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| import os | |
| import gradio as gr | |
| from transformers import pipeline, DetrForObjectDetection, DetrConfig, DetrImageProcessor | |
| import numpy as np | |
| import cv2 | |
| from PIL import Image | |
| # Pre-load the base configuration and models (without setting a threshold yet) | |
| base_config = DetrConfig.from_pretrained("facebook/detr-resnet-50") | |
| base_model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50", config=base_config) | |
| base_processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50") | |
| def load_model(threshold): | |
| # Adjust the configuration for the current threshold | |
| config = DetrConfig.from_pretrained("facebook/detr-resnet-50", threshold=threshold) | |
| # Create a new model instance with the updated configuration | |
| model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50", config=config) | |
| # Image processor does not need to be re-loaded | |
| return pipeline(task='object-detection', model=model, image_processor=base_processor) | |
| # Initialize the pipeline with a default threshold | |
| od_pipe = load_model(0.25) # Set a default threshold here | |
| def draw_detections(image, detections): | |
| np_image = np.array(image) | |
| np_image = cv2.cvtColor(np_image, cv2.COLOR_RGB2BGR) | |
| for detection in detections: | |
| score = detection['score'] | |
| label = detection['label'] | |
| box = detection['box'] | |
| x_min, y_min = box['xmin'], box['ymin'] | |
| x_max, y_max = box['xmax'], box['ymax'] | |
| cv2.rectangle(np_image, (x_min, y_min), (x_max, y_max), (0, 255, 0), 2) | |
| cv2.putText(np_image, f"{label} {score:.2f}", (x_min, y_min - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2) | |
| final_image = cv2.cvtColor(np_image, cv2.COLOR_BGR2RGB) | |
| return Image.fromarray(final_image) | |
| def get_pipeline_prediction(threshold, pil_image): | |
| global od_pipe | |
| od_pipe = load_model(threshold) # reload model with the specified threshold | |
| try: | |
| if not isinstance(pil_image, Image.Image): | |
| pil_image = Image.fromarray(np.array(pil_image).astype('uint8'), 'RGB') | |
| result = od_pipe(pil_image) | |
| processed_image = draw_detections(pil_image, result) | |
| return processed_image, result | |
| except Exception as e: | |
| return pil_image, {"error": str(e)} | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown("## Object Detection") | |
| inp_image = gr.Image(label="Upload your image here") | |
| threshold_slider = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.25, label="Detection Threshold") | |
| run_button = gr.Button("Detect Objects") | |
| with gr.Column(): | |
| with gr.Tab("Annotated Image"): | |
| output_image = gr.Image() | |
| with gr.Tab("Detection Results"): | |
| output_data = gr.JSON() | |
| run_button.click(get_pipeline_prediction, inputs=[threshold_slider, inp_image], outputs=[output_image, output_data]) | |
| demo.launch() |