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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 | |
| def load_model(model_name, threshold): | |
| config = DetrConfig.from_pretrained(model_name, threshold=threshold) | |
| model = DetrForObjectDetection.from_pretrained(model_name, config=config) | |
| image_processor = DetrImageProcessor.from_pretrained(model_name) | |
| return pipeline(task='object-detection', model=model, image_processor=image_processor) | |
| od_pipe = load_model("facebook/detr-resnet-101", 0.25) # Default model | |
| def draw_detections(image, detections, model_name): | |
| np_image = np.array(image) | |
| np_image = cv2.cvtColor(np_image, cv2.COLOR_RGB2BGR) | |
| for detection in detections: | |
| if 'mask' in detection: | |
| # Interpret and visualize segmentation mask | |
| mask = detection['mask'] | |
| color = np.random.randint(0, 255, size=3) | |
| mask = np.round(mask * 255).astype(np.uint8) | |
| mask = cv2.resize(mask, (image.width, image.height)) | |
| mask_image = np.stack([mask]*3, axis=-1) | |
| np_image[mask == 255] = np_image[mask == 255] * 0.5 + color * 0.5 | |
| if 'box' in detection: | |
| # Visualize bounding box | |
| box = detection['box'] | |
| x_min, y_min, x_max, y_max = [int(b) for b in [box['xmin'], box['ymin'], box['xmax'], box['ymax']]] | |
| cv2.rectangle(np_image, (x_min, y_min), (x_max, y_max), (0, 255, 0), 2) | |
| final_image = cv2.cvtColor(np_image, cv2.COLOR_BGR2RGB) | |
| return Image.fromarray(final_image) | |
| def get_pipeline_prediction(model_name, threshold, pil_image): | |
| global od_pipe | |
| od_pipe = load_model(model_name, 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) | |
| print("Detection Output:", result) # Debug: Check the output structure | |
| processed_image = draw_detections(pil_image, result, model_name) | |
| description = f'Model used: {model_name}, Detection Threshold: {threshold}' | |
| return processed_image, result, description | |
| except Exception as e: | |
| return pil_image, {"error": str(e)}, "Failed to process image" | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown("## Object Detection") | |
| inp_image = gr.Image(label="Upload your image here") | |
| model_dropdown = gr.Dropdown(choices=["facebook/detr-resnet-50", "facebook/detr-resnet-50-panoptic", "facebook/detr-resnet-101", "facebook/detr-resnet-101-panoptic"], value="facebook/detr-resnet-101", label="Select Model") | |
| 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() | |
| with gr.Tab("Description"): | |
| description_output = gr.Textbox() | |
| run_button.click(get_pipeline_prediction, inputs=[model_dropdown, threshold_slider, inp_image], outputs=[output_image, output_data, description_output]) | |
| demo.launch() |