def sleep_to_evening():
print("calculating sleep time..")
task_year = (datetime.datetime.now() + datetime.timedelta(days=1)).year
task_month = (datetime.datetime.now() + datetime.timedelta(days=1)).month
task_day = datetime.datetime.now().day
task_hour = 21
task_minute = 0
sleeptime = datetime.datetime(task_year, task_month, task_day, task_hour, task_minute)-datetime.datetime.now()
print("Sleeping",sleeptime.seconds/60/60," hours")
send_to_telegram(chat,"Sleeping "+str(sleeptime.seconds/60/60)+" hours")
time.sleep(sleeptime.seconds)
print("job started..")
send_to_telegram(chat,"job started..")
def separate():
gpu_count = 1
chat = PASTE_GROUP_ID
screenshots_path = "/home/alex/storage/shop_screens/video/"
processed_path = "/home/alex/storage/shop_screens/video/processed/"
shop_names = ["Altuf", "Avangard", "Mar", "Tag"]
if host_check("scriptlab.net"):
print("scriptlab.net - Ok")
else:
print("scriptlab.net - Unavailable. Exit")
exit()
if host_check("10.2.4.95"):
print("10.2.4.95 (images server) - ok")
else:
print("10.2.4.95 (images server) - Unavailable. Exit")
send_to_telegram(
chat,
"10.2.4.95 (images server) - Unavailable. Unable to terminate records. Exit"
)
exit()
if host_check("10.2.4.25"):
print("10.2.4.25 (SQL) - ok")
else:
print("10.2.4.25 (SQL) - Unavailable. Exit")
send_to_telegram(
chat,
"10.2.4.25 (SQL) - Unavailable. Unable to terminate records. Exit")
exit()
files_total_count = 0
print("files count..")
#send_to_telegram(chat,"files dividing started..")
for shop_id in range(4):
files = os.listdir(screenshots_path + shop_names[shop_id] + "/grabs/")
files = natsorted(files, alg=ns.PATH)
files_total_count += len(files)
print(files_total_count)
#send_to_telegram(chat,str(files_total_count)+" in queue")
files_count = 0
gpu_id = 0
conn = pymssql.connect(server='10.2.4.25',
user='******',
password=PASTE_PASS,
database='shopEvents')
cursor = conn.cursor()
# reset all to Not processed state
query = "delete from files_to_process"
cursor.execute(query)
conn.commit()
for shop_id in range(4):
print(shop_names[shop_id])
files = os.listdir(screenshots_path + shop_names[shop_id] + "/grabs/")
files = natsorted(files, alg=ns.PATH)
bar = progressbar.ProgressBar(maxval=len(files),
widgets=[
progressbar.Bar('=', '[', ']'), ' ',
progressbar.Percentage()
])
bar.start()
for file_id in range(len(files)):
bar.update(file_id + 1)
file_full_path = screenshots_path + shop_names[
shop_id] + "/grabs/" + files[file_id]
if os.stat(file_full_path).st_size == 0: #empty
os.remove(file_full_path)
continue
if file_full_path.find(".jpg") == -1: #not jpg
continue
query = "INSERT INTO files_to_process (file_id,file_full_path,shop_id,gpu_id,file_name) VALUES (" + str(
files_count) + ",'" + file_full_path + "'," + str(
shop_id) + "," + str(gpu_id) + ",'" + files[file_id] + "')"
cursor.execute(query)
conn.commit()
if (gpu_count > 1):
if files_count > files_total_count / (
gpu_count) and gpu_id < gpu_count:
print(file_full_path, shop_id, gpu_id)
gpu_id += 1
files_count = 0
else:
files_count += 1
else:
files_count += 1
bar.update(len(files))
print("\nprepared:", files_count, "files")
def terminator():
life_day_lenght = 60
chat = "-1001448066127"
if host_check("scriptlab.net"):
print("scriptlab.net - Ok")
else:
print("scriptlab.net - Unavailable. Exit")
exit()
if host_check("10.2.4.95"):
print("10.2.4.95 (images server) - ok")
else:
print("10.2.4.95 (images server) - Unavailable. Exit")
send_to_telegram(
chat,
"10.2.4.95 (images server) - Unavailable. Unable to terminate records. Exit"
)
exit()
if host_check("10.2.4.25"):
print("10.2.4.25 (SQL) - ok")
else:
print("10.2.4.25 (SQL) - Unavailable. Exit")
send_to_telegram(
chat,
"10.2.4.25 (SQL) - Unavailable. Unable to terminate records. Exit")
exit()
date_current = datetime.now()
#send_to_telegram(chat,"Removing old files..")
