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import torch
import cv2
import numpy as np
import gradio as gr
from PIL import Image
# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Load YOLOv5x model
model = torch.hub.load('ultralytics/yolov5', 'yolov5x', pretrained=True).to(device)
# Generate distinct colors for each class using HSV color space
def generate_distinct_colors(num_classes):
colors = {}
for i, class_name in enumerate(model.names):
# Use HSV to generate evenly distributed hues
hue = (i * 255 // num_classes)
# Convert HSV to BGR (OpenCV uses BGR)
hsv_color = np.uint8([[[hue, 255, 255]]])
bgr_color = cv2.cvtColor(hsv_color, cv2.COLOR_HSV2BGR)[0][0]
# Store as tuple for easier use
colors[class_name] = tuple(map(int, bgr_color))
return colors
# Generate colors once at startup
CLASS_COLORS = generate_distinct_colors(len(model.names))
def preprocess_image(image):
image = Image.fromarray(image)
image = image.convert("RGB")
return image
def detect_objects(image):
image = preprocess_image(image)
results = model(image)
image = np.array(image)
# Process all detections at once
detections = results.xyxy[0]
for *box, conf, cls in detections:
x1, y1, x2, y2 = map(int, box)
class_name = model.names[int(cls)]
confidence = conf.item() * 100
color = CLASS_COLORS[class_name]
# Draw rectangle and label
cv2.rectangle(image, (x1, y1), (x2, y2), color, 4)
label = f"{class_name} ({confidence:.1f}%)"
cv2.putText(image, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, color, 3, cv2.LINE_AA)
return image
# Gradio interface
iface = gr.Interface(
fn=detect_objects,
inputs=gr.Image(type="numpy", label="Upload Image"),
outputs=gr.Image(type="numpy", label="Detected Objects"),
title="Object Detection with YOLOv5",
description="Use webcam or upload an image to detect objects.",
allow_flagging="never",
examples=["spring_street_after.jpg", "pexels-hikaique-109919.jpg"]
)
iface.launch()