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| import gradio as gr | |
| import torch | |
| from transformers import DetrImageProcessor, DetrForObjectDetection | |
| from transformers import YolosImageProcessor, YolosForObjectDetection | |
| from transformers import DetrForSegmentation | |
| from PIL import Image, ImageDraw, ImageStat | |
| import requests | |
| from io import BytesIO | |
| import base64 | |
| from collections import Counter | |
| import logging | |
| from fastapi import FastAPI, File, UploadFile, HTTPException, Form | |
| from fastapi.responses import JSONResponse | |
| import uvicorn | |
| import pandas as pd | |
| import traceback | |
| import os | |
| # Set up logging | |
| logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") | |
| logger = logging.getLogger(__name__) | |
| # Constants | |
| CONFIDENCE_THRESHOLD = 0.5 | |
| VALID_MODELS = [ | |
| "facebook/detr-resnet-50", | |
| "facebook/detr-resnet-101", | |
| "facebook/detr-resnet-50-panoptic", | |
| "facebook/detr-resnet-101-panoptic", | |
| "hustvl/yolos-tiny", | |
| "hustvl/yolos-base" | |
| ] | |
| MODEL_DESCRIPTIONS = { | |
| "facebook/detr-resnet-50": "DETR with ResNet-50 backbone for object detection. Fast and accurate for general use.", | |
| "facebook/detr-resnet-101": "DETR with ResNet-101 backbone for object detection. More accurate but slower than ResNet-50.", | |
| "facebook/detr-resnet-50-panoptic": "DETR with ResNet-50 for panoptic segmentation. Detects objects and segments scenes.", | |
| "facebook/detr-resnet-101-panoptic": "DETR with ResNet-101 for panoptic segmentation. High accuracy for complex scenes.", | |
| "hustvl/yolos-tiny": "YOLOS Tiny model. Lightweight and fast, ideal for resource-constrained environments.", | |
| "hustvl/yolos-base": "YOLOS Base model. Balances speed and accuracy for object detection." | |
| } | |
| # Lazy model loading | |
| models = {} | |
| processors = {} | |
| def process(image, model_name): | |
| """Process an image and return detected image, objects, confidences, unique objects, unique confidences, and properties.""" | |
| try: | |
| if model_name not in VALID_MODELS: | |
| raise ValueError(f"Invalid model: {model_name}. Choose from: {VALID_MODELS}") | |
| # Load model and processor | |
| if model_name not in models: | |
| logger.info(f"Loading model: {model_name}") | |
| if "yolos" in model_name: | |
| models[model_name] = YolosForObjectDetection.from_pretrained(model_name) | |
| processors[model_name] = YolosImageProcessor.from_pretrained(model_name) | |
| elif "panoptic" in model_name: | |
| models[model_name] = DetrForSegmentation.from_pretrained(model_name) | |
| processors[model_name] = DetrImageProcessor.from_pretrained(model_name) | |
| else: | |
| models[model_name] = DetrForObjectDetection.from_pretrained(model_name) | |
| processors[model_name] = DetrImageProcessor.from_pretrained(model_name) | |
| model, processor = models[model_name], processors[model_name] | |
| inputs = processor(images=image, return_tensors="pt") | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| target_sizes = torch.tensor([image.size[::-1]]) | |
| draw = ImageDraw.Draw(image) | |
| object_names = [] | |
| confidence_scores = [] | |
| object_counter = Counter() | |
| if "panoptic" in model_name: | |
| processed_sizes = torch.tensor([[inputs["pixel_values"].shape[2], inputs["pixel_values"].shape[3]]]) | |
| results = processor.post_process_panoptic(outputs, target_sizes=target_sizes, processed_sizes=processed_sizes)[0] | |
| for segment in results["segments_info"]: | |
| label = segment["label_id"] | |
| label_name = model.config.id2label.get(label, "Unknown") | |
| score = segment.get("score", 1.0) | |
| if "masks" in results and segment["id"] < len(results["masks"]): | |
| mask = results["masks"][segment["id"]].cpu().numpy() | |
| if mask.shape[0] > 0 and mask.shape[1] > 0: | |