conn = pymssql.connect(server='10.2.4.25',
user='******',
password='******',
database='shopEvents')
cursor = conn.cursor()
time_limit = (
date_current -
dt.timedelta(days=life_day_lenght)).strftime('%Y-%m-%d %H:%M:%S')
# === Remove files with SQL
query = "SELECT path FROM files where date<'" + time_limit + "'"
cursor.execute(query)
answer = cursor.fetchall()
count_of_files_to_delete = len(answer)
log_message = "SQL: Removing " + str(count_of_files_to_delete) + " files"
print(log_message)
send_to_telegram(chat, log_message)
bar = progressbar.ProgressBar(maxval=count_of_files_to_delete,
widgets=[
progressbar.Bar('=', '[', ']'), ' ',
progressbar.Percentage()
])
i = 0
bar.start()
file_not_found_count = 0
file_removed_count = 0
for file_name_to_delete in answer:
if os.path.isfile(file_name_to_delete[0]):
os.remove(file_name_to_delete[0])
file_removed_count = file_removed_count + 1
else:
file_not_found_count = file_not_found_count + 1
bar.update(i + 1)
i = i + 1
bar.finish()
log_message = "Removed: " + str(file_removed_count) + " files"
print(log_message)
if file_removed_count:
send_to_telegram(chat, log_message)
if file_not_found_count:
log_message = "Not found " + str(file_not_found_count) + " files"
print(log_message)
send_to_telegram(chat, log_message)
# === Remove SQL reocords: files
print("Removing SQL records: files")
query = "DELETE FROM files where date<'" + time_limit + "'"
cursor.execute(query)
conn.commit()
# === Remove SQL records: events
print("Removing SQL records: events")
query = "DELETE FROM events WHERE eventDate<'" + time_limit + "'"
cursor.execute(query)
conn.commit()
# === File system: remove files
#print("File system: Removing old files")
file_date = filedate()
files_removed_count = 0
shares_path = "/mnt/shares/"
for root, subdirs, files in os.walk(shares_path):
list_file_path = os.path.join(root, 'my-directory-list.txt')
with open(list_file_path, 'wb') as list_file:
log_message = "Processing " + str(
len(files)) + " files in directory: " + list_file_path
print(log_message)
#send_to_telegram(chat,log_message)
bar = progressbar.ProgressBar(maxval=len(files),
widgets=[
progressbar.Bar('=', '[', ']'),
' ',
progressbar.Percentage()
])
i = 0
bar.start()
for filename in files:
if ".jpg" in filename:
#print(filename)
file_date.update(filename)
file_path = os.path.join(root, filename)
#time_difference=(date_current-datetime.fromtimestamp( os.path.getctime(file_path) )).total_seconds()
#day_difference=int(time_difference/60/60/24)
time_difference = date_current - file_date.dateFormat()
day_difference = int(time_difference.total_seconds() / 60 /
60 / 24)
if day_difference > life_day_lenght:
os.remove(file_path)
files_removed_count += 1
#else:
# print("New file "+str(time_difference)+": ("+str(day_difference)+"<="+str(life_day_lenght)+"): "+file_path)
bar.update(i + 1)
i = i + 1
bar.finish()
log_message = "Removed by file system: " + str(
files_removed_count) + " files"
print(log_message)
#send_to_telegram(chat,log_message)
files_removed_count = 0
log_message = "Terminator job complete. normal exit"
print(log_message)
send_to_telegram(chat, log_message)
def main(argv):
ops_shop_id = ""
ops_gpu = 0
try:
opts, args = getopt.getopt(
argv, "hd:g:s:f:t",
["interval=", "gpu=", "shop_id=", "from=", "to="])
except getopt.GetoptError:
print('detection_9_off.py -d 2019-05-22 -g 0 -s 0 -f 0 -t 100')
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print('detection_9_off.py -d 2019-05-22 -g 0 -s 0 -f 0 -t 100')
