import os from pathlib import Path import cv2 import numpy as np import shutil from module import database, detection_utils, configparser # Paths models_path = os.getcwd() + '/models/' output_path = os.getcwd() + '/output/' object_detection_image_path = os.getcwd() + '/output/object_detection/' # Config yolo_ignored_labels = str( configparser.any_config(section='yolo')['ignored_labels']).split(',') def bool_label_is_ignored(label): for ignored_label in yolo_ignored_labels: if ignored_label == label: return True return False def filter_negative_values(array_boxes): result = [] for value in array_boxes: result.append(0 if value < 0 else value) return result
import os from pathlib import Path import cv2 import numpy as np import shutil from module import database, detection_utils, configparser # Paths models_path = os.getcwd() + '/models/' output_path = os.getcwd() + '/output/' object_detection_image_path = os.getcwd() + '/output/object_detection/' # Config yolo_ignored_labels = str( configparser.any_config(section='yolo')['ignored_labels']).split(',') cv2_imshow_enabled = True if configparser.any_config( section='app')['cv2_imshow_enabled'] == 'True' else False print('[Info] cv2_imshow_enabled is ' + str(cv2_imshow_enabled)) def bool_label_is_ignored(label): for ignored_label in yolo_ignored_labels: if ignored_label == label: return True return False def filter_negative_values(array_boxes): result = [] for value in array_boxes:
import os import sys from module import configparser, process_utils, database, detection_utils from objects import SrFile from pathlib import Path import psycopg2 import time super_resolution_config = configparser.any_config(filename=os.getcwd() + '/config.ini', section='super_resolution') use_gpu = super_resolution_config['use_gpu'] == 'True' if use_gpu is False: os.environ["CUDA_VISIBLE_DEVICES"] = "-1" print('GPU Disabled at config') from libraries.fast_srgan import infer_oi from module import gpu_utils from module.vehicle_color import vehicle_color_detect # Check does system has GPU support print('GPU support available: ' + str(gpu_utils.is_gpu_available())) # Parse configs app_config = configparser.any_config(filename=os.getcwd() + '/config.ini', section='app') process_sleep_seconds = app_config['process_sleep_seconds'] max_width = int(super_resolution_config['max_width']) max_height = int(super_resolution_config['max_height']) # Output path output_root_folder_path = app_config['output_folder']
from pathlib import Path from module import configparser import numpy as np import shutil import imutils import pickle import cv2 import os # App config app_config = configparser.any_config(filename=os.getcwd() + '/config.ini', section='app') output_root_folder_path = app_config['output_folder'] # Config face_recognition_config = configparser.any_config(filename=os.getcwd() + '/config.ini', section='facerecognition') file_name_prefix = str(face_recognition_config['file_name_prefix']) cv2_imshow_enabled = True if configparser.any_config(section='app')['cv2_imshow_enabled'] == 'True' else False # Paths recognizer_path = None label_encoder_path = None output_faces_path = None output_faces_dataset = None # Set paths output_root_path = face_recognition_config['output_root_path'] if str(output_root_path) == 'cwd': # Stock paths recognizer_path = output_root_folder_path + '/faces_models/' + 'recognizer.pickle' label_encoder_path = output_root_folder_path + '/faces_models/' + 'label_encoder.pickle' output_faces_path = output_root_folder_path + '/faces/'
import os from datetime import datetime from argparse import ArgumentParser from module import fileutils, object_detection, configparser, actions, process_utils from objects import File import psycopg2 import sys import time # Config app_config = configparser.any_config(filename=os.getcwd() + '/config.ini', section='app') move_to_processed = app_config['move_to_processed'] == 'True' process_sleep_seconds = int(app_config['process_sleep_seconds']) # Get current time offset ts = time.time() utc_offset = (datetime.fromtimestamp(ts) - datetime.utcfromtimestamp(ts)).total_seconds() time_offset_hours = int(utc_offset / 60 / 60) print('Time offset: ' + str(time_offset_hours)) # Specify your names and folders at config.ini # split them by a,b,c,d names = ['App1'] folders = [os.getcwd() + '/images/'] # Process arguments parser = ArgumentParser() parser.add_argument( '--bool_slave_node',
