input arguments to execute openCV code - Python [closed]

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Hi, i'm triyng to execute a script but this needs arguments to execute. I use Windows plataform and i tried to use a bat to execute python:

python recognize_video.py %input% %input2% %input3% %input4% %input5%

with the arguments:

@echo off
set input= /face_detection_model/
echo %input%

set input2= openface_nn4.small2.v1.t7
echo %input2%

set input3= /output/recognizer.pickle
echo %input3%

set input4= /output/le.pickle
echo %input4%

set input5= 0.5
echo %input4%

But it doestn work, maybe i'm not understanding what it's expected from the arguments, please help. i will appreciated.

The python code is the attached :

from imutils.video import VideoStream
from imutils.video import FPS
import numpy as np
import argparse
import imutils
import pickle
import time
import cv2
import os

# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--detector", required=True, 
    help="path to OpenCV's deep learning face detector")
ap.add_argument("-m", "--embedding-model", required=True,
    help="path to OpenCV's deep learning face embedding model")
ap.add_argument("-r", "--recognizer", required=True,
    help="path to model trained to recognize faces")
ap.add_argument("-l", "--le", required=True,
    help="path to label encoder")
ap.add_argument("-c", "--confidence", type=float, default=0.5,
    help="minimum probability to filter weak detections")
args = vars(ap.parse_args())

# load our serialized face detector from disk
print("[INFO] loading face detector...")
protoPath = os.path.sep.join([args["detector"], "deploy.prototxt"])
modelPath = os.path.sep.join([args["detector"],
    "res10_300x300_ssd_iter_140000.caffemodel"])
detector = cv2.dnn.readNetFromCaffe(protoPath, modelPath)

# load our serialized face embedding model from disk
print("[INFO] loading face recognizer...")
embedder = cv2.dnn.readNetFromTorch(args["embedding_model"])

# load the actual face recognition model along with the label encoder
recognizer = pickle.loads(open(args["recognizer"], "rb").read())
le = pickle.loads(open(args["le"], "rb").read())

# initialize the video stream, then allow the camera sensor to warm up
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
time.sleep(2.0)

# start the FPS throughput estimator
fps = FPS().start()

# loop over frames from the video file stream
while True:
    # grab the frame from the threaded video stream
    frame = vs.read()

    # resize the frame to have a width of 600 pixels (while
    # maintaining the aspect ratio), and then grab the image
    # dimensions
    frame = imutils.resize(frame, width=600)
    (h, w) = frame.shape[:2]

    # construct a blob from the image
    imageBlob = cv2.dnn.blobFromImage(
        cv2.resize(frame, (300, 300)), 1.0, (300, 300),
        (104.0, 177.0, 123.0), swapRB=False, crop=False)

    # apply OpenCV's deep learning-based face detector to localize
    # faces in the input image
    detector.setInput(imageBlob)
    detections = detector.forward()

    # loop over the detections
    for i in range(0, detections.shape[2]):
        # extract the confidence (i.e., probability) associated with
        # the prediction
        confidence = detections[0, 0, i, 2]

        # filter out weak detections
        if confidence > args["confidence"]:
            # compute the (x, y)-coordinates of the bounding box for
            # the face
            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
            (startX, startY, endX, endY) = box.astype ...