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import gradio as gr
import cv2
import torch
import numpy as np
# Load the YOLOv5 model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
# Function to run inference on an image and count objects
def run_inference(image):
# Convert the image from PIL format to a format compatible with OpenCV
image = np.array(image)
# Run YOLOv5 inference
results = model(image)
# Extract detection results
detections = results.pandas().xyxy[0]
# Count objects by category
object_counts = detections['name'].value_counts()
# Create a formatted string to show object counts
count_text = "\n".join([f"{obj}: {count}" for obj, count in object_counts.items()])
# Convert the annotated image from BGR to RGB for display
annotated_image = results.render()[0]
annotated_image = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
return annotated_image, count_text
# Create the Gradio interface
interface = gr.Interface(
fn=run_inference,
inputs=gr.Image(type="pil"),
outputs=[
gr.Image(type="pil"),
gr.Textbox(label="Object Counts", lines=5, interactive=False) # Display counts as text
],
title="YOLOv5 Object Detection with Counts",
description="Upload an image to run YOLOv5 object detection, see the annotated results, and view the count of detected objects by category."
)
# Launch the app
interface.launch()
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