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CORS Updated #12706

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CORS Updated
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164 changes: 164 additions & 0 deletions app.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,164 @@
import os
import subprocess
from datetime import datetime

import cv2
import numpy as np
from flask import Flask, Response, jsonify, render_template, send_from_directory

# from flask_wtf import FlaskForm
# from wtforms import FileField, SubmitField
# from werkzeug.utils import secure_filename
# import os
# from wtforms.validators import InputRequired


app = Flask(__name__)

streaming_active = False
output_folder = "videos"
video_writer = None
# class UploadFileForm(FlaskForm):
# file = FileField("File", validators=[InputRequired()])
# submit = SubmitField("Upload File")


@app.route("/")
def index():
return render_template("index.html")


# @app.route('/home', methods=['GET',"POST"])
# def home():
# form = UploadFileForm()
# if form.validate_on_submit():
# file = form.file.data # First grab the file
# file.save(os.path.join(os.path.abspath(os.path.dirname(__file__)),app.config['UPLOAD_FOLDER'],secure_filename(file.filename))) # Then save the file
# return "File has been uploaded."
# return render_template('index.html', form=form)


@app.route("/start_stream")
def start_stream():
global streaming_active
global out
if not streaming_active:
streaming_active = True
start_recording()

return jsonify({"status": "success", "message": "Streaming started and recording initiated"})
else:
return jsonify({"status": "error", "message": "Streaming is already active"})


@app.route("/stop_stream")
def stop_stream():
global streaming_active
if streaming_active:
streaming_active = False
stop_recording()

return jsonify({"status": "success", "message": "Streaming stopped and recording saved"})
else:
return jsonify({"status": "error", "message": "Streaming is not active"})


def start_recording():
global video_writer

filename = f"recording_{datetime.now().strftime('%Y%m%d_%H%M%S')}.mp4"
fourcc = cv2.VideoWriter_fourcc(*"mp4v") # You can change the codec as needed
frame_size = (640, 480) # Adjust the frame size as needed
# video_writer = cv2.VideoWriter(filename, fourcc, 10.0, frame_size)
video_writer = cv2.VideoWriter(os.path.join(output_folder, filename), fourcc, 40.0, frame_size)


def stop_recording():
global video_writer

if video_writer is not None:
video_writer.release()
video_writer = None


@app.route("/video_feed")
def video_feed():
return Response(generate_frames(), mimetype="multipart/x-mixed-replace; boundary=frame")


@app.route("/static/<path:filename>")
def static_files(filename):
return send_from_directory("static", filename)


@app.route("/videos")
def list_videos():
videos = [video for video in os.listdir("videos") if video.endswith(".mp4")]
return jsonify(videos)


@app.route("/video/<filename>")
def stream_video(filename):
return send_from_directory("videos", filename)


@app.route("/detection/<filename>")
# def detection(filename):
# # Placeholder for detection logic
# print(f"Detection started for {filename}")
# return jsonify({'status': 'Detection started for ' + filename})
def detection(filename):
try:
print(f"filename################={filename}")
# Construct the command string
command = f"python3 detect.py --source ./videos/{filename}"

# Execute the command
process = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
stdout, stderr = process.communicate()

if process.returncode != 0:
# Handle errors if the command failed
return jsonify({"status": "Error", "message": stderr.decode()}), 500

# Return success response
return jsonify({"status": "Detection Done for " + filename})
except Exception as e:
# Handle any exceptions
return jsonify({"status": "Error", "message": str(e)}), 500


def generate_frames():
global video_writer
folder_path = "/tmp/camera_save_tutorial"
while streaming_active:
image_files = [f for f in os.listdir(folder_path) if f.endswith((".jpg", ".png"))]
if image_files:
try:
latest_image = max(image_files, key=lambda x: os.path.getctime(os.path.join(folder_path, x)))
image_path = os.path.join(folder_path, latest_image)

frame = cv2.imread(image_path)
if frame is None:
raise FileNotFoundError("Empty image file or format not supported")

_, buffer = cv2.imencode(".jpg", frame)
frame = buffer.tobytes()

