def getWord(self, img_file):
##基于exif信息文字朝向检测
img_exif = Image.open(img_file)
file_name = os.path.basename(img_file)
file_path = os.path.dirname(img_file)
img = cv2.imread(img_file)
is_exif = False
try:
for orientation in ExifTags.TAGS.keys():
if ExifTags.TAGS[orientation] == 'Orientation': break
exif = dict(img_exif._getexif().items())
if exif[orientation] == 3:
img = Image.fromarray(img).transpose(Image.ROTATE_180)
is_exif = True
elif exif[orientation] == 8:
img = Image.fromarray(img).transpose(Image.ROTATE_270)
is_exif = True
elif exif[orientation] == 6:
img = Image.fromarray(img).transpose(Image.ROTATE_90)
is_exif = True
except:
pass
# print (img_file)
# print (img)
angle = 0
if img is not None:
##基于tensorflow文字朝向检测
if adjust and is_exif is False:
angle = self.angle_detect_dnn(img=np.copy(img))
if angle == 90:
img = Image.fromarray(img).transpose(Image.ROTATE_270)
elif angle == 180:
img = Image.fromarray(img).transpose(Image.ROTATE_180)
elif angle == 270:
img = Image.fromarray(img).transpose(Image.ROTATE_90)
#霍夫矫正
if is_exif is False:
angle = rectifyImgAngle(img_file)
img = cv2.imread(img_file)
image = np.array(img)
# image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
data, drawUrl = text_ocr(image, scale, maxScale, TEXT_LINE_SCORE,
file_path, file_name)
res = {
'data': data,
'errCode': 0,
'drawUrl': drawUrl,
'angle': angle
}
else:
res = {'data': [], 'errCode': 3, 'drawUrl': '', 'angle': angle}
return res
def job(uid, url, imgString, iscut, isclass, billModel, ip):
now = get_now()
if url is not None:
img = read_url_img(url)
elif imgString is not None:
img = base64_to_PIL(imgString)
else:
img = None
if img is not None:
image = np.array(img)
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
data = text_ocr(image, scale, maxScale, TEXT_LINE_SCORE)
res = {'data': data, 'errCode': 0}
else:
res = {'data': [], 'errCode': 3}
return res
import json
import os
from PIL import Image
import numpy as np
from config import scale,maxScale,TEXT_LINE_SCORE
from pprint import pprint
import cv2
from dnn.main import text_ocr
img = Image.open(os.environ["OCR_IMAGE_PATH"]).convert('RGB')
image = np.array(img)
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
data = text_ocr(image,scale,maxScale,TEXT_LINE_SCORE)
print(json.dumps(data))
import sys
sys.path.append('/Users/admin/Downloads/darknet-ocr-master')
from dnn.main import text_ocr
for f in files:
if f in done or f in known_problem:
print(f, "done", file=sys.stderr)
continue
try:
print(f + "\t", end="")
sys.stdout.flush()
# print(f, file=sys.stderr)
image = cv2.imread(f)
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
p = 100
image = 255 - cv2.copyMakeBorder(255 - image, p, p, p, p,
cv.BORDER_CONSTANT, (0, 0, 0))
# cv2.imshow('',image);cv.waitKey(0)
# print(image,scale,maxScale,TEXT_LINE_SCORE)
data = text_ocr(image, 200, 300, 0.0)
o = ''.join([x['text'] for x in data])
print(o)
print(f + "\t" + o, file=sys.stderr)
sys.stdout.flush()
except:
print(f, "fail", file=sys.stderr)
def job(path):
img = cv2.imread(path)
image = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
data = text_ocr(image, scale_d, maxScale, TEXT_LINE_SCORE)
res = {'data':data,'errCode':0}
print(res)