def test():
os.chdir(_BASEDIR)
print("recognize", tr.recognize("imgs/line.png"))
img_path = "imgs/id_card.jpeg"
# img_path = "imgs/name_card.jpg"
img_pil = Image.open(img_path)
print(img_pil.size)
color_pil = img_pil.convert("RGB")
gray_pil = img_pil.convert("L")
img_draw = ImageDraw.Draw(color_pil)
colors = ['red', 'green', 'blue', "purple"]
t = time.time()
n = 1
for _ in range(n):
tr.detect(gray_pil, flag=tr.FLAG_RECT)
print("time", (time.time() - t) / n)
results = tr.run(gray_pil, flag=tr.FLAG_ROTATED_RECT)
for i, rect in enumerate(results):
cx, cy, w, h, a = tuple(rect[0])
print(i, "\t", rect[1], rect[2])
box = cv2.boxPoints(((cx, cy), (w, h), a))
box = np.int0(np.round(box))
for p1, p2 in [(0, 1), (1, 2), (2, 3), (3, 0)]:
img_draw.line(xy=(box[p1][0], box[p1][1], box[p2][0], box[p2][1]),
fill=colors[i % len(colors)],
width=2)
color_pil.show()
def test():
print("recognize", tr.recognize("imgs/line.png"))
img_path = "imgs/id_card.jpeg"
# img_path = "imgs/name_card.jpg"
img_pil = Image.open(img_path)
try:
if hasattr(img_pil, '_getexif'):
# from PIL import ExifTags
# for orientation in ExifTags.TAGS.keys():
# if ExifTags.TAGS[orientation] == 'Orientation':
# break
orientation = 274
exif = dict(img_pil._getexif().items())
if exif[orientation] == 3:
img_pil = img_pil.rotate(180, expand=True)
elif exif[orientation] == 6:
img_pil = img_pil.rotate(270, expand=True)
elif exif[orientation] == 8:
img_pil = img_pil.rotate(90, expand=True)
except:
pass
MAX_SIZE = 1600
if img_pil.height > MAX_SIZE or img_pil.width > MAX_SIZE:
scale = max(img_pil.height / MAX_SIZE, img_pil.width / MAX_SIZE)
new_width = int(img_pil.width / scale + 0.5)
new_height = int(img_pil.height / scale + 0.5)
img_pil = img_pil.resize((new_width, new_height), Image.ANTIALIAS)
color_pil = img_pil.convert("RGB")
gray_pil = img_pil.convert("L")
img_draw = ImageDraw.Draw(color_pil)
colors = ['red', 'green', 'blue', "purple"]
t = time.time()
n = 1
for _ in range(n):
tr.detect(gray_pil, flag=tr.FLAG_RECT)
print("time", (time.time() - t) / n)
results = tr.run(gray_pil, flag=tr.FLAG_ROTATED_RECT)
for i, rect in enumerate(results):
cx, cy, w, h, a = tuple(rect[0])
print(i, "\t", rect[1], rect[2])
box = cv2.boxPoints(((cx, cy), (w, h), a))
box = np.int0(np.round(box))
for p1, p2 in [(0, 1), (1, 2), (2, 3), (3, 0)]:
img_draw.line(xy=(box[p1][0], box[p1][1], box[p2][0], box[p2][1]),
fill=colors[i % len(colors)],
width=2)
color_pil.show()
def local_ocr2():
# 接收图片 二进制流
upload_file = request.files.get('file')
# print(upload_file)
if upload_file:
upload_file = Image.open(BytesIO(upload_file.read()))
upload_file = upload_file.convert("RGB")
# print(tr.run(img.copy().convert("L"), flag=tr.FLAG_ROTATED_RECT))
result = tr.recognize(upload_file.copy().convert("L"))
# print(result)
return {"res": result}
return 400
def plate_recognize(self):
dirs = PlateRecognize().os_path()
# print(dirs)
plate_list = {}
for filename in dirs:
pth = self.path + os.sep + filename
plate = tr.recognize(pth)[0].upper()
last_num = re.findall(r"[0-9A-Z]{4}$", plate)
if plate[:3].isalpha():
char = []
for i in last_num[0]:
if i == "I":
char.append("1")
elif i == "O":
char.append("0")
elif i == "Z":
char.append("2")
elif i == "J":
char.append("1")
elif i == "L":
char.append("2")
else:
char.append(i)
#print(char)
real_plate = ""
for i in plate[:3]:
real_plate += i
for i in char:
real_plate += i
#real_plate = real_plate + char
plate_list.update({filename: real_plate})
else:
# x = plate[:2].isalpha()
# print(x)
old_plate = ""
for i in plate:
if i != "-":
old_plate += i
plate_list.update({filename: old_plate})
return plate_list
def do_process(frame):
vehicle, LpImg, cor = get_plate(frame)