# Write the frame to the video file if recording is active
if video_writer is not None:
video_writer.write(cv2.imdecode(np.frombuffer(frame, dtype=np.uint8), 1))

yield (b"--frame\r\n" b"Content-Type: image/jpeg\r\n\r\n" + frame + b"\r\n")
# time.sleep(0.05)

except Exception as e:
print(f"Error processing image: {e}")
continue

else:
yield (b"--frame\r\n" b"Content-Type: image/jpeg\r\n\r\n" + b"\r\n")


if __name__ == "__main__":
app.run(debug=True)
8 changes: 4 additions & 4 deletions detect.py
Original file line number Diff line number Diff line change
Expand Up @@ -69,7 +69,7 @@
@smart_inference_mode()
def run(
weights=ROOT / "yolov5s.pt", # model path or triton URL
source=ROOT / "data/images", # file/dir/URL/glob/screen/0(webcam)
source=ROOT / "videos", # file/dir/URL/glob/screen/0(webcam)
data=ROOT / "data/coco128.yaml", # dataset.yaml path
imgsz=(640, 640), # inference size (height, width)
conf_thres=0.25, # confidence threshold
Expand Down Expand Up @@ -158,9 +158,9 @@ def run(
source = check_file(source) # download

# Directories
save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
(save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir

# save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
# (save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
save_dir = Path(project)
# Load model
device = select_device(device)
model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
Expand Down
1 change: 1 addition & 0 deletions export.py
Original file line number Diff line number Diff line change
Expand Up @@ -449,6 +449,7 @@ def transform_fn(data_item):
Quantization transform function.

Extracts and preprocess input data from dataloader item for quantization.

Parameters:
data_item: Tuple with data item produced by DataLoader during iteration
Returns:
Expand Down
85 changes: 85 additions & 0 deletions static/script.js
Original file line number Diff line number Diff line change
@@ -0,0 +1,85 @@
function startStream() {

fetch('/start_stream')
.then(response => response.json())
.then(data => {
console.log('Start Stream:', data);
document.getElementById('videoStream').src = '/video_feed';
});
}

function stopStream() {

fetch('/stop_stream')
.then(response => response.json())
.then(data => {
console.log('Stop Stream:', data);
document.getElementById('videoStream').src = '';
});
}


document.addEventListener('DOMContentLoaded', function() {
const videoDropdown = document.getElementById('videoDropdown');
//const playButton = document.getElementById('playButton');
// const detectionButton = document.getElementById('detectionButton');
const videoPlayer = document.getElementById('videoPlayer');
detectionButton= document.querySelector(".detectionButton");
playButton= document.querySelector(".playButton");



fetch('/videos').then(response => response.json()).then(videos => {
videos.forEach(video => {
let option = document.createElement('option');
option.value = video;
option.textContent = video;
videoDropdown.appendChild(option);
});
});

videoDropdown.onchange = function() {
if (this.value) {
playButton.style.display = 'block';
detectionButton.style.display='block'

} else {
playButton.style.display = 'none';
detectionButton.style.display = 'none';
}
};

playButton.onclick = function() {

videoPlayer.innerHTML = `<video id = vi src="/video/${videoDropdown.value}" controls></video>`;
// function playVideo(videoSource) {
// var videoPlayer = document.getElementById("videoPlayer");
// videoPlayer.src = videoSource;
// videoPlayer.load();
// videoPlayer.play();
// toggleDropdown(); // Close the dropdown after selecting a video
// }
// playVideo(`/videos/${videoDropdown.value}`);

};

detectionButton.onclick = function() {

this.innerHTML="<div class= 'loader'></div>"
// setTimeout(()=>{
// this.innerHTML="Detection Done";
// this.style="background : #f1f5f4; color: #333; pointer-events: none";
// },2000)

fetch(`/detection/${videoDropdown.value}`).then(response => response.json()).then(data => {
this.innerHTML="Detection Done";
this.style="background : #f1f5f4; color: #333; pointer-events: none";
alert(data.status);
})
.catch(error => {

loadingMessage.style.display = 'none';
console.error('Error:', error);
});
};
});
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