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):
        self.save_path = save_path

        self.sqr_percent = sqr_percent

        self.conn = pyodbc.connect('DSN=MSSQL;UID=usrMB;PWD=asdqaz')

        self.cam_zone_command = "SELECT id_cam, id_type_zone, sensitivity, x1, y1 ,x2, y2, x3, y3, x4, y4 " \
                                "FROM CamZones WHERE id_cam = %s AND id_type_zone IN (SELECT id_type_zone FROM TypesProcessingZone WHERE id_type_proces$

        self.sql_command = 'SELECT id, id_video, shopNo, date_add, id_cam, id_type_processing, data_processing, alarm ' \
                           'FROM TasksForAnalisis WHERE id_type_processing = 1'

        self.update_task_alarm = "UPDATE TasksForAnalisis SET alarm = %s WHERE id = %s"

        self.insert_to_detected = "INSERT INTO DetectedObjects(ID_task_for_analisis,ID_type_object,X1,Y1,X2,Y2) " \
                                  "VALUES (%s,%s,%s,%s,%s,%s)"

        self.times_for_video = "SELECT link_download FROM times_for_video WHERE id = %s"

    def close_connection(self):
        self.conn.close()

    def get_db_version(self):
        cursor = self.conn.cursor()
        cursor.execute("SELECT @@version;")
        row = cursor.fetchone()
        while row:
            print(row)
            break

    def get_thresh_interest_zones(self, cursor, sql_str):
        interest_zones = []
        yolo_threshs = []
        yolo_thresh = 0.13
        cursor.execute(sql_str)
        for row in cursor.fetchall():
            print('SUBQUERY:', row)
            yolo_threshs.append(row[2])
            zone = []

            zone.append(row[3])
            zone.append(row[4])
            zone.append(row[5])
            zone.append(row[6])

            zone.append(row[7])
            zone.append(row[8])
            zone.append(row[9])
            zone.append(row[10])
            interest_zones.append(zone)
        print("ZONES_INTEREST:", interest_zones)
        if len(yolo_threshs) > 0:
            yolo_thresh = min([float(t) for t in yolo_threshs])
        return yolo_thresh, interest_zones


    def process(self):
        self.neuro_network = Neuro()

        cursor = self.conn.cursor()
        cursor.execute(self.sql_command)
        results = cursor.fetchall()
        for result in results:
            print("MAIN:", result)
            id_task, id_video = result[0], result[1]
            # по иден?^?и?^?ика?^?о?^??^? видео в?^??^?а?^?им ?^??^??^?лк?^? на ?^?ка?^?ивание
            cursor.execute(self.times_for_video % id_video)
            link_for_download = cursor.fetchone()[0]
            print('Link for download', link_for_download)
            if link_for_download is None or link_for_download == '':
                print('Not valid link, continue')
                continue
            # е?^?ли ?^??^??^?лка на ?^?ка?^?ивание не п?^??^??^?а, ?^?ка?^?аем ?^?айл
            file_name = 'temp.jpg'
            if not Frame.download_frame(link_for_download, file_name):
                print('Image not downloaded, continue')
                continue
            # в?^??^?а?^?им из ?^?абли?^??^? CamZones ин?^?о?^?ма?^?и?^? о по?^?оге и зона?^? ин?^?е?^?е?^?а
            id_cam, id_type_processing = result[4], result[5]
            print("id_cam", id_cam, " id_type_processing", id_type_processing)
            sql_cam_zones = self.cam_zone_command % (id_cam)
            yolo_thresh, interest_zones = self.get_thresh_interest_zones(cursor, sql_cam_zones)
            print("yolo_thresh", yolo_thresh)
            print("interest_zones", interest_zones)
            import os
            images, frames = Frame.image_treatment(os.getcwd()+'/'+file_name, 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
                id_type_object = self.neuro_network.our_class_db_mapper(p)
                insert = self.insert_to_detected % (id_task, id_type_object, frames[i][0], frames[i][1], frames[i][2], frames[i][3])
                cursor.execute(insert)
                self.conn.commit()
            cursor.execute(self.update_task_alarm % (alarm, id_task))
            self.conn.commit()
    print("end of processing")

if __name__ == '__main__':
    p = Processing('/home/user1/', 40)
    p.get_db_version()
    p.process()