sys.exit()
elif opt in ("-d", "--date"):
ops_date = arg
elif opt in ("-g", "--gpu"):
ops_gpu = int(arg)
elif opt in ("-s", "--shop_id"):
ops_shop_id = int(arg)
elif opt in ("-f", "--from"):
ops_from = int(arg)
elif opt in ("-t", "--to"):
ops_from = int(arg)
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"] = str(ops_gpu)
screenshots_path = "/home/alex/storage/shop_screens/video/"
processed_path = "/home/alex/storage/shop_screens/video/processed/"
save_images_path = "/home/alex/storage/rcimages/"
shop_names = [
"Altuf", "Avangard", "Mar", "Tag", "Sklad", "SkladSM1", "SkladSM2"
]
NUM_CLASSES = 18
PATH_TO_CKPT = '../../inference_v7_12097/frozen_inference_graph.pb'
PATH_TO_LABELS = '../training/mscoco_label_map.pbtxt'
score_limit = 0.7
sizeMin = 10
sizeMax = 1090
font = cv2.FONT_HERSHEY_SIMPLEX
#sleep_to_evening()
prepare_data()
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
file_date = filedate()
with open('sql.pass', 'r') as sql_pass_file:
sql_pass = sql_pass_file.read()
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
conn = pymssql.connect(server='10.2.4.124',
user='******',
password=sql_pass,
database='shopEvents')
cursor = conn.cursor()
while (True):
detectedImagesCount = 0
query = "SELECT file_full_path,shop_id,file_name FROM files_to_process where gpu_id = " + str(
ops_gpu) + " ORDER BY file_id"
cursor.execute(query)
sql_answer = cursor.fetchall()
send_to_telegram(
chat,
"detection " + str(len(sql_answer)) + " files started")
print("len(sql_answer)", len(sql_answer))
for record_current in sql_answer:
file_full_path = record_current[0]
shop_id = record_current[1]
file_name_sql = record_current[2]
file_sql_query = ""
if not os.path.isfile(file_full_path):
continue
file_date.update(file_name_sql)
image_np = cv2.imread(file_full_path)
image_original = image_np.copy()
#RECOGNIZE++
image_np_expanded = np.expand_dims(image_np, axis=0)
image_tensor = detection_graph.get_tensor_by_name(
'image_tensor:0')
boxes = detection_graph.get_tensor_by_name(
'detection_boxes:0')
scores = detection_graph.get_tensor_by_name(
'detection_scores:0')
classes = detection_graph.get_tensor_by_name(
'detection_classes:0')
num_detections = detection_graph.get_tensor_by_name(
'num_detections:0')
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
final_score = np.squeeze(scores)
#get objects detected count
objectsDetectedCount = 0
score_summ = 0
npsboxes = np.squeeze(boxes)
imageheight, imagewidth = image_np.shape[:2]
for i in range(100):
current_class_id = np.squeeze(classes).astype(
np.int32)[i]
current_class_name = category_index[current_class_id][
'name']
if (scores is None or final_score[i] > score_limit
) and "banknotes" in current_class_name:
rx1 = int(npsboxes[i][1] * imagewidth) #1 xLeft
ry1 = int(npsboxes[i][0] * imageheight) #0 yTop
rx2 = int(npsboxes[i][3] * imagewidth) #3 xRight
ry2 = int(npsboxes[i][2] * imageheight) #2 yBottom
xlen = rx2 - rx1
ylen = ry2 - ry1
if xlen > sizeMin and xlen < sizeMax and ylen > sizeMin and ylen < sizeMax:
file_name = file_name_sql.replace(".jpg", "")
file_name_all_boxes = file_name + "_all_boxes.jpg"
save_path_all_boxes = save_images_path + str(
shop_id
) + "/original/" + file_name_all_boxes
file_name_original = file_name + ".jpg"
save_path_original = save_images_path + str(
shop_id
) + "/original/" + file_name_original
file_name_box = file_name + "_" + str(
objectsDetectedCount) + ".jpg"
