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()
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