def detection_plate(data_dir):
'''
Detect images in data dir.
Args:
img_array -- The numpy array of image to be predicted.
'''
try:
from hyperlpr import HyperLPR_PlateRecogntion
print(os.path.split(os.path.realpath(__file__))[0])
print('Start')
print('dir:', data_dir)
result_list = []
for img_name in os.listdir(data_dir):
print('img name:', img_name)
if (img_name.lower().find('.png') < 0
and img_name.lower().find('.jpeg') < 0
and img_name.lower().find('.jpg') < 0
and img_name.lower().find('.bmp') < 0):
continue
img_path = os.path.join(data_dir, img_name)
print('Start read img')
image = cv2.imread(img_path)
print('Start lpr detection')
result = HyperLPR_PlateRecogntion(
image,
path=os.path.join(
os.path.split(os.path.realpath(__file__))[0], 'hypercar'))
result_list.append(result)
except Exception as e:
print(e)
def detection_plate_one_img(img_array):
'''
Detect one image.
Args:
img_array -- The numpy array of image to be predicted.
'''
try:
from hyperlpr import HyperLPR_PlateRecogntion
result_list = HyperLPR_PlateRecogntion(
img_array,
path=os.path.join(
os.path.split(os.path.realpath(__file__))[0], 'hypercar'))
return result_list
except Exception as e:
print(e)
def lpr(file_name):
body = app.current_request.raw_body
# parsed = parse_qs(request.raw_body, encoding='utf-8')
temp_file = os.getcwd() + file_name
with open(temp_file, 'wb') as f:
f.write(body)
# 使用Opencv转换一下图片格式和名称
img = cv2.imread(temp_file)
# 识别结果
data = HyperLPR_PlateRecogntion(img)
if data:
for i in data:
if i[1] > 0.8:
return Response({'lp': i[0]})
else:
return Response('error happens')
def post(self):
"""识别车牌
"""
body = self.get_body_json()
url = body.pop("url")
image = io.imread(url)
info = HyperLPR_PlateRecogntion(image)
if info:
item = info[0]
logging.info("识别车牌：%s", info)
self.success(data={
"lp": item[0],
"precision": item[1],
"pos": item[2]
})
return
logging.info("识别失败：%s", url)
self.success(status="unknown", message="车牌识别失败")
def upload_to_s3(file_name):
# get raw body of PUT request
body = app.current_request.raw_body
# write body to tmp file
tmp_file_name = os.getcwd() + file_name
with open(tmp_file_name, 'wb') as tmp_file:
tmp_file.write(body)
# 使用Opencv转换一下图片格式和名称
img = cv2.imread(tmp_file_name)
# 识别结果
data = HyperLPR_PlateRecogntion(img)
if data:
for i in data:
if i[1] > 0.8:
os.remove(tmp_file_name)
return Response(body='lpr: {}'.format(i[0]),
status_code=200,
headers={'Content-Type': 'text/plain'})
else:
return Response(body='error: {}'.format('error happens'),
status_code=400,
headers={'Content-Type': 'text/plain'})
import cv2
import os
from hyperlpr import HyperLPR_PlateRecogntion
img_dir = "/home/ubuntu/Desktop/2019 Semester 1/Project/DemoHyperLPR/img/"
for filename in os.listdir(img_dir):
print("Working on image: "+filename)
if '.jpg' in filename:
img = cv2.imread(img_dir + filename)
recognitionResult = HyperLPR_PlateRecogntion(img)
maxConfidence = 0
maxRow = 0
imageName = 'default'
# identify the result with the highest confidence
for i in recognitionResult:
if i[1] > maxConfidence:
maxConfidence = i[1]
maxRow = i
if maxConfidence > 0.8:
imageName = maxRow[0]
os.rename(img_dir+filename, img_dir+imageName+'.jpg')
return image_code
def base64_to_image(base64_code):
img_data = base64.b64decode(base64_code)
img_array = np.fromstring(img_data, np.uint8)
img = cv2.imdecode(img_array, cv2.COLOR_RGB2BGR)
return img
aa=image_to_base64(image)
data={}
data['image']=aa
bb=base64_to_image(data['image'])
#print(HyperLPR_PlateRecogntion(bb)[0][0],filename[0].strip('.jpg'))
temp=[bb.shape]
if(len(HyperLPR_PlateRecogntion(bb))==0):
shutil.copyfile(path,"/Users/reocar/Documents/interface/lost_pic/"+filename[i])
output_[filename[i].strip('.jpg')]=temp
continue
if(len(HyperLPR_PlateRecogntion(bb)[0][0])!=7):
temp.append(HyperLPR_PlateRecogntion(bb)[0][0][0:7])
else:
temp.append(HyperLPR_PlateRecogntion(bb)[0][0])
output[filename[i].strip('.jpg')]=temp
def recognize():
"""
处理上传文件并识别车牌号码
:return: dict
"""
if not check_uploaded_file1('photo', request.files):
return make_api_response(2, '未找到匹配的图片')
photo = request.files.get('photo')
if not check_uploaded_file2(photo.filename):
return make_api_response(2, '未接收到任何图片，请重新上传图片')
if not (photo and allowed_file(photo.filename)):
return make_api_response(2, '上传的文件格式无效。')
relative_path = save_photo(photo)
photo_path = Path(relative_path)
if not photo_path.is_file():
return make_api_response(2, '图片文件上传失败')
if not check_uploaded_file3(relative_path):
return make_api_response(2, '图片大小为0，请重新上传图片')
image = Image.open(relative_path)
img_dpi = "%dx%d" % (image.size[0], image.size[1])
img_format = image.format
img_size = "%dKB" % (os.path.getsize(relative_path) / 1024)
start = time.clock()
# 识别车牌号码
image = cv2.imread(relative_path)
if image is None:
return make_api_response(status=1,
msg='OpenCV读取图片失败',
result_photo=photo_path.name,
img_dpi=img_dpi,
img_format=img_format,
img_size=img_size)
result_list = HyperLPR_PlateRecogntion(image)
elapsed = "%.2fs" % (time.clock() - start)
if not (result_list and len(result_list)
== 1) or not (result_list[0] and len(result_list[0]) == 3):
return make_api_response(status=1,
msg='操作成功，但未找到有效车牌号',
result_photo=photo_path.name,
img_dpi=img_dpi,
img_format=img_format,
img_size=img_size,
used_time=elapsed)
plate = result_list[0][0]
confidence = result_list[0][1]
location = result_list[0][2]
top_left_x = location[0]
top_left_y = location[1]
top_right_x = location[2]
top_right_y = location[1]
bottom_right_x = location[2]
bottom_right_y = location[3]
bottom_left_x = location[0]
bottom_left_y = location[3]
rectangle_point_locations = [(top_left_x, top_left_y),
(top_right_x, top_right_y),
(bottom_right_x, bottom_right_y),
(bottom_left_x, bottom_left_y)]
result_photo = mark_photo(relative_path, (top_left_x, top_left_y),
(bottom_right_x, bottom_right_y))
return make_api_response(status=0,
msg='车牌号码识别成功......',
plate=plate,
confidence=float(confidence),
result_photo=Path(result_photo).name,
location=rectangle_point_locations,
img_dpi=img_dpi,
img_format=img_format,
img_size=img_size,
used_time=elapsed)