save_path_box = save_images_path + str(
shop_id) + "/boxed/" + file_name_box
file_sql_query = file_sql_query + "INSERT INTO files (path,date,shop_id) VALUES ('" + save_path_box + "','" + file_date.sqlFormat(
) + "'," + str(shop_id) + ");"
query = "INSERT INTO events (eventDate,objectsCount,middleScore,FileName,shop_id,box_id,box_left,box_right,box_top,box_bottom,filename_original,filename_box,file_source_path) VALUES ('" + file_date.sqlFormat(
) + "',1," + str(
final_score[i]
) + ",'" + file_name_all_boxes + "'," + str(
shop_id
) + "," + str(
objectsDetectedCount
) + "," + str(rx1) + "," + str(
rx2
) + "," + str(ry1) + "," + str(
ry2
) + ",'" + file_name_original + "','" + file_name_box + "','" + file_full_path + "')"
cursor.execute(query)
conn.commit()
score_summ = score_summ + final_score[i]
image_np_current_box = image_original.copy()
object_description = str(
round(final_score[i] * 100)) + "%"
fontScale = 0.5
lineType = 2
#boxes
cv2.rectangle(image_np_current_box, (rx1, ry1),
(rx2, ry2), (255, 0, 0),
2) #regular box
cv2.rectangle(image_np_current_box,
(rx1, ry1 + 1),
(rx1 + 200, ry1 - 15),
(0, 255, 0),
-1) #text background
cv2.rectangle(image_np, (rx1, ry1), (rx2, ry2),
(0, 255, 0), 2) #regular box
cv2.rectangle(image_np, (rx1, ry1 + 1),
(rx1 + 200, ry1 - 15),
(0, 255, 0),
-1) #text background
cv2.putText(image_np_current_box,
object_description, (rx1, ry1),
cv2.FONT_HERSHEY_SIMPLEX,
fontScale, (0, 0, 0), lineType)
cv2.putText(image_np, object_description,
(rx1, ry1),
cv2.FONT_HERSHEY_SIMPLEX,
fontScale, (0, 0, 0), lineType)
#save ONE BOX
cv2.imwrite(save_path_box,
image_np_current_box)
print("cv2.imwrite image_np_current_box:",
save_path_box)
objectsDetectedCount += 1
if objectsDetectedCount > 0:
score_summString = '%.2f' % (
score_summ / objectsDetectedCount) #middle
score_summStringH = '%.2f' % (
score_summ / objectsDetectedCount * 100) #middle
else:
score_summString = "0"
score_summStringH = "0"
cv2.putText(
image_np,
str(objectsDetectedCount) + ":" + score_summStringH +
"%", (10, 150), font, 1, (255, 255, 255), 1,
cv2.LINE_AA)
#RECOGNIZE--
if objectsDetectedCount > 0:
#save ALL BOXES
cv2.imwrite(save_path_all_boxes, image_np)
print("cv2.imwrite image_np:", save_path_all_boxes)
detectedImagesCount += 1
file_sql_query = file_sql_query + "INSERT INTO files (path,date,shop_id) VALUES ('" + save_path_all_boxes + "','" + file_date.sqlFormat(
) + "'," + str(shop_id) + ");"
file_full_path_new = processed_path + shop_names[
shop_id] + "_" + file_name_sql
os.rename(file_full_path, file_full_path_new)
print("os.rename From:", file_full_path, "To:",
file_full_path_new)
# == Save filename to sql
file_sql_query = file_sql_query + "INSERT INTO files (path,date,shop_id) VALUES ('" + file_full_path_new + "','" + file_date.sqlFormat(
) + "'," + str(shop_id) + ");"
cursor.execute(file_sql_query)
conn.commit()
#remove record from file_to_process
file_to_process_query = "DELETE FROM files_to_process where file_full_path='" + file_full_path + "'"
cursor.execute(file_to_process_query)
conn.commit()
sleep_to_evening()
prepare_data()
#chat = "-1001448066127"
chat = "-227727734"
if host_check("scriptlab.net"):
print("scriptlab.net - Ok")
else:
print("scriptlab.net - Unavailable. Exit")
exit()
if host_check("10.2.4.95"):
print("10.2.4.95 (images server) - ok")
else:
print("10.2.4.95 (images server) - Unavailable. Exit")
send_to_telegram(
chat,
