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Create app.py
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import gradio as gr
import cv2
from ultralytics import YOLO
# Load the pretrained YOLOv5 model
yolo_model = YOLO("yolov5s.pt") # Load the small version of YOLOv5
# Function to perform object detection with a configurable confidence threshold
def detect_objects(frame, confidence_threshold=0.5):
# Convert the frame from BGR (OpenCV) to RGB
image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Perform inference with YOLO, passing the confidence threshold
results = yolo_model(image, conf=confidence_threshold) # Set confidence threshold
# Draw bounding boxes and labels on the image
annotated_image = results.plot() # This automatically draws boxes and labels
# Convert the image back to BGR for displaying in Gradio
annotated_image = cv2.cvtColor(annotated_image, cv2.COLOR_RGB2BGR)
return annotated_image
# Gradio interface to use the webcam for real-time object detection
# Added a slider for the confidence threshold
iface = gr.Interface(fn=detect_objects,
inputs=[
gr.Video(source="webcam", type="numpy"), # Webcam input
gr.Slider(minimum=0.0, maximum=1.0, default=0.5, label="Confidence Threshold") # Confidence slider
],
outputs="image") # Show output image with bounding boxes
iface.launch()