import pyodbc
from neuro import Neuro
from frame import Frame

server = '10.0.0.43'
database = 'mb'
username = 'usrMB'
password = 'asdqaz'


class Processing(object):
    def __init__(self, save_path, sqr_percent, classes):
        self.conn = pyodbc.connect('DSN=MSSQL;UID=usrMB;PWD=asdqaz')
        self.get_tasks = 'SELECT ID_task_for_analisis, ID_Request ' \
                         'FROM TasksForAnalisis WHERE id_type_processing = 1 AND Date_completed IS NULL'
        self.get_photo_video = 'SELECT is_photo, Link_Download, time_st, Time_fin, ID_Video FROM PhotoVideoRequests ' \
                               'WHERE ID_request = %s'
        self.update_detected_items = 'INSERT INTO DetectedItems(ID_Video, ID_task_for_analisis, ID_Type_item, ' \
                                     'X_top_right, Y_top_right, X_bottom_left, Y_bottom_left, Date_add) ' \
                                     'VALUES (%s,%s,%s,%s,%s,%s,%s,%s)'
        self.update_event_alarm = ''
        self.update_task_for_analisis_date_completed = 'UPDATE TasksForAnalisis SET ' \
                                                       'Date_comleted = CONVERT(time, SYSDATETIME())' \
                                                       ' WHERE ID_task_for_analisis = %s'


    def process(self):
        cursor = self.conn.cursor()
        cursor.execute(self.get_tasks)
        results = cursor.fetchall()
        for result in results:
            print("TasksForAnalisis field:", result)
            task_id, request_id = result[0], result[1]
            # get photo_video
            cursor.execute(self.get_photo_video % request_id)
            photo_videos = cursor.fetchall()
            for photo_video in photo_videos:
                is_photo, link_for_download, time_start, time_end, video_id = photo_video[0], photo_video[1],  \
                                                                              photo_video[2], photo_video[3], \
                                                                              photo_video[4]
                # download file by link... #image_path, save_path, interest_zones=None, yolo_thresh=0.13, sqr_percent=40
                images, frames = Frame.image_treatment(image_path, self.save_path, interest_zones, yolo_thresh,
                                                       self.sqr_percent)
                print("images:", images)
                print('frames:', frames)
                alarm = 0
                for i in range(len(images)):
                    p = self.neuro_network.predict(images[i])
                    print('predict:', p, self.neuro_network.get_real_class_name_by_number(p))
                    if self.neuro_network.is_alarm_class(p):
                       alarm = 1
                    if self.neuro_network.get_real_class_name_by_number(p) in self.classes:
                        continue
                    type_object_id = self.neuro_network.our_class_db_mapper(p)
                    insert = self.update_detected_items % (video_id, task_id, type_object_id, frames[i][0],
                                                           frames[i][1], frames[i][2], frames[i][3])
                    cursor.execute(insert)
                    self.conn.commit()
            cursor.execute(self.update_task_for_analisis_date_completed % task_id)
            self.conn.commit()