ppocr /bin/bash
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sudo docker container exec -it ppocr /bin/bash
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```
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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。
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@ -23,6 +23,7 @@ import cv2
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import numpy as np
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import json
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from PIL import Image, ImageDraw, ImageFont
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import math
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def parse_args():
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@ -127,6 +128,18 @@ def resize_img(img, input_size=600):
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def draw_ocr(image, boxes, txts, scores, draw_txt=True, drop_score=0.5):
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"""
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Visualize the results of OCR detection and recognition
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args:
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image(Image): image from Image.open
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boxes(list): boxes with shape(N, 4, 2)
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txts(list): the texts
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scores(list): txxs corresponding scores
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draw_txt(bool): whether draw text or not
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drop_score(float): only scores greater than drop_threshold will be visualized
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return(array):
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the visualized img
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"""
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from PIL import Image, ImageDraw, ImageFont
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img = image.copy()
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@ -154,35 +167,123 @@ def draw_ocr(image, boxes, txts, scores, draw_txt=True, drop_score=0.5):
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fill='red')
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if draw_txt:
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txt_color = (0, 0, 0)
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img = np.array(resize_img(img))
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_h = img.shape[0]
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blank_img = np.ones(shape=[_h, 600], dtype=np.int8) * 255
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blank_img = Image.fromarray(blank_img).convert("RGB")
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draw_txt = ImageDraw.Draw(blank_img)
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img = np.array(resize_img(img, input_size=600))
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txt_img = text_visual(
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txts, scores, img_h=img.shape[0], img_w=600, threshold=drop_score)
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img = np.concatenate([np.array(img), np.array(txt_img)], axis=1)
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font_size = 20
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gap = 20
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title = "index text score"
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font = ImageFont.truetype(
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"./doc/simfang.ttf", font_size, encoding="utf-8")
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draw_txt.text((20, 0), title, txt_color, font=font)
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count = 0
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for idx, txt in enumerate(txts):
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if scores[idx] < drop_score:
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continue
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font = ImageFont.truetype(
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"./doc/simfang.ttf", font_size, encoding="utf-8")
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new_txt = str(idx) + ': ' + txt + ' ' + '%.3f' % (scores[idx])
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draw_txt.text(
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(20, gap * (count + 1)), new_txt, txt_color, font=font)
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count += 1
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img = np.concatenate([np.array(img), np.array(blank_img)], axis=1)
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return img
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def str_count(s):
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"""
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Count the number of Chinese characters,
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a single English character and a single number
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equal to half the length of Chinese characters.
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args:
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s(string): the input of string
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return(int):
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the number of Chinese characters
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"""
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import string
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count_zh = count_pu = 0
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s_len = len(s)
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en_dg_count = 0
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for c in s:
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if c in string.ascii_letters or c.isdigit() or c.isspace():
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en_dg_count += 1
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elif c.isalpha():
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count_zh += 1
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else:
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count_pu += 1
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return s_len - math.ceil(en_dg_count / 2)
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def text_visual(texts, scores, img_h=400, img_w=600, threshold=0.):
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"""
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create new blank img and draw txt on it
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args:
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texts(list): the text will be draw
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scores(list|None): corresponding score of each txt
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img_h(int): the height of blank img
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img_w(int): the width of blank img
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return(array):
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"""
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if scores is not None:
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assert len(texts) == len(
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scores), "The number of txts and corresponding scores must match"
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def create_blank_img():
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blank_img = np.ones(shape=[img_h, img_w], dtype=np.int8) * 255
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blank_img[:, img_w - 1:] = 0
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blank_img = Image.fromarray(blank_img).convert("RGB")
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draw_txt = ImageDraw.Draw(blank_img)
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return blank_img, draw_txt
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blank_img, draw_txt = create_blank_img()
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font_size = 20
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txt_color = (0, 0, 0)
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font = ImageFont.truetype(
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"../../doc/simfang.ttf", font_size, encoding="utf-8")
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gap = font_size + 5
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txt_img_list = []
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count, index = 0, 0
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for idx, txt in enumerate(texts):
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index += 1
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if scores[idx] < threshold:
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index -= 1
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continue
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first_line = True
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while str_count(txt) >= img_w // font_size - 4:
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tmp = txt
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txt = tmp[:img_w // font_size - 4]
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if first_line:
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new_txt = str(index) + ': ' + txt
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first_line = False
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else:
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new_txt = ' ' + txt
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draw_txt.text((0, gap * (count + 1)), new_txt, txt_color, font=font)
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txt = tmp[img_w // font_size - 4:]
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count += 1
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if count >= img_h // gap - 1:
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txt_img_list.append(np.array(blank_img))
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blank_img, draw_txt = create_blank_img()
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count = 0
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if first_line:
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new_txt = str(index) + ': ' + txt + ' ' + '%.3f' % (scores[idx])
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else:
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new_txt = " " + txt + " " + '%.3f' % (scores[idx])
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draw_txt.text((0, gap * (count + 1)), new_txt, txt_color, font=font)
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count += 1
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# whether add new blank img or not
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if count >= img_h // gap - 1 and idx + 1 < len(texts):
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txt_img_list.append(np.array(blank_img))
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blank_img, draw_txt = create_blank_img()
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count = 0
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txt_img_list.append(np.array(blank_img))
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if len(txt_img_list) == 1:
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blank_img = np.array(txt_img_list[0])
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else:
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blank_img = np.concatenate(txt_img_list, axis=1)
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# cv2.imwrite("./draw_txt.jpg", np.array(blank_img))
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return np.array(blank_img)
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if __name__ == '__main__':
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text = [
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"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",
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"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",
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"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"
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]
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img = text_visual(text, scores=[0.999, 0.999, 0.999], img_h=100)
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cv2.imwrite("./draw_txt.jpg", np.array(img))
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"""
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test_img = "./doc/test_v2"
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predict_txt = "./doc/predict.txt"
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f = open(predict_txt, 'r')
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@ -202,3 +303,4 @@ if __name__ == '__main__':
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new_img = draw_ocr(image, boxes, txts, scores, draw_txt=True)
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cv2.imwrite(img_name, new_img)
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"""
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