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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_detect 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
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