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| import json | |
| def preprocess_data(data): | |
| inference_id = data.get("inference_id", "N/A") | |
| time_taken = data.get("time", "N/A") | |
| image_width = data["image"].get("width", "N/A") | |
| image_height = data["image"].get("height", "N/A") | |
| description = (f"Image Dimensions: {image_width}x{image_height}\n" | |
| f"Detected Objects:\n") | |
| for idx, prediction in enumerate(data["predictions"], start=1): | |
| x = prediction.get("x", "N/A") | |
| y = prediction.get("y", "N/A") | |
| width = prediction.get("width", "N/A") | |
| height = prediction.get("height", "N/A") | |
| confidence = prediction.get("confidence", "N/A") | |
| detected_class = prediction.get("class", "N/A") | |
| class_id = prediction.get("class_id", "N/A") | |
| detection_id = prediction.get("detection_id", "N/A") | |
| if confidence > 0.5: | |
| description += (f"\nDetection {idx}:\n" | |
| f"- Class: {detected_class} (ID: {class_id})\n" | |
| f"- Bounding Box: Center({x}, {y}), Width: {width}, Height: {height}\n" | |
| f"- Confidence: {confidence:.2f}\n") | |
| return description | |