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import gradio as gr
from PIL import Image
import cv2
from ultralytics import YOLO
# Load model (ensure the path to the weights file is correct)
model_path = "car_logos.pt"
detection_model = YOLO(model_path)
def predict_image(pil_image):
"""Process an image and return the annotated image."""
# Convert PIL image to NumPy array
frame = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR)
# Run YOLO model
results = detection_model.predict(frame, conf=0.5, iou=0.6)
# Annotate the image
annotated_frame = results[0].plot()
out_pil_image = Image.fromarray(annotated_frame[..., ::-1])
return out_pil_image
def predict_video(video_path):
"""Process a video and return the path to the annotated output video."""
cap = cv2.VideoCapture(video_path)
output_frames = []
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
results = detection_model.predict(frame, conf=0.5, iou=0.6)
annotated_frame = results[0].plot()
output_frames.append(annotated_frame)
cap.release()
if output_frames:
height, width, _ = output_frames[0].shape
out_path = "output_video.mp4"
out = cv2.VideoWriter(out_path, cv2.VideoWriter_fourcc(*"mp4v"), 30, (width, height))
for frame in output_frames:
out.write(frame)
out.release()
return out_path
else:
return "No frames processed."
def create_gradio_interface():
with gr.Blocks() as demo:
with gr.Tab("Upload and Honda or Toyota Logo image"):
gr.Markdown("### Upload Honda or Toyota for Object Detection")
image_input = gr.Image(type="pil", label="Input Image")
image_output = gr.Image(type="pil", label="Annotated Image")
image_button = gr.Button("Process Image")
image_button.click(fn=predict_image, inputs=image_input, outputs=image_output)
with gr.Tab("Video Upload"):
gr.Markdown("### Upload a Video for Object Detection")
video_input = gr.Video(label="Input Video")
video_output = gr.File(label="Annotated Video")
video_button = gr.Button("Process Video")
video_button.click(fn=predict_video, inputs=video_input, outputs=video_output)
demo.launch()
create_gradio_interface()
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