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November 28, 2025 04:07
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| from __future__ import annotations | |
| import os | |
| import sys | |
| import argparse | |
| import json | |
| from inference_sdk import InferenceHTTPClient | |
| from PIL import Image, ImageDraw, ImageFont | |
| API_KEY = "4Ffffb9rznBnFJyZbsHv" | |
| MODEL_ID = "emergency-vehicles-i10gn/2" | |
| API_URL = "https://serverless.roboflow.com" | |
| def get_text_size(text, font): | |
| """Cross-version text size helper.""" | |
| try: | |
| # Pillow ≥ 10 (new) | |
| bbox = font.getbbox(text) | |
| width = bbox[2] - bbox[0] | |
| height = bbox[3] - bbox[1] | |
| return width, height | |
| except: | |
| # Older Pillow fallback | |
| return font.getsize(text) | |
| def draw_boxes(image_path, predictions, output_path="output.jpg"): | |
| img = Image.open(image_path).convert("RGB") | |
| draw = ImageDraw.Draw(img) | |
| # Load font | |
| try: | |
| font = ImageFont.truetype("arial.ttf", 20) | |
| except: | |
| font = ImageFont.load_default() | |
| for p in predictions: | |
| x = p.get("x") | |
| y = p.get("y") | |
| w = p.get("width") | |
| h = p.get("height") | |
| if None in (x, y, w, h): | |
| continue | |
| # Convert center to corners | |
| x1 = x - w / 2 | |
| y1 = y - h / 2 | |
| x2 = x + w / 2 | |
| y2 = y + h / 2 | |
| label = p.get("class", "Object") | |
| conf = p.get("confidence", 0.0) | |
| text = f"{label} {conf:.2f}" | |
| # NEW: Safe text sizing | |
| text_w, text_h = get_text_size(text, font) | |
| # Draw rectangle | |
| draw.rectangle([x1, y1, x2, y2], outline="red", width=3) | |
| # Draw label background | |
| text_y = max(0, y1 - text_h) | |
| draw.rectangle([x1, text_y, x1 + text_w, text_y + text_h], fill="red") | |
| # Draw text | |
| draw.text((x1, text_y), text, fill="white", font=font) | |
| img.save(output_path) | |
| print(f"✔ Output saved as {output_path}") | |
| def main(argv=None): | |
| argv = argv if argv is not None else sys.argv[1:] | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("image", help="Path to image") | |
| args = parser.parse_args(argv) | |
| if not os.path.isfile(args.image): | |
| print("❌ Error: image not found:", args.image) | |
| sys.exit(2) | |
| client = InferenceHTTPClient(api_url=API_URL, api_key=API_KEY) | |
| print("🔍 Running inference...") | |
| result = client.infer(args.image, model_id=MODEL_ID) | |
| preds = result.get("predictions", []) | |
| print(json.dumps(preds, indent=2)) | |
| if preds: | |
| draw_boxes(args.image, preds) | |
| else: | |
| print("⚠ No objects detected.") | |
| if __name__ == "__main__": | |
| main() |
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