Jam Indonesia Python

 !pip install pytz

from datetime import datetime
import pytz
from time import gmtime, strftime,sleep
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning) 

import warnings
warnings.filterwarnings("ignore")
warnings.filterwarnings("ignore", category=DeprecationWarning)
warnings.filterwarnings("ignore", module="sklearn")

import warnings
# Menyembunyikan semua peringatan
warnings.filterwarnings("ignore")
# Menyembunyikan peringatan spesifik, misalnya UserWarning
warnings.filterwarnings("ignore", category=UserWarning)

jakarta_tz = pytz.timezone('Asia/Jakarta') 
jakarta_time = datetime.now(jakarta_tz) 
time_str = jakarta_time.strftime("%H:%M:%S")
print("Waktu di Indonesia/Jakarta:", time_str)

 tgl=strftime("%Y-%m-%d", gmtime()) #u waktu
jam=strftime("%H:%M:%S", gmtime()) #u waktu
jakarta_tz = pytz.timezone('Asia/Jakarta') 
jakarta_time = datetime.now(jakarta_tz) 
jam = jakarta_time.strftime("%H:%M:%S")

+++++++++++++
Contoh penerapan :

def updateDB(cursor,db,idx):
    sql = "UPDATE `tb_temp`  set `status`='1',`status2`='1' where id='%s'" % (idx)
    #print(sql)
    v=0
    try:
        cursor.execute(sql)
        db.commit()
        v=1
    except:
        db.rollback()
        return v


# In[3]:E:\xampp\htdocs\appPlatkendaraan\ypathfile



#pathme='E:\xampp\htdocs\appPlatkendaraan\ypathfile'
pathme='E:\\xampp\\htdocs\\appPlatkendaraan\\ypathfile'

sfr = SimpleFacerec()
AL=pathme+"/dataset/"
print (AL)
sfr.load_encoding_images(AL) 


# In[ ]:


IMG=""
kon=True
db = MySQLdb.connect("localhost","root","","24_eparkir")
cursor = db.cursor()

while (kon):  
    ada=0
    ID=''
    gambar1=''
    gambar2=''
    NAME1=''
    NAME2=''

    try:
        cursor.execute("SELECT `id`,`gambar1`,`gambar2` FROM `tb_temp` where `status`='0' order by `id` desc limit 0,1")
        db.commit()
        for row in cursor.fetchall():
            ID=row[0]
            gambar1=row[1]
            gambar2=row[2] 
            ada=1 

            AL=pathme+'/'+gambar1
            print("Face:"+AL)
            frame= cv2.imread(r''+AL)
            face_locations, face_names = sfr.detect_known_faces(frame)
            for face_loc, name in zip(face_locations, face_names):
                y1, x2, y2, x1 = face_loc[0], face_loc[1], face_loc[2], face_loc[3]
                
                ar=name.split('-')
                NAME1=ar[0]
                print ('GET1='+NAME1)
                #updateDB(cursor,db,ID)
                #kon=False
            ###################################

            AL=pathme+'/'+gambar2
            print("Plat:"+AL)
            img = cv.imread(r''+AL)

            img = cv.resize(img, (int(img.shape[1]*.4),int(img.shape[0]*.4)))
            img_gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)  
            kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE,(20,20))
            img_opening = cv.morphologyEx(img_gray, cv.MORPH_OPEN, kernel)
            img_norm = img_gray - img_opening
            (thresh, img_norm_bw) = cv.threshold(img_norm, 0, 255, cv.THRESH_BINARY + cv.THRESH_OTSU)
            (thresh, img_without_norm_bw) = cv.threshold(img_gray, 0, 255, cv.THRESH_BINARY + cv.THRESH_OTSU)

            contours_vehicle, hierarchy = cv.findContours(img_norm_bw, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE) 
            index_plate_candidate = []
            index_counter_contour_vehicle = 0

            for contour_vehicle in contours_vehicle:
                x,y,w,h = cv.boundingRect(contour_vehicle)
                aspect_ratio = w/h
                if w >= 200 and aspect_ratio <= 4 : 
                    index_plate_candidate.append(index_counter_contour_vehicle)
                index_counter_contour_vehicle += 1

            img_show_plate = img.copy() 
            img_show_plate_bw = cv.cvtColor(img_norm_bw, cv.COLOR_GRAY2RGB)

            if len(index_plate_candidate) == 0:
                print("Plat nomor tidak ditemukan")

            elif len(index_plate_candidate) == 1:
                x_plate,y_plate,w_plate,h_plate = cv.boundingRect(contours_vehicle[index_plate_candidate[0]])
                cv.rectangle(img_show_plate,(x_plate,y_plate),(x_plate+w_plate,y_plate+h_plate),(0,255,0),5)
                cv.rectangle(img_show_plate_bw,(x_plate,y_plate),(x_plate+w_plate,y_plate+h_plate),(0,255,0),5)
                img_plate_gray = img_gray[y_plate:y_plate+h_plate, x_plate:x_plate+w_plate]
            else:
                #print('Dapat dua lokasi plat, pilih lokasi plat kedua')
                x_plate,y_plate,w_plate,h_plate = cv.boundingRect(contours_vehicle[index_plate_candidate[1]])
                cv.rectangle(img_show_plate,(x_plate,y_plate),(x_plate+w_plate,y_plate+h_plate),(0,255,0),5)
                cv.rectangle(img_show_plate_bw,(x_plate,y_plate),(x_plate+w_plate,y_plate+h_plate),(0,255,0),5)
                img_plate_gray = img_gray[y_plate:y_plate+h_plate, x_plate:x_plate+w_plate]

