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model_postM_invoice.py
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model_postM_invoice.py
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# -*- coding: utf-8 -*-
from config import opencvFlag, GPU, IMGSIZE, ocrFlag
if not GPU:
import os
os.environ["CUDA_VISIBLE_DEVICES"] = '' ##不启用GPU
if ocrFlag == 'torch':
from crnn.crnn_torch import crnnOcr as crnnOcr ##torch版本ocr
elif ocrFlag == 'keras':
from crnn.crnn_keras import crnnOcr as crnnOcr ##keras版本OCR
import time
import cv2
import numpy as np
from PIL import Image
from glob import glob
from text.detector.detectors import TextDetector
from apphelper.image import get_boxes, letterbox_image
from text.opencv_dnn_detect import angle_detect ##文字方向检测,支持dnn/tensorflow
from apphelper.image import estimate_skew_angle, rotate_cut_img, xy_rotate_box, sort_box, box_rotate, solve
if opencvFlag == 'opencv':
from text import opencv_dnn_detect as detect ##opencv dnn model for darknet
elif opencvFlag == 'darknet':
from text import darknet_detect as detect
else:
## keras版本文字检测
from text import keras_detectM_invoice as detect
print("Text detect engine:{}".format(opencvFlag))
def text_detect(img,
MAX_HORIZONTAL_GAP=30,
MIN_V_OVERLAPS=0.6,
MIN_SIZE_SIM=0.6,
TEXT_PROPOSALS_MIN_SCORE=0.7,
TEXT_PROPOSALS_NMS_THRESH=0.3,
TEXT_LINE_NMS_THRESH=0.3,
):
boxes, scores = detect.text_detect(np.array(img))
boxes = np.array(boxes, dtype=np.float32)
scores = np.array(scores, dtype=np.float32)
textdetector = TextDetector(MAX_HORIZONTAL_GAP, MIN_V_OVERLAPS, MIN_SIZE_SIM)
shape = img.shape[:2]
boxes = textdetector.detect(boxes,
scores[:, np.newaxis],
shape,
TEXT_PROPOSALS_MIN_SCORE,
TEXT_PROPOSALS_NMS_THRESH,
TEXT_LINE_NMS_THRESH,
)
text_recs = get_boxes(boxes)
newBox = []
rx = 1
ry = 1
for box in text_recs:
x1, y1 = (box[0], box[1])
x2, y2 = (box[2], box[3])
x3, y3 = (box[6], box[7])
x4, y4 = (box[4], box[5])
newBox.append([x1 * rx, y1 * ry, x2 * rx, y2 * ry, x3 * rx, y3 * ry, x4 * rx, y4 * ry])
return newBox
def crnnRec(im, boxes, leftAdjust=False, rightAdjust=False, alph=0.2, f=1.0):
"""
crnn模型，ocr识别
leftAdjust,rightAdjust 是否左右调整box 边界误差，解决文字漏检
"""
results = []
im = Image.fromarray(im)
for index, box in enumerate(boxes):
degree, w, h, cx, cy = solve(box)
partImg, newW, newH = rotate_cut_img(im, degree, box, w, h, leftAdjust, rightAdjust, alph)
text = crnnOcr(partImg.convert('L'))
if text.strip() != u'':
results.append({'cx': cx * f, 'cy': cy * f, 'text': text, 'w': newW * f, 'h': newH * f,
'degree': degree * 180.0 / np.pi})
return results
def eval_angle(im, detectAngle=False):
"""
估计图片偏移角度
@@param:im
@@param:detectAngle 是否检测文字朝向
"""
angle = 0
img = np.array(im)
if detectAngle:
angle = angle_detect(img=np.copy(img)) ##文字朝向检测
if angle == 90:
im = Image.fromarray(im).transpose(Image.ROTATE_90)
elif angle == 180:
im = Image.fromarray(im).transpose(Image.ROTATE_180)
elif angle == 270:
im = Image.fromarray(im).transpose(Image.ROTATE_270)
img = np.array(im)
return angle, img
def model(img, detectAngle=False, config={}, leftAdjust=False, rightAdjust=False, alph=0.2):
"""
@@param:img,
@@param:ifadjustDegree 调整文字识别倾斜角度
@@param:detectAngle,是否检测文字朝向
"""
angle, img = eval_angle(img, detectAngle=detectAngle) ##文字方向检测
if opencvFlag != 'keras':
img, f = letterbox_image(Image.fromarray(img), IMGSIZE) ## pad
img = np.array(img)
else:
f = 1.0 ##解决box在原图坐标不一致问题
config['img'] = img
text_recs = text_detect(**config) ##文字检测
newBox = sort_box(text_recs) ##行文本识别
result = crnnRec(np.array(img), newBox, leftAdjust, rightAdjust, alph, 1.0 / f)
return img, result, angle
####################################################################################################
from PIL import Image
from apphelper.image import union_rbox
import os
import torch
from apphelper.image import xy_rotate_box, box_rotate, solve
import cv2
import tensorflow as tf
os.environ["CUDA_VISIBLE_DEVICES"] = '0' #指定第一块GPU可用
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.3 # 程序最多只能占用指定gpu30%的显存
sess = tf.Session(config = config)
def ocr(img):
h, w = img.shape[:2]
_, result, angle = model(img,
detectAngle=True, ##是否进行文字方向检测
config=dict(MAX_HORIZONTAL_GAP=50, ##字符之间的最大间隔，用于文本行的合并
MIN_V_OVERLAPS=0.6,
MIN_SIZE_SIM=0.6,
TEXT_PROPOSALS_MIN_SCORE=0.1,
TEXT_PROPOSALS_NMS_THRESH=0.3,
TEXT_LINE_NMS_THRESH=0.7, ##文本行之间测iou值
),
leftAdjust=True, ##对检测的文本行进行向左延伸
rightAdjust=True, ##对检测的文本行进行向右延伸
alph=0.01, ##对检测的文本行进行向右、左延伸的倍数
)
# res5 = []
# for line in result:
# res5.append(line['text'])
# return {"text": {str(k): v for k, v in enumerate(res5)}}
return result