Ejemplo n.º 1
0
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)
Ejemplo n.º 2
0
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)
Ejemplo n.º 3
0
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')
Ejemplo n.º 4
0
    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")
Ejemplo n.º 5
0
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'})
Ejemplo n.º 6
0
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')
Ejemplo n.º 7
0
        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

    




Ejemplo n.º 8
0
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)