"10.2.4.95 (images server) - Unavailable. Unable to add records. Exit")
exit()
if host_check("10.2.4.124"):
print("10.2.4.124 (SQL) - ok")
else:
print("10.2.4.124 (SQL) - Unavailable. Exit")
send_to_telegram(
chat, "10.2.4.124 (SQL) - Unavailable. Unable to add records. Exit")
exit()
def load_image_into_numpy_array(image):
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape(
def main(argv):
ops_shop_id = ""
ops_gpu = 0
#ops_date=""
try:
opts, args = getopt.getopt(argv, "hd:g:s:",
["interval=", "gpu=", "shop_id="])
except getopt.GetoptError:
print('detection_6_off.py -d 2019-05-22 -g 0 -s 0')
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print('detection_6_off.py -d 2019-05-22 -g 0 -s 0')
sys.exit()
elif opt in ("-d", "--date"):
ops_date = arg
elif opt in ("-g", "--gpu"):
ops_gpu = int(arg)
elif opt in ("-s", "--shop_id"):
ops_shop_id = int(arg)
#print ('Interval: ', ops_interval)
print('Gpu: ', ops_gpu)
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"] = str(ops_gpu)
screenshots_path = "/mnt/shares/shop_screens/"
processed_path = "/mnt/shares/shop_screens/processed/"
shop_names = ["Altuf", "Avangard", "Mar", "Tag"]
#NUM_CLASSES = 1
NUM_CLASSES = 18
#PATH_TO_CKPT = '../../banknotes_inference_graph_v3_20904/frozen_inference_graph.pb'
PATH_TO_CKPT = '../../inference_v6_10591/frozen_inference_graph.pb'
#PATH_TO_LABELS = '../training/object-detection.pbtxt'
PATH_TO_LABELS = '../training/mscoco_label_map.pbtxt'
save_images_path = "/mnt/shares/recognized_images/"
score_limit = 0.1
sizeMin = 10
sizeMax = 1090
font = cv2.FONT_HERSHEY_SIMPLEX
conn = pymssql.connect(server='10.2.4.25',
user='******',
password='******',
database='shopEvents')
cursor = conn.cursor()
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
file_date = filedate()
# ==== Temporary code
# query = "SELECT path FROM files where path<>'' ORDER BY path"
# cursor.execute(query)
# sql_answer=cursor.fetchall()
# # == prepare array
# files_from_sql=[]
# for record_current in sql_answer:
# files_from_sql.append(record_current[0])
# print("Recognized files array size: "+str(len(files_from_sql)))
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
#step in every shop
for shop_id in range(4):
if ops_shop_id != "" and shop_id != int(ops_shop_id):
continue
#dtnow=datetime.datetime.now()
#today=dtnow.strftime("%Y-%m-%d_08-56")
files = os.listdir(screenshots_path + shop_names[shop_id] +
"/grabs/")
#images = list(filter(lambda x: today in x, files))
if len(files) > 0:
send_to_telegram(
chat,
str(len(files)) + " " + shop_names[shop_id] + " begin")
detectedImagesCount = 0
bar = progressbar.ProgressBar(maxval=len(files),
widgets=[
progressbar.Bar(
'=', '[', ']'), ' ',
progressbar.Percentage()
])
bar.start()
#files=files.sort()
files = natsorted(files, alg=ns.PATH)
print("Processing " + str(len(files)) + " files of " +
shop_names[shop_id])
for file_id in range(len(files)):
bar.update(file_id + 1)
file_sql_query = ""
file_full_path = screenshots_path + shop_names[
shop_id] + "/grabs/" + files[file_id]
if os.stat(file_full_path).st_size == 0:
#drop file
os.remove(file_full_path)
#print(files[file_id],"removed as empty")
continue
if file_full_path.find(".jpg") == -1:
#print(files[file_id],"skipped as not jpg file")
continue
file_date.update(files[file_id])