from skimage.metrics import structural_similarity as ssim from pathlib import Path from module import configparser, database, process_utils from objects import SrFile import shutil import os import cv2 import sys import time # Parse configs app_config = configparser.any_config(filename=os.getcwd() + '/config.ini', section='app') similarity_config = configparser.any_config(filename=os.getcwd() + '/config.ini', section='similarity') process_sleep_seconds = app_config['process_sleep_seconds'] delete_files = similarity_config['delete_files'] == 'True' # Define paths output_root_folder_path = os.getcwd() + '/output/' test_move_path = output_root_folder_path + 'recycle/' # Check directory existence Path(test_move_path).mkdir(parents=True, exist_ok=True) # Similarity level similarity_level = 0.30 # Image compare process
import os import psycopg2 from module import configparser from argparse import ArgumentParser # Process arguments parser = ArgumentParser() parser.add_argument('--bool_slave_node', type=str, help='Multi node support, give string True as input if slave.') args = parser.parse_args() if args.bool_slave_node == 'True': print('[INFO] Process running in slave mode!') params = configparser.any_config( filename=os.getcwd() + ('/config_slave.ini' if args.bool_slave_node == 'True' else '/config.ini'), section='postgresql' ) # connection = psycopg2.connect(**params) # print(connection.get_backend_pid()) def db_connected(): connection = psycopg2.connect(**params) try: cur = connection.cursor() cur.execute('SELECT 1') cur.close() return True except psycopg2.OperationalError: return False
from pathlib import Path from module import configparser import time import os import sys import cv2 # Paths grab_image_output_path = os.getcwd() + '/images/' # Check folder existence Path(grab_image_output_path).mkdir(parents=True, exist_ok=True) # Config sleep_seconds = float( configparser.any_config(section='streamgrab')['sleep_seconds']) jpeg_stream_names = configparser.any_config( section='streamgrab')['jpeg_stream_names'].split(',') steam_urls_config = configparser.any_config( section='streamgrab')['jpeg_streams'].split(',') def grab(): for stream_name, stream_url in zip(jpeg_stream_names, steam_urls_config): cap = cv2.VideoCapture(stream_url) ret, img = cap.read() # Create file name from date time now = datetime.now() output_file_name = str(stream_name + '_' + now.strftime("%d_%m_%Y_%H_%M_%S") + '.jpg')
import os from os import environ from pathlib import Path import imutils from libraries.openalpr_64.openalpr import Alpr from module import configparser import numpy as np import cv2 from objects import Plate # Custom config open_alpr_config = configparser.any_config(filename=os.getcwd() + '/config.ini', section='openalpr') # Paths car_labels_path = os.getcwd() + '/output/car/' rotation_temp_images_path = os.getcwd() + '/output/rotation_temp/' alpr_dir = os.getcwd() + '/libraries/openalpr_64' open_alpr_conf = os.getcwd() + '/libraries/openalpr_64/openalpr.conf' open_alpr_runtime_data = os.getcwd() + '/libraries/openalpr_64/runtime_data' def detect_license_plate(input_image, file_name, use_rotation=False): try: if open_alpr_config['enabled'] == 'True': region = open_alpr_config['region'] # Validate file path
from pathlib import Path from module import configparser import numpy as np import shutil import imutils import pickle import cv2 import os # Config face_recognition_config = configparser.any_config(filename=os.getcwd() + '/config.ini', section='facerecognition') file_name_prefix = str(face_recognition_config['file_name_prefix']) # Paths recognizer_path = None label_encoder_path = None output_faces_path = None output_faces_dataset = None # Set paths output_root_path = face_recognition_config['output_root_path'] if str(output_root_path) == 'cwd': # Stock paths recognizer_path = os.getcwd( ) + '/output/faces_models/' + 'recognizer.pickle' label_encoder_path = os.getcwd( ) + '/output/faces_models/' + 'label_encoder.pickle' output_faces_path = os.getcwd() + '/output/faces/' output_faces_dataset = os.getcwd() + '/output/faces_dataset/'
# Responsible for first and second stage object detection # then sorts objects to specific categories (labels) # which are in corresponding folders, # makes database intelligence inserts import os from pathlib import Path import cv2 import numpy as np import shutil from module import database, detection_utils, configparser # App config app_config = configparser.any_config(filename=os.getcwd() + '/config.ini', section='app') output_root_folder_path = app_config['output_folder'] # Paths models_path = os.getcwd() + '/models/' output_path = output_root_folder_path object_detection_image_path = output_root_folder_path + '/object_detection/' # Config yolo_ignored_labels = str( configparser.any_config(section='yolo')['ignored_labels']).split(',') cv2_imshow_enabled = True if configparser.any_config( section='app')['cv2_imshow_enabled'] == 'True' else False print('[Info] cv2_imshow_enabled is ' + str(cv2_imshow_enabled)) def bool_label_is_ignored(label):