| mask_image = Image.fromarray((mask * 255).astype("uint8")) | |
| colored_mask = Image.new("RGBA", image.size, (0, 0, 0, 0)) | |
| mask_draw = ImageDraw.Draw(colored_mask) | |
| r, g, b = (segment["id"] * 50) % 255, (segment["id"] * 100) % 255, (segment["id"] * 150) % 255 | |
| mask_draw.bitmap((0, 0), mask_image, fill=(r, g, b, 128)) | |
| image = Image.alpha_composite(image.convert("RGBA"), colored_mask).convert("RGB") | |
| draw = ImageDraw.Draw(image) | |
| if score > CONFIDENCE_THRESHOLD: | |
| object_names.append(label_name) | |
| confidence_scores.append(float(score)) | |
| object_counter[label_name] = float(score) | |
| else: | |
| results = processor.post_process_object_detection(outputs, target_sizes=target_sizes)[0] | |
| for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): | |
| if score > CONFIDENCE_THRESHOLD: | |
| x, y, x2, y2 = box.tolist() | |
| draw.rectangle([x, y, x2, y2], outline="#32CD32", width=2) | |
| label_name = model.config.id2label.get(label.item(), "Unknown") | |
| # Place text at top-right corner, outside the box, with smaller size | |
| text = f"{label_name}: {score:.2f}" | |
| text_bbox = draw.textbbox((0, 0), text) | |
| text_width, text_height = text_bbox[2] - text_bbox[0], text_bbox[3] - text_bbox[1] | |
| draw.text((x2 - text_width - 2, y - text_height - 2), text, fill="#32CD32") | |
| object_names.append(label_name) | |
| confidence_scores.append(float(score)) | |
| object_counter[label_name] = float(score) | |
| unique_objects = list(object_counter.keys()) | |
| unique_confidences = [object_counter[obj] for obj in unique_objects] | |
| # Image properties | |
| file_size = "Unknown" | |
| if hasattr(image, "fp") and image.fp is not None: | |
| buffered = BytesIO() | |
| image.save(buffered, format="PNG") | |
| file_size = f"{len(buffered.getvalue()) / 1024:.2f} KB" | |
| # Color statistics | |
| try: | |
| stat = ImageStat.Stat(image) | |
| color_stats = { | |
| "mean": [f"{m:.2f}" for m in stat.mean], | |
| "stddev": [f"{s:.2f}" for s in stat.stddev] | |
| } | |
| except Exception as e: | |
| logger.error(f"Error calculating color statistics: {str(e)}") | |
| color_stats = {"mean": "Error", "stddev": "Error"} | |
| properties = { | |
| "Format": image.format if hasattr(image, "format") and image.format else "Unknown", | |
| "Size": f"{image.width}x{image.height}", | |
| "Width": f"{image.width} px", | |
| "Height": f"{image.height} px", | |
| "Mode": image.mode, | |
| "Aspect Ratio": f"{round(image.width / image.height, 2) if image.height != 0 else 'Undefined'}", | |
| "File Size": file_size, | |
| "Mean (R,G,B)": ", ".join(color_stats["mean"]) if isinstance(color_stats["mean"], list) else color_stats["mean"], | |
| "StdDev (R,G,B)": ", ".join(color_stats["stddev"]) if isinstance(color_stats["stddev"], list) else color_stats["stddev"] | |
| } | |
| return image, object_names, confidence_scores, unique_objects, unique_confidences, properties | |
| except Exception as e: | |
| logger.error(f"Error in process: {str(e)}\n{traceback.format_exc()}") | |
| raise | |
| # FastAPI Setup | |
| app = FastAPI(title="Object Detection API") | |
| async def detect_objects_endpoint( | |
| file: UploadFile = File(None), | |
| image_url: str = Form(None), | |
| model_name: str = Form(VALID_MODELS[0]) | |
| ): | |
| """FastAPI endpoint to detect objects in an image from file or URL.""" | |
| try: | |
| if (file is None and not image_url) or (file is not None and image_url): | |
| raise HTTPException(status_code=400, detail="Provide either an image file or an image URL, but not both.") | |
| if file: | |
| if not file.content_type.startswith("image/"): | |
| raise HTTPException(status_code=400, detail="File must be an image") | |
| contents = await file.read() | |
| image = Image.open(BytesIO(contents)).convert("RGB") | |
| else: | |
| response = requests.get(image_url, timeout=10) | |