            (thresh, img_plate_bw) = cv.threshold(img_plate_gray, 0, 255, cv.THRESH_BINARY + cv.THRESH_OTSU)
            kernel = cv.getStructuringElement(cv.MORPH_CROSS, (3,3))
            img_plate_bw = cv.morphologyEx(img_plate_bw, cv.MORPH_OPEN, kernel) # apply morph open
            contours_plate, hierarchy = cv.findContours(img_plate_bw, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE) 

            index_chars_candidate = [] #index
            index_counter_contour_plate = 0 #idx
            img_plate_rgb = cv.cvtColor(img_plate_gray,cv.COLOR_GRAY2BGR)
            img_plate_bw_rgb = cv.cvtColor(img_plate_bw, cv.COLOR_GRAY2RGB)
            for contour_plate in contours_plate:
                x_char,y_char,w_char,h_char = cv.boundingRect(contour_plate)
                if h_char >= 40 and h_char <= 60 and w_char >=10:
                    index_chars_candidate.append(index_counter_contour_plate)
                    cv.rectangle(img_plate_rgb,(x_char,y_char),(x_char+w_char,y_char+h_char),(0,255,0),5)
                    cv.rectangle(img_plate_bw_rgb,(x_char,y_char),(x_char+w_char,y_char+h_char),(0,255,0),5)

                index_counter_contour_plate += 1

            if index_chars_candidate == []:
                print('Karakter tidak tersegmentasi')
            else:
                score_chars_candidate = np.zeros(len(index_chars_candidate))
                counter_index_chars_candidate = 0
                for chars_candidateA in index_chars_candidate:
                    xA,yA,wA,hA = cv.boundingRect(contours_plate[chars_candidateA])
                    for chars_candidateB in index_chars_candidate:
                        if chars_candidateA == chars_candidateB:
                            continue
                        else: 
                            xB,yB,wB,hB = cv.boundingRect(contours_plate[chars_candidateB]) 
                            y_difference = abs(yA - yB)
                            if y_difference < 11:
                                score_chars_candidate[counter_index_chars_candidate] = score_chars_candidate[counter_index_chars_candidate] + 1 

                    counter_index_chars_candidate += 1

                index_chars = [] 
                chars_counter = 0 
                for score in score_chars_candidate:
                    if score == max(score_chars_candidate): 
                        index_chars.append(index_chars_candidate[chars_counter])
                    chars_counter += 1

                x_coors = []
                for char in index_chars:
                    x, y, w, h = cv.boundingRect(contours_plate[char])
                    x_coors.append(x) 
                x_coors = sorted(x_coors) 
                index_chars_sorted = []
                for x_coor in x_coors:
                    for char in index_chars:
                        x, y, w, h = cv.boundingRect(contours_plate[char])
                        if x_coors[x_coors.index(x_coor)] == x:
                            index_chars_sorted.append(char) 

                num_plate = []
                for char_sorted in index_chars_sorted:
                    x,y,w,h = cv.boundingRect(contours_plate[char_sorted])
                    char_crop = cv.cvtColor(img_plate_bw[y:y+h,x:x+w], cv.COLOR_GRAY2BGR)
                    char_crop = cv.resize(char_crop, (img_width, img_height))
                    img_array = keras.preprocessing.image.img_to_array(char_crop)
                    img_array = tf.expand_dims(img_array, 0)
                    predictions = model.predict(img_array)
                    score = tf.nn.softmax(predictions[0]) 
                    num_plate.append(class_names[np.argmax(score)])
                    #print(class_names[np.argmax(score)], end='')
                plate_number = ''
                for a in num_plate:
                    plate_number += a 

            print(plate_number)
            NAME2=plate_number
            print ('GET2='+NAME2)
            updateDB(cursor,db,ID)

            tgl=strftime("%Y-%m-%d", gmtime()) #u waktu
            jam=strftime("%H:%M:%S", gmtime()) #u waktu
            jakarta_tz = pytz.timezone('Asia/Jakarta') 
            jakarta_time = datetime.now(jakarta_tz) 
            jam = jakarta_time.strftime("%H:%M:%S")

            sql2="INSERT INTO `tb_lewat` (`id_lewat`, `tanggal`, `jam`, `gambar_wajah`, `id_pemilik`, `status_wajah`, `gambar_nomor`, `id_kendaraan`, `status_kendaraan`, `status_kepemilikan`, `keterangan`, `temp1`, `temp2`) VALUES ('', '%s', '%s', '%s', '%s', '%s','%s', '%s', '%s', '%s', '%s', '%s', '%s')" % (tgl,jam,gambar1,NAME1,'Proses',gambar2,NAME2,'Proses','','',NAME1,NAME2)
            print(sql2)
            try:
                cursor.execute(sql2)
                db.commit()
            except:
                db.rollback()
    except:
        print('No data....')

cursor.close()
db.close()