app.py

import os
import gradio as gr
from transformers import pipeline, DetrForObjectDetection, DetrConfig, DetrImageProcessor
import numpy as np
import cv2
from PIL import Image

# Initialize the model
config = DetrConfig.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50", config=config)
image_processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")

# Initialize the pipeline
od_pipe = pipeline(task='object-detection', model=model, image_processor=image_processor)

def draw_detections(image, detections):
    # Convert PIL image to a numpy array
    np_image = np.array(image)
    
    # Convert RGB to BGR for OpenCV
    np_image = cv2.cvtColor(np_image, cv2.COLOR_RGB2BGR)
    
    for detection in detections:
        score = detection['score']
        label = detection['label']
        box = detection['box']
        x_min = box['xmin']
        y_min = box['ymin']
        x_max = box['xmax']
        y_max = box['ymax']

        # Draw rectangles and label with a larger font size
        cv2.rectangle(np_image, (x_min, y_min), (x_max, y_max), (0, 255, 0), 2)
        label_text = f'{label} {score:.2f}'
        label_size = cv2.getTextSize(label_text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)[0]
        label_x = x_min
        label_y = y_min - label_size[1] if y_min - label_size[1] > 10 else y_min + label_size[1]
        cv2.putText(np_image, label_text, (label_x, label_y), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2)
    
    # Convert BGR to RGB for displaying
    final_image = cv2.cvtColor(np_image, cv2.COLOR_BGR2RGB)
    final_pil_image = Image.fromarray(final_image)
    return final_pil_image

def get_pipeline_prediction(pil_image):
    try:
        pipeline_output = od_pipe(pil_image)
        processed_image = draw_detections(pil_image, pipeline_output)
        return processed_image, pipeline_output
    except Exception as e:
        print(f"An error occurred: {str(e)}")
        return pil_image, {"error": str(e)}

# Setting up Gradio interface with tabs for the outputs
demo = gr.Interface(
    fn=get_pipeline_prediction,
    inputs=gr.inputs.Image(label="Input image", type="pil"),
    outputs=[
        gr.outputs.Image(type="pil", label="Annotated Image"),
        gr.outputs.JSON(label="Detected Objects")
    ],
    outputs_per_tab =[['image'], ['json']],
    tabs=["Annotated Image", "Detection Results"]
)

demo.launch()