app.py

from transformers import DetrImageProcessor, DetrForObjectDetection
import torch
from PIL import Image, ImageDraw, ImageFont  # Import ImageFont
import gradio as gr
import requests
import random

def detect_objects(image):
    # Load the pre-trained DETR model
    processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
    model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")

    inputs = processor(images=image, return_tensors="pt")
    outputs = model(**inputs)

    # convert outputs (bounding boxes and class logits) to COCO API
    # let's only keep detections with score > 0.9
    target_sizes = torch.tensor([image.size[::-1]])
    results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]

    # Draw bounding boxes and labels on the image
    draw = ImageDraw.Draw(image)
    for i, (score, label, box) in enumerate(zip(results["scores"], results["labels"], results["boxes"])):
        box = [round(i, 2) for i in box.tolist()]
        color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
        draw.rectangle(box, outline=color, width=3)
        label_text = f"{model.config.id2label[label.item()]}: {round(score.item(), 2)}"
        # Larger and bolder font
        draw.text((box[0], box[1]), label_text, fill=color,)

    return image

def detect_labels(image):
    # Load the pre-trained DETR model
    processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
    model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")

    inputs = processor(images=image, return_tensors="pt")
    outputs = model(**inputs)

    # convert outputs (bounding boxes and class logits) to COCO API
    # let's only keep detections with score > 0.9
    target_sizes = torch.tensor([image.size[::-1]])
    results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]

    labels = []
    for label_id in results["labels"]:
        labels.append(model.config.id2label[label_id.item()])

    return labels

def upload_image_with_boxes(file):
    image = Image.open(file.name)
    image_with_boxes = detect_objects(image)
    return image_with_boxes

def upload_image_with_labels(file):
    image = Image.open(file.name)
    labels = detect_labels(image)
    return ", ".join(labels)

iface_boxes = gr.Interface(
    fn=upload_image_with_boxes,
    inputs="file",
    outputs="image",
    title="Object Detection with Boxes",
    description="Upload an image and detect objects using DETR model. Boxes will be drawn around the detected objects.",
    allow_flagging=False
)

iface_labels = gr.Interface(
    fn=upload_image_with_labels,
    inputs="file",
    outputs="text",
    title="Detected Object Labels",
    description="Upload an image and get the detected object labels using DETR model.",
    allow_flagging=False
)

iface_boxes.launch()
iface_labels.launch()