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from ultralytics import YOLO
import cv2
import numpy as np
import gradio as gr
import torch
# Load YOLOv5n6 model
model = YOLO('yolov5n6.pt')
# Set the confidence threshold and IOU
model.conf = 0.25 # confidence threshold
model.iou = 0.45 # IOU threshold
model.agnostic = False
model.multi_label = False
model.max_det = 100 # max number of detections
# Low-resolution for inference
LOW_RES = (320, 180)
def detect_and_draw(frame):
# Create low-res copy
low_res_frame = cv2.resize(frame, LOW_RES)
# Perform detection
results = model(low_res_frame, verbose=False)
# Scale bounding boxes
scale_x = frame.shape[1] / LOW_RES[0]
scale_y = frame.shape[0] / LOW_RES[1]
# Draw bounding boxes on high-res frame
for detection in results[0].boxes.data:
x1, y1, x2, y2, conf, cls = detection
x1, y1, x2, y2 = int(x1*scale_x), int(y1*scale_y), int(x2*scale_x), int(y2*scale_y)
label = f"{results[0].names[int(cls)]} {conf:.2f}"
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
return frame
# Define your stream URL
stream_url = "https://edge01.london.nginx.hdontap.com/hosb5/ng_showcase-coke_bottle-street_fixed.stream/chunklist_w464099566.m3u8"
def process_stream():
cap = cv2.VideoCapture(stream_url)
frame_count = 0
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
frame_count += 3
if frame_count % 30 == 0:
result = detect_and_draw(frame)
result_rgb = cv2.cvtColor(result, cv2.COLOR_BGR2RGB)
yield result_rgb
cap.release()
iface = gr.Interface(
fn=process_stream,
inputs=None,
outputs="image",
live=True,
title="Fast Real-time Object Detection with High-Res Output",
description="Live stream processed with YOLOv5n6 on low-res frames, results drawn on high-res frames."
)
if __name__ == "__main__":
if torch.cuda.is_available():
model.to('cuda')
iface.queue()
iface.launch()
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