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import gradio as gr
import cv2
from PIL import Image
import numpy as np
from ultralytics import YOLO
from huggingface_hub import hf_hub_download
import os
# Verify that Hugging Face repo and file paths are correct
REPO_ID = "StephanST/WALDO30" # Update if the repository ID is different
MODEL_FILENAME = "WALDO30_yolov8m_640x640.pt"
# Download the model from Hugging Face
try:
model_path = hf_hub_download(repo_id=REPO_ID, filename=MODEL_FILENAME)
except Exception as e:
raise RuntimeError(f"Failed to download model from Hugging Face. Verify `repo_id` and `filename`. Error: {e}")
# Load the YOLOv8 model
try:
model = YOLO(model_path) # Ensure the model path is correct
except Exception as e:
raise RuntimeError(f"Failed to load the YOLO model. Verify the model file at `{model_path}`. Error: {e}")
# Detection function for images
def detect_on_image(image):
try:
results = model(image) # Perform detection
annotated_frame = results[0].plot() # Get annotated image
return Image.fromarray(annotated_frame)
except Exception as e:
raise RuntimeError(f"Error during image processing: {e}")
# Detection function for videos
def detect_on_video(video):
try:
temp_video_path = "processed_video.mp4"
cap = cv2.VideoCapture(video)
fourcc = cv2.VideoWriter_fourcc(*"mp4v")
out = cv2.VideoWriter(temp_video_path, fourcc, cap.get(cv2.CAP_PROP_FPS),
(int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))))
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
results = model(frame) # Perform detection
annotated_frame = results[0].plot() # Get annotated frame
out.write(annotated_frame)
cap.release()
out.release()
return temp_video_path
except Exception as e:
raise RuntimeError(f"Error during video processing: {e}")
# Gradio Interface
image_input = gr.Image(type="pil", label="Upload Image")
video_input = gr.Video(label="Upload Video") # Removed invalid `type` argument
image_output = gr.Image(type="pil", label="Detected Image")
video_output = gr.Video(label="Detected Video")
app = gr.Interface(
fn=[detect_on_image, detect_on_video],
inputs=[image_input, video_input],
outputs=[image_output, video_output],
title="WALDO30 YOLOv8 Object Detection",
description="Upload an image or video to see object detection results using the WALDO30 YOLOv8 model."
)
if __name__ == "__main__":
app.launch()
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