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import os
import cv2
import random
import matplotlib.pyplot as plt
from ultralytics import YOLO
import gradio as gr
# Load the trained YOLO model
MODEL_PATH = "best.pt" # Replace with your model's path
model = YOLO(MODEL_PATH)
def random_color():
"""Generate a random color for bounding boxes."""
return (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
def detect_and_visualize(image_path):
"""
Perform object detection and visualize the results.
Args:
image_path (str): Path to the input image.
Returns:
Annotated image as an array.
Detection details as a dictionary.
"""
# Perform object detection
results = model(image_path)
# Read the input image
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # Convert to RGB
detections = []
for result in results:
boxes = result.boxes.xyxy.cpu().numpy() # Bounding box coordinates
confidences = result.boxes.conf.cpu().numpy() # Confidence scores
class_ids = result.boxes.cls.cpu().numpy().astype(int) # Class IDs
for box, confidence, class_id in zip(boxes, confidences, class_ids):
x_min, y_min, x_max, y_max = map(int, box)
class_name = model.names[class_id]
# Draw bounding box and label
color = random_color()
cv2.rectangle(image, (x_min, y_min), (x_max, y_max), color, 2)
label = f"{class_name} {confidence:.2f}"
cv2.putText(image, label, (x_min, y_min - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
# Append detection details
detections.append({
"label": class_name,
"confidence": float(confidence),
"bounding_box": {
"x1": x_min,
"y1": y_min,
"x2": x_max,
"y2": y_max
}
})
# Optionally save the annotated image
output_path = "output/annotated_image.jpg"
os.makedirs(os.path.dirname(output_path), exist_ok=True)
cv2.imwrite(output_path, cv2.cvtColor(image, cv2.COLOR_RGB2BGR))
print(f"Annotated image saved to {output_path}")
return cv2.cvtColor(image, cv2.COLOR_RGB2BGR), detections
# Gradio interface
def gradio_interface(image):
"""
Gradio-compatible wrapper for object detection.
Args:
image (numpy.array): Input image.
Returns:
Annotated image and detection details.
"""
temp_input_path = "temp_input.jpg"
cv2.imwrite(temp_input_path, cv2.cvtColor(image, cv2.COLOR_RGB2BGR)) # Save temp file for YOLO
annotated_image, detections = detect_and_visualize(temp_input_path)
os.remove(temp_input_path) # Clean up temp file
return annotated_image, detections
# Define Gradio interface
interface = gr.Interface(
fn=gradio_interface,
inputs=gr.Image(type="numpy", label="Upload Image"),
outputs=[
gr.Image(type="numpy", label="Annotated Image"),
gr.JSON(label="Detection Details")
],
title="YOLO Object Detection",
description="Upload an image to detect objects and view annotated results along with detailed detection data."
)
if __name__ == "__main__":
interface.launch()
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