مشكلة في كل مرة اريد تشغيل الكود يجب ادخال مسار الملفات ضمن terminal

مرحبا..

لدي خوارزمية yolo v3 الذي تقوم بتحديد ماهية الاشياء الموجودة في الصورة 

لايوجد بها مشاكل لكن في كل مرة اريد تشغيل الكود يجب ادخال مسار الملفات التالة ضمن terminal :

python yolo.py --image dog.jpg --config yolov3.cfg --weights yolov3.weights --classes yolov3.txt

هل يمكنني تثبيتهم ضمن الكود؟؟؟؟

import cv2
import pafy
import argparse
import numpy as np

image="dog.jpg"
classesf="yolov3.txt"
weights="yolov3.weights"
config="yolov3.cfg"
scale=0.00392
classes=None


with open(classesf,'r') as f:
    classes=[line.strip() for line in f.readlines()]
COLORS =np.random.uniform(0,255,size=(len(classes),3))
net=cv2.dnn.readNet(weights,config)


ap = argparse.ArgumentParser()
ap.add_argument('-i', '--image', required=True, 
                help = 'path to input image')
ap.add_argument('-c', '-c/--yolov3.cfg', required=True,
                help = 'path to yolo config file')
ap.add_argument('-w', '--weights', required=True,
                help = 'path to yolo pre-trained weights')
ap.add_argument('-cl', '--classes', required=True,
                help = 'path to text file containing class names')
args = ap.parse_args()


def get_output_layers(net):
    
    layer_names = net.getLayerNames()
    try:
        output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]
    except:
        output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

    return output_layers


def draw_prediction(img, class_id, confidence, x, y, x_plus_w, y_plus_h):

    label = str(classes[class_id])

    color = COLORS[class_id]

    cv2.rectangle(img, (x,y), (x_plus_w,y_plus_h), color, 2)

    cv2.putText(img, label, (x-10,y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)


image = cv2.imread(args.image)
def video_detector():
    while True:
        image = cv2.imread(args.image)
        Width = image.shape[1]
        Height = image.shape[0]
        blob = cv2.dnn.blobFromImage(image, 0.00392, (416,416), (0,0,0), True, crop=False)
        net.setInput(blob)
        outs = net.forward(get_output_layers(net))
        class_ids = []
        confidences = []
        boxes = []
        conf_threshold = 0.5
        nms_threshold = 0.4
        scale = 0.00392
        classes = None


        for out in outs:
            for detection in out:
                scores = detection[5:]
                class_id = np.argmax(scores)
                confidence = scores[class_id]
                if confidence > 0.5:
                    center_x = int(detection[0] * Width)
                    center_y = int(detection[1] * Height)
                    w = int(detection[2] * Width)
                    h = int(detection[3] * Height)
                    x = center_x - w / 2
                    y = center_y - h / 2
                    class_ids.append(class_id)
                    confidences.append(float(confidence))
                    boxes.append([x, y, w, h])


        indices = cv2.dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold)

        for i in indices:
            try:
                box = boxes[i]
            except:
                i = i[0]
                box = boxes[i]
            
            x = box[0]
            y = box[1]
            w = box[2]
            h = box[3]
            draw_prediction(image, class_ids[i], confidences[i], round(x), round(y), round(x+w), round(y+h))

        cv2.imshow("object detection", image)
        cv2.waitKey()
        if cv2.waitKey(1) & 0xff ==ord('q'):
            break
        cv2.imwrite("object-detection.jpg", image)
        cv2.destroyAllWindows()


conn = psycopg2.connect("dbname=Management_Center user=postgres password=123456")

[postgresql]
host=localhost
database=Management_Center
user=postgres
password=123456

if __name__ == "__main__":
    video_detector()