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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("int")

            # extract the face ROI
            face = frame[startY:endY, startX:endX]
            (fH, fW) = face.shape[:2]

            # ensure the face width and height are sufficiently large
            if fW < 20 or fH < 20:
                continue

            # construct a blob for the face ROI, then pass the blob
            # through our face embedding model to obtain the 128-d
            # quantification of the face
            faceBlob = cv2.dnn.blobFromImage(face, 1.0 / 255,
            (96, 96), (0, 0, 0), swapRB=True, crop=False)
            embedder.setInput(faceBlob)
            vec = embedder.forward()

            # perform classification to recognize the face
            preds = recognizer.predict_proba(vec)[0]
            j = np.argmax(preds)
            proba = preds[j]
            name = le.classes_[j]

            # draw the bounding box of the face along with the
            # associated probability
            text = "{}: {:.2f}%".format(name, proba * 100)
            y = startY - 10 if startY - 10 > 10 else startY + 10
            cv2.rectangle(frame, (startX, startY), (endX, endY),
                (0, 0, 255), 2)
            cv2.putText(frame, text, (startX, y),
                cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)

    # update the FPS counter
    fps.update()

    # show the output frame
    cv2.imshow("Frame", frame)
    key = cv2.waitKey(1) & 0xFF

    # if the `q` key was pressed, break from the loop
    if key == ord("q"):
        break

# stop the timer and display FPS information
fps.stop()
print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))

# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()