# Temporary code
# if file_full_path in files_from_sql:
# file_full_path_new = processed_path+shop_names[shop_id]+"_"+files[file_id]
# os.rename(file_full_path,file_full_path_new)
# move_sql_query = "INSERT INTO files (path,date,shop_id) VALUES ('"+file_full_path_new+"','"+file_date.sqlFormat()+"',"+str(shop_id)+");"
# cursor.execute(move_sql_query)
# conn.commit()
# continue
# ============ print(shop_id,"recognizing:",files[file_id],file_id,"of",len(files))
#print(file_date.sqlFormat())
#print("y",file_date.year)
#print("date extracted from filename")
#exit()
image_np = cv2.imread(file_full_path)
#image_original = image_np
image_original = image_np.copy()
#image_original = copy.deepcopy(image_np)
#RECOGNIZE++
image_np_expanded = np.expand_dims(image_np, axis=0)
image_tensor = detection_graph.get_tensor_by_name(
'image_tensor:0')
boxes = detection_graph.get_tensor_by_name(
'detection_boxes:0')
scores = detection_graph.get_tensor_by_name(
'detection_scores:0')
classes = detection_graph.get_tensor_by_name(
'detection_classes:0')
num_detections = detection_graph.get_tensor_by_name(
'num_detections:0')
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
final_score = np.squeeze(scores)
#get objects detected count
objectsDetectedCount = 0
score_summ = 0
npsboxes = np.squeeze(boxes)
imageheight, imagewidth = image_np.shape[:2]
for i in range(100):
current_class_id = np.squeeze(classes).astype(
np.int32)[i]
current_class_name = category_index[current_class_id][
'name']
if (scores is None or final_score[i] > score_limit
) and "banknotes" in current_class_name:
rx1 = int(npsboxes[i][1] * imagewidth) #1 xLeft
ry1 = int(npsboxes[i][0] * imageheight) #0 yTop
rx2 = int(npsboxes[i][3] * imagewidth) #3 xRight
ry2 = int(npsboxes[i][2] * imageheight) #2 yBottom
xlen = rx2 - rx1
ylen = ry2 - ry1
if xlen > sizeMin and xlen < sizeMax and ylen > sizeMin and ylen < sizeMax:
file_name = files[file_id].replace(".jpg", "")
file_name_all_boxes = file_name + "_all_boxes.jpg"
save_path_all_boxes = save_images_path + str(
shop_id
) + "/original/" + file_name_all_boxes
file_name_original = file_name + ".jpg"
save_path_original = save_images_path + str(
shop_id
) + "/original/" + file_name_original
file_name_box = file_name + "_" + str(
objectsDetectedCount) + ".jpg"
save_path_box = save_images_path + str(
shop_id) + "/boxed/" + file_name_box
file_sql_query = file_sql_query + "INSERT INTO files (path,date,shop_id) VALUES ('" + save_path_box + "','" + file_date.sqlFormat(
) + "'," + str(shop_id) + ");"
query = "INSERT INTO events (eventDate,objectsCount,middleScore,FileName,shop_id,box_id,box_left,box_right,box_top,box_bottom,filename_original,filename_box,file_source_path) VALUES ('" + file_date.sqlFormat(
) + "',1," + str(
final_score[i]
) + ",'" + file_name_all_boxes + "'," + str(
shop_id
) + "," + str(
objectsDetectedCount
) + "," + str(rx1) + "," + str(
rx2
) + "," + str(ry1) + "," + str(
ry2
) + ",'" + file_name_original + "','" + file_name_box + "','" + file_full_path + "')"
cursor.execute(query)
conn.commit()
score_summ = score_summ + final_score[i]
image_np_current_box = image_original.copy()
object_description = str(
round(final_score[i] *
100)) + "% " + current_class_name
fontScale = 0.5
lineType = 2
#boxes
cv2.rectangle(image_np_current_box, (rx1, ry1),
(rx2, ry2), (255, 0, 0),
2) #regular box
cv2.rectangle(image_np_current_box,
(rx1, ry1 + 1),
(rx1 + 200, ry1 - 15),
(0, 255, 0),
-1) #text background
cv2.rectangle(image_np, (rx1, ry1), (rx2, ry2),
(0, 255, 0), 2) #regular box
cv2.rectangle(image_np, (rx1, ry1 + 1),
(rx1 + 200, ry1 - 15),
(0, 255, 0),