| response.raise_for_status() | |
| image = Image.open(BytesIO(response.content)).convert("RGB") | |
| if model_name not in VALID_MODELS: | |
| raise HTTPException(status_code=400, detail=f"Invalid model. Choose from: {VALID_MODELS}") | |
| detected_image, detected_objects, detected_confidences, unique_objects, unique_confidences, _ = process(image, model_name) | |
| buffered = BytesIO() | |
| detected_image.save(buffered, format="PNG") | |
| img_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8") | |
| img_url = f"data:image/png;base64,{img_base64}" | |
| return JSONResponse(content={ | |
| "image_url": img_url, | |
| "detected_objects": detected_objects, | |
| "confidence_scores": detected_confidences, | |
| "unique_objects": unique_objects, | |
| "unique_confidence_scores": unique_confidences | |
| }) | |
| except Exception as e: | |
| logger.error(f"Error in FastAPI endpoint: {str(e)}\n{traceback.format_exc()}") | |
| raise HTTPException(status_code=500, detail=f"Error processing image: {str(e)}") | |
| # Gradio UI | |
| def create_gradio_ui(): | |
| with gr.Blocks(theme=gr.themes.Default(primary_hue="blue", secondary_hue="gray")) as demo: | |
| gr.Markdown( | |
| """ | |
| # 🚀 Object Detection App | |
| Upload an image or provide a URL to detect objects using state-of-the-art transformer models (DETR, YOLOS). | |
| """ | |
| ) | |
| with gr.Tabs(): | |
| with gr.Tab("📷 Image Upload"): | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| gr.Markdown("### Input") | |
| model_choice = gr.Dropdown( | |
| choices=VALID_MODELS, | |
| value=VALID_MODELS[0], | |
| label="🔎 Select Model", | |
| info="Choose a model for object detection or panoptic segmentation." | |
| ) | |
| model_info = gr.Markdown( | |
| f"**Model Info**: {MODEL_DESCRIPTIONS[VALID_MODELS[0]]}", | |
| visible=True | |
| ) | |
| image_input = gr.Image(type="pil", label="📷 Upload Image") | |
| image_url_input = gr.Textbox( | |
| label="🔗 Image URL", | |
| placeholder="https://example.com/image.jpg" | |
| ) | |
| with gr.Row(): | |
| submit_btn = gr.Button("✨ Detect", variant="primary") | |
| clear_btn = gr.Button("🗑️ Clear", variant="secondary") | |
| model_choice.change( | |
| fn=lambda model_name: f"**Model Info**: {MODEL_DESCRIPTIONS.get(model_name, 'No description available.')}", | |
| inputs=model_choice, | |
| outputs=model_info | |
| ) | |
| with gr.Column(scale=2): | |
| gr.Markdown("### Results") | |
| error_output = gr.Textbox( | |
| label="⚠️ Errors", | |
| visible=False, | |
| lines=3, | |
| max_lines=5 | |
| ) | |
| output_image = gr.Image( | |
| type="pil", | |
| label="🎯 Detected Image", | |
| interactive=False | |
| ) | |
| with gr.Row(): | |
| objects_output = gr.DataFrame( | |
| label="📋 Detected Objects", | |
| interactive=False, | |
| value=None | |
| ) | |
| unique_objects_output = gr.DataFrame( | |
| label="🔍 Unique Objects", | |
| interactive=False, | |
| value=None | |
| ) | |
| properties_output = gr.DataFrame( | |
| label="📄 Image Properties", | |
| interactive=False, | |
| value=None | |
| ) | |
| def process_for_gradio(image, url, model_name): | |
| try: | |
| if image is None and not url: | |
| return None, None, None, None, "Please provide an image or URL" | |
| if image and url: | |
| return None, None, None, None, "Please provide either an image or URL, not both" | |
| if url: | |
| response = requests.get(url, timeout=10) | |
| response.raise_for_status() | |
| image = Image.open(BytesIO(response.content)).convert("RGB") | |
| detected_image, objects, scores, unique_objects, unique_scores, properties = process(image, model_name) | |
| objects_df = pd.DataFrame({ | |
| "Object": objects, | |
| "Confidence Score": [f"{score:.2f}" for score in scores] | |
| }) if objects else pd.DataFrame(columns=["Object", "Confidence Score"]) | |
| unique_objects_df = pd.DataFrame({ | |
