app.py

import gradio as gr
from PIL import Image
import numpy as np
import torch
from torchvision.models.detection import fasterrcnn_resnet50_fpn_v2, FasterRCNN_ResNet50_FPN_V2_Weights
from torchvision.utils import draw_bounding_boxes
import matplotlib.pyplot as plt
from io import BytesIO

weights = FasterRCNN_ResNet50_FPN_V2_Weights.DEFAULT
categories = weights.meta["categories"]
img_preprocess = weights.transforms()

def load_model():
    model = fasterrcnn_resnet50_fpn_v2(weights=weights, box_score_thresh=0.5)
    model.eval()
    return model

model = load_model()

def make_prediction(img):
    img_processed = img_preprocess(img)
    prediction = model(img_processed.unsqueeze(0))
    prediction = prediction[0]
    prediction["labels"] = [categories[label] for label in prediction["labels"]]
    return prediction

def create_image_with_bboxes(img, prediction):
    img_tensor = torch.tensor(img)
    img_with_bboxes = draw_bounding_boxes(img_tensor, boxes=prediction["boxes"], labels=prediction["labels"],
                                          colors=["red" if label=="person" else "green" for label in prediction["labels"]], width=2)
    img_with_bboxes_np = img_with_bboxes.detach().numpy().transpose(1,2,0)
    return img_with_bboxes_np

def process_image(image):
    img = Image.fromarray(image.astype('uint8'), 'RGB')
    prediction = make_prediction(img)
    img_with_bbox = create_image_with_bboxes(np.array(img).transpose(2,0,1), prediction)

    fig = plt.figure(figsize=(12,12))
    ax = fig.add_subplot(111)
    plt.imshow(img_with_bbox)
    plt.xticks([],[])
    plt.yticks([],[])
    ax.spines[["top", "bottom", "right", "left"]].set_visible(False)

    plt.tight_layout()
    plt.close(fig)

    # Save plot to a BytesIO object
    img_bytes = BytesIO()
    fig.savefig(img_bytes, format='png')
    img_bytes.seek(0)

    # Create a summary of detected objects
    detected_objects = []
    for label, score in zip(prediction["labels"], prediction["scores"]):
        detected_objects.append(f"{label}: {score:.2f}")

    prediction_data = {k: (v.tolist() if isinstance(v, torch.Tensor) else v) for k, v in prediction.items()}
    return Image.open(img_bytes), detected_objects, prediction_data

gr.Interface(
    fn=process_image,
    inputs=gr.Image(type="numpy"),
    outputs=[gr.Image(type="pil"), gr.Textbox(), gr.JSON()],
    title="OBJECT_DETECTOR_254",
    description="Upload an image to detect objects and display bounding boxes along with a summary of detected objects.",
).launch()