-1) #text background
cv2.putText(image_np_current_box,
object_description, (rx1, ry1),
cv2.FONT_HERSHEY_SIMPLEX,
fontScale, (0, 0, 0), lineType)
cv2.putText(image_np, object_description,
(rx1, ry1),
cv2.FONT_HERSHEY_SIMPLEX,
fontScale, (0, 0, 0), lineType)
#save ONE BOX
cv2.imwrite(save_path_box,
image_np_current_box)
objectsDetectedCount += 1
if objectsDetectedCount > 0:
score_summString = '%.2f' % (
score_summ / objectsDetectedCount) #middle
score_summStringH = '%.2f' % (
score_summ / objectsDetectedCount * 100) #middle
else:
score_summString = "0"
score_summStringH = "0"
cv2.putText(
image_np,
str(objectsDetectedCount) + ":" + score_summStringH +
"%", (10, 150), font, 1, (255, 255, 255), 1,
cv2.LINE_AA)
#RECOGNIZE--
if objectsDetectedCount > 0:
#save ORIGINAL
cv2.imwrite(save_path_original, image_original)
#save ALL BOXES
cv2.imwrite(save_path_all_boxes, image_np)
detectedImagesCount += 1
file_sql_query = file_sql_query + "INSERT INTO files (path,date,shop_id) VALUES ('" + save_path_original + "','" + file_date.sqlFormat(
) + "'," + str(shop_id) + ");"
file_sql_query = file_sql_query + "INSERT INTO files (path,date,shop_id) VALUES ('" + save_path_all_boxes + "','" + file_date.sqlFormat(
) + "'," + str(shop_id) + ");"
#drop source file
#os.remove(file_full_path) # <============== move
file_full_path_new = processed_path + shop_names[
shop_id] + "_" + files[file_id]
os.rename(file_full_path, file_full_path_new)
# == Save filename to sql
move_sql_query = "INSERT INTO files (path,date,shop_id) VALUES ('" + file_full_path_new + "','" + file_date.sqlFormat(
) + "'," + str(shop_id) + ");"
cursor.execute(move_sql_query)
conn.commit()
bar.finish()
send_to_telegram(
chat,
str(detectedImagesCount) + " detected in " +
shop_names[shop_id])
print("job complete. normal exit")
send_to_telegram(chat, "job complete. normal exit")
import shutil
chat = "-1001448066127"
if host_check("scriptlab.net"):
print("scriptlab.net - Ok")
else:
print("scriptlab.net - Unavailable. Exit")
exit()
if host_check("10.2.4.85"):
print("10.2.4.85 (source images) - ok")
else:
print("10.2.4.85 (source images) - Unavailable. Exit")
send_to_telegram(
chat,
"10.2.4.85 (source images) - Unavailable. Unable to terminate records. Exit"
)
exit()
if host_check("10.2.5.191"):
print("10.2.5.191 (destination path) - ok")
else:
print("10.2.5.191 (destination path) - Unavailable. Exit")
send_to_telegram(
chat,
"10.2.5.191 (destination path) - Unavailable. Unable to terminate records. Exit"
)
exit()
def load_image_into_numpy_array(image):
'======================================== SAMPLE 0 ========================================',
'')
lex_result = lex_result.replace(
'================================================================================',
'')
lex_result = lex_result.replace(lex_source, '')
lex_result = lex_result.replace('@gpt2robot', '')
lex_result = '.'.join(
lex_result.split('.')[:3]) + '.' #crop left 4 sentences
#translate
if source_language != 'en':
lex_result = translate_text(source_language, lex_result)
#send to telegram
#with open("chat.txt",'r') as lex_file:
# chat=lex_file.read()
send_to_telegram(chat, lex_result, message_id)
print('waiting..')
#else:
# lex_file.close()
#lex--
#logger.info("Generating predictions...")
#predict_fn(network, text, params)
sys.exit()
# Train eval loop
while True:
start = time.time()
network.train(input_fn=partial(input_fn, eval=False),
steps=params["train_steps"])