if (len(LpImg)): #check if there is at least one license image
plate_image = cv2.convertScaleAbs(LpImg[0], alpha=(170.0))
gray = cv2.cvtColor(plate_image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (7, 7), 0)
binary = cv2.threshold(gray, 195, 255, 4)[1]
binary = binary[30:-30, :]
binary = cv2.resize(binary,
(int(binary.shape[1]), int(binary.shape[0] * 0.6)))
# plate_image = plate_image[30:-30,:]
# plate_image=cv2.resize(plate_image,(int(plate_image.shape[1]),int(plate_image.shape[0]*0.6)))
res = tr.recognize(binary)
return res
def image_to_text(image_path, index, actual_filename):
"""
:param image_path: pdf 切割后每个小图片的路径
:param index: 索引
:param serial_num: pdf 序列号，也叫车辆架构号
:return:
"""
image = Image.open(image_path)
# index=0 表头信息
if index == 0:
scope = constants.header_scope
result = {}
for k, v in scope.items():
# 序列号从文件名中取，不在图片中进行识别
if k == 'serialnum':
continue
img = cut_img(img=image, coordinate=v)
# 表头高度有轻微变化，用run_angle检测识别出范围
tr_result = tr.run_angle(img)
text = None
for item in tr_result:
if item[1] == 'r':
continue
else:
text = correct_text(k, item[1])
result[k] = text
log.info('index:{0}, text:{1}'.format(*(index, result)))
redis.hash_set(actual_filename, index, json.dumps(result))
else:
scope = constants.normal_scope
result = {}
for k, v in scope.items():
img = cut_img(img=image, coordinate=v)
# 列名有很多重复的，判断是否和最新识别过的相同，避免在ORC的时候浪费时间
if k == 'column_name':
np_column = np.array(img)
global np_base_columns, text_base_columns
# 判断和之前的列名是否一样，避免重复识别，提高速度
if (np_column.shape == np_base_columns.shape) and (not np.any(
cv2.subtract(np_base_columns, np_column))):
result[k] = text_base_columns
continue
else:
result[k] = correct_text(k, tr.recognize(img)[0])
# 更新要对比的基础列名
text_base_columns = result[k]
np_base_columns = np_column
# 这种需要单独处理 如果用 tr.recognize 有些测试值空格识别不出来，会粘在一起
# 如果title1 里面有K，则显示用title1
elif k == 'test_value' and 'K' in result['title1']:
text = ''
tr_result = tr.run_angle(img)
for i, item in enumerate(tr_result):
# 第四列是-TOL，填充一个空置
if i == 3:
text = text + ' ' + 'null' + ' ' + item[1]
else:
text = text + ' ' + item[1]
# 末尾填充null，为了防止BONUS有些pdf里没有值，没法一一对应
text = text + ' ' + 'null'
text = text.strip()
# 如果加了 'null' 之后，column_name 和test_value 长度不同，说明不需要null占位，需要去除
if len(result['column_name'].split(' ')) != len(
text.split(' ')):
text = text.replace(' null', '')
result[k] = text
print(result)
else:
result[k] = correct_text(k, tr.recognize(img)[0])
log.info('index:{0}, text:{1}'.format(*(index, result)))
redis.hash_set(actual_filename, index, json.dumps(result))
import tr
# detect text lines, return list of (x, y, w, h)
print(tr.detect("imgs/web.png"))
# recognize text line, return (text, confidence)
print(tr.recognize("imgs/line.png"))
# detect and recognize, return list of ((x, y, w, h), text, confidence)
print(tr.run("imgs/name_card.jpg"))
np.set_printoptions(suppress=True) # 取消科学计数法输出
# # Use TR package
# In[ ]:
# coding: utf-8
import tr
import sys, cv2, time, os
from PIL import Image, ImageDraw, ImageFont
import numpy as np
#print("recognize", tr.recognize("imgs/line.png"))
print("recognize", tr.recognize("E:/work/code/check_web/data/icon/icon_chima4.png"))
# img_path = "imgs/id_card.jpeg"
img_path = "E:/work/code/ocr/新增-商品手册/784381.jpg"
# img_path = "imgs/name_card.jpg"
# img_path = "E:/work/code/check_web/data/icon/temp_tishi41.png"
img_pil = Image.open(img_path)
try:
if hasattr(img_pil, '_getexif'):
# from PIL import ExifTags
# for orientation in ExifTags.TAGS.keys():
# if ExifTags.TAGS[orientation] == 'Orientation':
# break
orientation = 274
exif = dict(img_pil._getexif().items())
def test():
os.chdir(_BASEDIR)
print("recognize", tr.recognize("imgs/line.png"))
txt = tr.run("imgs/line.png")[0][1]
print(txt)