| "Unique Object": unique_objects, | |
| "Confidence Score": [f"{score:.2f}" for score in unique_scores] | |
| }) if unique_objects else pd.DataFrame(columns=["Unique Object", "Confidence Score"]) | |
| properties_df = pd.DataFrame([properties]) if properties else pd.DataFrame(columns=properties.keys()) | |
| return detected_image, objects_df, unique_objects_df, properties_df, "" | |
| except Exception as e: | |
| error_msg = f"Error processing image: {str(e)}" | |
| logger.error(f"{error_msg}\n{traceback.format_exc()}") | |
| return None, None, None, None, error_msg | |
| submit_btn.click( | |
| fn=process_for_gradio, | |
| inputs=[image_input, image_url_input, model_choice], | |
| outputs=[output_image, objects_output, unique_objects_output, properties_output, error_output] | |
| ) | |
| clear_btn.click( | |
| fn=lambda: [None, "", None, None, None, None], | |
| inputs=None, | |
| outputs=[image_input, image_url_input, output_image, objects_output, unique_objects_output, properties_output, error_output] | |
| ) | |
| with gr.Tab("🔗 URL Input"): | |
| gr.Markdown("### Process Image from URL") | |
| image_url_input = gr.Textbox( | |
| label="🔗 Image URL", | |
| placeholder="https://example.com/image.jpg" | |
| ) | |
| url_model_choice = gr.Dropdown( | |
| choices=VALID_MODELS, | |
| value=VALID_MODELS[0], | |
| label="🔎 Select Model" | |
| ) | |
| url_model_info = gr.Markdown( | |
| f"**Model Info**: {MODEL_DESCRIPTIONS[VALID_MODELS[0]]}", | |
| visible=True | |
| ) | |
| url_submit_btn = gr.Button("🔄 Process URL", variant="primary") | |
| url_output = gr.JSON(label="API Response") | |
| url_model_choice.change( | |
| fn=lambda model_name: f"**Model Info**: {MODEL_DESCRIPTIONS.get(model_name, 'No description available.')}", | |
| inputs=url_model_choice, | |
| outputs=url_model_info | |
| ) | |
| def process_url_for_gradio(url, model_name): | |
| try: | |
| response = requests.get(url, timeout=10) | |
| response.raise_for_status() | |
| image = Image.open(BytesIO(response.content)).convert("RGB") | |
| detected_image, objects, scores, unique_objects, unique_scores, _ = process(image, model_name) | |
| buffered = BytesIO() | |
| detected_image.save(buffered, format="PNG") | |
| img_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8") | |
| return { | |
| "image_url": f"data:image/png;base64,{img_base64}", | |
| "detected_objects": objects, | |
| "confidence_scores": scores, | |
| "unique_objects": unique_objects, | |
| "unique_confidence_scores": unique_scores | |
| } | |
| except Exception as e: | |
| error_msg = f"Error processing URL: {str(e)}" | |
| logger.error(f"{error_msg}\n{traceback.format_exc()}") | |
| return {"error": error_msg} | |
| url_submit_btn.click( | |
| fn=process_url_for_gradio, | |
| inputs=[image_url_input, url_model_choice], | |
| outputs=[url_output] | |
| ) | |
| with gr.Tab("ℹ️ Help"): | |
| gr.Markdown( | |
| """ | |
| ## How to Use | |
| - **Image Upload**: Select a model, upload an image or provide a URL, and click "Detect" to see detected objects and image properties. | |
| - **URL Input**: Enter an image URL, select a model, and click "Process URL" to get results in JSON format. | |
| - **Models**: Choose from DETR (object detection or panoptic segmentation) or YOLOS (lightweight detection). | |
| - **Clear**: Reset all inputs and outputs using the "Clear" button. | |
| - **Errors**: Check the error box for any processing issues. | |
| ## Tips | |
| - Use high-quality images for better detection results. | |
| - Panoptic models (e.g., DETR-ResNet-50-panoptic) provide segmentation masks for complex scenes. | |
| - For faster processing, try YOLOS-Tiny on resource-constrained devices. | |
| """ | |
| ) | |
| return demo | |
| if __name__ == "__main__": | |
| demo = create_gradio_ui() | |
| demo.launch() | |
| # To run FastAPI, use: uvicorn object_detection:app --host 0.0.0.0 --port 8000 |