QNetworkReply is empty

[lagarcia]

Hello, I am new to using QT and I am working on it in my final year project. I am consuming an api made in python for facial recognition, to which I pass the path of an image and it returns the identification of the people present in it. The problem that I present is that when I consume the service, when I print the answer it comes out empty. From the same python if I managed to consume the api and get the response, converting it to json. Here is the api code in python and the one in Qt to consume the microservice.

Code in Qt

QString a="E:/fotos movil/carlos marx/IMG_2925.jpg";
QVariantMap feed;
feed.insert("ruta", QVariant(a).toString());
QByteArray payload=QJsonDocument::fromVariant(feed).toJson();

QUrl myurl;
myurl.setScheme("http");
myurl.setHost("localhost");
myurl.setPort(5001);
myurl.setPath("/reconocer");

QNetworkRequest request;
request.setUrl(myurl);
request.setHeader(QNetworkRequest::ContentTypeHead er, "application/json");

QNetworkAccessManager *restclient;
restclient = new QNetworkAccessManager(this);
QNetworkReply *reply = restclient->post(request,payload);

qDebug() << reply->readAll(); // it is empty


api code in python
import face_recognition
import cv2
import numpy as np
from flask import Flask, jsonify
import requests
import json
from flask import request

app = Flask(__name__)


@app.route("/reconocer",methods=['POST'])
def reconocer():

# Load a sample picture and learn how to recognize it.
s1_image = face_recognition.load_image_file("lena.png")
s1_face_encoding = face_recognition.face_encodings(s1_image)[0]

# # Load a second sample picture and learn how to recognize it.
s2_image = face_recognition.load_image_file("vidal.jpg")
s2_face_encoding = face_recognition.face_encodings(s2_image)[0]

s3_image = face_recognition.load_image_file("viltres.jpg")
s3_face_encoding = face_recognition.face_encodings(s3_image)[0]

# Create arrays of known face encodings and their names
known_face_encodings = [
s1_face_encoding,
s2_face_encoding,
s2_face_encoding
]
known_face_names = [
"Lena",
"Vidal",
"viltres"
]

# Initialize some variables
face_locations = []
face_encodings = []
face_names = []

frame = cv2.imread(request.json["ruta"])

# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_frame[:, :, ::-1]

# Only process every other frame of video to save time

# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)

face_names = []


for face_encoding in face_encodings:
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(known_face_encoding s, face_encoding)
name = "Desconocido"

# # If a match was found in known_face_encodings, just use the first one.
# if True in matches:
# first_match_index = matches.index(True)
# name = known_face_names[first_match_index]

# Or instead, use the known face with the smallest distance to the new face
face_distances = face_recognition.face_distance(known_face_encoding s, face_encoding)
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
name = known_face_names[best_match_index]

face_names.append(name)



p= {'personas': face_names}
print (p)
return jsonify(p)

if __name__ == "__main__":
app.run(host='localhost', port='5001', threaded=True)

I would appreciate if anyone can guide me on what I can do to access the answer that returns python through the dictionary. Taking into account that it was verified that the api is receiving the image path and is identifying correctly.

[d_stranz]

In your QNetworkAccessManager::post() command, you do not wait for the QNetworkAccessManager::finished() signal before trying to read the data. The QNetworkReply instance is returned in this signal, and at that point the data will be ready to read.

So you need to define a slot to connect to that signal, and in that slot you can then call QNetworkReply::readAll() using the pointer to the instance that is passed by the signal.

[lagarcia]

Thank you very much, really, I have been working on this for days, it has solved a problem that although it seems simple, I did not find the solution, it is the first time I am in the forum, and I did not expect such a quick and effective response, thanks again.